基于激光SLAM和AprilTag融合的温室移动机器人自主导航方法
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江苏省农业科技自主创新资金项目(CX(22)5009)


Autonomous Navigation Method of Greenhouse Mobile Robot Based on Laser SLAM and AprilTag
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    摘要:

    为提高温室环境下移动机器人自主导航精度与效率,提出一种多传感器融合的自主导航方法。设计搭建了基于多维激光雷达、高清工业相机与边缘计算设备的温室移动机器人平台。为提高建图效率与精度,采用Gmapping算法构建二维格栅地图,其输入为三维激光雷达点云滤波后二维点云数据以及采用RF2O算法得到的里程计数据。针对设施栽培垄道狭长、对称、重复的环境特点,提出AprilTag定位校正方法,解决移动机器人定位丢失问题。使用Dijkstra算法和DWA算法分别规划机器人全局和局部导航路径。基于三维运动捕捉系统评估移动机器人定位精度,试验结果表明,在速度0.4、0.3、0.2 m/s下,移动机器人纵向定位误差平均值均不大于0.066 m,标准差均不大于0.049 m;横向定位误差平均值均不大于0.117 m,标准差均不大于0.092 m。在生产温室内开展了移动机器人自主导航性能评估试验,试验结果表明,在速度0.4、0.3、0.2 m/s下,移动机器人实际行驶轨迹与期望轨迹之间横向偏差平均值均不大于0.050 m,标准差均不大于0.032 m,航向偏差平均值均不大于2.2°,标准差均不大于1.4°。移动机器人的定位与导航精度能够满足温室内的连续稳定作业需求。

    Abstract:

    To enhance the precision and effectiveness of autonomous navigation for mobile robots in a greenhouse environment, a localization and autonomous navigation method was proposed by fusing laser simultaneous localization and mapping (SLAM) and AprilTag visual fiducial system. A mobile robot platform for greenhouse was developed based on multi-dimensional LiDAR, high-definition industrial cameras, and edge computing devices. Firstly, in order to improve the efficiency and accuracy of map building, Gmapping algorithm was used to construct a two-dimension grid map by taking two-dimension LiDAR point cloud processed by three-dimension LiDAR point cloud and the odometer data obtained by RF2O algorithm as inputs. Then AprilTag positioning correction method was proposed to solve the positioning loss problem of the mobile robot for the environmental characteristics of narrow, symmetrical, and repetitive facility cultivation ridges. Finally, a combination of Dijkstra algorithm and dynamic window approach (DWA) algorithm was used to plan the global and local navigation paths. The positioning accuracy of mobile robot was evaluated based on a three-dimensional motion capture system in laboratory. The experimental results showed that at speeds of 0.4 m/s, 0.3 m/s and 0.2 m/s, the average longitudinal positioning error of the mobile robot was no more than 0.066 m, and the standard deviation was no more than 0.049 m. The average lateral positioning error was no more than 0.117 m, and the standard deviation was no more than 0.092 m. Autonomous navigation performance evaluation tests of mobile robot were carried out in a greenhouse environment. The experimental results showed that at speeds of 0.4 m/s, 0.3 m/s and 0.2 m/s, the average lateral deviation between the actual driving trajectory of the mobile robot and the expected trajectory was no more than 0.050 m, the standard deviation was no more than 0.032 m, and the average heading deviation was no more than 2.2°, the standard deviation was no more than 1.4°. The positioning and navigation accuracy of mobile robots can meet the continuous and stable operation requirements in greenhouse.

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张文翔,卢鑫羽,张兵园,贡宇,任妮,张美娜.基于激光SLAM和AprilTag融合的温室移动机器人自主导航方法[J].农业机械学报,2025,56(1):123-132. ZHANG Wenxiang, LU Xinyu, ZHANG Bingyuan, GONG Yu, REN Ni, ZHANG Meina. Autonomous Navigation Method of Greenhouse Mobile Robot Based on Laser SLAM and AprilTag[J]. Transactions of the Chinese Society for Agricultural Machinery,2025,56(1):123-132.

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  • 收稿日期:2024-01-22
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  • 在线发布日期: 2025-01-10
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