Neural networks for robotics: an engineering perspective

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Content: 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