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"

1 黄永， 姚文静， 龙科军 . 公路客运车辆侧翻事故特性及防控研究[J]. 公路与汽运， 2013(2): 85-88.
Huang Yong , Yao Wen-jing , Long Ke-jun . The characteristic and prevention research on rollover accident of highway passenger vehicles[J]. Highways & Automotive Applications, 2013(2): 85-88.
2 Scheuer A , Fraichard T . Continuous-curvature path planning for multiple car-like vehicles[C]∥IEEE-RSJ International Conference on Intelligent Robots and Systems, Grenoble, 1997: 997-1003.
3 Elbanhawi M , Simic M , Jazar R N . Continuous path smoothing for car-like robots using B-spline curves[J]. Journal of Intelligent & Robotic Systems, 2015, 80(1): 23-56.
4 Ji J , Khajepour A , Melek W , et al . Path planning and tracking for vehicle collision avoidance based on model predictive control with multi-constraints[J]. IEEE Transactions on Vehicular Technology, 2017, 66(2): 952-964.
5 Brown M , Funke J , Erlien S , et al . Safe driving envelopes for path tracking in autonomous vehicles[J]. Control Engineering Practice, 2017, 61: 307-316.
6 宁江坤 . 自主行驶车辆的避障控制算法研究[D]. 西安： 西安工业大学电子信息工程学院, 2016.
Ning Jiang-kun . Research on obstacle avoidance control algorithm for autonomous vehicles[D]. Xi’an: School of Electronic Information Engineering, Xi’an Technological University, 2016.
7 宋晓琳, 潘鲁彬, 曹昊天 . 基于改进智能水滴算法的汽车避障局部路径规划[J]. 汽车工程, 2016, 38(2): 185-191.
Song Xiao-lin , Pan Lu-bin , Cao Hao-tian . Local path planning for vehicle obstacle avoidance based on improved intelligent water drops algorithm[J]. Automotive Engineering, 2016, 38(2): 185-191.
8 陈谋, 肖键, 姜长生 . 基于改进蚁群算法的无人机三维航路规划[J]. 吉林大学学报: 工学版, 2008, 38(4): 991-995.
Chen Mou , Xiao Jian , Jiang Chang-sheng . Three dimensional path planning of UAV with improved ant algorithm[J]. Journal of Jilin University (Engineering and Technology Edition), 2008, 38(4): 991-995.
9 康冰, 王曦辉, 刘富 . 基于改进蚁群算法的搜索机器人路径规划[J]. 吉林大学学报: 工学版, 2014, 44(4): 1062-1068.
Kang Bing , Wang Xi-hui , Liu Fu . Path planning of searching robot based on improved ant colony algorithm[J]. Journal of Jilin University (Engineering and Technology Edition), 2014, 44(4): 1062-1068.
10 Karaman S , Frazzoli E . Sampling-based algorithms for optimal motion planning[J]. The International Journal of Robotics Research, 2011, 30(7): 846-894.
11 Ha J S , Choi H L , Jeon J H . Iterative methods for efficient sampling-based optimal motion planning of nonlinear systems[J]. International Journal of Applied Mathematics & Computer Science, 2018, 28(1): 155-168.
12 Laumond J P , Sekhavat S , Lamiraux F . Guidelines in nonholonomic motion planning for mobile robots[J]. Robot Motion Planning & Control, 1998, 229: 1-53．
13 Kuffner J J , Lavalle S M . RRT-connect: an efficient approach to single-query path planning[C]∥IEEE International Conference on Robotics and Automation, San Francisco, 2002: 995-1001.
14 Lavalle S M . Motion planning[J]. Robotics & Automation Magazine IEEE, 2011, 18(2): 108-118.
15 杜明博, 梅涛, 陈佳佳, 等 . 复杂环境下基于RRT的智能车辆运动规划算法[J]. 机器人, 2015, 37(4): 443-450.
Du Ming-Bo , Mei Tao , Chen Jia-jia , et al . RRT-based motion planning algorithm for intelligent vehicle in complex environments[J]. Robot, 2015, 37(4): 443-450.
16 余卓平, 卫烨, 熊璐, 等 . 城市工况下基于改进RRT的无人车运动规划算法[J]. 汽车技术, 2018(8): 10-17.
Yu Zhuo-ping , Wei Ye , Xiong Lu , et al . An improved RRT-based motion planning algorithm for autonomous vehicle in urban environment[J].Automobile Technology, 2018(8): 10-17.
17 宋晓琳, 周南, 黄正瑜, 等 . 改进RRT在汽车避障局部路径规划中的应用[J]. 湖南大学学报: 自然科学版, 2017, 44(4): 30-37.
Song Xiao-lin , Zhou Nan , Huang Zheng-yu , et al . An improved RRT algorithm of local path planning for vehicle collision avoidance[J]. Journal of Hunan University (Natural Sciences), 2017, 44(4): 30-37.
18 Lavalle S M . Rapidly-exploring random trees：a new tool for path planning[J]. Algorithmic & Computational Robotics New Directions, 1998(11): 293-308.
19 宗长富, 韩小健, 赵伟强, 等 . 基于动态LTR的客车防侧翻控制[J]. 中国公路学报, 2016, 29(9): 136-142.
Zong Chang-fu , Han Xiao-jian , Zhao Wei-qiang , et al . Anti-rollover control of bus based on dynamic LTR[J]. China Journal of Highway and Transport, 2016, 29(9): 136-142.
20 Ericson C . 实时碰撞检测算法技术[M]. 刘天慧, 译. 北京: 清华大学出版社, 2010.
21 Hill J D , Boyle L N . Driver stress as influenced by driving maneuvers and roadway conditions[J]. Transportation Research Part F, 2007, 10(3): 177-186.
22 Elbenhawi M , Simic M , Nakhaie J G . Continuous-curvature bounded trajectory planning using parametric splines[J]. Frontiers in Artificial Intelligence & Applications, 2014, 262: 513-522.
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