Adaptive Neural Network Control of Time Delay Teleoperation System Based on Model Approximation

Wang, Yaxiang; Tian, Jiawei; Liu, Yan; Yang, Bo; Liu, Shan; Yin, Lirong; Zheng, Wenfeng

doi:10.3390/s21227443

Cited by 16 publications

(10 citation statements)

References 37 publications

(54 reference statements)

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“…The algorithm based on data learning can not only learn the feature detector like Quarknetworks, but also learn the feature descriptor. With the improvement of machine learning [20][21][22][23][24], Simoserra et al proposed Deepdesc [25] for key point descriptor learning. This method uses a convolutional neural network to learn the discriminant representation of image blocks (patches), trains a Siamese network with paired inputs, and processes a large number of paired image blocks by combining the random extraction of training sets and the mining strategy for patch pairs that are difficult to classify.…”

Section: Introductionmentioning

confidence: 99%

Soft Tissue Feature Tracking Based on Deep Matching Network

Lu¹,

Liu²,

Hou³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Research in the field of medical image is an important part of the medical robot to operate human organs. A medical robot is the intersection of multi-disciplinary research fields, in which medical image is an important direction and has achieved fruitful results. In this paper, a method of soft tissue surface feature tracking based on a depth matching network is proposed. This method is described based on the triangular matching algorithm. First, we construct a self-made sample set for training the depth matching network from the first N frames of speckle matching data obtained by the triangle matching algorithm. The depth matching network is pre-trained on the ORL face data set and then trained on the self-made training set. After the training, the speckle matching is carried out in the subsequent frames to obtain the speckle matching matrix between the subsequent frames and the first frame. From this matrix, the inter-frame feature matching results can be obtained. In this way, the inter-frame speckle tracking is completed. On this basis, the results of this method are compared with the matching results based on the convolutional neural network. The experimental results show that the proposed method has higher matching accuracy. In particular, the accuracy of the MNIST handwritten data set has reached more than 90%.

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Section: Introductionmentioning

confidence: 99%

Soft Tissue Feature Tracking Based on Deep Matching Network

Lu¹,

Liu²,

Hou³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…The multiresolution registration strategy's key is a multiresolution sampling of the reference image and floating image multiresolution technology. Wavelet transform [43], Laplacian pyramid [44], Gauss pyramid [45], average pyramid, and sampling pyramid are widely studied. This paper needs to obtain low-resolution images from the original high-resolution image, so a Gaussian pyramid in downsampling is used.…”

Section: Multiresolution Strategymentioning

confidence: 99%

2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation

et al. 2022

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Image-guided surgery (IGS) can reduce the risk of tissue damage and improve the accuracy and targeting of lesions by increasing the surgery’s visual field. Three-dimensional (3D) medical images can provide spatial location information to determine the location of lesions and plan the operation process. For real-time tracking and adjusting the spatial position of surgical instruments, two-dimensional (2D) images provide real-time intraoperative information. In this experiment, 2D/3D medical image registration algorithm based on the gray level is studied, and the registration based on normalized cross-correlation is realized. The Gaussian Laplacian second-order differential operator is introduced as a new similarity measure to increase edge information and internal detail information to solve single information and small convergence regions of the normalized cross-correlation algorithm. The multiresolution strategy improves the registration accuracy and efficiency to solve the low efficiency of the normalized cross-correlation algorithm.

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“…For the above two problems to be solved in this paper, from the mentioned teleoperation system's research status, we can conclude that there are mainly adaptive control methods to solve communication channel delay and system dynamic parameter uncertainty (Polushin et al, 2010;Haddadi et al, 2015;Wang et al, 2021).…”

Section: Introductionmentioning

confidence: 98%

Adaptive control of time delay teleoperation system with uncertain dynamics

et al. 2022

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A bilateral adaptive control method based on PEB control structure is designed for a class of time-delay force feedback teleoperation system without external interference and internal friction to study the uncertainty of dynamic parameters and time delay. The stability and tracking performances of the closed-loop constant time delay teleoperation system are analyzed by Lyapunov stability theory. Finally, the controller designed in this paper is successfully applied to the teleoperation system composed of a two-degree of freedom rotating manipulator as the master robot and the slave robot. The simulation is carried out in no operator and environment force or with operator and environment force. The adaptive bilateral control method's control performance is compared with that of the traditional time-delay teleoperation system. Finally, it is verified that the method has good control performance.

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Adaptive Neural Network Control of Time Delay Teleoperation System Based on Model Approximation

Cited by 16 publications

References 37 publications

Soft Tissue Feature Tracking Based on Deep Matching Network

Soft Tissue Feature Tracking Based on Deep Matching Network

2D/3D Multimode Medical Image Registration Based on Normalized Cross-Correlation

Adaptive control of time delay teleoperation system with uncertain dynamics

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