A unified approach for the control of multifingered robot hands

We have proposed a Whole Finger Manipulation (WFM) to improve robustness of manipulation by multifingered robot hand. The WFM is characterized by (1) multiple contact points between an object and surfaces of the inner links of multifingered robot hand, (2) a manipulation by utilizing a sliding at each contact point. In this paper, considering the kinematics and dynamics of the WFM, we propose a control scheme which guarantees the manipulation stability. The proposed control law consists of both tracking control of multifingered robot hand and impedance control which enforces the motion of the contact point in the normal direction and internal force. The minimal internal force is defined as the internal force which generate the minimal contact force at the instant. The conditions for being manipulated an object stably are presented. The experimental results to verify the validity of the proposed control law are shown.

The control of multi-fingered robot hands has been the subject of interest. To manipulate some object with regrasping motion, there are many parameters to be determined; the grasping points, grasping forces, regrasping phases, finger allocation and so on. It is difficult to optimize such manipulation parameters for achieving effective manipulation. We propose a generation method of regrasping motion for a four-fingered robot hand using evolutionary programming (EP). In this case, EP determines not only finger motions, but also rotation of the target object in order to increase its manipulability. In manipulating an object like a rectangular solid, it is very important to change the object directions and to grasp another face according to the task progress because the grasping faces tend to be limited to only two. The evolutionary optimization method is generally able to find optimal solutions without a supervisor after much iteration, which makes it almost impractical to apply a real robot directly. Therefore we control the real robot hand with an optimal controller that has been generated in computer simulations. We show the effectiveness of the proposed acquisition method for the regrasping motion with experimental results.

The control of multi-fingered robot hands has been the subject of recent interest. To manipulate some object with it, it is necessary to determine the contact points and grasping forces at the fingertips, but it is difficult to optimize such manipulation parameters for performing effective manipulation. In this paper, we propose an acquisition method for re-grasping motion for a four-fingered robot hand using evolutionary programming (EP). In this case, EP determines not only when and where the fingers are moved, but also which finger is operated at the next contact point according to the attitude changes of object grasping with the grasping stability. Evolutionary programming is generally able to find an optimal solution without a supervisor after much iteration, which is almost impractical using a real robot. Therefore we control the real robot hand with the optimal controller that has been obtained in computer simulations. We show the effectiveness of the proposed acquisition method for the re-grasping motion with experimental results.

By considering the dexterous multi-fingered manipulation problem as a hybrid one, we at first propose a MLD model which encapsulates occurrence of impacts, switching between types of contact motion, and phases of continuous motion. Then we deal with planning and tracking problem of the derived MLD system, propose to realize dexterous hand manipulation via MIQP. The effectiveness of the proposed approach is validated by simulation results

Manipulation using dextrous robot hands has been an exciting yet frustrating research topic for the last several years. While significant progress has occurred in the design, construction, and low level control of robotic hands, researchers are up against fundamental problems in developing algorithms for real-time computations in multi-sensory processing and motor control. The aim of this book is to explore parallels in sensorimotor integration in dextrous robot and human hands, addressing the basic question of how the next generation of dextrous hands should evolve. By bringing together experimental psychologists, kinesiologists, computer scientists, electrical engineers, and mechanical engineers, the book covers topics that range from human hand usage in prehension and exploration, to the design and use of robotic sensors and multi-fingered hands, and to control and computational architectures for dextrous hand usage. While the ultimate goal of capturing human hand versatility remains elusive, this book makes an important contribution to the design and control of future dextrous robot hands through a simple underlying message: a topic as complex as dextrous manipulation would best be addressed by collaborative, interdisciplinary research, combining high level and low level views, drawing parallels between human studies and analytic approaches, and integrating sensory data with motor commands. As seen in this text, success has been made through the establishment of such collaborative efforts. The future will hold up to expectations only as researchers become aware of advances in parallel fields and as a common vocabulary emerges from integrated perceptions about manipulation.

One of the major challenges in robotics consists in developing successful control strategies for robotic grasping devices. In this scenario, one of the most interesting approaches regards the exploitation of surface electromyography(sEMG). In this work, we propose a novel sEMG-based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">minimally supervised</i> regression approach capable of performing nonlinear fitting without the necessity for point-by-point training data labelling. The proposed method exploits a differentiable version of the Dynamic Time Warping (DTW) similarity – referred to as soft-DTW divergence – as loss function for a flexible neural network architecture. This is a different paradigm with respect to state-of-the-art approaches in which sEMG-based control of robot hands is mainly realized using supervised or unsupervised machine learning based regression. An experimental session was carried out involving 10 healthy subjects in an offline experiment for systematic and statistical evaluations, and an online experiment for the evaluation of the control of a robot hand. The reported results demonstrate that the proposed soft-DTW neural network can be trained by means of a labelling that does not require to be temporally aligned with the sEMG training dataset, while reporting performances comparable with a standard mean square error(MSE)-based neural network. Also, the subjects were able to successfully control a robot hand for grasping motions and tasks with error levels comparable to state-of-the-art regression approaches.

An integrative approach to the biomechanical function and neuromuscular control of the fingers

This paper presents the tuning of PID control parameters for position feedback control of motor and the position reference design for grasping task by a 7-degree of freedom (DOF) three-fingered robotic hand. The system of the robotic hand consists of DC-motors, magnetic encoder and a motion controller unit. The robotic hand has three fingers where each finger has two joints and two links made of perspective plastic material. The PID tuning is implemented to all six motors that are used to move all joints of the three fingers. Trial and error method is used for tuning the parameter of PID control to improve the transient response performance of the motors. For the position reference design, a set of position data is identified to produce the grasping trajectory for all finger joints. The trajectory for the robotic hand performs two types of finger configuration for grasping two different shapes of household solid objects. Both works were implemented via programming in Motion Manager Application. It has been observed that the robot hand has performed well with the tuned PID parameters by reducing the overshoot, rise time (T r ), peak time (T p ), settling time (T s ) and steady state error (e ss ). Besides, the robot hand has also succeeded the grasping task with the proposed position reference trajectory.

Advanced Human-In-The-Loop (HITL) control strategies for robot hands based on surface electromyography (sEMG) are among major research questions in robotics. Due to intrinsic complexity and inaccuracy of labeling procedures, unsupervised regression of sEMG signals has been employed in literature, however showing several limitations in realizing multiple grasping motion control. In this work, we propose a novel Human-Robot interface (HRi) based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">self-supervised</i> regression of sEMG signals, combining Non-Negative Matrix Factorization (NMF) with Deep Neural Networks (DNN) in order to both avoid explicit labeling procedures and have powerful nonlinear fitting capabilities. Experiments involving 10 healthy subjects were carried out, consisting of an offline session for systematic evaluations and comparisons with traditional unsupervised approaches, and an online session for assessing real-time control of a wearable anthropomorphic robot hand. The offline results demonstrate that the proposed self-supervised regression approach overcame traditional unsupervised methods, even considering different robot hands with dissimilar kinematic structures. Furthermore, the subjects were able to successfully perform online control of multiple grasping motions of a real wearable robot hand, reporting for high reliability over repeated grasp-transportation-release tasks with different objects. Statistical support is provided along with experimental outcomes.

Grasping unknown objects with multi-fingered hands is challenging due to incomplete information regarding scene geometry and the complicated control and planning of robot hands. We propose a method for grasping unknown objects with multi-fingered hands based on shape complementarity between the robot hand and the object. Taking as input a point cloud of the scene we locally perform shape completion and then we search for hand poses and finger configurations that optimize a local shape complementarity metric. We validate the proposed approach in MuJoCo physics engine. Our experiments show that the explicit consideration of shape complementarity of the hand leads to robust grasping of unknown objects.

Problem statement: Sensitization of robot hand is still remaining as crucial issue since most of robot hand systems nowadays are only capable to grasp a predefined specific object. It is still difficult for robot hand system to realize human-like tactile sensation. Some common problems in robot hand system are low accuracy sensing device, sensors are not robust enough for long time work and heavy duties, inconsistence tactile sensing detection and difficulties in control of sensing fusion with robot trajectory. These problems are apparently drawback the progress to commercializing robot hands as real consumer products. Approach: This study presented the application of optical three-axis tactile sensor to robot hand to improve sensitization quality in robotic hand system. The proposed tactile sensor system was designed with compliance modules to communicate with robot hand control system. The sensing principle used in this tactile sensor comparatively provides better sensing accuracy to detect contact phenomena from acquisition of three axial directions of forces. Methodology of force and slippage detection in the tactile sensor system was presented. Accordingly, the optimization of robot hand control algorithm to comply with the tactile sensor system was presented and verified in experiment of grasping and twisting. Results: The tactile sensor presented in this study is capable of detecting normal and shear force simultaneously. The proposed methodology was verified in experiment with paper cup and water, in which the result shows the robot control system managed to respond to the proposed object stiffness distinction parameters and effectively respond to sudden change of object weight during grasping. An experiment of grasping and twisting motions was conducted using a bottle cap. In order to perform simultaneous grasping and twisting tasks, optimization of the control algorithm was conducted with additional parameters to satisfy the desired tasks. Conclusion: Experimental result shows that the robot hand managed to perform grasping and twisting of bottle cap smoothly. The overall results revealed good performance of the proposed optical three-axis tactile sensor system and robot hand control algorithm for future application in a real artificial robot hand. In addition, slippage sensation measured in a robot control system could contribute a better maneuvering of the robot arm-finger system.

Recent advances in rehabilitation robotics suggest that it may be possible for hand-amputated subjects to recover at least a significant part of the lost hand functionality. The control of robotic prosthetic hands using non-invasive techniques is still a challenge in real life: myoelectric prostheses give limited control capabilities, the control is often unnatural and must be learned through long training times. Meanwhile, scientific literature results are promising but they are still far from fulfilling real-life needs. This work aims to close this gap by allowing worldwide research groups to develop and test movement recognition and force control algorithms on a benchmark scientific database. The database is targeted at studying the relationship between surface electromyography, hand kinematics and hand forces, with the final goal of developing non-invasive, naturally controlled, robotic hand prostheses. The validation section verifies that the data are similar to data acquired in real-life conditions, and that recognition of different hand tasks by applying state-of-the-art signal features and machine-learning algorithms is possible.

For a several decades, myoelectric control of robotic prosthesis has used electromyograpy (EMG) as its control input to infer human intention. In this paper, we propose to use impedance change of musculoskeletal system to estimate kinematics change of human hand in prosthesis control based on its several features superior to the EMG as follows: clearer signal with much less noise so is less delay caused by filtering and little change of the signal at stationary state of hand motion. We investigated these features of electrical impedance myography (EIM) through several experiments. The result shows it is only minute change of signal occurs at the stationary pose. Although it is sensitive to the motion of other parts (e.g. elbow), it is surely a promising signal for the control of robotic prosthetic hand.

Dexterous robot hand is an important integrated product of bionics and robotics.In modern life, it has gradually become the focus and hot spot.This paper introduces various types of famous dexterous robot hand and its development.The intelligent computation of robot hand also improves to adapt the more complicated using environment.The paper focuses on the study of the theoretical calculation in grasping control.The theoretical characteristics of intelligent computation are analyzed.In the end, the conclusion of grasping control is summarized.

A remote actuation system was developed for the modular control of tendon-driven robot hands, such as the “BairClaw” introduced here. Motor-actuated tendons are routed over cantilever-based load cells for direct measurement of tendon tensions. Compression springs are used to maintain tendon tension and absorb unexpected loads. Tendons are routed to the robot hand through a low friction PTFE housing. The index finger of the BairClaw has four degrees-of-freedom (three flexion-extension, one adduction-abduction) and a Hall effect sensor-based joint angle measurement system. The BairClaw was designed around a biomimetic multimodal tactile sensor that can measure temperature, vibration, and skin deformation associated with forces and contact location. Importantly, the exoskeleton-like design of the BairClaw finger enables rich tactile sensing capabilities in concert with a functional distal interphalangeal joint. The testbed can be used for human-like haptic exploration and, when all BairClaw digits are complete, for sensory event-driven grasp and manipulation.