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This paper aims to present an impedance control method with mixed H₂/H_∞ synthesis and relaxed passivity for a cable-driven series elastic actuator to be applied for physical human–robot interaction.

To shape the system’s impedance to match a desired dynamic model, the impedance control problem was reformulated into an impedance matching structure. The desired competing performance requirements as well as constraints from the physical system can be characterized with weighting functions for respective signals. Considering the frequency properties of human movements, the passivity constraint for stable human–robot interaction, which is required on the entire frequency spectrum and may bring conservative solutions, has been relaxed in such a way that it only restrains the low frequency band. Thus, impedance control became a mixed H₂/H_∞ synthesis problem, and a dynamic output feedback controller can be obtained.

The proposed impedance control strategy has been tested for various desired impedance with both simulation and experiments on the cable-driven series elastic actuator platform. The actual interaction torque tracked well the desired torque within the desired norm bounds, and the control input was regulated below the motor velocity limit. The closed loop system can guarantee relaxed passivity at low frequency. Both simulation and experimental results have validated the feasibility and efficacy of the proposed method.

This impedance control strategy with mixed H₂/H_∞ synthesis and relaxed passivity provides a novel, effective and less conservative method for physical human–robot interaction control.

Robonaut is a humanoid robot designed by the Robotic Systems Technology Branch at NASA's Johnson Space Center in a collaborative effort with Defense Advanced Research Projects Agency. This paper describes the implementation of haptic feedback into Robonaut and Robosim, the computer simulation of Robotonaut. In the first experiment, we measured the effects of varying feedback to a teleoperator during a handrail grasp task. Second, we conducted a teleoperated task, inserting a flexible beam into an instrumented receptacle. In the third experiment, we used Robonaut to perform a two‐arm task where a compliant ball was translated in the robot's workspace. The experimental results are encouraging as the Dexterous Robotics Lab continues to implement force feedback into its teleoperator hardware architecture.

The working hypothesis, on which this paper is built, is that it is advantageous to look at protocols of robot rehabilitation in the general context of human-robot interaction in haptic dyads. The purpose of this paper is to propose a new method to detect and evaluate an index of active participation (AC index), underlying the performance of robot-assisted movements. This is important for avoiding the slacking phenomenon that affects robot therapy.

The evaluation of the AC index is based on a novel technique of assistance which does not use constant or elastic forces but trains of small force impulses, with amplitude adapted to the level of impairment and a frequency of 2 Hz, which is suggested by recent results in the field of intermittent motor control. A preliminary feasibility test of the proposed method was carried out during a haptic reaching task in the absence of visual feedback, for a group of five stroke patients and an equal group of healthy subjects.

The AC index appears to be stable and sensitive to training in both populations of subjects.

The main original element of this study is the proposal of the new AC index of voluntary control associated with the new method of pulsed haptic interaction.

The purpose of this paper is to design an exoskeleton robot and present a corresponding rehabilitation training method for patients in different rehabilitation stages.

This paper presents a lightweight seven-degrees-of-freedom (DOF) cable-driven exoskeleton robot that is wearable and adjustable. After decoupling joint movement caused by a cable-driven mechanism, active rehabilitation training mode and passive rehabilitation training mode are proposed to improve the effect of rehabilitation training.

Simulations and experiments have been carried out, and the results validated the feasibility of the proposed mechanism and methods by a fine rehabilitative effect with different persons.

This paper designed a 7-DOF cable-driven exoskeleton robot that is suitable for patients of different body measurements and proposed the active rehabilitation training mode and passive rehabilitation training mode based on the cable-driven exoskeleton robot.

This paper aims to propose an intuitive shared control strategy to control a humanoid manipulator that can fully combine the advantages of humans and machines to produce a stronger intelligent form.

The working space of an operator’s arm and that of a manipulator are matched, and a genetic algorithm that limits the position of the manipulator’s elbow joint is used to find the optimal solution. Then, the mapping of the operator’s action to that of manipulators is realized. The controls of the human and robot are integrated. First, the current action of the operator is input. Second, the target object is predicted according to the maximum entropy hypothesis. Third, the joint angle of the manipulator is interpolated based on time. Finally, the confidence and weight of the current moment are calculated.

The modified weight adjustment method is the optimal way to adjust the weight during the task. In terms of time and accuracy, the experimental results of single target obstacle avoidance grabbing and multi-target predictive grabbing show that the shared control mode can provide full play to the advantages of humans and robots to accomplish the target task faster and more accurately than the control merely by a human or robot on its own.

A flexible and highly anthropomorphic human–robot action mapping method is proposed, which provides operator decisions in the shared control process. The shared control between human and the robot is realized, and it enhances the rapidity and intelligence, paving a new way for a novel human–robot collaboration.

Offering unskilled people training in engineering and vocational skills helps to decrease unemployment rate. The purpose of this paper is to augment actual hands-on conventional vocational training methods with virtual haptic simulations as part of computer-based vocational education and training.

This paper discusses the design of a bi-manual virtual multi-modal training interface for learning basic skills in surface mount device hand soldering. This research aims to analyze human hand dexterity of novices and experts at micro level skill knowledge capture by simulating and tracking the users’ actions in the manual soldering process through a multi-modal user interface.

Haptic feedback can enhance the experience of a virtual training environment for the end user and can provide a supplementary modality for imparting tangible principles to increase effectiveness. This will improve the teaching and learning of engineering and vocational skills with touch-based haptics technology, targeted toward teachers and students of various disciplines in engineering. Compared with the traditional training methods for learning soldering skills, the proposed method shows more efficiency in faster skill acquisition and skill learning.

In this study, the authors proposed a novel bi-manual virtual training simulator model for teaching soldering skills for surface mount technology and inspection. This research aims to investigate the acquisition of soldering skills through virtual environment, with and without haptic feedback. This acts as a basic-level training simulator that provides introductory training in soldering skills and can help initially unskilled people find educational opportunities and job offers in the electronics industry.

This paper presents a survey of research into interactive robotic systems for the purpose of identifying the state of the art capabilities as well as the extant gaps in this emerging field. Communication is multimodal. Multimodality is a representation of many modes chosen from rhetorical aspects for its communication potentials. The author seeks to define the available automation capabilities in communication using multimodalities that will support a proposed Interactive Robot System (IRS) as an AI mounted robotic platform to advance the speed and quality of military operational and tactical decision making.

This review will begin by presenting key developments in the robotic interaction field with the objective of identifying essential technological developments that set conditions for robotic platforms to function autonomously. After surveying the key aspects in Human Robot Interaction (HRI), Unmanned Autonomous System (UAS), visualization, Virtual Environment (VE) and prediction, the paper then proceeds to describe the gaps in the application areas that will require extension and integration to enable the prototyping of the IRS. A brief examination of other work in HRI-related fields concludes with a recapitulation of the IRS challenge that will set conditions for future success.

Using insights from a balanced cross section of sources from the government, academic, and commercial entities that contribute to HRI a multimodal IRS in military communication is introduced. Multimodal IRS (MIRS) in military communication has yet to be deployed.

Multimodal robotic interface for the MIRS is an interdisciplinary endeavour. This is not realistic that one can comprehend all expert and related knowledge and skills to design and develop such multimodal interactive robotic interface. In this brief preliminary survey, the author has discussed extant AI, robotics, NLP, CV, VDM, and VE applications that is directly related to multimodal interaction. Each mode of this multimodal communication is an active research area. Multimodal human/military robot communication is the ultimate goal of this research.

A multimodal autonomous robot in military communication using speech, images, gestures, VST and VE has yet to be deployed. Autonomous multimodal communication is expected to open wider possibilities for all armed forces. Given the density of the land domain, the army is in a position to exploit the opportunities for human–machine teaming (HMT) exposure. Naval and air forces will adopt platform specific suites for specially selected operators to integrate with and leverage this emerging technology. The possession of a flexible communications means that readily adapts to virtual training will enhance planning and mission rehearsals tremendously.

Interaction, perception, cognition and visualization based multimodal communication system is yet missing. Options to communicate, express and convey information in HMT setting with multiple options, suggestions and recommendations will certainly enhance military communication, strength, engagement, security, cognition, perception as well as the ability to act confidently for a successful mission.

The objective is to develop a multimodal autonomous interactive robot for military communications. This survey reports the state of the art, what exists and what is missing, what can be done and possibilities of extension that support the military in maintaining effective communication using multimodalities. There are some separate ongoing progresses, such as in machine-enabled speech, image recognition, tracking, visualizations for situational awareness, and virtual environments. At this time, there is no integrated approach for multimodal human robot interaction that proposes a flexible and agile communication. The report briefly introduces the research proposal about multimodal interactive robot in military communication.

The purpose of this paper is to propose a control algorithm to improve the backdrivability performance of minimally invasive surgical robotic arms, so that precise manual manipulations of robotic arms can be performed in the preoperative operation.

First, the flexible-joint dynamic model of the 3-degree of freedom remote center motion (RCM) mechanisms of minimally invasive surgery (MIS) robot is derived and its dynamic parameters and friction parameters are identified. Next, the angular velocities and angular accelerations of joints are estimated in real time by the designed Kalman filter. Finally, a control algorithm based on Kalman filter is proposed to enhance the backdrivability of RCM mechanisms by compensating for the internally generated gravitational, frictional and inertial resistances experienced during the positioning and orientating.

The parameter identification for RCM mechanisms can be experimentally evaluated from comparison between the measured torques and the reconstructed torques. The accuracy and convergence of the real-time estimation of angular velocity and acceleration of the joint by the designed Kalman filter can be verified from corresponding simulation experiments. Manual adjustment experiments and animal experiments validate the effectiveness of the proposed backdrivability control algorithm.

The backdrivability control algorithm presented in this paper is a universal method to enhance the manual operation performance of robots, which can be used not only in the medical robot preoperative manual manipulation but also in robot haptic interaction, industrial robot direct teaching and active rehabilitation training of rehabilitation robot and so on.

Compared with other backdrivability design methods, the proposed algorithm achieves good backdrivability for RCM mechanisms without using force sensors and accelerometers. In addition, this paper presents a new static friction compensation approach for a joint moving with very low velocity.

Teleoperated minimally invasive surgical robots can significantly enhance a surgeon's accuracy, dexterity and visualization. However, current commercially available systems do not include significant haptic (force and tactile) feedback to the operator. This paper describes experiments to characterize this problem, as well as several methods to provide haptic feedback in order to improve surgeon's performance. There exist a variety of sensing and control methods that enable haptic feedback, although a number of practical considerations, e.g. cost, complexity and biocompatibility, present significant challenges. The ability of teleoperated robot‐assisted surgical systems to measure and display haptic information leads to a number of additional exciting clinical and scientific opportunities, such as active operator assistance through “virtual fixtures” and the automatic acquisition of tissue properties.

The current difficulties of distribution network working robots are mainly in the performance and operation mode. On the one hand, high-altitude power operation tasks require high load-carrying capacity and dexterity of the robot; on the other hand, the fully autonomous mode is uncontrollable and the teleoperation mode has a high failure rate. Therefore, this study aims to design a distribution network operation robot named Sky-Worker to solve the above two problems.

The heterogeneous arms of Sky-Worker are driven by hydraulics and electric motors to solve the contradiction between high load-carrying capacity and high flexibility. A human–robot collaborative shared control architecture is built to realize real-time human intervention during autonomous operation, and control weights are dynamically assigned based on energy optimization.

Simulations and tests show that Sky-Worker has good dexterity while having a high load capacity. Based on Sky-Worker, multiuser tests and practical application experiments show that the designed shared-control mode effectively improves the success rate and efficiency of operations compared with other current operation modes.

The designed heterogeneous dual-arm distribution robot aims to better serve distribution line operation tasks.

For the first time, the integration of hydraulic and motor drives into a distribution network operation robot has achieved better overall performance. A human–robot cooperative shared control framework is proposed for remote live-line working robots, which provides better operation results than other current operation modes.