Design and Development of an Intelligent Robotic Feeding Control System for Sheep

Haina Jiang, Haijun Li () and Guoxing Cai
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Haina Jiang: College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Haijun Li: College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Guoxing Cai: College of Electromechanical Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China

Agriculture, 2025, vol. 15, issue 18, 1-20

Abstract: With the widespread adoption of intelligent technologies in animal husbandry, traditional manual feeding methods can no longer meet the demands for precision and efficiency in modern sheep farming. To address this gap, we present an intelligent robotic feeding system designed to enhance feeding efficiency, reduce labor intensity, and enable precise delivery of feed. This system, developed on the ROS platform, integrates LiDAR-based SLAM with point cloud rendering and an Octomap 3D grid map. It combines an improved bidirectional RRT* algorithm with Dynamic Window Approach (DWA) for efficient path planning and uses 3D LiDAR data along with the RANSAC algorithm for slope detection and navigation information extraction. The YOLOv8s model is utilized for precise sheep pen marker identification, while integration with weighing sensors and a farm management system ensures accurate feed distribution control. The main research contribution lies in the development of a comprehensive, multi-sensor fusion system capable of achieving autonomous feeding in dynamic and complex environments. Experimental results show that the system achieves centimeter-level accuracy in localization and attitude control, with FAST-LIO2 maintaining precision within 1° of attitude angle errors. Compared to baseline performance, the system reduces node count by 17.67%, shortens path length by 0.58 cm, and cuts computation time by 42.97%. At a speed of 0.8 m/s, the robot achieves a maximum longitudinal deviation of 7.5 cm and a maximum heading error of 5.6°, while straight-line deviation remains within ±2.2 cm. In a 30 kg feeding task, the system demonstrates zero feed wastage, highlighting its potential for intelligent feeding in modern sheep farming.

Keywords: smart feeding; sheep robot; vision SLAM; path planning; target recognition (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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