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Titlebook: Soft Computing for Intelligent Robotic Systems; Lakhmi C. Jain,Toshio Fukuda Book 1998 Springer-Verlag Berlin Heidelberg 1998 Intelligente

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发表于 2025-3-21 17:45:29 | 显示全部楼层 |阅读模式
书目名称Soft Computing for Intelligent Robotic Systems
编辑Lakhmi C. Jain,Toshio Fukuda
视频video
概述Most up-to-date techniques in robotics
丛书名称Studies in Fuzziness and Soft Computing
图书封面Titlebook: Soft Computing for Intelligent Robotic Systems;  Lakhmi C. Jain,Toshio Fukuda Book 1998 Springer-Verlag Berlin Heidelberg 1998 Intelligente
描述Research results using some of the most advanced soft computing techniques in intelligent robotic systems are presented. The main purpose of this book is to show how the power of soft computing techniques can be exploited in intelligent robotic systems. The main emphasis is on control system for a mobile robot, behavior arbitration for a mobile robot, reinforcement learning of a robot, manipulation of a robot, collision avoidance and automatic design of robots..This book will be useful for application engineers, scientists and researchers who wish to use some of the most advanced soft computing techniques in robotics.
出版日期Book 1998
关键词Intelligente Roboter; Sonar; Trend; behavior; control; control system; evolution; learning; navigation; netwo
版次1
doihttps://doi.org/10.1007/978-3-7908-1882-6
isbn_softcover978-3-662-13003-2
isbn_ebook978-3-7908-1882-6Series ISSN 1434-9922 Series E-ISSN 1860-0808
issn_series 1434-9922
copyrightSpringer-Verlag Berlin Heidelberg 1998
The information of publication is updating

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Neural Networks for Visual Servoing in Robotics,This chapter introduces an application of artificial neural network techniques to robotic control. Arm movements are controlled using visual features. The neurocontroller adapts on-line without any prior knowledge of the system geometry.
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Lakhmi C. Jain,Toshio FukudaMost up-to-date techniques in robotics
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Studies in Fuzziness and Soft Computinghttp://image.papertrans.cn/s/image/870475.jpg
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https://doi.org/10.1007/978-3-7908-1882-6Intelligente Roboter; Sonar; Trend; behavior; control; control system; evolution; learning; navigation; netwo
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978-3-662-13003-2Springer-Verlag Berlin Heidelberg 1998
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发表于 2025-3-23 02:23:58 | 显示全部楼层
Neural Reinforcement Learning for Robot Navigation,ure 1 illustrates what we mean by an agent in this chapter. Experiments in learning an obstacle-avoidance behavior of the robot Khepera are presented. It is shown that neural RL is more suitable in real world applications.
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