Optimization of refueling docking trajectory for multi-UAV formation

多无人机编队加油对接轨迹优化

¹ Key laboratory of Aviation Aerodynamic Numerical Simulation, Aeronautical Computing Technique Research Institute, Xi'an 710065, China
² Key Laboratory of Aeronautical Science and Technology for Airborne Ballistic Computer, Aeronautical Computing Technique Research Institute, Xi'an 710065, China

Received: 10 March 2025

Abstract

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.

摘要

针对空中加油过程中无人受油机自主对接轨迹优化问题, 采用高精度的计算流体力学方法计算无人受油机在加油机后的危险区域, 将该区域作为对接轨迹规划时飞行环境中的障碍物。提出一种改进蚁群算法, 使用反向学习组成更优的初始解, 大大提高算法收敛速度; 再通过模糊控制自适应调节对接过程的航迹长度和飞行安全性2个代价函数的权重, 在保持受油机飞行安全的前提下缩短航迹长度。仿真结果表明, 提出的改进蚁群算法能够实现单个无人受油机及多机编队加油的自主对接轨迹规划, 与传统的蚁群算法相比飞行安全性更高且收敛速度更快。