Research on Fast Identification and Location of Contour Features of Electric Vehicle Charging Port in Complex Scenes

Quan, Pengkun; Lou, Ya’nan; Lin, Haoyu; Liang, Zhiming; Di, Shichun

doi:10.1109/access.2021.3092210

Cited by 18 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The pooling layer down sampled these features to achieve data dimensionality reduction. The fully connected layer transformed the extracted local features into feature vectors, which passed to the Bi-LSTM neural network for prediction: The prediction is computed using the equation as explained [39][40][41]:…”

Section: The Decision Layer Fusion Network Modeling Framework Of the ...mentioning

confidence: 99%

Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging

Yan,

Liu,

Wang

et al. 2024

Foods

View full text Add to dashboard Cite

Rapid non-destructive testing technologies are effectively used to analyze and evaluate the linoleic acid content while processing fresh meat products. In current study, hyperspectral imaging (HSI) technology was combined with deep learning optimization algorithm to model and analyze the linoleic acid content in 252 mixed red meat samples. A comparative study was conducted by experimenting mixed sample data preprocessing methods and feature wavelength extraction methods depending on the distribution of linoleic acid content. Initially, convolutional neural network Bi-directional long short-term memory (CNN-Bi-LSTM) model was constructed to reduce the loss of the fully connected layer extracted feature information and optimize the prediction effect. In addition, the prediction process of overfitting phenomenon in the CNN-Bi-LSTM model was also targeted. The Bayesian-CNN-Bi-LSTM (Bayes-CNN-Bi-LSTM) model was proposed to improve the linoleic acid prediction in red meat through iterative optimization of Gaussian process acceleration function. Results showed that best preprocessing effect was achieved by using the detrending algorithm, while 11 feature wavelengths extracted by variable combination population analysis (VCPA) method effectively contained characteristic group information of linoleic acid. The Bi-directional LSTM (Bi-LSTM) model combined with the feature extraction data set of VCPA method predicted 0.860 Rp2 value of linoleic acid content in red meat. The CNN-Bi-LSTM model achieved an Rp2 of 0.889, and the optimized Bayes-CNN-Bi-LSTM model was constructed to achieve the best prediction with an Rp2 of 0.909. This study provided a reference for the rapid synchronous detection of mixed sample indicators, and a theoretical basis for the development of hyperspectral on-line detection equipment.

show abstract

Section: The Decision Layer Fusion Network Modeling Framework Of the ...mentioning

confidence: 99%

Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging

Yan,

Liu,

Wang

et al. 2024

Foods

View full text Add to dashboard Cite

show abstract

“…Currently, the positioning of automatic charging systems relies predominantly on machine vision technology at the charging port [4][5][6][7]. In structured environments, machine vision systems exhibit commendable stability and high precision, meeting the requirements of robot-based electric vehicle (EV) automatic charging solutions for precise plug insertion.…”

Section: Introductionmentioning

confidence: 99%

Precision Data-Driven Collision Localization with a Dedicated Matrix Template for Electric Vehicle Automatic Charging

Lin,

Quan,

Liang

et al. 2024

Electronics

Self Cite

View full text Add to dashboard Cite

With the increasing maturity of autonomous driving technology and automated valet parking, public awareness of robot-based automatic charging for electric vehicles has gradually increased. The positioning of the charging port for electric vehicles is a prerequisite for achieving automatic charging. The common approach is to use visual methods for charging port positioning. However, due to factors such as external light conditions, humidity, and temperature, the visual system may experience insufficient positioning accuracy, leading to difficulties in executing the charging plug-in task. To address this issue, this paper proposes a data-driven collision localization method based on the vibration signal generated by the contact. During the data collection process, we first introduce a collision point matrix template suitable for automatic charging plug-in. This template covers the entire charging port and supports the acquisition of dense collision vibration data. Using this collision point matrix template, the collision localization problem can be transformed into a classification problem of collision vibration information corresponding to different collision points. Then, the collision vibration data obtained, based on this template, are used to train the collision localization model, which mainly consists of an echo state network (ESN) and support vector machine (SVM). The AUBO-i5 6-DOF articulated robot is employed to test the proposed collision localization method under different joint configurations. The simulated experimental results demonstrate the effectiveness of the proposed collision localization method, showcasing a promising localization accuracy and root mean square error (RMSE).

show abstract

“…In the rapidly advancing field of artificial intelligence, the utilization of robots for the automatic charging of electric vehicles is regarded as an ideal solution. Currently, research in the domain of automatic electric vehicle charging primarily focuses on the design of charging robots [1,2] and the visual positioning of charging ports [3][4][5][6][7]. However, a notable gap remains in the research concerning the safety aspects of robots during the charging plug-in process.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach

Lin,

Quan,

Liang

et al. 2024

Applied Sciences

Self Cite

View full text Add to dashboard Cite

With the rise of electric vehicles, autonomous driving, and valet parking technologies, considerable research has been dedicated to automatic charging solutions. While the current focus lies on charging robot design and the visual positioning of charging ports, a notable gap exists in addressing safety aspects during the charging plug-in process. This study aims to bridge this gap by proposing a collision classification scheme for robot manipulators in automatic electric vehicle charging scenarios. In situations with minimal visual positioning deviation, robots employ impedance control for effective insertion. Significant deviations may lead to potential collisions with other vehicle parts, demanding discrimination through a global visual system. For moderate deviations, where a robot’s end-effector encounters difficulty in insertion, existing methods prove inadequate. To address this, we propose a novel data-driven collision classification method, utilizing vibration signals generated during collisions, integrating the robust light gradient boosting machine (LightGBM) algorithm. This approach effectively discerns the acceptability of collision contacts in scenarios involving moderate deviations. Considering the impact of passing vehicles introducing environmental noise, a noise suppression module is introduced into the proposed collision classification method, leveraging empirical mode decomposition (EMD) to enhance its robustness in noisy charging scenarios. This study significantly contributes to the safety of automatic charging processes, offering a practical and applicable collision classification solution tailored to diverse noisy scenarios and potential contact forms encountered by charging robots. The experimental results affirm the effectiveness of the collision classification method, integrating LightGBM and EMD, and highlight its promising prediction accuracy. These findings offer valuable perspectives to steer future research endeavors in the domain of autonomous charging systems.

show abstract

Research on Fast Identification and Location of Contour Features of Electric Vehicle Charging Port in Complex Scenes

Cited by 18 publications

References 23 publications

Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging

Predictive Analysis of Linoleic Acid in Red Meat Employing Advanced Ensemble Models of Bayesian and CNN-Bi-LSTM Decision Layer Fusion Based Hyperspectral Imaging

Precision Data-Driven Collision Localization with a Dedicated Matrix Template for Electric Vehicle Automatic Charging

Enhancing Safety in Automatic Electric Vehicle Charging: A Novel Collision Classification Approach

Contact Info

Product

Resources

About