基于三次B样条曲线拟合的智能车轨迹跟踪算法
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摘要
针对传统几何轨迹跟踪算法切向角获取依赖高精度惯导设备的问题,提出了基于三次B样条曲线拟合的轨迹跟踪算法。首先,通过对先验地图中的离散轨迹点进行拟合生成平滑轨迹线;然后,根据轨迹方程通过插值法重新生成离散路点,并计算各个路点处的切向角,从而实现了对多传感器融合轨迹的优化与跟踪。在真实的智能车实验平台上,用所提算法对20 km/h低速绕圈和60 km/h较高速度直道两种典型场景进行了在真实道路下的跟踪测试。在低速大曲率和较高速度直道两种典型场景下,所提算法轨迹跟踪的最大横向误差均保持在0.3 m以内。实验结果表明,该算法有效解决了传统几何轨迹跟踪算法对惯导设备依赖的问题,同时保持了较好的跟踪性能。
The tangential angle acquisition of the traditional geometric path tracking algorithm depends on high precision inertial navigation equipments. In order to solve the problem, a new path tracking algorithm based on cubic B-spline curve fitting was proposed. Firstly, the smooth path was generated by fitting the discrete path points in the priori map. Then, the discrete path points were regenerated by using an interpolation method according to the path equation, and the tangential angle at each point was calculated to realize the optimization and tracking of the multi-sensor fusion path. On the real intelligent vehicle experiment platform, the 20 km/h low-speed-circle and the 60 km/h high-speed-straight-path tracking tests for the proposed algorithm were carried out under the two real road scenes. Under the two typical test scenarios of low-speed-largecurvature and high-speed-straight-path, the maximum lateral error of path tracking of the proposed algorithm is kept within 0. 3 m. The experimental results show that, the proposed algorithm can effectively solve the problem of traditional geometric path tracking algorithm's dependence on inertial navigation device, and maintain good tracking performance at the same time.
The tangential angle acquisition of the traditional geometric path tracking algorithm depends on high precision inertial navigation equipments. In order to solve the problem, a new path tracking algorithm based on cubic B-spline curve fitting was proposed. Firstly, the smooth path was generated by fitting the discrete path points in the priori map. Then, the discrete path points were regenerated by using an interpolation method according to the path equation, and the tangential angle at each point was calculated to realize the optimization and tracking of the multi-sensor fusion path. On the real intelligent vehicle experiment platform, the 20 km/h low-speed-circle and the 60 km/h high-speed-straight-path tracking tests for the proposed algorithm were carried out under the two real road scenes. Under the two typical test scenarios of low-speed-largecurvature and high-speed-straight-path, the maximum lateral error of path tracking of the proposed algorithm is kept within 0. 3 m. The experimental results show that, the proposed algorithm can effectively solve the problem of traditional geometric path tracking algorithm's dependence on inertial navigation device, and maintain good tracking performance at the same time.
引文
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[4]赵熙俊,陈慧岩.智能车辆路径跟踪横向控制方法的研究[J].汽车工程,2011,33(5):382-387.(ZHAO X J,CHEN H Y.A study on lateral control method for the path tracking of intelligent vehicles[J].Automotive Engineering,2011,33(5):382-387.)
[5]ZHU Q,HUANG Z H,LIU D X,et al.An adaptive path tracking method for autonomous land vehicle based on neural dynamic programming[C]//Proceedings of the 2016 IEEE International Conference on Mechatronics and Automation.Piscataway,NJ:IEEE,2016:1429-1434.
[6]CHOI J W,CURRY R E,ELKAIM G H.Continuous curvature path generation based on bézier curves for autonomous vehicles[J].IAENG International Journal of Applied Mathematics,2010,40(2):91-101.
[7]MARTIN T C,ORCHARD M E,SNCHEZ P V.Design and simulation of control strategies for trajectory tracking in an autonomous ground vehicle[J].Management and Control of Production and Logistics,2013,46(24):118-123.
[8]SHAN Y X,YANG W,CHEN C,et al.CF-Pursuit:a pursuit method with a clothoid fitting and a fuzzy controller for autonomous vehicles[EB/OL].[2017-08-16].http://journals.sagepub.com/doi/full/10.5772/61391.
[9]ELBANHAWI M,SIMIC M,JAZAR R.Receding horizon lateral vehicle control for pure pursuit path tracking[J].Journal of Vibration and Control,2016,24(3):1-24.
[10]HAN L,YASHIRO H,NEJAD H T N,et al.Bézier curve based path planning for autonomous vehicle in urban environment[C]//Proceedings of the 2010 IEEE Intelligent Vehicles Symposium.Piscataway,NJ:IEEE,2010:1036-1042.
[11]CHOI J W,CURRY R E,ELKAIM G H.Curvature-continuous trajectory generation with corridor constraint for autonomous ground vehicles[C]//Proceedings of the 2010 49th IEEE Conference on Decision and Control.Piscataway,NJ:IEEE,2010:7166-7171.
[12]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 C,HE Y Q,BU C G,et al.Feasible trajectory generation for autonomous vehicles based on quartic Bézier curve[J].Acta Automatica Sinica,2015,41(3):486-496.)
[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 L L,LONG Z W,ZHOU K J.Non-time trajectory tracking method based on Bezier curve for robot[J].Chinese Journal of Scientific Instrument,2016,37(7):1564-1572.)
[14]TSAI P S,WANG L S,CHANG F R,et al.Systematic backstepping design for B-spline trajectory tracking control of the mobile robot in hierarchical model[C]//Proceedings of the 2004 IEEE International Conference on Networking,Sensing and Control.Piscataway,NJ:IEEE,2004:713-718.
[15]HOFFMANN G M,TOMLIN C J,MONTEMERLO M,et al.Autonomous automobile trajectory tracking for off-road driving:controller design,experimental validation and racing[C]//Proceedings of the 2007 American Control Conference.Piscataway,NJ:IEEE,2007:2296-2301.
[16]ELBANHAWI M,SIMIC M,JAZAR R N.Continuous path smoothing for car-like robots using b-spline curves[J].Journal of Intelligent&Robotic Systems,2015,80(Suppl.1):23-56.
[17]张永华,杜煜,张汇,等.基于惯导航向角的智能车几何轨迹跟踪算法[J].北京联合大学学报:自然科学版,2017,31(4):54-60.(ZHANG Y H,DU Y,ZHANG H,et al.Geometric-based path tracking algorithm of the intelligent vehicle based on Inertial azimuth angle[J].Journal of Beijing Union University(Natural Sciences),2017,31(4):54-60.)