# Adaptive Hybrid Local-Global Sampling for Fast Informed Sampling-Based Optimal Path Planning

arXiv: https://arxiv.org/abs/2208.09318 · HTML: https://mlsanity.com/paper/2208.09318/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2022.

## Built on by (most cited first, 1 of 1)

- [Optimal task and motion planning and execution for human-robot multi-agent systems in dynamic environments](https://mlsanity.com/paper/2303.14874/) (2023)

## Abstract (arXiv)

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.