Memetic Optimization of Collaborative Human-UAV-Truck Search-and-Rescue Task Scheduling in Earthquakes
Kang-Cong Lv, Zhi-Yuan Zhang, Lin-Yuan Bai, Xunlin Jiang, Yu‐Jun Zheng · Unmanned Systems · 2025
Unmanned aerial vehicles (UAVs) have become increasingly used in search and rescue tasks in disasters. However, there are many tasks that cannot be completed solely by UAVs; instead, they should be completed by cooperation of UAVs and human rescuers. Moreover, as battery capacity is a major limiting factor in UAV operations, using ground vehicles (trucks) to provide mobile battery swapping services is an efficient approach. To this end, this paper studies a problem of collaborative human-UAV-truck search-and-rescue task scheduling in earthquakes, where UAVs are used to quickly search for survivors who are expected to be ultimately rescued by human rescuers, while a truck serves as a mobile battery depot for UAVs, such that the survivors can be rescued as many as possible and as early as possible. To solve the problem, we propose a memetic algorithm that combines the ecogeography-based optimization metaheuristic for global exploration, adaptive local search for improving solution accuracy, and a Gaussian binary simulated annealing method for truck path planning. Numerical experiments on a set of real-world problem instances demonstrate the effectiveness and efficiency of the proposed method.