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Books on the topic 'Legged robotics control soft robotics robotics'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

Fodor, János. Aspects of Soft Computing, Intelligent Robotics and Control. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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2

Castillo, Oscar. Soft Computing for Intelligent Control and Mobile Robotics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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3

Fodor, János, and Janusz Kacprzyk, eds. Aspects of Soft Computing, Intelligent Robotics and Control. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03633-0.

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4

Fodor, János, and Robert Fullér, eds. Advances in Soft Computing, Intelligent Robotics and Control. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05945-7.

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5

Castillo, Oscar, Janusz Kacprzyk, and Witold Pedrycz, eds. Soft Computing for Intelligent Control and Mobile Robotics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15534-5.

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6

Sadati, Nasser. Hybrid control and motion planning of dynamical legged locomotion. Hoboken, N.J: Wiley, 2012.

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7

Stefan, Wermter, Palm Günther, and Elshaw Mark, eds. Biomimetic neural learning for intelligent robots: Intelligent systems, cognitive robotics, intelligent robots. Berlin: Springer, 2005.

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8

Melin, Patricia. Soft Computing Applications in Optimization, Control, and Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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9

Design and control of intelligent robotic systems. Berlin: Springer, 2009.

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10

International Symposium on Intelligent Automation and Control (2nd 1996 Montpellier, France). Intelligent automation and control: Recent trends in development and applications : proceedings of the World Automation Congress (WAC '96), May 28-30, 1996, Montpellier, France. Edited by Jamshidi Mohammad, Yuh Junku, Dauchez Pierre, and World Automation Congress (1996 : Montpellier, France). Albuquerque: TSI Press, 1996.

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11

J, Buckley James. Fuzzy and neural: Interactions and applications. Heidelberg: Physica-Verlag, 1998.

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12

World Automation Congress (4th 2000 Maui, Hawaii). Underwater vehicle technology: Proceedings of the Symposium on Underwater Robotic Technology (SURT 2000) at the Fourth Biannual World Automation Congress (WAC 2000), June 12-15, 2000, Maui, Hawaii, USA. Edited by Choi Song K and Yuh Junku. Albuquerque, NM: TSI Press, 2002.

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13

IEEE International Conference on Fuzzy Systems (10th 2001 Melbourne, Australia). The 10th IEEE International Conference on Fuzzy Systems: Meeting the grand challenge : machines that serve people : December, 2001, Sunday 2nd to Wednesday 5th. Piscataway, NJ: IEEE, 2001.

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14

Fodor, János. Aspects of Soft Computing, Intelligent Robotics and Control. Springer, 2012.

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15

Fodor, János, and Robert Fullér. Advances in Soft Computing, Intelligent Robotics and Control. Springer, 2014.

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16

Castillo, Oscar, and Witold Pedrycz. Soft Computing for Intelligent Control and Mobile Robotics. Springer, 2014.

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17

Castillo, Oscar, and Witold Pedrycz. Soft Computing for Intelligent Control and Mobile Robotics. Springer, 2011.

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18

Gruver, William A., Nasser Sadati, Guy A. Dumont, and Kaveh Akabri Hamed. Hybrid Control and Motion Planning of Dynamical Legged Locomotion. Wiley & Sons, Incorporated, John, 2012.

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19

Gruver, William A., Nasser Sadati, Guy A. Dumont, and Kaveh Akabri Hamed. Hybrid Control and Motion Planning of Dynamical Legged Locomotion. Wiley & Sons, Incorporated, John, 2012.

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20

Gruver, William A., Nasser Sadati, Guy A. Dumont, and Kaveh Akabri Hamed. Hybrid Control and Motion Planning of Dynamical Legged Locomotion. Wiley & Sons, Incorporated, John, 2012.

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21

Quinn, Roger D., and Roy E. Ritzmann. Principles and mechanisms learned from insects and applied to robotics. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0042.

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This chapter explores how insects have provided inspiration for robotic designs over recent decades as biologists have continued to learn more about these complex invertebrate systems. Initial legged robots typically mimicked insects only in terms of their basic six-legged (hexapod) designs and walking gaits. Since then robots have been developed that take advantage of insect leg and wing designs, compliant structures, movement behaviors, reflexes, and even local neural control systems identified in their central nervous systems. Future robots may be controlled with models of entire insect nervous systems including their brains.
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22

Tzafestas, Spyros G. Soft Computing in Systems and Control Technology (World Scientific Series in Robotics and Intelligent Systems , Vol 18). World Scientific Publishing Company, 1999.

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23

Wang, Lingfeng, Kay Chen Tan, and Dikai Liu. Design and Control of Intelligent Robotic Systems. Springer, 2010.

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24

Wang, Lingfeng, Kay Chen Tan, and Dikai Liu. Design and Control of Intelligent Robotic Systems. Springer, 2009.

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25

Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience (Lecture Notes in Computer Science). Springer, 2005.

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26

World Automation Congress - Advances in Robotics, Manufacturing, Automation, Control, Soft Computing, Multimedia, and Image Processing May 10-14, 1998 Anchorage Alaska CD-Rom. TSI Press, 2000.

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27

World Automation Congress - Advances in Robotics, Manufacturing, Automation, Control, Soft Computing, Multimedia, Image Processing, Medical Imaging and Financial Engineering June 11-16, 2000 Wailea, Maui, Hawaii CD-Rom. TSI Press, 2000.

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28

World Automation Congress Volume 4 Advances in Robotics, Manufacturing, Automation, Control, Soft Computing, Multimedia, Biomedicine, Image Processing and Financial Engineering June 28-July 1, 2004 Seville, Spain CD-Rom. TSI Press, 2004.

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29

Song K. Choi & Junku Yuh. Underwater Vehicle Technology Volume 12. TSI Press, 2002.

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30

Metta, Giorgio. Humans and humanoids. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0047.

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This chapter outlines a number of research lines that, starting from the observation of nature, attempt to mimic human behavior in humanoid robots. Humanoid robotics is one of the most exciting proving grounds for the development of biologically inspired hardware and software—machines that try to recreate billions of years of evolution with some of the abilities and characteristics of living beings. Humanoids could be especially useful for their ability to “live” in human-populated environments, occupying the same physical space as people and using tools that have been designed for people. Natural human–robot interaction is also an important facet of humanoid research. Finally, learning and adapting from experience, the hallmark of human intelligence, may require some approximation to the human body in order to attain similar capacities to humans. This chapter focuses particularly on compliant actuation, soft robotics, biomimetic robot vision, robot touch, and brain-inspired motor control in the context of the iCub humanoid robot.
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