Adaptive hybrid local–global sampling for fast informed sampling-based optimal path planning
Marco Faroni, Nicola Pedrocchi, Manuel Beschi
Abstract
Abstract This paper improves the performance of RRT $$^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> -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 $$^*$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mo>∗</mml:mo> </mml:msup> </mml:math> ) in several simulated and real-world scenarios. An open-source, ROS-compatible implementation of the algorithm is publicly available.