Neural networks in robotics
著者
書誌事項
Neural networks in robotics
(The Kluwer international series in engineering and computer science)
Kluwer Academic, c1993
大学図書館所蔵 全28件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
"Most of the papers contained in this book were presented at the First Workshop on Neural Networks in Robotics, sponsored by University of Southern California's Center for Neural Engineering, October 1991."
Includes bibliographical references and index
内容説明・目次
内容説明
Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots were created. The behavior of biological systems provides both the inspiration and the challenge for robotics. The goal is to build robots which can emulate the ability of living organisms to integrate perceptual inputs smoothly with motor responses, even in the presence of novel stimuli and changes in the environment. The ability of living systems to learn and to adapt provides the standard against which robotic systems are judged. In order to emulate these abilities, a number of investigators have attempted to create robot controllers which are modelled on known processes in the brain and musculo-skeletal system. Several of these models are described in this book.
On the other hand, connectionist (artificial neural network) formulations are attractive for the computation of inverse kinematics and dynamics of robots, because they can be trained for this purpose without explicit programming. Some of the computational advantages and problems of this approach are also presented.
For any serious student of robotics, Neural Networks in Robotics provides an indispensable reference to the work of major researchers in the field. Similarly, since robotics is an outstanding application area for artificial neural networks, Neural Networks in Robotics is equally important to workers in connectionism and to students for sensormonitor control in living systems.
目次
Foreword. Introduction. Section I: Trajectory Generation. 1. Learning Global Topological Properties of Robot Kinematic Mapping for Neural Network-Based Configuration Control. 2. A One-Eyed Self Learning Robot Manipulator. 3. A CMAC Neural Network for the Kinematic Control of Walking Machine. 4. Neurocontroller Selective Learning from Man-in-the-Loop Feedback Control Actions. 5. Application of Self-Organizing Neural Networks for Mobile Robot Environment. 6. A Neural Network Based Inverse Kinematics Solution in Robotics. 7. Hopefield Net Generation and Encoding of Trajectories in Contained Environment. Section II: Recurrent Networks. 8. Some Preliminary Comparisons Between a Neural Adaptive Controller and a Model Reference Adaptive Controller. 9. Stable Nonlinear System Identification Using Neural Network Models. 10. Modeling of Robot Dynamics by Neural Networks with Dynamic Neurons. 11. Neural Networks Learning Rules for Control: Uniform Dynamic Backpropagation, and the Heavy Adaptive Learning Rule. 12. Parameter Learning and Compliance Control Using Neural Networks. 13. Generalisation and Extension of Motor Programs for a sequential Recurrent Network. 14. Temporally Continuous vs. Clocked Networks. Section III: Hybrid Controllers. 15. Fast Sensorimotor Skill Acquisition Based on Rule-Based Training of Neural Nets. 16. Control of Grasping in Robot Hands by Neural Networks and Expert Systems. 17. Robot Task Planning Using a Connectionist/Symbolic System. Section IV: Sensing. 18. Senses, Skills, Reactions and Reflexes Learning Automatic Behaviors in Multi-Sensory Robotic Systems. 19. A New Neural Net Approachto Robot 3D Perception and Visuo-Motion Coordination. 20. Connectivity Graphs for Space-Variant Active Vision. 21. Competitive Learning for Color Space Division. 22. Learning to Understand and Control in a World of Events. 23. Self-Selection of Input Stimuli for Improving Performance. Section V: Biological Systems. 24. A Biologically-Inspired Architecture for Reactive Motor Control. 25. Equilibria Dynamics of a Neural Network Model for Opponent Muscle Control. 26. Developmental Robotics -- A New Approach to the Specification of Robot Programs. 27. A Kinematics and Dynamics Robot Control System Based on Cerbro-Cerebellar Interaction Modelling. 28. What Frogs' Brains Tell Robots' Schemas. 29. Modulation of Robotic Motor Synergies Using Reinforcement Learning Optimization. 30. Using Optimal Control to Model Trajectory Formation and Perturbation Response in a Prehension Task. Index.
「Nielsen BookData」 より