A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles

Unmanned aerial vehicle (UAV) flocking control with obstacle avoidance is a many-objective optimization problem for centralized algorithms. A UAV flocking distributed optimization control frame is designed to render the many-objective optimization problem into a multi-objective optimization solved by a single UAV. For different objectives, two kinds of criteria are raised to guarantee flight safety: the hard constraints that must be satisfied and the soft ones that will be optimized. Considering the restrictions of onboard computing resources, multi-objective pigeon-inspired optimization (MPIO) is modified based on the hierarchical learning behavior in pigeon flocks. On such a basis, a UAV distributed flocking control algorithm based on the modified MPIO is proposed to coordinate UAVs to fly in a stable formation under complex environments. Comparison experiments with basic MPIO and a modified non-dominated sorting genetic algorithm (NSGA-II) are carried out to show the feasibility, validity, and superiority of the proposed algorithm.