Parallelized Control-Aware Motion Planning With Learned Controller Proxies

Abstract

Kinodynamic motion planning enables autonomous robots to find efficient paths while minimizing energy expenditure and avoiding hazards in the environment. However, during plan execution, the controller may deviate from the collision-free path found by the planner due to discrepancies between planning and control, causing inaccurate estimation of path costs and potentially collisions with obstacles. While this can be mitigated by incorporating the vehicle controller into planning, these approaches are generally bottlenecked by the high computation cost of simulating the vehicle dynamics and controller. This letter presents the Parallel Closed-Loop RRT* motion planner that uses a fast neural network controller as a substitute for a computationally-demanding controller during planning. Using a neural network controller and parallelizing the planning process makes closed-loop planning tractable for vehicles with nonlinear dynamics and significantly reduces planning time. Experiments on a simulated underwater vehicle with a model predictive controller demonstrate that our approach yields feasible plans that are more likely to be successfully executed without collisions compared to planners that do not consider the controller.