Kinematic Model based Real-time Path Planning Method with Guide Line for Autonomous Vehicle
详细信息 查看官网全文
- 英文论文题名：Kinematic Model based Real-time Path Planning Method with Guide Line for Autonomous Vehicle
- 论文作者：Shuaishuai Yang ; Zhuping Wang ; Hao Zhang
- 英文论文作者：Shuaishuai Yang ; Zhuping Wang ; Hao Zhang ; College of Electronics and Information Engineering ; Tongji University
- 年：2017
- 作者机构：College of Electronics and Information Engineering,Tongji University;
- 英文论文关键词：Path planning ; kinematic model ; autonomous vehicle
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（A）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
- 语种：英文
- 分类号：U463.6
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：5
- 文件大小：331k
- 原文格式：D
- 会议级别：全国
摘要
This paper presents a kinematic model based path planning algorithm for autonomous vehicle. Polynomial parameterization is used to generate the trajectory. All constraints including kinematic constraints and obstacles are represented by polynomial parameters. Using this parameterization method the velocity can be planned simultaneously. Road information is taken into account in the object function through a guide line generated by Bezier curve. Solving optimization problem with constraint, a trajectory is generated by the planner. Compared with traditional one, the trajectory generated by the proposed method satisfies kinematic constraints of vehicle. Simulation results demonstrate the validity of the proposed algorithm.
This paper presents a kinematic model based path planning algorithm for autonomous vehicle. Polynomial parameterization is used to generate the trajectory. All constraints including kinematic constraints and obstacles are represented by polynomial parameters. Using this parameterization method the velocity can be planned simultaneously. Road information is taken into account in the object function through a guide line generated by Bezier curve. Solving optimization problem with constraint, a trajectory is generated by the planner. Compared with traditional one, the trajectory generated by the proposed method satisfies kinematic constraints of vehicle. Simulation results demonstrate the validity of the proposed algorithm.
This paper presents a kinematic model based path planning algorithm for autonomous vehicle. Polynomial parameterization is used to generate the trajectory. All constraints including kinematic constraints and obstacles are represented by polynomial parameters. Using this parameterization method the velocity can be planned simultaneously. Road information is taken into account in the object function through a guide line generated by Bezier curve. Solving optimization problem with constraint, a trajectory is generated by the planner. Compared with traditional one, the trajectory generated by the proposed method satisfies kinematic constraints of vehicle. Simulation results demonstrate the validity of the proposed algorithm.
引文
[1]D.Gonzalez,J.Perez,A Review of Motion Planning Techniques for Automated Vehicles,IEEE Trans.on Intelligent Transportation Systems,17(4):1-11,2015.
[2]P.Hart,N.Nilsson and B.Raphael,A Formal Basis for the Heuristic Determination of Minimum Cost Paths,IEEE Transactions on Systems Science and Cybernetics,4(2):100-107,1968.
[3]E.Dijkstra,A note on two problems in connexion with graphs,Numerische Mathematik,1(1):269-271,1959.
[4]J.Kuffner,S.Lavalle,RRT-connect:An Efficient Approach to Single-query Path Planning,IEEE International Conference on Robotics and Automation,2:998-1001,2003.
[5]L.Ma,J.Xue,K.Kawabara,et al,Efficient Sampling-Based Motion Planning for On-Road Autonomous Driving,IEEE Transactions on Intelligent Transportation Systems,16(4):1-16,2015.
[6]N.Melchior,Particle RRT for Path Planning with Uncertainty,IEEE International Conference on Robotics and Automation,1617-1624,2007.
[7]S.Karaman,M.Walter,A.Perez,et al,Anytime Motion Planning using the RRT*,IEEE International Conference on Robotics and Automation,47(10):1478-1483,2011.
[8]Z.Qu,J.Wang and C.Plaisted,A new analytical solution to mobile robot trajectory generation in the presence of moving obstacles,IEEE Transactions on Robotics,20(6):978-993,2004.
[9]T.Shim,G.Adireddy and H.Yuan,Autonomous vehicle collision avoidance system using path planning and modelpredictive-control-based active front steering and wheel torque control,Journal of Automobile Engineering,226(6):767-778,2012.
[10]W.Luo,J.Peng,W.Liu,et al,A Unified Optimization Method for Real-Time Trajectory Generation of Mobile Robots with Kinodynamic Constraints in Dynamic Environment,6th IEEE Conference on Robotics,Automation and Mechatronics,112-118,2013.
[11]G.Frain,Curves and Surfaces for Computer Aided Geometric Design:A Practical Guide,Mathematics of Computation,55(192):96,1992
[12]D.Dolgov,S.Thrun,M.Montemerlo,et al,Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments,International Journal of Robotics Research,29(5):485-501,2010.
[2]P.Hart,N.Nilsson and B.Raphael,A Formal Basis for the Heuristic Determination of Minimum Cost Paths,IEEE Transactions on Systems Science and Cybernetics,4(2):100-107,1968.
[3]E.Dijkstra,A note on two problems in connexion with graphs,Numerische Mathematik,1(1):269-271,1959.
[4]J.Kuffner,S.Lavalle,RRT-connect:An Efficient Approach to Single-query Path Planning,IEEE International Conference on Robotics and Automation,2:998-1001,2003.
[5]L.Ma,J.Xue,K.Kawabara,et al,Efficient Sampling-Based Motion Planning for On-Road Autonomous Driving,IEEE Transactions on Intelligent Transportation Systems,16(4):1-16,2015.
[6]N.Melchior,Particle RRT for Path Planning with Uncertainty,IEEE International Conference on Robotics and Automation,1617-1624,2007.
[7]S.Karaman,M.Walter,A.Perez,et al,Anytime Motion Planning using the RRT*,IEEE International Conference on Robotics and Automation,47(10):1478-1483,2011.
[8]Z.Qu,J.Wang and C.Plaisted,A new analytical solution to mobile robot trajectory generation in the presence of moving obstacles,IEEE Transactions on Robotics,20(6):978-993,2004.
[9]T.Shim,G.Adireddy and H.Yuan,Autonomous vehicle collision avoidance system using path planning and modelpredictive-control-based active front steering and wheel torque control,Journal of Automobile Engineering,226(6):767-778,2012.
[10]W.Luo,J.Peng,W.Liu,et al,A Unified Optimization Method for Real-Time Trajectory Generation of Mobile Robots with Kinodynamic Constraints in Dynamic Environment,6th IEEE Conference on Robotics,Automation and Mechatronics,112-118,2013.
[11]G.Frain,Curves and Surfaces for Computer Aided Geometric Design:A Practical Guide,Mathematics of Computation,55(192):96,1992
[12]D.Dolgov,S.Thrun,M.Montemerlo,et al,Path Planning for Autonomous Vehicles in Unknown Semi-structured Environments,International Journal of Robotics Research,29(5):485-501,2010.