000			04069nam a2200493Ii 4500
001			9781351231794
003			FlBoTFG
005			20220509193016.0
006			m o d
007			cr
008			181112s2018 fluab ob 001 0 eng d
020			_a9781351231794 _q(e-book : PDF)
035			_a(OCoLC)1053796340
040			_aFlBoTFG _cFlBoTFG _erda
041	1		_aeng
072		7	_aTEC _x007000 _2bisacsh
072		7	_aTEC _x008000 _2bisacsh
072		7	_aTHRB _2bicscc
100	1		_aArana-Daniel, Nancy, _eauthor.
245	1	0	_aNeural Networks for Robotics : _bAn Engineering Perspective / _cby Nancy Arana-Daniel, Alma Y. Alanis and Carlos Lopez-Franco.
250			_aFirst edition.
264		1	_aBoca Raton, FL : _bCRC Press, _c2018.
300			_a1 online resource (227 pages) : _b176 illustrations, text file, PDF
336			_atext _2rdacontent
337			_acomputer _2rdamedia
338			_aonline resource _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0	0	_tChapter 1 Recurrent High Order Neural Networks for rough terrain cost mapping -- _t1.1 Introduction -- _t1.2 Recurrent High Order Neural Networks, RHONN -- _t1.3 Experimental results: identification of costs maps using RHONNs -- _t1.4 Conclusions -- _t--Chapter 2 Geometric Neural Networks for object recognition -- _t2.1 Object recognition and geometric representations of objects -- _t2.2 Geometric algebra: An overview -- _t2.3 Clifford SVM -- _t2.4 Conformal neuron and hyper-conformal neurons -- _t2.5 Conclusions -- _t--Chapter 3 Non-holonomic Mobile Robot Control using Recurrent High Order Neural Networks -- _t3.1 Introduction -- _t3.2 RHONN to Identify Uncertain Discrete-Time Nonlinear Systems -- _t3.3 Neural Identification -- _t3.4 Inverse Optimal Neural Control -- _t3.5 IONC for Non-holonomic Mobile Robots -- _t3.6 Conclusions -- _t--Chapter 4 Neural Networks for Autonomous Navigation on Nonholonomic Mobile Robots -- _t4.1 Introduction -- _t4.2 Simultaneous Localization and Mapping -- _t4.3 Reinforcement Learning -- _t4.4 Inverse Optimal Neural Controller -- _t4.5 Experimental Results -- _t4.6 Conclusions -- _t--Chapter 5 Holonomic Robot Control using Neural Networks -- _t5.1 Introduction -- _t5.2 Optimal Control -- _t5.3 Inverse Optimal Control -- _t5.4 Holonomic robot -- _t5.5 Visual feedback -- _t5.6 Simulation -- _t5.7 Conclusions -- _t--Chapter 6 Neural network based controller for Unmanned Aerial Vehicles -- _t6.1 Introduction -- _t6.2 Quadrotor dynamic modeling -- _t6.3 Hexarotor dynamic modeling -- _t6.4 Neural Network based PID -- _t6.5 Visual Servo Control -- _t6.6 Simulation results -- _t6.7 Experimental Results -- _t6.8 Conclusions
520	3		_aThe 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.
530			_aAlso available in print format.
650		7	_aTECHNOLOGY & ENGINEERING / Electronics / General. _2bisacsh
650		7	_acost mapping of environments. _2bisacsh
650		7	_aground and aerial robots. _2bisacsh
650		7	_aintelligent control. _2bisacsh
650		7	_apattern classification. _2bisacsh
650		7	_arobot navigation. _2bisacsh
650		0	_aRobots _xControl systems.
650		0	_aNeural networks (Computer science)
655		0	_aElectronic books.
700	1		_aAlanis, Alma Y., _eauthor.
700	1		_aLopez-Franco, Carlos, _eauthor.
710	2		_aTaylor and Francis.
776	0	8	_iPrint version: _z9780815378686
856	4	0	_uhttps://www.taylorfrancis.com/books/9781351231794 _zClick here to view.
999			_c128131 _d128131