A New Method for Multi-UAV Cooperative Mission Planning Under Fault

Abstract

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.

A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm

“…While building a framework for dynamic scheduling, some sudden problems can be successfully solved [27,28]. These solutions for dynamic scheduling can be divided into two categories: one, as in [29], introduces a distributed algorithmic framework to solve directly; and the other, as in this paper, presents subjective judgement conditions and categorizes the solution.…”

“…Additionally, the PSO algorithm has been employed in additional combinations as a heuristic method: in their studies, Geng et al [26] proposed a quantified particle swarm optimization algorithm for the task allocation problem of UAV clusters;. Shao et al [27] proposed a hybrid strategy based on discrete particle swarm optimization for the many-to-one task planning problem in the case of a constructed a quantized particle swarm optimization technique. The simulated annealing algorithm is enhanced by Chen et al [28] using a Levi distribution strategy, and it works well for both dynamic and static task assignment models.…”