配电室巡检机器人多目标点路径规划算法  

Multi-target point path planning algorithm for inspection robots in power distribution rooms

作　　者：黄淼 郝真鸣[1,2] 冉宁 戴少石 郝晋渊 Huang Miao;Hao Zhenming;Ran Ning;Dai Shaoshi;Hao Jinyuan(College of Electronic Informational Engineering,Hebei University,Baoding 071002,China;Laboratory of IoT Technology,Hebei University,Baoding 071002,China;Huaneng Shangan Power Plant,Shijiazhuang 050000,China;HBU-UCLAN School of Media,Communication and Creative Industries,Hebei University,Baoding 071002,China)

机构地区：[1]河北大学电子信息工程学院,保定071002 [2]河北大学物联网智能技术研究中心,保定071002 [3]华能上安电厂,石家庄050000 [4]河北大学中央兰开夏传媒与创意学院,保定071002

出　　处：《电子测量技术》2024年第16期49-57,共9页Electronic Measurement Technology

基　　金：国家自然科学基金(62373132);中央引导地方科技发展资金项目(236Z1602G);教育部“春晖计划”合作科研项目(HZKY20220257);石家庄市驻冀高校基础研究项目(241791367A);河北大学优秀青年科研创新团队建设项目(QNTD202411)资助。

摘　　要：配电室巡检机器人由于其特殊的应用环境,会使应用于多目标点路径规划的传统启发式算法求解结果劣化,从而无法得到实际应用意义上的全局最优解。针对上述问题,本文提出了一种基于改进灰狼优化与A*串联融合的多目标点路径规划算法。首先,使用前置A*配合栅格距离公式计算任意两目标点间的栅格距离;然后,采用改进了输入变量编码方式及收敛因子式的灰狼算法规划出多目标点的最优巡航顺序向量;最后,依次规划最优巡航顺序向量相邻两个目标点之间的路径,最终得出多目标点全局闭环规划路径。仿真结果表明,改进灰狼算法得出的最优路径长度相较于传统灰狼算法最大减少18.1%;融合算法相较于传统单一算法优化,其路径规划结果遍历栅格减少了808个,路径长度减少76.4%。Due to the special application environment of the power distribution room inspection robot,the traditional heuristic algorithm used for multi-objective point path planning may deteriorate the solution results,thus failing to obtain the globally optimal solution in practical applications.In response to the above issues,this paper proposes a multi-objective point path planning algorithm based on the serial fusion of improved grey wolf optimization and A*.Firstly,the pre-A*algorithm is used in conjunction with the grid distance formula to calculate the grid distance between any two target points.Then,an improved grey wolf algorithm,which has modified the input variable encoding method and convergence factor formula,is adopted to plan the optimal cruise sequence vector for multiple target points.Finally,the path between adjacent target points in the optimal cruise sequence vector is planned in sequence,and the globally closed-loop planning path for multiple target points is finally obtained.The simulation results show that the optimal path length obtained by the improved grey wolf algorithm is reduced by a maximum of 18.1%compared with the traditional grey wolf algorithm.Compared with the traditional single algorithm optimization,the fusion algorithm reduces the traversed grids by 808 and the path length by 76.4%.

关 键 词：巡检机器人 多目标点路径规划 灰狼优化算法 融合算法 

分 类 号：TN96[电子电信—信号与信息处理] TP11[电子电信—信息与通信工程]