Dexterous Robot Hand Research Articles

Study of ultra-miniature giant magneto resistance sensor system based on 3D static magnetic analysis technique

In this study, a method for the modeling of ferromagnetic component of an ultra-miniature giant magneto resistance (GMR) sensor system is presented. The goal of the modeling is to design appropriate angular sensor system for the highly integrated DLR/HIT II 5-finger dexterous robot hand. A 3D static magnetic analysis modeling is used to avoid sensor damage by the excessive magnetic intensity and disorientation when other magnetic fields disturb the primary field. Using the finite element results, appropriate shape and size of ferromagnetic component is compared and optimized. A simplified signal processing circuit is also given for the ultra-miniature GMR sensor system. Finally experimental results show an angular accuracy of less than ± 1 ° with only the residual offset compensation of ultra-miniature GMR sensor system is obtained. Integration experiences reveal the challenges associated with obtaining the required capabilities within the desired size.

High Speed and Accurate Posture Estimation of Human Hand Using Multilevel Database

The authors propose a hand posture estimation system in real time and with high accuracy, for robot hand control and human interface with hand motions without no sensors attached to the users. This method searches the similar image quickly from a large volume of previously-sorted image database which contains complicated shapes and self-occlusions of the human hand. Because the system doesn't need any special peripheral equipment such as range sensor and PC cluster, and works with a note PC using a single high-speed camera, the user can control and make a dexterous robot hand behave as he/she does, and use the system as an information input device by moving his/her hand and fingers.

Surface EMG in advanced hand prosthetics

One of the major problems when dealing with highly dexterous, active hand prostheses is their control by the patient wearing them. With the advances in mechatronics, building prosthetic hands with multiple active degrees of freedom is realisable, but actively controlling the position and especially the exerted force of each finger cannot yet be done naturally. This paper deals with advanced robotic hand control via surface electromyography. Building upon recent results, we show that machine learning, together with a simple downsampling algorithm, can be effectively used to control on-line, in real time, finger position as well as finger force of a highly dexterous robotic hand. The system determines the type of grasp a human subject is willing to use, and the required amount of force involved, with a high degree of accuracy. This represents a remarkable improvement with respect to the state-of-the-art of feed-forward control of dexterous mechanical hands, and opens up a scenario in which amputees will be able to control hand prostheses in a much finer way than it has so far been possible.

Origami Folding by a Robotic Hand

Dexterous manipulation by a robotic hand is a difficult problem involving (1) how to design a robot that gives the capability to achieve the task and (2) how to control the designed robot to actually conduct the task. In this paper, we take a task-oriented approach called “task capture” to construct a dexterous robot hand system. Before designing the robot, we analyze how a human being conducts the task, focusing on how the target object is manipulated rather than trying to imitate human finger movement. Based on the captured task, we design a robot that manipulates an object in the same way as a human being may do, with a mechanism as simple as possible, rather than concerning human appearance. As a target task, we choose origami paper folding. We first analyze the difficulty of origami manipulation and design a robotic mechanism that folds an origami form, the Tadpole, based on the proposed approach. The proof of how well the “task capture” approach works is demonstrated by a simple robot we developed, which folds a Tadpole consecutively.

Kinematic enveloping grasp planning method for robotic dexterous hands and three-dimensional objects

SUMMARYThree-dimensional (3D) enveloping grasps for dexterous robotic hands possess several advantages over other types of grasps. This paper describes a new method for kinematic 3D enveloping grasp planning. A new idea for grading the 3D grasp search domain for a given object is proposed. The grading method analyzes the curvature pattern and effective diameter of the object, and grades object regions according to their suitability for grasping. A new approach is also proposed for modeling the fingers of the dexterous hand. The grasp planning method is demonstrated for a three-fingered, six degrees-of-freedom, dexterous hand and several 3D objects containing both convex and concave surface patches. Human-like high-quality grasps are generated in less than 20 s per object.

Design and control of a shape memory alloy based dexterous robot hand

Modern externally powered upper-body prostheses are conventionally actuated by electricservomotors. Although these motors achieve reasonable kinematic performance, theyare voluminous and heavy. Deterring factors such as these lead to a substantialproportion of upper extremity amputees avoiding the use of their prostheses.Therefore, it is apparent that there exists a need for functional prosthetic devicesthat are compact and lightweight. The realization of such a device requires analternative actuation technology, and biological inspiration suggests that tendon basedsystems are advantageous. Shape memory alloys are a type of smart materialthat exhibit an actuation mechanism resembling the biological equivalent. Assuch, shape memory alloy enabled devices promise to be of major importancein the future of dexterous robotics, and of prosthetics in particular. This paperinvestigates the design, instrumentation, and control issues surrounding the practicalapplication of shape memory alloys as artificial muscles in a three-fingered robothand.

Dexterous Robot Hand Control with Data Glove by Human Imitation

The purpose of the study is to obtain the automatic and optimal matching between a motion-measurement device such as a data glove and an output device such as a dexterous robot hand, where there are many differences in the numbers of degree of freedom, sensor and actuator positions, and data format, by means of motion imitation by the humans. Through the algorithm proposed here, a system engineer or user need no labor of determining the values of gains and parameters to be used. In the system, a subject with data glove imitated the same motion with a dexterous robot hand which was moving according to a certain mathematical function. Autoregressive models were adapted to the matching, where each joint angle in the robot and data glove data of the human were used as object and explanatory variables respectively. The partial regression coefficients were estimated by means of singular value decomposition with a system-noise reduction algorithm utilizing statistical properties. The experimental results showed that the robot hand was controlled with high accuracy with small delay, suggesting that the method proposed in this study is proper and easy way and is adaptive to many other systems between a pair of motion-measurement device and output device.

Estimation of hand preshaping during human grasping by using instrumented glove

In this paper a neural network model for spatio-temporal coordination of hand gesture during prehension is proposed. The model includes a simplified control strategy for whole hand shaping during grasping tasks, that provides a realistic coordination among fingers. This strategy uses the increasing evidence that supports the view of a synergistic control of whole fingers during prehension. In this control scheme, only two parameters are needed to define the evolution of hand shape during the task performance. The proposal involves the design and development of a Library of Hand Gestures consisting of motor primitives for finger pre-shaping of an anthropomorphic dextrous hand. Through computer simulations, we show how neural dynamics of the model leads to simulated grasping movements with human-like kinematic features. The model can provide clear-cut predictions for experimental evaluation at both the behavioural and neural levels as well as a neural control system for a dextrous robotic hand.

2P2-B21 遭遇型マスタハンドにおける母指の制御手法

We have developed an encounter-type master hand to control a dexterous robot hand for telexistence. The master hand does not hinder the operator's behavior. To cover wide workspace of operator's thumb, our master hand has 4 DOF and 4 sensors on thumb. In this paper, we describe the mechanism and control method for the thumb of our master hand.

A neural network model for coordination of hand gesture during reach to grasp

In this paper a neural network model for spatio-temporal coordination of hand gesture during prehension is proposed. The model includes a simplified control strategy for whole hand shaping during grasping tasks, that provides a realistic coordination among fingers. This strategy uses the increasing evidence that supports the view of a synergistic control of whole fingers during prehension. In this control scheme, only two parameters are needed to define the evolution of hand shape during the task performance. The proposal involves the design and development of a Library of Hand Gestures consisting of motor primitives for finger pre-shaping of an anthropomorphic dextrous hand. Through computer simulations, we show how neural dynamics of the model leads to simulated grasping movements with human-like kinematic features. The model can provide clear-cut predictions for experimental evaluation at both the behavioural and neural levels as well as a neural control system for a dextrous robotic hand.

Model-based visual hand posture tracking for guiding a dexterous robotic hand

Visual hand gesture based interfaces have been widely used for navigation of virtual environments and control of a robot arm in robotic systems. In fact, hand movement has abundant powers of expression with complex finger joint motions. In this paper, the full degree-of-freedom hand motion is tracked in a vision-based mode towards natural and intuitive control of a dexterous robotic hand. The main obstacle to handle human hand posture tracking is the high dimensionality. A novel three-dimensional model-based approach is proposed. It utilizes a genetic algorithm-based global optimization procedure after each particle-filtering step, which deals with the tracking in high-dimensional and multi-modal state space reasonably well. Experimental results show that present approach improves performance of hand posture tracking significantly and provides a feasible solution for guiding a dexterous hand.

Design of a novel dexterous robot hand

Design of a novel dexterous robot hand

CPG Based Manipulation: Measurement of Human's Rhythmic Finger Gaits

A number of researches for the dextrous manipulation have been studied in robotics and physiologic fields independently. Multi-fingered manipulation for dextrous robot hands should be based on the analysis of human skills. The purpose of this study is to apply the CPG-based control to the dextrous manipulation. In this paper, CPG-based manipulation is proposed for the rotating manipulation. Manipulation patterns of fingers are investigated when a subject rotates a cylindrical object. Typical contact patterns during the rotating manipulation are measured by FSRs. Based on the observation, the neural circuit model which generates a similar contact pattern is constructed. The experimental results by the computer simulation suggest that rotating manipulation can be performed using the constructed CPG model.

Design and control of a cable-free and single-motor-driven dexterous robot hand

This paper presents a new design for a multi-fingered dexterous robot hand. Based on an analysis of currently available hands, two improvements are proposed for better mechanism, actuation, and functionality. We designed a new cable-free, rigid connection, fifteen DOFs robot hand mechanism having one DC-motor, fifteen bidirectional clutches, and the capability for holding cylinder object. The finger mechanism and motion transmission chain of this robot hand are described. Hand control structure and finger control structure are analyzed. In addition, simulation is perfonned in Matlab® SimMechanics toolbox. Two design problems are also discussed and summarized. Copyright © 2003 IFAC.

Developing haptic and visual perceptual categories for reaching and grasping with a humanoid robot

Properties of the human embodiment — sensorimotor apparatus and neurological structure — participate directly in the growth and development of cognitive processes against enormous worst case complexity. It is our position that relationships between morphology and perception over time lead to increasingly comprehensive models that describe the agent’s relationship to the world. We are applying insight derived from neuroscience, neurology, and developmental psychology to the design of advanced robot architectures. To investigate developmental processes, we have begun to approximate the human sensorimotor configuration and to engage sensory and motor subsystems in developmental sequences. Many such sequences have been documented in studies of infant development, so we intend to bootstrap cognitive structures in robots by emulating some of these growth processes that bear an essential resemblance to the human morphology. In this paper, we will show two related examples in which a humanoid robot determines the models and representations that govern its behavior. The first is a model that captures the dynamics of a haptic exploration of an object with a dextrous robot hand that supports skillful grasping. The second example constructs constellations of visual features to predict relative hand/object postures that lead reliably to haptic utility. The result is a first step in a trajectory toward associative visual-haptic categories that bounds the incremental complexity of each stage of development.

A Planning System for Object Reconfiguration with Robotic Hand and the Real Time Realization of the Motion

An object manipulation algorithm for a three-fingered dextrous robotic hand mounted on a 6 DOF robotic arm is proposed. The paper is divided into two parts. The first part describes a (currently) off-line planning system for object reconfiguration design based on simulated annealing, while the second part deals with the real time realization of the determined motion path. The presented model-based motion planner algorithm for object re-configuration uses simulated annealing (SA) algorithm for generating the relative motion between the object and the end-points of the fingers.

Robonaut: NASA's space humanoid

To meet the dexterous manipulation needs of future missions, NASA is developing Robonaut, an astronaut-sized robot with two arms, two five-fingered hands, a head and a torso. Robonaut is advancing the state of the art in anthropomorphic robotic systems, dexterous robotic hands, modular robotic systems components and telepresence control systems.

Manipulability of cooperating robots with unactuated joints and closed-chain mechanisms

We study the differential kinematics and the kineto-static manipulability indices of multiple cooperating robot arms, including active and passive joints. The kinematic manipulability indices are derived extending previous results on cooperating robots without passive joints. The force manipulability analysis for cooperative robot systems cannot be straightforwardly derived by duality arguments as it can with conventional arms, rather a distinction between active and passive force manipulability must be introduced. Results in the paper apply directly to the analysis of cooperating robots, parallel robots, dextrous robotic hands and legged vehicles, and, in general, to closed kinematic chains.

Rolling bodies with regular surface: controllability theory and applications

Pairs of bodies with regular rigid surfaces rolling onto each other in space form a nonholonomic system of a rather general type, posing several interesting control problems of which not much is known. The nonholonomy of such systems can be exploited in practical devices, which is very useful in robotic applications. In order to achieve all potential benefits, a deeper understanding of these types of systems and more practical algorithms for planning and controlling their motions are necessary. In this paper, we study the controllability aspect of this problem, giving a complete description of the reachable manifold for general pairs of bodies, and a constructive controllability algorithm for planning rolling motions for dexterous robot hands.

Design and Implementation of a Lightweight, Large Workspace, Non-Anthropomorphic Dexterous Hand

The design and implementation of a novel dexterous robotic hand is described. To Provide flexibility of application the design features three fingers, each position or force controllable in X-Y-Z, and a large workspace (400 mm dia. × 75 mm deep). These requirements plus the need for adequate strength (i.e. force and stiffness) are in conflict with the need to keep the mass within the payload capacity of common robots. These contrary objectives are met by designing a novel hybrid parallel-serial finger mechanism, a lightweight frame and pneumatic servos for finger actuation. The implemented design is verified experimentally, and may be scaled for other applications.