基于改进RRT算法的RoboCup机器人动态路径规划

doi:10.13973/j.cnki.robot.2017.0008

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (838 KB) HTML ( ) 输出: BibTeX \| EndNote (RIS)
引用本文:
刘成菊, 韩俊强, 安康. 基于改进RRT算法的RoboCup机器人动态路径规划[J]. 机器人, 2017, 39(1): 8-15.DOI: 10.13973/j.cnki.robot.2017.0008. LIU Chengju, HAN Junqiang, AN Kang. Dynamic Path Planning Based on an Improved RRT Algorithm for RoboCup Robot. ROBOT, 2017, 39(1): 8-15. DOI: 10.13973/j.cnki.robot.2017.0008.

摘要针对足球机器人在场上采用反应式方法避障时存在的速度慢、效果差的问题，采用改进的快速扩展随机树（RRT）算法设计了一种能够适应机器人足球赛场动态移动障碍环境的路径规划器．首先，引入基本的快速扩展随机树算法，针对其随机性强、路径过长的缺点，提出了以一定概率选择目标点、增加引力分量以及路径平滑处理等改进方式；引入路径缓存区以及动态扩展随机树的方法来解决移动障碍物环境中的路径规划问题．复杂障碍物环境中的仿真实验表明，改进的规划路径长度比基本快速扩展随机树算法所得路径缩短约20%．最终将策略移植到实体NAO机器人上参加RoboCup比赛，取得世界八强的成绩．

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	刘成菊
	韩俊强
	安康

关键词 ：仿人机器人, 路径规划, 快速扩展随机树, 动态环境

Abstract：To solve the problem of slow speed and low efficiency of the soccer robots using reactive obstacle avoidance strategy at soccer game, an improved rapidly-exploring random tree (RRT) algorithm is adopted, and a path planner based on this algorithm is designed to adapt to the dynamic moving obstacles environment in the robot soccer field. Firstly, the basic RRT algorithm is introduced, and some improvement ways to solve the disadvantages of strong randomness and long path length are proposed, such as probabilistically selecting the target node, adding the gravity component, and smoothing the path. The path planning in dynamic moving obstacles environment is solved by using the methods of path buffer and dynamic-expanding random tree. The simulation in complex obstacle environment shows that the path generated by the improved algorithm is about 20% shorter than the path generated by the basic RRT algorithm. Finally, the strategy is applied to the physical NAO robot to participate in the RoboCup competition and the top eight grade is achieved.

Key words： humanoid robot path planning rapidly-exploring random tree dynamic environment

收稿日期: 2016-11-01 录用日期: 2016-12-09

TP242.6

基金资助:国家自然科学基金（61673300，61573260），中央高校基本科研业务费专项

通讯作者: 安康，ankang526@foxmail.com E-mail: ankang526@foxmail.com

作者简介: 刘成菊（1980-），女，博士，副教授．研究领域：仿生技术，机器人运动控制，进化计算．
韩俊强（1992-），男，硕士．研究领域：机器人运动控制．
安康（1981-）男，博士，讲师．研究领域：机器人运动控制，被动行走．

链接本文:

https://robot.sia.cn/CN/10.13973/j.cnki.robot.2017.0008 或 https://robot.sia.cn/CN/Y2017/V39/I1/8

[1] LaValle S M. Rapidly-exploring random trees:A new tool for path planning[R]. Ames, USA:Computer Science Department, Iowa State University, 1998.
[2] Kuffner J J, LaValle S M. RRT-connect:An efficient approach to single-query path planning[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2000:995-1001.
[3] Esposito J, Kim J, Kumar V. Adaptive RRTs for validating hybrid robotic control systems[M]//Algorithmic Foundations of Robotics VI. Berlin, Germany:Springer, 2005:107-121.
[4] Karaman S, Walter M R, Perez A, et al. Anytime motion planning using the RRT[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2011:1478-1483.
[5] 李威洲.基于RRT的复杂环境下机器人路径规划[D].哈尔滨:哈尔滨工程大学,2012.Li W Z. Robot motion planning based on rapid-exploring random trees in complex environment[D]. Harbin:Harbin Engineering University, 2012.
[6] 王全.基于RRT的全局路径规划方法及其应用研究[D].长沙:国防科学技术大学,2014.Wang Q. Research on rapidly-exploring random trees based global path planning and its application[D]. Changsha:National University of Defense Technology, 2014.
[7] Du M B, Chen J J, Zhao P, et al. An improved RRT-based motion planner for autonomous vehicle in cluttered environments[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2014:4674-4679.
[8] 宋金泽,戴斌,单恩忠,等.一种改进的RRT路径规划算法[J].电子学报,2010,38(2A):225-228.Song J Z, Dai B, Shan E Z, et al. An improved RRT path planning algorithm[J]. Acta Electronica Sinica, 2010, 38(2A):225-228.
[9] Wu D, Sun Y J, Wang X, et al. An improved RRT algorithm for crane path planning[J]. International Journal of Robotics and Automation, 2016, 31(2):84-92.
[10] Kala R. Rapidly exploring random graphs:Motion planning of multiple mobile robots[J]. Advanced Robotics, 2013, 27(14):1113-1122.
[11] Khatib O. Real-time obstacle avoidance for manipulators and mobile robots[J]. International Journal of Robotics Research, 1986, 5(1):90-98.
[12] 郝利波.基于改进RRT与人工势场混合算法的足球机器人路径规划研究[D].西安:西安科技大学,2011.Hao L B. Based on improved RRT with artificial potential field hybrid algorithm of robot soccer path planning research[D]. Xi'an:Xi'an University of Science and Technology, 2011.
[13] Yuan X, Zhu Q D, Yan Y J. Collision avoidance planning in multi-robot system based on improved artificial potential field and rules[J]. Journal of Harbin Institute of Technology, 2009, 16(3):413-418.
[14] Hu Y L, Zhang Q S. Multi-robots path planning based on improved artificial potential field method[M]//Advanced Materials Research. Clausthal-Zellerfeld, Germany:Trans Tech Publications, 2012:937-940.
[15] Abbadi A, Matousek R, Minar P, et al. RRTs review and options[M]//Computational Engineering in Systems Applications (vol. II). WSEAS Press, 2011:194-199.
[16] Wang W, Li Y. A multi-RRTs framework for robot path planning in high-dimensional configuration space with narrow passages[C]//IEEE International Conference on Mechatronics and Automation. Piscataway, USA:IEEE, 2009:4952-4957.
[17] Bruce J, Veloso M M. Real-time randomized path planning for robot navigation[M]//Lecture Notes in Computer Science, vol.2752. Berlin, Germany:Springer, 2002:288-295.