Dynamic Programming Principles for Sampling-based Motion Planners
نویسندگان
چکیده
Recent randomized asymptotically optimal motion planners incrementally construct an approximate, discrete, sparse representation of the underlying continuous search space, and use locally optimal solutions, along with a local reassignment of the edges, to obtain optimal solutions encoded in the current graph/tree data structure. Careful analysis reveals that all these algorithms can be interpreted as implementing asynchronous dynamic programming (DP) iterations in the underlying graph. Based on this key insight, we utilize existing DP strategies, such as value iteration (VI) or policy iteration (PI) to solve problems arising in randomized sampled-based motion planning. In this work, we show how DP algorithms can be utilized in the framework of samplingbased algorithms. This connection yields different types of algorithms which have asymptotic optimality guarantees, faster convergence rates to the optimal solution and lend themselves readily to different execution models (sequential, parallel).
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تاریخ انتشار 2015