[1]蒲兴成,张军,张毅.基于神经网络的改进行为协调控制及其在智能轮椅路径规划中的应用[J].智能系统学报,2011,6(5):456-463.
　PU Xingcheng,ZHANG Jun,ZHANG Yi.Modified behavior coordination for intelligent wheelchair path planning based on a neural network[J].CAAI Transactions on Intelligent Systems,2011,6(5):456-463.
点击复制

基于神经网络的改进行为协调控制及其在智能轮椅路径规划中的应用

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 6 期数: 2011年第5期 页码: 456-463 栏目: 学术论文—智能系统 出版日期: 2011-10-30
Title:
Modified behavior coordination for intelligent wheelchair path planning based on a neural network
文章编号:
1673-4785(2011)05-0456-08
作者:
蒲兴成1，张军2，张毅2
1.重庆邮电大学 数理学院，重庆 400065；
2.重庆邮电大学 自动化学院，重庆 400065
Author(s):
PU Xingcheng1， ZHANG Jun2， ZHANG Yi2
1.Mathematics and Physics College, Chongqing University of Posts and Telecommunications, Chongqing 400065, China；
2.Automation College, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
关键词:
机器人; 智能轮椅; 非结构化环境; 路径规划; 神经网络; 行为协调控制器
Keywords:
robot;  intelligent wheelchair;  unstructured environment;  path planning;  neural network;  behavior coordination control
分类号:
TP24
文献标志码:
A
摘要:
针对传统的基于行为的智能轮椅的路径规划方法在室外非结构环境下的路径规划效果差的问题，提出一种新的智能轮椅的路径规划算法.该算法利用模糊逻辑设计了基本控制行为，并在此基础上结合大量实际经验使用神经网络设计了行为协调控制器.改进的算法将仲裁机制和命令融合机制2种行为协调方法有效结合起来，并吸收了这2种行为协调方法的优点，从而改善了系统的反应速度，极大提高了控制精确；另一方面，该算法还可以识别陷阱区域并通过自主改变行为的权重方法控制轮椅逃出陷阱区域，因而具备了较强的人工智能特征.仿真和实物实验验证了该算法智能性高且实现简单，适用于室外非结构化环境下的机器人路径规划.
Abstract:
In order to solve the poor effects of traditional behaviorbased path planning of intelligent wheelchairs in an outdoor unstructured environment, a new path planning method was proposed in this paper. The new algorithm uses fuzzy logic to design basic control behavior, and on this basis applies a neural network to design behavior coordination by combining a large amount of practical experience. The improved algorithm can combine arbitration mechanisms with fusion mechanisms successfully; it absorbs the major advantage of these two original algorithms and improves response speed of the system while enhancing the control accuracy significantly. On the other hand, the method can identify trap area and control the wheelchair escape from the trap by changing the behavior weights independently, therefore displaying strong artificial intelligence characteristics. The simulation and real experimental results verify that the algorithm is capable of advanced intelligence and can be implemented easily. Additionally, it can be used in an outdoor unstructured environment for robot path planning.
参考文献/References:
［1］LATOMBE J C. Robot motion planning［M］. Boston, USA: Kluwer Academic Publishers, 1991: 143176.
［2］刘一松,魏宁,孙亚民.基于栅格法的虚拟人快速路径规划［J］.计算机工程与设计, 2008, 29(5): 12291231.
?LIU Yisong, WEI Ning, SUN Yamin. Path planning algorithm based on grid method for virtual human［J］. Computer Engineering and Design, 2008, 29(5): 12291231.
［3］杨淮清,肖兴贵,姚栋.一种基于可视图法的机器人全局路径规划算法［J］.沈阳工业大学学报, 2009, 31(2): 225229.
?YANG Huaiqing, XIAO Xinggui, YAO Dong. A Vgraph based global path planning algorithm for mobile robot［J］. Journal of Shenyang University of Technology, 2009, 31(2): 225229.
［4］陈家照,张中位,徐福后.改进的概率路径图法［J］.计算机工程与应用, 2009, 45(10): 5455.
CHEN Jiazhao, ZHANG Zhongwei, XU Fuhou. Modified probabilistic roadmap method［J］. Computer Engineering and Applications, 2009, 45(10): 5455.
［5］鲍庆勇,李舜酩,沈峘,等.自主移动机器人局部路径规划综述［J］.传感器与微系统, 2009, 28(9): 14.
BAO Qingyong, LI Shunming, SHEN Huan, et al. Survey of local path planning of autonomous mobile robot［J］. Transducer and Microsystem Technologies, 2009, 28(9): 14.
［6］朱毅,张涛,宋靖雁.非完整移动机器人的人工势场法路径规划［J］.控制理论与应用, 2010, 27(2): 152158.
?ZHU Yi, ZHANG Tao, SONG Jingyan. Path planning for nonholonomic mobile robots using artificial potential field method［J］. Control Theory & Application, 2010, 27(2): 152158.
［7］傅卫平,张鹏飞,杨世强.利用机器人行为动力学与滚动窗口路径规划［J］.计算机工程与应用, 2009, 45(2): 212215.
FU Weiping, ZHANG Pengfei, YANG Shiqiang. Behavioral dynamics of mobile robot and rolling windows algorithm to path planning［J］. Computer Engineering and Applications, 2009, 45(2): 212215.
［8］宋勇，李贻斌，李彩虹.递归神经网络的进化机器人路径规划方法［J］.哈尔滨工程大学学报， 2009， 30（8）： 898902.
SONG Yong, LI Yibin, LI Caihong. Path planning based on a recurrent neural network for an evolutionary robot［J］. Journal of Haribn Engineering University, 2009, 30(8): 898902.
［9］赵先章,常红星,曾隽芳,等.一种基于粒子群算法的移动机器人路径规划方法［J］.计算机应用研究, 2007, 24(3): 181186.
ZHAO Xianzhang, CHANG Hongxing, ZENG Junfang, et al. Path planning method for mobile robot based on particle swarm algorithm［J］. Application Research of Computers, 2007, 24(3): 181186.
［10］郝博,秦丽娟,姜明洋.基于改进遗传算法的移动机器人路径规划方法研究［J］.计算机工程与科学, 2010, 32(7): 104107.
HAO Bo, QIN Lijuan, JIANG Mingyang. Research on the path planning methods for mobile robots based on an improved genetic algorithm［J］. Computer Engineering ＆ Science, 2010, 32(7): 104107.
［11］郝冬,刘斌.基于模糊逻辑行为融合路径规划方法［J］.计算机工程与设计, 2009, 30(3): 660663.
HAO Dong, LIU Bin. Behavior fusion path planning method for mobile robot based on fuzzy logic［J］. Computer Engineering and Design, 2009, 30(3): 660663.
［12］BROOKS R. A robust layered control system for a mobile robot［J］. IEEE Journal of Robotics and Automation, 1986, 2(1):1423.
［13］ARKIN R C. Motor schema based navigation for a mobile robot: an approach to programming by behavior［C］//Proceedings of the IEEE International Conference on Robotics and Automation. Raleigh, USA, 1987: 264271.
［14］ARKIN R C. Behaviorbased robotics［M］. Cambridge, USA: the MIT Press, 1998.
［15］PIRJANIAN P. Behavior coordination mechanisms: stateoftheart, Technical Report IRIS99375［R］. Los Angeles, USA: Institute for Robotics and Intelligent Systems, University of Southern California, 1999.
［16］ABREU A. Fuzzy behaviors and behavior arbitration in autonomous vehicles［C］//Proceedings of the 9th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence. London, UK: SpringerVerlag, 1999: 237251.
［17］PEREZ M C. A proposal of behavior based control architecture with reinforcement learning for an autonomous underwater robot［D］. Girona, Spain: University of Girona, 2003.
相似文献/References:
[1]方勇纯.机器人视觉伺服研究综述[J].智能系统学报,2008,3(2):109.
　FANG Yong-chun.A survey of robot visual servoing[J].CAAI Transactions on Intelligent Systems,2008,3():109.
[2]王立权,刘秉昊,吴健荣,等.6R关节型机器人运动学建模[J].智能系统学报,2010,5(2):156.
　WANG Li-quan,LIU Bing-hao,WU Jian-rong,et al.Modeling and implementing the inverse kinematics ofa six revolute joint robot[J].CAAI Transactions on Intelligent Systems,2010,5():156.
[3]孙宁,方勇纯.一类欠驱动系统的控制方法综述[J].智能系统学报,2011,6(3):200.
　SUN Ning,FANG Yongchun.A review for the control of a class of underactuated systems[J].CAAI Transactions on Intelligent Systems,2011,6():200.
[4]李晓飞,刘宏.机器人听觉声源定位研究综述[J].智能系统学报,2012,7(1):9.
　LI Xiaofei,LIU Hong.A survey of sound source localization for robot audition[J].CAAI Transactions on Intelligent Systems,2012,7():9.
[5]孙凤池,宋萌,刘光.一种无线传感器信号衰减自适应测距模型[J].智能系统学报,2012,7(3):214.
　SUN Fengchi,SONG Meng,LIU Guang.An adaptive ranging model based on energy distance loss of wireless sensors[J].CAAI Transactions on Intelligent Systems,2012,7():214.
[6]孙玉香,曹会彬,冯勇,等.具有拨土功能的轮腿一体化机器人结构设计[J].智能系统学报,2012,7(5):409.
　SUN Yuxiang,CAO Huibin,FENG Yong,et al.Structure design on a legwheeledintegration robotwith an earthmoving function[J].CAAI Transactions on Intelligent Systems,2012,7():409.
[7]伍明,李琳琳,李承剑.基于协方差交集的多机器人协作目标跟踪算法[J].智能系统学报,2013,8(1):66.[doi:10.3969/j.issn.1673-4785.201204022]
　WU Ming,LI Linlin,LI Chengjian.An algorithm of multi robot cooperative object tracking based on covariance intersection[J].CAAI Transactions on Intelligent Systems,2013,8():66.[doi:10.3969/j.issn.1673-4785.201204022]
[8]肖国宝,严宣辉.一种基于改进Theta *的机器人路径规划算法[J].智能系统学报,2013,8(1):58.[doi:10.3969/j.issn.1673-4785.201208032]
　XIAO Guobao,YAN Xuanhui.A path planning algorithm based on improved Theta * for mobile robot[J].CAAI Transactions on Intelligent Systems,2013,8():58.[doi:10.3969/j.issn.1673-4785.201208032]
[9]李大伟,贾鹏飞,李卫国,等.一种基于卡尔曼滤波与模糊算法的变电站机器人组合导航及控制系统设计[J].智能系统学报,2013,8(3):226.
　LI Dawei,JIA Pengfei,LI Weiguo,et al.A kind of integrated navigation and control system design for substation robot based on the Kalman filtering and fuzzy algorithm[J].CAAI Transactions on Intelligent Systems,2013,8():226.
[10]裴振兵,陈雪波.改进蚁群算法及其在机器人避障中的应用[J].智能系统学报,2015,10(1):90.[doi:10.3969/j.issn.1673-4785.201311018]
　PEI Zhenbing,CHEN Xuebo.Improved ant colony algorithm and its application in obstacle avoidance for robot[J].CAAI Transactions on Intelligent Systems,2015,10():90.[doi:10.3969/j.issn.1673-4785.201311018]

备注/Memo

收稿日期： 2011-01-16.
基金项目：科技部国际合作资助项目（2010DFA12160）;重庆市科委资助项目(CSCT，2010AA2055).
通信作者：蒲兴成.E-mail：puxingcheng@sina.com.
作者简介：
蒲兴成,男,1973年生,副教授,博士,主要研究方向为非线性系统、随机系统和智能控制等.主持和参与省部级基金项目8项,发表学术论文40余篇，出版学术专著1部、教材1部.
张军，男，1985年生，硕士研究生，主要研究方向为人工智能和机器人控制.
张毅，男，1966年生，教授，博士生导师，中国人工智能学会理事、青年工作委员会副主任、智能机器人专业委员会委员,中国计量测试学会高级会员,《机器人技术及其应用》杂志理事.主要研究方向为智能服务机器人、信息无障碍技术、智能人机交互技术.申请国家发明专利20项，获得国家发明专利10项，发表学术论文150余篇，其中被SCI、EI、ISTP检索80余篇，出版专著2部，撰写教材3部.

更新日期/Last Update: 2011-11-16
Copyright © 《 智能系统学报》 编辑部
地址：（150001）黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话：0451- 82534001、82518134　邮箱：tis@vip.sina.com