计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

一种基于航迹片段的多蚁群协同规划算法

刘慧娟,蔡 超,孙希霞   

  1. (华中科技大学自动化学院多谱信息处理技术国防科技重点实验室,武汉430074)
  • 收稿日期:2013-12-19 出版日期:2014-11-15 发布日期:2014-11-13
  • 作者简介:刘慧娟(1989 - ),女,硕士,主研方向:飞行器路径规划,计算机视觉;蔡 超,副教授、博士;孙希霞,博士研究生。
  • 基金项目:
    :国家部委基金资助项目。

A Multiple Ant Colony Collaborative Planning Algorithm Based on Trajectory Segment

LIU Huijuan,CAI Chao,SUN Xixia   

  1. (State Key Laboratory for Multi-spectral Information Processing Technologies,School of Automation,Huazhong University of Science and Technology,Wuhan 430074,China)
  • Received:2013-12-19 Online:2014-11-15 Published:2014-11-13

摘要: 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。

关键词: 协同航迹规划, 网络图, 多子群, 蚁群算法, 异质信息素

Abstract: 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.

Key words: collaborative trajectory planning, network graph, multi-subgroup, ant colony algorithm, heterogeneous pheromone

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