Innovative Technologies for the Next Generation of Robotic Hands

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 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.

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.

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.

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.

Advanced robots are desired to improve their reasoning ability in increasingly practical situations to quickly respond to urgent challenges such as search and rescue, public security, and autonomous driving. Advances in sensor technology and intelligent algorithms are accelerating the development of autonomous systems, allowing them to interact with dynamic environments more independently and intelligently, completing relatively complicated tasks. Nevertheless, the safety and security problems are yet to be comprehensively addressed. Currently, many efforts have been made to solve this problem by improving the unimodal perception ability of intelligent systems. Methods using surveillance cameras to collect large amounts of visual data and process them using deep networks. Various types of range scanner can sense and reconstruct the surrounding environment through precise distance measurements. However, in the above cases, there may be a lack of training data, fixed cameras cannot adequately and dynamically model the surroundings, unimodal analysis is hard to capture both appearance and depth information. Therefore, higher levels of perception and reasoning abilities are required to handle unexpected conditions and scenarios efficiently. In such cases, mobile robots should be fully aware of the dynamic environment, prioritize, and continuously analyze critical situations, share information, and collaboration under limited computing power and resources. Furthermore, considering the robots are operating alongside humans, reliable and interpretable robot behavior is highly preferred for more natural interactions.&#13;\n&#13;\nThis study aims to develop reasoning strategies for autonomous robots to improve robotic intelligence under different assumptions and conditions. The framework allows one or several robots to address safety or security problems in a targeted manner in partially known environments, where time, dynamics, and interactions play a significant role. The objective is to identify the unique individual whose activity, in space and time, appear different from the other people in the scene. The activities considered not only the motion pattern, but also the object associated with this person. Unlike traditional abnormal detection, the uniqueness reasoning in this work does not require a predefined expected pattern. A modular analysis is also applied to decompose the entire reasoning process into submodules such as perception, analysis, and inference, to achieve algorithmic flexibility and interpretability. Probabilistic modeling is used to associate spatial relationships between semantic labels and their temporal changes. The relationships are described in either instant spatial spaces (semantic-interaction feature) or spatiotemporal spaces (spatiotemporal feature), and it provides essential information for robot navigation, situation awareness, and task planning. Unlike existing research, this is the first time that such a mission is implemented on mobile robot platforms by using the probabilistic fusion of multimodal information.&#13;\n&#13;\nConsidering how evidence information is obtained and linked in the process of reasoning, we study the problem of uniqueness reasoning at three levels. The first level is called knowledge-based single robot uniqueness reasoning and runs on a single robot platform. At this level, we focus on a knowledge-based approach, where knowledge is provided in terms of how motion and an object associated with a person can be used to infer his/her uniqueness, as determined by human expertise. Because such a method has advantages in dealing with structured missions and with limited training data. Qualitative rules are applied to extract human activities from observations, and then the uniqueness of each person is judged by associating the prior knowledge with the performed activities. Experiments were conducted in indoor environments with various degrees of occlusion, and the experimental results showed the rationality and accuracy of the reasoning.&#13;\n&#13;\nHowever, though the knowledge-based method can successfully address specific tasks, it lacks flexibility and generality to adapt to different situations. The assumption of prior knowledge should be replaced by general analytical modeling. Thus, the second level is analytics-based single robot uniqueness reasoning. For one thing, it applies quantitative analysis to model human activities from observations rather than using qualitative rules. For another, instead of leveraging prior knowledge, the distribution of the features characterizing a human activity is constructed and the activity of an individual is analyzed within the distribution to determine if it is unique. Experiments were conducted in both indoor and urban outdoor environments. The experimental results demonstrated the rationality, accuracy, and efficiency of the reasoning.&#13;\n&#13;\nMoreover, since many applications require to work in an unstructured and large-scale environment as well as a long-term operation, the sensing and reasoning capabilities of a single robot are limited by occlusion, sensor failure, and narrow field of view. Robots are desired to share information and perform collaborative reasoning. Therefore, if say the first two levels are mainly using sensory data to implement the algorithm, the third level is more focused on applying the algorithm to the robot platform. The third level is analytics-based multi-robot uniqueness reasoning, in which a multi-robot system is adopted as the platform. By obtaining the relative pose between robots, local observations can be transformed and transmitted among the multi-robot system. Since it is a distributed system, each robot still first performs its local uniqueness reasoning in the case of long-distance and poor communication. Meanwhile, the system performs a re-judgment based on the shared global observation and then shares the judgment results back to each robot. Experiments were carried out in various unstructured environments and contained day and night situations. The experimental results showed the rationality, accuracy, and robustness of the reasoning. This is the first time that reasoning is categorized by considering the way of obtaining and linking evidence information. This study has potential applications in a variety of situations, and it can serve as a guide for further robotic motion planning and human-robot interaction.

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.

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.

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.

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.

The human hand provides excellent grasp capabilities by using the interaction of the thumb and other fingers, and thus, how to dextrously interact with each other can represent the performance of the robot hand. This paper presents the kinematic design optimization for an anthropomorphic robot hand based on the interactivity of fingers. We propose a new performance index, called ‘interactivity of fingers (IF),’ which is useful for quantifying the multifingered precision grasping capability of the robot hand. Using the IF, the kinematic model of an anthropomorphic robot hand is optimized. The optimization is performed by using the genetic algorithm, and position and orientation of the thumb’s saddle joint are systematically determined. In order to verify the usefulness of the proposed performance index, IF of existing hands and that of the optimized model are compared. As a result, the IF of the optimized robot hand is almost five times larger than those of the other hands and the effectiveness of the proposed method is discussed.

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.

PurposeThe purpose of this paper is to study the adaptability of the tracked robot in complex working environment. It proposes an angle-changeable tracked robot with human–robot interaction in unstructured environment. The study aims to present the mechanical structure and human–robot interaction control system of the tracked robot and analyze the static stability of the robot working in three terrains, i.e. rugged terrain, sloped terrain and stairs.Design/methodology/approachThe paper presents the mechanical structure and human–robot interaction control system of the tracked robot. To prevent the detachment of the tracks during obstacle navigation, a new type of passively adaptive device based on the relationship between the track’s variable angle and the forces is presented. Then three types of rough terrain are chosen to analyze the static stability of the tracked robot, i.e. rugged terrain, sloped terrain and stairs.FindingsThis paper provides the design method of the tracked robot. Owing to its appropriate dimensions, good mass distribution and limited velocity, the tracked robot remains stable on the complex terrains. The experimental results verify the effectiveness of the design method.Originality/valueThe theoretical analysis of this paper provides basic reference for the structural design of tracked robots.

Real world virtual world communication and interaction will be a cornerstone of future intelligent manufacturing ecosystems. Human robotic interaction is considered to be the basic element of factories of the future. Despite the advancement of different technologies such as wearables and Augmented Reality (AR), human-robot interaction (HRI) is still extremely challenging. Whilst progress has been made in the development of different mechanisms to support HRI, there are issues with cost, naturalistic and intuitive interaction, and communication across heterogeneous systems. To mitigate these limitations, RoSTAR is proposed. RoSTAR is a novel open-source HRI system based on the Robot Operating System (ROS) and Augmented Reality. An AR Head Mounted Display (HMD) is deployed. It enables the user to interact and communicate through a ROS powered robotic arm. A model of the robot arm is imported directly into the Unity Game engine, and any interactions with this virtual robotic arm are communicated to the ROS robotic arm. This system has the potential to be used for different process tasks, such as robotic gluing, dispensing and arc welding as part of an interoperable, low cost, portable and naturalistically interactive experience.