Enabling Artificial Intelligence Studies in Off-Road Mobility Through Physics-Based Simulation of Multiagent Scenarios
Abstract
We describe a simulation environment that enables the design and testing of control policies for off-road mobility of autonomous agents. The environment is demonstrated in conjunction with the training and assessment of a reinforcement learning policy that uses sensor fusion and interagent communication to enable the movement of mixed convoys of human-driven and autonomous vehicles. Policies learned on rigid terrain are shown to transfer to hard (silt-like) and soft (snow-like) deformable terrains. The environment described performs the following: multivehicle multibody dynamics cosimulation in a time/space-coherent infrastructure that relies on the Message Passing Interface standard for low-latency parallel computing; sensor simulation (e.g., camera, GPU, IMU); simulation of a virtual world that can be altered by the agents present in the simulation; training that uses reinforcement learning to “teach” the autonomous vehicles to drive in an obstacle-riddled course. The software stack described is open source. Relevant movies: Project Chrono. Off-road AV simulations, 20202.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 08, 2022
- Source ID
- 10.1115/1.4053321
Entities
People
- Aaron Young
- Alessandro Tasora
- Asher Elmquist
- Dan Negrut
- Jay Taves
- Radu Serban
- Simone Benatti
Organizations
- Army Research Office
- National Science Foundation
- United States Army
- United States Department of Transportation
- University of Parma
- University of Wisconsin–Madison