Aiming to streamline farm inspections and reduce labor intensity, researchers at the Key Laboratory of Smart Agriculture, China Agriculture University, Beijing have developed an autonomous robot called Poultry Patrolman. This innovative robot is engineered to independently navigate the narrow lanes of high-density stacked poultry houses, enhancing the efficiency of environmental inspections.
Technology Behind the Robot
The Poultry Patrolman utilizes 2D LiDAR sensors, enabling it to ‘see’ its surroundings and accurately interpret sensor data. To correct any movement discrepancies, the robot employs a smart algorithm known as Full Samples Consensus (F-SAC), allowing it to reliably detect lane edges and steer accordingly.
To enhance tracking accuracy, the researchers implemented a specialized optimization approach named Collaborative Hybrid Genetic-Particle Swarm Optimization (CHGAPSO). This method fine-tunes the robot’s steering system, while an EKF-PID control system ensures that the robot follows its designated path smoothly and precisely.
Results of the Study
Experimental evaluations revealed that the F-SAC algorithm achieved a maximum absolute angular error of just 2.328°, with an impressive average angular error of 0.116% and a line fitting accuracy of 98.3%. Furthermore, the CHGAPSO algorithm outperformed alternative methods in optimizing control parameters across four trajectory types: straight line, sinusoidal curve, composite curve, and noisy straight line.
The EKF-PID control system demonstrated stable lane-following capabilities, maintaining lateral steady-state errors within 2 cm at varied initial poses and speeds of 0.2 m/s, 0.4 m/s, and 0.6 m/s. These results affirm the reliability and efficiency of the proposed navigation framework for autonomous inspections within poultry houses.
The study, entitled Lane navigation control method and equipment of chicken house based on 2D LiDAR, has been published in the journal Computers and Electronics in Agriculture.
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