An Emergency Rescue Reconnaissance UAV Nest Site Selection Method Considering Regional Differentiated Coverage and Rescue Satisfaction

Traditional emergency reconnaissance UAV nest deployment methods face limitations such as blind coverage, delayed response, single coverage targets, and undifferentiated regional priorities. This paper proposes an optimized deployment approach that maximizes response satisfaction and regional coverage, achieving comprehensive coverage while avoiding resource waste and blind zones. UAV nest location needs to consider several factors, including UAV nest coverage, distribution of mandatory coverage areas, scope of critical areas, and various constraints. Mandatory coverage areas are disaster-prone zones identified from historical data, requiring focused reconnaissance. Critical areas are regions with high population density and critical infrastructure concentration. Constraints contain the nest coverage radius constraints, surplus coverage constraints, economic cost constraints, nest distance constraints, nest synergy constraints, and regional boundary constraints. We developed an improved multi-dimensional ant colony optimization algorithm tailored to the problem characteristics, which incorporates multi-dimensional pheromones representing coverage potential, cost efficiency, and spatial constraints, along with adaptive updating and dynamic selection mechanisms for effective problem-solving. This paper takes Nanjing, Jiangsu Province as an analysis case. And the solution achieved 100% regional coverage, redundant coverage of critical zones, and seamless inter-nest collaboration. Sensitivity analysis confirmed the model’s robustness and effectiveness under varying coverage radius and budget conditions.