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The purpose of this paper is to present a direct force control which uses two closed-loop controller for one-degree-of-freedom human-machine system to synchronize the human position and machine position, and minimize the human-machine force. In addition, the friction is compensated to promote the performance of the human-machine system.

The dynamic of the human-machine system is mathematically modeled. The control strategy is designed using two closed-loop controllers, including a PID controller and a PI controller. The frictions, which exist in the rotary joint and the hydraulic wall, are compensated separately using the Friedland’s observer and Dahl’s observer.

When human-machine system moves at low velocity, there exists a significant amount of static friction that hinders the system movements. The simulation results show that the system gives a better performance in human-machine position synchronization and human-machine force minimization when the friction is compensated.

The acquired results are based on simulation not experiment.

This paper is the first to apply the electrohydraulic servo systems to both actuate the human-machine system, and use the direct force control strategy consisting of two closed-loop controllers. It is also the first to compensate the friction both in the robot joint and hydraulic wall.

Haptic interfaces enable person‐machine communication through touch, and most commonly, in response to user movements. We comment on a distinct property of haptic interfaces, that of providing for simultaneous information exchange between a user and a machine. We also comment on the fact that, like other kinds of displays, they can take advantage of both the strengths and the limitations of human perception. The paper then proceeds with a description of the components and the modus operandi of haptic interfaces, followed by a list of current and prospective applications and a discussion of a cross‐section of current device designs.

The purpose of this paper is to propose an innovative method to extend the operating range of the laser tracking system and improve the accuracy and automation of boresighting by designing a measurement instrument. Boresighting is a process that aligns the direction of special equipment with the aircraft reference axis. Sometimes the accurate measurement and adjustment of the equipment and the aircraft are hard to achieve.

The aircraft is moved by an automatic adjustment system which consists of three numerical control positioners. For obtaining the position of the bore axis, an instrument with two measurement points is designed. Based on the multivariate normal distribution hypothesis, an uncertainty evaluation method for the aiming points is introduced. The accuracy of the measurement point is described by an uncertainty ellipsoid. A compensation and calibration method is proposed to decrease the effect of manufacturing error and deflection error by the finite element analysis.

The experimental results of the boresighting measurement prove that the proposed method is effective and reliable in digital assembly. The measurement accuracy of the angle between the bore axis and the reference axis is about ±0.004°. In addition, the measurement result is mainly influenced by the position error of the instrument.

The results of this study will provide a new way to obtain and control the installation deviation of part in aircraft digital assembly and will help to improve the precision and efficiency. This measurement method can be applied to obtain the axis of a deep blind hole.

Considering the increasing impact of Artificial Intelligence (AI) on financial technology (FinTech), the purpose of this paper is to propose a research framework to better understand robo-advisor adoption by a wide range of potential customers. It also predicts that personal and sociodemographic variables (familiarity with robots, age, gender and country) moderate the main relationships.

Data from a web survey of 765 North American, British and Portuguese potential users of robo-advisor services confirm the validity of the measurement scales and provide the input for structural equation modeling and multisample analyses of the hypotheses.

Consumers’ attitudes toward robo-advisors, together with mass media and interpersonal subjective norms, are found to be the key determinants of adoption. The influences of perceived usefulness and attitude are slightly higher for users with a higher level of familiarity with robots; in turn, subjective norms are significantly more relevant for users with a lower familiarity and for customers from Anglo-Saxon countries.

Banks and other firms in the finance industry should design robo-advisors to be used by a wide spectrum of consumers. Marketing tactics applied should consider the customer’s level of familiarity with robots.

This research identifies the key drivers of robo-advisor adoption and the moderating effect of personal and sociodemographic variables. It contributes to understanding consumers’ perceptions regarding the introduction of AI in FinTech.

This study aims to understand the role of digital literacies as a moderator between employee engagement and its antecedents, namely, workplace digitalisation and innovative culture.

A total of 256 valid samples were used in the analysis. The respondents were individuals used as management-level executives in companies located in Selangor/Kuala Lumpur. The model was tested using structural equation modelling.

The findings reveal that there exists a significant association between employee engagement and its antecedents, namely, workplace digitalisation and innovative culture. Digital literacies are found to moderate the relationships between workplace digitalisation-employee engagement and innovative culture-employee engagement.

This paper provides new insight to the practitioners about the role of digital literacies in raising employee engagement in the digital workplace.

These findings enrich the literature on employee engagement, whereby, improving employee digital literacies strengthens employee acceptance to workplace digitalisation and benefit from the innovative culture to stay engaged.

Robot tools, or in more general terms, end‐of‐arm tools, or robot end‐effectors are general purpose, programmable or task‐oriented devices connected between the robot wrist and the object or load to be manipulated and/or processed by the robot. They can offer and/or limit the versatility of grasping and/or processing of different components, sensing their characteristics and working together with the robot control system to provide a reliable “service” throughout the component manipulation cycle. Reconfigurable robot tooling enables the robot to rapidly change its end‐effectors or fingers of its end‐effectors, typically under programmable software control. The importance of providing lean‐flexibility by means of reconfigurable, automated robot hand changers (ARHC), particularly in small‐batch robotic welding, assembly, machine loading and in other flexible robot cells, is discussed with examples. Some known systems are demonstrated and the “Ranky‐type” ARHC design is illustrated in more detail.

Humanoid robot has similar shape and action characteristics as humans, and it can complete some basic tasks instead of humans without changing the human environment, which makes humanoid robot become the best structure and help form for robot to provide services for human beings.

The mobile operation control of humanoid robot is generated by the walking movement of humanoid robot's feet, and the robot's hand and arm complete grasping and other operations together.

On the basis of humanoid robot, the integrated system of software and hardware based on the KM34Z256 humanoid robot is described first, and a series of kinematics discussion on its mobile operation is carried out.

The research based on this project shows that the target recognition and positioning method is not only accurate and of high energy but also can realize the mobile operation of humanoid robot.

The purpose of this review paper is to address the substantial challenges of the outdated exoskeletons used for rehabilitation and further study the current advancements in this field. The shortcomings and technological developments in sensing the input signals to enable the desired motions, actuation, control and training methods are explained for further improvements in exoskeleton research.

Search platforms such as Web of Science, IEEE, Scopus and PubMed were used to collect the literature. The total number of recent articles referred to in this review paper with relevant keywords is filtered to 143.

Exoskeletons are getting smarter often with the integration of various modern tools to enhance the effectiveness of rehabilitation. The recent applications of bio signal sensing for rehabilitation to perform user-desired actions promote the development of independent exoskeleton systems. The modern concepts of artificial intelligence and machine learning enable the implementation of brain–computer interfacing (BCI) and hybrid BCIs in exoskeletons. Likewise, novel actuation techniques are necessary to overcome the significant challenges seen in conventional exoskeletons, such as the high-power requirements, poor back drivability, bulkiness and low energy efficiency. Implementation of suitable controller algorithms facilitates the instantaneous correction of actuation signals for all joints to obtain the desired motion. Furthermore, applying the traditional rehabilitation training methods is monotonous and exhausting for the user and the trainer. The incorporation of games, virtual reality (VR) and augmented reality (AR) technologies in exoskeletons has made rehabilitation training far more effective in recent times. The combination of electroencephalogram and electromyography-based hybrid BCI is desirable for signal sensing and controlling the exoskeletons based on user intentions. The challenges faced with actuation can be resolved by developing advanced power sources with minimal size and weight, easy portability, lower cost and good energy storage capacity. Implementation of novel smart materials enables a colossal scope for actuation in future exoskeleton developments. Improved versions of sliding mode control reported in the literature are suitable for robust control of nonlinear exoskeleton models. Optimizing the controller parameters with the help of evolutionary algorithms is also an effective method for exoskeleton control. The experiments using VR/AR and games for rehabilitation training yielded promising results as the performance of patients improved substantially.

Robotic exoskeleton-based rehabilitation will help to reduce the fatigue of physiotherapists. Repeated and intention-based exercise will improve the recovery of the affected part at a faster pace. Improved rehabilitation training methods like VR/AR-based technologies help in motivating the subject.

The paper describes the recent methods for signal sensing, actuation, control and rehabilitation training approaches used in developing exoskeletons. All these areas are key elements in an exoskeleton where the review papers are published very limitedly. Therefore, this paper will stand as a guide for the researchers working in this domain.