Lane navigation control method and equipment of chicken house based on 2D LiDAR

To enhance the efficiency of poultry farm management and reduce labor intensity, this study developed an autonomous inspection robot, named Poultry-Patrolman, for operation in high-density stacked-cage poultry houses. To address the challenges of precise navigation within narrow operation lanes, a comprehensive perception and control framework was proposed, with emphasis on data preprocessing, edge fitting, and adaptive control strategies. On the perception front, raw two-dimensional (2D) LiDAR data were transformed from polar to Cartesian coordinates and corrected for motion distortion based on odometry measurements between consecutive frames. For robust lane boundary extraction, a Full Sample Consensus (F-SAC) algorithm was proposed and applied to the segmented cloud points to perform edge fitting, from which a linear navigation line was generated to compute real-time deviation. On the control side, a Collaborative Hybrid Genetic-Particle Swarm Optimization (CHGAPSO) algorithm was employed to optimize the parameters of a PID controller. The optimized PID parameters, together with the navigation deviation, were integrated into an EKF-PID framework to achieve smooth and accurate trajectory tracking. Experimental results demonstrate that the F-SAC algorithm achieved a maximum absolute angular error of 2.328°, an average angular error of 0.116°, and a line fitting accuracy of 98.3 %. The CHGAPSO algorithm outperformed other methods in optimizing control parameters across four trajectory types: straight line, sinusoidal curve, composite curve, and noisy straight line. Furthermore, the EKF-PID control system demonstrated stable lane-following performance, consistently maintaining lateral steady-state errors within 2 cm under various initial poses at speeds of 0.2 m/s, 0.4 m/s, and 0.6 m/s. These findings validate the effectiveness and reliability of the proposed navigation framework for autonomous poultry house inspection.