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This paper aims to develop a wearable-based human-manipulator interface which integrates the interval Kalman filter (IKF), unscented Kalman filter (UKF), over damping method (ODM) and adaptive multispace transformation (AMT) to perform immersive human-manipulator interaction by interacting the natural and continuous motion of the human operator’s hand with the robot manipulator.

The interface requires that a wearable watch is tightly worn on the operator’s hand to track the continuous movements of the operator’s hand. Nevertheless, the measurement errors generated by the sensor error and tracking failure signicantly occur several times, which means that the measurement is not determined with sufficient accuracy. Due to this fact, IKF and UKF are used to compensate for the noisy and incomplete measurements, and ODM is established to eliminate the influence of the error signals like data jitter. Furthermore, to be subject to the inherent perceptive limitations of the human operator and the motor, AMT that focuses on a secondary treatment is also introduced.

Experimental studies on the GOOGOL GRB3016 robot show that such a wearable-based interface that incorporates the feedback mechanism and hybrid filters can operate the robot manipulator more flexibly and advantageously even if the operator is nonprofessional; the feedback mechanism introduced here can successfully assist in improving the performance of the interface.

The interface uses one wearable watch to simultaneously track the orientation and position of the operator’s hand; it is not only avoids problems of occlusion, identification and limited operating space, but also realizes a kind of two-way human-manipulator interaction, a feedback mechanism can be triggered in the watch to reflect the system states in real time. Furthermore, the interface gets rid of the synchronization question in posture estimation, as hybrid filters work independently to compensate the noisy measurements respectively.

The purpose of this paper is to present a markerless human–manipulators interface which maps the position and orientation of human end-effector (EE, the center of the palm) to those of robot EE so that the robot could copy the movement of the operator hand.

The tracking system of this human–manipulators interface comprises five Leap Motions (LMs) which not only makes up the narrow workspace drawback of one LM but also provides redundancies to improve the data precision. However, because of the native noises and tracking errors of the LMs, the measurement errors increase over time. To address this problem, two filter tools are integrated to obtain the relatively accurate estimation of the human EE, that is, Particle Filter for position estimation and Kalman Filter for orientation estimation. Because the operator has inherent perceptive limitations, the motions of the manipulator may be out of sync with the hand motions, so that it is hard to complete with the high performance manipulation. Therefore, in this paper, an over-damping method is adopted to improve reliability and accuracy.

A series of human–manipulators interaction experiments were carried out to verify the proposed system. Compared with the markerless and contactless methods(Kofman et al., 2007; Du and Zhang, 2015), the method described in this study is more accurate and efficient.

The proposed method would not hinder most natural human limb motion and allows the operator to concentrate on his/her own task, making it perform high-precision manipulations efficiently.

This paper aims to present an intuitive control system for robotic manipulators that pairs a Leap Motion, a low-cost optical tracking and gesture recognition device, with the ability to record and replay trajectories and operation to create an intuitive method of controlling and programming a robotic manipulator. This system was designed to be extensible and includes modules and methods for obstacle detection and dynamic trajectory modification for obstacle avoidance.

The presented control architecture, while portable to any robotic platform, was designed to actuate a six degree-of-freedom robotic manipulator of our own design. From the data collected by the Leap Motion, the manipulator was controlled by mapping the position and orientation of the human hand to values in the joint space of the robot. Additional recording and playback functionality was implemented to allow for the robot to repeat the desired tasks once the task had been demonstrated and recorded.

Experiments were conducted on our custom-built robotic manipulator by first using a simulation model to characterize and quantify the robot’s tracking of the Leap Motion generated trajectory. Tests were conducted in the Gazebo simulation software in conjunction with Robot Operating System, where results were collected by recording both the real-time input from the Leap Motion sensor, and the corresponding pose data. The results of these experiments show that the goal of accurate and real-time control of the robot was achieved and validated our methods of transcribing, recording and repeating six degree-of-freedom trajectories from the Leap Motion camera.

As robots evolve in complexity, the methods of programming them need to evolve to become more intuitive. Humans instinctively teach by demonstrating the task to a given subject, who then observes the various poses and tries to replicate the motions. This work aims to integrate the natural human teaching methods into robotics programming through an intuitive, demonstration-based programming method.

The aim of this paper is to present a novel methodology which incorporates Camshift, Kalman filter (KFs) and adaptive multi-space transformation (AMT) for a human-robot interface, which perfects human intelligence and teleoperation.

In the proposed method, an inertial measurement unit is used to measure the orientation of the human hand, and a Camshift algorithm is used to track the human hand using a three-dimensional camera. Although the location and the orientation of the human can be obtained from the two sensors, the measurement error increases over time due to the noise of the devices and the tracking errors. KFs are used to estimate the location and the orientation of the human hand. Moreover, to be subject to the perceptive limitations and the motor limitations, human operator is hard to carry out the high precision operation. An AMT method is proposed to assist the operator to improve accuracy and reliability in determining the pose of the robot.

The experimental results show that this method would not hinder most natural human-limb motion and allows the operator to concentrate on his/her own task. Compared with the non-contacting marker-less method (Kofman et al., 2007), this method proves more accurate and stable.

The human-robot interface system was experimentally verified in a laboratory environment, and the results indicate that such a system can complete high-precision manipulation efficiently.

This paper aims to propose a new technique for programming robotized machining tasks based on intuitive human–machine interaction. This will enable operators to create robot programs for small-batch production in a fast and easy way, reducing the required time to accomplish the programming tasks.

This technique makes use of online walk-through path guidance using an external force/torque sensor, and simple and intuitive visual programming, by a demonstration method and symbolic task-level programming.

Thanks to this technique, the operator can easily program robots without learning every robot-specific language and can design new tasks for industrial robots based on manual guidance.

The main contribution of the paper is a new procedure to program machining tasks based on manual guidance (walk-through teaching method) and user-friendly visual programming. Up to now, the acquisition of paths and the task programming were done in separate steps and in separate machines. The authors propose a procedure for using a tablet as the only user interface to acquire paths and to make a program to use this path for machining tasks.

This paper aims to solve the problem between detection efficiency and performance in grasp commodities rapidly. A fast detection and grasping method based on improved faster R-CNN is purposed and applied to the mobile manipulator to grab commodities on the shelf.

To reduce the time cost of algorithm, a new structure of neural network based on faster R CNN is designed. To select the anchor box reasonably according to the data set, the data set-adaptive algorithm for choosing anchor box is presented; multiple models of ten types of daily objects are trained for the validation of the improved faster R-CNN. The proposed algorithm is deployed to the self-developed mobile manipulator, and three experiments are designed to evaluate the proposed method.

The result indicates that the proposed method is successfully performed on the mobile manipulator; it not only accomplishes the detection effectively but also grasps the objects on the shelf successfully.

The proposed method can improve the efficiency of faster R-CNN, maintain excellent performance, meet the requirement of real-time detection, and the self-developed mobile manipulator can accomplish the task of grasping objects.

The purpose of this paper is to provide a hybrid adaptive impedance-leader-follower control algorithm for multi-arm coordination manipulators, which is significant for dealing with the problems of kinematics inconsistency and error accumulation of interactive force in multi-arm system.

This paper utilized a motion mapping theory in Cartesian space to establish a centralized dynamic leader-follower control algorithm which helped to reduce the possibility of kinematics inconsistency for multiple manipulators. A virtual linear spring model (VLSM) was presented based on a recognition approach of characteristic marker. This paper accomplished an adaptive impedance control algorithm based on the VLSM, which took into account the non-rigid contact characteristic. Experimentally demonstrated results showed the proposed algorithm guarantees that the motion and interactive forces asymptotically converge to the prescribed values.

The hybrid control method improves the accuracy and reliability of multi-arm coordination system, which presents a new control framework for multiple manipulators.

This algorithm has significant commercial applications, as a means of controlling multi-arm coordination manipulators that could serve to handle large objects and assemble complicated objects in industrial and hazardous environment.

This work presented a new control framework for multiple coordination manipulators, which can ensure consistent kinematics and reduce the influence of error accumulation, and thus can improve the accuracy and reliability of multi-arm coordination system.

The purpose of this paper is to describe the integration of a gesture control system for industrial collaborative robot. Human and robot collaborative systems can be a viable manufacturing solution, but efficient control and communication are required for operations to be carried out effectively and safely.

The integrated system consists of facial recognition, static pose recognition and dynamic hand motion tracking. Each sub-system has been tested in isolation before integration and demonstration of a sample task.

It is demonstrated that the combination of multiple gesture control methods can increase its potential applications for industrial robots.

The novelty of the system is the combination of a dual gesture controls method which allows operators to command an industrial robot by posing hand gestures as well as control the robot motion by moving one of their hands in front of the sensor. A facial verification system is integrated to improve the robustness, reliability and security of the control system which also allows assignment of permission levels to different users.

Recent years have seen a technological change, Industry 4.0, in the manufacturing industry. Human–robot cooperation, a new application, is increasing and facilitating collaboration without fences, cages or any kind of separation. The purpose of the paper is to review mainstream academic publications to evaluate the current status of human–robot cooperation and identify potential areas of further research.

A systematic literature review is offered that searches, appraises, synthetizes and analyses relevant works.

The authors report the prevailing status of human–robot collaboration, human factors, complexity/ programming, safety, collision avoidance, instructing the robot system and other aspects of human–robot collaboration.

This paper identifies new directions and potential research in practice of human–robot collaboration, such as measuring the degree of collaboration, integrating human–robot cooperation into teamwork theories, effective functional relocation of the robot and product design for human robot collaboration.

This paper will be useful for three cohorts of readers, namely, the manufacturers who require a baseline for development and deployment of robots; users of robots-seeking manufacturing advantage and researchers looking for new directions for further exploration of human–machine collaboration.

The purpose of this paper is to provide a novel training-responding controlling approach for human–robot interaction. The approach is inspired by the processes of muscle memory and conditioned reflex. The approach is significant for dealing with the problems of robot’s redundant movements and operator’s fatigue in human–robot interaction system.

This paper presented a directional double clustering algorithm (DDCA) to achieve the training process. The DDCA ensured that the initial clustering centers uniformly distributed in every desired cluster. A minimal resource allocation network was used to construct a memory responding algorithm (MRA). When the human–robot interaction system needed to carry out a task for more than one time, the desired movements of the robot were given by the MRA without repeated training. Experimentally demonstrated results showed the proposed training-responding controlling approach could successfully accomplish human–robot interaction tasks.

The training-responding controlling approach improved the robustness and reliability of the human–robot interaction system, which presented a novel controlling method for the operator.

This approach has significant commercial applications, as a means of controlling for human–robot interaction could serve to point to the desired target and arrive at the appointed positions in industrial and household environment.

This work presented a novel training-responding human-robot controlling method. The human-robot controlling method dealt with the problems of robot’s redundant movements and operator’s fatigue. To the authors’ knowledge, the working processes of muscle memory and conditioned reflex have not been reported to apply to human-robot controlling.