Neural networks for robotics : an engineering perspective /: an engineering perspective. ([2019])
- Record Type:
- Book
- Title:
- Neural networks for robotics : an engineering perspective /: an engineering perspective. ([2019])
- Main Title:
- Neural networks for robotics : an engineering perspective
- Further Information:
- Note: Nancy Arana-Daniel, Carlos Lopez-Franco, Alma Y. Alanis.
- Authors:
- Arana-Daniel, Nancy
- Other Names:
- Lopez-Franco, Carlos
Alanis, Alma Y - Contents:
- Cover; Half Title; Title; Copyright; Dedication; Contents; Preface; Abbreviations; Chapter 1 Recurrent High Order Neural Networks for Rough Terrain Cost Mapping; 1.1 Introduction; 1.1.1 Mapping background; 1.2 Recurrent High Order Neural Networks, RHONN; 1.2.1 RHONN order; 1.2.2 Neural network training; 1.2.2.1 Kalman lter; 1.2.2.2 Kalman lter training; 1.2.2.3 Extended Kalman lter-based training algorithm, EKF; 1.3 Experimental Results: Identi cation of Costs Maps Using RHONNs; 1.3.1 Synthetic dynamic environments; 1.3.1.1 Synthetic dynamic random environment number 1 1.3.1.2 Synthetic dynamic random environment number 21.3.1.3 Synthetic dynamic random environment number 3; 1.3.2 Experiments using real terrain maps; 1.3.2.1 Real terrain map: grove environment; 1.3.2.2 Real terrain map: golf course; 1.3.2.3 Real terrain map: forest; 1.3.2.4 Real terrain map: rural area; 1.4 Conclusions; Chapter 2 Geometric Neural Networks for Object Recognition; 2.1 Object Recognition and Geometric Representations of Objects; 2.1.1 Geometric representations and descriptors of real objects; 2.2 Geometric Algebra: An Overview; 2.2.1 The geometric algebra of n-D space 2.2.2 The geometric algebra of 3-D space2.2.3 Conformal geometric algebra; 2.2.4 Hyperconformal geometric algebra; 2.2.5 Generalization of G6; 3 into G2n; n; 2.3 Cli ord SVM; 2.3.1 Quaternion valued support vector classi er; 2.3.2 Experimental results; 2.4 Conformal Neuron and Hyper-Conformal Neuron; 2.4.1 Hyperellipsoidal neuron;Cover; Half Title; Title; Copyright; Dedication; Contents; Preface; Abbreviations; Chapter 1 Recurrent High Order Neural Networks for Rough Terrain Cost Mapping; 1.1 Introduction; 1.1.1 Mapping background; 1.2 Recurrent High Order Neural Networks, RHONN; 1.2.1 RHONN order; 1.2.2 Neural network training; 1.2.2.1 Kalman lter; 1.2.2.2 Kalman lter training; 1.2.2.3 Extended Kalman lter-based training algorithm, EKF; 1.3 Experimental Results: Identi cation of Costs Maps Using RHONNs; 1.3.1 Synthetic dynamic environments; 1.3.1.1 Synthetic dynamic random environment number 1 1.3.1.2 Synthetic dynamic random environment number 21.3.1.3 Synthetic dynamic random environment number 3; 1.3.2 Experiments using real terrain maps; 1.3.2.1 Real terrain map: grove environment; 1.3.2.2 Real terrain map: golf course; 1.3.2.3 Real terrain map: forest; 1.3.2.4 Real terrain map: rural area; 1.4 Conclusions; Chapter 2 Geometric Neural Networks for Object Recognition; 2.1 Object Recognition and Geometric Representations of Objects; 2.1.1 Geometric representations and descriptors of real objects; 2.2 Geometric Algebra: An Overview; 2.2.1 The geometric algebra of n-D space 2.2.2 The geometric algebra of 3-D space2.2.3 Conformal geometric algebra; 2.2.4 Hyperconformal geometric algebra; 2.2.5 Generalization of G6; 3 into G2n; n; 2.3 Cli ord SVM; 2.3.1 Quaternion valued support vector classi er; 2.3.2 Experimental results; 2.4 Conformal Neuron and Hyper-Conformal Neuron; 2.4.1 Hyperellipsoidal neuron; 2.4.2 Experimental results; 2.5 Conclusions; Chapter 3 Non-Holonomic Robot Control Using RHONN; 3.1 Introduction; 3.2 RHONN to Identify Uncertain Discrete-Time Nonlinear Systems; 3.3 Neural Identi cation; 3.4 Inverse Optimal Neural Control 3.5 IONC for Non-Holonomic Mobile Robots3.5.1 Robot model; 3.5.2 Wheeled robot; 3.5.2.1 Controller design; 3.5.2.2 Neural identi cation of a wheeled robot; 3.5.2.3 Inverse optimal control of a wheeled robot; 3.5.2.4 Experimental results; 3.5.3 Tracked robot; 3.5.3.1 Controller design; 3.5.3.2 Results; 3.6 Conclusions; Chapter 4 NN for Autonomous Navigation on Non-Holonomic Robots; 4.1 Introduction; 4.2 Simultaneous Localization and Mapping; 4.2.1 Prediction; 4.2.2 Observations; 4.2.3 Status update; 4.3 Reinforcement Learning; 4.4 Inverse Optimal Neural Controller 4.4.1 Planning-Identi er-Controller4.5 Experimental Results; 4.6 Conclusions; Chapter 5 Holonomic Robot Control Using Neural Networks; 5.1 Introduction; 5.2 Optimal Control; 5.3 Inverse Optimal Control; 5.4 Holonomic Robot; 5.4.1 Motor dynamics; 5.4.2 Neural identi cation design; 5.4.3 Control design; 5.4.4 Omnidirectional mobile robot kinematics; 5.5 Visual Feedback; 5.6 Simulation; 5.7 Conclusions; Chapter 6 Neural Network-Based Controller for Unmanned Aerial Vehicles; 6.1 Introduction; 6.2 Quadrotor Dynamic Modeling; 6.3 Hexarotor Dynamic Modeling; 6.4 Neural Network-Based PID … (more)
- Publisher Details:
- Boca Raton, FL : CRC Press/Taylor & Francis Group
- Publication Date:
- 2019
- Extent:
- 1 online resource
- Subjects:
- 629.8/92632
Robots -- Control systems
Neural networks (Computer science)
TECHNOLOGY & ENGINEERING / Engineering (General)
Neural networks (Computer science)
Robots -- Control systems
Electronic books
Electronic books - Languages:
- English
- ISBNs:
- 9781351231794
1351231790
9781351231787
1351231782
9781351231770
1351231774 - Related ISBNs:
- 9780815378686
0815378688 - Notes:
- Note: Description based on online resource; title from digital title page (viewed on October 30, 2018).
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- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
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- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.338689
- Ingest File:
- 01_287.xml