The purpose of this study is to enhance the effectiveness of path planning algorithms in environments with narrow passages.
The proposed approach combines artificial potential field (APF) and probabilistic roadmap (PRM) methodologies to improve path planning in environments with narrow passages. The technique first identifies narrow passage areas and applies a specialized potential field to assign high values within these regions. This adjustment guides the PRM algorithm to scatter samples more strategically, increasing the likelihood of efficient path discovery.
The experimental results demonstrate that the proposed technique significantly outperforms standard PRM, A* algorithm and recent APF-based techniques in environments with narrow passages, as validated on the Pioneer P3DX robot.
This research introduces a new hybrid path planning technique that uniquely combines APF with PRM techniques to address the specific challenge of navigating narrow passages in complex environments. The proposed technique introduces a new APF modeling strategy designed specifically to fix the narrow passage problem.
