无人机碰撞规避路径规划算法研究
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摘要
无人机(unmanned aerial vehicle,UAV)技术是目前国内外的研究热点。无人机系统正向着智能化、自主化的方向发展,其中路径规划是无人机自主控制的重要组成部分及无人机飞行安全的重要保障。为优化无人机障碍规避路径规划算法,分别设立静态障碍物和动态障碍物环境,基于最小规避距离和航程比这2个指标,比较分析了人工势场法、模糊逻辑算法和蚁群算法对无人机碰撞规避路径规划的性能,并针对人工势场法易陷入局部极小值的缺陷提出了通过增加垂直引导斥力来使无人机逃离局部极小值的改进措施,实验仿真严谨可靠,为进一步融合多种算法、优化现有路径规划算法奠定了基础。
The unmanned aerial vehicle( UAV) has been a research hotspot worldwide. The UAV system is developing to be more and more intelligent and autonomous. UAV path planning is an important part of UAV autonomous control and the important guarantee of UAV's safety. For the purpose of improving the collision avoidance and path planning algorithms,the artificial potential field,fuzzy logic algorithm and ant colony algorithm are simulated respectively in the static obstacle and dynamic obstacle environments, and compared based on the minimum avoidance distance and range ratio. Meanwhile,an improved algorithm of artificial potential field is proposed,and the improvement helps the UAV escape the local minimum by introducing the vertical guidance repulsion. The simulation results are rigorous and reliable,which lay a foundation for the further fusion of multiple algorithms and improving the path planning algorithms.
The unmanned aerial vehicle( UAV) has been a research hotspot worldwide. The UAV system is developing to be more and more intelligent and autonomous. UAV path planning is an important part of UAV autonomous control and the important guarantee of UAV's safety. For the purpose of improving the collision avoidance and path planning algorithms,the artificial potential field,fuzzy logic algorithm and ant colony algorithm are simulated respectively in the static obstacle and dynamic obstacle environments, and compared based on the minimum avoidance distance and range ratio. Meanwhile,an improved algorithm of artificial potential field is proposed,and the improvement helps the UAV escape the local minimum by introducing the vertical guidance repulsion. The simulation results are rigorous and reliable,which lay a foundation for the further fusion of multiple algorithms and improving the path planning algorithms.
引文
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[14]ZHAO Q,ZHEN Z,CHEN G,et al. Path Planning of UAVs Formation Based on Improved Ant Colony Optimization Algorithm[C]∥Proceedings of IEEE Conference on Guidance,Navigation and Control,2015:1549-1552
[15]赖智铭,郭躬德.基于自适应阈值蚁群算法的路径规划算法[J].计算机系统应用,2014,23(2):113-118LAI Zhiming,GUO Gongde. Ant Colony Optimization Based on Self-Adaption Threshold for Path Planning[J]. Computer System Applications,2014,23(2):113-118(in Chinese)
[16]李擎,张超,陈鹏,等.一种基于粒子群参数优化的改进蚁群算法及其应用[J].控制与决策,2013,28(6):873-879LI Qing,ZHANG Chao,CHEN Peng,et al. Improved Ant Colony Optimization Algorithm Based on Particle Swarm Optimization[J]. Control and Decision,2013,28(6):873-879(in Chinese)
[17]方群,徐青.基于改进粒子群算法的无人机三维航迹规划[J].西北工业大学学报,2017,35(1):66-73FANG Qun,XU Qing. 3D Route Planning for UAV Based on Improved PSO Algorithem[J]. Journal of Northwestern Polytechnical University,2017,35(1):66-73(in Chinese)
[18]尹高扬,周绍磊,吴青坡.无人机快速三维航迹规划算法[J].西北工业大学学报,2016,34(4):564-570YING Gaoyang,ZHOU Shaolei,WU Qingpo. Efficient Path Planning Algorithm in Three Dimensions for UAV[J]. Journal of Northwestern Polytechnical University,2016,34(4):564-570(in Chinese)
[2]孟蕊,苏维均,连晓峰.基于动态模糊人工势场法的移动机器人路径规划[J].计算机工程与设计,2010,31(7):1558-1561MENG Rui,SU Weijun,LIAN Xiaofeng. Mobile Robot Path Planning Based on Dynamic Fuzzy Artificial Potential Field Method[J]. Computer Engineering and Design,2010,31(7):1558-1561(in Chinese)
[3]丁家如,杜昌平,赵耀,等.基于改进人工势场法的无人机路径规划算法[J].计算机应用,2016,36(1):287-290DING Jiaru,DU Changping,ZHAO Yao,et al. Path Planning Algorithm for Unmanned Aerial Vehicles Based on Improved Artificial Potential Field[J]. Journal of Computer Applications,2016,36(1):287-290(in Chinese)
[4] ZHANG T,ZHU Y,SONG J. Real-Time Motion Planning for Mobile Robots by Means of Artificial Potential Field Method in Unknown Environment[J]. Industrial Robot,2013,37(4):384-400
[5]李擎,张超,韩彩卫,等.动态环境下基于模糊逻辑算法的移动机器人路径规划[J].中南大学学报,2013(增刊2):104-108LI Qing,ZHANG Chao,HAN Caiwei,et al. Path Planning Based on Fuzzy Logic Algorithm for Mobile Robots in Dynamic Environments[J]. Journal of Central South University,2013(suppl 2):104-108(in Chinese)
[6] KARIM B,ZHU Q. A Fuzzy Logic Behavior Architecture Controller for a Mobile Robot Path Planning in Multi-Obstacles Environment[J]. Research Journal of Applied Sciences Engineering&Technology,2013,5(14):3835-3842
[7] LI Q,ZHANG C,HAN C,et al. Path Planning Based on Fuzzy Logic Algorithm for Mobile Robots in Static Environment[C]∥Proceedings of IEEE Conference on Control and Decision Conference,2013:2866-2871
[8]顾辰.改进的A*算法在机器人路径规划中的应用[J].电子设计工程,2014(19):96-98GU Chen. Application of Improved A*Algorithm in Robot Path Planning[J]. Electronic Design Engineering,2014(19):96-98(in Chinese)
[9]占伟伟,王伟,陈能成,等.一种利用改进A*算法的无人机航迹规划[J].武汉大学学报(信息科学版),2015,40(3):315-320ZHAN Weiwei,WANG Wei,CHEN Nengcheng,et al. Path Planning Strategies for UAV Based on Improved A*Algorithm[J].Geomatics and Information Science of Wuhan University,2015,40(3):315-320(in Chinese)
[10]屈耀红,肖自兵,袁冬莉.基于风场信息的无人机在线航迹规划方法[J].西北工业大学学报,2012,30(4):576-581QU Yaohong,XIAO Zibing,YUAN Dongli. An Effective Method of UAV Flight Path Planning On-Line in Wind Field Using Improved A*Searching Algorithm[J]. Journal of Northwestern Polytechnical University,2012,30(4):576-581(in Chinese)
[11]徐翔,梁瑞仕,杨会志.基于改进遗传算法的智能体路径规划仿真[J].计算机仿真,2014,31(6):357-361XU Xiang,LIANG Ruishi,YANG Huizhi. Path Planning for Agent Based on Improved Genetic Algorithm[J]. Computer Simulation,2014,31(6):357-361(in Chinese)
[12]康冰,王曦辉,刘富.基于改进蚁群算法的搜索机器人路径规划[J].吉林大学学报,2014,44(4):1062-1068KANG Bing,WANG Xihui,LIU Fu. Path Planning of Searching Robot Based on Improved Ant Colony Algorithm[J]. Journal of Jilin University,2014,44(4):1062-1068(in Chinese)
[13]刘洋,章卫国,李广文,史静平.一种三维环境中的无人机多路径规划方法[J].西北工业大学学报,2014,32(3):412-416LIU Yang,ZHANG Weiguo,LI Guangwen,Shi Jingping. A Multi-Path Planning Method for Unmanned Aerial Vehicle(UAV)in 3D Environment[J]. Journal of Northwestern Polytechnical University,2014,32(3):412-416(in Chinese)
[14]ZHAO Q,ZHEN Z,CHEN G,et al. Path Planning of UAVs Formation Based on Improved Ant Colony Optimization Algorithm[C]∥Proceedings of IEEE Conference on Guidance,Navigation and Control,2015:1549-1552
[15]赖智铭,郭躬德.基于自适应阈值蚁群算法的路径规划算法[J].计算机系统应用,2014,23(2):113-118LAI Zhiming,GUO Gongde. Ant Colony Optimization Based on Self-Adaption Threshold for Path Planning[J]. Computer System Applications,2014,23(2):113-118(in Chinese)
[16]李擎,张超,陈鹏,等.一种基于粒子群参数优化的改进蚁群算法及其应用[J].控制与决策,2013,28(6):873-879LI Qing,ZHANG Chao,CHEN Peng,et al. Improved Ant Colony Optimization Algorithm Based on Particle Swarm Optimization[J]. Control and Decision,2013,28(6):873-879(in Chinese)
[17]方群,徐青.基于改进粒子群算法的无人机三维航迹规划[J].西北工业大学学报,2017,35(1):66-73FANG Qun,XU Qing. 3D Route Planning for UAV Based on Improved PSO Algorithem[J]. Journal of Northwestern Polytechnical University,2017,35(1):66-73(in Chinese)
[18]尹高扬,周绍磊,吴青坡.无人机快速三维航迹规划算法[J].西北工业大学学报,2016,34(4):564-570YING Gaoyang,ZHOU Shaolei,WU Qingpo. Efficient Path Planning Algorithm in Three Dimensions for UAV[J]. Journal of Northwestern Polytechnical University,2016,34(4):564-570(in Chinese)