面向城市环境的四旋翼无人机在线避障航迹规划方法
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摘要
针对面向城市环境的四旋翼无人机的在线避障航迹规划问题,分别研究了常用的快速扩展随机树(Rapidly-exploring Random Tree,RRT)和人工势场的改进算法。为了解决RRT算法收敛速度慢、航迹曲折的问题,首先利用概率引导的方式对随机树的生长方向进行引导,然后对航迹进行裁减和B样条曲线平滑处理,生成满足四旋翼无人机性能要求的可行航迹;为了解决人工势场法陷入局部极小值和振荡的问题,首先利用改进的势场函数生成初始航迹,然后利用航迹点裁剪和B样条曲线进行优化,得到最终规划航迹。最后在城市环境模型下,从算法规划时间、规划航迹长度和转折角度3个方面将改进RRT算法与改进人工势场法进行仿真比较,结果表明改进RRT算法更适用于四旋翼的在线避障航迹规划。
Aiming at the online obstacle avoidance and path planning problem of quadrotor for urban environment,this paper studied the improved algorithm of rapidly-exploring random tree(RRT) and artificial potential field.In order to solve the problem of slow convergence speed and tortuous path of RRT algorithm,first,the probability guidance is used to guide the growth direction of random tree,and then the track is cut and the B-spline curve is smoothed to generate a feasible track that satisfies the performance requirements of quadrotor.In order to solve the problems that the artificial potential field method is trapped into local minimums and oscillations,the initial path is first generated by using the improved potential field function,and then the path planning is optimized by using path point clipping and B-spline curve.Finally,under the urban environment model,the improved RRT algorithm is compared with the improved artificial potential field method from the aspects of algorithm planning time,planning track length and turning angle.The results show that the improved RRT algorithm is more suitable for online obstacle avoidance and path planning of quadrotor.
Aiming at the online obstacle avoidance and path planning problem of quadrotor for urban environment,this paper studied the improved algorithm of rapidly-exploring random tree(RRT) and artificial potential field.In order to solve the problem of slow convergence speed and tortuous path of RRT algorithm,first,the probability guidance is used to guide the growth direction of random tree,and then the track is cut and the B-spline curve is smoothed to generate a feasible track that satisfies the performance requirements of quadrotor.In order to solve the problems that the artificial potential field method is trapped into local minimums and oscillations,the initial path is first generated by using the improved potential field function,and then the path planning is optimized by using path point clipping and B-spline curve.Finally,under the urban environment model,the improved RRT algorithm is compared with the improved artificial potential field method from the aspects of algorithm planning time,planning track length and turning angle.The results show that the improved RRT algorithm is more suitable for online obstacle avoidance and path planning of quadrotor.
引文
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[13] WANG K,ZHANG Y W,ZHOU D X.Research on flight path planning algorithms based on Four-rotor[J].Computer Simulation,2014,31(5):84-88.(in Chinese)王凯,张摇维,周德新.基于四旋翼飞行器的飞行航迹规划方法研究[J].计算机仿真,2014,31(5):84-88.
[14] WANG D W,ZHU M F,LIU H.Rapidly-exploring random tree algorithm based on dynamic Step[J].Computer Technology and Development,2016,26(3):105-107,112.(in Chinese)王道威,朱明富,刘慧.动态步长的RRT 路径规划算法[J].计算机技术与发展,2016,26(3):105-107,112.
[15] ELBANHAWI M,SIMIC M,JAZAR R.Randomized Bidirec- tional B-Spline Parameterization Motion Planning[J].IEEE Transactions on Intelligent Transportation Systerms,2016,17(2):406-419.
[16] GUO H T.Research on B-spline Curves and Surface[D].Hefei:Hefei University of Technology,2012:4-11.(in Chinese)郭怀天.B样条曲线及曲面研究[D].合肥:合肥工业大学,2012:4-11.
[2] ZAMMIT C,VAN KAMPEN E.Comparison between A* and RRT Algorithms for UAV Path Planning[C]//2018 AIAA Guidance,Navigation,and Control Conference.Florida:American Institute of Aeronautics and Astronautics,2018:1-23.
[3] GURUJI A K,AGARWAL H,PARSEDIVA D K.Time-Efficient A* Algorithm for Robot Path Planning[J].Pcocedia Technology,2016,23:144-149.
[4] ZHAO T,PAN X J,HE Q F.Application of Dynamic Ant Colony Algorithm in Route Planning for UAV[C]//Seventh International Conference on Information Science and Technology.Da Nang:Transactions on Cybernetics,2017:433-437.
[5] SHEN J.Robot Anti-Collision Path Planning Under Complex Dynamic Environment[J].Machinery Design & Manufacture,2017(11):255-258.(in Chinese)沈杰.复杂动态环境下机器人避撞路径规划[J].机械设计与制造,2017(11):255-258.
[6] ZHANG L,XU S,YAO S M,et al.Robot Path Planning Based on Improved Artificial Bee Colony Algorithm Under Complica-ted Environment[J].Machinery Design & Manufacture,2017(10):252-255.(in Chinese)张林,徐曙,姚森敏,等.复杂环境下基于改进人工蜂群算法的机器人路径规划[J].机械设计与制造,2017(10):252-255.
[7] LI L P,SU Z,XIE Y G,et al.Path planning of robot fish’s based on genetic algorithm[J].Ordnance Industry Automation,2015,34(12):93-96.(in Chinese)李连鹏,苏中,谢迎刚,等.基于遗传算法的机器鱼水中航迹规划[J].兵工自动化,2015,34(12):93-96.
[8] XI J R,YANG J X,ZHANG B L,et al.UAV Three Dimensional Real-time Route Planning[J].Computer Measurement & Control,2016,24(6):181-184.(in Chinese)席剑锐,杨金孝,张博亮,等.无人机三维实时航迹规划[J].计算机测量与控制,2016,24(6):181-184.
[9] LIU Y.Research on UAV path planning in dynamic environment [D].Xi’an:Northwestern Polytechnical University,2015:3-5.(in Chinese)刘洋.动态环境中的无人机路径规划研究[D].西安:西北工业大学,2015:3-5.
[10] YANG S Y,LIU Y Y,YANG W L.A local dynamic probabilistic roadmap method for unknown environment [J].Aeronautical Science & Technology,2016,27(4):69-73.(in Chinese)杨盛毅,柳阳阳,杨伟力.一种未知环境下的局部动态概率路线图法[J].航空科学技术,2016,27(4):69-73.
[11] LA VALLE S M.Rapidly-Exploring Random Tree:a new tool for path planning[R].Ames:Iowa State University,1998.
[12] HUANG J J.Research on Methods of Path Planning and Re- planning for UAVs[D].Harbin:Harbin Institute of Technology,2013:11-12.(in Chinese)黄建军.无人机自主飞行轨迹规划与重规划方法研究[D].哈尔滨:哈尔滨工业大学,2013:11-12.
[13] WANG K,ZHANG Y W,ZHOU D X.Research on flight path planning algorithms based on Four-rotor[J].Computer Simulation,2014,31(5):84-88.(in Chinese)王凯,张摇维,周德新.基于四旋翼飞行器的飞行航迹规划方法研究[J].计算机仿真,2014,31(5):84-88.
[14] WANG D W,ZHU M F,LIU H.Rapidly-exploring random tree algorithm based on dynamic Step[J].Computer Technology and Development,2016,26(3):105-107,112.(in Chinese)王道威,朱明富,刘慧.动态步长的RRT 路径规划算法[J].计算机技术与发展,2016,26(3):105-107,112.
[15] ELBANHAWI M,SIMIC M,JAZAR R.Randomized Bidirec- tional B-Spline Parameterization Motion Planning[J].IEEE Transactions on Intelligent Transportation Systerms,2016,17(2):406-419.
[16] GUO H T.Research on B-spline Curves and Surface[D].Hefei:Hefei University of Technology,2012:4-11.(in Chinese)郭怀天.B样条曲线及曲面研究[D].合肥:合肥工业大学,2012:4-11.