Hierarchical Trajectory Planning Algorithm based on Differential Flatness

doi:10.12382/bgxb.2021.0756

Abstract

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

CLC Number:

TJ810.2

ZHOU Xiaotian, REN Hongbin, SU Bo, QI Zhiquan, WANG Yang. Hierarchical Trajectory Planning Algorithm based on Differential Flatness[J]. Acta Armamentarii, 2023, 44(2): 394-405.

Figures/Tables 15

Fig.1 Obstacle avoidance trajectory planning

Fig.2 Mapping between state space and flat space

Fig.3 Vehicle kinematics model

Fig.4 Flow chart of the algorithm

Fig.5 Schematic diagram of obstacle avoidance by lane changing

Table 1 Simulation experiment parameters

参数	数值
汽车质量m/kg	1217
转动惯量I_z/(kg·m²)	1020
质心到前轴距离L_f/m	1.165
质心到后轴距离L_r/m	1.265
前轮转向刚度C_f/(N·rad^-1)	40000
后轮转向刚度C_r/(N·rad^-1)	40000
车道有效宽度D/m	3.6

Fig.6 Quintic polynomial sampling process

Fig.7 Simulation process of hierarchical trajectory planning based on differential flatness

Fig.8 Hardware-in-the-loop (HIL) experimental platform

Fig.9 Lane changing simulation process

Fig.10 Simulation results of lane changingtrajectory tracking

Fig.11 Lane changing performance parameters of the vehicle

Fig.12 Self-driving platform

Fig.13 Lane changing traffic scene

Fig.14 Tracking result for lane changing trajectory

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