Target Search in Dynamic Environments With Multiple Solar-Powered UAVs

This article focuses on target search in dynamic environments using multiple solar-powered unmanned aerial vehicles (SUAVs) with the probability map method. First, the energy model of the SUAV and an external solar environment model are established. Second, the Gaussian mixture model (GMM) is used to extract the prior probability distribution of the target to obtain multiple search areas composed of two-dimensional Gaussian distributions. Then, dynamic task allocation of the SUAVs based on the environment is performed utilizing the GMM information. Next, the SUAVs search for the target according to the assignment and plan a path with the energy model and probability established before, and the probability map is updated in real time according to the detection result of the SUAVs and the velocity vector field of the environment. In this study, two probability map update methods, namely the equivalent area iteration (EAI) and probability estimated diffusion (PED) methods, are proposed for the situations of precise and imprecise environmental information, respectively. Finally, comparative simulations are carried out for these two different situations to prove the improvement obtained using SUAVs.