A Decision Support System For Robotic Motion Planning Using Artificial Neural Networks PDF Download

Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download A Decision Support System For Robotic Motion Planning Using Artificial Neural Networks PDF full book. Access full book title A Decision Support System For Robotic Motion Planning Using Artificial Neural Networks.

Repetitive Motion Planning and Control of Redundant Robot Manipulators

Repetitive Motion Planning and Control of Redundant Robot Manipulators
Author: Yunong Zhang
Publisher: Springer Science & Business Media
Total Pages: 201
Release: 2014-07-08
Genre: Technology & Engineering
ISBN: 3642375189

Download Repetitive Motion Planning and Control of Redundant Robot Manipulators Book in PDF, ePub and Kindle

Repetitive Motion Planning and Control of Redundant Robot Manipulators presents four typical motion planning schemes based on optimization techniques, including the fundamental RMP scheme and its extensions. These schemes are unified as quadratic programs (QPs), which are solved by neural networks or numerical algorithms. The RMP schemes are demonstrated effectively by the simulation results based on various robotic models; the experiments applying the fundamental RMP scheme to a physical robot manipulator are also presented. As the schemes and the corresponding solvers presented in the book have solved the non-repetitive motion problems existing in redundant robot manipulators, it is of particular use in applying theoretical research based on the quadratic program for redundant robot manipulators in industrial situations. This book will be a valuable reference work for engineers, researchers, advanced undergraduate and graduate students in robotics fields. Yunong Zhang is a professor at The School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China; Zhijun Zhang is a research fellow working at the same institute.


Connectionist Robot Motion Planning

Connectionist Robot Motion Planning
Author: Bartlett Mel
Publisher: Elsevier
Total Pages: 183
Release: 2013-07-19
Genre: Technology & Engineering
ISBN: 0323141269

Download Connectionist Robot Motion Planning Book in PDF, ePub and Kindle

Connectionist Robot Motion Planning: A Neurally-Inspired Approach to Visually-Guided Reaching is the third series in a cluster of books on robotics and related areas as part of the Perspectives in Artificial Intelligence Series. This series focuses on an experimental paradigm using the MURPHY system to tackle critical issues surrounding robot motion planning. MURPHY is a robot-camera system developed to explore an approach to the kinematics of sensory-motor learning and control for a multi-link arm. Organized into eight chapters, this book describes the guiding of a multi-link arm to visual targets in a cluttered workspace. It primarily focuses on “ecological solutions that are relevant to the typical visually guided reaching behaviors of humans and animals in natural environments. Algorithms that work well in unmodeled workspaces whose effective layouts can change from moment to moment with movements of the eyes, head, limbs, and body are also presented. This book also examines the strengths of neurally inspired connectionist representations and the utility of heuristic search when good performance, even if suboptimal, is adequate for the task. The co-evolution of MURPHY’s design with the brain, presumably in response to similar computational pressures, is described in the concluding chapters, specifically presenting the division of labor between programmed-feedforward and visual-feedback modes of limb control. Design engineers in the fields of biology, neurophysiology, and cognitive psychology will find this book of great value.


Robot Motion Planning

Robot Motion Planning
Author: Jean-Claude Latombe
Publisher: Springer Science & Business Media
Total Pages: 668
Release: 2012-12-06
Genre: Technology & Engineering
ISBN: 1461540224

Download Robot Motion Planning Book in PDF, ePub and Kindle

One of the ultimate goals in Robotics is to create autonomous robots. Such robots will accept high-level descriptions of tasks and will execute them without further human intervention. The input descriptions will specify what the user wants done rather than how to do it. The robots will be any kind of versatile mechanical device equipped with actuators and sensors under the control of a computing system. Making progress toward autonomous robots is of major practical inter est in a wide variety of application domains including manufacturing, construction, waste management, space exploration, undersea work, as sistance for the disabled, and medical surgery. It is also of great technical interest, especially for Computer Science, because it raises challenging and rich computational issues from which new concepts of broad useful ness are likely to emerge. Developing the technologies necessary for autonomous robots is a formidable undertaking with deep interweaved ramifications in auto mated reasoning, perception and control. It raises many important prob lems. One of them - motion planning - is the central theme of this book. It can be loosely stated as follows: How can a robot decide what motions to perform in order to achieve goal arrangements of physical objects? This capability is eminently necessary since, by definition, a robot accomplishes tasks by moving in the real world. The minimum one would expect from an autonomous robot is the ability to plan its x Preface own motions.


Kinematic Control of Redundant Robot Arms Using Neural Networks

Kinematic Control of Redundant Robot Arms Using Neural Networks
Author: Shuai Li
Publisher: John Wiley & Sons
Total Pages: 278
Release: 2019-02-12
Genre: Technology & Engineering
ISBN: 1119556996

Download Kinematic Control of Redundant Robot Arms Using Neural Networks Book in PDF, ePub and Kindle

Presents pioneering and comprehensive work on engaging movement in robotic arms, with a specific focus on neural networks This book presents and investigates different methods and schemes for the control of robotic arms whilst exploring the field from all angles. On a more specific level, it deals with the dynamic-neural-network based kinematic control of redundant robot arms by using theoretical tools and simulations. Kinematic Control of Redundant Robot Arms Using Neural Networks is divided into three parts: Neural Networks for Serial Robot Arm Control; Neural Networks for Parallel Robot Control; and Neural Networks for Cooperative Control. The book starts by covering zeroing neural networks for control, and follows up with chapters on adaptive dynamic programming neural networks for control; projection neural networks for robot arm control; and neural learning and control co-design for robot arm control. Next, it looks at robust neural controller design for robot arm control and teaches readers how to use neural networks to avoid robot singularity. It then instructs on neural network based Stewart platform control and neural network based learning and control co-design for Stewart platform control. The book finishes with a section on zeroing neural networks for robot arm motion generation. Provides comprehensive understanding on robot arm control aided with neural networks Presents neural network-based control techniques for single robot arms, parallel robot arms (Stewart platforms), and cooperative robot arms Provides a comparison of, and the advantages of, using neural networks for control purposes rather than traditional control based methods Includes simulation and modelling tasks (e.g., MATLAB) for onward application for research and engineering development By focusing on robot arm control aided by neural networks whilst examining central topics surrounding the field, Kinematic Control of Redundant Robot Arms Using Neural Networks is an excellent book for graduate students and academic and industrial researchers studying neural dynamics, neural networks, analog and digital circuits, mechatronics, and mechanical engineering.


Industrial Assembly

Industrial Assembly
Author: Shimon Y. Nof
Publisher: Springer Science & Business Media
Total Pages: 510
Release: 2012-12-06
Genre: Business & Economics
ISBN: 1461563933

Download Industrial Assembly Book in PDF, ePub and Kindle

Industrial Assembly is a rapidly changing field with significant importance in production. This book is the first of its kind to combine technology, design, methods, and planning and control models of assembly operations and systems. With the increasing importance of assembly in industry and of simultaneous engineering approaches, this timely publication provides: comprehensive coverage of technological, engineering, and management aspects of this field; multi-disciplinary approaches to rationalization of assembly operations and systems; explanation of qualitative models, information technologies, and design techniques, which have been practised effectively in industrial assembly; as well as theoretical foundations and emerging trends that shape the future of assembly.


IJCNN '93 - Nagoya

IJCNN '93 - Nagoya
Author:
Publisher:
Total Pages: 1136
Release: 1993
Genre: Neural Networks (computer Science)
ISBN:

Download IJCNN '93 - Nagoya Book in PDF, ePub and Kindle


Vision-Based Mobile Robot Control and Path Planning Algorithms in Obstacle Environments Using Type-2 Fuzzy Logic

Vision-Based Mobile Robot Control and Path Planning Algorithms in Obstacle Environments Using Type-2 Fuzzy Logic
Author: Mahmut Dirik
Publisher: Springer Nature
Total Pages: 143
Release: 2021-03-01
Genre: Technology & Engineering
ISBN: 3030692477

Download Vision-Based Mobile Robot Control and Path Planning Algorithms in Obstacle Environments Using Type-2 Fuzzy Logic Book in PDF, ePub and Kindle

The book includes topics, such as: path planning, avoiding obstacles, following the path, go-to-goal control, localization, and visual-based motion control. The theoretical concepts are illustrated with a developed control architecture with soft computing and artificial intelligence methods. The proposed vision-based motion control strategy involves three stages. The first stage consists of the overhead camera calibration and the configuration of the working environment. The second stage consists of a path planning strategy using several traditional path planning algorithms and proposed planning algorithm. The third stage consists of the path tracking process using previously developed Gauss and Decision Tree control approaches and the proposed Type-1 and Type-2 controllers. Two kinematic structures are utilized to acquire the input values of controllers. These are Triangle Shape-Based Controller Design, which was previously developed and Distance-Based Triangle Structure that is used for the first time in conducted experiments. Four different control algorithms, Type-1 fuzzy logic, Type-2 Fuzzy Logic, Decision Tree Control, and Gaussian Control have been used in overall system design. The developed system includes several modules that simplify characterizing the motion control of the robot and ensure that it maintains a safe distance without colliding with any obstacles on the way to the target. The topics of the book are extremely relevant in many areas of research, as well as in education in courses in computer science, electrical and mechanical engineering and in mathematics at the graduate and undergraduate levels.


Adaptive Neural Network Control Of Robotic Manipulators

Adaptive Neural Network Control Of Robotic Manipulators
Author: Sam Shuzhi Ge
Publisher: World Scientific
Total Pages: 397
Release: 1998-12-04
Genre: Technology & Engineering
ISBN: 9814496227

Download Adaptive Neural Network Control Of Robotic Manipulators Book in PDF, ePub and Kindle

Recently, there has been considerable research interest in neural network control of robots, and satisfactory results have been obtained in solving some of the special issues associated with the problems of robot control in an “on-and-off” fashion. This book is dedicated to issues on adaptive control of robots based on neural networks. The text has been carefully tailored to (i) give a comprehensive study of robot dynamics, (ii) present structured network models for robots, and (iii) provide systematic approaches for neural network based adaptive controller design for rigid robots, flexible joint robots, and robots in constraint motion. Rigorous proof of the stability properties of adaptive neural network controllers is provided. Simulation examples are also presented to verify the effectiveness of the controllers, and practical implementation issues associated with the controllers are also discussed.