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Sample frames which depicts that Baxter’s left arm copies motions of operator’s left arm, confirming the feasibility of the proposed motion capturing scheme
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Disturbance observer (DOB) based controller performs well in estimating and compensating for perturbation when the external or internal unknown disturbance is slowly time varying. However, to some extent, robot manipulators usually work in complex environment with high-frequency disturbance. Thereby, to enhance tracking performance in a teleoperati...
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One of the biggest challenges in the bilateral teleoperation of robot manipulators is to get transparency and telepresence between the local (master) and the remote (slave) manipulator. For these goals, it is advisable to transfer contact force measurements from one side to the other through the communication channel. However, there may be some rea...
Citations
... There is still need to improve anti-disturbance capabilities for AFS. Disturbance observer based control (DOBC) is an effective rejection by using the estimated values of disturbances, which has been used successfully in robots, motor drives and missiles [9][10][11][12][13][14][15][16][17][18][19]. In [20], disturbance observer (DO) and H ∞ are used in flexible spacecraft with unknown disturbances. ...
An improved anti-disturbance strategy is proposed to guarantee lateral stability for electric vehicles with external disturbance and input time delay. Firstly, the T-S fuzzy model is applied to describe active front wheel steering system (AFS). Based on the obtained model, a new collective observers including disturbance observer and state observer are structured to estimate disturbance and state simultaneously. Then, a compound control is designed by using the estimation values of collective observers. During the design process, a novel path-independent fuzzy Lyapunov-Krasovskii function (FLKF) and slack variable matrices are introduced to reduce conservatism. Finally, two simulation cases are implemented on Matlab/Simulink-Carsim to show the effectiveness of the proposed method.
... The Canny operator is used to analyze the low-frequency coefficients of motion images of two kinds of source snowboarding Y j,A (low-frequency coefficient of TOF depth image) and Y j,B (low-frequency coefficient of high-speed vision sensor image) for the extraction of edge features and then construct binary edge feature Z j,A and Z j,B [14]. The construction formula is as follows: ...
Due to the influence of environmental interference and too fast speed, there are some problems in ski motion capture, such as inaccurate motion capture, motion delay, and motion loss, resulting in the inconsistency between the actual motion of later athletes and the motion of virtual characters. To solve the above problems, a real-time skiing motion capture method of snowboarders based on a 3D vision sensor is proposed. This method combines the Time of Fight (TOF) camera and high-speed vision sensor to form a motion acquisition system. The collected motion images are fused to form a complete motion image, and the pose is solved. The pose data is bound with the constructed virtual character model to drive the virtual model to synchronously complete the snowboarding motion and realize the real-time capture of skiing motion. The results show that the motion accuracy of the system is as high as 98.6%, which improves the capture effect, and the motion matching proportion is better and more practical. It is also excellent in the investigation of motion delay and motion loss.
... This allows the operator to convey the desired motion to the robot intuitively and in real time, without the need to use the control interface. Several authors address the issues that arise in real-time robot teleoperation, such as delays and tremor, using variable gain [11,12], predictive [13], and fuzzy [14,15] controllers. ...
Increasing the accessibility of collaborative robotics requires interfaces that support intuitive teleoperation. One possibility for an intuitive interface is offered by wearable systems that measure the operator's movement and use the information for robot control. Such wearable systems should preserve the operator's movement capabilities and, thus, their ability to flexibly operate in the workspace. This paper presents a novel wireless wearable system that uses only inertial measurement units (IMUs) to determine the orientation of the operator's upper body parts. An algorithm was developed to transform the measured orientations to movement commands for an industrial collaborative robot. The algorithm includes a calibration procedure, which aligns the coordinate systems of all IMUs, the operator, and the robot, and the transformation of the operator's relative hand motions to the movement of the robot's end effector, which takes into account the operator's orientation relative to the robot. The developed system is demonstrated with an example of an industrial application in which a workpiece needs to be inserted into a fixture. The robot's motion is compared between the developed system and a standard robot controller. The results confirm that the developed system is intuitive, allows for flexible control, and is robust enough for use in industrial collaborative robotic applications.
... with Θ � Θ − Θ * . Based on the disturbance observer [31] and fuzzy logic rules, the control law of this robot manipulator is developed as ...
Robot manipulators have been extensively used in complex environments to complete diverse tasks. The teleoperation control based on human-like adaptivity in the robot manipulator is a growing and challenging field. This paper developed a disturbance-observer-based fuzzy control framework for a robot manipulator using an electromyography- (EMG-) driven neuromusculoskeletal (NMS) model. The motion intention (desired torque) was estimated by the EMG-driven NMS model with EMG signals and joint angles from the user. The desired torque was transmitted into the desired velocity for the robot manipulator system through an admittance filter. In the robot manipulator system, a fuzzy logic system, utilizing an integral Lyapunov function, was applied for robot manipulator systems subject to model uncertainties and external disturbances. To compensate for the external disturbances, fuzzy approximation errors, and nonlinear dynamics, a disturbance observer was integrated into the controller. The developed control algorithm was validated with a 2-DOFs robot manipulator in simulation. The results indicate the proposed control framework is effective and crucial for the applications in robot manipulator control.
This paper provides solutions for a single-leader-dual-follower (SLDF) teleoperation system to collaboratively transport an object. First, to regulate the direct-teleoperated follower robot (DFR), we employ a relative pose transformation algorithm, based on “re-fixing” the leader and DFR together, to enable that the operator can ergonomically guide DFR without requiring any specific initial position, ensuring a higher teleoperation precision at the same time. Second, to regulate the assistive follower robot (AFR), we provide an efficient technique to acquire the right orientation to achieve holding. In addition, we devise an adjustable artificial potential field method (APFM) to autonomously regulate AFR's position to a ready-to-hold pose, where the operator's motion is involved. At last, based on the combination of the auto-regressive model and the impedance model, we generate a reference trajectory for AFR to follow, which enables the followers to hold a rigid or deformable object with a desired contact force. Simulations and experimental results verify the feasibility and effectiveness of the proposed method.
Gesture recognition based on wearable devices is one of the vital components of human–computer interaction systems. Compared with skeleton-based recognition in computer vision, gesture recognition using wearable sensors has attracted wide attention for its robustness and convenience. Recently, many studies have proposed deep learning methods based on surface electromyography (sEMG) signals for gesture classification; however, most of the existing datasets are built for surface EMG signals, and there is a lack of datasets for multi-category gestures. Due to model limitations and inadequate classification data, the recognition accuracy of these methods cannot satisfy multi-gesture interaction scenarios. In this paper, a multi-category dataset containing 20 gestures is recorded with the help of a wearable device that can acquire surface electromyographic and inertial (IMU) signals. Various two-stream deep learning models are established and improved further. The basic convolutional neural network (CNN), recurrent neural network (RNN), and Transformer models are experimented on with our dataset as the classifier. The CNN and the RNN models’ test accuracy is over 95%; however, the Transformer model has a lower test accuracy of 71.68%. After further improvements, the CNN model is introduced into the residual network and augmented to the CNN-Res model, achieving 98.24% accuracy; moreover, it has the shortest training and testing time. Then, after combining the RNN model and the CNN-Res model, the long short term memory (LSTM)-Res model and gate recurrent unit (GRU)-Res model achieve the highest classification accuracy of 99.67% and 99.49%, respectively. Finally, the fusion of the Transformer model and the CNN model enables the Transformer-CNN model to be constructed. Such improvement dramatically boosts the performance of the Transformer module, increasing the recognition accuracy from 71.86% to 98.96%.
