An Innovative Approach for Mission Sharing and Route Planning of Swarm Unmanned Aerial Vehicles in Disaster Management

Abstract

Fast and effective response in disaster situations is critical for the success of rescue operations. In this context, swarm Unmanned Aerial Vehicles (UAVs) play an important role in disaster response by rapidly scanning large areas and performing situation assessments. In this paper, we propose an innovative method for task allocation and route planning for swarm UAVs. By combining Genetic Algorithm (GA) and Ant Colony Optimization (ACO) techniques, this method aims to ensure the most efficient movement of UAVs. First, clusters are created using GA to determine the regions of the disaster area that need to be scanned. At this stage, factors such as the capacities of the UAVs, their flight times, and the breadth of their mission areas are taken into account. Each UAV is optimized to scan a specific area assigned to it. Once the clusters are formed, the routes of the UAVs within each cluster are determined by the Ant Colony Algorithm (ACA). The route planning is tested both on Google Maps and in a simulation environment. Google Maps is used to evaluate the accuracy and feasibility of route planning based on real-world conditions, while the simulation environment provides the opportunity to test the behavior of the UAVs and the effectiveness of the routes in a virtual setting. With real-time data integration, the UAVs' route planning can be updated instantly and quickly adapted to emergency situations.