ENABLING ARTIFICIAL INTELLIGENCE STUDIES IN OFF-ROAD MOBILITY THROUGH PHYSICS-BASED SIMULATION OF MULTI-AGENT SCENARIOS

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 design and assessment of a reinforcement learning policy that uses sensor fusion and inter-agent communication to enable the movement of mixed convoys of conventional and autonomous vehicles. Policies learned on rigid terrain are shown to transfer to hard (silt-like) and soft (snow-like) deformable terrains. The enabling simulation environment, which is Chrono-centric, is used as follows: the training occurs in the GymChrono learning environment using PyChrono, the Python interface to Chrono. The GymChrono-generated policy is subsequently deployed for testing in SynChrono, a scalable, cluster-deployable multi-agent testing infrastructure that uses MPI. The Chrono::Sensor module simulates sensing channels used in the learning and inference processes. The software stack described is open source. Relevant movies: [1].