[1]钱夔,宋爱国,章华涛,等.基于自适应模糊神经网络的机器人路径规划方法[J].东南大学学报(自然科学版),2012,42(4):637-642.[doi:10.3969/j.issn.1001-0505.2012.04.012]
 Qian Kui,Song Aiguo,Zhang Huatao,et al.Path planning for mobile robot based on adaptive fuzzy neural network[J].Journal of Southeast University (Natural Science Edition),2012,42(4):637-642.[doi:10.3969/j.issn.1001-0505.2012.04.012]
点击复制

基于自适应模糊神经网络的机器人路径规划方法()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
42
期数:
2012年第4期
页码:
637-642
栏目:
自动化
出版日期:
2012-07-20

文章信息/Info

Title:
Path planning for mobile robot based on adaptive fuzzy neural network
作者:
钱夔 宋爱国 章华涛 熊鹏文
东南大学仪器科学与工程学院, 南京 210096
Author(s):
Qian Kui Song Aiguo Zhang Huatao Xiong Pengwen
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
关键词:
自适应模糊神经网络 导航 陷阱问题 虚目标 路径规划
Keywords:
adaptive fuzzy neural network navigation trap problem virtual target path planning
分类号:
TP242.6
DOI:
10.3969/j.issn.1001-0505.2012.04.012
摘要:
为了解决传统反应式导航中的复杂陷阱问题,优化导航控制,减少计算复杂度,提出了基于自适应模糊神经网络的机器人导航控制及改进型虚目标路径规划方法.首先根据移动机器人运动学模型,融合神经网络的自主学习功能与模糊控制的模糊推理能力,提出了基于自适应模糊神经网络的机器人导航控制器,将生成的Takagi-Sugeno型模糊推理系统作为机器人局部反应控制的参考模型.该自适应模糊神经网络控制器实时输出扰动角度,在线调整移动机器人的预瞄准方向,使移动机器人能够无碰撞趋向目标.然后,提出了一种改进型虚目标方法,优先选择机器人可能逃脱陷阱状态的路径,简化了设计难度,改变了虚目标切换方式,避免了大量复杂计算.实验结果表明,提出的方法可以帮助机器人在全局信息未知的复杂环境中导航,在趋近目标点的过程中能有效避障,无冗余路径产生,且轨迹平滑.
Abstract:
To solve the complex trap problems in the traditional reactive navigation and optimize navigation control with reduction of computational complexity, a navigation method that combines mobile robot navigation control based on adaptive fuzzy neural network and an improved virtual target path planning is proposed. First, a mobile robot controller based on the kinematic model combining the learning ability of neural network and the fuzzy reasoning of fuzzy control is designed, resulting in Takagi-Sugeno fuzzy system which is used as the reference model in local reaction control. The controller outputs disturbance angle for real-time adjustment of the direction of robot, and the mobile robot tends to the target without collision by means of the controller. Then, an improved virtual target method is applied to solve local trap problem. The robot may prefer the path to escape from the trap state. This approach can simplify the design difficulty, change the virtual target switching mode, and reduce a large number of complex calculations. The experimental results show that the proposed method can help the mobile robot navigate in unknown complex environments and approach the target without collisions and redundant paths, and the trajectory is smooth.

参考文献/References:

[1] Chiu S L.Fuzzy model identification based on duster estimation [J].Journal of Intelligent and Fuzzy systems,1994,2(3):267-278.
[2] Wang M,Liu J N K.Fuzzy logic-based real-time robot navigation in unkown environment with dead ends [J].Robotics and Autonomous Systems,2008,56(7):625-643.
[3] 乔俊飞,樊瑞元,韩红桂,等.机器人动态神经网络导航算法的研究和实现[J].控制理论与应用,2010,27(1):111-115.
  Qiao Junfei,Fan Ruiyuan,Han Honggui,et al.Research and realization of dynamic neural network navigation algorithm for mobile robot[J].Control Theory and Application,2010,27(1):111-115.(in Chinese)
[4] Er M J,Gao Y.Robust adaptive control of robot manipulators using generalized fuzzy neural networks[J].IEEE Transactions on Industrial Electronics,2003,50(3):620-628.
[5] Wang J S,Lee C S G.Structure and learning in self-adaptive neural fuzzy inference systems[J].International Journal of Fuzzy Systems,2000,2(1):12-22.
[6] Xu W,Tso S.Sensor-based fuzzy reactive navigation of a mobile robot through local target switching [J].IEEE Transactions on Systems,Man and Cybernetics,1999,29(3):451-459.
[7] Toibero J M,Roberti F,Carelli R.Stable contour-following control of wheeled mobile robot [J].Robotica,2009,27(1):1-12.
[8] 刘宏林,罗杨宇,李成荣.基于模糊控制器的未知环境下移动机器人导航[J].计算机仿真,2011,28(1):201-205.
  Liu Honglin,Luo Yangyu,Li Chengrong.Fuzzy controller for mobile robot navigation under unknown environments [J].Computer Simulation,2011,28(1):201-205.(in Chinese)
[9] Istvn E,Gbor H.Artificial neural network based mobile robot navigation[C] //Proceedings of 6th IEEE International Symposium on Intelligent Signal Processing.Budapest,Hungary,2009:241-246.
[10] 张惠娣,刘士荣,俞金寿.基于动力学系统方法的自主移动机器人行为设计[J].华东理工大学学报:自然科学版,2008,34(6):843-849.
  Zhang Huidi,Liu Shirong,Yu Jinshou.Designing behaviors of autonomous mobile robots based on dynamical system approach[J].Journal of East China University of Science and Technology:Natural Science Edition,2008,34(6):843-849.(in Chinese)
[11] 蔡自兴,贺汉根,陈虹.未知环境中移动机器人导航控制研究的若干问题[J].控制与决策,2002,17(4):385-390.
  Cai Zixing,He Hangen,Chen Hong.Some issues for mobile robots navigation under unknown environments[J].Control and Decision,2002,17(4):385-390.(in Chinese)
[12] 王永骏,涂键.神经元网络控制[M].北京:机械工业出版社,1998:204-207.

相似文献/References:

[1]宋茂忠.一种新颖的四进制八相时空调制通信定位综合化系统[J].东南大学学报(自然科学版),2002,32(1):56.[doi:10.3969/j.issn.1001-0505.2002.01.013]
 Song Maozhong.A new integrated system of communication and radiolocation with quaternary 8 phases space-time modulation[J].Journal of Southeast University (Natural Science Edition),2002,32(4):56.[doi:10.3969/j.issn.1001-0505.2002.01.013]
[2]杨莉,袁信.小波分析在SAR图像边缘检测中的应用[J].东南大学学报(自然科学版),2001,31(2):54.[doi:10.3969/j.issn.1001-0505.2001.02.014]
 Yang Li,Yuan Xin.Application of Wavelet in SAR Image Edge Detection[J].Journal of Southeast University (Natural Science Edition),2001,31(4):54.[doi:10.3969/j.issn.1001-0505.2001.02.014]

备注/Memo

备注/Memo:
作者简介: 钱夔(1987—),男,博士生; 宋爱国(联系人),男,博士,教授,博士生导师, a.g.song@seu.edu.cn.
基金项目: 国家高技术研究发展计划(863计划)资助项目(2006AA04Z246)、教育部重大创新工程培育资金资助项目(708045).
引文格式: 钱夔,宋爱国,章华涛,等.基于自适应模糊神经网络的机器人路径规划方法[J].东南大学学报:自然科学版,2012,42(4):637-642. [doi:10.3969/j.issn.1001-0505.2012.04.012]
更新日期/Last Update: 2012-07-20