摘要
为解决牛场人工推翻饲料劳动强度大、工作时间长等问题,设计了一种基于激光雷达同步定位与建图(Simultaneous localization and mapping,SLAM)的牛场智能推翻草机器人自主导航系统,以期实现机器人在牛场环境中自主导航完成推翻草任务。自主导航系统通过激光雷达感知牛场环境,使用加载里程计信息的Cartographer算法构建牛场环境地图,采用未加载里程计信息的自适应蒙特卡洛定位(Adaptive Monte Carlo localization,AMCL)算法实现机器人的定位,并采用迪杰斯特拉算法(Dijkstra)规划机器人推翻草工作路径。试验表明,在构建牛场环境地图时采用机器人加载里程计信息的方式,横纵向偏差最大值低于未加载里程计信息时构建的地图,分别为0.02 m和0.14 m;在实现机器人的定位与导航时采用未加载里程计信息的方式,横纵向偏差最大值及航向偏角最大值分别小于0.04 m、0.10 m和11°,且导航精度高于加载里程计信息时的数值,满足牛场环境中推翻草作业时的导航精度要求。
To solve the problems of high labor intensity and long working time of artificial overthrowing of feed in the pasture,an autonomous navigation system based on LiDAR for synchronous positioning and map building was designed to realize robot navigation and grass turning in pasture environment.The autonomous navigation system platform perceived the pasture environment through LiDAR,a ranch environment map was constructed by using Cartographer algorithm loaded with odometer information,the AMCL algorithm was used which did not load the odometer information to achieve robot positioning,and Dijkstra algorithm was used to plan the robot to overthrow the grass work path.The experiment showed that when constructing the ranch environment map,the maximum deviation of the robot loading odometer information was lower than that of the map when the odometer information was loaded,which was 0.02 m and 0.14 m,respectively,and the maximum value of the horizontal and vertical deviation and the maximum heading declination angle were less than 0.04 m,0.10 m and 11° when the positioning and navigation of the robot were realized,and the navigation accuracy was higher than the value when loading the odometer information.All the results showed that the navigation accuracy can meet the requirements of overthrowing grass operations in a pasture environment.
作者
宋怀波
段援朝
李嵘
焦义涛
王政
SONG Huaibo;DUAN Yuanchao;LI Rong;JIAO Yitao;WANG Zheng(College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling,Shaanxi 712100,China;Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs,Yangling,Shaanxi 712100,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2023年第2期293-301,共9页
Transactions of the Chinese Society for Agricultural Machinery
基金
陕西省技术创新引导计划项目(2022QFY11-02)。
作者简介
宋怀波(1980-),男,教授,博士,主要从事图像处理和机器学习研究,E⁃mail:songhuaibo@nwsuaf.edu.cn。
