A New Method on Trajectory Planning for Mobile Robots Using Bezier Curves

doi:10.16182/j.issn1004731x.joss.201803024

Abstract

Abstract: In view ofthe trajectory planning for the mobile robots, an efficient method based on Bezier curves is proposed, which produces better solution with shorter path and faster trajectory, compared with the existing methods. The problem of trajectory planning is divided into path planning and velocity planning. A Bezier curve is used to connect each two line-segments to obtain shorter paths. It is proved that the order of the used Bezier curves is at least five to ensure the curvature continuity of the whole path. Taking a differential-driven wheeled mobile as an example, to follow the planned path steadily and smoothly, velocity profile satisfying the constraints of kinematics and dynamics is generated. The simulation results verify the feasibility of the method proposed in this paper.

Key words: trajectory planning, path planning, Bezier curves, velocity planning

CLC Number:

TP242.6

Sun Lei, Zhang Lishuang, Zhou Lu, Zhang Xuebo. A New Method on Trajectory Planning for Mobile Robots Using Bezier Curves[J]. Journal of System Simulation, 2018, 30(3): 962-968.

References 10

[1]	Lau B, Sprunk C, Burgard W.Kinodynamic Motion Planning for Mobile Robots Using Splines[C] // International Conference on Intelligent Robots and Systems, St. Louis, USA, 2009: 2427-2433.
[2]	Zhu D, Yan M.Survey on technology of mobile robot path planning[J]. Control and Decision (S1001-0920), 2010, 25(7): 961-967.
[3]	Morales Y, Watanabe A.Including Human Factors for Planning Comfortable Paths[C]//International Conference on Robotics and Automation, Washington, USA, 2015: 6153-6159.
[4]	Dubins L.On Curves of Minimal Length with a Constraint on Average Curvature, and with Prescribed Initial and Terminal Positions and Tangents[J]. Amer. J. Math (S0002-9327), 1957, 79: 497-516.
[5]	Kjaergaard M, Andersen N A, Ravn O.Generic Trajectory Representation and Trajectory Following for Wheeled Robots[C]//International Conference on Robotics and Automation, Hong Kong, China, 2014: 4073-4080.
[6]	Chen C, He Y.Quartic Bezier Curve Based Trajectory Generation for Autonomous Vehicles with Curvature and Velocity Constraints[C] // International Conference on Robotics and Automation, Hong Kong, China, 2014: 6108-6113.
[7]	Piazzi A, BiancoC G L, Romano M. 3-splines for the Smooth Path Generation of Wheeled Mobile Robots[J], IEEE Trans. Robotics (S1552-3098), 2007, 23(5): 1089-1095.
[8]	Ghilardelli F, Lini G, Piazzi A.Path Generation Using-splines for a Truck and Trailer Vehicle[J]. IEEE Trans. On Automation Science and Engineering (S1545-5955), 2014, 11(1):187-203.
[9]	Duncan M.Applied Geometry for Computer Graphics and CAD[M]. Berlin: Springer, 2005.
[10]	Lepetic M, Klancar G, Skrjanc I.Time Optimal Path Planning Considering Acceleration Limits[J]. Robotics and Autonomous Systems (S0921-8890), 2003, 45(3): 199-210.

[1]	Sen Zhang, Mengyan Zhang, Jingping Shao, Jiexin Pu. Multi-UAVs 3D Path Planning Method Based on Random Strategy Search [J]. Journal of System Simulation, 2022, 34(6): 1286-1295.
[2]	Dun Meng, Zhuo Hu, Huajun Zhang. Simulation of Multi-layer Ship Evacuation System Based on Improved A* Algorithm [J]. Journal of System Simulation, 2022, 34(6): 1375-1382.
[3]	Xiangyang Deng, Limin Zhang, Wei Fang, Miao Tang. Robot Path Planning Based on Bidirectional Aggregation Ant Colony Optimization [J]. Journal of System Simulation, 2022, 34(5): 1101-1108.
[4]	Zhaoqiang Li, Shiyu Zhang. Research on 3D Path Planning Algorithm Based on Fast RRT Algorithm [J]. Journal of System Simulation, 2022, 34(3): 503-511.
[5]	Qiming Wang, Gaoqiang Zong, Jinming Xu. Optimal Path Planning for Multi-stage Automatic Parking and Simulation Analysis [J]. Journal of System Simulation, 2022, 34(2): 385-395.
[6]	Zeng Sijie, Yan Liang, Duan Xiaojun. Planning and Analysis on UAV Trajectory Based on PCE Method [J]. Journal of System Simulation, 2022, 34(1): 145-152.
[7]	Duan Xianyin, Zhang Can, Zhu Zerun, Sun Zhaoyang, Jiang Guozhang, Xiang Feng. Trajectory Planning of 6-DOF Manipulator Based on Inverse Multi-objective Optimization [J]. Journal of System Simulation, 2021, 33(9): 2128-2137.
[8]	Chen Zhimei, Li Min, Shao Xuejuan, Zhao Zhicheng. Obstacle Avoidance Path Planning of Bridge Crane Based on Improved RRT Algorithm [J]. Journal of System Simulation, 2021, 33(8): 1832-1838.
[9]	Li Qi, Wang Wei. Path Designing of Multi-omnidirectional Wheel Collaborative Sorting Platform [J]. Journal of System Simulation, 2021, 33(3): 698-709.
[10]	Jiang Zerui, Yang Lijun, Li Jun, Jiao Xiaolong, Zheng Hang. Kinematics Analysis and Simulation of Automatically Tracking Dental Surgery Lamp [J]. Journal of System Simulation, 2021, 33(12): 2864-2879.
[11]	Huang Xiaodong, Yuan Haitao, Bi Jing, Liu Tao. DQN-based Path Planning Method and Simulation for Submarine and Warship in Naval Battlefield [J]. Journal of System Simulation, 2021, 33(10): 2440-2448.
[12]	Lin Bin, Han Guanghui, Song Chenchen, Zhang Yajing. Traversal Path Planning and Simulation of Robot based on Radiation Scanning [J]. Journal of System Simulation, 2021, 33(1): 84-90.
[13]	Xiu Supu, Wen Yuanqiao, Xiao Changshi, Yuan Haiwen, Zhan Wenqiang. Design and Simulation on Autonomous Landing of a Quad Tilt Rotor [J]. Journal of System Simulation, 2020, 32(9): 1676-1685.
[14]	Zhu Daqi, Zhu Tingting, Yan Mingzhong. Multi-AUV Complete Coverage Path Planning Based on Improved Neural Network [J]. Journal of System Simulation, 2020, 32(8): 1505-1514.
[15]	You Bo, Wang Mingrui, Li Zhi, Ding Liang. Trajectory Planning of Wheeled Mobile Robot Based on Model Predictive Control [J]. Journal of System Simulation, 2020, 32(4): 591-600.