提出单/多机器人农田全覆盖路径规划方法，提升农业巡检效率。
Coverage Path Planning in Precision Agriculture: Algorithms, Applications, and Key Benefits
- 单机用波前算法生成扫描序列，多机分区域并行作业。
- 多机方案将田地划为凸多边形，实现任务最优分配与最短覆盖时间。
- 适用于智能农机、精准农业，可降低人工巡检成本。
覆盖路径规划（CPP）是计算移动机器人在特定区域内完成全覆盖扫描的最优路径任务。配备传感器和摄像头的机器人可采集大量作物健康、土壤状况及天气数据，经高级分析后辅助农事决策，提升整体农场管理效率。本文展示两种方法：单机器人采用波前覆盖算法生成所需路径序列；多机器人方案分为两步：首先将农田划分为凸多边形区域以实现任务最优分配，其次为每个区域生成最短覆盖路径，确保整体覆盖时间最小化。
原文摘要 · Abstract (English)
Coverage path planning (CPP) is the task of computing an optimal path within a region to completely scan or survey an area of interest using one or multiple mobile robots. Robots equipped with sensors and cameras can collect vast amounts of data on crop health, soil conditions, and weather patterns. Advanced analytics can then be applied to this data to make informed decisions, improving overall farm management. In this paper, we will demonstrate one approach to find the optimal coverage path of an agricultural field using a single robot, and one using multiple robots. For the single robot, we used a wavefront coverage algorithm that generates a sequence of locations that the robot needs to follow. For the multi-robot approach, the proposed approach consists of two steps: dividing the agricultural field into convex polygonal areas to optimally distribute them among the robots, and generating an optimal coverage path to ensure minimum coverage time for each of the polygonal areas.
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