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兵工学报 ›› 2023, Vol. 44 ›› Issue (2): 394-405.doi: 10.12382/bgxb.2021.0756

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基于微分平坦的分层轨迹规划算法

周孝添1, 任宏斌1,*(), 苏波2, 齐志权2, 汪洋2   

  1. 1 北京理工大学 机械与车辆学院, 北京 100081
    2 中国北方车辆研究所, 北京 100072
  • 收稿日期:2021-11-09 上线日期:2022-06-10
  • 通讯作者:
  • 基金资助:
    国家自然科学基金项目(52002025)

Hierarchical Trajectory Planning Algorithm based on Differential Flatness

ZHOU Xiaotian1, REN Hongbin1,*(), SU Bo2, QI Zhiquan2, WANG Yang2   

  1. 1 School of Mechanical Engineering,Beijing Institute of Technology, Beijing 100081, China
    2 China North Vehicle Research Institute, Beijing 100072, China
  • Received:2021-11-09 Online:2022-06-10

摘要:

为充分考虑横纵向耦合和汽车运动学特性对轨迹规划的影响,提出一种分层优化的轨迹规划算法框架。利用贝塞尔曲线的凸包性设计安全走廊约束,以轨迹平滑性为目标函数得到一个基于贝塞尔曲线节点的下层规划器。在上层规划器中,基于下层规划器求解得到的横纵向贝塞尔曲线和车辆运动学模型的微分平坦输出进行三维耦合,构建满足车辆乘坐舒适性、高效性和安全性的目标函数,利用粒子群优化算法对贝塞尔轨迹初始参数进行二次优化得到综合性能最优的行驶轨迹。仿真结果表明:新算法在保证安全性的同时,具有良好的乘坐舒适性和可跟踪性;由于二次规划与粒子群优化算法的求解效率高,此框架实时性强,具有概率完备性。

关键词: 轨迹规划, 微分平坦, 贝塞尔曲线, 二次规划, 粒子群优化算法

Abstract:

To fully consider the influence of transverse and longitudinal coupling and vehicle kinematics on trajectory planning, a hierarchical optimization-based trajectory planning algorithm framework is proposed. The safe corridor constraint is designed with the convex hull of a Bezier curve. Taking the trajectory smoothness as the objective function, we obtain a lower planner based on Bezier curve nodes. In the upper planner, based on the transverse and longitudinal Bezier curves solved by the lower planner and the differentially flat output of the vehicle kinematics model, the objective function meeting the vehicle ride comfort, efficiency and safety requirements is constructed, and quadratic optimization is applied to the initial parameters of the Bezier trajectory by particle swarm optimization algorithm to obtain the driving trajectory with the best comprehensive performance. The simulation results show that: the algorithm has good ride comfort and traceability while ensuring safety; due to the high efficiency of quadratic programming and particle swarm optimization, this framework has strong real-time and probabilistic completeness.

Key words: trajectory planning, differential flatness, bezier curve, quadratic programming, particle swarm optimization algorithm

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