Opti-UAV SAR: A unified multi-UAV–target assignment framework for post-disaster search and rescue

This paper presents Opti-UAV SAR, an advanced optimization framework for post-disaster search and rescue (SAR) operations, designed to enhance both the efficiency and effectiveness of SAR missions. Leveraging Unmanned Aerial Vehicles (UAVs) equipped with intelligent sensing and communication capabilities, the framework aims to maximize the detection accuracy of individuals and assess damage in disaster-affected zones. In addition to traditional search functions, the framework integrates AI-based computer vision technique to automatically analyze UAV-captured imagery for structural damage and hazard identification, improving situational awareness and decision-making. At the core of the solution is a three-phase UAV-task allocation strategy that enables dynamic and scalable mission planning. This strategy combines: (i) a priority-based drone assignment mechanism that considers target criticality and environmental urgency, (ii) Bayesian optimization for resource-efficient deployment tuning under uncertainty, and (iii) a graph theory-based coordination method for robust coverage and inter-UAV communication. Compared to conventional assignment techniques, Opti-UAV SAR significantly improves detection accuracy, reduces response time, and lowers computational overhead. Simulation results confirm that the proposed approach provides a practical, intelligent, and highly adaptable solution for real-world SAR operations in complex and evolving post-disaster environments.