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Author: Cong Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 89
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Book Description
In a sensing rich system, a large amount of data can be obtained over time and utilized to improve the performance and functionality of a robotic system. Data-driven approaches emphasize on the utilization of auxiliary sensors, sensor fusion, and data learning. Real-time control systems of robotic systems often run at kilo-Hertz sampling frequencies. New data is obtained from a variety of feedback sources every one or a few milliseconds. Auxiliary sensors provide additional feedbacks and enable sensor fusion. This dissertation presents a series of data-driven approaches to improve the sensing and control of robot manipulators from several aspects, including sensor fusion for motion sensing, statistical learning for feedback compensation, nonparametric learning control, and intelligent modeling and identification. In regard to the limited sensing capability of conventional indirect drive-trains of industrial robots, a sensor fusion approach based on auxiliary optical and inertial sensors is introduced for direct motion sensing of robot end-effectors. The approach is especially useful to applications where high accuracy is required for end-effector performance in real-time. Meanwhile, for the scenarios where auxiliary sensor are not allowed, a statistical learning algorithms is developed for sensing compensation so that control of systems with limited feedback capability can be significantly improved. A major application of the approach is vision guidance of industrial robots. The proposed learning approach can significantly increase the visual tracking bandwidth without requiring high-speed cameras. Besides improving the sensing capability of robots, nonparametric learning control is developed to control systems with complex dynamics. A major motivation of the approach is robotic laser and plasma cutting. Furthermore, to obtain high-fidelity models more efficiently, planning and learning algorithms are discussed for intelligent system modeling and identification. The applications of the proposed approaches range from vision guided robotic material handling to precision robotic machining. Various tests are designed to validate the proposed approaches.
Author: Cong Wang
Publisher:
ISBN:
Category :
Languages : en
Pages : 89
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Book Description
In a sensing rich system, a large amount of data can be obtained over time and utilized to improve the performance and functionality of a robotic system. Data-driven approaches emphasize on the utilization of auxiliary sensors, sensor fusion, and data learning. Real-time control systems of robotic systems often run at kilo-Hertz sampling frequencies. New data is obtained from a variety of feedback sources every one or a few milliseconds. Auxiliary sensors provide additional feedbacks and enable sensor fusion. This dissertation presents a series of data-driven approaches to improve the sensing and control of robot manipulators from several aspects, including sensor fusion for motion sensing, statistical learning for feedback compensation, nonparametric learning control, and intelligent modeling and identification. In regard to the limited sensing capability of conventional indirect drive-trains of industrial robots, a sensor fusion approach based on auxiliary optical and inertial sensors is introduced for direct motion sensing of robot end-effectors. The approach is especially useful to applications where high accuracy is required for end-effector performance in real-time. Meanwhile, for the scenarios where auxiliary sensor are not allowed, a statistical learning algorithms is developed for sensing compensation so that control of systems with limited feedback capability can be significantly improved. A major application of the approach is vision guidance of industrial robots. The proposed learning approach can significantly increase the visual tracking bandwidth without requiring high-speed cameras. Besides improving the sensing capability of robots, nonparametric learning control is developed to control systems with complex dynamics. A major motivation of the approach is robotic laser and plasma cutting. Furthermore, to obtain high-fidelity models more efficiently, planning and learning algorithms are discussed for intelligent system modeling and identification. The applications of the proposed approaches range from vision guided robotic material handling to precision robotic machining. Various tests are designed to validate the proposed approaches.
Author: Kyu Min Park
Publisher: Springer Nature
ISBN: 3031301951
Category : Technology & Engineering
Languages : en
Pages : 133
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Book Description
This book provides a concise survey and description of recent collision detection methods for robot manipulators. Beginning with a review of robot kinodynamic models and preliminaries on basic statistical learning methods, the book covers fundamental aspects of the collision detection problem, from collision types and collision detection performance criteria to model-free versus model-based methods, and the more recent data-driven learning-based approaches to collision detection. Special effort has been given to describing and evaluating existing methods with a unified set of notation, systematically categorizing these methods according to a basic set of criteria, and summarizing the advantages and disadvantages of each method. This book is the first to comprehensively organize the growing body of learning-based collision detection methods, ranging from basic supervised learning methods to more advanced approaches based on unsupervised learning and transfer learning techniques. Step-by-step implementation details and pseudocode descriptions are provided for key algorithms. Collision detection performance is measured with respect to both conventional criteria such as detection delay and the number of false alarms, as well as criteria that measure generalization capability for learning-based methods. Whether it be for research or commercial applications, in settings ranging from industrial factories to physical human–robot interaction experiments, this book can help the reader choose and successfully implement the most appropriate detection method that suits their robot system and application.
Author: 王奎
Publisher:
ISBN:
Category : Robotics
Languages : en
Pages : 198
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Book Description
Author: Ciro Natale
Publisher: Springer Science & Business Media
ISBN: 354000159X
Category : Technology & Engineering
Languages : en
Pages : 117
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Book Description
Robot interaction control is one of the most challenging targets for industrial robotics. While it would provide the robotic systems with a high degree of autonomy, its effectiveness is limited by the complexity of this problem and by the necessity of special sensors (six-dof force sensors). On the other hand, the control methodologies to be adopted for addressing this problem can be considered mature and well-assessed. All the known interaction control strategies (e.g. impedance, direct force control) are tackled and reshuffled in a geometrically consistent way for simplification of the task specification and enhancement of the execution performance. This book represents the first step towards the application of theoretical results at an industrial level; in fact each proposed control algorithm is experimentally tested here on an industrial robotic setup.
Author: Yi Zheng
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Perceiving and manipulating deformable objects with the sense of touch are essential skills in everyday life. However, it remains difficult for robots to autonomously manipulate deformable objects using tactile sensing because of numerous perception, modeling, planning, and control challenges. We believe this is partially due to two fundamental challenges: (1) Establishing a physics-based model describing physical interactions between deformable tactile sensors and deformable objects is difficult; (2) Modern tactile sensors provide high-dimensional data, which is beneficial for perception but impedes the development of practical planning and control strategies. To address these challenges, we developed systematic frameworks for the tactile-driven manipulation of deformable objects that integrates state-of-the-art tactile sensing with well-established tools used by other robotics communities. In Study \#1, we showed how a robot can learn to manipulate a deformable, thin-shell object via tactile sensor feedback using model-free reinforcement learning methods. A page flipping task was learned on a real robot using a two-stage approach. First, we learned nominal page flipping trajectories by constructing a reward function that quantifies functional task performance from the perspective of tactile sensing. Second, we learned adapted trajectories using tactile-driven perceptual coupling, with an intuitive assumption that, while the functional page flipping trajectories for different task contexts (page sizes) might differ, similar tactile sensing feedback should be expected. In Study \#2, we showed how a robot can use tactile sensor feedback to control the pose and tension of a deformable linear object (elastic cable). For a cable manipulation task, low-dimensional latent space features were extracted from high-dimensional raw tactile sensor data using unsupervised learning methods, and a dynamics model was constructed in the latent space using supervised learning methods. The dynamics model was integrated with an optimization-based, model predictive controller for end-to-end, tactile-driven motion planning and control on a real robot. In summary, we developed frameworks for the tactile-driven manipulation of deformable objects that either circumvents sensor modeling difficulties or constructs a dynamics model directly from tactile feedback and uses the model for planning and control. This work provides a foundation for the further development of systematic frameworks that can address complex, tactile-driven manipulation problems.
Author: Claude Samson
Publisher:
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 394
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Book Description
A complete approach to the problem of controlling robot manipulators needs to bring together three scientific branches: computer science, mechanics, and automatic control.
Author: Bijoy K. Ghosh
Publisher: Elsevier
ISBN: 0080503071
Category : Technology & Engineering
Languages : en
Pages : 442
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Book Description
Microcomputer technology and micromechanical design have contributed to recent rapid advances in Robotics. Particular advances have been made in sensor technology that allow robotic systems to gather data and react "intelligently" in flexible manufacturing systems. The analysis and recording of the data are vital to controlling the robot.In order to solve problems in control and planning for a Robotic system it is necessary to meet the growing need for the integration of sensors in to the system. Control in Robotics and Automation addresses this need. This book covers integration planning and control based on prior knowledge and real-time sensory information. A new task-oriented approach to sensing, planning and control introduces an event-based method for system design together with task planning and three dimensional modeling in the execution of remote operations.Typical remote systems are teleoperated and provide work efficiencies that are on the order of ten times slower than what is directly achievable by humans. Consequently, the effective integration of automation into teleoperated remote systems offers potential to improve remote system work efficiency. The authors introduce visually guided control systems and study the role of computer vision in autonomously guiding a robot system. - Sensor-Based Planning and Control in an Event-Based Approach - Visually Guided Sensing and Control - Multiple Sensor Fuson in Planning and Control - System Integration and Implementation - Practical Applications
Author: Mohammad Yazdani
Publisher: Eliva Press
ISBN: 9994984829
Category : Technology & Engineering
Languages : en
Pages : 23
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Book Description
In recent decades, robotic manipulators have played a very important role in the development of industry and automation. Nowadays, robotic manipulators are performing their tasks alongside human users by having some unique characteristics such as durability in polluted and dangerous environments, repeatability, high accuracy, the ability to move heavy objects, etc. The nonlinear, time-varying and complex dynamics of robotic manipulators and the effects of perturbation on their performance has been faced control engineers with serious challenges to design high performance controller for them. Hence, the focus of this book is on the modeling and control of robotic systems based on sliding mode control as one of the most powerful nonlinear robust control methods to overcome these problems. In this book, methods and principles of position control and position/force control in certain and uncertain environments are introduced. Also, considering the many problems and challenges that torque/force sensors create in the control of robotic systems, approaches to eliminate them are explained. The soft sensor method used in this book to design the controller is based on wavelet neural networks, which have a high ability to detect discontinuities and sudden jumps that commonly occur in the robots working environment. One of the unique features of this book is the use of new and applicable ideas for designing sensor-less robust controller in robotic systems.
Author: Min Wu
Publisher: Logos Verlag Berlin GmbH
ISBN: 383255484X
Category : Technology & Engineering
Languages : en
Pages : 212
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Book Description
In recent years, researchers have achieved great success in guaranteeing safety in human-robot interaction, yielding a new generation of robots that can work with humans in close proximity, known as collaborative robots (cobots). However, due to the lack of ability to understand and coordinate with their human partners, the ``co'' in most cobots still refers to ``coexistence'' rather than ``collaboration''. This thesis aims to develop an adaptive learning and control framework with a novel physical and data-driven approach towards a real collaborative robot. The first part focuses on online human motion prediction. A comprehensive study on various motion prediction techniques is presented, including their scope of application, accuracy in different time scales, and implementation complexity. Based on this study, a hybrid approach that combines physically well-understood models with data-driven learning techniques is proposed and validated through a motion data set. The second part addresses interaction control in human-robot collaboration. An adaptive impedance control scheme with human reference estimation is presented. Reinforcement learning is used to find optimal control parameters to minimize a task-orient cost function without fully knowing the system dynamic. The proposed framework is experimentally validated through two benchmark applications for human-robot collaboration: object handover and cooperative object handling. Results show that the robot can provide reliable online human motion prediction, react early to human motion variation, make proactive contributions to physical collaborations, and behave compliantly in response to human forces.
Author: Gaynor E. Taylor
Publisher: Springer Science & Business Media
ISBN: 3642840124
Category : Computers
Languages : en
Pages : 454
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Book Description
This volume contains a series of papers originally presented at a NATO Advanced Research Workshop (ARW) entitled Kinematic and Dynamic Issues in Sensor Based Control. The workshop, one of a series concerned with topics in sensory robotics, took place at II Ciocco, Castelvecchio di Pascoli, Italy in October 1987. Attendance was by invitation only and the majority of participants are recognised leaders in their field- some from the robotics community, others with a more general control background. The main topics of interest were grouped into eight sessions represented by the eight main sections of the book: 1: Modelling Techniques: General Kinematic and Dynamic Issues 2: Sensor Signal Processing 3: Force Control 4: Further Control Topics 5: Vision Based Control 6: Further Kinematic and Dynamic Issues 7: Computational Issues 8: Learning from Sensor Input Also included are brief reports of the roundtable discussions which sought to determine important future directions of research in this area. My thanks to all those who made the workshop possible: The NATO Scientific Affairs Division and the panel on Sensory Systems for Robotic Control who provided most of the financial support; the workshop committee, Dr. B. Espiau, Dr. P. Coiffet, Dr. P.
