Research on Fast Recognition and Localization of an Electric Vehicle Charging Port Based on a Cluster Template Matching Algorithm

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

doi:10.3390/s22093599

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…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

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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).

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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

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“…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

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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.

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A review of robotic charging for electric vehicles

Saputra,

Nor,

Rijanto

et al. 2023

Int J Intell Robot Appl

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Research on Fast Recognition and Localization of an Electric Vehicle Charging Port Based on a Cluster Template Matching Algorithm

Cited by 5 publications

References 27 publications

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

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

A review of robotic charging for electric vehicles

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