Assessment of the In-House Laboratory Independent Research at the Army's Research, Development, and Engineering Centers (2020) / Chapter Skim
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3 Computational Sciences
3 Computational Sciences
Pages 29-36
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From page 29... ...
During each presentation the panel engaged in question-and-answer sessions with the presenter, and a general discussion with RDEC staff after the panel had formulated initial impressions and developed additional questions during its closed-session deliberations, conducted after the RDEC staff had concluded their presentations. ARMAMENT RESEARCH, DEVELOPMENT, AND ENGINEERING CENTER Project: Heterogeneous Visual Perception for Collaborative Localization of Autonomous Vehicle Teams This project addresses the problem of coordinated perception for a team of robotic agents.
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From page 30... ...
The numerical method used a commercially available FE package, Abaqus, for the stress analysis, together with specialized algorithms or scripts written by the investigator using MATLAB and Python to alter the interface shapes. An iterative process was used to adjust the positions of nodal points along the boundaries of contacting bodies using a zero-order optimization method.
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From page 31... ...
The researcher demonstrated awareness of literature in the area and followed sound research practices, investigating the sensitivity of results on parameters such as network architecture and exploring the robustness of predictive capabilities through characterization tests across multiple data sets. Meaningful results have been realized including a validation of the hypothesis that dropout-based variational inference can be used to build a meaningful predictive distribution.
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From page 32... ...
TANK AUTOMOTIVE RESEARCH, DEVELOPMENT, AND ENGINEERING CENTER Project: Behavior-Based Convoy Formation Control for Multi-Robot Teams A centralized approach to convoy formation is known to be both computationally demanding and susceptible to a single point failure. The research project seeks a robust decentralized approach to this problem that requires no shared communication among the vehicles.
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From page 33... ...
Since no explicit model is assumed, reinforcement learning ideas are used to update the control policy based on the current level of trust. This research supports autonomous driving behaviors for ground vehicles, which is one of the critical aspects of the U.S.
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From page 34... ...
The basic idea applied to answer these questions is to collect data from several field tests (as part of the Expedited Warfighter Experiment) and analyze the data using a Bayesian network.
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From page 35... ...
the ARL-developed multiscale framework ensures more efficient parallel computing scalability for large-scale simulation models in the context of off-road mobility simulation, and (3) the developed multiscale tire–soil interaction model allows for running the tire–soil interaction simulations with reasonable computational time.
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From page 36... ...
Consequently, efforts in all areas of computational science, including multiscale materials simulations, deep learning algorithms, multi-agent optimization, and vehicle autonomy, all of which are widely applicable to the Army mission, need to be supported across basic and applied research programs, across organizations such as ARL and ARDEC. The ILIR program is an important source of support for such programs that link basic and applied research efforts.
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