动态环境中的无人机路径规划方法

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

西北工业大学自动化学院, 西安 710129

详细信息

中图分类号: V249
计量
- 文章访问数: 2027
- HTML全文浏览量: 234
- PDF下载量: 1864
- 被引次数: 0
出版历程
- 收稿日期: 2013-04-07
- 网络出版日期: 2014-02-20

Path planning of UAV in dynamic environment

College of Automation, Northwestern Polytechnical University, Xi'an 710129, China

摘要

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.
- 路径规划 /
- 无人机 /
- 动态环境 /
- 蚁群算法
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.
- path planning /
- unmanned aerial vehicle /
- dynamic environment /
- ant colony algorithm

HTML全文

参考文献(10)

[1]	Kavraki L E, Svestka P, Latombe J C, et al.Randomized preprocessing of configuration space for fast path planning[C]// IEEE International Conference on Robotics and Automation.San Diego:IEEE, 1994:3020-3026
[2]	曾碧, 杨宜民.动态环境下基于蚁群算法的实时路径规划方法[J].计算机应用研究, 2010, 27(3):860-863 Zeng Bi, Yang Yimin.Method of real time path planning based on ant colony algorithm in dynamic environment[J].Application Research of Computers, 2010, 27(3):860-863(in Chinese)
[3]	朱毅, 张涛, 程农, 等.动态环境下基于子目标的移动机器人路径规划方法[J].系统仿真学报, 2010, 22(增刊1):254-257 Zhu Yi, Zhang Tao, Cheng Nong, et al.Sub-goal based path planning method for mobile robot under dynamic environment[J].Journal of System Simulation, 2010, 22(Supplement 1):254-257(in Chinese)
[4]	肖本贤, 齐东流, 刘海霞, 等.动态环境中基于模糊神经网络的AGV路径规划[J].系统仿真学报, 2006, 18(9):2401-2404 Xiao Benxian, Qi Dongliu, Liu Haixia, et al.AGV path planning in the dynamic environment based-on fuzzy neural network[J].Journal of System Simulation, 2006, 18(9):2401-2404(in Chinese)
[5]	刘国栋, 谢宏斌, 李春光.动态环境中基于遗传算法的移动机器人路径规划的方法[J].机器人, 2003, 25(7):327-330 Liu Guodong, Xie Hongbin, Li Chunguang.Method of mobile robot path planning in dynamic environment based on genetic algorithm[J].Robot, 2003, 25(7): 327-330(in Chinese)
[6]	沈晶, 顾国昌, 刘海波.未知动态环境中基于分层强化学习的移动机器人路径规划[J].机器人, 2006, 28(5):544-547 Shen Jing, Gu Guochang, Liu Haibo.Mobile robot path planning based on hierarchical reinforcement learning in unknown dynamic environment[J].Robot, 2006, 28(5):544-547(in Chinese)
[7]	Van Den Berg J, Overmars M.Kinodynamic motion planning on roadmaps in dynamic environments[C]//Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.San Diego:IEEE, 2007:4253-4258
[8]	Colorni A, Dorigo M, Maniezzo V.Distributed optimization by ant colonies[C]//The 1st European Conference on Artificial Life.Paris:Elsevier Publishing, 1991:134-142
[9]	张晓勇, 吴敏, 彭军, 等.机器人救援的目标吸引动态路径规划蚁群算法[J].系统仿真学报, 2011, 23(9):1854-1859 Zhang Xiaoyong, Wu Min, Peng Jun, et al.Target attraction based ant colony for dynamic path planning of rescue robot[J].Journal of System Simulation, 2011, 23(9):1854-1859(in Chinese)
[10]	柳长安, 鄢小虎, 刘春阳, 等.基于改进蚁群算法的移动机器人动态路径规划方法[J].电子学报, 2011, 39(5): 1220- 1224 Liu Chang'an, Yan Xiaohu, Liu Chunyang, et al.Dynamic path planning for mobile robot based on improved ant colony optimization algorithm[J].Acta Electronica Sinica, 2011, 39(5):1220-1224(in Chinese)