Collaborative Human–UAV Search and Rescue for Missing Tourists in Nature Reserves

The use of unmanned aerial vehicles (UAVs) is becoming commonplace in search-and-rescue tasks in complex terrains. In the literature, there are a number of studies on UAV search with the objective of minimizing search time and/or maximizing detection probability. However, little effort has been devoted to collaborative human and UAV search, which is necessary in many applications in which humans must ultimately reach the target. In this paper, we present a collaborative human–UAV search-planning problem, the objective of which is to minimize the expected time for human rescuers to reach the target. For this highly complex problem, traditional exact algorithms would be very time-consuming or even impractical for solving even relatively small instances. We propose an evolutionary algorithm that uses biogeography-inspired operators to efficiently evolve a population of candidate solutions to the optimal or near-optimal solution within an acceptable time. Computational experiments demonstrate the advantages of our algorithm over many popular algorithms. The proposed method has been successfully applied to two real-world search-and-rescue operations to find missing tourists in a nature reserve in China. Compared with the old method used by the rescue department, our method shortened the time required for reaching the targets by approximately 79 and 147 minutes in the two cases, respectively, providing a great improvement in the life-critical operations.