Robot Hand System Research Articles

A Skin-Inspired High-Performance Tactile Sensor for Accurate Recognition of Object Softness.

High-performance tactile sensors with skin-sensing properties are crucial for intelligent perception in next-generation smart devices. However, previous studies have mainly focused on the sensitivity and response range of tactile sensation while neglecting the ability to recognize object softness. Therefore, achieving a precise perception of the softness remains a challenge. Here, we report an integrated tactile sensor consisting of a central hole gradient structure pressure sensor and a planar structure strain sensor. The recognition of softness and tactile perception is achieved through the synergistic effect of pressure sensors that sense the applied pressure and strain sensors that recognize the strain of the target object. The results indicate that the softness evaluation parameter (SC) of the integrated structural tactile sensor increases from 0.14 to 0.47 along with Young's modulus of the object decreasing from 2.74 to 0.45 MPa, demonstrating accurate softness recognition. It also exhibits a high sensitivity of 10.55 kPa-1 and an ultrawide linear range of 0-1000 kPa, showing an excellent tactile sensing capability. Further, an intelligent robotic hand system based on integrated structural tactile sensors was developed, which can identify the softness of soft foam and glass and grasp them accurately, indicating human skin-like sensing and grasping capabilities.

Task-Oriented Training by a Personalized Electromyography-Driven Soft Robotic Hand in Chronic Stroke: A Randomized Controlled Trial

Background Intensive task-oriented training has shown promise in enhancing distal motor function among patients with chronic stroke. A personalized electromyography (EMG)-driven soft robotic hand was developed to assist task-oriented object-manipulation training effectively. Objective. To compare the effectiveness of task-oriented training using the EMG-driven soft robotic hand. Methods A single-blinded, randomized controlled trial was conducted with 34 chronic stroke survivors. The subjects were randomly assigned to the Hand Task (HT) group (n = 17) or the control (CON) group (n = 17). The HT group received 45 minutes of task-oriented training by manipulating small objects with the robotic hand for 20 sessions, while the CON group received 45 minutes of hand-functional exercises without objects using the same robot. Fugl-Meyer assessment (FMA-UE), Action Research Arm Test (ARAT), Modified Ashworth Score (MAS), Box and Block test (BBT), Maximum Grip Strength, and active range of motion (AROM) of fingers were assessed at baseline, after intervention, and 3 months follow-up. The muscle co-contraction index (CI) was analyzed to evaluate the session-by-session variation of upper limb EMG patterns. Results The HT group showed more significant improvement in FMA-UE (wrist/hand, shoulder/elbow) compared to the CON group (P < .05). At 3-month follow-up, the HT group demonstrated significant improvements in FMA-UE, ARAT, BBT, MAS (finger), and AROMs (P < .05). The HT group exhibited a more significant decrease in muscle co-contractions compared to the CON group (P < .05). Conclusions EMG-driven task-oriented training with the personalized soft robotic hand was a practical approach to improving motor function and muscle coordination. Clinical Trial Registry Name: Soft Robotic Hand System for Stroke Rehabilitation. Clinical Trial Registration-URL: https://clinicaltrials.gov/. Unique Identifier: NCT03286309.

Ion trap and release dynamics enables nonintrusive tactile augmentation in monolithic sensory neuron.

An iontronic-based artificial tactile nerve is a promising technology for emulating the tactile recognition and learning of human skin with low power consumption. However, its weak tactile memory and complex integration structure remain challenging. We present an ion trap and release dynamics (iTRD)-driven, neuro-inspired monolithic artificial tactile neuron (NeuroMAT) that can achieve tactile perception and memory consolidation in a single device. Through the tactile-driven release of ions initially trapped within iTRD-iongel, NeuroMAT only generates nonintrusive synaptic memory signals when mechanical stress is applied under voltage stimulation. The induced tactile memory is augmented by auxiliary voltage pulses independent of tactile sensing signals. We integrate NeuroMAT with an anthropomorphic robotic hand system to imitate memory-based human motion; the robust tactile memory of NeuroMAT enables the hand to consistently perform reliable gripping motion.

The MSC Prosthetic Hand: Rapid, Powerful, and Intuitive

Amputees suffer from the weight, insufficient power, and uncomfortable control methods of their prostheses. Recent studies have introduced many ideas for both hardware and software to tackle these problems. In this letter, the authors introduce a preliminary platform of a robotic prosthetic hand system called the MSC hand that integrates effective mechanical mechanisms and intuitive control methods. The hand adopts mode-switchable twisted string actuators to provide a wide range of grasping speed (closing speed of 0.8 s) and grasping force (pinch force of 45 N) with a light weight of 390 g. All the fingers and the thumb flex and extend actively, and the thumb is also able to abduct and adduct passively. The active fingers are controlled by surface electromyographic signals, and a learning-based neurophysiologic model is used to estimate human intention for each finger. The model provides independent intentions for each finger, so the simultaneous and proportional control of multiple fingers is possible in real time. The performance of the MSC hand was verified through standardized experiments such as online simulation and the box and block test. In addition, a demonstration of gripping various objects was performed. The results showed rapid and precise gripping and intuitive control over the tasks.

Bayesian optimization for improving the cycle time of soft robotic hand system

Bayesian optimization for improving the cycle time of soft robotic hand system

Systematic object-invariant in-hand manipulation via reconfigurable underactuation: Introducing the RUTH gripper

We introduce a reconfigurable underactuated robot hand able to perform systematic prehensile in-hand manipulations regardless of object size or shape. The hand utilizes a two-degree-of-freedom five-bar linkage as the palm of the gripper, with three three-phalanx underactuated fingers, jointly controlled by a single actuator, connected to the mobile revolute joints of the palm. Three actuators are used in the robot hand system in total, one for controlling the force exerted on objects by the fingers through an underactuated tendon system, and two for changing the configuration of the palm and, thus, the positioning of the fingers. This novel layout allows decoupling grasping and manipulation, facilitating the planning and execution of in-hand manipulation operations. The reconfigurable palm provides the hand with a large grasping versatility, and allows easy computation of a map between task space and joint space for manipulation based on distance-based linkage kinematics. The motion of objects of different sizes and shapes from one pose to another is then straightforward and systematic, provided the objects are kept grasped. This is guaranteed independently and passively by the underactuated fingers using a custom tendon routing method, which allows no tendon length variation when the relative finger base positions change with palm reconfigurations. We analyze the theoretical grasping workspace and grasping and manipulation capability of the hand, present algorithms for computing the manipulation map and in-hand manipulation planning, and evaluate all these experimentally. Numerical and empirical results of several manipulation trajectories with objects of different size and shape clearly demonstrate the viability of the proposed concept.

Model-Free Tracking Control with Prescribed Performance for a Shape Memory Alloy-Based Robotic Hand

The shape memory alloy (SMA)-based robotic hand has been a new emerging technology with potential applications ranging from life service to surgical treatment, because of the characteristics of SMA, such as high power-to-weight ratio, small volume and low driving voltage. However, due to the complex dynamic model and nonlinear aspects of SMA, it is complicated to control an SMA-based robotic hand. This paper presents a novel model free adaptive control for the SMA-based robotic hand system. By applying the Taylor series expansion method and the differential mean value theorem, the SMA based robotic hand system can be transformed into an equivalent linearization model, which merely depends on measurement data without any information on the system. Combined with prescribed performance control, the novel control method can constrain the tracking error in a preassigned domain. Experiments are conducted on the SMA-based robotic hand system to verify the performance of the presented control method.

Proximity-based non-contact perception and omnidirectional point-cloud generation based on hierarchical information on fingertip proximity sensors

In this study, we examine robotic proximity perception for both reactive control and 3D point-cloud acquisition using a multi-fingered robot hand. A reactive controller adjusts the relative position and orientation between the fingertips and the target object, while the robot-hand system gathers point-cloud data related to the shape of the object. An experimental result showed that the accuracy of the point-cloud generated using proximity perception was equivalent to or better than that of the point-cloud generated using a vision sensor. Based on the results, we propose proximity-based point-cloud acquisition using ‘non-contact perception’ motion to search the object's surface while preventing unintended collisions. The motion is then combined with the approaching and twisting motions of the robot hand. This enables multi-angles object detection complementarily with reactive fingertip control. Our experiments reveal that the proposed method can generate data-rich point-clouds for various general objects.

Development of a High-Speed, Low-Latency Telemanipulated Robot Hand System

This paper focuses on development of a high-speed, low-latency telemanipulated robot hand system, evaluation of the system, and demonstration of the system. The characteristics of the developed system are the followings: non-contact, high-speed 3D visual sensing of the human hand, intuitive motion mapping between human hands and robot hands, and low-latency, fast responsiveness to human hand motion. Such a high-speed, low-latency telemanipulated robot hand system can be considered to be more effective from the viewpoint of usability. The developed system consists of a high-speed vision system, a high-speed robot hand, and a real-time controller. For the developed system, we propose new methods of 3D sensing, mapping between the human hand and the robot hand, and the robot hand control. We evaluated the performance (latency and responsiveness) of the developed system. As a result, the latency of the developed system is so small that humans cannot recognize the latency. In addition, we conducted experiments of opening/closing motion, object grasping, and moving object grasping as demonstrations. Finally, we confirmed the validity and effectiveness of the developed system and proposed method.

Using Miniaturized Strain Sensors to Provide a Sense of Touch in a Humanoid Robotic Arm

Recent advancements in sensing technology have led to developments of lighter and smaller control systems for prosthetic and biomedical applications. In this paper, we develop a bio-inspired sensory system for a master-slave force-sensing robotic hand which allow accurate control and provide a natural sense of touch to humanoid robotic hand based on force information derived from a smart glove equipped. The slave robotic hand is fabricated using three-dimensional (3D) printing technology, with servo motors to actuate the hand components. A glove with miniaturised flexible sensors attached serves as the master robotic hand, providing movement and force signals for the slave to emulate. The signals from the force sensors are used to moderate the movement of the slave hand’s fingers, so allowing delicate objects to be handled without the risk of breakage. We show that this is a practical and versatile method to improve robotic handling, and that with careful selection and tuning, it is possible to track the master hand’s applied force to within 0.1 Newtons. The success of this approach will pave the way for the development of novel control systems using low-cost bio-inspired strain and force sensors for prosthetics applications.

An open-source anthropomorphic robot hand system: HRI hand

We present an open-source anthropomorphic robot hand system called HRI hand. Our robot hand system was developed with a focus on the end-effector role of the collaborative robot manipulator. HRI hand is a research platform that can be built at a lower price (approximately $500, using only 3D printing) than commercial end-effectors. Moreover, it was designed as a two four-bar linkage for the under-actuated mechanism and provides pre-shaping motion similar to the human hand prior to touching an object. A URDF, python node, and rviz package is also provided to support the Robot Operating System (ROS). All hardware CAD design files and software source codes have been released and can be easily assembled and modified. The system proposed in this paper is developed with a five-finger structure, but each finger is modularized, so it can be developed with end-effectors of various shapes depending on the shape of the palm.

Robotic hand system design for mirror therapy rehabilitation after stroke

This paper developed a robotics-assisted device for the stroke patients to perform the hand rehabilitation. Not only the system can perform passive range of motion exercises for impaired hand, but also can perform mirror therapy for pinching and hand grasping motions under the guidance of the posture sensing glove worn on patient’s functional hand. Moreover, the framework and operation flow of the developed system has been and delineated in this paper. Practical results with human subjects are shown in this paper to examine the usability of proposed system, trial experiment of advance mirror therapy that use the proposed system to interact with realities is also presented in this paper.

Development of Wireless Controlling and Monitoring System for Robotic Hand Using Zigbee Protocol

Nowadays, the robotic arm is fast becoming the most popular robotic form used in the industry among others. Therefore, the issues regarding remote monitoring and controlling system are very important, which measures different environmental parameters at a distance away from the room and sets various condition for a desired environment through a wireless communication system operated from a central room. Thus, it is crucial to create a programming system which can control the movement of each part of the industrial robot in order to ensure it functions properly. EDARM ED-7100 is one of the simplest models of the robotic arm, which has a manual controller to control the movement of the robotic arm. In order to improve this control system, a new controller system was redesigned in this work by using Zigbee. It is a communication protocol for safety and economic data communication in an industrial field, where the wired communication is either expensive or difficult under physical and experimental conditions, such as the worker cannot recognize the error through the manufacturing process. Hence, this paper introduced a system that used microcontroller (AT89S52) with wireless devices (Zigbee) and sensors to control the robotic hand (EDARM ED-7100) and to monitor the information regarding the robot's parameter using WiFi technology. A mathematical model was derived through an empirical method to specify the robot's configuration changes. In this work, the ability of controlling system had increased, as well as hardware, while the necessities of other similar equipment for data communication were minimized. In addition, it presents the comparison of two controlling systems: using the Zigbee and without using it. Based from the experiment it can be safely concluded that the robotic arm's movement had followed a linear function.

Safe grasping system for robotic hand with fingers

Safe grasping system for robotic hand with fingers

Planning of Knotting Based on Manipulation Skills with Consideration of Robot Mechanism/Motion and Its Realization by a Robot Hand System

This paper demonstrates the relationship between the production process of a knot and manipulation skills. First, we define the description (rope intersections, grasp type and fixation positions) of a knot. Second, we clarify the characteristics of the manipulation skills from the viewpoint of the knot description. Next, in order to obtain the production process of the knot, we propose an analysis method based on the structure of the knot and the characteristics of the manipulation skills. Using the proposed analysis method, we analyzed eight kinds of knots, formed with a single rope, two ropes or a single rope and an object. Finally, in order to validate the production process obtained by the proposed analysis method, we show experimental results of an overhand knot and a half hitch produced by using a robot hand system.

Real-Time Control of an Exoskeleton Hand Robot with Myoelectric Pattern Recognition.

Robot-assisted training provides an effective approach to neurological injury rehabilitation. To meet the challenge of hand rehabilitation after neurological injuries, this study presents an advanced myoelectric pattern recognition scheme for real-time intention-driven control of a hand exoskeleton. The developed scheme detects and recognizes user's intention of six different hand motions using four channels of surface electromyography (EMG) signals acquired from the forearm and hand muscles, and then drives the exoskeleton to assist the user accomplish the intended motion. The system was tested with eight neurologically intact subjects and two individuals with spinal cord injury (SCI). The overall control accuracy was [Formula: see text] for the neurologically intact subjects and [Formula: see text] for the SCI subjects. The total lag of the system was approximately 250[Formula: see text]ms including data acquisition, transmission and processing. One SCI subject also participated in training sessions in his second and third visits. Both the control accuracy and efficiency tended to improve. These results show great potential for applying the advanced myoelectric pattern recognition control of the wearable robotic hand system toward improving hand function after neurological injuries.

Adaptive tactile control for in-hand manipulation tasks of deformable objects

Tactile sensors are key components for a robot hand system, which are usually used to obtain the object’s features. The use of tactile sensors to obtain information from the objects is an open topic of research. In this paper, a new strategy for in-hand extraction of object’s properties and for control of the interaction forces with robot fingers, mainly based on tactile data, is presented. The scope of this strategy is to grasp and manipulate solid objects, including rigid and soft bodies. Assuming that the hand is in an initial configuration in which the object is grasped, the properties’ extraction approach is executed. After the extraction of properties is finished, the object can be classified in regard to a general body listing: rigid body, soft elastic body, or soft plastic object. Once the object is classified, for in-hand manipulation tasks, the contact points between the object grasped and the fingers are maintained using the information given by the tactile sensors in order to perform manipulation tasks. Each task is defined by a sequence of basic actions, in which the contact points and applied forces are adapted depending on the action to be performed and the estimated features for the object. The presented approach tries to imitate the behavior of human beings, in which the applied forces by the fingers are changed when the human estimates the rigidity of a body and when the fingers react to unexpected movements of the object to keep the contact points.

Multi-finger myoelectric signals for controlling a virtual robotic prosthetic hand

The paper investigates how multi-finger myoelectric signals could be used to control a virtual robotic prosthetic hand created by using robot operating system (ROS). Both off-line and online experiment phases are conducted by using ten electrodes and performing eight selected multi-finger motions on four healthy subjects. Classification accuracy and confusion matrix of eight time domain (TD)-features and two algorithms are compared during the off-line phase. Then the delay time and accuracy of online control of six selected TD-features and support vector machine (SVM) algorithm are presented with and without visual feedback from the virtual robotic prosthetic hand system. The experimental results show that different feature extraction principles have significant influence on online experiment performance when using SVM without visual feedback (SVMO), and the SVM with visual feedback (SVMW) has improved the online classification and recognition accuracy of eight multi-finger motions through all selected TD features.

POSITIONING CONTROL PERFORMANCES OF A ROBOTIC HAND SYSTEM

Hazardous environments such as in industry sector with high chemical usage give high risks to the safety of workers. These risks can be reduced by designing robotic hand that is able to replace human works. For the industry purpose, the robotic hand needs to have a higher performance in accuracy, stability and consistency. However, the current robotic hand in industry is not flexible, which means it cannot be used for different tasks. Therefore, a multi-purpose robotic hand was developed. In this paper, the objectives of this research are to design and develop a PID controller for improving the performances of a robotic hand system. The experimental results prove that the PID controller shows good performances with the steady state error less than 0.110 for the input reference, 300 respectively.

多指ロボットハンドによる折り紙動作のためのスプライン関数を用いた軌道生成

One of recent important topics of dexterous manipulation is to manipulate flexible objects such as rope, cloth, or paper. We have also developed a dexterous robot hand system that can achieve Origami tasks. In this study, its trajectory generation method is improved so as to speed up the operation. The trajectory is generated with spline functions and its coefficient parameters are determined using a constrained nonlinear optimization. The optimization is executed with considering the torques of joints and the collision between the robot hand and obstacles. As a result, the time of operation has been shortened to 43% of the time in the previous method.