Toward a real-time framework for solving the kinodynamic motion planning problem

In this paper we propose a framework combining techniques from sampling-based motion planning, machine learning, and trajectory optimization to address the kinodynamic motion planning problem in real-time environments. This framework relies on a look-up table that stores precomputed optimal solutions to boundary value problems (assuming no obstacles), which form the directed edges of a precomputed motion planning roadmap. A sampling-based motion planning algorithm then leverages such a precomputed roadmap to compute online an obstacle-free trajectory. Machine learning techniques are employed to minimize the number of online solutions to boundary value problems required to compute the neighborhoods of the start state and goal regions. This approach is demonstrated to reduce online planning times up to six orders of magnitude. Simulation results are presented and discussed. Problem-specific framework modifications are then discussed that would allow further computation time reductions.