Optimizing UAV flocks for emergency response in post-disaster industrial zones

Abstract

In the aftermath of natural disasters striking industrial zones, unmanned aerial vehicles (UAV) flocks-based autonomous search and rescue (SAR) operations have greater potential than manual searches in complex and hazardous environments. This article introduces a novel method that combines detection point allocation with trajectory planning. Specifically, a two-stage optimization model is presented that takes into account the technical limitations of airborne emergency rescue equipment and the performance constraints of UAVs, aiming to maximize the SAR success rate and reduce operation time. The proposed heuristic algorithm first assigns detection points to the UAV flock, and then is used to iteratively generate a set of optimal detection points for the UAV flock. The effectiveness of the proposed method is validated via simulation experiments conducted in two virtual three-dimensional chemical industrial areas. Numerical analysis shows that the proposed method increases the average efficiency in SAR operations by 90.80% compared to the seven commonly used methods.