TIE: Time-Informed Exploration For Robot Motion Planning
This addresses the need for more efficient robot motion planning in dynamic environments, though it appears incremental as it builds on existing reachability analysis and sampling-based methods.
The paper tackles the problem of accelerating convergence in anytime sampling-based motion planning for robots by introducing a Time-Informed Set (TIS) to focus exploration on trajectories that can improve the current best solution, resulting in faster convergence as shown in benchmarking experiments.
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 state and control constraints. However, an intelligent exploration strategy is required to accelerate their convergence and avoid redundant computations. Using ideas from reachability analysis, this work defines a "Time-Informed Set", that focuses the search for time-optimal kino-dynamic planning after an initial solution is found. Such a Time-Informed Set (TIS) includes all trajectories that can potentially improve the current best solution and hence exploration outside this set is redundant. Benchmarking experiments show that an exploration strategy based on the TIS can accelerate the convergence of sampling-based kino-dynamic motion planners.