A Genetic Algorithm‐Based Approach for Collision Avoidance in a Multi‐UAV Disaster Mitigation Deployment

ABSTRACT This research delves into the intricacies of designing trajectories for unmanned aerial vehicles (UAVs) within a multi‐UAV system, specifically addressing the challenges presented during simultaneous rescue operations in neighboring states. The unique scenario introduces a potential risk of UAVs from one state intersecting with those from others, leading to communication issues and the looming threat of collisions. These collisions not only cause delays in emergency operations but also result in additional costs for repairing damaged UAV components. In response to this critical challenge, the study proposes an innovative approach utilizing Genetic Algorithms to facilitate collision avoidance in a multi‐UAV environment, tailored explicitly for disaster mitigation scenarios. This technique is an efficient solution to enhance the safety and effectiveness of UAV operations during disaster response and relief efforts. The proposed trajectory planning method uses a genetic algorithm, with the fitness function strategically designed to optimize two pivotal objectives: utility (maximizing the number of people saved postdisaster) and collision avoidance (minimizing conflicts between multiple UAVs as they navigate predetermined paths). The overarching goal of this approach is to strike a balance, aiming to maximize utility while concurrently minimizing the risk of collisions. By adopting this approach, the research significantly contributes to advancing the field of disaster response strategies, enhancing the overall efficiency of multi‐UAV systems in complex and dynamic environments. The proposed solution not only addresses the immediate challenges posed by potential collisions but also underscores the importance of optimizing UAV trajectories to achieve maximum utility in postdisaster scenarios.