In Hand Object Localization And Control Enabling Dexterous Manipulation With Robotic Hands PDF Download
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| Author | : Martin Pfanne |
| Publisher | : Springer Nature |
| Total Pages | : 213 |
| Release | : 2022-08-31 |
| Genre | : Technology & Engineering |
| ISBN | : 3031069676 |
Download In-Hand Object Localization and Control: Enabling Dexterous Manipulation with Robotic Hands Book in PDF, ePub and Kindle
This book introduces a novel model-based dexterous manipulation framework, which, thanks to its precision and versatility, significantly advances the capabilities of robotic hands compared to the previous state of the art. This is achieved by combining a novel grasp state estimation algorithm, the first to integrate information from tactile sensing, proprioception and vision, with an impedance-based in-hand object controller, which enables leading manipulation capabilities, including finger gaiting. The developed concept is implemented on one of the most advanced robotic manipulators, the DLR humanoid robot David, and evaluated in a range of challenging real-world manipulation scenarios and tasks. This book greatly benefits researchers in the field of robotics that study robotic hands and dexterous manipulation topics, as well as developers and engineers working on industrial automation applications involving grippers and robotic manipulators.
| Author | : Tetsuyou Watanabe |
| Publisher | : Academic Press |
| Total Pages | : 218 |
| Release | : 2018-06-26 |
| Genre | : Technology & Engineering |
| ISBN | : 0128133961 |
Download Human Inspired Dexterity in Robotic Manipulation Book in PDF, ePub and Kindle
Human Inspired Dexterity in Robotic Manipulation provides up-to-date research and information on how to imitate humans and realize robotic manipulation. Approaches from both software and hardware viewpoints are shown, with sections discussing, and highlighting, case studies that demonstrate how human manipulation techniques or skills can be transferred to robotic manipulation. From the hardware viewpoint, the book discusses important human hand structures that are key for robotic hand design and how they should be embedded for dexterous manipulation. This book is ideal for the research communities in robotics, mechatronics and automation. Investigates current research direction in robotic manipulation Shows how human manipulation techniques and skills can be transferred to robotic manipulation Identifies key human hand structures for robotic hand design and how they should be embedded in the robotic hand for dexterous manipulation
| Author | : Qiang Li |
| Publisher | : Academic Press |
| Total Pages | : 374 |
| Release | : 2022-04-02 |
| Genre | : Computers |
| ISBN | : 0323904173 |
Download Tactile Sensing, Skill Learning, and Robotic Dexterous Manipulation Book in PDF, ePub and Kindle
Tactile Sensing, Skill Learning and Robotic Dexterous Manipulation focuses on cross-disciplinary lines of research and groundbreaking research ideas in three research lines: tactile sensing, skill learning and dexterous control. The book introduces recent work about human dexterous skill representation and learning, along with discussions of tactile sensing and its applications on unknown objects’ property recognition and reconstruction. Sections also introduce the adaptive control schema and its learning by imitation and exploration. Other chapters describe the fundamental part of relevant research, paying attention to the connection among different fields and showing the state-of-the-art in related branches. The book summarizes the different approaches and discusses the pros and cons of each. Chapters not only describe the research but also include basic knowledge that can help readers understand the proposed work, making it an excellent resource for researchers and professionals who work in the robotics industry, haptics and in machine learning. Provides a review of tactile perception and the latest advances in the use of robotic dexterous manipulation Presents the most detailed work on synthesizing intelligent tactile perception, skill learning and adaptive control Introduces recent work on human’s dexterous skill representation and learning and the adaptive control schema and its learning by imitation and exploration Reveals and illustrates how robots can improve dexterity by modern tactile sensing, interactive perception, learning and adaptive control approaches
| Author | : Peter K. Allen |
| Publisher | : |
| Total Pages | : 9 |
| Release | : 1998 |
| Genre | : |
| ISBN | : |
Download Intelligent Sensor-Based Manipulation with Robotic Hands Book in PDF, ePub and Kindle
Our hand research has focused on enhancing the dexterity of robotic hands and understanding the nature of dexterous manipulation. The premise of the research is that incorporating task level understanding into a manipulation system simplifies robot planning and increases autonomy. The study of task level strategies for dexterous manipulation has led to development of several novel techniques for controlling the fingertip forces during manipulation and fingertip motion planning. The insights into increased autonomy have led to the development of a novel technique for teleoperating robot hands. The traditional technique of teleoperating a robot hand is to use a Dataglove or exoskeleton master; there is a direct mapping from the human hand to the robot hand. This approach has several limitations which we have addressed by using a simpler control interface with a joystick or keyboard. Enhancing the robot hand's autonomy allows for simpler control strategies and gives it greater functionality than by traditional means. Control of the hand is shared between the user and the robot. We have developed a prototype teleoperation system using a Utah/MIT hand. Our research will ultimately have application in medicine and industry, for enhancement of prosthetic hands and the development of more complex robotic grippers.
| Author | : Qi Zhu |
| Publisher | : |
| Total Pages | : |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
Download Teleoperated Grasping Using an Upgraded Haptic-Enabled Human-Like Robotic Hand and a CyberTouch Glove Book in PDF, ePub and Kindle
Grasping, the skill to hold objects and tools while doing in-hand manipulation, still is in many cases an unsolvable problem for robotics, but a natural act for humans. An efficient grasping requires not only human-like robotic hands with articulated fingers but also tactile, force, and kinesthetic sensors for the precise control of the forces and motions exerted during the manipulation. As a fully autonomous robotic dexterous manipulation is too difficult to develop for changing and unstructured environments, an alternative approach is to combine the low-level robot computer control with the higher-level perception and task planning abilities of a human operator equipped with an adequate human-computer interface (HCI). This thesis presents theoretical and experimental contributions to the development of an upgraded haptic-enabled anthropomorphic Ring Ada dexterous robotic hand and a biology-inspired synergistic real-time control system for teleoperated grasping of different objects using a CyberTouch HCI data glove. A fuzzy logic controller module was developed to efficiently control the underactuated Ring Ada' robotic hand during grasping. A machine learning classification system was developed to recognize grasped objects. Experiments have convincingly demonstrated that our novel Ring Ada robotic hand equipped with kinematic position sensors and touch sensors is able to efficiently grasp different lightweight objects through teleoperation.
| Author | : Prashant Rao |
| Publisher | : |
| Total Pages | : 200 |
| Release | : 2018 |
| Genre | : |
| ISBN | : |
Download Stability Through Design Book in PDF, ePub and Kindle
While dexterous robotic manipulation research has made significant advancements in the last two decades in areas of sensors, control strategies, perception and planning, the abilities of robotic hands in unstructured and unpredictable environments are limited. Specifically, a few researchers have shown promising manipulation results with stiffness controllers, which allow for the generation of fingertip forces as a function of displacement. In terms of mechanical design, robotic hands have been converging towards low-inertia, passively compliant, tendon-driven strategies for agility and robustness against environmental impacts. As tendon driven robotic fingers are serial chain systems, various routing strategies with passively compliant tendons lead to unique multi-articular stiffness coupling between the degrees of freedom. The performance of manipulation controllers is highly dependent on the passive properties of the fingers. While tendon-driven robotic fingers are widely accepted to be advantageous for manipulation and stiffness controllers have shown promising results, currently there is no methodology for a thorough analysis of the effects of various compliance arrangements in the fingers on the closed loop properties of stiffness controllers. As a result, we don't have the ability to reliably predict the boundaries of stable operation and determine the limitations due to mechanical parameters for tendon-driven robotic fingers. We also don't have a quantifiable way of exploiting the design of mechanical elements to intrinsically improve the dexterity and robustness of robotic in-hand manipulation by augmenting the controllers. In this dissertation, I present a systematic methodology for analyzing the effects arrangements of passive compliance on tendon driven robotic joints implementing stiffness control strategies. To begin, I develop generalizable comprehensive mechanical models of compliant tendon driven robotic fingers. Then, I identify the various arrangements of passive stiffness elements and analyze their role in the performance of various stiffness control strategies and subsequently towards dexterity. I have analyzed the achievable joint stiffness control boundaries of tendon-driven robotic fingers implementing joint stiffness control leading to a first of its kind generalizable stability boundary that can be applied to robotic fingers with any degrees of freedom and tendon routing strategy. Then, I extend the analysis to Cartesian fingertip space as object manipulation requires accurate control of fingertip force directions and magnitudes. I use the analysis to identify all the mechanical design features and dynamic parameters that have a direct impact on controller stability. I have isolated compliance in parallel to actuators as a significant element for optimization. Optimal linear and nonlinear parallel compliance found using the analysis improved the stability and force tracking accuracy of Cartesian stiffness control even in the presence of external forces. Such features are ideal for in-hand manipulation. Finally, I extend the stability analysis to object-space stiffness controller and optimize linear and nonlinear parallel compliance for improved dexterity, accuracy and robustness of in-hand manipulation. My research not only allows for an accurate prediction of the behavior of stiffness controlled tendon-driven robotic hands but also leads to a mechanical design paradigm informed by the stability of robotic hands allowing for the design of intrinsically stable, robust and dexterous robotic hands that take us one step closer to human-like dexterity
| Author | : Kien Cuong Nguyen |
| Publisher | : |
| Total Pages | : 159 |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
Download Control of an Anthropomorphic Arm-hand Robot for Grasping and Dexterous Manipulation Book in PDF, ePub and Kindle
This thesis deals with the control of an anthropomorphic arm-hand robot by focusing on two aspects: the control of the fingertip force and the coordination between the arm and the hand. The force control of a robotic finger remains difficult despite the advances in current state-of-art. This is due to the small size of the finger, its low communication bandwidth, the lack of precision of the position sensors and the significant backlash in the actuation systems. A new approach controlling the fingertip force by adjusting the joint torque saturation parameter shows better results. Not limited to pure force control, this control method is proved to also have good performance when applying to indirect and hybrid position/force control. Usually ignored in literature while considering dexterous manipulation, the position and movement of the arm play a very important role. Many in-hand manipulation tasks cannot be realized without a proper movement of the arm. One typical example is the rotation of the manipulated object relative to the palm without moving the fingers thanks to inertial and gravitational effects. Besides, arm movement is also an important factor contributing to the appearance of the grasping gestures. In this thesis, the movement of the grasped object under gravitational effect was analyzed and a grasping strategy was elaborated. In addition to this, some mechanical constraints (tenodesis effect in particular) contributing to the human natural gestures were deciphered and such natural gestures were reproduced on an anthropomorphic arm-hand robot in redundant grasping situations.
| Author | : Ankit Chaudhary |
| Publisher | : Springer |
| Total Pages | : 108 |
| Release | : 2017-06-05 |
| Genre | : Technology & Engineering |
| ISBN | : 9811047987 |
Download Robust Hand Gesture Recognition for Robotic Hand Control Book in PDF, ePub and Kindle
This book focuses on light invariant bare hand gesture recognition while there is no restriction on the types of gestures. Observations and results have confirmed that this research work can be used to remotely control a robotic hand using hand gestures. The system developed here is also able to recognize hand gestures in different lighting conditions. The pre-processing is performed by developing an image-cropping algorithm that ensures only the area of interest is included in the segmented image. The segmented image is compared with a predefined gesture set which must be installed in the recognition system. These images are stored and feature vectors are extracted from them. These feature vectors are subsequently presented using an orientation histogram, which provides a view of the edges in the form of frequency. Thereby, if the same gesture is shown twice in different lighting intensities, both repetitions will map to the same gesture in the stored data. The mapping of the segmented image's orientation histogram is firstly done using the Euclidian distance method. Secondly, the supervised neural network is trained for the same, producing better recognition results. An approach to controlling electro-mechanical robotic hands using dynamic hand gestures is also presented using a robot simulator. Such robotic hands have applications in commercial, military or emergency operations where human life cannot be risked. For such applications, an artificial robotic hand is required to perform real-time operations. This robotic hand should be able to move its fingers in the same manner as a human hand. For this purpose, hand geometry parameters are obtained using a webcam and also using KINECT. The parameter detection is direction invariant in both methods. Once the hand parameters are obtained, the fingers’ angle information is obtained by performing a geometrical analysis. An artificial neural network is also implemented to calculate the angles. These two methods can be used with only one hand, either right or left. A separate method that is applicable to both hands simultaneously is also developed and fingers angles are calculated. The contents of this book will be useful for researchers and professional engineers working on robotic arm/hand systems.
| Author | : Beatriz León |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 263 |
| Release | : 2013-09-29 |
| Genre | : Technology & Engineering |
| ISBN | : 3319018337 |
Download From Robot to Human Grasping Simulation Book in PDF, ePub and Kindle
The human hand and its dexterity in grasping and manipulating objects are some of the hallmarks of the human species. For years, anatomic and biomechanical studies have deepened the understanding of the human hand’s functioning and, in parallel, the robotics community has been working on the design of robotic hands capable of manipulating objects with a performance similar to that of the human hand. However, although many researchers have partially studied various aspects, to date there has been no comprehensive characterization of the human hand’s function for grasping and manipulation of everyday life objects. This monograph explores the hypothesis that the confluence of both scientific fields, the biomechanical study of the human hand and the analysis of robotic manipulation of objects, would greatly benefit and advance both disciplines through simulation. Therefore, in this book, the current knowledge of robotics and biomechanics guides the design and implementation of a simulation framework focused on manipulation interactions that allows the study of the grasp through simulation. As a result, a valuable framework for the study of the grasp, with relevant applications in several fields such as robotics, biomechanics, ergonomics, rehabilitation and medicine, has been made available to these communities.
| Author | : Shenli Yuan |
| Publisher | : |
| Total Pages | : |
| Release | : 2022 |
| Genre | : |
| ISBN | : |
Download Robot In-hand Manipulation Using Roller Graspers Book in PDF, ePub and Kindle
This work describes the development of a class of dexterous robot hands that use steerable, continuously rotating fingertips to perform complex in-hand manipulation of grasped objects, a task that has alluded many widely used robot graspers. While a typical laboratory robot with a parallel-jaw gripper can perform basic pick-and-place tasks, they lack the fine manipulation proficiency needed for more demanding, precise and varied tasks. Inspired by the human hand, many research efforts are focused on creating and controlling human-like (anthropomorphic) hands in the hopes of duplicating human dexterity. One goal of these designs has been to enable robotic in-hand manipulation. Except for small motions, in-hand manipulation with these hands requires grasp-gaiting, a complex process where the fingers "walk" across the object, making and breaking contacts in order to propel the object to a desired pose, which can be an inefficient and difficult approach for a variety of tasks. So far, the fragility, cost, and complexity of these devices have precluded use outside of a laboratory setting. While we share with other researchers the goal of enabling in-hand manipulation, we achieve it by completely different means. We have developed a series of highly non-anthropomorphic grasping devices - the Roller Graspers - that use rotating fingertips to perform full six-degrees-of-freedom (DoF) in-hand manipulation on grasped objects. We do this by intelligently driving the fingertips across the object. The first Roller Grasper used steerable cylindrical fingertip rollers mounted on a pivoting linkage, for a total of three DoF for each of its three fingers. Using scripted motion control, it demonstrated the feasibility of our in-hand manipulation concept. The second and third versions used spherical fingertips which afforded better grasp stability and range of motion. These designs also supported our development of a hierarchical manipulation architecture that allowed the roller graspers to achieve autonomous in-hand manipulation. The manipulation architecture consisted of a sample-based high-level planner and a heuristic low-level policy that allowed the grasper to perform full 6-DoF manipulation of objects with a variety of shapes and sizes. The final version of our hand, called Tactile-Enabled Roller Grasper (TERG), incorporated a novel tactile sensing system that could extract the surface contour of a grasped object as well as the shear force applied at the contact location, even while the fingertips were rotating. This enabled more diverse and robust in-hand manipulation that was not possible in the previous generations.
