Neural Networks for Robotics: An Engineering Perspective
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Book Description
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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.
- 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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Table of Contents
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Chapter 1 Recurrent High Order Neural Networks for rough terrain cost mapping
1.1 Introduction
1.2 Recurrent High Order Neural Networks, RHONN
1.3 Experimental results: identification of costs maps using RHONNs
1.4 Conclusions
Chapter 2 Geometric Neural Networks for object recognition
2.1 Object recognition and geometric representations of objects
2.2 Geometric algebra: An overview
2.3 Clifford SVM
2.4 Conformal neuron and hyper-conformal neurons
2.5 Conclusions
Chapter 3 Non-holonomic Mobile Robot Control using Recurrent High Order Neural Networks
3.1 Introduction
3.2 RHONN to Identify Uncertain Discrete-Time Nonlinear Systems
3.3 Neural Identification
3.4 Inverse Optimal Neural Control
3.5 IONC for Non-holonomic Mobile Robots
3.6 Conclusions
Chapter 4 Neural Networks for Autonomous Navigation on Nonholonomic Mobile Robots
4.1 Introduction
4.2 Simultaneous Localization and Mapping
4.3 Reinforcement Learning
4.4 Inverse Optimal Neural Controller
4.5 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
5.5 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
6.5 Visual Servo Control
6.6 Simulation results
6.7 Experimental Results
6.8 Conclusions