期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于内发动机机制的机器人趋光控制 被引量：3

Robot Phototaxis Control Based on Intrinsic Motivation Mechanism
在线阅读 下载PDF
导出
摘要 针对移动机器人的趋光问题,提出了一种基于内发动机机制的控制方法.该方法以生物体感觉运动系统的学习机制为基础,通过评价、行为选择以及取向和决策环节的强化实现对机器人最优趋光控制策略的搜索,使机器人在未知环境下,通过自主的学习和训练,逐渐掌握趋光移动技能.采用马尔科夫定理证明了学习过程的收敛性;仿真实验证明了基于内发动机机制趋光控制方法的有效性;通过与人工势场法的比较,说明了该方法的精确性. For the mobile robot phototaxis control problem, the control method was proposed based on intrinsic motivation mechanism. According to sensorimotor system learning mechanism the robot achieved the optimal control method through the strengthening links of evaluation, behavioral choices, tropism and decision-making. The robot obtained the independent learning skills in unknown environment and gradually mastered the skills phototaxis through learning and training. The convergence of the algorithm was proved by Markov theorem. Simulation results show the effectiveness of the method. Comparison with the artificial potential method proves the accuracy of this method.
作者 庞涛 阮晓钢 陈静 任红格
机构地区 北京工业大学电子信息与控制工程学院 沈阳航空航天大学电信学院
出处 《北京工业大学学报》 CAS CSCD 北大核心 2014年第1期32-37,共6页 Journal of Beijing University of Technology
基金 国家"973"计划资助项目(2012CB720000) 国家自然科学基金资助项目(61101161) 高等学校博士学科点专项科研基金资助项目(20101103110007)
关键词 机器人 认知 内发动机 趋光技能 感觉运动系统 robot cognitive intrinsic motivation phototaxis skill sensorimotor system
分类号 TP242.6 [自动化与计算机技术—检测技术与自动化装置]
  • 相关文献

参考文献12

  • 1RICHARD A W, SEVAN G F, JORDAN B P. Embodiedevolution: distributing an evolutionary algorithm in a population of robots [ J ]. Robotics and Autonomous Systems, 2002, 39(1): 1-18.
  • 2CRESPI A, LACHAT D, PASQUIER A. Controlling swimming and crawling in a fish robot using a central pattern generator[ J]. Autonomous Robots, 2008, 25 ( 1/ 2) : 3-13.
  • 3SHELLEY R. Cooperative phototaxis using networked mobile sensors and centroidal voronoi tessellations [ C ] // Proceedings of the American Control Conference. New York: IEEE, 2009 : 3274-3279.
  • 4JOSE A F, GERARDO G A, MIGUEL A M. Behavioral control through evolutionary neurocontrollers for autonomous mobile robot navigation [ J 1. Robotics and Autonomous Systems, 2009, 57(4): 411-419.
  • 5DAI Li-zhen, RUAN Xiao-gang, WANG Guan-wei, et al. Neural networks based autonomous learning for a desktop robot [ C ] // Proceedings of the World Congress on Intelligent Control and Automation. New York: IEEE, 2012 : 739-742.
  • 6OUDEYER P, KAPLAN F. What is intrinsic motivation? a typology of computational approaches[J]. Frontiers in Neurorobotics, 2007, 1(6): 1-14.
  • 7ASADA M, UCHIBE E, HOSODA K. Cooperative behavior acquisition for mobile robots in dynamically changing real worlds via vision-based reinforcement learning and development [J]. Artificial Intelligence, 1999, 110(2): 275-292.
  • 8SINGH S, LEWIS Richard L, BARTO Andrew G, et al. Intrinsically motivated reinforcement learning : an evolutionary perspective [ J ]. IEEE Transactions on Autonomous Mental Development, 2010, 2 (2) : 70-82.
  • 9ZORAN M, MARKO M, MIHAILO L, et al. Neural network reinforcement learning for visual control of robot manipulators [ J ]. Expert Systems with Applications, 2013, 40(5) : 1721-1736.
  • 10PRADHAN S K, SUBUDHI B. Real-time adaptive control of a flexible manipulator using reinforcement learning[ J]. IEEE Transactions on Automation Science and Engineering, 2012, 9(2): 237-249.

二级参考文献14

  • 1Baird L C. Residual algorithms: Reinforcement learning with function approximation. In: Proceedings of the 12th International Conference on Machine Learning (ICML95), Tahoe City, California, USA, 1995. 30～37
  • 2Rumelhart D E et al. Learning internal representations by error propagation. In: Rumelhart D E et al, eds. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol.1,Cambridge, MA: MIT Press,1986. 318～362
  • 3Cybenko G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems, 1989, 2: 303～314
  • 4Baird L C, Moore A. Gradient descent for general reinforcement learning. In: Kearns M S, Solla S A, Cohn D A eds. Advances in Neural Information Processing Systems 11, Cambrige, MA: MIT Press, 1999. 968～974
  • 5Bertsekas D P, Tsitsiklis J N. Gradient convergence in gradient methods with errors. SIAM Journal on Optimization, 2000, 10(3): 627～642
  • 6Heger M. The loss from imperfect value functions in expectation-based and minimax-based tasks. Machine Learning, 1996, 22(1): 197～225
  • 7Sutton R. Generalization in reinforcement learning: Successful examples using sparse coarse coding. In: Touretzky D S, Mozer M C, Hasselmo M E eds. Advances in Neural Information Processing Systems 8, Cambrige, MA: MIT Press, 1996. 1038～1044
  • 8Kaelbling L P et al. Reinforcement learning: A survey. Jour- nal of Artificial Intelligence Research, 1996, 4: 237～285
  • 9Tesauro G J. Temporal difference learning and TD-gammon. Communications of the ACM, 1995, 38(3):58～68
  • 10Crites R H, Barto A G. Elevator group control using multiple reinforcement learning agents. Machine Learning, 1998, 33(2/3):235～262

共引文献21

同被引文献20

引证文献3

二级引证文献15

北京工业大学学报
2014年 第1期

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