基于混合人工势场与蚁群算法的多飞行器冲突解脱方法

Conflict Resolution Method for Multiple Aircraft Based on Hybrid Artificial Potential Field and Ant Colony Algorithm

复杂低空空域环境下多飞行器冲突解脱方法可以有效地提供冲突解脱策略,避免飞行器之间发生危险接近事故或者碰撞,从而保障空域运行安全.目前飞行器冲突解脱方法主要可以分为集中式和分布式.然而基于人工势场法等分布式方法虽然计算速度快,但可能会产生不切实际的解;基于进化算法等集中式方法可靠性高,但是计算量大,响应速度较慢,实时性差.本文结合人工势场法与蚁群算法的优点提出改进混合冲突解脱方法,首先利用人工势场法迅速得到近似可行的冲突解脱路径,然后将方案调整、编码得到“权威蚂蚁”,由“权威蚂蚁”衍生“权威蚁群”,利用“权威蚁群”始化信息素矩阵,基于蚁群算法,求得含有飞行规划约束的解脱方案.并通过与传统的人工势场法与蚁群算法进行比较,验证了改进算法在时效性和可行性上的优点.

Conflict resolution methods for multiple aircrafts in complex low-altitude airspace environment can effectively provide conflict resolution strategies to avoid dangerous approach accidents or collisions between aircrafts,thus ensuring airspace operation safety.At present,aircraft conflict resolution methods can be divided into centralized and distributed methods.However,distributed methods such as artificial potential field method may produce unrealistic solutions although they have fast computation speed.Centralized methods such as evolutionary algorithms have high reliability,but they are computationally intensive,slow in response and poor in real-time.Based on the advantages of artificial potential field method and ant colony algorithm,this paper proposed an improved hybrid conflict resolution method.Firstly,the artificial potential field method was used to quickly obtain an approximately feasible conflict resolution path.Secondly,the scheme was adjusted and coded to obtain“authoritative ants”.The“authoritative ant colony”was derived from the“authoritative ant colony”and the pheromone matrix was initialized by the“authoritative ant colony”.Based on ant colony algorithm,a solution with flight planning constraints was obtained.Compared with the traditional artificial potential field method and ant colony algorithm,the advantages of the improved algorithm in timeliness and feasibility were verified.