A path planning method for UAVs based on multi-objective pigeon-inspired optimisation and differential evolution

Inspired by the behaviour of pigeon flocks, an improved method of path planning and autonomous formation for unmanned aerial vehicles based on the pigeon-inspired optimisation and differential evolution is proposed in this paper. Firstly, the mathematical model for UAV path planning is devised as a multi-objective optimisation with three indices, i.e., the length of a path, the sinuosity of a path, and the risk of a path. Then, the method integrated by pigeon-inspired optimisation and mutation strategies of differential evolution is developed to optimise feasible paths. Besides, Pareto dominance is applied to select the global best position of a pigeon. Finally, a series of simulation results compared with standard particle swarm optimisation algorithm and standard differential evolution algorithm show the effectiveness of our method.

Automated summary

This paper proposes an improved method of path planning and autonomous formation for unmanned aerial vehicles based on pigeon-inspired optimization and differential evolution. The mathematical model for UAV path planning is devised as a multi-objective optimization, and the method integrated by pigeon-inspired optimization and mutation strategies of differential evolution is developed to optimize feasible paths. Simulation results show the effectiveness of the proposed method compared with standard particle swarm optimization and standard differential evolution algorithms.