Mobile robot following method based on UWB and single-line lidar

This paper investigates the autonomous follower robot in the context of human-machine integration. A robust and efficient robot decision module is proposed to recognize, locate, and re-identify the target while handling situations of lost targets. First, the DBSCAN clustering algorithm is employed to recognize and locate the target based on point cloud data acquired by the single-line lidar. To identify the target pedestrian, the location information from Ultra-Wide Band (UWB) is utilized. Sensor data fusion with nonlinear least squares optimization enables the extraction of coordinate information in the lidar coordinate system. Subsequently, coordinate transformation is applied to obtain the position information in the robot's coordinate system. To enhance the robustness of the mobile robot's following process, a multimodal behavior controller is proposed based on the data from two sensors. This controller allows the robot to switch between different modes, facilitating the retrieval of the target even under external disturbances. Experimental validation demonstrates the effectiveness of the proposed multi-sensor fusion method in reducing the tracking error of the mobile robot by 55%.