Design and testing of the laser navigation system for sheep farm inspection robots

Inspection robots have been widely used in modern agriculture. The high navigation accuracy is often required to rapidly position in complex dynamic environments in sheep farms. In this study, the 3D laser positioning and autonomous navigation were developed to tailor to the sheep farm. Firstly, a fusion approach was employed to combine the 3D LiDAR and an inertial measurement unit (IMU), in order to perceive the sheep farm environment. A tightly coupled radar-inertial navigation and mapping (RNAM) algorithm was used to construct the navigation map. Secondly, a viewpoint visibility approach was employed to preliminarily filter the dynamic point clouds. Egocentric ratio of pSeudo occupancy-based dynamic object removal (ERASOR) was integrated to enhance the dynamic point detection. The height and distance features were combined to further filter out the interference. Thirdly, the precise local positioning was achieved in an error state extended Kalman filter (ESEKF) using laser odometry and IMU. Stable global positioning was realized to enhance the adaptive monte carlo algorithm with the normal distribution transform-iterative closest point (NDT-ICP). Finally, a path planning was constructed to combine the A* algorithm with the timed-elastic-band (TEB) algorithm. Experimental results demonstrate that the filtering algorithm significantly improved the robotic positioning accuracy, compared with the conventional simultaneous localization and mapping (SLAM) algorithms without dynamic point cloud filtering. The average lateral and longitudinal deviations reached 35.2% and 28.7%, respectively. The overall positioning accuracy increased by 31.8%. The robot exhibited an average heading deviation below 2.4° with a standard deviation under 3.2° at the speeds of 0.3-0.5 m/s. While both lateral and longitudinal deviations were maintained, the average values were below 3.5 cm and standard deviations below 2.9 cm. Among the three motion modes—forward, backward, and line-changing- the forward mode demonstrated the highest accuracy, while the backward and line-changing modes shared slightly reduced precision. Nevertheless, all modes fully met the requirements of autonomous navigation in the agricultural robots. The high-precision map construction, positioning, and navigation were realized under complex dynamic environments in sheep farms. The 3D laser positioning and navigation can lay a foundation for the autonomous mobile platforms of the agricultural robots within sheep farm settings in complex environments.