果园绿篱修剪机械手路径规划算法研究  

Research on the path planning algorithm of an orchard hedge pruning manipulator

作　　者：夏长高[1] 许秋月 韩江义[1] XIA Changgao;XU Qiuyue;HAN Jiangyi(School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学汽车与交通工程学院,江苏镇江212013

出　　处：《重庆理工大学学报（自然科学）》2023年第1期132-139,共8页Journal of Chongqing University of Technology：Natural Science

基　　金：江苏省科技计划项目(BE2018343-1)。

摘　　要：为提高规划效率,缩短路径长度,保证绿篱修剪机械手的平稳进行,提出了一种基于引力思想和目标偏移概率的快速搜索随机树算法(a-bRRT^(*)),将目标偏转概率和引力思想引入渐近最优快速搜索随机树RRT^(*)算法中,可以兼顾规划效率和路径长度。比较基于目标偏移概率快速搜索随机树(bias-RRT)、RRT^(*)和a-bRRT^(*)算法,结果表明:在修剪树篱顶面时,a-bRRT^(*)算法较bias-RRT算法的路径长度缩短66.32%,规划时间较RRT^(*)降低44.19%;修剪树篱侧面时,a-bRRT^(*)算法较bias-RRT算法的路径长度缩短67.17%,规划时间比RRT^(*)算法降低73.87%。仿真表明:提出的a-bRRT^(*)算法大幅提高了搜索效率,缩短了路径长度。To improve planning efficiency, shorten path length and ensure a smooth operation of a hedgerow pruning manipulator, this paper proposes a rapidly-exploring random tree algorithm(a-bRRT^(*)) based on gravity and target offset probability. The algorithm introduces the idea of target deflection probability and the gravitational force into the asymptotically optimal rapid exploration random tree RRT^(*)algorithm, which can balance planning efficiency and path length. The comparison of the rapidly exploring random trees based on target offset probability(bias-RRT) with RRT^(*)and a-bRRT^(*)shows that, when the top surface of hedge is being pruned, the path length of a-bRRT^(*)algorithm is 66.32% shorter than bias-RRT, and the planning time is 44.19% shorter than RRT^(*). When the side of the hedge is being trimmed, the path length of a-bRRT^(*)algorithm is 67.17% shorter than bias-RRT, and the planning time is 73.87% shorter than RRT^(*). The simulation results show that the proposed a-bRRT^(*)algorithm greatly improves the search efficiency and shortens the path length.

关 键 词：快速探索随机树 运动学 路径规划 绿篱修剪机械手 

分 类 号：TP241.3[自动化与计算机技术—检测技术与自动化装置]