Path planning for agricultural robots in wild livestock farm environments

Path planning for field agricultural robots must satisfy several criteria: establishing feeding routes, maintaining gentle slopes, approaching multiple livestock observation points, ensuring timely environmental monitoring, and achieving high efficiency. The complex terrain of outdoor farming areas poses a challenge. Traditional A* algorithms, which generate only the shortest path, fail to meet these requirements and often produce paths that lack smoothness. Therefore, identifying the most suitable path, rather than merely the shortest one, is essential. This study introduced a path-planning algorithm tailored to field-based livestock farming environments, building upon the traditional A* algorithm. It constructed a digital elevation model, integrated an artificial potential field for evaluating multiple target points, calculated terrain slope, optimized the search neighborhood based on robot traversability, and employed Bézier curve segmentation for path optimization. This method segmented the path into multiple curves by evaluating the slopes of the lines connecting adjacent nodes, ensuring a smoother and more efficient route. The experimental results demonstrate its superiority to traditional A*, ensuring paths near multiple target points, significantly reducing the search space, and resulting in over 69.4% faster search speeds. Bézier curve segmentation delivers smoother paths conforming to robot trajectories.