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基于改进人工势场算法的煤矿井下机器人路径规划

薛光辉 王梓杰 王一凡 李亚男 刘文海

薛光辉,王梓杰,王一凡,等. 基于改进人工势场算法的煤矿井下机器人路径规划[J]. 工矿自动化,2024,50(5):6-13.  doi: 10.13272/j.issn.1671-251x.2024030014
引用本文: 薛光辉,王梓杰,王一凡,等. 基于改进人工势场算法的煤矿井下机器人路径规划[J]. 工矿自动化,2024,50(5):6-13.  doi: 10.13272/j.issn.1671-251x.2024030014
XUE Guanghui, WANG Zijie, WANG Yifan, et al. 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
Citation: XUE Guanghui, WANG Zijie, WANG Yifan, et al. 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

基于改进人工势场算法的煤矿井下机器人路径规划

doi: 10.13272/j.issn.1671-251x.2024030014
基金项目: 国家自然科学基金项目面上项目(51874308);国家重点基础研究发展计划(973计划)项目(2014CB046306)。
详细信息
    作者简介:

    薛光辉(1977—),男,河南汝州人,副教授,博士,主要从事煤矿机器人、煤矿设备自动化与智能化、设备状态检测与健康诊断、无线传感器网络等方面的研究工作,E-mail:xgh@cumtb.edu.cn

  • 中图分类号: TD67

Path planning of coal mine underground robot based on improved artificial potential field algorithm

  • 摘要: 路径规划是煤矿机器人在煤矿井下狭小巷道空间中应用亟待解决的关键技术之一。针对传统人工势场(APF)算法在狭小巷道环境中规划出的路径可能离巷道边界过近,以及在障碍物附近易出现目标不可达和路径振荡等问题,提出了一种基于改进APF算法的煤矿机器人路径规划方法。参考《煤矿安全规程》有关规定建立了巷道两帮边界势场,将机器人行驶路径尽量规划在巷道中间,以提高机器人行驶安全性;在障碍物斥力势场中引入调节因子,以解决目标不可达问题;引入转角限制系数以平滑规划出的路径,减少振荡,提高规划效率,保证规划路径的安全性。仿真结果表明:当目标点离障碍物很近时,改进APF算法可成功规划出能够抵达目标点的路径;改进APF算法规划周期数较传统算法平均减少了14.48%,转向角度变化累计值平均减少了87.41%,曲率绝对值之和平均减少了78.09%,表明改进APF算法规划的路径更加平滑,路径长度更短,规划效率和安全性更高。

     

  • 图  1  APF算法原理

    Figure  1.  Principle of artificial potential field algorithm

    图  2  目标不可达情况

    Figure  2.  Target unreachable situation

    图  3  路径振荡原理

    Figure  3.  Principle of path oscillation

    图  4  巷道内斥力势场强度

    Figure  4.  Strength of repulsive potential field in roadways

    图  5  基于改进APF算法的煤矿井下机器人路径规划流程

    Figure  5.  Path planning flow of coal mine underground robots based on improved artificial potential field algorithm

    图  6  煤矿井下局部环境地图

    Figure  6.  Local environmental map of underground coal mine

    图  7  斥力势场修正前后路径规划仿真结果

    Figure  7.  Simulation results of path planning before and after repulsive potential field correction

    图  8  引入转角限制系数后仿真结果

    Figure  8.  Simulation results after introducing corner restriction coefficient

    图  9  1个障碍物时路径规划结果

    Figure  9.  Path planning results with one obstacle

    图  10  2个障碍物时路径规划结果

    Figure  10.  Path planning results with two obstacles

    图  11  3个障碍物时路径规划结果

    Figure  11.  Path planning results with three obstacles

    表  1  引入转角限制系数前后路径规划性能对比

    Table  1.   Performance comparison of path planning before and after introducing corner constraint coefficients

    评价指标算法改进前算法改进后
    路径长度/m27.4223.83
    规划周期数190165
    转向角度变化累计值/rad17.456.88
    曲率绝对值之和/m−173.0612.39
    下载: 导出CSV

    表  2  多个障碍物情况下改进前后路径规划仿真结果

    Table  2.   Simulation results of path planning before and after improvement under multiple obstacles

    评价指标1个障碍物2个障碍物3个障碍物
    改进前改进后改进前改进后改进前改进后
    路径长度/m19.4816.8328.2523.8224.2620.83
    规划周期数135117196165168144
    转向角度变化累计值/rad29.653.8743.365.2538.314.45
    曲率绝对值之和/m−1135.3936.60265.0949.58215.9343.08
    下载: 导出CSV
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  • 收稿日期:  2024-03-06
  • 修回日期:  2024-05-15
  • 网络出版日期:  2024-06-13

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