A path-planning method for coal mine robot based on improved probability road map algorithm
-
摘要: 路径规划是煤矿机器人在煤矿井下非结构化狭长受限空间中应用亟待解决的关键技术之一。针对传统概率路线图（PRM）算法在空间狭长封闭巷道环境中难以保障采样的节点均匀分布于自由空间中导致路径规划失效，以及节点可能距离障碍物较近导致规划的路径可通行性差等问题，提出了一种基于改进PRM算法的煤矿机器人路径规划方法。在构造阶段引入人工势场法，将落在障碍物中的节点沿与其距离最近自由空间中的节点连线方向推至自由空间，并在障碍物边缘建立斥力场，实现节点的均匀分布且使其距离障碍物有一定距离；在查询阶段融合D* Lite算法，当遇到动态障碍物或前方无法通行时可实现路径的重规划。仿真结果表明：改进PRM算法的节点均匀分布在自由空间中，且均距离障碍物一定距离，提高了路径规划的安全性；当节点数为100个时，改进PRM算法成功率较传统PRM算法提高了25%；随着节点数增加，传统PRM算法和改进PRM算法路径规划成功次数均呈增长趋势，但改进PRM算法在效率方面优势更明显；当节点数为400个时，改进PRM算法运行效率较传统PRM算法提高了35.13%，且规划的路径更平滑，路径长度更短；当障碍物突然出现时，改进PRM算法能够实现路径的重规划。Abstract: Path planning is a key technology that urgently need to be solved in application of coal mine robots in unstructured narrow confined spaces underground. The traditional probabilistic road map (PRM) algorithms are difficult to ensure uniform distribution of sampled nodes in free space in narrow and enclosed roadway environments, resulting in path planning failure. Nodes may be close to obstacles, resulting in poor passability of the planned path. In order to solve the above problems, a path-planning method for coal mine robot based on improved PRM algorithm is proposed. In the constructive phase, the artificial potential field method is introduced to push the node falling in the obstacle to the free space along the direction of the connection line of the node in the free space nearest to it. The repulsive force field is established at the edge of the obstacle to realize uniform distribution of nodes and make them a certain distance from the obstacle. In the query phase, the D* Lite algorithm is integrated to achieve path re-planning when encountering dynamic obstacles or when the front is impassable. The simulation results show that the nodes of the improved PRM algorithm are uniformly distributed in free space and are at a certain distance from obstacles. It improves the safety of path planning. When the number of nodes is 100, the success rate of the improved PRM algorithm is 25% higher than that of the traditional PRM algorithm. As the number of nodes increases, the number of successful path-planning attempts for both traditional and improved PRM algorithms shows an increasing trend. But the improved PRM algorithm has a more significant advantage in efficiency. When the number of nodes is 400, the operational efficiency of the improved PRM algorithm is 35.13% higher than that of the traditional PRM algorithms. The planned path is smoother and the path length is shorter. When obstacles suddenly appear, the improved PRM algorithm can achieve path re-planning.
-
表 1 不同节点数量时改进前后PRM算法路径规划结果统计
Table 1. Path planning results statistics of probabilistic road map algorithm before and after improvement with different number of sampling nodes
算法 节点
数/个成功
次数运行
时间/s路径
长度/m转折点
数/个路径数
量/条传统PRM算法 100 10 3.14 420.35 10 4 200 30 9.78 390.39 8 3 300 61 14.09 381.09 7 2 400 100 29.92 372.78 6 1 改进PRM算法 100 35 2.63 400.93 6 3 200 66 5.13 382.64 5 2 300 97 9.87 372.46 4 1 400 100 19.41 369.57 4 1 
下载: 导出CSV
-
[1] 周李兵. 煤矿井下无轨胶轮车无人驾驶系统研究[J]. 工矿自动化,2022,48(6):36-48.ZHOU Libing. Research on unmanned driving system of underground trackless rubber-tyred vehicle in coal mine[J]. Journal of Mine Automation,2022,48(6):36-48. [2] 薛光辉,候称心,张云飞,等. 煤矿巷道修复重载作业机器人现状与发展趋势[J]. 工矿自动化,2020,46(9):8-14.XUE Guanghui,HOU Chenxin,ZHANG Yunfei,et al. Current situation and development trend of heavy-duty operation robot for coal mine roadway repair[J]. Industry and Mine Automation,2020,46(9):8-14. [3] 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. [4] 金祖进,程刚,郭锋,等. 煤矿搜救机器人最优路径规划算法[J]. 工矿自动化,2018,44(10):24-28.JIN Zujin,CHENG Gang,GUO Feng,et,al. Optimal path planning algorithm for coal mine search and rescue robot[J]. Industry and Mine Automation,2018,44(10):24-28. [5] 王国法,赵国瑞,任怀伟. 智慧煤矿与智能化开采关键核心技术分析[J]. 煤炭学报,2019,44(1):34-41.WANG Guofa,ZHAO Guorui,REN Huaiwei. Analysis on key technologies of intelligent coal mine and intelligent mining[J]. Journal of China Coal Society,2019,44(1):34-41. [6] 葛世荣,胡而已,李允旺. 煤矿机器人技术新进展及新方向[J]. 煤炭学报,2023,48(1):54-73. doi: 10.13225/j.cnki.jccs.2022.1661GE Shirong,HU Eryi,LI Yunwang. New progress and direction of robot technology in coal mine[J]. Journal of China Coal Society,2023,48(1):54-73. doi: 10.13225/j.cnki.jccs.2022.1661 [7] 田子建,高学浩,张梦霞. 基于改进人工势场的矿井导航装置路径规划[J]. 煤炭学报,2016,41(增刊2):589-597.TIAN Zijian,GAO Xuehao,ZHANG Mengxia. Path planning based on the improved artificial potential field of coal mine dynamic target navigation[J]. Journal of China Coal Society,2016,41(S2):589-597. [8] GAO Yongxin,DAI Zhonglin,YUAN Jing. A multiobjective hybrid optimization algorithm for path planning of coal mine patrol robot[J]. Computational Intelligence and Neuroscience,2022,2022:9094572. DOI: 10.1155/2022/9094572. [9] SONG Baoye,MIAO Huimin,XU Lin. Path planning for coal mine robot via improved ant colony optimization algorithm[J]. Systems Science & Control Engineering,2021,9(1):283-289. [10] 陶德俊,姜媛媛,刘延彬,等. 煤矿救援机器人路径平滑算法研究[J]. 工矿自动化,2019,45(10):49-54.TAO Dejun,JIANG Yuanyuan,LIU Yanbin,et al. Research on path smoothing algorithm of coal mine rescue robot[J]. Industry and Mine Automation,2019,45(10):49-54. [11] MAO Ruiqing,MA Xiliang. Research on path planning method of coal mine robot to avoid obstacle in gas distribution area[J]. Journal of Robotics,2016,2016:120-125. [12] 鲍久圣,张牧野,葛世荣,等. 基于改进A*和人工势场算法的无轨胶轮车井下无人驾驶路径规划[J]. 煤炭学报,2022,47(3):1347-1360.BAO Jiusheng,ZHANG Muye,GE Shirong,et al. Underground driverless path planning of trackless rubber tyred vehicle based on improved A* and artificial potential field algorithm[J]. Journal of China Coal Society,2022,47(3):1347-1360. [13] 代嘉惠. 大功率本安驱动煤矿救援机器人定位与建图算法研究[D]. 重庆: 重庆大学, 2019.DAI Jiahui. Study on localization and mapping algorithm of high-power intrinsically safe coal mine rescue robot[D]. Chongqing: Chongqing University, 2019. [14] 金书奎,寇子明,吴娟. 煤矿水泵房巡检机器人路径规划与跟踪算法的研究[J]. 煤炭科学技术,2022,50(5):253-262.JIN Shukui,KOU Ziming,WU Juan. Research on path planning and tracking algorithm of inspection robot in coal mine water[J]. Coal Science and Technology,2022,50(5):253-262. [15] 田洪清,王建强,黄荷叶,等. 越野环境下基于势能场模型的智能车概率图路径规划方法[J]. 兵工学报,2021,42(7):1496-1505. doi: 10.3969/j.issn.1000-1093.2021.07.017TIAN Hongqing,WANG Jianqiang,HUANG Heye,et al. Probabilistic roadmap method for path planning of intelligent vehicle based on artificial potential field model in off-road environment[J]. Acta Armamentarii,2021,42(7):1496-1505. doi: 10.3969/j.issn.1000-1093.2021.07.017 [16] SULAIMAN S,SUDHEER A P. Modeling of a wheeled humanoid robot and hybrid algorithm-based path planning of wheel base for the dynamic obstacles avoidance[J]. Industrial Robot,2022,49(6):1058-1076. doi: 10.1108/IR-12-2021-0298 [17] KAVRAKI L E,SVESTKA P,LATOMBE J C,et al. Probabilistic roadmaps for path planning in high-dimensional configuration spaces[J]. IEEE Transactions on Robotics and Automation,1996,12(4):566-580. doi: 10.1109/70.508439 [18] KHATIB O. Real-time obstacle avoidance for manipulators and mobile robots[J]. The International Journal of Robotics Research,1986,5(1):90-98. doi: 10.1177/027836498600500106 [19] 杨奇峰,曲道奎,徐方. 基于障碍物运动预测的移动机器人路径规划[J]. 计算机工程与设计,2021,42(1):182-188. doi: 10.16208/j.issn1000-7024.2021.01.027YANG Qifeng,QU Daokui,XU Fang. Path planning of mobile robot based on obstacle motion prediction[J]. Computer Engineering and Design,2021,42(1):182-188. doi: 10.16208/j.issn1000-7024.2021.01.027 [20] 杜轩,欧资臻. 改进D* Lite和人工势场法的移动机器人路径规划研究[J]. 制造业自动化,2022,44(2):153-158.DU Xuan,OU Zizhen. Research on the path planning of mobile robots to improve the method of D* Lite and artificial potential field[J]. Manufacturing Automation,2022,44(2):153-158. [21] KOENIG S, LIKHACHEV M. D* lite[C]. AAAI Conference on Artificial Intelligence, Palo Alto, 2002: 476-483. [22] 周非同. 室内移动机器人导航系统研究与设计[D]. 合肥: 中国科学技术大学, 2019.ZHOU Feitong. Research and design of indoor mobile robot's navigation system[D]. Hefei: University of Science and Technology of China, 2019. [23] 黄鲁,周非同. 基于路径优化D* Lite算法的移动机器人路径规划[J]. 控制与决策,2020,35(4):877-884.HUANG Lu,ZHOU Feitong. Path planning of moving robot based on path optimization of D* Lite algorithm[J]. Control and Decision,2020,35(4):877-884. -
点击查看大图
图(5) / 表(1)
计量
- 文章访问数: 959
- HTML全文浏览量: 29
- PDF下载量: 27
- 被引次数: 0
