Anthropomorphic Robotic Hand Research Articles

Exploration and reconstruction of unknown object by active touch of robot hand

This paper proposes a method of exploring the global shape of an unknown object using information on local geometric features. In the first, we introduce a rolling and sliding motion of a fingertip with a force/torque sensor to estimate an unknown local curvature. Also, a recognition algorithm for local geometry using normal curvature equations is presented, which are composed of principal curvatures and principal direction. Finally, to reconstruct the global shape of the object, we propose an interpolation method using principal curvatures at contact points. The proposed method is verified using a hand-arm system consisting of an industrial robot arm and an anthropomorphic robot hand with a 6-axis force/torque sensor. The effectiveness of the proposed method is experimentally validated for different type of objects.

PARLOMA – A Novel Human-Robot Interaction System for Deaf-Blind Remote Communication

Deaf-blindness forces people to live in isolation. At present, there is no existing technological solution enabling two (or many) deaf-blind people to communicate remotely among themselves in tactile Sign Language (t-SL). When resorting to t-SL, deaf-blind people can communicate only with people physically present in the same place, because they are required to reciprocally explore their hands to exchange messages. We present a preliminary version of PARLOMA, a novel system to enable remote communication between deaf-blind persons. It is composed of a low-cost depth sensor as the only input device, paired with a robotic hand as the output device. Essentially, any user can perform hand-shapes in front of the depth sensor. The system is able to recognize a set of hand-shapes that are sent over the web and reproduced by an anthropomorphic robotic hand. PARLOMA can work as a “telephone” for deaf-blind people. Hence, it will dramatically improve the quality of life of deaf-blind persons. PARLOMA has been presented and supported by the main Italian deaf-blind association, Lega del Filo d'Oro. End users are involved in the design phase.

Adaptive Backstepping Position Control of Pneumatic Anthropomorphic Robotic Hand

This paper presents a nonlinear adaptive backstepping algorithm for position control of an anthropomorphic robotic hand. The contraction force of the pneumatic artificial muscle (PAM) actuators has been modeled based on an empirical approach and the overall finger is represented as a nonlinear second order system, taking into account the system uncertainty caused by hysteresis phenomenon in PAM actuators. Adaptive backstepping controller has been developed by formulating the estimator of the system uncertainty. A cascade control system is developed by combining a conventional PID control, as an inner loop controller, with the adaptive backstepping position control as the outer loop of the controller. Finally, a simulation test is conducted to evaluate the performance of the proposed controller.

Human-inspired feedback synergies for environmental interaction with a dexterous robotic hand

Effortless control of the human hand is mediated by the physical and neural couplings inherent in the structure of the hand. This concept was explored for environmental interaction tasks with the human hand, and a novel human-inspired feedback synergy (HFS) controller was developed for a robotic hand which synchronized position and force feedback signals to mimic observed human hand motions. This was achieved by first recording the finger joint motion profiles of human test subjects, where it was observed that the subjects would extend their fingers to maintain a natural hand posture when interacting with different surfaces. The resulting human joint angle data were used as inspiration to develop the HFS controller for the anthropomorphic robotic hand, which incorporated finger abduction and force feedback in the control laws for finger extension. Experimental results showed that by projecting a broader view of the tasks at hand to each specific joint, the HFS controller produced hand motion profiles that closely mimic the observed human responses and allowed the robotic manipulator to interact with the surfaces while maintaining a natural hand posture. Additionally, the HFS controller enabled the robotic hand to autonomously traverse vertical step discontinuities without prior knowledge of the environment, visual feedback, or traditional trajectory planning techniques.

Design of an underactuated anthropomorphic hand with mechanically implemented postural synergies

Neurology shows that human controls dozens of muscles for hand poses in a coordinated manner and such coordination is referred as to a postural synergy. The concept of postural synergies was recently adopted in the control of robotic hands. With the synergies implemented digitally in a controller, all the motors in an anthropomorphic robotic hand can be controlled via a few synergy inputs. Aiming at exploring alternative approaches for synergy realization, this paper proposes to implement the postural synergies using a mechanical transmission unit. Two rotation inputs can be scaled, combined, and mapped to the rotary outputs to drive an anthropomorphic hand, enabling not only various grasping tasks but also a manipulation motion. Synergy synthesis and design of the anthropomorphic hand are firstly presented. The transmission unit as the implementation of the postural synergies is then elaborated. Tests were performed to quantify how well the synergies could be reproduced via this transmission unit. The results suggest it might be promising to construct a low cost yet versatile prosthetic hand by implementing the postural synergies mechanically.

Low-cost design and fabrication of an anthropomorphic robotic hand.

Human hand signifies a magnificent and challenging example for scientists and engineers trying to replicate its complex structure and functionality. This paper proposes a bio-mechatronic approach for the design of an anthropomorphic artificial hand capable of performing basic human hand motions with fundamental gripping functionality. The dexterity of the artificial hand is exhibited by imitating the natural motion of the human fingers. Imitation is produced according to the data acquired from the flex sensors attached to the human fingers. In order to have proper gripping, closed-loop control is implemented using the tactile sensors. Feedback for the closed-loop control is provided by force sensing resistors (FSRs), attached on the fingertips of the robotic hand. These sensors also enable handling of fragile objects. The mathematical model is derived using forward kinematics and also simulated on MATLAB to ascertain the position of robotic fingers in 3D space.

텐던-튜브를 이용한 인체모방형 로봇핸드 및 암 개발

텐던-튜브를 이용한 인체모방형 로봇핸드 및 암 개발

Design of a myoelectric prosthetic hand implementing postural synergy mechanically

Purpose – This paper aims to describe the design of a multi-degree of freedom (DOF) prosthetic hand prototype implementing postural synergy mechanically, which is actuated by two motors via a transmission unit, and is controlled using surface electromyography (sEMG) signal. Design/methodology/approach – First, an anthropomorphic robotic hand is designed to imitate the human hand. The robotic hand has 18 DOF, 12 of which are actively driven by Bowden cables. Next, a set of different grasp modes are performed on a “full actuation” robotic hand, and principal component analysis (PCA) method is used to extract the first two postural synergies. Then, they are used to design a differential pulley-based transmission unit using two independent inputs to drive 12 output tendons. Finally, two control signals extracted from six channels of sEMG signals are used to proportionally control the two motors for achieving hand posture synthesis. Findings – Using a differential pulley-based mechanical transmission unit to implement the synthesis of the first two postural synergies can make the prosthetic hand achieve different grasps by two motors, such as power, precision and lateral grasps. It is also feasible to control this “two actuation” prosthetic hand by relating the two-dimensional sEMG inputs with the first two postural synergies. Originality/value – Mechanical implantation of postural synergies reduces the number of independent actuators without sacrificing the prosthetic hand’s versatility and simplifies its controller. Two-dimensional control extracted from sEMG is mapped into the combination coefficients of postural synergy synthesis. It shows potential application in the practical prosthetic hand.

Feasibility of teleoperations with multi-fingered robotic hand for safe extravehicular manipulations

Extra-Vehicular Activity (EVA) plays such a key role that more and more time is devoted to it in space missions. Nevertheless, EVA presents so many intrinsic critical aspects to result highly hazardous for the human operators. This is why a convenient alternative can be offered by telerobotic manipulations, with multi-fingered robotic hands working in teleoperated mode, to safely and remotely replicate the capabilities of the operator's hands. But at present, remotely controlled robotic hands cannot provide the same dexterity of humans, so this work is intended to experimentally evaluate their feasibility and technological limits when operator's hand gestures are one-to-one mapped directly to a robotic hand device. In particular we demonstrated how state-of-the art sensory gloves, used to measure angles of human finger's joints, can introduce averaged errors of 4.6 degrees in angles, and that these errors increase to 6.5 degrees when remotely replicated by standard anthropomorphic robotic hands.

Manos robóticas antropomórficas: una revisión

This paper presents a review on main topic regarding to anthropomorphic robotic hands developed in the last years, taking into account the more important mechatronics designs submit on the literature, and making a comparison between them. The next chapters deepen on level of anthropomorphism and dexterity in advanced actuated hands and upper limbs prostheses, as well as a brief overview on issues such as grasping, transmission mechanisms, sensory and actuator system, and also a short introduction on under-actuated robotic hands is reported.

The Pransky interview: Rich Walker, Managing Director at the Shadow Robot Company

Purpose – This paper aims to present a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experiences of a prominent robotic industry engineer-turned entrepreneur regarding the evolution, commercialisation and challenges of bringing a technological invention to market. Design/methodology/approach – The interviewee is Rich Walker, the Managing Director at the Shadow Robot Company in London, UK. Rich Walker joined the Shadow team before going to University, and in this interview, he describes his 25-year journey with the company. Findings – While he was studying mathematics at Cambridge, Rich Walker's passion for cutting edge robotics led him to write software in 1989, unpaid, as part of a team at the pioneering the Shadow Robot Company. Walker was soon involved in developing robotic hands based on pneumatic muscles, which resulted in eight patents, with Walker named as one of the inventors. Walker and the small team have since grown Shadow to become a thriving global business that designs and manufactures state-of-the-art anthropomorphic robot hands and related systems, with 20 R&D and production engineers in London, Brest and Shanghai. Originality/value – Walker's experiences with a small entrepreneurial robot start-up has led him not only to become a leader at Shadow for new robotics applications but he is also active in developing and implementing European projects, and he consults as an expert in small and medium enterprises (SMEs) and innovators.

Exploration of unknown object by active touch of robot hand

This paper proposes a method of exploring the local shape of an unknown object using the force and torque information obtained from active touch. In the first, we present a method to estimate an unknown curvature, using rolling and sliding motion with a force/torque sensor attached to the fingertip of the hand. Then, the normal curvature equation from 2D curvatures is obtained. Finally we present a reconstruction algorithm of local geometry by using a normal curvature equation, which is composed of principal curvatures and principal directions. The method is tested by using a hand-arm system consisting of an industrial robot arm and an anthropomorphic robot hand with 6-axis force/torque sensor. The feasibility of the proposed method is experimentally validated for objects with simple geometries such as cylinder, spheres etc.

The DEXMART hand: Mechatronic design and experimental evaluation of synergy-based control for human-like grasping

This paper summarizes recent activities carried out for the development of an innovative anthropomorphic robotic hand called the DEXMART Hand. The main goal of this research is to face the problems that affect current robotic hands by introducing suitable design solutions aimed at achieving simplification and cost reduction while possibly enhancing robustness and performance. While certain aspects of the DEXMART Hand development have been presented in previous papers, this paper is the first to give a comprehensive description of the final hand version and its use to replicate human-like grasping. In this paper, particular emphasis is placed on the kinematics of the fingers and of the thumb, the wrist architecture, the dimensioning of the actuation system, and the final implementation of the position, force and tactile sensors. The paper focuses also on how these solutions have been integrated into the mechanical structure of this innovative robotic hand to enable precise force and displacement control of the whole system. Another important aspect is the lack of suitable control tools that severely limits the development of robotic hand applications. To address this issue, a new method for the observation of human hand behavior during interaction with common day-to-day objects by means of a 3D computer vision system is presented in this work together with a strategy for mapping human hand postures to the robotic hand. A simple control strategy based on postural synergies has been used to reduce the complexity of the grasp planning problem. As a preliminary evaluation of the DEXMART Hand’s capabilities, this approach has been adopted in this paper to simplify and speed up the transfer of human actions to the robotic hand, showing its effectiveness in reproducing human-like grasping.

Development of a tendon-driven robotic finger for an anthropomorphic robotic hand

Our paper proposes a tendon-driven robotic finger based on an anatomical model of a human finger and a suitable method for its analysis. Our study aims to realize an anthropomorphic robotic hand that has the same characteristics and dexterity as that of a human hand, and it also aims to identify the advantages of the human musculoskeletal structure for application to the design and control of robot manipulators. When designing an anthropomorphic robotic hand, several devices are required to apply the human finger structure to a tendon-driven robotic finger. Reasons for this include that one of the human finger muscles, namely, the lumbrical muscle, is situated between tendons, which is an unfavorable configuration for the tendon-driven mechanism. Second, unlike a standard pulley used in a tendon-driven mechanism, some moment arms of the human finger change nonlinearly according to the joint angle. In our robotic finger design, we address these difficulties by rearranging its tendons and develop a mechanism to change the moment arm. We also propose a method to analyze and control this robotic fingers coordinating joints using non-stretch branching tendons based on the human extensor mechanism with a virtual tendon Jacobian matrix and the advantage is that this constraint virtually reduces the degrees-of-freedom (DOF) of the mechanism. Further, we build a prototype to confirm its motion using this method. In addition, we show that the state with the reduced DOF can be lost by external forces acting on the mechanism, and this condition can be changed manually by adjusting the tendon forces. This makes it possible to control the virtual DOFs to satisfy the requirements of the task. Finally, we discuss the benefits from anthropomorphic structures including the tendon arrangement, which mimic the human lumbrical muscle, and the above mentioned mechanism with non-linear moment arms from the perspective that there are two states of DOFs. These insights may provide new perspectives in the design of robotic hands.

Postural synergies of the UB Hand IV for human-like grasping

In this paper, the postural synergy configuration subspace given by the fundamental eigengrasps of the UB Hand IV is derived from experiments, and a simplified synergy-based strategy for planning grasps is proposed. The objectives of this work are, on one side, the simplification of grasp synthesis in a configuration space of reduced dimensions and, on the other side, the attainment of a human-like behavior for anthropomorphic hands. A reference set of 36 hand postures, chosen with the goal of covering the entire grasp variety referring to a recently proposed taxonomy, has been considered for the evaluation of the hand synergies. With the aim of defining general properties of the three predominant synergies, the reference set of hand postures has been applied to other two anthropomorphic robot hands, and the obtained synergies have been compared with the ones computed considering the UB Hand IV kinematics. Moreover, the synthesis of new grasps, not contained in the reference set of hand postures, has also been achieved by means of the synergy subspace. The experiments carried out demonstrate that the adopted synergy-based planning method works efficiently for all the considered grasps even if not contained in the reference set used for the evaluation of the postural synergies.

Bioinspired Fingertip for Anthropomorphic Robotic Hands

Background: An artificial fingertip with mechanical features and appearance similar to the human fingertip could represent a significant step forward towards the development of the next generation artificial hands. However, so far, a fingertip showing a good trade-off among mechanical features, appearance and anthropomorphism, along with its 3D computational model, is still missing.Objective: To explore and develop an artificial fingertip demonstrating a mechanical response similar to the human fingertip, in order to improve the grasp stability of robotic hands.Methods: Taking inspiration from the multi-layered structure of the human finger, novel artificial fingertips, composed of a rigid core and covered by layers of polymeric materials with different degrees of stiffness and topped by a hard nail were developed. An accurate 3D finite element (FE) model was also developed in order to simulate and evaluate the internal mechanical behavior of the prototypes under external indentations. The mechanical response of the prototypes was assessed and compared with that of the human fingertip and the FE model results, under different experimental conditions. Finally, the artificial fingertips were integrated into an anthropomorphic robotic hand and evaluated in grip tests, in order to compare the grasp stability with respect to conventional stiff (metal) fingertips.Results: The developed prototypes demonstrated a response to compression tests similar to the human finger and the FE model showed a discrete accuracy (mean error 7%). Finally, an increased ability (by 96%) in stably holding objects during precision grips with respect to conventional stiff fingers was demonstrated.Conclusion: Multi-layered biomimetic fingertips can improve grasp stability and cosmetic appearance of anthropomorphic robot hands.

Human Hand Motion Analysis and Synthesis of Optimal Power Grasps for a Robotic Hand

Biologically inspired robotic systems can find important applications in biomedical robotics, since studying and replicating human behaviour can provide new insights into motor recovery, functional substitution and human-robot interaction. The analysis of human hand motion is essential for collecting information about human hand movements useful for generalizing reaching and grasping actions on a robotic system. This paper focuses on the definition and extraction of quantitative indicators for describing optimal hand grasping postures and replicating them on an anthropomorphic robotic hand. A motion analysis has been carried out on six healthy human subjects performing a transverse volar grasp. The extracted indicators point to invariant grasping behaviours between the involved subjects, thus providing some constraints for identifying the optimal grasping configuration. Hence, an optimization algorithm based on the Nelder-Mead simplex method has been developed for determining the optimal grasp configuration of a robotic hand, grounded on the aforementioned constraints. It is characterized by a reduced computational cost. The grasp stability has been tested by introducing a quality index that satisfies the form-closure property. The grasping strategy has been validated by means of simulation tests and experimental trials on an arm-hand robotic system. The obtained results have shown the effectiveness of the extracted indicators to reduce the non-linear optimization problem complexity and lead to the synthesis of a grasping posture able to replicate the human behaviour while ensuring grasp stability. The experimental results have also highlighted the limitations of the adopted robotic platform (mainly due to the mechanical structure) to achieve the optimal grasp configuration.

Bioinspired Fingertip for Anthropomorphic Robotic Hands

BACKGROUND: An artificial fingertip with mechanical features and appearance similar to the human fingertip could represent a significant step forward towards the development of the next generation artificial hands. However, so far, a fingertip showing a good trade-off among mechanical features, appearance and anthropomorphism, along with its 3D computational model, is still missing. OBJECTIVE: To explore and develop an artificial fingertip demonstrating a mechanical response similar to the human fingertip, in order to improve the grasp stability of robotic hands. METHODS: Taking inspiration from the multi-layered structure of the human finger, novel artificial fingertips, composed of a rigid core and covered by layers of polymeric materials with different degrees of stiffness and topped by a hard nail were developed. An accurate 3D finite element (FE) model was also developed in order to simulate and evaluate the internal mechanical behavior of the prototypes under external indentations. The mechanical response of the prototypes was assessed and compared with that of the human fingertip and the FE model results, under different experimental conditions. Finally, the artificial fingertips were integrated into an anthropomorphic robotic hand and evaluated in grip tests, in order to compare the grasp stability with respect to conventional stiff (metal) fingertips. RESULTS: The developed prototypes demonstrated a response to compression tests similar to the human finger and the FE model showed a discrete accuracy (mean error 7%). Finally, an increased ability (by 96%) in stably holding objects during precision grips with respect to conventional stiff fingers was demonstrated. CONCLUSION: Multi-layered biomimetic fingertips can improve grasp stability and cosmetic appearance of anthropomorphic robot hands.

Anthropomorphic tendon-driven robotic hands can exceed human grasping capabilities following optimization

How functional versatility emerges in vertebrate limbs in spite of their anatomical complexity is a longstanding question. In particular, fingers are actuated by numerous muscles pulling on tendons following intricate paths. In contrast, the tendon-driven robotic hands with intuitive tendon routings preferred by roboticists for their ease of analysis and control do not perform at the level of their biological counterparts. Thus there is much debate on whether and how the anatomy of the human hand contributes to grasp capabilities. These parallel questions in biology and robotics arise partly because it is unclear how the number and routing of tendons offer functional benefits. We use a novel computational approach that analyzes tendon-driven systems and quantifies grasp quality to compare the precision grasp capabilities of thousands of robotic index finger and thumb designs vs. the capabilities measured in human hands. Our exhaustive search finds that neither the symmetrical designs sometimes preferred by roboticists nor randomly generated designs approach the grasp capabilities of the human hand (they are on average 73% weaker). However, optimizing for anatomically plausible asymmetry in joint centers, tendon routings, and maximal tendon tensions produces designs that can exceed the human hand by 13–45%, and outperform the preferred robotic designs by up to 435%. Thus, the grasp capabilities of prosthetic or anthropomorphic hands can be greatly improved by judiciously altering design parameters, at times in counter-intuitive ways. Moreover we conclude that, in addition to its other capabilities, the human hand’s anatomy is very advantageous for precision grasp as it greatly outperforms numerous alternative robotic designs.

Analysis of Inverse Kinamtics of an Anthropomorphic Robotic hand

In this paper, a new method for solving the inverse kinematics of the fingers of an anthropomorphic hand is proposed. Solution of inverse kinematic equations is a complex problem, the complexity comes from the nonlinearity of joint space and Cartesian space mapping and having multiple solutions.This is a typical problem in robotics that needs to be solved to control the fingers of an anthropomorphic robotic hand to perform tasks it is designated to do. With more complex structures operating in a 3-dimensional space deducing a mathematical soluation for the inverse kinematics may prove challenging. In this paper, using the ability of ANFIS (Adaptive Neuro-Fuzzy Inference System) to learn from training data, it is possible to create ANFIS network, an implementation of a representative fuzzy inference system using ANFIS structure, with limited mathematical representation of the system. The main advantages of this method with respect to the other methods are implementation is easy, very fast and shorter computation time and better response with acceptable error.