期刊文献+

自主机器人视觉与行为模型及避障研究 被引量:5

Research on Vision-Action Model of Autonomous Robot and Obstacle Avoiding
下载PDF
导出
摘要 主动的障碍物探测与避障行为控制一直是自主机器人的重点研究课题 .在以往的研究中 ,机器人的视觉系统与电动机驱动系统往往是各自独立的构成控制体系 ,人们很少关注它们之间的关系 ,这样就从本质上割裂了两者之间的紧密耦合关系 ,减少了获取更多信息的途径 .本文主要研究如何建立机器人视觉与电动机驱动系统关系的视觉与行为模型 ,通过这种紧密的耦合关系为机器人运动提供更丰富的信息来源 .此外 ,在该模型的基础上引入了强化学习 ,引导机器人进行动态避障 .实验表明该方法是可靠的 。 Active obstacle detecting and avoiding for autonomous robot are significant research fields.In the past,the vision system and motor controlling system of the robot have been developed independently.Most people ignored the relationship of these two systems,separated the close coupling,and reduced the ways to obtain more information.Here,a Vision-Action model is developed to describe the relationship between the robot vision and motor action,in which the coupling provides more information resources of the environment.In addition,reinforced learning is used for autonomous robot to avoid the dynamic obstacle based on this Vision-Action model.Our experiments in the method were carried out stably and in real time.
出处 《电子学报》 EI CAS CSCD 北大核心 2003年第z1期2197-2200,共4页 Acta Electronica Sinica
基金 国家 8 63计划 (No .2 0 0 2AA7340 0 1 )
关键词 视觉与行为模型 动态避障 强化学习 光流场 vision-action model obstacle avoiding reinforcement learning optical flow fields
  • 相关文献

参考文献6

  • 1[1]Boley D L,Sutherland K T. A rapidly converging recursive method for mobile robot localization [ J ]. International Journal of Robotics Research, 1998,17(10): 1027 - 1039.
  • 2[2]Ohya A, Kosaka A Kak. Vision-based navigation by a mobile robot with obstacle using single-camera vision and ultrasonic sensing [ J]. IEEE Transactions on Robotics and Automation, 1998,14(6) :969 - 978.
  • 3[3]Brooks R A. A robust layered control system for a mobile robot [ J].IEEE Journal of Robotics and Automation, 1986, RA-2( 1 ): 14 - 23.
  • 4[4]Yao Shu,Zhang Bo.The learning convergence of CMAC in cyclic learning[J] .Journal of Computer Science and Technology, 1994,9(4) :320- 328.
  • 5[5]Horn B, Schunch B. Determining optical flow [ J ]. Artificial Intelligence, 1981,17:185 - 203.
  • 6[7]WatkinsCJCH, Dayan P. Q-learning [J].Machine Learning, 1992,8: 279 - 292.

同被引文献48

引证文献5

二级引证文献40

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部