A Dual-Scale Flight Trajectory Planning Scheme for UAV Swarms

Recently, natural disasters such as earthquakes, floods, and fires have posed significant challenges to search and rescue (SAR) operations. The rapid and accurate gathering of environmental information from affected areas, along with the efficient transportation of relief supplies and personnel, is crucial to the success of post-disaster response efforts. Traditional ground-based SAR methods are often hindered by complex terrain and disrupted transportation infrastructure, which makes it challenging to reach the core areas of disaster-affected zones quickly. In this case, the Unmanned Aerial Vehicle (UAV)-based technology has emerged as a promising solution for post-disaster relief, offering high flexibility and rapid response capabilities. However, the autonomous flight control of UAVs in such complicated environments remains a challenge, especially in areas with numerous obstacles. Planning UAV flight trajectories efficiently and safely in dynamic environments remains an open problem. This study proposes a dual-scale trajectory planning scheme to improve the efficiency and safety of UAVs in post-disaster relief efforts. Specifically, the A* algorithm is employed for large-scale trajectory planning to avoid large, fixed obstacles, while the connected potential field method is used for small-scale trajectory planning and swarm formation to dynamically avoid moving obstacles and maintain communication connectivity in real time. By combining these two approaches, the proposed scheme enables efficient and safe UAV flight control in disaster areas, thereby enhancing the overall effectiveness of SAR operations.