基于DAV_DWA算法的农业机器人局部路径规划  

Local Path Planning for Agricultural Robots Based on DAV_DWA

作　　者：汪小旵[1,2] 祁子涵 杨震宇 王得志 黄慧星 卢美光 WANG Xiaochan;QI Zihan;YANG Zhenyu;WANG Dezhi;HUANG Huixing;LU Meiguang(College of Engineering,Nanjing Agricultural University,Nanjing 210031,China;Jiangsu Province Engineering Lab for Modern Facility Agriculture Technology and Equipment,Nanjing 210031,China)

机构地区：[1]南京农业大学工学院,南京210031 [2]江苏省现代设施农业技术与装备实验室,南京210031

出　　处：《农业机械学报》2025年第2期105-114,共10页Transactions of the Chinese Society for Agricultural Machinery

基　　金：江苏省农业科技自主创新资金项目(CX(21)2006)。

摘　　要：为解决农业机器人在示范温室工作通道行驶中难以避让动态障碍物、易陷入局部最小值、无法到达目标点等问题,提出了基于双障碍物评价函数、自适应权重和虚拟目标法的动态窗口法(Dual obstacle cost function,adaptive weights and virtual target_dynamic window approach,DAV_DWA)来实现机器人局部路径规划。首先,采用动静双策略的避障方法,将动态和静态障碍物安全距离划分为2个评价函数,降低动态障碍物碰撞风险且防止对静态障碍物过度避障;其次,提出评价函数权重自适应策略,根据2种障碍物距离实现自适应调整各评价函数权重,以增强机器人在不同复杂环境中的路径寻优能力;最后,提出虚拟目标法,使其脱离局部最小值后继续导航,增强其对于局部最小值的路径规划能力。对比仿真试验和温室实地试验结果表明,在仿真环境中,相较于其他算法,DAV_DWA算法在保证安全性的前提下,能够在更短的时间内,以更短的路径到达目标点;温室障碍物场景中,机器人可以完成自主导航任务,且定位误差不大于0.12 m,跟踪误差不大于0.10 m,符合实际需求。In order to solve the current stage of agricultural robots in the working channel of the demonstration greenhouse,dynamic obstacle processing is difficult,poor target accessibility,easy to fall into the local minimum and so on,the dual obstacle cost function,adaptive weights and virtual target_dynamic window approach(DAV_DWA)was proposed to achieve greenhouse robot local path planning.Firstly,a dynamic-static dual-strategy obstacle avoidance method was adopted,which divided the safety distance of dynamic and static obstacles into two evaluation functions to reduce the collision risk of dynamic obstacles and prevent excessive obstacle avoidance of static obstacles.Secondly,an adaptive strategy for evaluation function weights was proposed,adaptive adjustment of the weights of each evaluation function according to two obstacle distances to enhance the robot's path-finding ability in different complex environments.Finally,the virtual goal method was proposed to enable it to continue navigation after detaching from the local minimum,so as to enhance its path planning ability for the local minimum.Comparative simulation experiments and greenhouse experiments were carried out,and the results showed that compared with other algorithms,DAV_DWA was able to reach the target point with a shorter path in a shorter time under the premise of guaranteeing the safety;in the greenhouse scenario,the robot can complete the autonomous navigation task,and the positioning error was no more than 0.12 m,and tracking error was no more than 0.10 m,which was in line with the practical requirements.

关 键 词：农业机器人 路径规划 动态窗口法 参数自适应 局部最小值 

分 类 号：TP242.6[自动化与计算机技术—检测技术与自动化装置]