Time-Informed Exploration For Robot Motion Planning
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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. This work defines a "Time Informed Set", using ideas 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 Time Informed Set can accelerate the convergence of sampling-based kino-dynamic motion planners.
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