基于改进A^(*)算法+LM-BZS算法的农业机器人路径规划

Path Planning of Agricultural Robot Based on Improved A^(*)and LM-BZS Algorithms

针对目前农业机器人在全局路径规划过程中存在规划效率低、规划路径折线段多、折线角度大、作业不稳定等问题,以果园履带机器人为运动学模型,提出一种基于改进A^(*)算法+低阶多段贝塞尔曲线拼接(Low-order multi-segment Bezier curve splicing,LM-BZS)算法的路径规划方法。首先,根据先验地图获取果园环境信息,将果树和障碍物视作不可通行区域,并结合机器人本体尺寸,对不可通行区域进行膨胀拟合处理;然后,利用改进A^(*)算法搜索路径,对初步生成路径进行树行节点调整;最后,采用LM-BZS算法对调整后的路径点进行优化处理,生成符合果园履带机器人作业要求的行驶路径。仿真试验结果表明,相较于传统A^(*)算法,本文所提出的改进算法在无障碍和有障碍环境中,路径规划时间分别减少76.75%、86.40%,节点评估数量分别减少36.68%、39.37%;经LM-BZS算法优化所得路径在无障碍环境中,相较于传统A^(*)算法和高阶贝塞尔算法,平均曲率分别降低45.81%、18.94%;在有障碍环境中平均曲率分别降低56.98%、27.81%。场地试验结果表明,果园履带机器人在对本文算法生成路径进行跟踪行驶时,在无障碍和有障碍环境中,最大横向误差分别为0.428、0.491 m,平均横向误差分别为0.232、0.276 m,平均航向偏差分别为11.06°、13.76°,符合果园履带机器人自主行驶条件。

In order to solve the problems of low planning efficiency,many polyline segments of the planning path,large polyline angle and unstable operation of agricultural robots in the process of global path planning,a path planning method based on improved A^(*)algorithm and low-order multi-segment Bezier curve splicing(LM-BZS)algorithms was proposed by taking the orchard crawler robot as the kinematic model.To begin with,the orchard environment information was obtained according to the prior map,the fruit trees and the obstacles were regarded as impassable regions,and the impassable regions were expanded and fitted according to the dimensions of the robot body.And then,the improved A^(*)algorithm was used to search for the path,and the tree row nodes were adjusted for the preliminary generation path.In the end,the LM-BZS algorithm was used to optimize the adjusted path points to generate a driving path that meets the operation requirements of the orchard crawler robot.The simulation results manifested that compared with the traditional A^(*)algorithm,the improved algorithm proposed reduced the path planning time by 76.75%and 86.40%,and the number of evaluation nodes by 36.68%and 39.37%,respectively in the barrier-free and obstacle environments.In the barrier-free environment,the average curvature of the path optimized by the LM-BZS algorithm was reduced by 45.81%and 18.94%compared with that of the traditional A^(*)algorithm and the high-order Bezier curve algorithm,respectively,and the average curvature was reduced by 56.98%and 27.81%compared with that of the traditional A^(*)algorithm and the higher-order Bezier curve algorithm in the obstacle environment.The field test results manifested that in the barrier-free and obstacle environment,the maximum lateral error was 0.428 m and 0.491 m,the average lateral error was 0.232 m and 0.276 m,and the average course deviation was 11.06°and 13.76°respectively,which was in line with the autonomous driving conditions of the orchard crawler robot.