摘要
多架无人机协同执行任务已经成为未来无人机发展的重要趋势,多无人机协同航迹规划是保障多无人机协同完成任务的重要手段之一。文章系统梳理了多无人机协同航迹规划研究现状,归纳现有多无人机协同航迹规划方法,从搭建航迹规划仿真地图、明确多机协同评价指标和选择规划算法三个方面阐述多无人机协同航迹规划实现方法,最后对协同航迹规划技术的发展进行了展望:提高无人机群的自主性、协同性和规划算法性能是多无人机协同航迹规划的发展趋势,最终有望实现无人机群在任何情况下都能够快速自主规划最优航迹并同时保持多无人机空间和时间上的协同。研究高精度的无人机航迹规划仿真地图、合理的多机协同结构对提高多无人机协同航迹规划技术也具有较大的实践意义。
引文
[1]Bodenhorn C,Galkowski P,Stiles P.Personalizing onboard route re-planning for recon,attack,and special operations missions//The Proceedings of American Helicopter Society Conference,1997:128
[2]Sullivan J M.Evolution or revolution?The raise of UAVs.IEEETechnology and Society Magazine,2006,25(3):43
[3]叶媛媛.多UCAV协同任务规划方法研究[学位论文].长沙:国防科学技术大学,2005
[4]朱战霞,郑莉莉.无人机编队飞行控制器设计.飞行力学,2007(04):22
[5]Sanchez L J L,Pestana J,Puente P,et al.A System for the design and development of vision-based multi-robot quadrotor swarms//International Conference on Unmanned Aircraft Systems(ICUAS),2014:640
[6]Borrelli F,Subramanian D,Raghunathan A U,et al.MILP and NLP techniques for centralized trajectory planning of multiple unmanned air vehicles//American Control Conference:IEEE Press,2006:5763
[7]袁利平,夏洁,陈宗基.多无人机协同路径规划研究综述.飞行力学,2009(05):1
[8]Farina A,Graziano A,Mariani F,et al.Voronoi Diagrams-ASurvey of a Fundamental Geometric Data Structure//ACM Computing Surveys,2010:345
[9]Overmars M H,Mark H O T.A random approach to motion planning.RUU-CS,1992(1):92
[10]周明.遗传算法原理及应用.北京:国防工业出版社,1999
[11]Gilmore J F,Czuchry A J.A neural network model for route planning constraint integration//International Joint Conference on Neural Networks:IEEE Press,2002:221
[12]陈冬.基于粒子群优化算法的无人机航迹规划[学位论文].西安:西北工业大学,2007
[13]程晓明.无人机双机协同航迹规划技术研究[学位论文].南京:南京航空航天大学,2015