Dynamic collision avoidance for cooperative fixed-wing UAV swarm based on normalized artificial potential field optimization

Cooperative path planning is an important area in fixed-wing UAV swarm. However, avoiding multiple time-varying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment. Firstly, a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension, which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation. Then, taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm, a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed. Finally, several simulations are carried out to illustrate superiority and effectiveness.