摘要
以四轮差速机器人作为试验平台,提出一种基于单线激光雷达获取环境信息的方法,研究机器人在树行中心线的导航性能以及机器人到达行尾进行自主换行的能力.首先,由单线激光雷达获取环境信息,利用距离阈值法、自适应密度聚类算法、改进的最小二乘法等算法处理激光数据,得到树行中心线作为机器人理论导航路径,以机器人与导航路径的横向偏移量、航向偏角和线速度作为输入量来设计控制模型实时跟踪导航路径,到达行尾时能够自主感知并执行掉头转弯至下一行动作;行间自主导航试验中,机器人线速度分别为0.2、0.4和0.6 m/s,且机器人初始位置横向偏移量不大于0.8 m、航向偏角不大于15°时,导航轨迹与树行中心线的平均横向偏差为3.69 cm,最大横向偏差为11 cm;在自主换行测试中,机器人能够准确进行掉头转弯到下一行,然后继续进行行间导航.果园自主导航系统具有良好的运行稳定性和较高的导航精度,能够满足果园作业机器人的自主行驶精度要求.
A four-wheel differential robot was used as experimental platform to obtain environmental information based on single-line liDAR,and the navigation performance of the robot in the center line of the tree and the autonomous line breaking of the robot at the end of the line were investigated.The environmental information was obtained by single laser radar,and the method of distance threshold,the adaptive density clustering algorithm and the improved method of least square algorithm processing laser data were used to obtain the center line of the tree line as robot navigation theory path.The robot and navigation path horizontal offset,heading angle and velocity were selected as input to design the control model of real-time tracking navigation path.The results show that when the robot reaches the end of line,it can automatically sense and execute the turn to the next line.In the inter-row autonomous navigation experiment,when the robot linear velocities are 0.2,0.4 and 0.6 m/s with the initial lateral offset of robot not more than 0.8 m and the heading angle not more than 15°,the average lateral deviation between the navigation trajectory and the center line of tree line is 3.69 cm,and the maximum lateral deviation is 11 cm.In the autonomous line-breaking test,the robot can accurately turn around to the next line and then continue to navigate between the lines.The proposed orchard autonomous navigation system has good operation stability and high navigation accuracy,which can meet the autonomous driving accuracy requirement of orchard robot.
作者
吴城
李晓娟
WU Cheng;LI Xiaojuan(School of Mechanical Engineering,Xinjiang University,Urumqi,Xinjiang 830047,China)
出处
《江苏大学学报（自然科学版）》
CAS
北大核心
2023年第5期530-539,共10页
Journal of Jiangsu University：Natural Science Edition
基金
新疆维吾尔自治区创新团队项目(2020D14034)。
