Authors: AmirMohammad Tahmasbi, MohammadSaleh Faghfoorian, Saeed Khodaygan, Aniket Bera
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.
Source: http://arxiv.org/abs/2410.24205v1
