Development of a vision based pose estimation system for robotic machining and improving its accuracy using LSTM neural networks and sparse regression

Bilal, Diyar Khalis; Ünel, Mustafa; Tunç, Lütfi; Gonul, Bora

doi:10.1016/j.rcim.2021.102262

Cited by 38 publications

(9 citation statements)

References 23 publications

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“…However, the image‐based visual servo system lacks the feedback of the target position, which is only suitable for cases where the visual projection relationship is relatively simple. Bilal et al (2022) developed an attitude estimation system based on hand–eye camera for CR processing, with the long‐term and short‐term memory (LSTM) NN and sparse regression applied to improve the accuracy of attitude estimation. Compared with the proposed method based on LSTM, the proposed method based on sparse regression provided a saving model with better results.…”

Section: Overview Of Control Theory and Methodsmentioning

confidence: 99%

A review for control theory and condition monitoring on construction robots

Shi

Bai

et al. 2023

Journal of Field Robotics

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The application of robotic technologies in building construction leads to great convenience and productivity improvement, and construction robots (CRs) bring enormous opportunities for the way we conduct design and construction. To get a better understanding of the trends and track the application of CRs for on‐site conditions, this paper conducts a systematic review of control models and status monitoring of CRs, which are two key aspects that determine construction accuracy and efficiency. Control accuracy and flexibility are primary needs for CRs applied in different scenes, so the control methods based on driving models are vitally important. Status monitoring on CRs contains knowledge in fault detection, intelligence maintenance, and fault‐tolerant control, and multiple objectives need to be met and optimized in the whole drive chain. Moreover, the state‐of‐the‐art is comprehensively summarized, and new insights are also provided to carry on promising researches.

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Section: Overview Of Control Theory and Methodsmentioning

confidence: 99%

A review for control theory and condition monitoring on construction robots

Shi

Bai

et al. 2023

Journal of Field Robotics

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“…CNN-LSTM [28]: a one-versus-rest filter bank common spatial pattern (OVR-FBCSP), CNN and long short-term memory (LSTM) [29] -based hybrid deep neural network to decode the EEG signals of motion imagination.…”

Section: Benchmark Methodsmentioning

confidence: 99%

Considerate motion imagination classification method using deep learning

Yan

Yang²,

Yu³

2022

PLoS ONE

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In order to improve the classification accuracy of motion imagination, a considerate motion imagination classification method using deep learning is proposed. Specifically, based on a graph structure suitable for electroencephalography as input, the proposed model can accurately represent the distribution of electroencephalography electrodes in non-Euclidean space and fully consider the spatial correlation between electrodes. In addition, the spatial-spectral-temporal multi-dimensional feature information was extracted from the spatial-temporal graph representation and spatial-spectral graph representation transformed from the original electroencephalography signal using the dual branch architecture. Finally, the attention mechanism and global feature aggregation module were designed and combined with graph convolution to adaptively capture the dynamic correlation intensity and effective feature of electroencephalography signals in various dimensions. A series of contrast experiments and ablation experiments on several different public brain-computer interface datasets demonstrated that the excellence of proposed method. It is worth mentioning that, the proposed model is a general framework for the classification of electroencephalography signals, which is suitable for emotion recognition, sleep staging and other fields based on electroencephalography research. Moreover, the model has the potential to be applied in the medical field of motion imagination rehabilitation in real life.

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“…The vanilla RNN is capable of handling moderate-sized sequences, but it underperforms with long sequences, which is referred to as its short-term memory problem mentioned in ref . To address this problem, some more powerful advanced RNNs have been proposed.…”

Section: Encoder–decoder Rnn With Attentionmentioning

confidence: 99%

Bioinspired Encoder–Decoder Recurrent Neural Network with Attention for Hydroprocessing Unit Modeling

Yang,

Moreira,

2023

Ind. Eng. Chem. Res.

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The hydroprocessing technique is used to refine crude oil and produce lighter, valuable products. Developing models of these units is crucial for predicting the process dynamics and facilitating optimization and control. In this research, we develop attention-based encoder–decoder recurrent neural network (A-ED-RNN) models, employing various RNN cells such as bioinspired neural circuit policies (NCPs), gated recurrent unit (GRU), and long short-term memory (LSTM), to predict diesel and jet production rates within an industrial hydroprocessing unit. A key innovation is integrating the NCP into the A-ED-RNN models, harnessing its advanced computational power to attain enhanced performance with a smaller model size compared to that of GRU and LSTM cells. The developed RNN models effectively capture the dynamics of diesel and jet production, surpassing the traditional data-driven models. Notably, the NCP-based A-ED-RNN model demonstrates superior memory efficiency and predictive ability, standing out among all of the developed RNN models, underscoring its potential for modeling complex processes.

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Development of a vision based pose estimation system for robotic machining and improving its accuracy using LSTM neural networks and sparse regression

Cited by 38 publications

References 23 publications

A review for control theory and condition monitoring on construction robots

A review for control theory and condition monitoring on construction robots

Considerate motion imagination classification method using deep learning

Bioinspired Encoder–Decoder Recurrent Neural Network with Attention for Hydroprocessing Unit Modeling

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