Relevant Region Sampling Strategy with Adaptive Heuristic for Asymptotically Optimal Path Planning
This work addresses efficiency in sampling-based path planning for robotics, representing an incremental improvement over existing methods like RRT#.
The paper tackles path planning in high-dimensional spaces by proposing a batch sampling method that samples in refined relevant regions using adaptive heuristics, resulting in improved initial solution quality and reduced computation time compared to related work, with simulations in SE(2) and SE(3) state spaces demonstrating its effectiveness.
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.