Accelerating sampling-based optimal path planning via adaptive informed sampling

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 reward from previous samples. The paper demonstrates that the resulting 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.