基于UWB/IMU/ODOM/LIDAR的温室机器人室内综合定位系统,Sensors

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基于UWB/IMU/ODOM/LIDAR的温室机器人室内综合定位系统
Sensors ( IF 3.4 ) Pub Date : 2022-06-25 , DOI: 10.3390/s22134819
Zhenhuan Long ₁ , Yang Xiang ₁ , Xiangming Lei ₁ , Yajun Li ₁ , Zhengfang Hu ₁ , Xiufeng Dai ₁

Affiliation

传统的移动机器人采用激光雷达进行室内全球定位导航，对地面环境有严格要求。在温室内复杂的地面条件下，机器人在长时间运行过程中容易出现因车轮打滑而产生的里程表累积误差（ODOM），从而降低了机器人定位测绘的准确性。针对上述问题，提出了基于UWB（超宽带）/IMU（惯性测量单元）/ODOM/LIDAR的一体化定位系统。首先，通过扩展卡尔曼滤波器（EKF）算法集成UWB/IMU/ODOM，得到估计的定位信息。其次，激光雷达通过自适应蒙特卡洛定位（AMCL）算法与已建立的二维（2D）地图相结合，实现机器人的全局定位。实验表明，基于UWB/IMU/ODOM/LIDAR的一体化定位系统有效降低了机器人在温室环境中的定位累积误差。在0.3 m/s、0.5 m/s和0.7 m/s三种移动速度下，最大横向误差小于0.1 m，最大横向均方根误差（RMSE）达到0.04 m。对于全球定位，x轴方向、y轴方向和整体定位的RMSE分别估计为0.092、0.069和0.079 m，得到系统的平均定位时间为72.1 ms。这对于需要精确定位和导航的温室环境中的机器人操作来说已经足够了。基于UWB/IMU/ODOM/LIDAR的一体化定位系统有效降低了机器人在温室环境中的定位累积误差。在0.3 m/s、0.5 m/s和0.7 m/s三种移动速度下，最大横向误差小于0.1 m，最大横向均方根误差（RMSE）达到0.04 m。对于全球定位，x轴方向、y轴方向和整体定位的RMSE分别估计为0.092、0.069和0.079 m，得到系统的平均定位时间为72.1 ms。这对于需要精确定位和导航的温室环境中的机器人操作来说已经足够了。基于UWB/IMU/ODOM/LIDAR的一体化定位系统有效降低了机器人在温室环境中的定位累积误差。在0.3 m/s、0.5 m/s和0.7 m/s三种移动速度下，最大横向误差小于0.1 m，最大横向均方根误差（RMSE）达到0.04 m。对于全球定位，x轴方向、y轴方向和整体定位的RMSE分别估计为0.092、0.069和0.079 m，得到系统的平均定位时间为72.1 ms。这对于需要精确定位和导航的温室环境中的机器人操作来说已经足够了。最大横向误差小于0.1 m，最大横向均方根误差（RMSE）达到0.04 m。对于全球定位，x轴方向、y轴方向和整体定位的RMSE分别估计为0.092、0.069和0.079 m，得到系统的平均定位时间为72.1 ms。这对于需要精确定位和导航的温室环境中的机器人操作来说已经足够了。最大横向误差小于0.1 m，最大横向均方根误差（RMSE）达到0.04 m。对于全球定位，x轴方向、y轴方向和整体定位的RMSE分别估计为0.092、0.069和0.079 m，得到系统的平均定位时间为72.1 ms。这对于需要精确定位和导航的温室环境中的机器人操作来说已经足够了。

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Integrated Indoor Positioning System of Greenhouse Robot Based on UWB/IMU/ODOM/LIDAR

Conventional mobile robots employ LIDAR for indoor global positioning and navigation, thus having strict requirements for the ground environment. Under the complicated ground conditions in the greenhouse, the accumulative error of odometer (ODOM) that arises from wheel slip is easy to occur during the long-time operation of the robot, which decreases the accuracy of robot positioning and mapping. To solve the above problem, an integrated positioning system based on UWB (ultra-wideband)/IMU (inertial measurement unit)/ODOM/LIDAR is proposed. First, UWB/IMU/ODOM is integrated by the Extended Kalman Filter (EKF) algorithm to obtain the estimated positioning information. Second, LIDAR is integrated with the established two-dimensional (2D) map by the Adaptive Monte Carlo Localization (AMCL) algorithm to achieve the global positioning of the robot. As indicated by the experiments, the integrated positioning system based on UWB/IMU/ODOM/LIDAR effectively reduced the positioning accumulative error of the robot in the greenhouse environment. At the three moving speeds, including 0.3 m/s, 0.5 m/s, and 0.7 m/s, the maximum lateral error is lower than 0.1 m, and the maximum lateral root mean square error (RMSE) reaches 0.04 m. For global positioning, the RMSEs of the x-axis direction, the y-axis direction, and the overall positioning are estimated as 0.092, 0.069, and 0.079 m, respectively, and the average positioning time of the system is obtained as 72.1 ms. This was sufficient for robot operation in greenhouse situations that need precise positioning and navigation.

更新日期：2022-06-25

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