Task Path Planning System for Autonomous Feeding Robots in Three-Dimensional Farms underYOLOV3 Algorithm

Traditional farm autonomous feeding robot path planning systems have low target detection accuracy, poor planning efficiency, and insufficient adaptability to environmental changes when facing complex farm environments. This article aims to solve the problems existing in traditional systems by introducing the YOLOv3 algorithm, in order to improve the performance and efficiency of robots in autonomous feeding tasks on farms. Firstly, this article utilizes the YOLOv3 algorithm to achieve target detection and localization in the system, and then uses sensor data fusion technology to perceive the environment and construct a map. It then integrates map information for path planning and optimization, achieving system adjustment and obstacle avoidance, and finally selects commonly used path planning algorithms for experimental comparison. The results show that the target detection accuracy of the task path planning system for autonomous feeding robots in three-dimensional farms based on the YOLOv3 algorithm is the highest, exceeding 99%. In addition, the operating efficiency of the system is also the highest, above 95%. In summary, the YOLOv3 algorithm is very suitable for the research of task path planning systems for autonomous feeding robots in three-dimensional farms.