To further understand the underlying mechanism of various reinforcement learning (RL) algorithms and also to better use the optimization theory to make further progress in RL, many researchers begin to revisit the lin...
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In basketball videos, the ball is always so small in the camera that its appearance feature is hard to be extracted. In this paper, we introduce a deep-learning technology to detect the basketball. Specifically, we tr...
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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.
Leveraging line features to improve localization accuracy of point-based visual-inertial SLAM (VINS) is gaining interest as they provide additional constraints on scene structure. However, real-time performance when i...
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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.
—To mitigate the radiologist’s workload, computer-aided diagnosis with the capability to review and analyze medical images is gradually deployed. Deep learning-based region of interest segmentation is among the most...
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Here, a robust adaptive trajectory tracking algorithm is proposed for free-form surface grinding robot (FSGR) in metal surface production line. Machine-learning method is used for robot dynamic approximation which is ...
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Visual object tracking has been a concern topic these years,and many trackers have achieved good results in various *** researches and breakthroughs have made many improvements to solve problems such as drift,lighting...
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Visual object tracking has been a concern topic these years,and many trackers have achieved good results in various *** researches and breakthroughs have made many improvements to solve problems such as drift,lighting,deformation and *** this paper,we improve the structure of the AlexNet[1] network by designing the three important influencing factors of the receptive field size,total network step size,and feature filling of the twin *** from this,we add a smoothing matrices and a background suppression matrices to effectively learn the features of the first few frames as much as *** multilayer feature elements can learn online about target appearance changes and background suppression,and we train them by using continuous video sequences.
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.
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