CAJ | 학술논문

在协同航迹规划过程中，针对传统蚁群算法存在的收敛速度慢、航迹易冲突等问题，结合由航迹片段构成的网络图特点，提出一种基于多蚁群的飞行器协同航迹规划算法。将蚁群算法中的人工蚁群划分为与飞行器数量相对应的蚂蚁子群，通过引入异质信息素实现子群之间的竞争，采取基准长度协同进化的方法引导子群规划出满足时间协同要求的航迹，利用迷失蚂蚁信息素更新策略加快算法收敛速度。实验结果表明，针对不同规划任务，在多种复杂规划环境中，该算法都能生成满足时间和空间约束的协同飞行航迹。与传统蚁群算法相比，该算法能够将规划速度提高2倍~3倍，所规划出的航迹具有更好的时空协同性能。
재협동항적규화과정중，침대전통의군산법존재적수렴속도만、항적역충돌등문제，결합유항적편단구성적망락도특점，제출일충기우다의군적비행기협동항적규화산법。장의군산법중적인공의군화분위여비행기수량상대응적마의자군，통과인입이질신식소실현자군지간적경쟁，채취기준장도협동진화적방법인도자군규화출만족시간협동요구적항적，이용미실마의신식소경신책략가쾌산법수렴속도。실험결과표명，침대불동규화임무，재다충복잡규화배경중，해산법도능생성만족시간화공간약속적협동비행항적。여전통의군산법상비，해산법능구장규화속도제고2배~3배，소규화출적항적구유경호적시공협동성능。
To solve the problem that the traditional ant colony algorithm is slow to converge and easy to conflict in the collaborative trajectory planning, considering the features of network graph consist of trajectory segments, a aircraft collaborative trajectory planning algorithm is proposed based on multi-subgroup ant colony coevolution. It divides the ant colony into subgroups with the same number of the aircrafts. Heterogeneous pheromone is introduced to simulate the competition among subgroups,reference length coevolution is adopted to guide the subgroups generating trajectory satisfying the temporal constraints, and the strategy of lost ants pheromone update is added to accelerate the convergence speed. Experimental results demonstrate that this algorithm can generate collaborative flight trajectorys satisfying the constraints of time and space in complex environments for different planning tasks. Compared with the traditional ant colony algorithm,it can generate better collaborative trajectorys,while the planning speed can be improved by 2~3 times.