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

Abstract

• Designed and developed a high-density stacked-cage poultry house inspection robot called Poultry-Patrolman. • A single-line LiDAR navigation line extraction method for high-density stacked-cage poultry house was proposed. • Implement control by combining the CHGAPSO algorithm with the EKF-PID algorithm. • The functionality of the development system has been validated within the high-density stack-cage poultry house. 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.