Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (5): 1428-1440.doi: 10.13229/j.cnki.jdxbgxb20181089

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Local path planning of bus based on RS-RRT algorithm

Xiao-jian HAN1(),Wei-qiang ZHAO1(),Li-jun CHEN2,Hong-yu ZHENG1,Yang LIU1,Chang-fu ZONG1   

  1. 1. State Key Laboratory of Automobile Simulation and Control, Jilin University, Changchun 130022, China
    2. Big Data and Network Management Center, Jilin University, Changchun 130022, China
  • Received:2018-10-29 Online:2019-09-01 Published:2019-09-11
  • Contact: Wei-qiang ZHAO E-mail:xiaojian9569@163.com;zwqjlu@163.com

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Abstract:

In order to solve the path planning problem of self-driving bus in the structured road environment, an improved path planning algorithm, named Regional-Sampling Rapidly-exploring Random Tree (RS-RRT) algorithm, was proposed for obstacle avoidance conditions. In the sampling phase, Gaussian distribution sampling and local biasing sampling were integrated to improve the search efficiency of the path planning algorithm. In the expansion phase of the random tree, considering the actual size of bus and obstacles, the Separating Axis Theorem (SAT) was used to detect the collision of bus and surrounding obstacles in real time. In the post-processing stage, considering the goal of safety and comfort，the driver's driving consensus, the safety distance model and path smoothing algorithm were combined to correct the planning path. In order to verify the effectiveness of the RS-RRT algorithm, the hardware-in-the-loop test bench of electro-hydraulic steering system for commercial vehicle was built. The simulation scenario was built by TruckSim, and the proposed algorithm was verified by the co-simulation software of MATLAB and TruckSim. The results show that compared with basic RRT and Goal-biasing RRT, the proposed RS-RRT algorithm has advantages in terms of number of nodes, path length and running time. The generated path can meet the dynamics and path tracking requirements of the bus.

Key words: vehicle engineering, local path planning, regional-sampling, collision detection, path smoothing

CLC Number: 

  • U461.1

Fig.1

Flow chart of RRT algorithm"

Fig.2

3-DOF vehicle model"

Fig.3

Flow chart of RS-RRT sampling"

Fig.4

Region of sampling points"

Fig.5

Dead zone of sampling"

Fig.6

Schematic diagram of separating axis theorem"

Fig.7

Post processing of considering driving consensus"

Fig.8

Calculation of vertex coordinates"

Fig.9

HIL test bench"

Table 1

Basic parameters of bus"

参 数 数值
车长/m 10.995
车宽/m 2.5
车高/m 3.58
整车质量/kg 11 400
簧上质量/kg 10 090
轴数 2
轴距/m 5.88
前轴与质心间距离/m 4.12
发动机功率/kW 206
最小转弯半径/m 10.75
最大侧向加速度/( m·s-2) 5.8
前轮最大转角/(°) 32.75
最大侧倾角/(°) 24.3

Table 2

Parameters of bus and obstacles"

车辆 车长/m 车宽/m 车高/m
A 4.714 1.81 1.44
B 10.995 2.5 3.58
C 5.087 1.868 1.5
自车 10.995 2.5 3.58

Table 3

Other parameters"

名 称 数值
目标偏向概率 0.1
搜索步长/m 4
搜索终止长度/m 2
侧向安全距离/m 10.995
安全距离裕度/m 4
最大允许曲率/m-1 0.095

Fig.10

Simulation scenario"

Fig.11

Sampling results of various sampling methods"

Table 4

Comparison results of obstacle avoidance"

类 型 RS-RRT Goal-biasing RRT RRT
平均节点数目/个 42 63 157
平均路径长度/m 160.852 163.67 165.18
平均耗时/ms 49.97 85.53 230

Fig.12

Planned path considering static obstacles and collision detection"

Fig.13

Planned path considering dynamic obstacles and collision detection"

Fig.14

Planned path after post processing"

Fig.15

Curvature of various paths"

Fig.16

Results of path tracking at vx =36 km/h"

Fig.17

Results of path tracking at vx =54 km/h"

Fig.18

Results of path tracking at vx =72 km/h"

Fig.19

Error value of path tracking"

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