[1]梁爽,曹其新,王雯珊,等.基于强化学习的多定位组件自动选择方法[J].智能系统学报编辑部,2016,11(2):149-154.[doi:10.11992/tis.201510031]
 LIANG Shuang,CAO Qixin,WANG Wenshan,et al.An automatic switching method for multiple location components based on reinforcement learning[J].CAAI Transactions on Intelligent Systems,2016,11(2):149-154.[doi:10.11992/tis.201510031]
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基于强化学习的多定位组件自动选择方法(/HTML)
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年2期
页码:
149-154
栏目:
出版日期:
2016-04-25

文章信息/Info

Title:
An automatic switching method for multiple location components based on reinforcement learning
作者:
梁爽1 曹其新1 王雯珊1 邹风山2
1. 上海交通大学 机器人研究所, 上海 200240;
2. 新松机器人有限公司 中央研究院, 辽宁 沈阳 110000
Author(s):
LIANG Shuang1 CAO Qixin1 WANG Wenshan1 ZOU Fengshan2
1. Research Institute of Robotics, Shanghai Jiaotong University, Shanghai 200240, China;
2. SIASUN Robot and Automation CO., LTD, Shenyang 110000, China
关键词:
移动机器人定位强化学习中间件:Monte Carlo方法多传感器模块化分布式系统
Keywords:
mobile robotlocationreinforcement learningmiddlewareMonte Carlomulti-sensormodularizationdistributed system
分类号:
TP242.6
DOI:
10.11992/tis.201510031
摘要:
在一个大规模的动态环境中,针对机器人各种定位传感器的局限性,提出了一种基于强化学习的定位组件自动选择方法。系统采用分布式架构,将机器人不同的定位传感器与定位方法封装为不同的组件。采用强化学习的方法,寻找最优策略,实现多定位组件的实时切换。仿真结果表明,该方法可以解决大型环境中,单一定位方法不能适用于整个环境的问题,能够依靠多定位组件提供可靠的机器人定位信息;环境发生改变时,通过学习的方法不需要重新配置组件,且与直接遍历组件后切换组件的方法相比,极大地减小了延时。
Abstract:
To address the limitations of location sensors in large-scale dynamic environments, an automatic switching method for multiple robotic components based on reinforcement learning is proposed. This system uses distributed architecture and encapsulates different location sensors and methods into different middleware components. Reinforcement learning is employed to find the optimal strategy for deciding how to switch between components in real time. The simulation result shows that this method can solve problems that a single location method cannot in a large-scale environment and can provide reliable location information depending on multiple location components. This method can also effectively reduce the time delay compared with a method that first traverses all the components directly and then switches components.

参考文献/References:

[1] 李群明, 熊蓉, 褚健. 室内自主移动机器人定位方法研究综述[J]. 机器人, 2003, 25(6): 560-567, 573. LI Qunming, XIONG Rong, CHU Jian. Localization approaches for indoor autonomous mobile robots: A review[J]. Robot, 2003, 25(6): 560-567, 573.
[2] 张雪华, 刘华平, 孙富春, 等. 采用Kinect的移动机器人目标跟踪[J]. 智能系统学报, 2014, 9(1): 34-39. ZHANG Xuehua, LIU Huaping, SUN Fuchun, et al. Target tracking of mobile robot using Kinect[J]. CAAI transactions on intelligent systems, 2014, 9(1): 34-39.
[3] ANDO N, SUEHIRO T, KITAGAKI K, et al. RT-middleware: distributed component middleware for RT (robot technology)[C]//Proceedings of 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005. (IROS 2005). Edmonton, Alta., Canada: IEEE, 2005: 3933-3938.
[4] ANDO N, SUEHIRO T, KOTOKU T. A software platform for component based RT-system development: OpenRTM-aist[M]//CARPIN S, NODA I, PAGELLO E, et al. Simulation, Modeling, and Programming for Autonomous Robots. Berlin Heidelberg: Springer, 2008: 87-98.
[5] 庄严, 王伟, 王珂, 等. 移动机器人基于激光测距和单目视觉的室内同时定位和地图构建[J]. 自动化学报, 2005, 31(6): 925-933. ZHUANG Yan, WANG Wei, WANG Ke, et al. Mobile robot indoor simultaneous localization and mapping using laser range finder and monocular vision[J]. Acta automatica sinica, 2005, 31(6): 925-933.
[6] GERKEY B P. AMCL[EB/OL]. http://www.ros.org/wiki/amcl, 2011.
[7] 刘洞波, 刘国荣, 胡慧, 等. 基于激光测距的温室移动机器人全局定位方法[J]. 农业机械学报, 2010, 41(5): 158-163. LIU Dongbo, LIU Guorong, HU Hui, et al. Method of mobile robot global localization based on laser range finder in greenhouse[J]. Transactions of the Chinese society for agricultural machinery, 2010, 41(5): 158-163.
[8] 李振伟, 陈翀, 赵有. 基于OpenCV的运动目标跟踪及其实现[J]. 现代电子技术, 2008, 31(20): 128-130, 138. LI Zhenwei, CHEN Chong, ZHAO You. Moving object tracking method and implement based on OpenCV[J]. Modern electronics technique, 2008, 31(20): 128-130, 138.
[9] 张汝波, 顾国昌, 刘照德, 等. 强化学习理论、算法及应用[J]. 控制理论与应用, 2000, 17(5): 637-642. ZHANG Rubo, GU Guochang, LIU Zhaode, et al. Reinforcement learning theory, algorithms and its application[J]. Control Theory & Applications, 2000, 17(5): 637-642.
[10] 黄炳强, 曹广益, 王占全. 强化学习原理、算法及应用[J]. 河北工业大学学报, 2007, 35(6): 34-38. HUANG Bingqiang, CAO Guangyi, WANG Zhanquan. Reinforcement learning theory, algorithms and application[J]. Journal of Hebei University of Technology, 2007, 35(6): 34-38.
[11] SUTTON R S, BARTO A G. Reinforcement learning: an Introduction[M]. Cambridge, Mass.: MIT Press, 1998: 114-116.
[12] WANG Wenshan, CAO Qixin, ZHU Xiaoxiao, et al. An automatic switching approach of robotic components for improving robot localization reliability in complicated environment[J]. Industrial robot: an international journal, 2014, 41(2): 135-144.

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备注/Memo

备注/Memo:
收稿日期:2015-10-29;改回日期:。
基金项目:国家自然科学基金项目(61273331).
作者简介:梁爽,女,1993年生,硕士研究生,主要研究方向为移动机器人路径规划及模块化机器人技术。曹其新,男,1960年生,教授,博士生导师,主要研究方向为智能机器人与模块化系统、机器视觉与模式识别、移动机器人、泛在机器人技术。被EI、SCI检索论文90余篇,获得发明和实用新型专利50余项。王雯珊,女,1986年生,博士研究生,主要研究方向为泛在机器人、任务规划。
通讯作者:曹其新.E-mail:qxcao@sjtu.edu.cn.
更新日期/Last Update: 1900-01-01