Shock Resistive Flexure-Based Anthropomorphic Hand with Enhanced Payload.

This paper introduces an anthropomorphic robot hand that has variable grasping stiffness to unknown objects without damage. The robot hand has four fingers, 13 DoFs (Degrees of Freedom) with three SEA (Series Elastic Actuator) modules in the index, middle, and ring finger. The performance for broader grasping stiffness using variable impedance control based on the parameter update block and the procedure of grasping capability evaluations of the robot hand are explained. The robot finger modules are constructed with a thumb, index, middle, and ring finger to complete the anthropomorphic type robot hand. The robot finger module has three DoFs and functions as one actuator based on an under-actuated mechanism. Additionally, external force measurement is possible in the fingertip force sensor and SEA (Series Elastic Actuator) module. For the variable impedance control implementation, a parameter update block is added to adjust the desired impedance parameter. To implement the parameter update block, we define and evaluate the correlation equation between the fingertip force, shore hardness, and damping ratio.

The paper describes work in progress at the University of Bologna concerning the design of a new anthropomorphic robot hand. The hand is based on the modular assembly of articulated fingers that adopt an original configuration of their structure, made with rigid links connected by elastic hinges that are coaxially crossed by flexible tendons. This innovative design is suitable to host distributed sensory equipment and continuous compliant cover, allowing a high level of anthropomorphism together with great structural simplification, reliability enhancement and cost reduction. Furthermore, the proposed solution is very flexible, as it can be adapted to many different hand configurations and is not dependent on a particular type of actuation, being compatible with future availability of any kind of artificial muscles.

The paper presented here approaches to solve the complexity of an anthropomorphic robotic hand. Robotics is now the most advancing field and there has always been an intention of mimicking human-like actuation and behavior. An anthropomorphic hand is one of the approaches to imitate human-like operations. In this paper, the idea of developing an anthropomorphic hand with 15 degrees of freedom and 5 actuators has been elaborated as well as the mechanical design, control system, composition, and peculiarities of the robotic hand have been discussed. The hand has an anthropomorphic appearance and also can perform human-like functionalities, for example, gripping and hand gestures representation. The results reveal that the hand is designed as one part and does not need any kind of assembly and it exhibits an excellent weight lifting capacity, since it is made of flexible thermoplastic polyurethane (TPU) material, and its elasticity also ensures that the hand is safe for interacting with humans as well. This hand may be used in a humanoid robot as well as a prosthetic hand. The limited number of actuators makes the control simpler and the hand lighter.

With demographic change driving the demand for robotic assistance in complex grasping tasks, robot hands are emerging as a promising solution. However, the growing interest in anthropomorphic robot hands is difficult to address: The complex and multifaceted mechanical design creates high entry hurdles for new designers. This slows the growth in the number of experts and complicates finding relevant information, even for experienced designers. This is particularly evident in the conceptual design phase, when determining joint configuration, their coordination, and relative allocation. Guidance in the form of a knowledge-based design assistance system could address these problems by automating the decision-making parts of the design process, presenting solutions with reference to literature and offering recommendations for the conceptual design. This dissertation addresses the question of how such a guidance can be developed. A hypotheses is formulated and three objectives are pursued to test it: (1) develop a literature-linked knowledge base on joint configuration and coordination of tendon-driven rigid-sequential anthropomorphic robot hands to systematically generate conceptual designs, (2) develop a method to identify optimal designs based on user requirements, and (3) develop an automated evaluation method to select the most suitable design from the generated options. For the first objective, a knowledge base is developed, organizing relevant information into a non-redundant list of 23 fields of interest and 30 principal solutions, each linked to its original references. Arranged in morphological boxes, the knowledge base is applied to a methodically derived joint configuration of the human hand to generate approximately 8.5 billion conceptual designs. For the second, a heuristic is developed to identify Pareto-optimal designs based on desired grasp types (performance) and a choice between dexterity and design simplicity (preference). Suitable joint configurations and coordination strategies, drawn from literature, are ranked according to the user’s preference. For the third, the developed control system evaluates designs using a utility analysis of the principal solutions, with scores adjusted by preference and relevance weights to align with the heuristic. The resulting guidance is implemented as a graphical user interface and verified to replicate the intended recommendations. An example application of the guidance presents, to the best of the author’s knowledge, the first task-based design of an anthropomorphic robot hand with references to original sources. By lowering entry barriers in early-stage anthropomorphic robot hand design, this dissertation lays a foundation for accelerating mechanical and control development, with potential to meet growing demand and reduce costs. It also presents the first framework for task-based robot hand design with meaningful results, despite gaps in the state of the art limiting a full analysis.

Anthropomorphic robotic hands have been widely investigated to dexterously manipulate objects because of their anatomical similarity to the human hand. However, the large dimension of configuration space challenges the real-time performance of existing grasp planning methods and drastically limits the application of anthropomorphic hands. In this letter, we propose a fast force-closure grasp synthesis (FFCGS) method for the anthropomorphic hand to efficiently grasp unknown objects. The FFCGS is implemented by using a signed distance field (SDF) as input. Firstly, a network that samples feasible 6D wrist poses is trained in an end-to-end fashion to reduce the dimension of search space. Furthermore, a fast optimization algorithm is presented to find finger configurations for force-closure precision grasp based on the differentiable Q-distance metric. We validate our method in both a simulated and a real-world environment. Experiment results show that the proposed FFCGS achieves a significantly improved performance in terms of time efficiency (5 times faster), grasp quality metrics, and success rate (5%-10% improvement) over benchmark methods. The outcomes of this study have great significance in promoting the motion planning of robot hand-arm systems and upper-limb prostheses.

We describe the design of the multi-finger anthropomorphic robotic hand for small-scale industrial applications, called AISRA (Anthropomorphic Interface for Stimulus Robust Applications), which can feel and sense the object that it is holding. The robotic hand was printed using the 3D printer and includes the servo bed for finger movement. The data for object recognition was collected using Leap Motion controller, and Naive Bayes classifier was used for training and classification. We have trained the robotic hand on several monotonous objects used in daily life using supervised machine learning techniques and the gesture data obtained from the Leap Motion controller. The mean accuracy of object recognition achieved is 92.1%. The Naive Bayes algorithm is suitable for using with the robotic hand to predict the shape objects in its hands based on the angular position of its figures. Leap Motion controller provides accurate results and helps to create a dataset of object examples in various forms for the AISRA robotic hand, and can be used to help developing and training 3D-printed anthropomorphic robotic hands. The experiments in object grasping experiments demonstrated that the AISRA robotic hand can grasp objects with different size and shape, and verified the feasibility of robot hand design using low-cost 3D printing technology. The implementation can be used for small-scale industrial applications.

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.

The human hand is a complex, highly-articulated system, which has been the source of inspiration in designing humanoid robotic and prosthetic hands. Understanding the functionality of the human hand is crucial for the design, efficient control and transfer of human versatility and dexterity to such anthropomorphic robotic hands. Although research in this area has made significant advances, the synthesis of grasp configurations, based on observed human grasping data, is still an unsolved and challenging task. In this work we derive a novel, constrained autoencoder model, that encodes human grasping data in a compact representation. This representation encodes both the grasp type in a three-dimensional latent space and the object size as an explicit parameter constraint allowing the direct synthesis of object-specific grasps. We train the model on 2250 grasps generated by 15 subjects using 35 diverse objects from the KIT and YCB object sets. In the evaluation we show that the synthesized grasp configurations are human-like and have a high probability of success under pose uncertainty.

The human palm is a remarkable and highly functional part of the hand that significantly contributes to dexterity, grasp versatility, and overall manipulation capability. The metacarpophalangeal joints (MCP) of the palm facilitate movement of the fingers for flexion, extension, abduction, adduction, and limited circumduction, which can be a challenge to replicate the same function in robotic hands by simple design. In this paper, we proposed a single actuated folding mechanism as the metacarpals and passive rotating MCP joints to perform abduction, adduction, and circumduction of the human hand. The proposed anthropomorphic hand called <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Folding Hand</b> which has a reconfigurable palm and five underactuated tendon-driven fingers. The design of the hand is compact and the price is low, with all six actuators and five sensors on the hand costing less than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\$}$</tex-math></inline-formula>180. Additionally, a methodology has been developed to comprehensively analyze the grasping capacity by combining the grasping quality and the grasping workspace. The experimental results show that the folding palm mechanism and the compliant rotating finger base can replicate human hand capabilities and performance precision and in-hand manipulation tasks.

We have presented the newly developed anthropomorphic robot hand named the KH Hand type S and its master slave system using the bilateral controller. The use of an elastic body has improved the robot hand in terms of weight, the backlash of the transmission, and friction between the gears. We have demonstrated the expression of the Japanese finger alphabet. We have also shown an experiment of a peg-in-hole task controlled by the bilateral controller. These results indicate that the KH Hand type S has a higher potential than previous robot hands in performing not only hand shape display tasks but also in grasping and manipulating objects in a manner like that of the human hand. In our future work, we are planning to study dexterous grasping and manipulation by the robot.

Dexterous anthropomorphic robotic hand has very important applications in service robot for cooperating with people in the daily living environment and using the tools specifically designed for people. In this paper, we designed a dexterous anthropomorphic robotic hand mimicking the muscle-tendon-ligament structure of the human hand. The robotic hand possesses the similar Degrees of Freedom (DOF) configuration of the human hand. Each DOF is driven with a pair of antagonistic pneumatic artificial muscles, and Bowden wires are carefully laid for mimicking the tendons of the human hand to facilitate the motion control of the robotic hand. Rubber bladders with force sensors are also employed as the skin of the palm and the finger pad to enable the force control and compliant manipulation of the robotic hand. As a result, the robotic hand can reproduce the manipulations of the human hand, and thus can be deployed on a robot to cooperate with people or on a homogeneous slave robot to improve its tele-operation performance.

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.

Abstract: Providing a robotic system with dexterous skills and autonomous capabilities is a key challenge in the field of humanoid robotics, particularly in areas such as industrial manufacturing, prosthetics, and orthopaedic rehabilitation. Providing such a system would be extremely useful in these areas. In order for its functionality to be fully realised, a multifingered robotic hand calls on a significantly higher level of actuation and transmission systems. Under actuation techniques provide the impression of being a workable solution for achieving high degrees of dexterity in robotic hands without the need for more complex mechanical design. One of the most defining characteristics of an under-actuated robotic hand is that the needed number of actuators to control the hand is fewer in number than the degree of freedom that the hand possesses. When compared to a fully actuated version of the same hand, an identical hand with under actuation offers a considerable reduction in the complexity of the control system and a large cost savings. The current study proposes the design and kinematic analysis of an anthropomorphic five-finger robotic hand. Four of the fingers and the thumb are under-actuated, and the hand has twenty-one degrees of freedom and twelve degrees of actuation.

Humanoid robots and robotic manipulators with good dexterity are promising to enhance the factory productivity in next generation. Dexterity of robotic manipulators can be achieved by equipping an anthropomorphic robot hand with multiple fingers. Additionally, in order to manipulate various tools that humans are using in daily life, the size and the exerting force of the robot hand should be similar to that humans are. In this paper, we proposed a human-sized multi-fingered robot hand with detachable mechanism at the wrist. The robot hand can be split at the wrist into the hand part and the actuator part. The fingers are driven by wires and are controlled by actuators embedded in the arm part. The driving force from the arm part is transmitted to the hand part by gear mechanism at the wrist. The developed robot hand has the size of 200[mm](length) × 78[mm](width) × 24.6[mm](thickness) and can exert 10[N] at the fingertip. The performance of the developed robot hand was shown by a motion control experiment.

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