计算机工程 ›› 2019, Vol. 45 ›› Issue (11): 275-280.doi: 10.19678/j.issn.1000-3428.0052945

• 开发研究与工程应用 • 上一篇    下一篇

多基地多无人机航迹避障任务规划

刘畅1a,1b, 谢文俊1a, 张鹏1a, 郭庆1a, 高超2   

  1. 1. 空军工程大学 a. 装备管理与无人机工程学院;b. 研究生院, 西安 710051;
    2. 中国卫星海上测控部, 江苏 江阴 214431
  • 收稿日期:2018-10-22 修回日期:2018-11-22 发布日期:2018-11-30
  • 作者简介:刘畅(1995-),男,硕士研究生,主研方向为无人机智能作战控制与任务规划、人机交互;谢文俊,教授;张鹏,副教授;郭庆,讲师;高超,助理工程师。
  • 基金项目:
    航空科学基金(20165596025)。

Mission Planning for Multi-base Multi-UAV Obstacle Avoidance

LIU Chang1a,1b, XIE Wenjun1a, ZHANG Peng1a, GUO Qing1a, GAO Chao2   

  1. 1a. College of Equipment Management and UAV Engineering;1b. Graduate School, Air Force Engineering University, Xi'an 710051, China;
    2. China Satellite Maritime TT & C Department, Jiangyin, Jiangsu 214431, China
  • Received:2018-10-22 Revised:2018-11-22 Published:2018-11-30

摘要: 在多目标群多基地多无人机协同任务规划环境中,可能存在多个突发威胁。针对该问题,提出一种周期性快速搜索遗传算法(PFSGA)与人工势场法(APF)的联合算法。以侦察任务为背景,将共同分配策略引入任务规划过程中,构建多基地多无人机协同任务规划模型,利用PFSGA算法进行初步的任务规划。在此基础上,考虑基地与目标群之间的突发威胁,应用APF进行航迹避障。仿真结果表明,该算法具有良好的避障功能,与遗传算法和APF的联合算法相比,PFSGA-APF联合算法可避免陷入局部最优且易于求得最优解。

关键词: 任务规划, 周期性快速搜索遗传算法, 无人机, 人工势场, 避障

Abstract: There may be multiple sudden threats in multi-target group,multi-base and multi-UAV collaborative mission planning environment.To address this problem,a joint algorithm of Periodic Fast Search Genetic Algorithm(PFSGA) and Artificial Potential Field(APF) method is proposed.Taking the reconnaissance mission as the background,the common assignment strategy is introduced into the mission planning,the multi-base multi-UAV cooperative mission planning model is constructed,and the PFSGA algorithm is used for initial mission planning.On this basis,sudden threats between bases and target groups are considered,and the APF method is used to avoid obstacles.Simulation results show that the proposed algorithm has good obstacle avoidance performance,and compared with joint algorithm of genetic algorithm and APF,the joint algorithm of PFSGA-APF can avoid falling into the local optimum and obtain the optimal solution easily.

Key words: mission planning, Periodic Fast Search Genetic Algorithm(PFSGA), Unmanned Aerial Vehicle(UAV), Artificial Potential Field(APF), obstacle avoidance

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