Hardcover. 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. The book offers an insight on artificial neural networks for giving a robot a high level of autonomous tasks, such as navigation, object recognition, and clustering, with real-time implementations. It provides methodologies for a wide range of artificial neural network architectures to solve problems in autonomous navigation and object recognition. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Bibliographic Details

Title: Neural Networks for Robotics (Hardcover)
Publisher: Taylor & Francis Inc, Bosa Roca
Publication Date: 2018
Language: English
ISBN 10: 0815378688
ISBN 13: 9780815378686
Binding: Hardcover
Condition: new

About this title

Synopsis

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.

About the Author

Nancy Arana-Daniel received her B.Sc. Degree from the University of Guadalajara in 2000, and her M. Sc. And Ph.D. degrees in electric engineering with the special field in computer science from Research Center of the National Polytechnic Institute and Advanced Studies, CINVESTAV, in 2003 and 2007, respectively. She is currently a research fellow at the University of Guadalajara, in the Department of Computer Science M�xico, where she is working at the Laboratory of Intelligent Systems and the Research Center for Control Systems and Artificial Intelligence. She is IEEE Senior member and a member of National System of Researchers (SNI-1). She has published several papers in International Journals and Conferences, and she has been technical manager of several projects that have been granted by the National Council of Science and Technology (CONACYT). Also, she has collaborated in an international project granted by OPTREAT. She is Associated Editor of the Journal of Franklin Institute (Elsevier). Her research interests focus on applications of Geometric Algebra to Geometric computing, machine learning, bio-inspired optimization, pattern recognition, and robot navigation.

Carlos Lopez-Franco gained his Ph.D. in Computer Science in 2007 from the Center of Research and Advanced Studies, CINVESTAV Unidad Guadalajara, Jalisco, M�xico. Currently, he is a full professor at the University of Guadalajara, M�xico, Department of Computer Science. He is currently working with the Intelligent Systems group, and he is the head of the department of Computer Sciences at CUCEI, Universidad de Guadalajara. His current research interests include geometric algebra, computer vision, robotics, and pattern recognition.

Alma Y. Alanis received her B.Sc degree from Instituto Tecnologico de Durango (ITD, Durango Campus, Durango) in 2002, and her M.Sc. and Ph.D. degrees in electrical engineering from the Advanced Studies and Research Center of the National Polytechnic Institute (CINVESTAV-IPN, Guadalajara Campus, Mexico) in 2004 and 2007, respectively. Since 2008, she has been with the University of Guadalajara, where she is currently a Chair Professor in the Department of Computer Science. She is also a member of the Mexican National Research System (SNI-2). She has published papers in recognized International Journals and Conferences, along with two international books. She is a Senior Member of the IEEE and Subject and Associated Editor of the Journal of Franklin Institute (Elsevier) and Intelligent Automation & Soft Computing (Taylor & Francis); moreover, she is currently serving on a number of IEEE and IFAC Conference Organizing Committees. In 2013, she received the grant for women in science by L'Oreal-UNESCO-AMC-CONACYT-CONALMEX. In 2015, she received the Marcos Moshinsky Research Award. Since 2008, she has been a member of the Accredited Assessors record (RCEA-CONACYT), evaluating a wide range of national research projects. She has belonged to important project evaluation committees for national and international research projects. Her research interests are in neural control, backstepping control, block control, and their applications to electrical machines, power systems, and robotics.

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