Optimization of refueling docking trajectory for multi-UAV formation

Aiming at the optimization of autonomous docking trajectory of unmanned receiver in the in-flight refueling, a high-precision computational fluid dynamics method was used to calculate the dangerous area behind the refueling aircraft as the obstacle in the flight environment in the docking trajectory planning. An improved ant colony algorithm is proposed, which uses reverse learning to form a better initial population and greatly improves the convergence speed of the algorithm. Then the weight of the two cost functions of track safety and track distance is adaptively adjusted by using the fuzzy control, and the track distance is shortened on the premise of keeping the flight safety of the oil receiving aircraft. The simulated results show that the improved ant colony algorithm can realize the autonomous docking trajectory planning of single unmanned oil receiver and multi-aircraft formation refueling, and has higher track safety and faster convergence speed than the traditional ant colony algorithm.