  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.
