Path Planning for Coal Mining Masonry Robots Combined With Trajectory Optimization
In underground coal mining, the efficiency of masonry robots is hindered by complex environmental conditions and pose constraints. This study proposes a novel path planning algorithm combining an improved Rapidly-exploring Random Tree (RRT) with Particle Swarm Optimization (PSO), followed by traject...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10872922/ |
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| author | Xingyi Qian Yan Wang |
| author_facet | Xingyi Qian Yan Wang |
| author_sort | Xingyi Qian |
| collection | DOAJ |
| description | In underground coal mining, the efficiency of masonry robots is hindered by complex environmental conditions and pose constraints. This study proposes a novel path planning algorithm combining an improved Rapidly-exploring Random Tree (RRT) with Particle Swarm Optimization (PSO), followed by trajectory optimization under mechanical constraints to identify the time-optimal path. The improved RRT incorporates dynamic sampling regions and tree reorganization to reduce redundancy and enhance efficiency. A dynamic step length strategy is also introduced to address obstacle avoidance in complex underground environments, ensuring robotic arm safety. The modified PSO algorithm is then used for path planning and trajectory optimization, incorporating obstacle avoidance and pose constraints. Simulation results show that the integrated algorithm significantly reduces path length, sampling points, and search time compared to traditional RRT, RRT*, and informed RRT*. Additionally, trajectory optimization with PSO, considering joint posture constraints, reduces operation time by approximately 13% compared to ant colony optimization. This research provides key technical insights for improving the efficiency and safety of masonry robots in coal mining. |
| format | Article |
| id | doaj-art-71c867522bb14438971c9dbe77f63349 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-71c867522bb14438971c9dbe77f633492025-02-11T00:01:03ZengIEEEIEEE Access2169-35362025-01-0113241972420610.1109/ACCESS.2025.353902310872922Path Planning for Coal Mining Masonry Robots Combined With Trajectory OptimizationXingyi Qian0https://orcid.org/0009-0009-6431-5929Yan Wang1https://orcid.org/0009-0005-4328-241XCollege of Mechanical Engineering, Liaoning Technical University, Fuxin, ChinaCollege of Mechanical Engineering, Liaoning Technical University, Fuxin, ChinaIn underground coal mining, the efficiency of masonry robots is hindered by complex environmental conditions and pose constraints. This study proposes a novel path planning algorithm combining an improved Rapidly-exploring Random Tree (RRT) with Particle Swarm Optimization (PSO), followed by trajectory optimization under mechanical constraints to identify the time-optimal path. The improved RRT incorporates dynamic sampling regions and tree reorganization to reduce redundancy and enhance efficiency. A dynamic step length strategy is also introduced to address obstacle avoidance in complex underground environments, ensuring robotic arm safety. The modified PSO algorithm is then used for path planning and trajectory optimization, incorporating obstacle avoidance and pose constraints. Simulation results show that the integrated algorithm significantly reduces path length, sampling points, and search time compared to traditional RRT, RRT*, and informed RRT*. Additionally, trajectory optimization with PSO, considering joint posture constraints, reduces operation time by approximately 13% compared to ant colony optimization. This research provides key technical insights for improving the efficiency and safety of masonry robots in coal mining.https://ieeexplore.ieee.org/document/10872922/Coal mine masonry robotpath planningRRT algorithmimproved particle swarm optimization algorithmtrajectory optimization |
| spellingShingle | Xingyi Qian Yan Wang Path Planning for Coal Mining Masonry Robots Combined With Trajectory Optimization IEEE Access Coal mine masonry robot path planning RRT algorithm improved particle swarm optimization algorithm trajectory optimization |
| title | Path Planning for Coal Mining Masonry Robots Combined With Trajectory Optimization |
| title_full | Path Planning for Coal Mining Masonry Robots Combined With Trajectory Optimization |
| title_fullStr | Path Planning for Coal Mining Masonry Robots Combined With Trajectory Optimization |
| title_full_unstemmed | Path Planning for Coal Mining Masonry Robots Combined With Trajectory Optimization |
| title_short | Path Planning for Coal Mining Masonry Robots Combined With Trajectory Optimization |
| title_sort | path planning for coal mining masonry robots combined with trajectory optimization |
| topic | Coal mine masonry robot path planning RRT algorithm improved particle swarm optimization algorithm trajectory optimization |
| url | https://ieeexplore.ieee.org/document/10872922/ |
| work_keys_str_mv | AT xingyiqian pathplanningforcoalminingmasonryrobotscombinedwithtrajectoryoptimization AT yanwang pathplanningforcoalminingmasonryrobotscombinedwithtrajectoryoptimization |