The computer vision techniques have been widely used in the robotic manipulation for perception and positioning. However, duo to the inaccuracy of camera calibration and measurement, there is a greatly need for more a...
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Object pose estimation is a core means for robots to understand and interact with their environment. For this task, monocular category-level methods are attractive as they require only a single RGB camera. However, cu...
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When confronting a practical dial-a-ride problem (DARP), addressing the transportation demands of joining and removal at any operational time is of practical significance yet a theoretical challenge. To this end, this...
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The use of multiple robots to manufacture composite components represents a critical development direction for fiber placement systems (FPSs). In multi-robotic fiber placement systems (MRFPSs) with heterogeneous mecha...
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The use of multiple robots to manufacture composite components represents a critical development direction for fiber placement systems (FPSs). In multi-robotic fiber placement systems (MRFPSs) with heterogeneous mechanical structures, robots collaborate to perform fiber placement tasks. Consequently, robot synchronization emerges as a primary factor in determining the performance of the fiber placement process. However, the difficulty in establishing accurate system models and the presence of disturbances are two significant challenges to achieving precise robot synchronization. Additionally, the system is expected to exhibit desirable dynamic characteristics, such as finite-time error convergence. To address these issues and requirements, we propose a novel adaptive finite-time synchronization control (AFSC) algorithm for the system. Specifically, a finite-time sliding mode observer is developed to handle kinematic uncertainty. A novel fast non-singular terminal sliding mode (FNTSM) manifold is constructed in the AFSC algorithm. Moreover, the control algorithm integrates an adaptive law to handle dynamic uncertainty and an adaptive term to counteract disturbances. Performance analysis demonstrates that the AFSC ensures that the coupled, synchronization, and tracking errors converge to zero within finite time. Furthermore, simulations and experiments are conducted to validate the effectiveness of the AFSC algorithm.
For traditional parameter estimation schemes of uncertain robot, most of them were proposed to identify unknown parameter with desired precision, but few of them focused on the convergence time. Recently finite-time e...
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ISBN:
(数字)9781728151694
ISBN:
(纸本)9781728151700
For traditional parameter estimation schemes of uncertain robot, most of them were proposed to identify unknown parameter with desired precision, but few of them focused on the convergence time. Recently finite-time estimation techniques have been proposed by scholars to achieve estimation in finite time. In this paper, we proposed a novel estimation scheme for uncertain robot systems with fixed time instead of finite time. In order to avoid using acceleration signals during the estimation, a kind of auxiliary filtering technique was employed. Besides, a continuous and recursive update law was employed for the parameter estimation such that the computational burdens of real-time inversion of square matrices could be avoided. Finally the effectiveness of the identification algorithm is verified based on a 2-DOF uncertain robot model.
In this paper, we propose a stiffness estimation and intention detection method for human-robot collaboration. The human arm endpoint stiffness can be obtained according to the muscle activation levels of the upper ar...
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ISBN:
(数字)9781728151694
ISBN:
(纸本)9781728151700
In this paper, we propose a stiffness estimation and intention detection method for human-robot collaboration. The human arm endpoint stiffness can be obtained according to the muscle activation levels of the upper arm and the human arm configurations. The estimated endpoint stiffness of human arm is matching to the robot arm joint stiffness through an appropriate mapping. The motion intention of human arm is detected based on the wrist configuration which is recognized by a Myo armband attached at the forearm of the operator. In order to reduce the time of feature engineering to ensure the performance of real-time collaboration, the wrist configuration recognition is realised based on the neural learning algorithm. The sEMG of the human forearm is directly fed into the neural network after processing by filters and sliding windows. The force sensor at the end of the robot arm is embedded in the feedback loop to make the robot arm better adapted to the operator's movement. The results of experiments performed on Baxter robot platform illustrate a good performance and verifies the proposed method.
As multiple glass bottle bodies appear in the same one image from a view, the high precision and speed localization of the bottle body is difficult for the traditional visual detection methods. To overcome the problem...
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As multiple glass bottle bodies appear in the same one image from a view, the high precision and speed localization of the bottle body is difficult for the traditional visual detection methods. To overcome the problem, the imaging system is first briefly presented. And a new visual detection framework, named BTMfast, for glass bottle body with binary template matching is proposed. First, the input image is down-sampled to reduce the computational complexity. The subimages containing body region are obtained according to the position priors. Then, the neck or body of a bottle is taken as the template, and the template image and each sumimage is segmented with OTSU segmentation algorithm. Thirdly, the central line of the body in the original image can be obtained by the binary template matching. Finally, the top boundary of the bottle mouth is enhanced by a filtering operation with a new kernel. And the mouth position is obtained by segmenting the filtered image and column scanning. Two datasets are created for evaluating the performance of the proposed method. Compared with many conventional approaches on the two datasets, our method can achieve the optimal result. The localization error is about 1.5, the consumed time is about 4.3 ms.
Anomaly detection refers to identifying the observation that deviates from the normal pattern, which has been an active research area in various domains. Recently, the increasing data scale, complexity, and dimension ...
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The network connectivity maintenance problem in formation control of mobile agents with double-integrator dynamics is addressed in this paper. Distributed potential field-based controllers are proposed for double-inte...
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The network connectivity maintenance problem in formation control of mobile agents with double-integrator dynamics is addressed in this paper. Distributed potential field-based controllers are proposed for double-integrator agent group to maintain the connectivity of communication graph until the desired formation is achieved. We also investigate the connectivity preserving formation control with a virtual leader. Providing that at least one agent has information about the leader, all agents can asymptotically reach the desired formation and attain the same velocity as the virtual leader. The asymptotic stability analysis is presented with Lyapunov-like tools including LaSalle's invariance principle and Barbalat's lemma. Simulation results on a group of agents with double-integrator dynamics are presented to demonstrate the effectiveness of the proposed strategies.
Purpose In this paper, electroencephalogram (EEG) is used to recognize the pattern of the different auxiliary forces (AFs) on the upper extremity. It is expected that the robot obtains human feedback information in or...
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