Abstract—A path planning algorithm for the unmanned vehicles
based on target-oriented rapidly-exploring random tree (RRT) is
proposed in this paper with the objective of improving search
efficiency for the basic RRT algorithm. According to the idea of
target-oriented search, the information of the target node is always
utilized for the global search which makes the algorithm
directional and fast. In the vicinity of the obstacle, the local path
planning is carried out by using the optimized RRT algorithm to
meet safety requirements of the vehicle. With the help of a heuristic
function for node evaluation, the random tree generated is pruned
and optimized with fewer search nodes in a shorter distance. The
B-spline curve is used for fitting the nodes to derive a smooth
feasible path such that the kinematic constraints of vehicle are
taken into consideration. The effectiveness and advantages of the
proposed algorithm are verified by simulation and road
experiment.
I. INTRODUCTION
In recent years following the rise of artificial intelligence,
there is a spectacular prosperity in the area of unmanned vehicle
with great technical developments and improved practicability.
It attracts a lot of researchers, engineers and practitioners doing
research, producing prototypes and even commercial products,
especially in the developed countries and China. The principle
of unmanned vehicle is to perceive the road environment
including road signs, vehicles, pedestrians, obstacles and other
information by the vehicle sensors, and automatically control
speed and steering of the vehicle to reach the target by following
a safe and feasible path planned. All the operations and
computation are carried out in real time without the help of
humans.
Path planning plays a very important role in developing
unmanned vehicle, since the knowledge from the environmental
perception processed by AI should be synthesized to make the
path decision for a car to follow. The path planned by the
*This work is supported by the National Natural Science Foundation of
China under Grants 61403025, 61433002 and 61503209. the Fundamental
Research Funds for the Central Universities under Grant 2017JBM015
Haijun Gong is with School of Electronic and Information Engineering,
Beijing Jiaotong University, Beijing 100044, China. He is also with Idriver+
Technologies Co. Ltd., Beijing 102208, China (e-mail:
16120198@bjtu.edu.cn)
Chenkun Yin is with School of Electronic and Information Engineering,
Beijing Jiaotong University, Beijing 100044, China (corresponding author,
phone:+86-10-51684105; e-mail: chkyin@bjtu.edu.cn).
Fang Zhang is with Idriver+ Technologies Co. Ltd., Beijing 102208, China
(e-mail:zhangfang@idriverplus.com).
Zhongsheng Hou is with School of Electronic and Information Engineering,
Beijing Jiaotong University, Beijing 100044, China (e-mail:
zhshhou@bjtu.edu.cn).
Ruikun Zhang is with School of Mathematics and Physics, Qingdao
University of Science and Technology, Qingdao 266061, China (e-mail:
rkzhang@qust.edu.cn).
unmanned vehicle must satisfy the nonholonomic constraints of
the vehicle and guarantee safety from the starting point to the
destination, namely collision with any other objects on the road
must be strictly avoided.
There are several traditional path planning algorithms
including polygon fitting method, genetic algorithm, grid
method, simulated annealing algorithm and so on [1-3]. These
methods however require modeling and description of the
obstacles in a deterministic space. Because of high complexity
of calculation, they are not suitable to generate the vehicle path
in a complex environments with many scattered obstacles. For
the typical graph search algorithms, such as A*, D* and artificial
potential field method, the requirements of optimality and
real-time may be met during path planning, but the
nonholonomic constraints of vehicle are not taken into
consideration, so that it may be infeasible for the car to move
along the path [4-5].
Rapidly-exploring Random Tree (RRT), proposed by
LaValle in 1998 [6], was developed from the optimal control
theory, nonholonomic planning and stochastic path planning.
Since the random sampling method is adopted, it does not need
any preprocessing for the state space and is suitable to address
the problem of path planning with complex constraints in high
dimensional space. There are wide applications of RRT in the
field of motion planning for robotics in recent years [7].
The basic RRT algorithm entirely uses random sampling
strategy, its search space is often large, which makes the search a
bit time-consuming for convergence. On account of the
disadvantages, different kinds of improvement for RRT
algorithm have been made depending on the actual conditions
[8-11]. Kuffner and LaValle proposed a two-way search RRT
algorithm [12], that is, two trees from the starting point and the
target point generate simultaneously in order to accelerate the
search speed. Moon and Chung proposed dual-tree RRT [13], in
which a workspace tree and a state tree are considered. Because
of the different topologies allowed for the two trees, the nodes
can expand in a flexible manner, and the efficiency and rate of
success for search has been significantly improved. The paths
generated by these algorithms are usually applied to robots, but
may be infeasible for unmanned vehicles since the kinematic
constraints for the vehicle are not necessarily met.
To overcome the shortcomings caused by randomness of the
basic RRT algorithm, the target-oriented idea is introduced to
the basic RRT algorithm in this paper and an improved RRT
algorithm based on target point for path planning is proposed in
view of high efficiency of perceiving and short path distance
needed for the unmanned vehicles. During the global search, the
information of the target point is fully utilized all the time to
generate a global path. In the vicinity of the obstacle, local path
A Path Planning Algorithm for Unmanned Vehicles Based on
Target-Oriented Rapidly-Exploring Random Tree*
Haijun Gong, Chenkun Yin, Fang Zhang, Zhongsheng Hou, Senior Member, IEEE and Ruikun Zhang
2017 11th Asian Control Conference (ASCC)
Gold Coast Convention Centre, Australia
December 17-20, 2017
978-1-5090-1572-6/17/$31.00 ©2017 IEEE 760
下载后可阅读完整内容，剩余5页未读，立即下载
weixin_38519619
- 粉丝: 2
- 资源: 905
我的内容管理
展开
最新资源
- WebLogic集群配置与管理实战指南
- AIX5.3上安装Weblogic 9.2详细步骤
- 面向对象编程模拟试题详解与解析
- Flex+FMS2.0中文教程：开发流媒体应用的实践指南
- PID调节深入解析：从入门到精通
- 数字水印技术：保护版权的新防线
- 8位数码管显示24小时制数字电子钟程序设计
- Mhdd免费版详细使用教程：硬盘检测与坏道屏蔽
- 操作系统期末复习指南：进程、线程与系统调用详解
- Cognos8性能优化指南：软件参数与报表设计调优
- Cognos8开发入门：从Transformer到ReportStudio
- Cisco 6509交换机配置全面指南
- C#入门：XML基础教程与实例解析
- Matlab振动分析详解：从单自由度到6自由度模型
- Eclipse JDT中的ASTParser详解与核心类介绍
- Java程序员必备资源网站大全
资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！
点击此处反馈
