Zonal RL-RRT: Integrated RL-RRT Path Planning with Collision Probability and Zone Connectivity

TL;DR

Zonal RL-RRT is a novel path planning algorithm that combines kd-tree based zone partitioning with reinforcement learning to improve efficiency and success rates in high-dimensional environments.

Contribution

It introduces a high-level decision framework using Q-learning for zone connectivity, significantly enhancing efficiency over existing sampling and heuristic methods.

Findings

3x faster than RRT/RRT* in forest-like maps

Outperforms heuristic-guided methods by 1.5x in runtime

Maintains high success rates across 2D to 6D environments

Abstract

Path planning in high-dimensional spaces poses significant challenges, particularly in achieving both time efficiency and a fair success rate. To address these issues, we introduce a novel path-planning algorithm, Zonal RL-RRT, that leverages kd-tree partitioning to segment the map into zones while addressing zone connectivity, ensuring seamless transitions between zones. By breaking down the complex environment into multiple zones and using Q-learning as the high-level decision-maker, our algorithm achieves a 3x improvement in time efficiency compared to basic sampling methods such as RRT and RRT* in forest-like maps. Our approach outperforms heuristic-guided methods like BIT* and Informed RRT* by 1.5x in terms of runtime while maintaining robust and reliable success rates across 2D to 6D environments. Compared to learning-based methods like NeuralRRT* and MPNetSMP, as well as the heuristic RRT*J, our algorithm demonstrates, on average, 1.5x better performance in the same environments. We also evaluate the effectiveness of our approach through simulations of the UR10e arm manipulator in the MuJoCo environment. A key observation of our approach lies in its use of zone partitioning and Reinforcement Learning (RL) for adaptive high-level planning allowing the algorithm to accommodate flexible policies across diverse environments, making it a versatile tool for advanced path planning.