Volume 48 Issue 3
Mar.  2022
Turn off MathJax
Article Contents
Abstract
References
Related Articles
ZHU Ziqi, LI Chuangye, DAI Wei. Path planning of coal gangue sorting robot based on G-RRT* algorithm[J]. Journal of Mine Automation，2022，48(3)：55-62. DOI: 10.13272/j.issn.1671-251x.2021090015
Citation: ZHU Ziqi, LI Chuangye, DAI Wei. Path planning of coal gangue sorting robot based on G-RRT* algorithm[J]. Journal of Mine Automation，2022，48(3)：55-62. DOI: 10.13272/j.issn.1671-251x.2021090015
shu

Path planning of coal gangue sorting robot based on G-RRT* algorithm

  • 1.

    Washing Center, CHN Energy Shendong Coal Group Co., Ltd., Yulin 719300, China

  • 2.

    Zhengzhou Institute of Electric and Mechanical Engineering, Zhengzhou 450015, China

  • 3.

    School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

More Information
  • Received Date: September 04, 2021
  • Revised Date: March 06, 2022
  • Available Online: March 21, 2022
    Graphical Abstract
    • Abstract
  • The coal gangue sorting environment is complex. In order to avoid the collision between robot and obstacles and improve sorting efficiency, it is necessary to carry out path planning for robot. The principle of coal gangue sorting system is analyzed. The path planning problem of coal gangue sorting robot is summed up as planning a collision-free path from a given starting point to a target point in the environment of obstacles, and the two constraints of high speed and avoiding collision with obstacles must be met at the same time. Combining the advantages of Cartesian space and joint space, a path planning scheme for coal gangue sorting robot with path planning in joint space and collision detection in Cartesian space is proposed. The scheme does not need to carry out kinematic inversion of the robot, and can avoid describing obstacles in joint space. In order to solve the problem of blindness in the improved rapidly-exploring random trees (RRT*) path planning algorithm, a variable probability target bias strategy is proposed and introduced into RRT* algorithm to obtain the G-RRT* algorithm. The target bias strategy with variable probability increases the target bias probability in the obstacle-free area so as to enhance the target orientation of the algorithm. In the obstacle area, the target bias probability value is reduced to ensure the obstacle avoidance capability of the algorithm. The G-RRT* algorithm combines the variable probability target bias strategy with RRT* algorithm. The G-RRT* algorithm not only retains the asymptotic optimal path length characteristic of RRT* algorithm, but also improves the target orientation of the algorithm, and can improve the path planning efficiency greatly. The experimental results show that compared with RRT-Connect algorithm and RRT algorithm with fixed probability target bias strategy, the G-RRT* algorithm can get the shortest average path length, and is more suitable for path planning of coal gangue sorting robot.
    • FullText(HTML)
    • References (21)
  • [1]
    董书宁,刘再斌,程建远,等. 煤炭智能开采地质保障技术及展望[J]. 煤田地质与勘探,2021,49(1):21-31. DOI: 10.3969/j.issn.1001-1986.2021.01.003

    DONG Shuning,LIU Zaibin,CHENG Jianyuan,et al. Technologies and prospect of geological guarantee for intelligent coal mining[J]. Coal Geology & Exploration,2021,49(1):21-31. DOI: 10.3969/j.issn.1001-1986.2021.01.003
    [2]
    徐亮. 我国煤炭开发建设现状与“十四五”展望[J]. 中国煤炭,2021,47(3):44-48. DOI: 10.3969/j.issn.1006-530X.2021.03.006

    XU Liang. The present situation and expectation of coal exploitation and construction in China[J]. China Coal,2021,47(3):44-48. DOI: 10.3969/j.issn.1006-530X.2021.03.006
    [3]
    张维宸. 能源安全视角下煤炭大国开采政策对比[J]. 国土资源情报,2021(3):16-27. DOI: 10.3969/j.issn.1674-3709.2021.03.003

    ZHANG Weichen. Comparison of mining policies in coal countries from the perspective of energy security[J]. Land and Resources Information,2021(3):16-27. DOI: 10.3969/j.issn.1674-3709.2021.03.003
    [4]
    张永超,于智伟,丁丽林. 基于强化学习的煤矸石分拣机械臂智能控制算法研究[J]. 工矿自动化,2021,47(1):36-42.

    ZHANG Yongchao,YU Zhiwei,DING Lilin. Research on intelligent control algorithm of coal gangue sorting robot arm based on reinforcement learning[J]. Industry and Mine Automation,2021,47(1):36-42.
    [5]
    蒋卫祥. 增量式矿石自动化分拣系统研究[J]. 矿业研究与开发,2020,40(11):150-155.

    JIANG Weixiang. Study on the incremental automatic ore sorting system[J]. Mining Research and Development,2020,40(11):150-155.
    [6]
    夏晶,张昊,周世宁,等. 煤矸分拣机器人动态拣取避障路径规划[J]. 煤炭学报,2021,46(增刊1):570-577.

    XIA Jing,ZHANG Hao,ZHOU Shining,et al. Dynamic picking and obstacle avoidance path planning of coal gangue sorting robot[J]. Journal of China Coal Society,2021,46(S1):570-577.
    [7]
    曹现刚,李宁,王鹏,等. 基于比例导引法的机械臂拣矸过程轨迹规划方法研究[J]. 煤炭工程,2019,51(5):154-158.

    CAO Xiangang,LI Ning,WANG Peng,et al. Research and simulation on priority and path planning of manipulator gangue picking[J]. Coal Engineering,2019,51(5):154-158.
    [8]
    曾俊宝,李硕,李一平,等. 便携式自主水下机器人控制系统研究与应用[J]. 机器人,2016,38(1):91-97.

    ZENG Junbao,LI Shuo,LI Yiping,et al. Research and application of the control system for a portable autonomous underwater vehicle[J]. Robot,2016,38(1):91-97.
    [9]
    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.
    [10]
    于乾坤,王国磊,任田雨,等. 一种移动喷涂机器人的高效站位优化方法[J]. 机器人,2017,39(2):249-256.

    YU Qiankun,WANG Guolei,REN Tianyu,et al. An efficient base position optimization method for mobile painting robot[J]. Robot,2017,39(2):249-256.
    [11]
    曹现刚,吴旭东,王鹏,等. 面向煤矸分拣机器人的多机械臂协同策略[J]. 煤炭学报,2019,44(增刊2):763-774.

    CAO Xiangang,WU Xudong,WANG Peng,et al. Collaborative strategy of multi-manipulator for coal-gangue sorting robot[J]. Journal of China Coal Society,2019,44(S2):763-774.
    [12]
    赵明辉,宣鹏程,张少宾. 并联煤矸石分拣机器人的结构设计及分析[J]. 机床与液压,2021,49(5):55-59. DOI: 10.3969/j.issn.1001-3881.2021.05.011

    ZHAO Minghui,XUAN Pengcheng,ZHANG Shaobin. Structure design and analysis for parallel gangue sorting robot[J]. Machine Tool & Hydraulics,2021,49(5):55-59. DOI: 10.3969/j.issn.1001-3881.2021.05.011
    [13]
    王鹏,曹现刚,马宏伟,等. 基于余弦定理−PID的煤矸石分拣机器人动态目标稳准抓取算法[J]. 煤炭学报,2020,45(12):4240-4247.

    WANG Peng,CAO Xiangang,MA Hongwei,et al. Dynamic target steady and accurate grasping algorithm of gangue sorting robot based on cosine theorem-PID[J]. Journal of China Coal Society,2020,45(12):4240-4247.
    [14]
    张永超,于智伟,丁丽林. 基于机器视觉的煤矸石检测研究[J]. 煤矿机械,2021,42(4):32-34.

    ZHANG Yongchao,YU Zhiwei,DING Lilin. Research on coal gangue detection based on machine vision[J]. Coal Mine Machinery,2021,42(4):32-34.
    [15]
    YU Xue,CHEN Weineng,GU Tianlong,et al. ACO-A*:Ant colony optimization plus A* for 3-D traveling in environments with dense obstacles[J]. IEEE Transactions on Evolutionary Computation,2019,23(4):617-631. DOI: 10.1109/TEVC.2018.2878221
    [16]
    LE A T, BUI M Q, LE T D, et al. D* Lite with Reset: Improved version of D* Lite for complex environment[C]//First IEEE International Conference on Robotic Computing, Taichung, 2017.
    [17]
    NAZARAHARI M,KHANMIRZA E,DOOSTIE S. Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm[J]. Expert Systems with Applications,2019,115:106-120. DOI: 10.1016/j.eswa.2018.08.008
    [18]
    SUN Ping,SHAN Rui. Predictive control with velocity observer for cushion robot based on PSO for path planning[J]. Journal of Systems Science and Complexity,2020,33(4):988-1011. DOI: 10.1007/s11424-020-8375-x
    [19]
    KARAMAN S,FRAZZOLI E. Sampling-based algorithms for optimal motion planning[J]. International Journal of Robotics Research,2011,30(7):846-894. DOI: 10.1177/0278364911406761
    [20]
    CAO Xiaoman,ZOU Xiangjun,JIA Chunyang,et al. RRT-based path planning for an intelligent litchi-picking manipulator[J]. Computers and Electronics in Agriculture,2019,156:105-118. DOI: 10.1016/j.compag.2018.10.031
    [21]
    张云峰,马振书,孙华刚,等. 基于改进快速扩展随机树的机械臂路径规划[J]. 火力与指挥控制,2016,41(5):25-30. DOI: 10.3969/j.issn.1002-0640.2016.05.006

    ZHANG Yunfeng,MA Zhenshu,SUN Huagang,et al. Path planning of manipulators based on improved rapidly-exploring random tree[J]. Fire Control & Command Control,2016,41(5):25-30. DOI: 10.3969/j.issn.1002-0640.2016.05.006
    • Related Articles

  • Related Articles

    [1]YU Yu. Research on 5G communication anti-interference technology and channel equalization algorithm for open-pit mines[J]. Journal of Mine Automation, 2024, 50(S2): 127-131.
    [2]WANG Limin, SUN Ruifeng, ZHAI Guodong, ZHANG Jiawei, XU Hong, ZHAO Jie, HUA Yihang. Path planning of coal mine foot robot by integrating improved A* algorithm and dynamic window approach[J]. Journal of Mine Automation, 2024, 50(6): 112-119. DOI: 10.13272/j.issn.1671-251x.2024020042
    [3]XUE Guanghui, WANG Zijie, WANG Yifan, LI Yanan, LIU Wenhai. Path planning of coal mine underground robot based on improved artificial potential field algorithm[J]. Journal of Mine Automation, 2024, 50(5): 6-13. DOI: 10.13272/j.issn.1671-251x.2024030014
    [4]MAO Qinghua, YAO Lijie, XUE Xusheng. Path planning algorithm for tracked directional drilling rigs in coal mines[J]. Journal of Mine Automation, 2024, 50(2): 18-27. DOI: 10.13272/j.issn.1671-251x.2023080085
    [5]ZHANG Zhiwei, MA Xiaoping, BAI Yateng, LEI Zhenya, LI Jiaming. Local path planning for mobile robots based on improved OpenPlanner algorithm[J]. Journal of Mine Automation, 2023, 49(12): 40-46. DOI: 10.13272/j.issn.1671-251x.18151
    [6]JIANG Yuanyuan, FENG Xueyan. Path planning of coal mine rescue robot based on improved A* algorithm[J]. Journal of Mine Automation, 2023, 49(8): 53-59. DOI: 10.13272/j.issn.1671-251x.2022120027
    [7]XUE Guanghui, LIU Shuang, WANG Zijie, LI Yanan. A path-planning method for coal mine robot based on improved probability road map algorithm[J]. Journal of Mine Automation, 2023, 49(6): 175-181. DOI: 10.13272/j.issn.1671-251x.18116
    [8]MA Hongwei, SUN Naxin, ZHANG Ye, WANG Peng, CAO Xiangang, XIA Jing. Track planning of coal gangue sorting robot for dynamic target stable grasping[J]. Journal of Mine Automation, 2022, 48(4): 20-30. DOI: 10.13272/j.issn.1671-251x.2021110050
    [9]JIANG Jianfeng, CHEN Sihua, YOU Lantao. Uplink rate enhancement algorithm for 5G network in intelligent mine[J]. Journal of Mine Automation, 2021, 47(12): 62-67. DOI: 10.13272/j.issn.1671-251x.2021080067
    [10]HUANG Yourui, LI Jing, HAN Tao, XU Shanyong. Research on path planning algorithm of robot in coal mine based on membrane computing[J]. Journal of Mine Automation, 2021, 47(11): 22-29. DOI: 10.13272/j.issn.1671-251x.17847

  • Cited by

    Periodical cited type(4)

    1. 王轶玮，李晓宇，翁智，白凤山. 基于改进Zero-DCE模型的矿井低照度图像增强方法. 工矿自动化. 2025(02): 57-64+99 . 本站查看
    2. 王泰基. 基于ITLBO-AFSA优化FCM算法的矿井图像增强. 工矿自动化. 2024(S1): 25-28 . 本站查看
    3. 王启飞，赵逸涵，刘帅，刘昊霖，孙英峰，李成武. 煤矿事故大数据驱动的风险治理模式研究综述. 中国安全科学学报. 2024(07): 28-37 .
    4. 樊丹丹. 虚拟现实技术下激光三维无障碍空间图像增强算法. 激光杂志. 2024(10): 125-129 .

    Other cited types(5)

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1273) PDF downloads (47) Cited by(9)
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co., Ltd. Visits：1225463    Baidu Analytics

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return