TIE: Time-Informed Exploration For Robot Motion Planning


Anytime sampling-based methods are an attractive technique for solving kino-dynamic motion planning problems. These algorithms scale well to higher dimensions and can efficiently handle stateand control constraints. However, an intelligent exploration strategy is required to accelerate their convergence and avoid redundant computations. This work defines a “Time Informed Set”, usingideas from reachability analysis, that focuses the search for time-optimal kino-dynamic planning after an initial solution is found. Such a Time Informed Set includes all trajectories that can potentially improve the current best solution. Exploration outside this set is hence redundant. Benchmarking experiments show that an exploration strategy based on the TIS can accelerate the convergence of sampling-based kino-dynamic motion planners.