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Vehicle Controls & Behaviors

Annual Plan

Deep Reinforcement Learning Approach to CPS Vehicle Re‐envisioning

Project Summary

Principal Investigators

  • Venkat N. Krovi (PI), Clemson University
  • Melissa Smith, Umesh Vaidya, Phanindra Tallapragada, Feng Luo. Raji; Rao (Co‐PIs), Clemson University

Faculty

  • Yiqiang Han, Clemson University

Students

  • Fei Ding, Max Faykus, Ameya Salvi, Jake Buzhardt, Alex Krolicki, Ajinkya Joglekar, Dan Zhang, Clemson University

Government

  • Denise Rizzo, David Gorsich, Jon Smereka, Mark Brudnak, U.S. Army GVSC

Industry

  • Karthik Krishnan, MSC Software/Hexagon

Project begins June 2020, estimated duration 2 years.

The Army’s plans for Next‐Gen Ground Vehicles, for reconnaissance, combat as well as logistics, seek to re‐envision functionality in new classes of Cyber‐Physical System (CPS) vehicle concepts. Modern day CPS Systems offer opportunities to extract superior performance (reconfigurability, robustness, reliability) from the underlying: (i) articulated‐wheeled electromechanical vehicle platforms (e.g. GroundX vehicles with embodied‐mechatronic preflexes); (ii) multi‐modal multi‐resolution sensor‐based spatio‐temporal information gathering; coupled closely with (iii) size, weight and power (SWaP) constrained algorithmic‐ intelligence to realize “real‐time Sense‐Think‐Act”. This inherent but unexploited capability offers enormous opportunity for superior mobility and information gathering in a range of outdoor terrains.

Intelligent orchestration/coordination of newly provisioned flexibility, from the myriad parameters, settings and behaviors, requires careful evaluation at both design stage and during operations. Our team proposes to develop a Deep Reinforcement Learning Enhanced Decision‐Support Framework for CPSSystem Evaluation enabled by multi‐domain physical and sensor‐intelligence modeling of the ground vehicle. The framework and methods will be applicable across: (i) varied size/weight/architecture CPS vehicles, sensor‐allocation schema, sensor‐intelligence modules, and/or motion planner‐controllers; and (ii) diverse benchmark test‐environments/scenarios. In latter stages of the project the framework will enable simulation‐based design‐refinement and hardware‐in‐the‐loop testing.

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