改进A*算法的采摘机器人路径规划与跟踪控制

doi:10.13733/j.jcam.issn.2095⁃5553.2022.03.019

摘要/Abstract

摘要： 为提高采摘机器人的工作效率和控制精准度，提出一种基于改进A*算法的路径规划与跟踪控制方法。首先建立移动采摘机器人的动力学模型，然后通过引入人工势场法改进了A*算法的效率，实现了对采摘机器人运动路径的快速规划，最后利用状态观测器估计出系统状态，并设计终端滑模控制律来准确跟踪路径指令，大大提高了控制精度。仿真结果表明：设计的改进A*算法相比于传统A*算法具有更高的运行效率和更短的路径长度，移动车和机械臂的运行时间分别为6 s和2 s，路径长度分别为47.82 m和11.25 m，设计的终端滑模控制相比于滑模控制具有更优的控制精度，移动车和机械臂的最大跟踪误差为0.2 m和0.04 m，能够使采摘机器人更高效和更精准地运行。

关键词: 采摘机器人, 改进A*算法, 路径规划, 跟踪控制, 状态观测器, 终端滑模控制

Abstract: To improve the working efficiency and control accuracy of picking robot, a path planning and tracking control method based on improved A* algorithm was proposed. Firstly, the dynamic model of the mobile picking robot was established, then the efficiency of A* algorithm was improved by introducing the artificial potential field method,after which the fast path planning of the picking robot was realized. Finally, the state of the system was estimated by using the state observer, and the terminal sliding mode control law was designed to accurately track the path command, which greatly improved the control accuracy. The simulation results showed that the design was improved, so that compared with the traditional A* algorithm, the improved algorithm had higher efficiency and shorter path length. The running time of the mobile vehicle and the manipulator was only 6 s and 2 s, respectively, and the path length was only 47.82 m and 11.25 m, respectively. The terminal sliding mode control had better control precision than sliding mode control. The maximum tracking error of the mobile vehicle and the manipulator was only 0.2 m and 0.04 m, respectively, which made the picking robot run more efficiently and accurately.

Key words: picking robot, improved A* algorithm, path planning, tracking control, state observer, terminal sliding mode control

中图分类号:

S24: TP273

代玉梅, 张瑞玲, 马黎. 改进A*算法的采摘机器人路径规划与跟踪控制[J]. 中国农机化学报, 2022, 43(3): 138-145.

Dai Yumei, Zhang Ruiling. Ma Li. Path planning and tracking control of picking robot based on improved A* algorithm[J]. Journal of Chinese Agricultural Mechanization, 2022, 43(3): 138-145.

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