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"This thesis deals with computationally efficient fuzzy and neural algorithms for mobile robot navigation in an unknown environment. Compared with burdensome and time consuming calculation of normal neural netwok, the RAM-based network and fuzzy logic methods require little computational load and are well suited to a microcontroller based system."--Abstract, leaf iii.
Starting with a summary of the history of Artificial Intelligence, this book makes the bridge to the modern debate on the definition of Intelligence and the path to building Intelligent Machines. Since the definition of Intelligence is itself subject to open debate, the quest for Intelligent machines is pursuing a moving target. Apparently, intelligent behaviour is, to a great extent, the result of using a sophisticated associative memory, more than the result of heavy processing. The book describes theories on how the brain works, associative memory models and how a particular model - the Sparse Distributed Memory (SDM) - can be used to navigate a robot based on visual memories. Other robot navigation methods are also comprehensively revised and compared to the method proposed. The performance of the SDM-based robot has been tested in different typical problems, such as illumination changes, occlusions and image noise, taking the SDM to the limits. The results are extensively discussed in the book.
This study addresses the issue of vision based mobile robot navigation in a partially cluttered indoor environment using a mapless navigation strategy. The work focuses on two key problems, namely vision based obstacle avoidance and vision based reactive navigation strategy. The estimation of optical flow plays a key role in vision based obstacle avoidance problems, however the current view is that this technique is too sensitive to noise and distortion under real conditions. Accordingly, practical applications in real time robotics remain scarce. This dissertation presents a novel methodology for vision based obstacle avoidance, using a hybrid architecture. This integrates an appearance-based obstacle detection method into an optical flow architecture based upon a behavioural control strategy that includes a new arbitration module. This enhances the overall performance of conventional optical flow based navigation systems, enabling a robot to successfully move around without experiencing collisions. Behaviour based approaches have become the dominant methodologies for designing control strategies for robot navigation. Two different behaviour based navigation architectures have been proposed for the second problem, using monocular vision as the primary sensor and equipped with a 2-D range finder. Both utilize an accelerated version of the Scale Invariant Feature Transform (SIFT) algorithm. The first architecture employs a qualitative-based control algorithm to steer the robot towards a goal whilst avoiding obstacles, whereas the second employs an intelligent control framework. This allows the components of soft computing to be integrated into the proposed SIFT-based navigation architecture, conserving the same set of behaviours and system structure of the previously defined architecture. The intelligent framework incorporates a novel distance estimation technique using the scale parameters obtained from the SIFT algorithm. The technique employs scale parameters and a corresponding zooming factor as inputs to train a neural network which results in the determination of physical distance. Furthermore a fuzzy controller is designed and integrated into this framework so as to estimate linear velocity, and a neural network based solution is adopted to estimate the steering direction of the robot. As a result, this intelligent iv approach allows the robot to successfully complete its task in a smooth and robust manner without experiencing collision. MS Robotics Studio software was used to simulate the systems, and a modified Pioneer 3-DX mobile robot was used for real-time implementation. Several realistic scenarios were developed and comprehensive experiments conducted to evaluate the performance of the proposed navigation systems. KEY WORDS: Mobile robot navigation using vision, Mapless navigation, Mobile robot architecture, Distance estimation, Vision for obstacle avoidance, Scale Invariant Feature Transforms, Intelligent framework.
Intelligent Mobile Robot Navigation builds upon the application of fuzzy logic to the area of intelligent control of mobile robots. Reactive, planned, and teleoperated techniques are considered, leading to the development of novel fuzzy control systems for perception and navigation of nonholonomic autonomous vehicles. The unique feature of this monograph lies in its comprehensive treatment of the problem, from the theoretical development of the various schemes down to the real-time implementation of algorithms on mobile robot prototypes. As such, the book spans different domains ranging from mobile robots to intelligent transportation systems, from automatic control to artificial intelligence.
This book highlights relevant studies and applications in the area of robotics, which reflect the latest research, from interdisciplinary theoretical studies and computational algorithm development, to representative applications. It presents chapters on advanced control, such as fuzzy, neural, backstepping, sliding mode, adaptive, predictive, diagnosis and fault tolerant control etc. and addresses topics including cloud robotics, cable-driven robots, two-wheeled robots, mobile robots, swarm robots, hybrid vehicle, and drones. Each chapter employs a uniform structure: background, motivation, quantitative development (equations), case studies/illustration/tutorial (simulations, experiences, curves, tables, etc.), allowing readers to easily tailor the techniques to their own applications.
This book contains an edited collection of eighteen contributions on soft and hard computing techniques and their applications to autonomous robotic systems. Each contribution has been exclusively written for this volume by a leading researcher. The volume demonstrates the various ways that the soft computing and hard computing techniques can be used in different integrated manners to better develop autonomous robotic systems that can perform various tasks of vision, perception, cognition, thinking, pattern recognition, decision-making, and reasoning and control, amongst others. Each chapter of the book is self-contained and points out the future direction of research. "It is a must reading for students and researchers interested in exploring the potentials of the fascinating field that will form the basis for the design of the intelligent machines of the future" (Madan M. Gupta)
Path planning based on artificial potential field method, particle swarm optimization algorithm and fuzzy logic controller for navigation in static and dynamic environments. Two schemes of motion controller are used. The first scheme is based on PID controller and second scheme is based on fuzzy logic controller. The PID controller parameters and parameters of membership functions have been optimized by using particle swarm optimization (PSO) algorithm.
This pioneering book describes the development of a robot mapping and navigation system inspired by models of the neural mechanisms underlying spatial navigation in the rodent hippocampus. Computational models of animal navigation systems have traditionally had limited performance when implemented on robots. This is the first research to test existing models of rodent spatial mapping and navigation on robots in large, challenging, real world environments.
The investigation reported in this thesis attempt to develop efficient techniques for the control of multiple mobile robots in an unknown environment. Mobile robots are key components in industrial automation, service provision, and unmanned space exploration. This thesis addresses eight different techniques for the navigation of multiple mobile robots. These are fuzzy logic, neural network, neuro-fuzzy, rule-base, rule-based-neuro-fuzzy, potential field, potential-field-neuro-fuzzy, and simulated-annealing- potential-field- neuro-fuzzy techniques. The main components of this thesis comprises of eight chapters. Following the literature survey in Chapter-2, Chapter-3 describes how to calculate the heading angle for the mobile robots in terms of left wheel velocity and right wheel velocity of the robot. In Chapter-4 a fuzzy logic technique has been analysed. The fuzzy logic technique uses different membership functions for navigation of the multiple mobile robots, which can perform obs ...
Artificial Intelligence is one of the new technologies that has contributed to the successful development and implementation of powerful and friendly control systems. These systems are more attractive to end-users shortening the gap between control theory applications. The IFAC Symposia on Artificial Intelligence in Real Time Control provides the forum to exchange ideas and results among the leading researchers and practitioners in the field. This publication brings together the papers presented at the latest in the series and provides a key evaluation of present and future developments of Artificial Intelligence in Real Time Control system technologies.