On Adaptive Grasp with Underactuated Anthropomorphic Hands

To automatically adapt to the shape of different objects with enough grasping force is a challenge in the design of under- actuated anthropomorphic hands, because the grasped object is easily ejected from the hands during underactuated grasping process. The goal of this paper is to develop a design method of underactuated anthropomorphic hands to guarantee reliable adaption to different grasped objects. An analysis method is developed to investigate the evolution of motion and force in the whole underactuated grasping process. Based on the evolution of motion and force, the underactuated grasping process is decomposed into four aspects including initial contact state, grasp terminal state, grasp trajectory and rate of progress. More- over, the influence factors of such four aspects are found as the form of the combinations of underactuated mechanism pa- rameters. According to the four aspects of the underactuated grasping process, this paper presents a stepwise parameter design method through optimization of parameter combinations step-by-step. The reliable adaptive grasp for a wide scale of grasped object size is achieved. Experimental setups are constructed to corroborate the results from the theory analysis and design.

The anthropomorphic design and experiments of HIT/DLR five-fingered dexterous hand

This paper presents a new developed anthropomorphic robot dexterous hand： HIT/DLR Hand II. The hand is composed of an independent palm and five identical modular fingers, and each finger has three degree of freedom （DOFs） and four joints. All the actuators and electronics are integrated in the finger body and the palm. Owing to using a new actuator, drivers and a novel arrangement, both the length and width of the finger is about two third of its formner version. By using the wire coupling mecha- nism, the distal phalanx transmission ratio is kept exactly 1 ： 1 in the whole movement range. The packing mechanism which is implemented directly in the finger body and palm not only reduces the size of whole hand but also make it more anthropomorphic. Additionally, the new designed force/torque and position sensors are integrated in the hand for increasing muhisensory capability. To evaluate the performances of the finger mechanism, the position and impedance control experiments are conducted.

Anthropomorphic hand based on twisted-string-driven da Vinci’s mechanism for approaching human dexterity and power of grasp

Designing anthropomorphic prosthetic hands that approach human-level performance remains a great challenge.Commercial prosthetics are still inferior to human hands in several important properties, such as weight, size, fingertip force,grasp velocity, and active and passive dexterities. We present a novel design based on the under-actuated da Vinci’s mechanism driven by a flexible twisted string actuator(TSA). The distributed drive scheme allows structural optimization using a motion capture database to reproduce the natural movement of human hands while keeping adaptability to free-form objects. The application of TSA realizes a high conversion from motor torque to tendon contraction force while keeping the structure light,flexible, and compact. Our anthropomorphic prosthetic hand, consisting of six active and 15 passive degrees of freedom, has a weight of 280 g, approximately 70% of that of a human hand. It passed 30 of the 33 Feix grasp tests on objects in daily living and retained a loading capacity of 5 kg. This simple but intelligent mechanism leads to excellent stability and adaptability and renders feasible wide applications in prosthetics and in service robots.

HIT anthropomorphic robotic hand and finger motion control

Nowadays many anthropomorphic robotic hands have been put forward. These hands emphasize different aspects according to their applications. HIT Anthropomorphic Robotic Hand (ARhand) is a simple, lightweight and dexterous design per the requirements of anthropomorphic robots. Underactuated self-adaptive theory is adopted to decrease the number of motors and weight. The fingers of HIT ARhand with multi phalanges have the same size as those of an adult hand. Force control is realized with the position sensor, joint torque sensor and fingertip torque sensor. From the 3D model, the whole hand, with the low power consumption DSP control board integrated in it, will weigh only 500 g. It will be assembled on a BIT-Anthropomorphic Robot.

A Self-sensing TSA-actuated Anthropomorphic Robot Hand

This paper introduces a self-sensing anthropomorphic robot hand driven by Twisted String Actuators(TSAs).The use of TSAs provides several advantages such as muscle-like structures,high transmission ratios,large output forces,high efficiency,compactness,inherent compliance,and the ability to transmit power over distances.However,conventional sensors used in TSA-actuated robotic hands increase stiffness,mass,volume,and complexity,making feedback control challenging.To address this issue,a novel self-sensing approach is proposed using strain-sensing string based on Conductive Polymer Composite(CPC).By measuring the resistance changes in the strain-sensing string,the bending angle of the robot hand's fingers can be estimated,enabling closed-loop control without external sensors.The developed self-sensing anthropomorphic robot hand comprises a 3D-printed structure with five fingers,a palm,five self-sensing TSAs,and a 3D-printed forearm.Experimental studies validate the self-sensing properties of the TSA and the anthropomorphic robot hand.Additionally,a real-time Virtual Reality(VR)monitoring system is implemented for visualizing and monitoring the robot hand's movements using its self-sensing capabilities.This research contributes valuable insights and advancements to the field of intelligent prosthetics and robotic end grippers.

Design and Myoelectric Control of an Anthropomorphic Prosthetic Hand

This paper presents an anthropomorphic prosthetic hand using flexure hinges, which is controlled by the surface electromyography （sEMG） signals from 2 electrodes only. The prosthetic hand has compact structure with 5 fingers and 4 Degree of Freedoms （DoFs） driven by 4 independent actuators. Helical springs are used as elastic joints and the joints of each finger are coupled by tendons. The myoelectric control system which can classify 8 prehensile hand gestures is built. Pattern recognition is employed where Mean Absolute Value （MAV）, Variance （VAR）, the fourth-order Autoregressive （AR） coefficient and Sample Entropy （SE） are chosen as the optimal feature set and Linear Discriminant Analysis （LDA） is utilized to reduce the dimension. A decision of hand gestures is generated by LDA classifier after the current projected feature set and the previous one are ＂pre-smoothed＂, and then the final decision is obtained when the current decision and previous decisions are ＂post-smoothed＂ from the decisions flow. The prosthetic hand can perform prehensile postures for activities of daily living and carry objects under the control of EMG signals.