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Adaptive Hybrid Local-Global Sampling for Fast Informed Sampling-Based Optimal Path Planning (2208.09318v2)

Published 19 Aug 2022 in cs.RO, cs.SY, and eess.SY

Abstract: This paper improves the performance of RRT$*$-like sampling-based path planners by combining admissible informed sampling and local sampling (i.e., sampling the neighborhood of the current solution). An adaptive strategy regulates the trade-off between exploration (admissible informed sampling) and exploitation (local sampling) based on online rewards from previous samples. The paper demonstrates that the algorithm is asymptotically optimal and has a better convergence rate than state-of-the-art path planners (e.g., Informed-RRT*) in several simulated and real-world scenarios. An open-source, ROS-compatible implementation of the algorithm is publicly available.

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