End-to-end learning for off-road terrain navigation using the Chrono open-source simulation platform

This contribution ( i ) describes an open-source, physics-based simulation infrastructure that can be used to learn and test control policies in off-road navigation; and ( ii ) demonstrates the use of the simulation platform in an end-to-end learning exercise that relies on simulated sensor data fusion (camera, GPS and IMU). For ( i ), the 0.5 million lines of open-source code support vehicle dynamics (wheeled/tracked vehicles, rovers), deformable non-deformable terrains, and virtual sensing. The library has a Python API for interfacing with existing Machine Learning frameworks. For (ii) , we use a Gator off-road vehicle to demonstrate how a policy learned on non-deformable terrain performs when used in hilly conditions while navigating around a course of randomly placed obstacles on deformable terrain. The hilly terrain covers an 80×80 m patch and the soil can be controlled by the user to assume various behavior, e.g. non-deformable, deformable hard (silt-like), deformable soft (snow-like), etc. To the best of our knowledge, there is no other open-source, physics-based engine that can be used to simulate off-road mobility of autonomous agents operating on deformable terrains. The results reported herein can be reproduced with models and data available in a public repository (UW-Madison Simulation Based Engineering Laboratory, Supporting models, scripts, data, https://go.wisc.edu/arflqq , 2021 ). Animations associated with the tests run are available online (UW-Madison Simulation Based Engineering Laboratory, Supporting simulations, https://go.wisc.edu/256xb9 , 2021 ).