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The purpose of this paper is to report research which led to the realization of a robot for miniaturized assembly endowed with high‐accuracy and high‐operative flexibility.

The proposed solution is a microassembly system composed of a Cartesian parallel robot with flexure revolute joints and a modular gripper with metamorphic fingertips, capable of adapting their shape to different micro‐objects. The fingertips are realized by electro‐discharge machining from a sheet of superelastic alloy. Thanks to its modularity, the gripper can be arranged with two opposite fingers or three fingers placed at 120°. The fingers are actuated by a piezoelectric linear motor with nanometric accuracy.

The experimental results on the prototype are very interesting. The measured positioning accuracy of the linear motors is 0.5 μm; the end‐effector positioning accuracy is lower, due to the non‐perfect kinematics and hysteresis of the flexure joints; however, these effects can be compensated by the direct measurement of the end effector position or by visual feedback. The metamorphic design of the fingertips remarkably increases the grasping force; moreover, the grasping is more stable and reliable.

The introduction of this microassembly system can fulfil the needs of a wide range of industrial applications, thanks to its accurate positioning in a relatively large workspace. The cost of the machine is relatively low, thanks to its modularity.

The combination of Cartesian parallel kinematics, cog‐free linear motors and superelastic flexure revolute joints allows one to obtain high‐positioning accuracy; the metamorphic fingertips enhance the grasping effectiveness and flexibility.

To identify the dexterity of spacesuit gloves, they need to undergo bending tests in the development process. The ideal way is to place a humanoid robotic hand into the spacesuit glove, mimicking the motions of a human hand and measuring the bending angle/force of the spacesuit glove. However, traditional robotic hands are too large to enter the narrow inner space of the spacesuit glove and perform measurements. This paper aims to design a humanoid robot hand that can wear spacesuit gloves and perform measurements.

The proposed humanoid robotic hand is composed of five modular fingers and a parallel wrist driven by electrical linear motors. The fingers and wrist can be delivered into the spacesuit glove separately and then assembled inside. A mathematical model of the robotic hand is formulated by using the geometric constraints and principle of virtual work to analyze the kinematics and statics of the robotic hand. This model allows for estimating the bending angle and output force/torque of the robotic hand through the displacement and force of the linear motors.

A prototype of the robotic hand, as well as its testing benches, was constructed to validate the presented methods. The experimental results show that the whole robotic hand can be transported to and assembled in a spacesuit glove to measure the motion characteristics of the glove.

The proposed humanoid robotic hand provides a new method for wearing and measuring the spacesuit glove. It can also be used to other gloves for special protective suits that have highly restricted internal space.

This paper aims to propose a novel method based on a gesture primitives analysis of human daily grasping tasks for designing dexterous hands with various grasping and in-hand manipulation abilities, which simplifies the complex and redundant humanoid five-finger hand system.

First, the authors developed the fingers and the joint configuration with a series of gesture primitives configurations and the modular virtual finger scheme, refined from the daily work gesture library by principal component analysis. Then, the authors optimized the joint degree-of-freedom configuration with the bionic design analysis of the anatomy, and the authors optimized the dexterity workspace. Furthermore, the adaptive fingertip and routing structure were designed based on the dexterous manipulation theory. Finally, the effectiveness of the design method was experimentally validated.

A novel lightweight three-finger and nine-degree-of-freedom dexterous hand with force/position perception was designed. The proposed routing structure was shown to have the capability of mapping the relationship between the joint space and actuator space. The adaptive fingertip with an embedded force sensor can effectively increase the robustness of the grasping operation. Moreover, the dexterous hand can grasp various objects in different configurations and perform in-hand manipulation dexterously.

The dexterous hand design developed in this study is less complex and performs better in dexterous manipulation than previous designs.

The purpose of this paper is to achieve stable grasping and dexterous in-hand manipulation, the control of the multi-fingered robotic hand is a difficult problem as the hand has many degrees of freedom with various grasp configurations.

To achieve this goal, a novel object-level impedance control framework with optimized grasp force and grasp quality is proposed for multi-fingered robotic hand grasping and in-hand manipulation. The minimal grasp force optimization aims to achieve stable grasping satisfying friction cone constraint while keeping appropriate contact forces without damage to the object. With the optimized grasp quality function, optimal grasp quality can be obtained by dynamically sliding on the object from initial grasp configuration to final grasp configuration. By the proposed controller, the in-hand manipulation of the grasped object can be achieved with compliance to the environment force. The control performance of the closed-loop robotic system is guaranteed by appropriately choosing the design parameters as proved by a Lyapunove function.

Simulations are conducted to validate the efficiency and performance of the proposed controller with a three-fingered robotic hand.

This paper presents a method for robotic optimal grasping and in-hand manipulation with a compliant controller. It may inspire other related researchers and has great potential for practical usage in a widespread of robot applications.

Accurate segmentation of artificial assembly action is the basis of autonomous industrial assembly robots. This paper aims to study the precise segmentation method of manual assembly action.

In this paper, a temporal-spatial-contact features segmentation system (TSCFSS) for manual assembly actions recognition and segmentation is proposed. The system consists of three stages: spatial features extraction, contact force features extraction and action segmentation in the temporal dimension. In the spatial features extraction stage, a vectors assembly graph (VAG) is proposed to precisely describe the motion state of the objects and relative position between objects in an RGB-D video frame. Then graph networks are used to extract the spatial features from the VAG. In the contact features extraction stage, a sliding window is used to cut contact force features between hands and tools/parts corresponding to the video frame. Finally, in the action segmentation stage, the spatial and contact features are concatenated as the input of temporal convolution networks for action recognition and segmentation. The experiments have been conducted on a new manual assembly data set containing RGB-D video and contact force.

In the experiments, the TSCFSS is used to recognize 11 kinds of assembly actions in demonstrations and outperforms the other comparative action identification methods.

A novel manual assembly actions precisely segmentation system, which fuses temporal features, spatial features and contact force features, has been proposed. The VAG, a symbolic knowledge representation for describing assembly scene state, is proposed, making action segmentation more convenient. A data set with RGB-D video and contact force is specifically tailored for researching manual assembly actions.

Electrical defects cover an important part of assembly defects and strongly affect the vehicle system performance. Almost 40% of assembly defects are classified as human errors and electrical connection failures represent a significant part of them. Humans still remain a cost-effective solution for the flexible manufacturing systems with increasing product complexity. So, understanding human behaviors is still a challenging task. The purpose of this study is to define, prioritize and validate the critical factors for the complexity of electrical connector plugin process.

The critical variables were defined by the expert team members. The required number of measurements and variables were revised resulting preliminary analysis of binary logistic regression. After the revision of measurement plan, the list of critical input variables and the mathematical model were defined. The model has been validated by the fitted values of the residuals (FITS analysis).

To the best of the authors’ knowledge, this is one of the limited studies, which defines the critical factors for electrical connection process complexity. Female connector harness length, connector width/height/length differences, operator sense of correct connector matching and ergonomy were defined as the factors with the highest impact on the failure occurrence. The obtained regression equation strongly correlates the failure probability.

The obtained mathematical model can be used in new model development processes both for the product and assembly process design (ergonomy, accessibility and lay-out).

The obtained risk factors demonstrated a strong correlation with assembly process complexity and failure rates. The output of this study would be used as an important guide for process (assembly line ergonomy, accessibility and lay-out) and product design in new model development and assembly ramp-up phases.

This paper aims to present a pose correction method based on integrated virtual impedance control for avoiding collision and reducing impact.

The authors first constructed the artificial potential field (APF) considering the geometric characteristics of the end-effector. The characteristics of the proposed field were analyzed considering the position and orientation misalignment. Then, an integrated virtual impedance control was proposed by adding resultant virtual repulsive force into traditional impedance control. Finally, the authors modified a correction trajectory for avoiding collision and reducing impact with virtual force and contact force.

The APF the authors constructed can get rid of a local minimum. Comparing with linear correction, this method is able to avoid collision effectively. When the capturing target has intrinsic estimation error, the pose correction can ensure smooth transitions among different stages.

This method can be implemented on a manipulator with inner position control. It can be applied to an industrial robot with applications on robotic assembly for achieving a softer and smoother process. The method can also be expanded to the kind of claw-shaped end-effectors for capturing target.

As the authors know, it is the first time that the characteristics of the end-effector are considered for avoiding collision in capturing application. The proposed integrated virtual impedance control can provide smooth transitions among different stages without switching different force/position controllers.

This study aims to introduce a multi-configuration, three-finger dexterous hand with integrated high-dimensional sensors and provides an analysis of its design, modeling and kinematics.

A mechanical design scheme of the three-finger dexterous hand with a reconfigurable palm is proposed based on the existing research on dexterous hands. The reconfigurable palm design enables the dexterous hand to achieve four grasping modes to adapt to multiple grasping tasks. To further enhance perception, two six-axis force and torque sensors are integrated into each finger. The forward and inverse kinematics equations of the dexterous hand are derived using the D-H method for kinematics modeling, thus providing a theoretical model for index analysis. The performance is evaluated using three widely applied indicators: workspace, interactivity of fingers and manipulability.

The results of kinematics analysis show that the proposed hand has excellent dexterity. Additionally, three different experiments are conducted based on the proposed hand. The performance of the dexterous hand is also verified by fingertip force, motion accuracy test, grasping and in-hand manipulation experiments based on Feix taxonomy. The results show that the dexterous hand has good grasping ability, reproducing 82% of the natural movement of the human hand in daily grasping activities and achieving in-hand manipulations such as translation and rotation.

A novel three-finger dexterous hand with multi-configuration and integrated high-dimensional sensors is proposed. It performs better than the previously designed dexterous hand in actual experiments and kinematic performance analysis.

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.