Configuration Design of an Under-Actuated Robotic Hand Based on Maximum Grasping Space

Capture is a key component for on?orbit service and space debris clean. The current research of capture on?orbit focuses on using special capture devices or full?actuated space arms to capture cooperative targets. However, the structures of current capture devices are complex, and both space debris and abandoned spacecraft are non?cooperative targets. To capture non?cooperative targets in space, a lightweight, less driven under?actuated robotic hand is proposed in this paper, which composed by tendon?pulley transmission and double?stage mechanisms, and always driven by only one motor in process of closing finger. Because of the expandability, general grasping model is constructed. The equivalent joint driving forces and general grasping force are analyzed based on the model and the principle of virtual work. Which reveal the relationship among tendon driving force, joint driving forces and grasping force. In order to configure the number of knuckles of finger, a new analysis method which takes the maximum grasping space into account, is proposed. Supposing the maximum grasped object is an envelope circle with diameter of 2.5m. In the condition, a finger grasping maximum envelope circle with different knuckles is modeled. And the finger lengths with corresponding knuckles are calculated out. The finger length which consists of three knuckles is the shortest among under?actuated fingers consists of not more than five knuckles. Finally, the principle prototype and prototype robotic hand which consists of two dingers are designed and assembled. Experiments indicate that the under?actuated robotic hand can satisfy the grasp requirements.

Slip Detection of Robotic Hand Based on Vibration Power of Pressure Center

This paper proposes an incipient slip detection method for a robotic hand based on the vibration power of the pressure center. Firstly,an array-type pressure sensor was planted into the soft skin of the robotic hand to measure the stick-slip vibration component of the pressure center generated in the process of slip of the grasped object. Secondly,the vibration power of the pressure center was calculated based on the measured stick-slip vibration component,and was used as a slip-detection function to judge the incipient slip of the grasped object. Finally,in order to use the same threshold value to judge incipient slip for different grasping forces,a weight coefficient was experimentally identified and used in the slip-detection function. The effectiveness of the proposed slip detection method was verified by experimental results,which showed that incipient slip can be detected by the proposed slip-detection function with the same threshold value for various materials,different slipping speeds grasping forces. In addition,multiple iterations of the experiment had demonstrated that the slip detection is repeatable.

FORCE OPTIMIZATION OF GRASPING BY ROBOTIC HANDS

It is important for robotic hands to obtain optimal grasping performance inthe meanwhile balancing external forces and maintaining grasp stability. The problem of forceoptimization of grasping is solved in the space of joint torques. A measure of grasping performanceis presented to protect joint actuators from working in heavy payloads. The joint torques arecalculated for the optimal performance under the frictional constraints and the physical limits ofmotor outputs. By formulating the grasping forces into the explicit function of joint torques, thefrictional constraints imposed on the grasping forces are transformed into the constraints on jointtorques. Without further simplification, the nonlinear frictional constraints can be simply handledin the process of optimization. Two numerical examples demonstrate the simplicity and effectivenessof the approach.

Machine Learning-Based Multi-Modal Information Perception for Soft Robotic Hands

This paper focuses on multi-modal Information Perception(IP)for Soft Robotic Hands(SRHs)using Machine Learning(ML)algorithms.A flexible Optical Fiber-based Curvature Sensor(OFCS)is fabricated,consisting of a Light-Emitting Diode(LED),photosensitive detector,and optical fiber.Bending the roughened optical fiber generates lower light intensity,which reflecting the curvature of the soft finger.Together with the curvature and pressure information,multi-modal IP is performed to improve the recognition accuracy.Recognitions of gesture,object shape,size,and weight are implemented with multiple ML approaches,including the Supervised Learning Algorithms(SLAs)of K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Logistic Regression(LR),and the unSupervised Learning Algorithm(un-SLA)of K-Means Clustering(KMC).Moreover,Optical Sensor Information(OSI),Pressure Sensor Information(PSI),and Double-Sensor Information(DSI)are adopted to compare the recognition accuracies.The experiment results demonstrate that the proposed sensors and recognition approaches are feasible and effective.The recognition accuracies obtained using the above ML algorithms and three modes of sensor information are higer than 85 percent for almost all combinations.Moreover,DSI is more accurate when compared to single modal sensor information and the KNN algorithm with a DSI outperforms the other combinations in recognition accuracy.

Synergistic control of soft robotic hands for human-like grasp postures

Although significant advances in the design of soft robotic hands have been made to mimic the structure of the human hands,there are great challenges to control them for coordinated and human-like postures.Based on the principle of postural synergies in the human hand,we present a synergistic approach for coordinated control of a soft robotic hand to replicate the human-like grasp postures.To this end,we firstly develop a kinematic model to describe the control variables and the various postures of the soft robotic hand.Based on the postural synergies,we use the developed model and Principal Component Analysis(PCA)method to describe the various postures of the soft robotic hand in a low-dimensional space formed by the synergies of actuator motions.Therefore,the coordinates of these synergies can be used as low-dimensional control inputs for the soft robotic hand with a higher-dimensional postural space.Finally,we establish an experimental platform on a customized soft robotic hand with6 pneumatical actuators to verify the effectiveness of the development.Experimental results demonstrate that with only a 2-dimensional control input,the soft robotic hand can reliably replicate 30 grasp postures in the Feix taxonomy of the human hand.

Hybrid Mapping Method:from Human to Robotic Hands with Dissimilar Kinematics

Mapping grasps from human to anthropomorphic robotic hands is an open issue in research,because the master hand and the slave hand have dissimilar kinematics.This paper proposes a hybrid mapping method to solve this problem.In the proposed method,fingers in the master and the slave hands are divided into vital and synergic fingers according to their contribution to the grasping task.The tip of the vital finger of the master hand is first mapped to that of the slave hand while ensuring that both are in simultaneous contact with the object to be grasped.Following postural synergy theory,joints of the other synergic fingers of the slave hand are then used to generate an anthropomorphic grasping configuration according to the shape of the object to be grasped.Following this,a human-guided impedance controller is used to reduce the pre-grasping error and realize compliant interaction with the environment.The proposed hybrid mapping method can not only generate the posture of the humanoid envelope but can also carry out impedance-adaptive matching.It was evaluated using simulations and an experiment involving an anthropomorphic robotic slave hand.

RLSESN-based PID adaptive control for a novel wearable rehabilitation robotic hand driven by PM-TS actuators

Purpose-The purpose of this paper is to develop a novel wearable rehabilitation robotic hand driven by Pneumatic Muscle-Torsion Spring(PM-TS)for finger therapy.PM has complex nonlinear dynamics,which makes PM modelling difficult.To realize high-accurate tracking for the robotic hand,an Echo State Network(ESN)-based PID adaptive controller is proposed,even though the plant model is unknown.Design/methodology/approach-To drive a single joint of rehabilitation robotic hand,the paper proposes a new PM-TS actuator comprising a Pneumatic Muscle(PM)and a Torsion Spring(TS).Based on the novel actuator,a wearable robotic hand is designed.By employing the model-free approximation capability of ESN,the RLSESN based PID adaptive controller is presented for improving the trajectory tracking performance of the rehabilitation robotic hand.An ESN together with Recursive Least Square(RLS)is called a RLSESN,where the ESN output weight matrix is updated by the online RLS learning algorithm.Findings–Practical experiments demonstrate the validity of the PM-TS actuator and indicate that the performance of the RLSESN based PID adaptive controller is better than that of the conventional PID controller.In addition,they also verify the effectiveness of the proposed rehabilitation robotic hand.Originality/value–A new PM-TS actuator configuration that uses a PM and a torsion spring for bi-directional movement of joint is presented.By utilizing the new PM-TS actuator,a novel wearable rehabilitation robotic hand for finger therapy is designed.Based on the unknown plant model,the RLSESN_PID controller is proposed to attain satisfactory performance.

Development of Gesture-Changeable under-actuated Humanoid Robotic Finger

Robotic fingers, which are the key parts of robot hand, are divided into two main kinds： dexterous fingers and under-actuated fingers. Although dexterous fingers are agile, they are too expensive. Under-actuated fingers can grasp objects self-adaptively, which makes them easy to control and low cost, on the contrary, under-actuated function makes fingers feel hard to grasp things agilely enough and make many gestures. For the purpose of designing a new finger which can grasp things dexterously, perform many gestures and feel easy to control and maintain, a concept called ＂gesture-changeable under-actuated＂ （GCUA） function is put forward. The GCUA function combines the advantages of dexterous fingers and under-actuated fingers： a pre-bending function is embedded into the under-actuated finger. The GCUA finger can not only perform self-adaptive grasping function, but also actively bend the middle joint of the finger. On the basis of the concept, a GCUA finger with 2 joints is designed, which is realized by the coordination of screw-nut transmission mechanism, flexible drawstring constraint and pulley-belt under-actuated mechanism. Principle analyses of its grasping and the design optimization of the GCUA finger are given. An important problem of how to stably grasp an object which is easy to glide is discussed. The force analysis on gliding object in grasping process is introduced in detail. A GCUA finger with 3 joints is developed. Many experiments of grasping different objects by of the finger were carried out. The experimental results show that the GCUA finger can effectively realize functions of pre-bending and self-adaptive grasping, the grasping processes are stable. The GCUA finger excels under-actuated fingers in dexterity and gesture actions and it is easier to control and cheaper than dexterous hands, becomes the third kinds of finger.

DESIGN OF A NOVEL DEXTEROUS ROBOT HAND

In the robotic community more and more hands are developed. Based on theexperience of HIT Hand and DLR Hand II, a smaller and easier manufactured dexterous robot hand withmultisen-sory function and high integration is jointly developed. The prototype of the hand issuccessfully built. It has 4 fingers in total 13-DOFs (degree of freedom). Each finger has 3-DOFsand 4 joints, the last 2 joints are mechanically coupled by means of four-bar linkage mechanism. Italso has an additional DOF to realize motion of the thumb relative to the palm. The fingertip forcecan reach up to 10 N. Full integration of mechanical body, actuation system, multisensory system andelectronics is a significant feature. DSP based control system is implemented in PCI busarchitecture and the serial communication between the hand and DSP needs only 2 lines.

Intelligent Control of Multi-fingered Robot Hand

The dynamics properties of a kind of multi-fingered robot hand is analyzed. It is pointed out that the dynamics property of this kind of multifingered robot hand in the approaching process is quite different from that in the grasping process and,different control algorithm should be taken in the two process. A position-force hybrid control algorithm is proposed which is applied to the control system of the University of Science and Technology Beijing double-thumb robot hand successfully.

On set-point tracking control of dexterous robot hand

A model-free set-point tracking control approach of multi-fingered robot hand is presented.The set-point tracking controller,which has the structural form of PD controller,is composed with a combination of feedforward term,feedback term and saturation control term.The controller does not require the explicit use of dynamic modeling parameters.Experiments performed on the HIT/DLR hand demonstrate the effectiveness of the proposed approach in performance improvement and real-time application.

METHOD OF CLASSIFYING GRASPS BY ROBOT HANDS

This research characterizes grasping by multifingered robot hands through investiga- tion of the space of contact forces into four subspaces , a method is developed to determine the di- mensions of the subspaces with respect to the connectivity of the object. The relationship reveals the differences between three types of grasps classified and indicates how the contact force can be decomposed corresponding to each type of grasp. The subspaces and the determination of their di- mensions are illlustrated by examples.

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.

Variations in the Biomechanics of 16 Palmar Hand Regions Related to Tomato Picking

The aim of this study is to systematically reveal the differences in the biomechanics of 16 hand regions related to bionic picking of tomatoes.The biomechanical properties(peak loading force,elastic coefficient,maximum percentage deformation and interaction contact mechanics between human hand and tomato fruit)of each hand region were experimentally measured and covariance analyzed.The results revealed that there were significant variations in the assessed biomechanical properties between the 16 hand regions(p<0.05).The maximum pain force threshold(peak loading force in I2 region)was 5.11 times higher than the minimum pain force threshold(in Th1 region).It was found that each hand region in its normal direction can elastically deform by at least 15.30%.The elastic coefficient of the 16 hand regions ranged from 0.22 to 2.29 N mm−1.The interaction contact force acting on the fruit surface was affected by the selected human factors and fruit features.The obtained covariance models can quantitatively predict all of the above biomechanical properties of 16 hand regions.The findings were closely related to hand grasping performance during tomato picking,such as soft contact,surface interaction,stable and dexterous grasping,provided a foundation for developing a high-performance tomato-picking bionic robotic hand.

Human-machine shared control:New avenue to dexterous prosthetic hand manipulation

At present,albeit the dexterous hand prostheses of multiple degrees of freedom(DOFs)have become prosperous on the market,the user’s demand on intuitively operating these devices have not been well addressed so that their acceptance rate is relatively low.The unintuitive control method and inadequate sensory feedback are frequently cited as the two barriers to the successful application of these dexterous products.Recently,driven by the wave of artificial intelligence(AI),a series of shared control methods have emerged,in which"bodily function"(myoelectric control)and"artificial intelligence"(local autonomy,computer vision,etc.)are tightly integrated,and provided a new conceptual solution for the intuitive operation of dexterous prostheses.In this paper,the background and development trends of this type of methods are described in detail,and the potential development directions and the key technologies that need breakthroughs are indicated.In practice,we instantiate this shared control strategy by proposing a new method combining simultaneous myoelectric control,multi-finger grasp autonomy,and augmented reality(AR)feedback together.This method"divides"the human sophisticated reach-and-grasp task into several subtasks,and then"conquers"them by using different strategies from either human or machine perspective.It is highly expected that the shared control methods with hybrid human-machine intelligence could address the control problem of dexterous prostheses.

Flexible Robotic Grasping Strategy with Constrained Region in Environment

Grasping is a significant yet challenging task for the robots. In this paper, the grasping problem for a class of dexterous robotic hands is investigated based on the novel concept of constrained region in environment, which is inspired by the grasping operations of the human beings. More precisely, constrained region in environment is formed by the environment, which integrates a bio-inspired co-sensing framework. By utilizing the concept of constrained region in environment, the grasping by robots can be effectively accomplished with relatively low-precision sensors. For the grasping of dexterous robotic hands, the attractive region in environment is first established by model primitives in the configuration space to generate offline grasping planning. Then, online dynamic adjustment is implemented by integrating the visual sensory and force sensory information, such that the uncertainty can be further eliminated and certain compliance can be obtained. In the end, an experimental example of BarrettHand is provided to show the effectiveness of our proposed grasping strategy based on constrained region in environment.

A 3D-printed Robot Hand with Three Linkage-driven Underactuated Fingers

In this study, we improved an underactuated finger mechanism by using Solidworks to simulate the grasp operation of a finger in some different situations. In addition, a robot palm is designed for the three-finger robot hand with the designed underactuated fingers. A Solidworks simulation was used to verify the rationality of the design. Some parts of the hand were modified to fit for 3D printing, and a prototype of the hand was produced by 3D printing, which could reduce the cost of the production process, as well as provide design flexibility and other advantages. Finally, some grasping experiments were made with the prototype. The results showed that the robot could grasp objects with different sizes, and further verified the rationality of the design and feasibility of fabricating the robot hand using 3D printing.