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基于G-RRT*算法的煤矸石分拣机器人路径规划

朱子祺 李创业 代伟

朱子祺,李创业,代伟. 基于G-RRT*算法的煤矸石分拣机器人路径规划[J]. 工矿自动化,2022,48(3):55-62.  doi: 10.13272/j.issn.1671-251x.2021090015
引用本文: 朱子祺,李创业,代伟. 基于G-RRT*算法的煤矸石分拣机器人路径规划[J]. 工矿自动化,2022,48(3):55-62.  doi: 10.13272/j.issn.1671-251x.2021090015
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

基于G-RRT*算法的煤矸石分拣机器人路径规划

doi: 10.13272/j.issn.1671-251x.2021090015
基金项目: 国家自然科学基金面上项目(61973306)。
详细信息
    作者简介:

    朱子祺(1982-),男,江苏徐州人,高级工程师,研究方向为煤炭加工及高质化、煤矿智能化,E-mail:Studyzhu@163.com

  • 中图分类号: TD67

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

  • 摘要: 由于煤矸石分拣环境复杂,为了避免机器人与障碍物发生碰撞,提高分拣效率,对机器人进行路径规划十分必要。分析了煤矸石分拣系统原理,将煤矸石分拣机器人路径规划问题归结为在障碍物环境下规划出一条从给定起点到目标点的无碰撞路径,且需同时满足速度快、避免与障碍物碰撞2个约束条件。结合笛卡尔空间和关节空间的优点,提出一种在关节空间进行路径规划、在笛卡尔空间进行碰撞检测的煤矸石分拣机器人路径规划方案,该方案不需要对机器人进行运动学求逆,且可避免在关节空间中描述障碍物。针对RRT*路径规划算法存在盲目性的问题,提出一种变概率的目标偏置策略,并将其引入RRT*算法,得到G-RRT*算法。变概率的目标偏置策略在无障碍物区域增大目标偏置概率值,以增强算法的目标导向性;而在障碍物区域减小目标偏置概率值,以保证算法的避障能力。G-RRT*算法将变概率的目标偏置策略与RRT*算法相结合,既保留了RRT*算法路径长度渐进最优的特点,也提高了算法的目标导向性,可极大地提高路径规划效率。实验结果表明,与加入固定概率目标偏置策略的RRT-Connect算法和RRT算法相比,采用G-RRT*算法得到的路径长度平均值最小,说明G-RRT*算法更适用于煤矸石分拣机器人路径规划。

     

  • 图  1  煤矸石分拣系统

    Figure  1.  Coal gangue sorting system

    图  2  机器人路径规划方案

    Figure  2.  Robot path planning scheme

    图  3  机器人机械结构

    Figure  3.  Robot mechanical structure

    图  4  机器人连杆和障碍物简化模型

    Figure  4.  Simplified model of robot link and obstacle

    图  5  RRT算法节点扩展

    Figure  5.  Node extension of RRT algorithm

    图  6  RRT*算法节点扩展

    Figure  6.  Node extension of RRT* algorithm

    图  7  目标偏置策略效果对比

    Figure  7.  Comparison of target bias strategy effects

    图  8  引入固定概率目标偏置的RRT算法实验结果

    Figure  8.  Experimental results of RRT algorithm introducing fixed probability target bias

    图  9  引入固定概率目标偏置的RRT-Connect算法实验结果

    Figure  9.  Experimental results of RRT-Connect algorithm introducing fixed probability target bias

    图  10  G-RRT*算法实验结果

    Figure  10.  Experimental results of G-RRT* algorithm

    图  11  3种算法实验结果对比

    Figure  11.  Comparison of experimental results of three algorithms

    表  1  算法性能参数对比

    Table  1.   Comparison of algorithm performance parameters

    算法平均时间/s路径长度
    平均值/rad
    RRT5.667.57
    RRT-Connect2.9811.86
    G-RRT*4.346.79
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-05
  • 修回日期:  2022-03-07
  • 网络出版日期:  2022-03-22

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