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动态环境中的无人机路径规划方法
引用本文:刘洋,章卫国,李广文,史静平.动态环境中的无人机路径规划方法[J].北京航空航天大学学报,2014,40(2):252-256.
作者姓名:刘洋  章卫国  李广文  史静平
作者单位:西北工业大学 自动化学院, 西安 710129
摘    要:为了解决动态环境中的路径规划问题,提出了一种引入时间轴的方法.在构型空间的基础上引入时间轴,将构型空间扩展为构型-时间空间,在构型-时间空间中可以表示动态障碍物所有时刻的位置.在路径生成阶段,提出了一种改进的蚁群算法,将方向信息作为启发信息引入蚁群算法中,使蚂蚁在初始搜索路径时更有针对性.仿真结果表明:构型-时间空间可以解决动态环境的表示问题,改进蚁群算法可以更快地收敛到全局最优解. 

关 键 词:路径规划    无人机    动态环境    蚁群算法
收稿时间:2013-04-07

Path planning of UAV in dynamic environment
Liu Yang,Zhang Weiguo,Li Guangwen,Shi Jingping.Path planning of UAV in dynamic environment[J].Journal of Beijing University of Aeronautics and Astronautics,2014,40(2):252-256.
Authors:Liu Yang  Zhang Weiguo  Li Guangwen  Shi Jingping
Institution:College of Automation, Northwestern Polytechnical University, Xi'an 710129, China
Abstract:In order to solve the problem of path planning in dynamic environment, a new method which introduced the time axis was given. Based on the configuration space (C space), the time axis was introduced to expand the C space to the configuration-time space (CT space), and the position of moving obstacles at all times could be expressed in the CT space. In the path generation stage, an improved ant colony algorithm was proposed. The heading information was introduced as heuristic information to the ant colony algorithm, and at the beginning of the algorithm ants could be guided to search the road map more efficiently. The simulation results show that the moving obstacles can be expressed well in the CT space. The improved ant colony algorithm is more efficient and it can converge to the best solution more quickly.
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