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

תקציר מקורי באנגליתarXiv:2410.24205v2 Announce Type: replace-cross Abstract: Path planning in complex environments poses significant challenges, particularly in achieving time efficiency while maintaining a fair success rate and path cost. 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 Value Iteration 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 t