Download or read book Dynamic Neural Networks for Robot Systems Data Driven and Model Based Applications written by Long Jin and published by Frontiers Media SA. This book was released on 2024-07-24 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural network control has been a research hotspot in academic fields due to the strong ability of computation. One of its wildly applied fields is robotics. In recent years, plenty of researchers have devised different types of dynamic neural network (DNN) to address complex control issues in robotics fields in reality. Redundant manipulators are no doubt indispensable devices in industrial production. There are various works on the redundancy resolution of redundant manipulators in performing a given task with the manipulator model information known. However, it becomes knotty for researchers to precisely control redundant manipulators with unknown model to complete a cyclic-motion generation CMG task, to some extent. It is worthwhile to investigate the data-driven scheme and the corresponding novel dynamic neural network (DNN), which exploits learning and control simultaneously. Therefore, it is of great significance to further research the special control features and solve challenging issues to improve control performance from several perspectives, such as accuracy, robustness, and solving speed.
Download or read book Neural Networks in Robotics written by George A. Bekey and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks in Robotics is the first book to present an integrated view of both the application of artificial neural networks to robot control and the neuromuscular models from which robots were created. The behavior of biological systems provides both the inspiration and the challenge for robotics. The goal is to build robots which can emulate the ability of living organisms to integrate perceptual inputs smoothly with motor responses, even in the presence of novel stimuli and changes in the environment. The ability of living systems to learn and to adapt provides the standard against which robotic systems are judged. In order to emulate these abilities, a number of investigators have attempted to create robot controllers which are modelled on known processes in the brain and musculo-skeletal system. Several of these models are described in this book. On the other hand, connectionist (artificial neural network) formulations are attractive for the computation of inverse kinematics and dynamics of robots, because they can be trained for this purpose without explicit programming. Some of the computational advantages and problems of this approach are also presented. For any serious student of robotics, Neural Networks in Robotics provides an indispensable reference to the work of major researchers in the field. Similarly, since robotics is an outstanding application area for artificial neural networks, Neural Networks in Robotics is equally important to workers in connectionism and to students for sensormonitor control in living systems.
Download or read book Competition Based Neural Networks with Robotic Applications written by Shuai Li and published by Springer. This book was released on 2017-05-30 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focused on solving competition-based problems, this book designs, proposes, develops, analyzes and simulates various neural network models depicted in centralized and distributed manners. Specifically, it defines four different classes of centralized models for investigating the resultant competition in a group of multiple agents. With regard to distributed competition with limited communication among agents, the book presents the first distributed WTA (Winners Take All) protocol, which it subsequently extends to the distributed coordination control of multiple robots. Illustrations, tables, and various simulative examples, as well as a healthy mix of plain and professional language, are used to explain the concepts and complex principles involved. Thus, the book provides readers in neurocomputing and robotics with a deeper understanding of the neural network approach to competition-based problem-solving, offers them an accessible introduction to modeling technology and the distributed coordination control of redundant robots, and equips them to use these technologies and approaches to solve concrete scientific and engineering problems.
Download or read book Neural Systems for Robotics written by Omid Omidvar and published by Elsevier. This book was released on 2012-12-02 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Systems for Robotics represents the most up-to-date developments in the rapidly growing aplication area of neural networks, which is one of the hottest application areas for neural networks technology. The book not only contains a comprehensive study of neurocontrollers in complex Robotics systems, written by highly respected researchers in the field but outlines a novel approach to solving Robotics problems. The importance of neural networks in all aspects of Robot arm manipulators, neurocontrol, and Robotic systems is also given thorough and in-depth coverage. All researchers and students dealing with Robotics will find Neural Systems for Robotics of immense interest and assistance. Focuses on the use of neural networks in robotics-one of the hottest application areas for neural networks technology Represents the most up-to-date developments in this rapidly growing application area of neural networks Contains a new and novel approach to solving Robotics problems
Download or read book Modeling Dynamic Systems for Multi step Prediction with Recurrent Neural Networks written by Nima Mohajerin and published by . This book was released on 2017 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis investigates the applicability of Recurrent Neural Networks (RNNs) and Deep Learning methods for multi-step prediction of robotic systems. The unmodeled dynamics and simplifying assumptions in classic modeling methods result in models that yield rapidly diverging predictions when the model is used in an iterative fashion, i.e., for multi-step prediction. However, the effect of the unmodeled dynamics can be captured by collecting datasets of the system. Deep Learning provides a strong set of tools to extract patterns from data, however, large datasets are commonly required for the methods to work well. Collecting a large amount of data from a robotic system can be a cumbersome and expensive approach. In this work, Deep Learning methods, particularly RNNs, are studied and employed for the purpose of learning models of two aerial vehicles from experimental data. The feasibility of employing RNNs is first studied to learn a model of a quadrotor based on a simulated dataset, which yields a Multi Layer Fully Connected (MLFC) architecture. Models can be learned for multi-step prediction, recovering excellent predictions over 500 timesteps in the presence of simulated disturbances to the robot and noise on the measurements. To learn models from experimental data, the RNN state initialization problem is defined and formulated. It is shown that the RNN state initialization problem can be addressed by creating and training an initialization network jointly with the multi-step prediction network, and the combination can be used in a black-box modeling approach such that the model produces predictions which are immediately accurate. The RNN based black-box methods are trained on an experimental dataset gathered from a quadrotor and a publicly available helicopter dataset. The quadrotor dataset, which encompasses approximately 4 hours of flight data in various regimes, has been released and is now available publicly online. Finally, a hybrid network, which combines the proposed RNN based black-box models with a physics based quadrotor model into a single RNN-based modeling system is introduced. The proposed hybrid network solves many of the limitations of the existing state of the art in long-term prediction for robotics systems. Trained on the quadrotor dataset, the hybrid model provides accurate body angular rate and velocity predictions of the vehicle over almost 2 seconds which is suitable to be used in a variety of model-based controller applications.
Download or read book Smart Materials in Additive Manufacturing Volume 3 written by Ali Zolfagharian and published by Elsevier. This book was released on 2024-07-25 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Smart Materials in Additive Manufacturing, Volume Three: 4D-Printed Robotic Materials, Sensors, and Actuators covers the principles, real-world use, and advances in the cutting-edge field of 4D printed smart robotic materials. It discusses the mechanics of these materials, techniques by which to manufacture them, and different applications. Detailed modeling and control techniques are outlined, illustrating their use in real-world settings. Shape memory polymers, hydrogels, shape memory alloys, biomaterials, natural fibers, dielectric elastomers, liquid crystal elastomers, electroactive polymers, and more materials are covered, featuring in-depth discussion of their responses to stimuli, fabrication, multi-physics modeling, control techniques, and applications. Discusses the design, modeling, simulation, and manufacturing processes of various 4D-printed robotic materials Outlines modeling and control techniques to illustrate the use of smart robotic materials Provides case studies demonstrating real-world situations where the techniques, materials, and concepts discussed have been successfully deployed Covers applications including robotics, metamaterials, micromachines, sensors, bioprinting, and actuators
Download or read book Data Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Download or read book Nature Inspired Robotics written by Jagjit Singh Dhatterwal and published by CRC Press. This book was released on 2024-07-24 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the theories and methods of Nature-Inspired Robotics in artificial intelligence. Software and hardware technologies, alongside theories and methods, illustrate the application of bio-inspired artificial intelligence. It includes discussions on topics such as Robot Control Manipulators, Geometric Transformation, Robotic Drive Systems and Nature Inspired Robotic Neural System. Elaborating upon recent progress made in five distinct configurations of nature-inspired computing, it explores the potential applications of this technology in two specific areas: neuromorphic computing systems and neuromorphic perceptual systems. · Discusses advances in cutting-edge technology in brain-inspired computing, perception technologies and aspects of neuromorphic electronics · Offers a thorough introduction to two-terminal neuromorphic memristors, including memristive devices and resistive switching mechanisms · Provides comprehensive explorations of spintronic neuromorphic devices and multi-terminal neuromorphic devices with cognitive behaviours · Includes cognitive behaviour of Inspired Robotics and cognitive technologies with applications in Artificial Intelligence · Contains practical discussions of neuromorphic devices based on chalcogenide and organic materials. This text acts as a reference book for students, scholars, and industry professionals.
Download or read book Intelligent Robotics and Applications written by Huayong Yang and published by Springer Nature. This book was released on 2023-10-09 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 9-volume set LNAI 14267-14275 constitutes the proceedings of the 16th International Conference on Intelligent Robotics and Applications, ICIRA 2023, which took place in Hangzhou, China, during July 5–7, 2023. The 413 papers included in these proceedings were carefully reviewed and selected from 630 submissions. They were organized in topical sections as follows: Part I: Human-Centric Technologies for Seamless Human-Robot Collaboration; Multimodal Collaborative Perception and Fusion; Intelligent Robot Perception in Unknown Environments; Vision-Based Human Robot Interaction and Application. Part II: Vision-Based Human Robot Interaction and Application; Reliable AI on Machine Human Reactions; Wearable Sensors and Robots; Wearable Robots for Assistance, Augmentation and Rehabilitation of Human Movements; Perception and Manipulation of Dexterous Hand for Humanoid Robot. Part III: Perception and Manipulation of Dexterous Hand for Humanoid Robot; Medical Imaging for Biomedical Robotics; Advanced Underwater Robot Technologies; Innovative Design and Performance Evaluation of Robot Mechanisms; Evaluation of Wearable Robots for Assistance and Rehabilitation; 3D Printing Soft Robots. Part IV: 3D Printing Soft Robots; Dielectric Elastomer Actuators for Soft Robotics; Human-like Locomotion and Manipulation; Pattern Recognition and Machine Learning for Smart Robots. Part V: Pattern Recognition and Machine Learning for Smart Robots; Robotic Tactile Sensation, Perception, and Applications; Advanced Sensing and Control Technology for Human-Robot Interaction; Knowledge-Based Robot Decision-Making and Manipulation; Design and Control of Legged Robots. Part VI: Design and Control of Legged Robots; Robots in Tunnelling and Underground Space; Robotic Machining of Complex Components; Clinically Oriented Design in Robotic Surgery and Rehabilitation; Visual and Visual-Tactile Perception for Robotics. Part VII: Visual and Visual-Tactile Perception for Robotics; Perception, Interaction, and Control of Wearable Robots; Marine Robotics and Applications; Multi-Robot Systems for Real World Applications; Physical and Neurological Human-Robot Interaction. Part VIII: Physical and Neurological Human-Robot Interaction; Advanced Motion Control Technologies for Mobile Robots; Intelligent Inspection Robotics; Robotics in Sustainable Manufacturing for Carbon Neutrality; Innovative Design and Performance Evaluation of Robot Mechanisms. Part IX: Innovative Design and Performance Evaluation of Robot Mechanisms; Cutting-Edge Research in Robotics.
Download or read book Intelligent Control and Applications for Robotics Volume II written by Yimin Zhou and published by Frontiers Media SA. This book was released on 2023-11-22 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Driven by sustaining demands from industrial automation, space applications and the lack of labor forces, robotics has received increasing attention from researchers in the field of automation and control. Optimizing control schemes is critical to fully exploit the potential of industrial and daily-use robots. Usually, accuracy and repeatability are measured to evaluate the performance of a robot, and deviation of the two parameters from normal status would inevitably leads to positional error and creates a problem for the process. Moreover, the repeatability of a robot is different in various parts of the working envelope, fluctuating with speed and payload. Due to the inherent complexity, an advanced learning methodology is crucial to the self-learning and fast adaptation to disturbances.
Download or read book Control Systems written by Jitendra R. Raol and published by CRC Press. This book was released on 2019-07-12 with total page 738 pages. Available in PDF, EPUB and Kindle. Book excerpt: Control Systems: Classical, Modern, and AI-Based Approaches provides a broad and comprehensive study of the principles, mathematics, and applications for those studying basic control in mechanical, electrical, aerospace, and other engineering disciplines. The text builds a strong mathematical foundation of control theory of linear, nonlinear, optimal, model predictive, robust, digital, and adaptive control systems, and it addresses applications in several emerging areas, such as aircraft, electro-mechanical, and some nonengineering systems: DC motor control, steel beam thickness control, drum boiler, motional control system, chemical reactor, head-disk assembly, pitch control of an aircraft, yaw-damper control, helicopter control, and tidal power control. Decentralized control, game-theoretic control, and control of hybrid systems are discussed. Also, control systems based on artificial neural networks, fuzzy logic, and genetic algorithms, termed as AI-based systems are studied and analyzed with applications such as auto-landing aircraft, industrial process control, active suspension system, fuzzy gain scheduling, PID control, and adaptive neuro control. Numerical coverage with MATLAB® is integrated, and numerous examples and exercises are included for each chapter. Associated MATLAB® code will be made available.
Download or read book Biological and Robotic Inter Limb Coordination written by Dai Owaki and published by Frontiers Media SA. This book was released on 2022-11-08 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Learning Control written by Dan Zhang and published by Elsevier. This book was released on 2020-12-05 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge technologies in the field of learning-based control. State-of-the-art techniques involving machine learning and artificial intelligence (AI) are covered, as are foundational control theories and more established techniques such as adaptive learning control, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications in robotics, AI, aircraft and other vehicles and complex dynamical systems. Computational methods for control systems, particularly those used for developing AI and other machine learning techniques, are also discussed at length. Provides foundational control theory concepts, along with advanced techniques and the latest advances in adaptive control and robotics Introduces state-of-the-art learning-based control technologies and their applications in robotics and other complex dynamical systems Demonstrates computational techniques for control systems Covers iterative learning impedance control in both human-robot interaction and collaborative robots
Download or read book Diagnosis and Fault tolerant Control 1 written by Vicenc Puig and published by John Wiley & Sons. This book was released on 2021-12-29 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances in fault diagnosis strategies for complex dynamic systems. Its impetus derives from the need for an overview of the challenges of the fault diagnosis technique, especially for those demanding systems that require reliability, availability, maintainability and safety to ensure efficient operations. Moreover, the need for a high degree of tolerance with respect to possible faults represents a further key point, primarily for complex systems, as modeling and control are inherently challenging, and maintenance is both expensive and safety-critical. Diagnosis and Fault-tolerant Control 1 also presents and compares different diagnosis schemes using established case studies that are widely used in related literature. The main features of this book regard the analysis, design and implementation of proper solutions for the problems of fault diagnosis in safety critical systems. The design of the considered solutions involves robust data-driven, model-based approaches.
Download or read book Production at the Leading Edge of Technology written by Mathias Liewald and published by Springer Nature. This book was released on 2023-02-01 with total page 857 pages. Available in PDF, EPUB and Kindle. Book excerpt: The German Academic Association for Production Technology (WGP) annually invites researchers coming from its institutes and from industry to contribute peer reviewed papers in the field of production technology. This congress proceedings provides recent research results and findings on leading-edge manufacturing processes. Main aim of this scientific congress is to push forward existing borders in production and to provide novel solutions of "Production at the Leading Edge of Manufacturing Technology”. Different sessions were held on the topics • Recent Developments in Manufacturing Processes • Advancements in Production Planning • New Approaches in Machine Learning • Aspects of Resilience of Production Processes • Creating Digital Twins for Production
Download or read book Neural Networks for Robotic Control written by Ali M. S. Zalzala and published by Prentice Hall. This book was released on 1996 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: 1. An overview of neural networks in control applications; 2. Artificial neural network based intelligent robot dynamic control; 3. Neural servo controller for position, force stabbing control of robotic manipulators; 4. Model-based adaptive neural structures for robotic control; 5. Intelligent co-ordination of multiple systems with neural networks; 6. Neural networks for mobile robot piloting control; 7. A neural network controller for the navigation and obstacle avoidance of a mobile robot; An ultrasonic 3-D robot vision system based on the statistical properties of artificial neural networks; Visual control of robotic manipulator based on neural networks; 10. Brain building for a biological robot; 11. Robustness of a distributed neural network controller for locomotion in a hexapod robot.
Download or read book Robot Intelligence Technology and Applications 5 written by Jong-Hwan Kim and published by Springer. This book was released on 2018-05-31 with total page 579 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes papers from the 5th International Conference on Robot Intelligence Technology and Applications held at KAIST, Daejeon, Korea on December 13–15, 2017. It covers the following areas: artificial intelligence, autonomous robot navigation, intelligent robot system design, intelligent sensing and control, and machine vision. The topics included in this book are deep learning, deep neural networks, image understanding, natural language processing, speech/voice/text recognition, reasoning & inference, sensor integration/fusion/perception, multisensor data fusion, navigation/SLAM/localization, distributed intelligent algorithms and techniques, ubiquitous computing, digital creatures, intelligent agents, computer vision, virtual/augmented reality, surveillance, pattern recognition, gesture recognition, fingerprint recognition, animation and virtual characters, and emerging applications. This book is a valuable resource for robotics scientists, computer scientists, artificial intelligence researchers and professionals in universities, research institutes and laboratories.