Research on path planning algorithm of robot in coal mine based on membrane computing
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摘要: 现有煤矿井下机器人路径规划算法采用固定步长和串行方式生成路径，存在成功率低、实时性差、效率低下等问题。将膜计算(MC)与Informed RRT*算法相结合，提出了一种煤矿井下机器人路径规划算法，即MC-IRRT*算法。该算法分为快速连通和路径寻优2个阶段。在快速连通阶段，构建多步长细胞型膜结构，根据空间区域的大小来调整步长：在可行空间较大的区域采用大步长搜索，加快搜索速度；在狭小的空间使用小步长搜索，使搜索空间更加精细，提高狭小空间路径搜索成功率。在路径寻优阶段，构建多采样点细胞型膜结构，通过多个基本膜并行计算，同时在多个椭圆区域内并行搜索最短可行路径，以节省时间，提高路径优化效率。简单场景实验结果表明，与Informed RRT*算法相比，MC-IRRT*算法在快速连通阶段和路径寻优阶段的搜索效率分别提高了76%，40%。复杂场景实验结果表明：RRT*算法和Informed RRT*算法路径规划失败，PQ-RRT*算法和MC-IRRT*算法均能成功寻得可行路径；与PQ-RRT*算法相比，MC-IRRT*算法的速率提高了12.79%，规划的路径长度缩短了8.18%；MC-IRRT*算法不仅可以迅速通过较窄可行区域，而且在路径转折处可以选择使用较小步长，使路径更加平滑。
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关键词:
- 煤矿机器人 /
- 路径规划 /
- 膜计算 /
- Informed RRT*算法 /
- MC-IRRT*算法 /
- 多步长搜索 /
- 并行搜索
Abstract: The existing path planning algorithm of robot in coal mine uses fixed step size and serial mode to generate path, which has problems such as low success rate, poor real-time performance and low efficiency.Combining membrane computing(MC)with Informed RRT* algorithm, this study proposes a path planning algorithm of robot in coal mine, namely MC-IRRT* algorithm.The algorithm is divided into two stages, namely fast connectivity and path optimization.In the fast connectivity stage, the multi-step cellular membrane structure is constructed, and the step size is adjusted according to the size of the space area.The large step search is used in the area with larger feasible space to accelerate the search speed.The small step search is used in the narrow space to make the search space more refined and improve the success rate of the narrow space path search.In the path optimization stage, a multi-sampling cellular membrane structure is constructed, and multiple basic membranes are calculated in parallel, and the shortest feasible path is searched in parallel in multiple elliptical areas at the same time to save time and improve the efficiency of path optimization.The simple scene experimental results show that compared with Informed RRT* algorithm, the search efficiency of MC-IRRT* algorithm in the fast connectivity stage and the path optimization phase is increased by 76% and 40% respectively.The complex scene experimental results show that the path planning of RRT* algorithm and Informed RRT* algorithm fails, and both PQ-RRT* algorithm and MC-IRRT* algorithm can find feasible paths successfully.Compared with PQ-RRT* algorithm, the rate of MC-IRRT* algorithm is increased by 12.79%, and the planned path length is shortened by 8.18%.The MC-IRRT* algorithm can not only pass through narrow feasible areas quickly, but also can choose to use smaller step at the turning point of the path so as to make the path smoother. -
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[1] 杨林,马宏伟,王岩,等.煤矿井下移动机器人运动规划方法研究[J].工矿自动化,2020,46(6):23-30. YANG Lin,MA Hongwei,WANG Yan,et al.Research on motion planning method of underground mobile robot[J].Industry and Mine Automation,2020,46(6):23-30.
[2] 袁晓明,郝明锐.煤矿辅助运输机器人关键技术研究[J].工矿自动化,2020,46(8):8-14. YUAN Xiaoming,HAO Mingrui.Research on key technologies of coal mine auxiliary transportation robot[J].Industry and Mine Automation,2020,46(8):8-14.
[3] 郑亮,孙龙龙,陈双.一种改进工业自动导引车路径规划算法[J].科学技术与工程,2021,21(16):6758-6763. ZHENG Liang,SUN Longlong,CHEN Shuang.An improved industrial automated guided vehicle path planning algorithm[J].Science Technology and Engineering,2021,21(16):6758-6763.
[4] MACWAN A,VILELA J,NEJAT G,et al.A multirobot path-planning strategy for autonomous wilderness search and rescue[J].IEEE Transactions on Cybernetics,2015,45(9):1784-1797.
[5] LIU L,LIN J,YAO J,et al.Path planning for smart car based on dijkstra algorithm and dynamic window approach[J].Wireless Communications and Mobile Computing,2021,2021(4):1-12.
[6] 李鹏,何宸宇,刘琪,等.基于A*扩展自适应蚁群算法的移动机器人路径规划[J].湖南科技大学学报(自然科学版),2021,36(2):85-92. LI Peng,HE Chenyu,LIU Qi,et al.Research on path planning of mobile robot based on A-star extended adaptive ant colony algorithm[J].Journal of Hunan University of Science & Technology(Natural Science Edition),2021,36(2):85-92.
[7] 李得伟,韩宝明,韩宇.一种逆向改进型A*路径搜索算法[J].系统仿真学报,2007,19(22):5175-5177. LI Dewei,HAN Baoming,HAN Yu.Conversely improved A star route search algorithm[J].Journal of System Simulation,2007,19(22):5175-5177.
[8] OH K S,KIM E T,CHO Y W.Path planning of a robot manipulator using retrieval RRT strategy[J].International Journal of Fuzzy Logic and Intelligent Systems,2007,7(2):138-142.
[9] WEI Kun,REN Bingyin.A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm[J].Sensors,2018,18(2):571.
[10] 林娜,张亚伦.自适应RRT无人机航路规划算法研究与仿真[J].计算机仿真,2015,32(1):73-77. LIN Na,ZHANG Yalun.Research and simulation on adaptive RRT algorithm for UAVs path planning[J].Computer Simulation,2015,32(1):73-77.
[11] KARAMAN S,FRAZZOLI E.Sampling-based algorithms for optimal motion planning[J].The International Journal of Robotics Research,2011,30(7):846-894.
[12] GAMMELL J D，SRINIVASA S S，BARFOOT T D.Informed RRT*:optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic[C]//International Conference on Intelligent Robots and Systems,Chicago,2014:2997-3004.
[13] 冯来春,梁华为,杜明博,等.基于A*引导域的RRT智能车辆路径规划算法[J].计算机系统应用,2017,26(8):127-133. FENG Laichun,LIANG Huawei,DU Mingbo,et al.Guiding-area RRT path planning algorithm based on A* for intelligent vehicle[J].Computer Systems & Applications,2017,26(8):127-133.
[14] XI Yingqi,SHEN Wei,ZHANG Wen,et al.A real-time dynamic path planning method combining artificial potential field method and biased target RRT algorithm[C]//The 2nd Asia Conference on Automation Engineering,Chengdu,2020:012015.
[15] HUANG J,SUN W.A method of feasible trajectory planning for UAV formation based on bi-directional fast search tree[J].Optik,2020,221:165213.
[16] LI Y,WEI W,GAO Y,et al.PQ-RRT*:an improved path planning algorithm for mobile robots[J].Expert Systems with Applications,2020,152:113425.
[17] LUO Siyu,LIU Shirong,ZHANG Botao,et al.Path planning algorithm based on Gb informed RRT* with heuristic bias[C]//36th Chinese Control Conference,Dalian,2017:297-302.
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