Multi-Regional Coverage Path Planning in Polygonal Regions for Multiple Unmanned Aerial Vehicles

The utilization of Unmanned Aerial Vehicles (UAVs) has seen a significant increase across many fields, including disaster rescue, agricultural plant protection, environmental monitoring and geographic mapping. These applications inherently demand highly efficient region full-coverage path planning techniques. On one hand, Although single UAV coverage has been widely explored, yet it often struggles to meet the escalating requirements of large-scale and time-sensitive tasks due to its limited efficiency. On the other hand, Meanwhile, multi-UAV collaboration has emerged as a promising solution alternative to overcome these limitations. In this study, a novel framework to optimize the multi-regional coverage path planning for multiple UAVs was proposed. First, the task regions were distributed to three UAVs based on the proposed minimum consumption ratio (MCR) method. This approach takes into account various factors, such as the area of regions and the capabilities of UAVs, ensuring a more rational allocation compared to traditional methods. Then, a unique method of rotating irregular polygonal regions was presented to solve the coverage path planning within regions. Unlike the conventional coordinate system transformation, this method rotates the regions directly themselves, which simplifying the calculation process. Subsequently, an improved shortest path planning method was proposed based on the ant colony algorithm, which determined the specific access point covering each region and the optimal flight path between multiple regions. Through a series of numerical experiments, compared with the traditional ant colony algorithm, the proposed method in this work increased efficiency. Particularly for heterogeneous UAVs, the method achieved more than a 14% reduction in time consumption, clearly highlighting the superiority of our proposed framework in optimizing the multi-UAV coverage path planning process.