Deep Learning on 3D Object Detection for Automatic Plug-in Charging Using a Mobile Manipulator

Zhengxue Zhou, Leihui Li, Xuping Zhang*

*Corresponding author for this work

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

Abstract

Increasing research attention has been attracted to automatic plug-in charging in an unmanned and dangerous environment. In this work, we develop an object detection solution based on deep learning on 3D point clouds using a mobile robot manipulator to provide mobility and manipulation. In this solution, the 3D point cloud technology is adopted to measure the shapes and depth information for plug-in charging. Then the deep learning is employed to deal with the uncertainty in 3D detection, such as inconsistent light conditions, irregular distribution, and structural ambiguity of point clouds. We utilize a mobile robot manipulator carrying a 3D camera and a gripper to detect the targeted objects and automate plug-in charging operations. The proposed 3D object detection principle and procedure for the automatic plug-in charging are presented in detail. The automatic plug-in charging testing is conducted to validate the developed 3D object detection algorithm using a mobile robot manipulator.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Automation (ICRA)
Number of pages7
PublisherIEEE
Publication date2021
Pages4148-4154
ISBN (Electronic)9781728190778
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Robotics and Automation (ICRA) -
Duration: 30 May 20214 Jun 2021
https://www.ieee-icra.org/

Conference

Conference2021 IEEE International Conference on Robotics and Automation (ICRA)
Period30/05/202104/06/2021
Internet address

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