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The purpose of this study is to develop a robotic hand–arm system for on-orbit servicing missions at the Tiangong-2 (TG-2) Space Laboratory.

The hand–arm system is mainly composed of a lightweight arm, a dexterous hand, an electrical cabinet, a global camera, a hand–eye camera and some human–machine interfaces. The 6-DOF lightweight arm and the 15-DOF dexterous hand adopt the modular design philosophy that greatly reduces the design cycle and cost. To reduce the computational burden on the central controller and simplify system maintenance, an electrical system which has a hierarchical structure is introduced.

The prototypical operating experiments completed in TG-2 space laboratory demonstrate the performance of the hand–arm system and lay foundations for the future applications of space manipulators.

The main contributions of this paper are as follows a robotic hand–arm system which can perform on-orbit servicing missions such as grasping the electric drill, screwing the bolt, unscrewing J599 electrical connector has been developed; a variable time step motion plan method is proposed to adjust the trajectories of the lightweight arm to reduce or eliminate the collision force; and a dexterous hand uses the coordinated grasp control based on the object Cartesian stiffness to realize stable grasp. To solve the kinematic mapping from the cyber glove commands to the dexterous hand, a fingertip-position-based method is proposed to acquire precise solutions.

The purpose of this paper is to present a novel data glove which can capture the motion of the arm and hand by inertial and magnetic sensors. The proposed data glove is used to provide the information of the gestures and teleoperate the robotic arm-hand.

The data glove comprises 18 low-cost inertial and magnetic measurement units (IMMUs) which not only make up the drawbacks of traditional data glove that only captures the incomplete gesture information but also provide a novel scheme of the robotic arm-hand teleoperation. The IMMUs are compact and small enough to wear on the upper arm, forearm, palm and fingers. The calibration method is proposed to improve the accuracy of measurements of units, and the orientations of each IMMU are estimated by a two-step optimal filter. The kinematic models of the arm, hand and fingers are integrated into the entire system to capture the motion gesture. A positon algorithm is also deduced to compute the positions of fingertips. With the proposed data glove, the robotic arm-hand can be teleoperated by the human arm, palm and fingers, thus establishing a novel robotic arm-hand teleoperation scheme.

Experimental results show that the proposed data glove can accurately and fully capture the fine gesture. Using the proposed data glove as the multiple input device has also proved to be a suitable teleoperating robotic arm-hand system.

Integrated with 18 low-cost and miniature IMMUs, the proposed data glove can give more information of the gesture than existing devices. Meanwhile, the proposed algorithms for motion capture determine the superior results. Furthermore, the accurately captured gestures can efficiently facilitate a novel teleoperation scheme to teleoperate the robotic arm-hand.

This paper aims to present a procedure to change the orientation of a grasped object using dexterous manipulation. The manipulation is controlled by teleoperation in a very simple way, with the commands introduced by an operator using a keyboard.

The paper shows a teleoperation scheme, hand kinematics and a manipulation strategy to manipulate different objects using the Schunk Dexterous Hand (SDH2). A state machine is used to model the teleoperation actions and the system states. A virtual link is used to include the contact point on the hand kinematics of the SDH2.

Experiments were conducted to evaluate the proposed approach with different objects, varying the initial grasp configuration and the sequence of actions commanded by the operator.

The proposed approach uses a shared telemanipulation schema to perform dexterous manipulation; in this schema, the operator sends high-level commands and a local system uses this information, jointly with tactile measurements and the current status of the system, to generate proper setpoints for the low-level control of the fingers, which may be a commercial close one. The main contribution of this work is the mentioned local system, simple enough for practical applications and robust enough to avoid object falls.

This paper aims to propose a robotic automation system for processing special-shaped thin-walled workpieces, which includes a measurement part and a processing part.

In the measurement part, to efficiently and accurately realize the three-dimensional camera hand-eye calibration based on a large amount of measurement data, this paper improves the traditional probabilistic method. To solve the problem of time-consuming in the extraction of point cloud features, this paper proposes a point cloud feature extraction method based on seed points. In the processing part, the authors design a new type of chamfering tool. During the process, the robot adopts admittance control to perform compensation according to the feedback of four sensors mounted on the tool.

Experiments show that the proposed system can make the tool smoothly fit the chamfered edge during processing and the machined chamfer meets the processing requirements of 0.5 × 0.5 to 0.9 × 0.9 mm².

The proposed design and approach can be applied on many types of special-shaped thin-walled parts. This will give a new solution for the automation integration problem in aerospace manufacturing.

A novel robotic automation system for processing special-shaped thin-walled workpieces is proposed and a new type of chamfering tool is designed. Furthermore, a more accurate probabilistic hand-eye calibration method and a more efficient point cloud extraction method are proposed, which are suitable for this system when comparing with the traditional methods.

The purpose of this paper is to design a multifingered dexterous hand grasping planning method that can efficiently perform grasping tasks on multiple dexterous hand platforms.

The grasping process is divided into two stages: offline and online. In the offline stage, the grasping solution form is improved based on the forward kinematic model of the dexterous hand. A comprehensive evaluation method of grasping quality is designed to obtain the optimal grasping solution offline data set. In the online stage, a safe and efficient selection strategy of the optimal grasping solution is proposed, which can quickly obtain the optimal grasping solution without collision.

The experiments verified that the method can be applied to different multifingered dexterous hands, and the average grasping success rate for objects with different structures is 91.7%, indicating a good grasping effect.

Using a forward kinematic model to generate initial grasping points can improve the generality of grasping planning methods and the quality of initial grasping solutions. The offline data set of optimized grasping solutions can be generated faster by the comprehensive evaluation method of grasping quality. Through the simple and fast obstacle avoidance strategy, the safe optimal grasping solution can be quickly obtained when performing a grasping task. The proposed method can be applied to automatic assembly scenarios where the end effector is a multifingered dexterous hand, which provides a technical solution for the promotion of multifingered dexterous hands in industrial scenarios.

Robot-assisted system for minimally invasive surgery (MIS) has been attracting more and more attentions. Compared with a traditional MIS, the robot-assisted system for MIS is able to overcome or reduce defects, such as poor hand-eye coordination, heavy labour intensity and limited motion of the instrument. The purpose of this paper is to design a novel robotic system for MIS applications.

A robotic system with three separate slave arms for MIS has been designed. In the proposed robot, a new mechanism was designed as the remote centre motion (RCM) mechanism to restrain the movement of instrument or laparoscope around the incision. Moreover, an improved instrument without coupling motion between wrist and grippers was developed to enhance its manipulability. A control system architecture was also developed, and an intuitive control method was applied to realize hand-eye coordination of the operator.

For the RCM mechanism, the workspace was analyzed and the positioning accuracy of the remote centre point was tested. The results show that the RCM mechanism can be applied to MIS. Furthermore, the master-slave trajectory tracking experiments reveal that slave robots are able to follow the movement of the master manipulators well. Finally, the feasibility of the robot-assisted system for MIS is proved by performing animal experiments successfully.

This paper offers a novel robotic system for MIS. It can accomplish the anticipated results.

The purpose of this paper is to develop an integrated, computer vision-based system to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In addition, a gesture recognition interface system was developed specially for individuals with upper-level spinal cord injuries including object tracking and face recognition to function as an efficient, hands-free WMRM controller.

Two Kinect® cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures and locate the operator's face for object positioning, and then send those as commands to control the WMRM. The other sensor was used to automatically recognize different daily living objects selected by the subjects. An object recognition module employing the Speeded Up Robust Features algorithm was implemented and recognition results were sent as a commands for “coarse positioning” of the robotic arm near the selected object. Automatic face detection was provided as a shortcut enabling the positing of the objects close by the subject's face.

The gesture recognition interface incorporated hand detection, tracking and recognition algorithms, and yielded a recognition accuracy of 97.5 percent for an eight-gesture lexicon. Tasks’ completion time were conducted to compare manual (gestures only) and semi-manual (gestures, automatic face detection, and object recognition) WMRM control modes. The use of automatic face and object detection significantly reduced the completion times for retrieving a variety of daily living objects.

Integration of three computer vision modules were used to construct an effective and hand-free interface for individuals with upper-limb mobility impairments to control a WMRM.

Despite the proven evidence of ever-growing productivity gains in the manufacturing industry as a result of years of research and investment in advanced technologies, such as robotics, the adoption of robots in construction is still lagging. The existing literature lacks technical frameworks and guidelines that account for the one-of-a-kind nature of construction projects and the myriad of materials and dimensional components in construction activities. This study seeks to address existing technical uncertainty and productivity issues associated with the application of robotics in the assembly-type manufacturing of industrialized construction.

To facilitate the selection of suitable robotic arms for industrialized construction activities, primarily assembly-type manufacturing tasks of offsite production processes, an activity-based ranking system based on axiomatic design principles is proposed. The proposed ranking system utilizes five functional requirements derived from robot characteristics—speed, payload, reach, degrees of freedom and position repeatability—to evaluate robot performance in an industrialized construction task using simulations of a framing station.

Based on design parameters obtained from activity-based simulations, seventy six robotic arms suitable for the framing task were scored and ranked. According to the sensitivity analysis of proposed functional requirements, speed is the key functional requirement that has a notable effect on productivity of a framing station and is thus the determinant in robot performance assessment for framing tasks.

The proposed ranking system is expected to augment automation in construction and serve as a preliminary guideline to help construction professionals in making informed decisions regarding the adoption of robotic arms.

Supernumerary robotic limbs (SRLs) are a new type of wearable robot, which improve the user’s operating and perceive the user’s environment by extra robotic limbs. There are some literature reviews about the SRLs’ key technology and development trend, but the design of SRLs has not been fully discussed and summarized. This paper aims to focus on the design of SRLs and provides a comprehensive review of the ontological structure design of SRLs.

In this paper, the related literature of SRLs is summarized and analyzed by VOSviewer. The structural features of different types of SRLs are extracted, and then discuss the design approach and characteristics of SRLs which are different from typical wearable robots.

The design concept of SRLs is different from the conventional wearable robots. SRLs have various reconfiguration and installed positions, and it will influence the safety and cooperativeness performance of SRLs.

This paper focuses on discussing the structural design of SRLs by literature review, and this review will help researchers understand the structural features of SRLs and key points of the ontological design of SRLs, which can be used as a reference for designing SRLs.

The idea is to exploit the natural stability and performance of the human arm during movement, execution and manipulation. The purpose of this paper is to remotely control a handling robot with a low cost but effective solution.

The developed approach is based on three different techniques to be able to ensure movement and pattern recognition of the operator’s arm as well as an effective control of the object manipulation task. In the first, the methodology works on the kinect-based gesture recognition of the operator’s arm. However, using only the vision-based approach for hand posture recognition cannot be the suitable solution mainly when the hand is occluded in such situations. The proposed approach supports the vision-based system by an electromyography (EMG)-based biofeedback system for posture recognition. Moreover, the novel approach appends to the vision system-based gesture control and the EMG-based posture recognition a force feedback to inform operator of the real grasping state.

The main finding is to have a robust method able to gesture-based control a robot manipulator during movement, manipulation and grasp. The proposed approach uses a real-time gesture control technique based on a kinect camera that can provide the exact position of each joint of the operator’s arm. The developed solution integrates also an EMG biofeedback and a force feedback in its control loop. In addition, the authors propose a high-friendly human-machine-interface (HMI) which allows user to control in real time a robotic arm. Robust trajectory tracking challenge has been solved by the implementation of the sliding mode controller. A fuzzy logic controller has been implemented to manage the grasping task based on the EMG signal. Experimental results have shown a high efficiency of the proposed approach.

There are some constraints when applying the proposed method, such as the sensibility of the desired trajectory generated by the human arm even in case of random and unwanted movements. This can damage the manipulated object during the teleoperation process. In this case, such operator skills are highly required.

The developed control approach can be used in all applications, which require real-time human robot cooperation.

The main advantage of the developed approach is that it benefits at the same time of three various techniques: EMG biofeedback, vision-based system and haptic feedback. In such situation, using only vision-based approaches mainly for the hand postures recognition is not effective. Therefore, the recognition should be based on the biofeedback naturally generated by the muscles responsible of each posture. Moreover, the use of force sensor in closed-loop control scheme without operator intervention is ineffective in the special cases in which the manipulated objects vary in a wide range with different metallic characteristics. Therefore, the use of human-in-the-loop technique can imitate the natural human postures in the grasping task.