Kinematic design optimization for anthropomorphic robot hand based on interactivity of fingers

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.

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.

In this study, a Crossed Flexural Hinge (CFH) structure was used for the design of a humanoid robot hand that can absorb any abrupt external force and that has a large payload, giving it the advantages of both rigid and compliant robots. Structural problems were identified through a 6 × 6 stiffness matrix to analyze whether CFH is suitable for use as an anthropomorphic robot hand. To reinforce the weak stiffness, a paired CFH (p-CFH) structure was proposed for the robot hand joints. In addition, it was verified through theoretical and experimental methods that p-CFH has superior stiffness characteristics compared to conventional CFH. When designing the anthropomorphic robot hand, p-CFH was appropriately deformed and applied. Using an underactuated wire mechanism suitable for the structure of the robot hand, it was possible to grasp objects of various shapes in a shape-adaptive manner. It was confirmed that the final anthropomorphic robot hand was able to stably hold an object of an unspecified shape without precisely controlling the motor. And the robot hand can also hold a heavy object due to the increased rigidity of the p-CFH. In addition, by conducting the qualitative impact test in which the robot was subjected to an impact in an arbitrary direction, it was confirmed that the robot, due to compliance of the joints, can absorb impact without incurring damage. Finally, a quantitative impact test was conducted in all directions, and the shock absorbing capability of anthropomorphic robot hand was verified through numerical comparison with the control model.

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.

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.

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.

In this work, we report on our progress in the development of a new anthropomorphic robot hand at the University of Malta. Following a brief overview of the work carried out in our laboratory so far, we discuss in some detail and through a fresh perspective a number of important lessons that can be learned from the human hand, and that can be very useful in the development of an artificial hand that can ultimately match the human counterpart in the execution of many tasks. We present a new design for a robot hand with joint position and grasping force sensing, based on the specific approaches brought up in this discussion, and targeted to reproduce many of the capabilities of the human hand. In particular, all of the actuation and sensing devices of the hand are located remotely from the device, therefore facilitating the development of a compact and lightweight hand design. A prototype of the new hand has been constructed and is presented herein. Finally, we present the initial results of our analysis to demonstrate how the magnitude and location of grasping forces can be inferred from the sensor readings.

We introduce MetaArms, wearable anthropomorphic robotic arms and hands with six degrees of freedom operated by the user's legs and feet. Our overall research goal is to re-imagine what our bodies can do with the aid of wearable robotics using a body-remapping approach. To this end, we present an initial exploratory case study. MetaArms' two robotic arms are controlled by the user's feet motion, and the robotic hands can grip objects according to the user's toes bending. Haptic feedback is also presented on the user's feet that correlate with the touched objects on the robotic hands, creating a closed-loop system. We present formal and informal evaluations of the system, the former using a 2D pointing task according to Fitts' Law. The overall throughput for 12 users of the system is reported as 1.01 bits/s (std 0.39). We also present informal feedback from over 230 users. We find that MetaArms demonstrate the feasibility of body-remapping approach in designing robotic limbs that may help us re-imagine what the human body could do.

Introduction We can easily manipulate a variety of objects with our hands. When exploring an object, we gather rich sensory information through both haptics and vision. The haptic and visual information obtained through such exploration is, in turn, key for realizing dexterous manipulation. Reproducing such codevelopment of sensing and adaptive/dexterous manipulation by a robotic hand is one of the ultimate goals of robotics, and further, it would be essential for understanding human object recognition and manipulation. Although many robotic hands have been developed, their performance is by far inferior to that of human hands. One reason for this performance difference may be due to differences in grasping strategies. Historically, research on robotic hands has mainly focused on pinching manipulation (e.g. Nagai and Yoshikawa, 1993) because the analysis was easy with point-contact conditions. Based on the analysis, roboticists applied control schemes using force/touch sensors at the fingertips (Kaneko et al ., 2007; Liu et al ., 2008). Since the contact points are restricted to the fingertips, it is easy for the robot to calculate how it grasps an object (e.g. a holding polygon) and how large it should exert force based on friction analysis. However, the resultant grasping is very brittle since a slip of just one of the contacting fingertips may lead to dropping the object.

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.

Anthropomorphic robot hand has been an interest of researchers to investigate principle and to realize dexterous manipulation. However, designing such a hand had been a challenge due to their large degree of freedom and limited payload. Those challenges result in lack of actuator power, lack of durability, and poor maintainability. To overcome such issues of anthropomorphic robot hand, we proposed novel mechanism of robot hand with detachable passive wire mechanism and hydrostatic actuator cluster. In this paper, basic concept of the proposed hand, hydrostatic actuator and hand development, and basic experiments with the developed hand is explained.

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.

Design robust controller for uncertain nonlinear systems most of time can be a challenging work.One of the most active research areas in this field is control of the robot arm.The control strategies for robotics are classified in two main groups: classical and non-classical methods, where the classical methods use the conventional control theory and nonclassical methods use the artificial intelligence theory such as fuzzy logic, neural networks and/or neuro-fuzzy.Control robot arm using classical controllers are often having lots of problems because robotic systems are always highly nonlinear.Accurate robot manipulator is difficult because some dynamic parameters such as compliance and friction are not well understood and some robot parameters such as inertia are difficult to measure accurately.Artificial control such as Fuzzy logic, neural network, genetic algorithm, and neurofuzzy control have been applied in many applications.Therefore, stable control of a nonlinear dynamic system such as a robot arm is challenging because of some mentioned issues.In this paper the intelligent control of robot arm using Adaptive Fuzzy Gain scheduling (AFGS) and comparison to fuzzy logic controller (FLC) and various performance indices like the RMS error, and Steady state error are used for test the controller performance.

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.