LAUSR

Aiming at the many-to-one mission planning problem in the case of UAV fault, a self-organizing solving method is designed. This method organically combines the situation assessment of single UAV with the collaborative optimization of multiple UAV. On the one hand, the situation assessment of single UAV is carried out based on Bayesian network, and the striking probability of each UAV is obtained. On the other hand, in order to solve the problem of multiple UAV cooperation, the improved discrete particle swarm optimization based mixed strategy (MSDPSO) is proposed. The algorithm has been improved in the following four aspects. Firstly, Sobol sequence is used to initialize the population to improve the coverage of solution space. Then, a nonlinear time-varying strategy is proposed to accelerate the convergence of the algorithm. Cauchy operator is also introduced to enhance the search space of discrete particle swarm optimization. At the same time, an adaptive cross learning strategy is proposed to enrich the diversity of the population, thereby improving the global optimization ability of the algorithm. In addition, the cubic spline interpolation is used to plan trajectory of UAV. Finally, improved discrete particle swarm optimization is used in three-dimensional space for simulation and comparison with both healthy and faulty UAV involved. Results show that the designed algorithm has significant improvement on solution optimality and convergence rate, which provides a theoretical basis for the application of multiple UAV collaborative task planning.