基于改进蚁群算法的突发威胁环境下多无人机协同规划航迹研究

Research on Multi-UAV Collaborative Track Planning in the Sudden Threat Environment Based on the Improved Ant Colony Algorithms

文章针对突发威胁环境引起多无人机协同飞行中碰撞和冲突、整体效能降低的问题,构建了带模糊因子的可重构三维动态航迹规划模型。针对传统蚁群算法收敛速度慢、较早陷入局部收敛的局限性,引进狼群分配原则以加快算法的收敛速度,引进轮盘赌选择原则以增加选择节点时的随机性,避免陷入局部最优,设置随机性的信息素浓度和启发因子权重参数以减少试错次数。仿真结果显示:与传统蚁群算法相比,综合航迹代价减少6.28%,航迹距离减少9.70%,算法运行时长降低15.82%。与文献【13】的改进蚁群算法相比综合航迹代价减少4.03%,航迹距离代价减少3.89%,算法运算时间降低2.54%。表明文中方法具有运算效率高、寻优能力强、避障成功率高的特点,能够在未知环境、快速飞行过程中自动避障以避免碰撞威胁,更适用于解决多无人机协同在线规避航迹规划问题。

Aiming at the problems of collision and conflict in multi-UAV coordinated flight caused by sudden threat environment and the reduction of the overall efficiency,a re-configurable 3D dynamic path planning model with fuzzy factors is constructed.In view of the limitations of traditional ant colony algorithms with slow convergence speed and early local convergence,the Wolf colony distribution principle is introduced to accelerate the convergence speed of the algorithm,the roulette selection principle increases the randomness of node selection,and avoid falling into the local optimum.The random pheromone concentration and inspiration factors weight parameters are set to reduce the number of trial and error.The simulation results show that the improved ant colony algorithm is superior to the traditional ant colony algorithm in dealing with the multi-UAV track planning problem,in which the cost of integrated track is reduced by 6.28%,the cost of track distance is shortened by 9.70%,and the operation time of the algorithm is reduced by 15.82%.Compared with the improved ant colony algorithm in literature[13],the integrated trace cost decreased by 4.03%,the cost of trace distance by 3.89%,and the algorithm operation time by 2.54%.The proposed method is characterized by high operational efficiency,strong optimization ability and high success rate of obstacle avoidance.It can automatically avoid obstacles to avoid the collision threat in unknown environment and fast flight process,and is more suitable for solving the problem of multi-UAV collaborative online avoidance of path planning.