Complete Coverage Path Planning for Data Collection with Multiple UAVs

Yuhong Chen, Zhiyu Mou, Bo Lin, Tengyu Zhang, Feifei Gao

Abstract: The utilization of unmanned aerial vehicles (UAVs) for communication data collection across all areas can be modeled as a complete coverage path planning (CCPP) problem. To address the challenge of lengthy coverage time in traditional CCPP algorithms, we propose a weighted balanced graph partitioning based complete coverage path planning scheme (WBGPP), which consists of two sub-algorithm: weighted balanced graph partitioning (Weighted B-GRAP) and single agent path planning (SAPP). The Weighted B-GRAP algorithm can decompose the multi-UAV CCPP problem into multiple single UAV CCPP problems by assigning each UAV a responsibility area according to its capability. Then, we optimize the backtracking strategy through breadth-first search and design a SAPP algorithm to reduce the number of repeated visits and shorten the coverage time. The simulation results show that the proposed WBGPP scheme effectively reduce the coverage time of multiple UAVs in CCPP problems and can be applied to various maps.