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兵工学报 ›› 2020, Vol. 41 ›› Issue (12): 2530-2539.doi: 10.3969/j.issn.1000-1093.2020.12.019

• 论文 • 上一篇    下一篇

基于改进A*算法的多无人机协同战术规划

张哲1, 吴剑1,2, 代冀阳1, 李品伟1   

  1. (1.南昌航空大学 信息工程学院, 江西 南昌 330063; 2.北京航空航天大学 可靠性与系统工程学院, 北京 100191)
  • 上线日期:2021-01-29
  • 作者简介:张哲(1995—),男,硕士研究生。E-mail:591222230@qq.com
  • 基金资助:
    国家自然科学基金项目(61663032);航空科学基金项目(2016ZC56003);南昌航空大学研究生创新专项基金项目(YC2019026)

Cooperative Tactical Planning for Multi-UAVs Based on Improved A* Algorithm

ZHANG Zhe1, WU Jian1,2, DAI Jiyang1, LI Pinwei1   

  1. (1.School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, Jiangxi, China;2.School of Reliability and System Engineering, Beihang University, Beijing 100191, China)
  • Online:2021-01-29

摘要: 多无人机协同作战是未来无人机作战方式的重要发展趋势。为增强多无人机系统的任务执行能力,提高系统整体作战效能并实现高效资源分配和调度,提出一种基于改进A*算法的多无人机协同战术规划方法。按照离线规划和重规划两方面,设计战役层和战术层的作战目标迭代优化方案;建立编队协同作战的数学模型,以编队成员间的时间协同和碰撞协同代价为变量,得到多约束条件下的综合编队目标函数;结合多层变步长搜索策略和单步扩展的搜索方式,基于改进A*算法,用于求解复杂战场环境下的多无人机编队协同作战航路。分别利用改进A*算法和传统A*算法进行对比仿真实验。仿真结果表明,多无人机协同战术规划方法能够较好地完成作战任务,改进A*算法能够获得更优的航路,从而验证了所提算法的有效性。

关键词: 无人机, 任务分配, 协同战术规划, A*算法

Abstract: Cooperative operation for multiple unmanned aerial vehicles (UAVs) is an important development trendency of combat mode of future UAVs. A cooperative tactical planning method based on improved A* algorithm is proposed for multi-UAVs. The proposed method is used to enhance the mission execution capability of multi-UAV system, improve the overall combat effectiveness, and achieve the efficient resource allocation and scheduling. An iterative optimization scheme for operational goals at the campaign and tactical levels is presented from the two aspects of offline planning and replanning. A mathematical model of formation cooperative operation is established, which takes the time coordination and collision coordination cost of formation members as variables and obtains the comprehensive formation objective function under multiple constraints. Moreover, an improved A* algorithm is developed to address the formation cooperative combat routes by employing the multi-layer variable step search strategy and the single step search method in complex combat environment. The simulation experiments were performed by using the improved A* algorithm and the traditional A* algorithm. The simulated results show that the tactical planning method can complete the combat task well and the improved A* algorithm can obtain the better routes, which proves the effectiveness of the algorithm.

Key words: unmannedaerialvehicle, taskallocation, collaborativetacticalplanning, A*algorithm

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