Regular Papers
International Journal of Control, Automation and Systems 2012; 10(5): 982-991
Published online September 30, 2012
https://doi.org/10.1007/s12555-012-0515-1
© The International Journal of Control, Automation, and Systems
Sampling-Based Retraction Method for Improving the Quality of Mobile Robot Path Planning
This paper presents a method for improving the quality of the initial path produced by the probabilistic roadmap (PRM)-based mobile robot path planner. The sampling-based retraction method modifies the initial path to achieve approximate maximum safety by removing unsafe and redundant sections. The updated directions and distances of the waypoints on the initial path are determined by approximately modeling clearances around the initial paths using random samples. The proposed me-thod can control the update speed to induce smooth convergence. The performance of the proposed method was verified by simulation.
Keywords Mobile robot, navigation, probabilistic roadmap
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Regular Papers
International Journal of Control, Automation and Systems 2012; 10(5): 982-991
Published online October 1, 2012 https://doi.org/10.1007/s12555-012-0515-1
Copyright © The International Journal of Control, Automation, and Systems.
Sampling-Based Retraction Method for Improving the Quality of Mobile Robot Path Planning
Byungjae Park, Jinwoo Choi, and Wan Kyun Chung*
POSTECH
Abstract
This paper presents a method for improving the quality of the initial path produced by the probabilistic roadmap (PRM)-based mobile robot path planner. The sampling-based retraction method modifies the initial path to achieve approximate maximum safety by removing unsafe and redundant sections. The updated directions and distances of the waypoints on the initial path are determined by approximately modeling clearances around the initial paths using random samples. The proposed me-thod can control the update speed to induce smooth convergence. The performance of the proposed method was verified by simulation.
Keywords: Mobile robot, navigation, probabilistic roadmap
Vol. 22, No. 9, pp. 2673~2953
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