Cooperative search for dynamic targets by multiple UAVs with communication data losses

This paper studies the problem of cooperative searching for dynamical moving targets by multiple unmanned aerial vehicles (UAVs). The environmental information possessed by UAVs is inconsistent due to packet losses of shared environmental information in communication channels and the discrepancies of detected information among different UAVs. To unify the environmental information among UAVs, the lost information is compensated for by an improved Least Square Method (LSM) which incorporates the target location model into the fitting function to enhance data fitting precision. The Weighted Averaging Method (WAM) is used to merge multiple source information where the weight coefficients are set based on the uncertain values of environmental information. To search for dynamic targets and then automatically re-enter into search areas for UAVs, a Modified Genetic Algorithm (MGA) and rolling optimization techniques are utilized to generate real-time paths for UAVs. Simulation results and comparison studies with existing methods validate the effectiveness of the above cooperative searching strategy.