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Full Description
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.
Contents
Recurrent High Order Neural Networks for rough terrain cost mapping. Geometric Neural Networks for object recognition. Non-holonomic Mobile Robot Control using Recurrent High Order Neural Networks. Neural Networks for Autonomous Navigation on Nonholonomic Mobile Robots. Holonomic Robot Control using Neural Networks. Neural network based controller for Unmanned Aerial Vehicles.
