基于贝塞尔曲线的无人车局部避障应用
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摘要
文中将基于贝塞尔曲线的路径规划方法应用在无人车局部避障中。首先通过最小化曲线的最大曲率与最小曲率之间的差值确定贝塞尔曲线所需的五个控制点,根据五个控制点确定贝塞尔曲线,再结合实验无人车和道路的实际情况从若干备选曲线中选择最优路线。实验仿真结果证明,该方法能够达到无人车局部避障的要求,能满足包括初始状态约束、目标状态约束、曲率连续约束等行驶条件。
The path planning method based on quartic Bezier curve is applied to the local obstacle avoidance of unmanned vehicles. The difference between the maximum curvature and minimum curvature of the minimum curve is used to determine the five control points required by Bezier curve. The Bezier curve is determined according to the five control points,and the optimal path is selected from some alternative curves by combining the experimental unmanned vehicle with actual road situation. The experimental results show that the proposed method can meet the requirements of unmanned vehicles for local obstacle avoidance,and satisfy the driving conditions of initial state constraint,target state constraint and curvature continuous constraint.
The path planning method based on quartic Bezier curve is applied to the local obstacle avoidance of unmanned vehicles. The difference between the maximum curvature and minimum curvature of the minimum curve is used to determine the five control points required by Bezier curve. The Bezier curve is determined according to the five control points,and the optimal path is selected from some alternative curves by combining the experimental unmanned vehicle with actual road situation. The experimental results show that the proposed method can meet the requirements of unmanned vehicles for local obstacle avoidance,and satisfy the driving conditions of initial state constraint,target state constraint and curvature continuous constraint.
引文
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[3]ZHANG L,SUN L,ZHANG S,et al.Trajectory planning for an indoor mobile robot using quintic Bezier curves[C]//2015IEEE International Conference on Robotics and Biomimetics.Zhuhai:IEEE,2015:757-762.
[4]CHU K,LEE M,SUNWOO M.Local path planning for offroad autonomous driving with avoidance of static obstacles[J].IEEE transactions on intelligent transportation systems,2012,13(4):1599-1616.
[5]BARRAQUAND J,LATOMBE J C.A Monte-Carlo algorithm for path planning with many degrees of freedom[C]//1990IEEE International Conference on Robotics and Automation.Cincinnati:IEEE,1990:1712-1717.
[6]KAVRAKI L,LATOMBE J C.Randomized preprocessing of configuration for fast path planning[C]//1994 IEEE International Conference on Robotics and Automation.San Diego:IEEE,1994:2138-2145.
[7]陈刚,沈林成.复杂环境下路径规划问题的遗传路径规划方法[J].机器人,2001,23(1):40-44.CHEN Gang,SHEN Lincheng.Genetic path planning algorithm for complex environment path planning[J].Robot,2001,23(1):40-44.
[8]FUNKE J,THEODOSIS P,HINDIYEH R,et al.Up to the limits:autonomous Audi TTS[C]//2012 IEEE Intelligent Vehicles Symposium.Alcala de Henares:IEEE,2012:541-547.
[9]JOLLY K G,KUMAR R S,VIJAYAKUMAR R.A Bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits[J].Robotics&autonomous systems,2009,57(1):23-33.
[10]CHOI J W,CURRY R E,ELKAIM G H.Curvature-continuous trajectory generation with corridor constraint for autonomous ground vehicles[C]//IEEE the 49th Conference on Decision and Control.Atlanta:IEEE,2011:7166-7171.
[11]陈成,何玉庆,卜春光,等.基于四阶贝塞尔曲线的无人车可行轨迹规划[J].自动化学报,2015,41(3):486-496.CHEN Cheng,HE Yuqing,BU Chunguang,et al.Feasible trajectory generation for autonomous vehicles based on quartic Bezier curve[J].Acta automatica Sinica,2015,41(3):486-496.
[12]DUNCAN M.Applied geometry for computer graphics and CAD[M].Berlin:Springer,2005.
[13]余伶俐,龙子威,周开军.基于贝塞尔曲线的机器人非时间轨迹跟踪方法[J].仪器仪表学报,2016,37(7):1564-1572.YU Lingli,LONG Ziwei,ZHOU Kaijun.Non-time trajectory tracking method based on Bezier curve for robot[J].Chinese journal of scientific instrument,2016,37(7):1564-1572.
[14]李振浩.基于序列二次规划:免疫记忆鱼群算法的局放超声定位研究[D].昆明:昆明理工大学,2017.LI Zhenhao.Partial discharge ultrasound localization research based on sequence quadratic programming&immune memory fish swarm algorithm(SQP-IMAFSA)[D].Kunming:Kunming University of Science and Technology,2017.
[15]胡玉文.城市环境中基于混合地图的智能车辆定位方法研究[D].北京:北京理工大学,2014.HU Yuwen.Hybrid map based localization method for unmanned ground vehicle in urban scenario[D].Beijing:Beijing Institute of Technology,2014.
[16]CHE H,WU Z,KANG R,et al.Global path planning for explosion-proof robot based on improved ant colony optimization[C]//2016 Asia-Pacific Conference Intelligent Robot Systems.Tokyo:IEEE,2016:36-40.