Neural Networks for Robotics

An Engineering Perspective

Nonfiction, Science & Nature, Technology, Electricity, Electronics
Cover of the book Neural Networks for Robotics by Nancy Arana-Daniel, Alma Y. Alanis, Carlos Lopez-Franco, CRC Press
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Author: Nancy Arana-Daniel, Alma Y. Alanis, Carlos Lopez-Franco ISBN: 9781351231770
Publisher: CRC Press Publication: September 6, 2018
Imprint: CRC Press Language: English
Author: Nancy Arana-Daniel, Alma Y. Alanis, Carlos Lopez-Franco
ISBN: 9781351231770
Publisher: CRC Press
Publication: September 6, 2018
Imprint: CRC Press
Language: English

The book offers an insight on artificial neural networks for giving a robot a high level of autonomous tasks, such as navigation, cost mapping, object recognition, intelligent control of ground and aerial robots, and clustering, with real-time implementations. The reader will learn various methodologies that can be used to solve each stage on autonomous navigation for robots, from object recognition, clustering of obstacles, cost mapping of environments, path planning, and vision to low level control. These methodologies include real-life scenarios to implement a wide range of artificial neural network architectures.

 

  • Includes real-time examples for various robotic platforms.
  • Discusses real-time implementation for land and aerial robots.
  • Presents solutions for problems encountered in autonomous navigation.
  • Explores the mathematical preliminaries needed to understand the proposed methodologies.
  • Integrates computing, communications, control, sensing, planning, and other techniques by means of artificial neural networks for robotics.
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The book offers an insight on artificial neural networks for giving a robot a high level of autonomous tasks, such as navigation, cost mapping, object recognition, intelligent control of ground and aerial robots, and clustering, with real-time implementations. The reader will learn various methodologies that can be used to solve each stage on autonomous navigation for robots, from object recognition, clustering of obstacles, cost mapping of environments, path planning, and vision to low level control. These methodologies include real-life scenarios to implement a wide range of artificial neural network architectures.

 

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