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Title: State Supervised Steering Function for Sampling-based Kinodynamic Planning
Sampling-based motion planners such as RRT* and BIT*, when applied to kinodynamic motion planning, rely on steering functions to generate time-optimal solutions connecting sampled states. Implementing exact steering functions requires either analytical solutions to the time-optimal control problem, or nonlinear programming (NLP) solvers to solve the boundary value problem given the system's kinodynamic equations. Unfortunately, analytical solutions are unavailable for many real-world domains, and NLP solvers are prohibitively computationally expensive, hence fast and optimal kinodynamic motion planning remains an open problem. We provide a solution to this problem by introducing State Supervised Steering Function (S3F), a novel approach to learn time-optimal steering functions. S3F is able to produce near-optimal solutions to the steering function orders of magnitude faster than its NLP counterpart. Experiments conducted on three challenging robot domains show that RRT* using S3F significantly outperforms state-of-the-art planning approaches on both solution cost and runtime. We further provide a proof of probabilistic completeness of RRT* modified to use S3F.  more » « less
Award ID(s):
1954778
NSF-PAR ID:
10327701
Author(s) / Creator(s):
;
Date Published:
Journal Name:
AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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