融合改进A^(*)算法与模糊PID的病死畜禽运输机器人路径规划与运动控制方法  

Path Planning and Motion Control Method for Sick and Dead Animal Transport Robots Integrating Improved A^(*)Algorithm and Fuzzy PID

作　　者：徐济双 焦俊[1] 李淼[2] 李华龙 杨选将 刘先旺 郭盼盼 麻之润 XU Jishuang;JIAO Jun;LI Miao;LI Huaong;YANG Xuanjiang;LIU Xianwang;GUO Panpan;MA Zhirun(School of Information and Computer Science,Anhui Agricultural University,Hefei 230036,China;Hefei Institute of Materials Chinese Academy of Sciences,Institute of Mechanical Intelligence,Hefei 230031,China)

机构地区：[1]安徽农业大学信息与计算机学院,安徽合肥230036 [2]中国科学院合肥物质科学研究院智能机械研究所,安徽合肥230031

出　　处：《智慧农业（中英文）》2023年第4期127-136,共10页Smart Agriculture

基　　金：国家重点研发计划(2022YFD2002104);国家自然科学基金项目(31902205)。

摘　　要：[目的/意义]为了实现病死畜禽无害化处理中心将病死畜禽从存储冷库运输并上料至无害化处理设备的智能化装备过程无人化,对运输机器人的路径规划与自主行走的关键技术难题进行研究。[方法]目前室内环境路径规划算法主要采用的是A*算法,但该算法拐点大、平滑性差、算法计算时间长、遍历节点多,为此提出基于改进的A*算法的病死畜禽无害化处理运输机器人路径规划方法和基于模糊比例积分微分(Proportional Integral Deriv-ative,PID)的运动控制方法,利用曼哈顿距离算法并增设附加值和权值改进启发函数,引入贝塞尔曲线函数优化路径;在规划路径后结合模糊PID算法控制运输机器人底盘的线速度与角速度实现追踪行走。[结果和讨论]开展传统A*算法与改进A^(*)算法的对比实验以及PID追踪实验。结果显示,改进后的A*算法节平均遍历节点由3 067个降至1 968个,算法平均时间由20.34 s减少到7.26 s,开展现场试验验证了该算法的有效性和可靠性。[结论]本研究提出的方法有效的缩短了病死畜禽运输机器人的路径规划时间且减少了遍历节点,提高了路径规划效率和路径平滑性,结合模糊PID算法可以实现运输机器人的稳定寻迹控制,有效解决传统的A*算法在运输机器人路径规划过程中存在的路径规划拐点大、平滑性差、算法计算时间长、遍历节点多等问题,满足病死畜禽无人上料技术需求。[Objective]A key challenge for the harmless treatment center of sick and dead animal is to prevent secondary environmental pollution,especially during the process of transporting the animals from cold storage to intelligent treatment facilities.In order to solve this problem and achieve the intelligent equipment process of transporting sick and dead animal from storage cold storage to harmless treatment equipment in the harmless treatment center,it is necessary to conduct in-depth research on the key technical problems of path planning and autonomous walking of transport robots.[Methods]A^(*)algorithm is mainly adopted for the robot path planning algorithm for indoor environments,but traditional A^(*)algorithms have some problems,such as having many inflection points,poor smoothness,long calculation time,and many traversal nodes.In order to solve these problems,a path planning method for the harmless treatment of diseased and dead animal using transport robots based on the improved A algorithm was constructed,as well as a motion control method based on fuzzy proportional integral differential(PID).The Manhattan distance method was used to replace the heuristic function of the traditional A^(*)algorithm,improving the efficiency of calculating the distance between the starting and ending points in the path planning process.Referring to the actual location of the harmless treatment site for sick and dead animal,vector cross product calculation was performed based on the vector from the starting point to the target point and the vector from the current position to the endpoint target.Additional values were added and dynamic adjustments were implemented,thereby changing the value of the heuristic function.In order to further improve the efficiency of path planning and reduce the search for nodes in the planning process,a method of adding function weights to the heuristic function was studied based on the actual situation on site,to change the weights according to different paths.When the current location node was rela

关 键 词：A^(*)算法 运输机器人 贝塞尔曲线 路径规划 模糊PID 病死畜禽 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]