LAUSR

Research on multiple unmanned aerial vehicles (UAVs) cooperative surveillance systems serving Internet of Things (IoT) applications, such as smart cities, precision logistics, etc., has become a hot topic. However, the target movement is unpredictable in an uncertain environment, and multiple UAVs are affected by obstacles or inaccessible regions, resulting in the decreased surveillance performance and even the loss of the target. This article is dedicated to determine the current surveillance environment through the 2-D laser scanner. At the cost of the energy consumption and the transmission unreliability, a multi-UAV cooperative path optimization (MCPO) model is designed to adjust the surveillance location of each UAV, which improves the target surveillance performance. Specifically, for different types of obstacles or inaccessible regions, we present a novel obstacle-avoidance selection strategy with two mechanisms in mind: 1) when some of UAVs encounter obstacles, but others can accurately monitor the target, a strict constraint mechanism is established to promptly adjust the surveillance location of each UAV, which ensures the accuracy of formation surveillance and 2) when all UAVs have to avoid obstacles, a fuzzy constraint mechanism is presented and combined with Lucas–Kanade (LK) method to expand the search range of the multi-UAV and enhance the flexible adjustment capability of the formation. To verify the superiority of the proposed optimization method, we develop a 3-D simulation experiment environment based on the UE4 platform and design several groups of experiments to analyze the effectiveness of MCPO. The experimental results demonstrate that MCPO can not only maintain the flight stability of multiple UAVs but also has satisfactory formation flexibility and surveillance accuracy.