Relevant Region Sampling Strategy with Adaptive Heuristic for Asymptotically Optimal Path Planning

TL;DR

This paper introduces an adaptive heuristic sampling strategy for asymptotically optimal path planning that focuses on promising regions, significantly improving efficiency and solution quality in high-dimensional spaces.

Contribution

It proposes a novel batch sampling method based on an adaptive Relevant Region, enhancing path planning efficiency over existing methods like RRT#.

Findings

Reduces planning time compared to related algorithms.

Produces higher quality initial solutions.

Validated through simulations in $SE(2)$ and $SE(3)$ spaces.

Abstract

Sampling-based planning algorithm is a powerful tool for solving planning problems in high-dimensional state spaces. In this article, we present a novel approach to sampling in the most promising regions, which significantly reduces planning time-consumption. The RRT# algorithm defines the Relevant Region based on the cost-to-come provided by the optimal forward-searching tree. However, it uses the cumulative cost of a direct connection between the current state and the goal state as the cost-to-go. To improve the path planning efficiency, we propose a batch sampling method that samples in a refined Relevant Region with a direct sampling strategy, which is defined according to the optimal cost-to-come and the adaptive cost-to-go, taking advantage of various sources of heuristic information. The proposed sampling approach allows the algorithm to build the search tree in the direction of the most promising area, resulting in a superior initial solution quality and reducing the overall computation time compared to related work. To validate the effectiveness of our method, we conducted several simulations in both $SE(2)$ and $SE(3)$ state spaces. And the simulation results demonstrate the superiorities of proposed algorithm.