Full coverage path planning for multiple farming machines based on an improved A* algorithm

Abstract The global agriculture industry, facing labor shortages and the need for enhanced production efficiency, turns to unmanned farm machinery as a solution. This study addresses the critical issue of path planning and scheduling for these machines, which often suffer from insufficient coverage, high duplication, and energy consumption when encountering large obstacles in the field. By integrating regional division and an improved A-Star algorithm, the research facilitates full coverage traversal within divided regions and explores the coordinated operation of multiple unmanned agricultural machinery units. The method achieves a 100% coverage rate and reduces operation time by 14.37%, significantly improving efficiency and coverage while lowering duplication rates, and demonstrating promising engineering application prospects in the field of agricultural automation