National Academies Press: OpenBook

2017-2018 Assessment of the Army Research Laboratory (2019)

Chapter: 6 Sciences for Maneuver

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Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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6

Sciences for Maneuver

The Panel on Mechanical Science and Engineering at the Army Research Laboratory (ARL) conducted its review of ARL’s vehicle intelligence (VI) programs—intelligence and control, machine-human interaction, and perception—at Aberdeen, Maryland, on July 18-20, 2017, and its review of ARL’s vehicle science and technology programs—platform mechanics, energy and propulsion, and logistics and sustainability—at Aberdeen Proving Ground, Maryland, on June 12-14, 2018. This chapter provides an evaluation of that work.

In general, research presentations and posters were professional, logical, content-rich, and useful. Clear growth in knowledge content by ARL researchers and support staff was demonstrated. Significant advances in the use of analytical and simulation tools were observed. The collaborative interactions—for example, the Collaborative Technology Alliances (CTAs) and Collaborative Research Alliances (CRAs)—continue to be productive. The board noted the various director-level responses to previous board recommendations. These positive responses are also reflected in the continuous improvement in campaign research performance.

The assessment of vehicle technologies (hardware) and vehicle intelligence (software) in separate and staggered annual reviews removes important features, challenges, and opportunities in the research. ARL could consider, as appropriate and relevant, review of both hardware and software research for specific topics. While ARL’s research approach increasingly includes the key pillars of science—theory, experiment, and computation—ARL needs to formally consider theory, experiment, and computation for all its projects. Last, in projects where it is not already done, ARL needs to develop and use models to understand the fundamental phenomena that are at the center of its research work.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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INTELLIGENCE AND CONTROL

Among the priorities that the intelligence and control effort supports are artificial intelligence and autonomy, robotics and autonomous systems, autonomous and intelligent ground systems to extend warfighter reach, collaborative and intelligent air systems with improved maneuverability, and autonomous and intelligent agents to achieve mission command and intelligence operations at all echelons.

Work done under intelligence and control addresses gaps in the area of learning in complex data, including artificial intelligence (AI) and machine learning (ML) with small samples, dirty data, and high clutter; AI and ML with highly heterogeneous data; and adversarial AI and ML in contested and deceptive environments.

Accomplishments and Advancements

The work presented to the review panel addressed issues in pattern recognition and segmentation in video streams, linking perception and cognition, development, and implementation to world models, and learning of terrain characteristics for improved robot navigation.

Overall, the efforts attempt to demonstrate (over a 5- to 7-year time frame) the operation of a heterogeneous team of largely autonomous robots, both on the ground and in air, providing a “security bubble” around a dismounted team of soldiers in military operations in an urban terrain setting.

Based on that scenario, many of the studied technical tasks have immediate relevance. These address perception that adapts to the environment, controls that learn from perception how to move on that terrain, planning that considers threats, and architectures that integrate world knowledge with perception.

Other parts of the work are more basic. These address certain issues that are less likely to be tackled by academic researchers—issues such as development of world models and linking perception with cognition.

Novel neural nets or perception algorithms, for instance, may help in a wide variety of tasks if they prove successful. The cognitive architecture work is of much longer range. It is unlikely that it will be integrated in the 5- to 7-year time frame; nevertheless, it is important to properly address longer term objectives and anticipated warfighter battle scenarios. As such, it is important that the Army continue to pay attention to such issues.

The research group included several early-career individuals who earned their Ph.D. degrees during the last decade or are currently working toward the degree. In general, the researchers exhibited substantial familiarity with the literature, with current and future needs, and with relevant existing techniques. Furthermore, the researchers were aware of the importance of presenting their work in professional conferences and publishing it in refereed journals. Evidence was presented of collaboration with academic researchers of significant relevant experience.

Four projects presented to the panel demonstrated a multifaceted strong effort in the area of automated and joint human-robot path planning. These efforts are autonomous mobile information collection using value of information-enhanced belief approach, context-driven visual search in complex environments, air-ground team surveillance for complex three-dimensional (3D) environments, and unsupervised semantic scene labeling for streaming data. These four projects complement each other well. They offer an opportunity to develop and use joint performance benchmarks and to compare performance and complexity of different approaches and algorithms on similar benchmark scenarios.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Unsupervised Semantic Scene Labeling for Streaming Data

This work has made significant progress over the past 2 years—now fully unsupervised, with a rational basis for selecting the parameters, and good results on merging nonadjacent blobs that belong to the same class. The results exceed the state of the art. The method processes single images and streams of image data. The researcher is developing important unsupervised capabilities.

World Model

This is a relatively small effort, trying to address a relatively big problem. World models for robots have gone through phases for many decades. The four-dimensional (4D) RCS model was proposed some three decades ago, before current robot capabilities had been developed. It is a good idea to once again think through the needs of a robot world model, and which elements could be made explicit in a central database versus distributed or kept implicit. This work is still in its infancy, and it is hard to predict where it will have its greatest impact going forward.

Cognitive Robotics: Linking Perception and Cognition

The cognitive model used here, ACT-R, has a wealth of data connecting it to human cognition. That makes it a reasonable basis on which to build a cognitive robot. This is a many-year effort. Over the past 2 years, this effort has made good progress on showing the generality of the underlying framework for relevant robotics tasks in perception and reasoning. The next technical tasks proposed are importing graphical data structures and doing probabilistic reasoning. If those capabilities can be integrated, that will enable more advances. The overall cognitive vision is exciting and important.

Online Learning for Robust Navigation

This project tries to learn control parameters for a tracked skid-steer vehicle, where the parameters are a function of different surfaces as inferred by an inexpensive onboard vision system. The system demonstrates a learning approach that measurably improves the estimate of future paths. The researcher is using a (disturbance estimation) technique that has worked well for many application areas. The system and the approach certainly show promise; it is important to quantify its capability.

Autonomous Mobile Information Collection Using a Value of Information-Enriched Belief Approach

This project uses value of information (VoI) as a central organizing principle for robot planning. The VoI can be used to guide robot exploration (where it should go, where it should point its sensors). The same principle can be used to infer value judgments made by human operators. The basic framework seems to be sound and can tie together work of several other projects. It will be important to determine the actual performance advantages and limitations once this is fully connected to real robot perception and mobility modules in a realistic environment and mission. The Partially Observable Markov Decision Process (POMDP)-based reward function (capturing mission-specific human knowledge) approach taken by this researcher has potential. POMDPs have been proven to be successful in many application arenas.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Deductive, Analogical, and Associative Reasoning in a Semantic Vector Space

This work is performing deductive and analogical reasoning in a 300-dimension vector space, where the location in that space is decided by data mining in a large corpus. Words are then connected to other words throughout that high-dimension space. This gives a statistical basis for semantic queries, such as looking for analogous concepts. The semantic vector space approach taken by this researcher has been found to be useful in other domains.

Air-Ground Robot Team Surveillance of Complex 3D Environments

This system combines human hints and automatic planning to provide time-constrained coverage plans for a mobile robot doing surveillance. This project is addressing a very interesting problem, and it is just at its beginning. This system will give soldiers a forward air and ground surveillance capability. Moreover, it will permit the injection of human knowledge to enhance route planning. This could help address the mathematical intractability of the NP-hard 3D surveillance task. Work was evaluated using a cluttered 3D urban environment. This is an excellent piece of work offering great potential and opportunities.

Challenges and Opportunities

It appears that all efforts can benefit from referral to benchmarks and test scenarios, and that the development of common benchmarks and test problems would benefit the group (and researchers in parallel efforts). A technique for detection or classification is never going to be “universal” in the sense that the scene has characteristics that affect the definition of the problem and that the detection or estimation or classification objectives are problem-dependent. The lack of common benchmarks and test scenarios make it difficult to assess the usefulness of the presented algorithms and solutions, and prevents meaningful technical exchange between researchers. Moreover, such benchmarks would significantly assist the group in meeting precise short-term objectives and planning for longer term goals.

A related issue is scenario relevance. A number of the reviewed studies could benefit from real or simulated data that are designed intentionally to be close to the scenarios anticipated by the warfighter. Development of such realistic “battle” scenarios would highlight challenges that may not be adequately addressed in the general literature, thereby increasing the value of the work to customers.

Another related issue is the complexity of the studied images. Detection of a movement in a single dark silhouette of a dancer against a white background is a very different problem from detecting hiding snipers in a noisy grayscale video stream of a large cluttered urban scene during combat. It may be useful to seek a common understanding of complexity for a library of images and video clips that can then be used jointly to assess the performance of algorithms, techniques, and associated speed-complexity trade-offs. Such characterization of the difficulty of the detection or classification or estimation problem in the context of the expected applications is important to assess progress of the research over time.

The problems attacked by the intelligence and control research group require that solutions address the following characteristics of the developed algorithms (as well as the algorithms to which they are compared)—complexity, scalability, robustness, and operation in noise. Most studies presented included some of these elements in the analysis, but their inclusion needs to be routine and systematic in all studies where they are applicable. An issue that the group could consider is quantifying complexity. Some problems will be solved as a consequence of technological (computing) progress (and it is therefore of interest to quantify the time frame). Other problems and algorithms are inherently of intractable

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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complexity, and would therefore require either polynomial-time approximations or injection of side information (such as aid by human intelligence) in order to make the problem mathematically tractable.

The following additional opportunities can be pursued to help the group’s researchers, management, and ARL leadership. ARL needs to develop a more precise statement of group goals (long- and short-term) and customers. The group and its members can benefit by articulating what will be realistically achievable within 2, 5, and 10 years. Systematic benchmark studies can greatly assist with this type of technology projection and planning.

Whenever applicable, studied techniques and algorithms could be presented along with an estimate of their complexity and scalability. It needs to be clear if the studied techniques can benefit from ongoing progress in computing technology (e.g., when their complexity is described by low-order polynomials) or if they still require low-complexity approximations or side information in order to become practical.

Whenever applicable, studied techniques and algorithms could address robustness to both small-scale changes and drifts and to larger scale failures and structural changes. Operation of algorithms and techniques could include a report on their nominal performance as well as on their performance in the presence of noise and interference (including the use of and development of realistic models of such noise and interference).

The general tendency in robotics research is to require that physical implementation become part of the presentation of new techniques and algorithms. While this approach has sometimes been criticized as thwarting theoretical research, it is increasingly recognized for providing an often much needed “reality check.” ARL owns and maintains a number of robotic platforms and, to the extent practicable, these needs to be used to test and demonstrate new approaches and new proposed methods. Moreover, testing on real robots is likely to reveal practical challenges for which new theory is needed. Work with physical platforms can also allow researchers to address fundamental hardware and performance limitations.

Discussion of techniques and algorithms could state fundamental limitations associated with the approach and methods being taken. This discussion can be very illuminating to the researchers and management, as well as future panels. Moreover, it can lead to substantive directions for future research.

ARL could consider pursuit of the following areas of research. Physical interactions with the environment other than planned manipulation—pushing, sliding, kicking, running into, and other forms of manipulation not using end effectors can often be useful. Robot models of human behavior—a robot needs to understand the mental model of its human teammates—may answer the following questions: What can the human see? How busy is the human? What is the human trying to do? While human modeling is difficult, it is of great importance to properly address that critical soldier-robot trust factor. The development of benchmark human-robot interaction/mutual-awareness models can be particularly useful. Interplay between robot motion and autonomy and the communications infrastructure—robots can communicate implicitly by their behaviors, or they can maneuver to enable chains of communications, including line-of-site communications that are harder to jam or intercept. A small fleet of robots can, for example, maneuver so as to maximize communications connectivity subject to interception and threat constraints.

Unsupervised Semantic Scene Labeling for Streaming Data

This research could benefit by addressing the following: How and what will agglomerative clustering methods be integrated into? How can supervision and expert knowledge be systematically incorporated? What benchmarks could be used to guide future developments? What is the plan for transitioning to the Tank Automotive Research, Development, and Engineering Center (TARDEC)?

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Additional questions on this research that when answered will lead to more progress on this important area might include the following: How can the work be generalized to segment objects that do not have a homogeneous appearance? It does a good job of handing objects with variance, but an object with disjoint classes (e.g., a Dalmatian dog, with black spots on a white background) will not be properly segmented as a distinct object—could the work be extended to pick one or more of the feature descriptors to ignore? What is an example use for this technique? If one of the potential uses, for example, is segmenting a dirt road from the surroundings, then that application suggests some performance criteria (speed, accuracy, reliability) and a demonstration scenario.

World Model

A world model will be very useful for future collaboration activities. The relationship of this work to future CRA projects and to other architecture projects needs to be clarified. Collaboration with others is needed.

Cognitive Robotics: Linking Perception and Cognition

The researcher and team could benefit from more precise near-term goals and a 5-year benchmark. It would be helpful to have a roadmap to show what additional work needs to be accomplished in this task before it becomes useful for a real robot demonstration scenario.

Online Learning for Robust Navigation

It will be important to do the following going forward: Implement a state-of-the-art control system as a benchmark, to see if the learning system really provides a performance advantage. Perform tests on a wider variety of surfaces to measure the effectiveness of the perception system on system performance. Examine how the disturbance estimation approach taken performs vis-à-vis an approach employing a higher fidelity slip model. Given its importance, other ground modeling issues could also be systematically addressed.

Autonomous Mobile Information Collection Using a Value of Information-Enriched Belief Approach

Injection of human knowledge can significantly increase the mathematical tractability of motion planning problems. As such, this research could investigate how this can systematically be done as mission is progressing (constraints permitting).

Intelligent Mobility (Minitar and RoboSimian)

It appears that the hardware is supported by useful simulation models and lower order control-relevant models. This could be carefully quantified by showing hardware data alongside supporting model-based simulation data. It would be good to see these platforms being more fully exploited by team members.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
×

Deductive, Analogical, and Associative Reasoning in a Semantic Vector Space

The biggest question on this work is who else is doing related projects. There is certainly related work that is kept proprietary within Google and other companies; there is almost certainly relevant work within the federal government. It will be important to stay in touch, as much as possible, with relevant work in those and other communities. This research could more fully utilize relevant semantic vector space literature in order to achieve the desired “Watson-like” reasoning. The research could examine what types of questions are mathematically tractable (polynomial time) and which are not (nonpolynomial time). The insertion of real-time expert knowledge can potentially (and significantly) help with the latter.

Context-Driven Visual Search in Complex Environments

This project will develop “focus of attention” based on mission constraints, temporal constraints, semantic constraints, and 3D depth cues. It has direct applicability for robot sensor pointing and processor allocation in performing real robotics missions.

It is straightforward to see how individual constraints will get integrated. It is more interesting to see how constraints will evolve over time. Once the perception system detects a window, for example, it is likely that other windows will be nearby and aligned? How will that sort of information be incorporated into the sensor aiming strategy?

The use of a pan-tilt-zoom camera for context-driven search is very important. The researcher could show how the pan-tilt-zoom camera reinforcement learning approach can be used to incorporate battle-relevant temporal and semantic mission constraints.

Air-Ground Robot Team Surveillance of Complex 3D Environments

Many other extensions to the research are possible, such as multiple hints coming in from the human, perhaps asynchronously during mission execution requiring real-time replanning; multiple robots doing the exploration; mixtures of air and ground vehicles; and surveillance of moving targets.

This research needs to carefully examine time-accuracy-geometry-human-intervention trade-offs and plan a transition to TARDEC and other customers.

Parsimonious Online Learning with Kernels

This is a very basic machine learning and function approximation technique. It appears to do near-optimal clustering with online learning to create an efficient and sparse data representation. It is not clear how sensitive the parameters are, or how much tuning needs to be done for different applications. Future work could demonstrate the advantages of this method in a concrete and practical example. The research uses a kernel approach for online sequential sparse classification. The research could systematically compare this approach with other approaches and also examine battle-relevant applications.

Optimized Output Codes for Deep Constrained Neural Nets

This work proposes a new encoding of neural network outputs that shows superior performance in terms of similar outputs to similar inputs and in terms of resistance to perturbation. The examples shown are of a relatively small case, learning recognition of hand-written digits after having been trained on hand-written numbers. It would be important to show similar advantages on larger and more diverse

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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data sets. This research examined how output codes can be optimized to assist with classification and lifelong learning issues—for example, reduction of catastrophic forgetting. It examined logistic regressive pairs, spectral hashing, and latent codes (zero shot learning). Two data sets were used. This research could show how this optimized code approach can be used within a realistic intelligence and control group battle-relevant benchmark.

MACHINE-HUMAN INTERACTION

The Army is positioning itself for a future in which humans and machines will closely collaborate to accomplish mission goals in dynamic, unpredictable environments. To this end, the machine-human interaction (MHI) effort focuses on developing basic research that will allow a machine to effectively and safely team with humans. The specific challenges being addressed include creating models of shared cognition in order to improve team performance, using multimodal sensors and technologies to promote effective communication, predicating a robot’s behavior on social and cultural information, and verifying the safety of new, artificial intelligence-driven technology in simulation prior to deployment.

ARL makes a distinction between machine-human interaction and human-machine interaction (HMI). MHI focuses on the development of algorithms that will allow a machine to more effectively communicate and act as a teammate to humans. HMI, on the other hand, examines the ways that people interact with robots. These closely related subfields of human-robot interaction (HRI) use different perspectives to look at the central HRI problem: How do and should people and robots interact? The work understandably combines simulation and research on real robots. Simulation experiments offer a means for rapid prototyping and inexpensive experimental validation. Research on real robots, although more challenging and expensive, is critical for verification of preliminary simulation experiments.

Transparency with respect to machine or robot behavior is an important underlying goal of ARL’s MHI program. Projects within the program attempt to produce not only accurate machine behavior but also behavior that will be understandable to a human operator and result in greater team performance, trust, and lower human workload.

Accomplishments and Advancements

ARL has a variety of MHI projects crosscutting a number of different HRI problems. The projects that the panel examined are each at different stages of development and maturity. As a whole, the projects demonstrate both challenges and opportunities for the efforts in this area.

Wingman Software Integration Laboratory

The Wingman Software Integration Laboratory project integrates autonomous control and targeting of an unmanned high-mobility multipurpose wheeled vehicle weapon system with a manned vehicle, resulting in a human-machine combat team. The project’s initial focus has been on the creation of an integrated simulation and testing environment that utilizes data from test locations in Michigan to generate realistic simulation and training situations for soldiers. The simulation environment successfully integrates the Unity game engine for targeting and the Anvil game engine for autonomous vehicle control using standard methods for communicating between game engines. This integrated system acts as a realistic software-in-the-loop simulation test-bed for the purpose of rapid prototyping in real-world vehicles and realistic experimentation of human-machine decision making and behavior.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Although only 6 months into the project, it is evident that this project is progressing rapidly and has already generated a number of significant accomplishments. The project has produced and demonstrated an initial version of the simulation environment for testing. These accomplishments are partially due to the partnerships between the research team and teams responsible for fielding autonomous components. Potential operational environments and concepts as discussed and developed at weekly meetings with a number of Department of Defense (DoD) partners including the U.S. Army TARDEC; the U.S. Naval Surface Warfare Center Dahlgren Division; the U.S. Army Armament Research, Development, and Engineering Center (ARDEC); the U.S. Army Maneuver Center of Excellence; the U.S. Army Test and Evaluation Command; and DCS Corporation. This large number of collaborators is evidence of significant Army support for the project. The project has resulted in the creation of several technical reports; while there are no publications to date, this is not surprising, given the short period of this project.

Data collection that is planned includes a simulation event at ARL to assess a warfighter machine interface for the roles in January-February 2018 and use of the Wingman System Integration Laboratory for training and human subjects’ data collection during warfighter experimentation in June-August 2018. The results from these experiments will be critical for determining the long-term applicability and impact of this research.

Leveraging Mutual Information to Enable Human in the Loop

This project explores the development of a type of sensor fusion application that combines physiology-based human neural classifiers generated from electroencephalogram (EEG) data with computer vision-based classifiers for object detection to create classifier ensembles that accurately identify objects in an image. The project examines a rapid serial visual presentation task in which a human subject is briefly presented with an image and must identify target items in the image. A significant portion of this research uses mutual information to evaluate the relevance and redundancy of the generated classifiers in relation to the target. Ideally, a set of highly relevant, minimally redundant classifiers will be identified maximizing performance. Results to date have been a mix of theoretical and empirical studies. These results indicate the strong potential of this approach to identify relevant classifiers but are less able to identify redundant classifiers.

Toward Natural Dialogue with Robots: BOT Language

This project examines natural bidirectional dialogue for human-machine teaming and collaboration. The project focuses on a search-and-navigation task in which a human commander uses dialogue to direct a robot during a search task. To date, the project has focused on the collection of a large corpus of data that will be used to automate portions of the overall system. One problem identified is that a large percentage of the data has not been collected from realistic users of the system, such as soldiers and army operators. The directions generated by real users may differ significantly from the directions created by university students or other naïve populations.

Assessing Vulnerabilities in Autonomy

This project attempts to assess the vulnerability of an autonomous vehicle convoy to attack by opposing forces. Although the project is well motivated and important, it lacks a well-defined research problem, realistic scenario, articulated metrics of success, and scientific approach. The project’s primary

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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results to date are the recognition that a trade-off exists between system safety and vulnerability and the recognition that subsystems of the overall system will also play a role in overall system vulnerability.

Challenges and Opportunities

ARL has a unique opportunity to conduct soldier-centric MHI studies. Conducting soldier-based studies would ensure both that the research conducted is impactful for the end user and that the work is grounded with respect to real-world applications. It has been noted, however, that there are significant challenges associated with the use of soldiers as human subjects. Among these are the facts that soldiers are an Internal Review Board-protected population, that soldiers are no longer located at the ARL Aberdeen location, and that soldiers may not have incentive to participate in such studies. On the other hand, it is also noted that ARL often hosts West Point cadets, who could also serve as an important human subject population. Overall, ARL is better positioned than other research centers to perform soldier-centered human subject studies, and the verification of these technologies could be accomplished using soldiers. It will be important to include soldiers, to the extent possible, from the beginning of the project, in order to ensure that the work proceeds in a direction of value for its end customer.

It is also critical that ARL MHI research focuses on realistic army missions as study scenarios. While these missions need not be so realistic as to warrant being classified, notional realism is nevertheless critical for ensuring the development of technologies of value to the Army and the DoD.

Given ARL’s expertise and resources, an opportunity exists to investigate and solve problems that, in some cases, are more complex than the problems tackled by academic researchers. Academic researchers may be drawn to collaborate with ARL on these realistic problems. One challenge to developing and maximizing the potential of these collaborations is the ability of ARL researchers to share code and data with extramural partners. ARL has had some success creating an Open Campus Initiative allowing extramural researchers to more easily work with ARL researchers. But the success of the Open Campus Initiative is uneven, with, for example, easier access for researchers at Adelphi than at Aberdeen. Further, not having an open network makes code and data sharing difficult.

Also, it is important that projects have a metric of success from the outset. These metrics will help researchers remain focused on the project’s research question, even in the case of Blue Sky projects.

Leveraging Mutual Information to Enable Humans in the Loop

This work would strongly benefit by using, to the extent possible, soldiers and intelligence analysts as human subjects. Subject-matter experts (SMEs) and realistic operators may use unique heuristics resulting in significantly different performance than naïve subjects. This would likely impact the types of classifiers identified as relevant. Moreover, using images from realistic scenarios could also influence performance and the types of classifiers identified as relevant and redundant.

Toward Natural Dialogue with Robots: BOT Language

The project employs a “Wizard of Oz”-style setup to create dialogue data and intends to move away from real environments toward simulation environments in order to speed up the data collection process. While the movement from simulation is understandable, it was noted that the experimenters will need to keep the subjects convinced that they are controlling a real robot. Providing computer-generated images of a simulated robot and environment may alert the subjects to fact that they are not really controlling a robot.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
×

Assessing Vulnerabilities in Autonomy

This project needs a well-defined and scoped research question with significant customer commitment. One possible approach might be to look at case studies of convoy attacks and identify common sets of vulnerabilities from these attacks. The project also needs precise metrics and measures of success. It is not currently clear if the project is attempting to generate as many vulnerabilities as possible, consider unique vulnerabilities resulting from autonomy, or some mix of these two. Last, the project needs to increase communication and buy-in from the customer. Weekly conference calls focused on specific, incremental project goals, rather than vulnerability brainstorming, might keep the project focused.

PERCEPTION

ARL seeks to be a premier research organization whose discoveries and innovations successfully transition to the field and support the Army’s long-term strategy of land power dominance. In pursuit of this vision, the perception group has committed to a long-term program emphasizing the key campaign initiative—Force Projection and Augmentation through Intelligent Vehicles.

The perception group’s activities have focused around their fiscal year (FY) 2020 goal: “Semantic labeling of an increasingly larger vocabulary of objects and behaviors to permit a richer, more detailed description of the environment.” Additional activities emphasize the practical aspects associated with ensuring correct spatial interpretation of sensory signals, so that the environmental descriptions are spatially accurate. For this review, evaluation of the group’s output is assessed according to the stated FY 2020 goal, to its potential impact to the field of perception, and to its long-term link to the key campaign initiative, as well as the current campaign—Sciences for Maneuver.

Accomplishments and Advancements

The perception group’s research activities are tightly focused on advancing theoretical and practical aspects of learning and estimation tasks of importance to Army capabilities and domains. Areas of study include object detection and learning, action learning, environmental learning, perception on robot platforms, and online parameter estimation for calibration of robot sensor suites. Importantly, all of the projects support progress toward the stated FY 2020 goal.

Overall, the presentations and demonstrated projects represent solid advances in their fields. The majority of the work indicates awareness of current research activities in the field of computer vision. The group’s research has stayed at the cutting edge, embracing new successful methodologies in computer vision and advancing existing approaches through intelligent analytical insights into the problem at hand.

The group has successfully leveraged past external collaborations to strengthen its intellectual capacity, consequently strengthening these ties. The group demonstrates a commitment to maintaining high visibility of the work through dissemination of research in conferences and, to a lesser degree, journals.

Object Learning

The recently initiated APPLE project focuses on techniques for object learning and refinement using fused color and depth data. To date, this project has generated useful reports on the state of the art, good progress on technology selection, and a roadmap for data collection. In the future, this project will integrate multiple technologies for shape and appearance capture, model creation and refinement, and representation. It is necessary to carefully choose data and modeling domains that are maximally

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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relevant to Army needs and scenarios. A related (and also new) project in object learning seeks to assess the impact of embodiment on agent-assisted training of humans to provide useful object views to an image-based model learner. As these projects mature, there will be an opportunity for cross-pollination of ideas.

Object Detection

One project and demonstration addressed the task of object detection—specifically, techniques for improvement of deep learning-based detection. A novel region-of-interest proposal mechanism provides “side information” that can be used to augment the set of examples used during training. Use of this additional information demonstrates some improvement in performance on an academic computer vision benchmark, and the demonstration included a close-to-real-time performance level. This work is interesting, but demonstration and performance characterization on data sets with close relevance to Army operations would be useful to assess the potential impact of the work.

Action Learning

Two projects address learning and representation of actions. One of these projects contributes a notable advance over the state of the art—namely, an unsupervised method for learning action attributes from data and segmenting video sequences into action primitives, which serve as a compact signature for the activity. A topically related project integrates textual features during training to improve the performance of a deep learning-based activity recognizer. There is an opportunity for these two projects to enhance one another.

Environmental Learning

ARL staff demonstrated an impressive integrated sensor suite for environmental sensing. This platform includes multiple depth and red, green, blue (RGB) sensors, easily and jointly calibrated using a fiducial. Data from this suite and lessons learned from integration could inform sensor platform configurations for future mapping robot designs. Also in this category is a project that integrates terrain and map data with hyperspectral measurements to perform improved classification of water and nonwater regions. Although narrowly scoped at present, affordable near-infrared (near-IR) hyperspectral imaging may add a useful new tool to sensing suites used in support of land operations.

Online Calibration of Proprioceptive and Exteroceptive Sensors

Known positioning of onboard sensors is essential to the correct spatial interpretation of data for modeling and estimation purposes. Having correctly calibrated sensors guarantees proper description of detected object and recognized activities within a world frame. ARL staff presented two projects covering calibration of onboard sensors, proprioceptive calibration using recursive filtering and exteroceptive calibration using a graph optimization. In both cases, the approaches are grounded on strong theoretical principles and numerical methods. Furthermore, the research activities are focused on problems that have contemporary value and are still insufficiently investigated by the perception community. The work is mature and demonstrates strong potential to transition to use within ARL’s robotic platforms, as well as to lead to new algorithms for maintaining correct sensor calibration during operations.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Perception on Robot Platforms

Several robotic demonstrations incorporating perception elements were provided and were generally impressive examples of technology integration. In addition to the multisensor platform and the object detection demonstrations mentioned earlier, the RoMan, Minitar, and RoboSimian platforms will offer highly useful test-beds for joint sensing and manipulation task embedding.

Challenges and Opportunities

Perception research has gone from being model-based to data driven on the strength of statistical machine learning and deep learning algorithms. At the moment, industry needs to dominate the target application domains of researchers and the data being collected. In the case of deep learning, ever more massive data sets are created by the collective efforts of the research community, sometimes with funding from industry. This situation is both a challenge and an opportunity. It is a challenge because Army needs are not necessarily serviced by these data sets. It is an opportunity because the generation of an appealing and challenging data set with long-term Army significance could easily be picked up by the research community through proper targeting. In doing so, ARL might even benefit from community contributions to the data set, as well as from the creativity of the research community with regard to ARL-relevant applications.

In a related vein, deep learning architectures are diverse, and their engineering has supplanted the traditional feature engineering strategy of the previous decade. ARL’s ability to attract new, knowledgeable talent will determine whether it keeps current with the trends in perception. Due to the rising popularity of machine learning, and deep learning in particular, ARL is in a good position to hire more experts in this area by exploiting existing academic relationships and cultivating new ones. The perception group has the opportunity to take advantage of the pipeline of experts graduating in machine learning, for both robotics and perception. Achieving critical mass in this area would help promote their stated goals.

The midterm (FY 2026) goal embodies difficult challenges that need significant research effort, including inference, dealing with context, and extracting relationships between objects. The goal is stated as follows: “Creation of the ability to infer purpose from the relationships between objects in the environment and behaviors (activity) exhibited by people (teammates, adversaries, and noncombatants) and place objects and behaviors into context.” The perception research community has not yet fully embraced activities that would support this end-goal. It is not clear to what degree model-based methods and data-driven methods will be needed, or combined, in achieving it. ARL has the opportunity to define canonical problems in this arena, as well as to curate unclassified data sets and scenarios that could both help push the state-of-the-art in this area of perception and be of utility to ARL mission scenarios.

PLATFORM MECHANICS

The platform mechanics program is charged with conducting fundamental research for enabling highly maneuverable high-speed air and ground vehicle platforms and subsystems for the future Army. These range from large combat/cargo vehicles to microscale devices. The science and technology (S&T) programs reviewed included fluids, structures and dynamics, actuation and mechanisms, and platform configuration concepts.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Accomplishments and Advancements

Responsive Interface Mechanics

The project on stimuli-responsive interface mechanics for nanocomposites focused on improving damping by modifying the polymer/fiber interface and using stimuli-responsive (photoreactive) molecules. The work hypothesized these modifications will increase the load-bearing capacity, Young’s modulus of the composite, and the interfacial strength between the carbon nanotubes (CNTs) and the surrounding matrix that will improve damping characteristics of the composite. This modeling and experimental work is intellectually fascinating. It could establish new ways of providing on-demand interfacial interactions in nanocomposites.

Controlling System Dynamics

Theoretical concepts for controlling system dynamics by using a linear state space model were elegantly explained in the project on Gramian-based control of unmanned aircraft system (UAS) disturbances. The presentation included a good plan to connect with the Army’s battlefield weather modeling community, although the experimental data showed considerable scatter, making comparison with simulations very challenging.

Four-Legged Mobility

The work on developing a four-legged mobility controller utilizing proprioception sensors in the project on fore-aft leg specialization control for a dynamic quadruped is equal to, or possibly superior to, similar work being pursued in academia and industries for achieving high-speed mobility over various terrains. It aligns well with the Army’s goal of agility. As opposed to Raibert’s (Carnegie Mellon University) approach of using similar front and rear controllers, the leg specialization controller provides more thrust with the rear legs and uses front legs for braking. More work needs to be completed to produce running robots. The Army investigators are collaborating with academic researchers.

Computational Fluid Dynamics/Computational Structural Dynamics Modeling and Correlation with Experiment

The project on computational fluid dynamics/computational structural dynamics (CFD/CSD) aeroelastic predictions of a semispan tiltrotor uses a state-of-the-art suite of structural and fluid computer codes, Helios, to computationally analyze aeroelastic models that exhibit whirl-flutter instabilities and correlate computed results with existing and future wind tunnel test data. In compliance with Reliance 21 (the overarching framework of the DoD’s S&T joint planning and coordination process), the Army needs to explore tiltrotor or tilting rotor technologies regardless of whether or not it procures a tiltrotor for FVL-M/Capability Set 3. The ARL team of investigators is collaborating with the Helios and the rotorcraft simulation software for aeroelastic analysis developers, and with academic investigators. The ARL team has successfully conducted coupled CFD/CSD transient analysis for linearly elastic structural response.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Energy-Efficient Multimodal Flight

The project on energy-efficient multimodal flight is focused on designing and testing tilting rotor configurations weighing less than 500 g. The project principally addresses the dynamics and control of the system and has successfully achieved the transition from hover to forward flight. Proposed collaborations with Bell Helicopter and the University of Pennsylvania are a commendable feature of the path forward.

Self-Manipulation

The project on self-manipulation presented a unique work including experiments, models, and advanced mathematics for systematically solving the research problem. Knowledge gained from studying cockroach motion was applied to design better robots. There were notable and meaningful collaborations with TARDEC—the customer—and with the John Hopkins University Applied Physics Laboratory for novel applications and efficient numerical algorithms.

Adaptive and Embedded Intelligence

The team for the project on adaptive and embedded intelligence is doing excellent work in materials engineering. It is examining the physical and electrical properties of galinstan (gallium-indium-tin) infused with polydimethylsiloxane with the goal of printing and manufacturing soft wearable electronics with distributed sensing and actuating capability through strain/stress sensitivity. The material could also be used for morphing wings, and for smart damping. It is an outstanding effort by ARL personnel and leveraged by a postdoctoral researcher who will continue the work as an assistant professor at the University of Alabama in 2018 Fall. This work promises to extend the stretchable electronics in ARL’s Sensors and Electron Devices Directorate (SEDD) and enable haptic interfaces that can be a very valuable natural, human-interface output mode for medical applications and other high-workload roles.

Aerodynamics at Low Reynolds Number

The careful experimental work included in the project on aerodynamics of aggressive low Reynolds number flight deals with generating a large gust and determining the resultant flow field. The comparison between gust-induced and sudden pitch-induced stalls is a productive direction to pursue. Of great interest is the pressure distribution generated on the airfoil due to the gust or by its own pitching motion.

Small, Low-Speed Rotors

The project on design, modeling, and analysis of small, low-speed rotors for mission-optimized multicopter UAS is an interesting study on optimization of rotor airfoil chord length distribution for a fixed airfoil (SDA1075 from Michael Selig at the University of Illinois, Urbana-Champaign) by using the XFOIL potential flow computational model and blade element theory. This approach makes good use of medium-fidelity tools for the design of isolated small UAS (fixed pitch) propellers/rotors for a specific size (9.6 in. diameter). The ARL investigators appear to be very knowledgeable in this area, including the effects of Reynolds number on thickness-to-chord ratio. They are awaiting data from collaborators at the Texas A&M University (for motor and battery models). The work has shown that very

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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large chord (10 percent) improves hover performance (which is somewhat counterintuitive, due to the Reynolds number effect).

Adaptive Turbomachinery Blade

The project on smart adaptive turbomachinery blade is an attempt to actively change the angle of attack on the turbine rotor blade in the hot section for expanding the speed envelope of a helicopter engine by up to 50 percent while maintaining efficiency (reducing aerodynamic stall). Predictions from multiple analysis codes agreed well with one another. Tests planned are awaiting the development of an adaptive model. The work has been patented, and General Electric has shown interest.

Flapping Wing

The impressive work in the project on flapping wing aerial vehicle system design uses a combination of relatively simple component models to produce a basic understanding of the control and the dynamics aspects of a flexible wing flapping UAS that involves complex fluid/structural/energy system interactions. Flight tests have shown considerable promise, and a key focus now seems to be determining the weakest links in our understanding of such systems and the most significant hurdles to improve the system performance. The basic capability, assuming that it is flexible enough to encompass other systems, promises to be very valuable.

Linear Quadratic Regulators

Another significant task in learning and adjusting weights for linear quadratic regulators (LQRs) that control design based on energy and agility metrics focused on a particular system comprising a hover to flight of a UAS biplane with four rotors was presented in the project on design and flight control of reconfigurable UAS. This effort conducts a valuable exploration of the ability to address major design changes to improve the system performance.

High-Fidelity Vertical Take-Off and Landing Flight Simulation

The project on a high-fidelity vertical take-off and landing (VTOL) flight simulator incorporated detailed physics (platform dynamics, controls, aerodynamics, sensors, etc.). It is capable of running higher-level physics—for example, vortex particle methods for rotor structure interactions—although such higher-level modeling will not currently run in real time. The project is a part of the large ARL simulator group.

Overview of Projects

The researchers are performing high-quality theoretical, computational, and experimental investigations relevant to the Army’s anticipated needs. The most outstanding accomplishments include developing the adaptive and embedded intelligence that employs basic science principles to develop a product with wide range of applications in the Army, and combining studies of the cockroach motion with models and advanced mathematics to design better robots. The tiltrotor aeroelastic work using computational and wind tunnel models presents a grand challenge and, if successful, will undoubtedly have important consequences for Army and DoD rotorcraft.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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The laboratory appears to be committed to nurturing new ideas through a variety of programs, including the laboratory director’s office, the availability of seed money, and the portfolio refresh process. Mentoring efforts appear to be effective.

Challenges and Opportunities

The tiltrotor aeroelastic stability test-bed work is a very substantial and important study of an aeroelastic tiltrotor model to be tested in the transonics dynamics tunnel at the National Aeronautics and Space Administration (NASA) Langley Research Center. The wind tunnel tests will begin in 2020, allowing enough time to computationally design the experiment. The personnel involved are dedicated to close collaboration between computations and experiment, and this approach is strongly encouraged and applauded. The history of this work is that a previous tiltrotor experiment led to data that have still not been entirely reconciled with computations. This remains an important opportunity to use lessons learned from this previous work and attempt to get a better correlation with new and hopefully better computational models.

The related project on improved rotorcraft load prediction through dynamic calibration involves dynamic tests with and without the rotor at various angles of attack with numerous sensors attached to different parts. Previous work exhibited large qualitative and quantitative differences between computed and experimentally measured normal rotor force. The expectation is that better prediction of rotor loads will reduce vibration, aircraft weight, and life-cycle cost. Wind tunnel tests are planned for fall 2018, and collaboration with U.S. Army Aviation and Missile Research, Development, and Engineering Center (AMRDEC) and NASA engineers is anticipated to improve upon the agreement between computed and test findings.

Researchers working on the project on stimuli-responsive interface mechanics for nanocomposites could estimate the amount of damping created by modifications of the interface between the nanoreinforcements and the surrounding matrix. The investigators for the project on Gramian-based control of UAS disturbances could compare techniques such as the singular value decomposition for deciding which disturbances can and could be rejected based on the controllability and the observability of the system, and explore why traditional disturbance rejection technique of changing the objective from “maintain position” to “maintain orientation” is inadequate. For the project on fore-aft leg specialization control for a dynamic quadruped, the investigators could conduct their examination using the reinforced learning weighted LQR gain controller to manage the speed versus energy objective. Additional insight could be gained by a deeper concentration on developing a dynamic model.

The project on CFD/CSD aeroelastic predictions of a semispan tiltrotor could first use a simpler aerodynamic model to complete computations in time to impact the design and execution of the wind tunnel test and use the more sophisticated and time-consuming FUN3D computational fluid dynamics model later. The investigators could consider the following: a tiltrotor version of the recently ended UH-60A air loads program seeking improved tiltrotor modeling tools to accurately predict whirl-flutter stability, exploring other CFD solvers toward validating FUN3D results, and identifying desirable tiltrotor features that require better computational models (e.g., thin tiltrotor wing).

The team exploring aerodynamics of aggressive low Reynolds number flight could collaborate with computational peers and study the same problem at higher Reynolds numbers (up to 105), since that would encompass gliders and all manner of birds. A formal collaboration with the teams working on design, modeling, and analysis of small, low-speed rotors for mission-optimized multicopter UAS could also be explored. These two teams, and those pursuing energy-efficient multimodal flights, flapping wing aerial vehicle system design, and design and flight control of reconfigurable UAS, are encouraged to

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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work together to address the aerodynamic aspects of their vehicles more specifically, and possibly engage some external advisors in this area.

Investigators of the project on design and flight control of reconfigurable UAS could also study the convergence of their learning algorithms, compare their shooting if starting from irregular initial values, and compare their controllers with a slightly more conventional controller of adjusting the weights. Researchers working on the high-fidelity VTOL flight simulator project could explore collaborating with the gaming industry personnel and with academics who have done related work on unmanned aerial vehicles (UAVs).

A significant challenge is recruiting and retaining highly qualified, talented, and motivated research and technical staff.

ENERGY AND PROPULSION

The energy and propulsion program includes S&T in energy storage for mobility, power/energy conversion, distribution and transfer, and intelligent power. The primary goal of this program is to enable adaptive vehicle configurations critical to future Army maneuverability. The S&T research areas reviewed by the panel included ignition mechanisms, micro-reactor heat sources, Li-ion batteries, and loss of lubrication mechanism in rotorcraft.

Accomplishments and Advancements

The program on tribology and lubrication science is well formulated and aimed at understanding fundamental physics. This program has demonstrated commendable use of experimental facilities in fundamental research. A significant practical accomplishment of this program has been identifying the role of heat-activated polymer for emergency lubrication.

There have been significant improvements in capabilities (facilities as well as people), quality of work, and presentations relative to the 2016 review. Some of the facilities at ARL are now among the best in the world. ARL has excellent research equipment, experimental facilities, and computational resources, including a spray combustion research laboratory, a small-engine altitude research facility, and a high-temperature propulsion materials laboratory.

Rotor-Body Coupling with Dynamic Calibration and Drive Train Modeling

This is very applied and essential work with the aim to develop a clean (correct) set of data for enabling the verification of CFD efforts across many institutions and organizations (military, commercial, and academic).

60 kW Man-Portable Inverter

This research is aimed at improving materials and details of the inverter design in order to reduce the weight from 1,000 pounds to a two-person lift. The project goals and methods are well defined. The problem formulation for this task has been driven by needs from the field developed from TARDEC. The approach and progress are solid and appropriate for the challenges in high-frequency switching, losses, and cooling, to name a few. There is good mix of conceptual and numerical models that guide the actual synthesis of a lighter and more compact inverter for vehicle-supported microgrids. The work is anticipated to yield several publications to provide lessons learned to the wider discipline of microgrids.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Advanced Li-Ion Batteries for Maneuver

The presenter highlighted an impressive achievement in manipulating the fundamental material properties to reach higher energy density without compromising the power and safety.

Challenges and Opportunities

The ARL research program on spray atomization and combustion is comprehensive. This program could benefit from partnerships with leading teams at national laboratories and universities. It will also be useful to look for collaboration with the Air Force on, for example, altitude light-off for fuels with Cetane Number below 35. Another example of the use of experimental facilities to resolve relevant practical problems is the use of the altitude testing facility in the resolution of the Gray Eagle turbocharger failure issue. Such accomplishments are commendable; however, care needs to be taken to ensure that experiments are well planned in terms of relevant dimensionless parameters rather than running an array of tests at various dimensional conditions. Although testing could serve to validate a design, it often does not contribute to new knowledge.

Excellent direction is being pursued on optimization of hybrid power systems given the broad needs of the army. The whole tactical unit energy independence project is very important and novel. There seems to be, however, a lack of system-level scalable models that can be used for the target specifications for advanced batteries, engines, capacitors, and burners. This is an area that, if addressed, could highlight the unique Army needs.

There is also considerable lack of modelers in this group. The excellent experimental work on combustion needs to be supported by physics-based and high-fidelity models. Borrowing expertise from the computational group might not be very helpful or as effective as creating internal expertise or leveraging leading external experts.

The group would also benefit from reduced-order models for systematic design of experiments and real-time control of combustion to probe and stretch the combustion stability limits under the fuel uncertainty. Perhaps the ignitability issue under very low cetane (strange) fuel could be then treated as a fault, because igniting this fuel needs an extraordinary sequence of actions and actuation, similar to a fault. If the goal of the team is to be able to find a robust ignition setting for a very wide range of fuel properties, the whole system will be suboptimal and sluggish.

Spark Initiated Ignition at Altitude

A broader range of the atomization parameter domain needs to be planned. Measurements need to be planned that give better understanding of the dominant physics. For example, a domain covering varying Reynolds numbers and Weber numbers needs to be examined. Perhaps some measurements of local gas velocity near the phase interface and identification of vortex structures would be helpful.

Petascale Simulation of Spray Breakup

At supercritical pressures, phase equilibrium needs to be used, allowing solution of gases in the liquid and vaporization of the liquid. The mixture can have a much higher critical pressure than the pure components, thereby allowing two phases to exist at a pressure that is supercritical for the original liquid. The diffused interface assumption is quite unsatisfactory. Molecular dynamics calculations show that the nonequilibrium region at the interface has a thickness of the order of 10 nanometers; this is an

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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order of magnitude thinner than the nonequilibrium region for a shock wave that is commonly taken as a discontinuity. The rational approach is to allow discontinuities in density, enthalpy, internal energy, and normal velocity at the phase interface. On each side of the discontinuity, there will be gradients of density due to composition gradients resulting from gas dissolving in the liquid and liquid vaporizing. Conservation equations for mass, momentum, and energy need to be applied at the interface in addition to the phase equilibrium relations.

EXERGY Energy Awareness

This project can have high impact. Since different units are configured differently with regard to energy-consuming assets, it appears that stochastic analysis is needed. Uncertainties also exist with regard to the operation of the individual assets (vehicles, equipment, etc.) and with regard to processes for transfer of the energy. Error bars on confidence levels might be high and therefore could be developed soon, at least for sample cases. This project is very necessary and worthy for army logistics and operational efficiency.

2025+ Tactical Unit Energy Independence Concept

This project articulated a significant effort for supporting the increased recharging needs for soldier equipment. The trend of speeding up the charging of lithium-ion batteries to 6-10 C rate needs to be matched with a power source for all the energy needed at silent mode. The researchers have many components and processes already identified, but the system-level integration for such a high-power source with so many energy conversions will be extremely challenging. The principal researcher was fully aware of these challenges and was collaborating with many institutions, organizations, and industry for getting models that would inform the integration and create a seamless platform for iterating. The control integration for the balance of the plant will be a key area that needs to be addressed as soon as possible so that it can inform the component sizing in order to satisfy the stringent constraints. Innovative balance of the plant is expected to be a critical phase of this project.

Spray Combustion Facility

The 150-bar spray chamber is a highly valuable facility. Improvements are desirable for the local imaging capability in order to yield better resolution for droplet and ligament determination. Also, an ability to measure gas velocities near the phase interface would be helpful to show how vortex structures affect the atomization.

Multifuel Microreactor

This concept is interesting and has promise; still, some proper comparison is needed—for example, how would the generation of electricity through heat compare with the generation of electricity through work (e.g., a turbine drives the electric generator)? In both cases, the power for pumping air and fuel could be included. Also, the combustion chamber needs to have a high surface-to-volume ratio only for the heat loss domain; it might be avoided in the region where most of the reaction occurs.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Advanced Li-Ion Batteries for Maneuver

A clarification of what is the state of the art and the Department of Energy (DOE) targets would have highlighted better the Army targets and associated plans for future directions. Scaling up the effort to pouch cells is being pursued with industry, although it is not clear that the existing industrial partner can facilitate the scale-up. There might be a need to team up with pilot battery manufacturing facilities such as at Argonne National Laboratory, Oak Ridge National Laboratory, Pennsylvania State University, and the University of Michigan to fabricate single-layer pouch cells first, before stacked multilayer pouches.

Overall, it is an excellent idea to have a close collaboration of Vehicle Technology Directorate (VTD) with SEDD. Both organizations will also benefit from building system-level models for assessing and establishing requirements for energy storage, since much of SEDD’s targets are coming from portable soldier power/energy specifications. There are many other studies that ARL pursues on batteries that can hopefully address the unique Army needs in cooling, fast charging, and advanced diagnostics for safety.

Dynamic Aeroelasticity Research of Aviation Turbocharger for UASs

It is not clear why ARL did not team up with BorgWarner or General Electric, who have the models for this type of coupling. They could then see if the existing models will work for high-altitude conditions and further improve or extend the models.

LOGISTICS AND SUSTAINABILITY

The paramount goal of the logistics and sustainability area is to establish a foundation for tomorrow’s Army that will lead to significant new mission capabilities, enhanced performance and effectiveness, lower maintenance and operational costs, increased reliability, and unmatched superior (and sustained) force projection.

Given that operations are associated with roughly 65 percent of a system’s life costs (35 percent for system procurement), it cannot be overemphasized how important logistics and sustainability are to future Army missions and making smart (self-sensing, self-adapting, and self-reporting) vehicles a reality. The area offers tremendous promise, particularly as the team moves forward in tying health monitoring to critical decision making, maintenance scheduling, and specifically controls, the latter permitting adaptive risk-based maneuvering for sustained safe reliable operations. The potential impact for the Army, the other forces (Air Force, Coast Guard, Marine Corps, Navy, and Space), and civilian and commercial systems is extraordinary.

Critical to the logistics and sustainability area mission are the targeted subareas of mechanical state awareness and reliability. Mechanical state awareness primarily addresses diagnostics (e.g., event signature sensing, characterization, detection, localization, and evolution). Reliability addresses prognostics (e.g., estimating remaining useful life), risk assessment (e.g., condition-based maintenance), multifunctional structures (e.g., embedded sensing via nanomaterials), and enhanced durability. This basic and applied research effort addresses the following critical issues and problems: deterministic, probabilistic/stochastic/statistical modeling; detecting and determining the progression of degradations at the material, component, subsystem, and system levels; and identifying new enabling materials, technologies, processes, methodologies, approaches, and algorithms. The research is also aimed at permitting the systematic design of new lightweight, higher strength, more durable systems with built-in intelligent diagnostics, predictable and graceful degradation, performance, health and usage state estimation, prognostics, and risk assessment to yield systems with increased reliability, enhanced resiliency, lower and more predictable maintenance, lower operational and life-of-the-system costs that enable intelligent

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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risk-based planning and execution of reduced-load maneuvers, prolonged operation, and more effective scheduling of maintenance and repair.

Given the ambitious goal of enabling the development of systems with a guaranteed 7-day maintenance-free operational period (MFOP)—a substantive paradigm shift—relevant accomplishments and advancements as well as challenges and opportunities were identified.

Accomplishments and Advancements

A great improvement was seen since the last review in 2016. This was evidenced, in part, via two relevant successes, a unifying MFOP theme, a spectrum of supporting activities, relevant publications (16 journal papers and 38 conference papers), and specific meritorious activity.

The two specific successes were the use of an acoustic emission (AE) sensing network to identify damage for a Blackhawk helicopter demonstrator composite section, and composite damage inference via electrical impedance spectroscopy (EIS) for carbon fiber reinforced polymer composites as used on General Atomic’s Gray Eagle. These successes are commendable, show great promise, and need to be built upon.

The specific goal of a guaranteed 7-day MFOP for vehicles is very good for ARL and the group. It provides a unifying goal that can be used to organize, direct, and focus ongoing and future work that is needed across the group and the Sciences for Maneuver Campaign. The 5-year plan that was presented was very informative. Given the difficulty associated with achieving an MFOP, there are significant challenges and opportunities to harness, as discussed below.

It was encouraging to see a broad spectrum of relevant activities being pursued to support the logistics and sustainability mission. These include the use of new embedded nanomaterials, new and smart sensing, model-based and data-based estimation, modeling at the materials and systems level (e.g., physics-based, CFD/CSD, and empirical), high-performance computing, event and damage signature identification, damage detection, damage evolution prediction, advanced algorithms, machine learning, model- and data-based risk assessment, new structures and vehicles, and additive manufacturing.

The researcher combined key pillars of science in the work (i.e., theory, experiment, and computation). The project on rotorcraft mechanical component modeling and fault dynamics is praiseworthy.

The proposed neoclassical Bayesian rotorcraft maintenance event tree (RMET) framework, capturing relevant fatigue paths, is essential for addressing the new MFOP paradigm shift that has been adopted. This framework is rich and offers great opportunities as well as challenges, discussed below.

Efforts to collaborate were identified within the group, with AMRDEC, with industry, and with universities via the Open Campus Initiative and hosting workshops. This activity needs to be continued and expanded.

The successes could be built upon by properly integrating scope, support, and resources/funding, and by strategically coordinating to substantively inform decision making—all essential to better meet the needs of tomorrow’s warfighter.

Covetics: Nanocarbon Metal Composites

The goal of developing advanced covetic-based materials with desirable properties (e.g., lighter, stronger) is commendable. While the pursuit may be considered risky, the potential reward could be significant. However, time will be required to properly characterize the approach and assess its true potential. The Open Campus and DOE connections, as well as the hosting of a covetics workshop, are also commendable. The overall quality of this work is very good, supported by publications.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Tunable Poly Urethane-Urea-Based Aligned Carbon Nanotubes Nanocomposites for Multifunctional Vehicle Applications

The goal of using CNTs for addressing today’s problems with composites (e.g., delamination, crack propagation, and damping) is commendable. While the pursuit may be considered risky, the potential reward could be significant. The Open Campus and Nano-Engineered Composite Aerospace Structures Consortium connections are commendable. The overall quality of this work is good.

State Estimation for Complex Dynamic Systems with Unstructured Uncertainty

This project presented a framework for estimating the state and input of a nonlinear system with an uncertain parameter. The approach is model-based, assumes a Gaussian joint state-input probability distribution (conditioned on output), and also assumes access to the system output in the presence of white Gaussian process and sensor noise. A maximum a posteriori estimation approach is taken. The examples considered are simple and academic in nature (e.g., Lorenz 96 with uncertain parameter driven by a sinusoidal input), but the approach was demonstrated to be effective. A practical Army-relevant composite airframe example is planned for the future. This will be an integrated diagnostics, prognostics, and real-time risk assessment demonstration. This work has plans for transition to AMRDEC by 2022. The quality of this work is very good.

Probabilistic Risk Assessment for Rotorcraft Fatigue Critical Structures

This project presented a neoclassical Bayesian RMET framework that captures targeted fatigue paths and multiple repair and inspection possibilities. The framework is rich in that it can be used to capture many fatigue/repair/inspection/failure modes. The quality of this work is very good.

Recurrent Neural Networks for Airframe Damage Prediction

This applied work uses Hsu-Nielson excitation (i.e., pencil breaks) as an acoustic source within a constrained laboratory setup in order to develop a recurrent neural network classifier that distinguishes between direct and reflected AE waves. This laboratory-based work directly supports the development of AE-based airframe damage detection and localization methods. The overall quality of this work is high.

Aerodynamic Interactions Modeling for Coaxial Rotor

Counter-rotating coaxial rotors offer several benefits over single-rotor systems: increased payload for the same engine power, no need for a tail rotor to balance yaw rotation, and no dissymmetry lift effect. However, the interaction between the rotors can be problematic—causing undesirable vibratory modes. This applied work uses CREATE and HELIOS in order to examine the associated complex coupled CFD/CSD interactions. This is a very challenging problem. The work will provide a foundation for future vertical lift work, eventually providing a tool for coaxial system design. AMRDEC is the primary transition partner. The overall quality of this work is high.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Rotorcraft Mechanical Component Dynamics and Fault Modeling

This work had a good combination of relevant problem formulation (addressing gear-tooth crack detection and propagation); sensing; cause-effect load-to-sensor understanding; modeling via finite-element/contact analysis framework to substantively support the above understanding and provide quantitative measures for subsequent decision making (e.g., rescheduling of maintenance, controls, scaling back maneuvers); and a very relevant application (e.g., variable speed transmission for helicopters like the UH-60). The work demonstrated planetary gear mesh stiffening and investigated tooth spall on dynamic transmission error. The use of a deep network to detect and classify gear tooth damage is promising. Future work may examine finite element model validation and differentiating between cracks and spalls. The new laboratory facilities will be very valuable. The overall quality of this work is high (with supporting publications).

Damage Detection Through Magnetostrictive Sensing

The use of embedded magnetostrictive Terfenol-D to detect early damage (without compromising strength) is very promising but requires additional modeling and testing. Critical here is developing a reliable and implementable sensing, detection, and localization criteria. The work can benefit from first principles modeling to complement and inform the ongoing empirical approach. A materials scientist could assist here. Future work could examine extent of damage. This work is planned to transition to AMRDEC and TARDEC. The overall quality of this work is good.

Embedded Self-Sensing Composite Materials for Army Vehicle Platforms

The use of embedded self-sensing nanomaterials within composite materials for sensing the onset of material damage is of great value to Army vehicle platforms. Additional modeling and testing are required in order to properly assess the true potential value of the electromechanical and optoelectronic nanomaterial approach being taken. Critical here is developing a reliable and implementable sensing, detection, and localization criteria, possibly using correlation techniques. It is good that the researcher will be working with a recently hired postdoctoral researcher. This can help address damage detection and localization. This work is planned for transition to AMRDEC and TARDEC. The overall quality of this work is good.

Multifunctional Platform for Additive Manufacturing with Novel Material Systems

The goal of developing a flexible, low-cost, multimaterial (multinozzle) printer that can be used for research and to print needed components on the battlefield can be very impactful. Moreover, the printer will have a built-in feedback system to regulate material properties in situ by exposure to a variety of stimuli—for example, ultraviolet light, localized temperature, electrical current, and magnetic field. Given the limitations of commercial printers—for example, simplicity, patents—the proposed printer may be of great value to ARL. The overall quality of this work is good.

Biaxial Fatigue of Carbon Fiber-Reinforced Polymers

The goal of understanding biaxial fatigue is very relevant to Army rotorcraft (e.g., rotor hub loading). Being able to detect subsurface damage is very important. This work, performed in collaboration

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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with Arizona State University, is promising and is already yielding valuable results (e.g., determining subsurface damage after a specified number of cycles). Being able to reliably predict, establish criteria, and detect subsurface damage at no later than 50 percent of fatigue life will require additional work (e.g., modeling). The quality of this work is excellent. This work is planned for transition to AMRDEC via publication._

Challenges and Opportunities

The issues being addressed by the logistics and sustainability group are formidable, requiring the full utilization of current and ongoing technological advances in materials science, electronics, computing, and sensing. In addition to building on the accomplishments and advances noted above, the group could beneficially pursue the following relevant challenges and opportunities.

The 7-day MFOP goal is formidable because it is inherently multidisciplinary, relying on the proper integration of advanced materials, system/component/material level modeling, smart sensing, high-performance computing (e.g., Monte Carlo simulations), and decision making (e.g., maintenance scheduling, adaptive maneuvering). Given this, it is suggested that the next 5-year plan more precisely reflect the complexity of the required short- and long-term cross-disciplinary interactions, goals, objectives and milestones. Specific questions that need to be addressed are: How will the team achieve the planned short-term FY 2022 UAS MFOP prototype goal with integrated sensing, deep learning, and risk analysis? What will be involved? Who will be involved? How will they be involved? Future talks and posters could provide relevant details. Relevant to the MFOP goal (and any other campaign/group goal) are the following key questions: How will the goal be achieved? What specific short-term objectives should be collectively targeted? What critical questions should be addressed? What relevant metrics should be used? How do you know when you are done or ready to transition to the next phase? What known knowledge and resources will be leveraged? Whenever possible, researchers need to cite relevant work (internal and external) and conduct relevant comparisons and trade studies, using suitable metrics to quantify success and progress, and motivate work ahead. These questions need to be addressed by the leadership (at all levels) as well as by individual researchers. In so doing, many opportunities will be created that can be used to tackle the very demanding challenges ahead.

The group is encouraged to pursue an integrated three-pronged modeling strategy: physics-based (deterministic) modeling, stochastic/probabilistic/statistical modeling, and hardware-based modeling—all supporting critical detection, prediction, and risk assessment issues. The RMET framework could properly integrate physics-based models with stochastic/probabilistic/statistical models. Here, model fidelity, purpose, and computational tractability need to be suitably examined to make essential tradeoffs. Because data from actual system degradations and failures are not readily available, or are time consuming and expensive to create, models are also needed in order to help train the new machine learning algorithms that are being (and will be) developed.

It is important that the team pursue this three-pronged strategy with the explicit goal of substantively tying the areas of system design, manufacturing, modeling, sensing, estimation, health monitoring, and controls.

New facilities (e.g., VTD Artificial Intelligence Laboratory for Risk Reliability and Resilience, and Structural Integrity and Durability Laboratory) will permit researchers to test their models and approaches and use the facilities as a vehicle to collaborate with internal and external industrial and academic partners. The facilities will be very valuable toward achieving MFOP goals.

While the two new facilities are highly lauded, more details on these successes, as well as future plans for these and similar pursuits, would have been very informative.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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The team needs to nurture existing partnerships and build new ones in order to maximally leverage knowledge across the nation and around the world.

Because it is impossible to do everything, researchers need to seek the right balance between modeling, theory, algorithmic rigor, experimentation, validation, and verification. More senior researchers can assist younger researchers to achieve this proper balance.

Given these factors, it is important that researchers address the following relevant questions: When does the model, theory, algorithm, or experiment break down? What are the fundamental limitations? Whenever possible, relevant comparisons and trade studies need to be conducted. Uncertainty—whether it is parametric, dynamic, system, or signal uncertainty—needs to be a uniformly guiding principle and a vehicle for relevant area-wide substantive exploration. Researchers need to consider how uncertainty impacts the problem.

These ideas need to be pursued with the explicit goal of substantively tying the areas of system design, modeling, manufacturing, smart sensing, estimation, health monitoring, and controls to mission-relevant decision making.

The group (and campaign) could aggressively advertise its amazing (and unique) suite of problems and resources so that more researchers, including women and underrepresented minority researchers, will seek to get involved. A coordinated strategy involving the six Services, industry, national laboratories, academia, and the media is very much needed.

State Estimation for Complex Dynamic Systems with Unstructured Uncertainty

This work can benefit from the development of a nonlinear benchmark example within the researcher’s new VTD Artificial Intelligence Laboratory for Risk Reliability and Resilience.

Probabilistic Risk Assessment for Rotorcraft Fatigue Critical Structures

This work can benefit from efforts to assess which fatigue paths are important. A series of successively more complex benchmarks can help focus the work and help other team members plug into the framework in a concrete way (e.g., developing physics-based models to generate data that may be useful for development purposes). Future work could also examine inspection-repair-life-performance trade-offs. Future work could also examine how to achieve robustness with respect to uncertainty in probability distributions, especially distributions based on limited data. A valuable outcome of the understanding that comes from this research could be to identify design approaches that facilitate improved probabilistic risk assessments.

Recurrent Neural Networks for Airframe Damage Prediction

It would be good to examine the impact of environmental uncertainty considering, for example, interfering noise sources, stochastic vibrations at sensor locations, energy-absorbing characteristics of wall materials, temperature, and humidity.

Aerodynamic Interactions Modeling for Coaxial Rotor

Future work could address critical validation issues. This could involve experimental work.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Damage Detection Through Magnetostrictive Sensing

Comparisons to other damage detection methods (e.g., eddy current) need to be made.

Embedded Self-Sensing Composite Materials for Army Vehicle Platforms

This project needs to specifically examine whether the method detects subsurface damage missed by surface sensing.

Multifunctional Platform for Additive Manufacturing with Novel Material Systems

The use of commercial off-the-shelf (COTS) parts will limit what can be achieved within cost constraints. Given this, the scope of this work needs to be clearly focused in order to properly address performance, fidelity, sensing, and cost trade-offs. This is particularly important if the materials to be produced require very precise properties. The work can benefit from relevant modeling to assess what can be realistically achieved. The researchers are encouraged to attend state-of-the-art additive manufacturing conferences.

OVERALL QUALITY OF THE WORK

In general, research presentations and posters were professional, logical, content-rich, and useful. Clear growth in knowledge content by ARL researchers and support staff was demonstrated. Significant advances in the use of analytical and simulation tools were observed. The collaborative interactions—for example, the CTAs and CRAs—continue to be productive. Various director-level responses to previous recommendations were provided. These positive responses are also reflected in the continuous improvement in campaign research performance.

Several research programs were outstanding. Three research programs stand out—research on low-ranked representation learning of action attributes (flexibility and extensibility) in focusing on human action attributes; research on autonomous mobile information collection using a value of information-enriched belief approach (projected functional stochastic gradient-based approach with teams of robots); and research and simulation work on the Wingman Software Integration Laboratory, which has a clear path to Army-relevant static and dynamic scenarios and multiple-machine and multiple-human interactions.

The overall technical quality of the intelligence and control effort is good, and has shown continual improvement—particularly since the 2016 assessment by the Army Research Laboratory Technical Assessment Board. The group has benefited from the hiring of highly skilled postdoctoral researchers, some of whom are being groomed to become full-time ARL employees. Publication in peer-reviewed journals and participation at professional conferences has continued to grow, coupled with increasing participation in other professional activities. Collaborations with peer communities and reputable academic groups appear to be healthy and provide the researchers with invaluable networking opportunities and options to leverage quality research elsewhere. The investment in quality research and development, especially in areas less likely to be pursued by academia, has increased the potential for impact. The connections between the individual research projects and the CTA and CRA programs are very useful and are commended. While it would be a mistake to expect all basic research to be tied into the CTAs, the CRAs provide rich sources of data and research problems and ready platforms for integration and

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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testing in a research-friendly environment. The CTAs and CRAs may naturally serve as a starting point for performance and testing benchmarks.

The research generated by the MHI group is generally of high quality and is focused on important MHI areas and largely comparable to university-led research. In particular, the posters and presentations typically contained acceptable technical content, experimental methods, presentation of data, and statistical analysis of results. The research reflected a broad understanding of the science and references to related work, indicating knowledge of research conducted elsewhere. The qualifications of the research teams were well matched to the research problems and employed acceptable and often state-of-the-art equipment and models. The research typically utilized an appropriate mix of theory and experimentation to arrive at well-reasoned conclusions. The Wingman Software Integration Laboratory was identified as a promising project potentially resulting in outstanding data and knowledge that could ultimately be transitioned to the field. The project is focused on an important topic, necessary for the deployment and implementation of human-machine teams with automated targeting. ARL has a strong set of well-qualified MHI researchers addressing important, Army-related problems. These researchers have a unique opportunity to generate mission-critical data from a population of specifically trained human subjects. Doing so would increase the impact and applicability of the research help the researchers to better understand the needs of the population they serve.

Overall, perception research is addressing cutting-edge problems, with meaningful and relevant results. The group demonstrated an appropriate mix of theory, computation, and experimentation. The group’s publication list and strategy spans the gamut from respected, application-based conference venues to well-regarded academic conferences and publications. There is an opportunity for the group to extend and enhance key projects to yield publications in the field’s best journals with some regularity. When considering the collective portfolio of individual researchers and researchers at leading universities and laboratories, the work achieved by ARL is comparable in scope and outcome. Together, the perception group’s projects reflect an understanding of relevant state of the art, while demonstrating a commitment to pursuing key open questions of Army relevance. ARL has attracted well-qualified research staff and provided them with excellent facilities for conducting cutting-edge research in perception. Several of the projects were particularly well presented and showed strong promise to transition to Army use. One such project is the online gyro calibration algorithm, while another is the embodied training project. They demonstrated solid understanding of the tactical ARL end point while bringing together the proper theory or practice, as needed.

In the platform mechanics area, the quality of work presented was high. The research activities showed a good balance between fundamental and applied research work. The number of articles in peer-reviewed journals, presentations at national conferences, service to the profession, and collaborations with scientists outside their group was commensurate with expectations for the group.

In the energy and propulsion area, overall, the quality of the posters and presentations was very good. The overall research program is commendable with relevant content. However, there is significant room for improvement in collaborations with external experts. Such activities will allow the ARL researchers to advance the state of the art beyond the current status. While some research programs (evidenced by the presentations or poster sessions) have very good external collaboration content and measurable contributions in advancing the state of the art, there are other programs that will benefit from better awareness of the specific technical field. ARL also needs to strive to establish better connections with the outside world. This will improve awareness of the state of the art and increase the productivity of the research programs. There are various ways to accomplish this objective. Attendance at technical conferences is one way to do this. Establishing a program that makes it possible to bring senior researchers in the field to ARL for extended stay will also be very useful. ARL needs to pursue aggressively partnering

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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opportunities with national laboratories and universities that are engaged in research programs on the forefront for specific subject matters.

In the logistics and sustainability area, while the overall technical quality of the work was deemed to be very good (16 journal publications and 38 conference papers since last review), the work and team can significantly benefit by building upon the preceding accomplishments and advancements to address the many challenges and opportunities that lay ahead.

CONCLUSIONS AND RECOMMENDATIONS

Several opportunities are identified for even greater advancement in this campaign’s research productivity. These include a need to increase the level of effort in several basic and applied research projects and internal collaborations; increase mentoring of junior research staff; increase use of Army (soldier) field experiences and scenarios, robots, and more relevant data sets in all the research; address systematically the complexity, scalability, robustness, uncertainty, and operations in noise and interference (i.e., boundary) operations; establish metrics and benchmarks within a push-pull research context; increase ARL presence and participation in journal publications and conference papers so as to define the problem set; and increase strategic, collaborative engagements with industry including via CTAs and CRAs.

According to the U.S. Army Operating Concept,1 human-machine interaction and teaming will be an important near and intermediate focus of research for the Army. The projects that were reviewed appear well positioned to provide mission-critical data and technologies toward developing enhanced human-machine teams. For the most part, these projects are progressing well and generating comparable technical quality to academic research. This finding is supported by the fact that the four projects reviewed have collectively published eight papers at peer-reviewed conferences or workshops. The venues for these publications are the same venues as for academic researchers.

The MHI research reviewed underutilizes military personnel as human subjects. The only reviewed project that has used military personnel is the Toward Natural Dialogue with Robots project; this and the BOT Language project plan to recruit a mix of military personnel and civilians as human subjects in the future, as available. MHI project researchers could, to the extent possible, use soldiers, cadets, and realistic army operators.

The MHI research reviewed underutilized realistic mission scenarios with military relevance. Three of the four projects reviewed did not employ a realistic mission scenario and relied on somewhat contrived notional missions. MHI project researchers need to use realistic relevant mission scenarios whenever possible.

ARL is conducting high-quality perception research that addresses important issues toward the near-term (FY 2020) goal of semantic labeling of an increasingly larger vocabulary of objects and behaviors to permit a richer, more detailed description of the environment. ARL has built up a strong group of researchers in perception, with appropriate resources and facilities to conduct important basic research. ARL has done a good job of disseminating its research through top-notch conferences and has formed strong collaborations with external partners.

Given that much of the ARL perception research is currently being evaluated on commercial or public domain data sets with limited obvious relevance to Army missions, an open question is whether the ARL research will perform similarly when it is transitioned to Army applications. It is well known that the performance of machine learning algorithms varies when different data sets are used. ARL may

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1The U.S. Army Operating Concept (AOC): Win in a Complex World—2020-2040, TRADOC Pamphlet 525-3-1, U.S. Army Training and Doctrine Command, Fort Eustis, Va., October 31, 2014, http://www.tradoc.army.mil/tpubs/pams/tp525-3-1.pdf.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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discover that its research is not addressing the right problem or the right solution for Army-specific applications. It is thus critically important for ARL perception research to be validated using Army-relevant data. While recognizing the challenges of generating such data, performance evaluation of the developed approaches to perception for Army-relevant missions is not possible without doing so and without validating again said data.

Recommendation: ARL should develop a research emphasis on the generation of Army-relevant data suitable for advancing data-driven perception methods and evaluating research in perception against mission-relevant outcomes.

Many of the active research projects are clearly motivated and informed by an Army-relevant use case. While motivation arising from general improvements in the state of the art and problems motivated by academic projects can be useful, a closer linkage to Army priorities and the technical roadmaps articulated by the group will help bind research and researchers more closely to organizational goals. Further, the research can be conducted with the ultimate sensor or platform constraints in mind, keeping in mind that vision-based research could eventually be embedded on mobile platforms in the field. Such constraints might influence the choice of approaches pursued in perception research. Such a linkage will help inform the project regarding platform constraints for deployment.

Recommendation: ARL should closely match all current projects and all new starts to a service-relevant goal in the organizational roadmap and employ platform/deployment constraints as research planning parameters. ARL should consult existing robotics roadmaps and organizational priorities and develop a story line showing how existing and new efforts feed together to develop the desired future capabilities.

While the ARL vehicle intelligence research is of high quality, most of this research is at the single-investigator level. Even projects that have closely related objectives or approaches are conducted without strong connections between them. Furthermore, it is not clear to what degree existing functional robot demonstrations capitalize on prior ARL research. Identifying synergistic ties between related projects (such as the use of common data sets, platforms, frameworks, and benchmarks) can speed innovative progress and increase potential impact. This could be achieved by identifying important Army-relevant use cases that inform the research projects, and then comparing and contrasting related ARL research in that context. The research would still be fundamental and basic, but informed by the specific Army application, as well as the advances made in other relevant areas of ARL research.

Recommendation: ARL should explore incentives and goals for increased internal collaboration across closely related projects, forming tighter connections among algorithms, sensor suites, robot platforms, and Army-relevant use cases. ARL reports and studies should describe how the work fits into the overall ARL mission (with specific customers targeted); what major test-beds will be exploited; how younger scientists are working with more senior scientists; and what expertise will be developed in house versus what will be imported from industry, academia, and other laboratories.

In addition, ARL could accelerate progress in related research areas by developing a strategy for centralizing internal expertise on nontrivial tools and techniques, such as methods for deep learning.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Centralized expertise would enable projects and experts to synergistically benefit each other and shorten the learning curve for investigators who are using techniques closely related to other projects.

Recommendation: ARL should centralize internal expertise on nontrivial tools and techniques, to shorten the learning curve and accelerate progress on related projects in perception.

While the ARL research in perception is being disseminated in conference publications, the ARL record is more limited for journal publications. In general, however, the quality of the ARL work is sufficient for publication in top-quality journals. Such publications would increase the visibility of this work, as well as the potential impact of ARL research on the state of the field.

Recommendation: ARL should increase the dissemination of its perception research through publications in top-quality journal publications.

In the platform mechanics area, the researchers are performing high-quality theoretical, computational, and experimental investigations relevant to Army’s anticipated needs. The most outstanding accomplishments include developing the adaptive and embedded intelligence that employs basic science principles to develop a product with a wide range of applications in the Army, and combining studies of the cockroach motion with models and advanced mathematics to design better robots. The tiltrotor aeroelastic work using computational and wind tunnel models presents a grand challenge and, if successful, will undoubtedly have important consequences for Army and DoD rotorcraft.

Recommendation: To further enhance the quality of research work in the platform mechanics area, the ARL researchers should

  • Discuss the physics behind the computational and experimental models in terms of governing equations, constitutive relations (material models), and initial and boundary conditions;
  • Use nondimensional variables when possible, and conduct screening and sensitivity studies to identify parameters that noticeably affect the desired outcome;
  • Verify codes by analyzing simple problems before using them for large-scale problems; and
  • Explore additional collaborations with academic and applied research laboratories.

In the energy and propulsion area, the objectives and the motivating research questions need to be clearly articulated quantitatively. This practice makes it easier to evaluate progress and ensure relevance. The Heilmeier Catechism2 provides an excellent framework to accomplish this objective. Research programs need to consist of parallel experimental, numerical, and analytical modeling elements. This is essential to advancing understanding and knowledge. It is not necessary that all three elements be conducted at ARL. Working with external institutions as partners will be fine, but these need to be a part of an integrated plan. For example, a very good experimental program on hybrid gears might fail to achieve its potential due lack of progress in parallel modeling efforts. All research programs need be described in terms of relevant fundamental dimensionless parameters. This was pointed out as an opportunity during the review 2 years ago and has been adopted well in some research programs. However, its absence is very visible in some other programs. A fundamental change in thinking process is

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2 See https://en.wikipedia.org/wiki/George_H._Heilmeier.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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required that will get all programs to embrace this basic concept. It is crucial that the experiments be planned in the space described by the relevant dimensionless parameters instead of planning in terms of physical quantities and then calculating the dimensionless parameters. To illustrate, an experiment in aerodynamics could be planned in terms of Reynolds number and Mach number, which will yield the operating temperature, velocities, and other parameter values rather than planning in terms of operating velocity, pressure, temperature and then calculating corresponding Mach number and Reynolds number. Following this principle, the ignition studies being conducted at ARL could be formulated and described in terms of relevant ranges of the Reynolds number and Weber number.

Recommendation: Research programs in the energy and propulsion area should include consideration of the following:

  • Research should be described in terms of relevant fundamental dimensionless parameters.
  • Experiments should be planned in the space described by the relevant dimensionless parameters instead of planning in terms of physical quantities and then calculating the dimensionless parameters.
  • Physics-based models should be integrated, guide, and be guided by off-line and online (onboard) data such as ignition feedback control.
  • Models should be developed for the analysis, design, and diagnostics of balance of plant.

The overall portfolio of the logistics and sustainability group was very good, but the formidable nature of the MFOP goal necessitates that modeling, experimental, computational, and algorithmic resources/expertise be more fully utilized. Given this, the group needs to work closely with one another—researcher to researcher and researchers with leadership—and identify collaborators (both internal and external). The group and collaborators could beneficially develop a set of test-beds, benchmarks, models, simulations, and data sets; develop supporting metrics to gauge the success of each project relative to the group mission and rigorously illustrate the use of model and data-based methods to detect a degradation signature to predict its progression and show how a mission can be prolonged and maintenance scheduling can be enhanced; and develop a plan for fully integrating all group members. The plan can include encouraging co-working on benchmarks, metrics, and papers; co-attending conferences; and co-working on the design of experiments. Future work needs to give more consideration to safety, maintenance scheduling trade-offs, and adaptive control for risk-based maneuvering.

Recommendation: The ARL logistics and sustainability group should aggressively take advantage of the forthcoming opportunities associated with integrating materials, smart sensing, big data, health monitoring, modeling, computing, and controls. The group should develop a new safety-based framework for qualifying vehicles.

While some benchmarks can emanate from the academic or scientific community, some need to come from the military research community. The team could use test-beds and benchmarks to serve as unifying umbrellas for the Sciences for Maneuver Campaign. A 5-year benchmark mission (possibly virtual or involving, for example, Building 570 with multiple ground and air robots) would help focus ARL’s efforts and the efforts of individual researchers. The benchmarking effort is expected to enhance essential and foundational modeling, simulation, and theoretical components.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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Recommendation: ARL should identify benchmarks that can be used to assess methods being presented and facilitate comparisons of ARL efforts as well as other state-of-the-art methods. These benchmarks should be ordered in terms of complexity of addressed scenarios (e.g., images and video clips) in order to systematically assess definitive and appreciable progress over time and from benchmark to benchmark. ARL should use these benchmarks to facilitate and measure the associated incremental or successive progress.

Suggested Citation:"6 Sciences for Maneuver." National Academies of Sciences, Engineering, and Medicine. 2019. 2017-2018 Assessment of the Army Research Laboratory. Washington, DC: The National Academies Press. doi: 10.17226/25419.
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The Army Research Laboratory (ARL) is the corporate laboratory for the U.S. army, which bridges scientific and military communities. The ARL is critical in maintaining the United States’ dominant military power through its advanced research and analysis capabilities. The National Academies of Sciences, Engineering, and Medicine's Army Research Laboratory Technical Assessment Board (ARLTAB) conducts biennial assessments of the scientific and technical quality of the facilities. These assessments are necessary to ensure that the ARL’s resources and quality of programs are maximized.

2017-2018 Assessment of the Army Research Laboratory includes findings and recommendations regarding the quality of the ARL’s research, development, and analysis programs. The report of the assessment is subdivided by the ARL’s Science and Technology campaigns, including Materials Research, Sciences for Lethality and Protection, Information Sciences, Computational Sciences, Sciences for Maneuver, Human Sciences, and Analysis and Assessment. This biennial report summarizes the findings for the 2017-2018 period.

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