Deep Learning-Based Connector Detection for Robotized Assembly of Automotive Wire Harnesses

TL;DR

This paper introduces a deep learning approach for detecting automotive wire harness connectors to improve robotized assembly, addressing current limitations in vision-based perception systems.

Contribution

It presents a novel deep learning-based connector detection method and a new dataset for training and evaluating models in automotive wire harness assembly.

Findings

Deep learning models effectively detect connectors in assembly tasks.

The approach improves perception accuracy for robotic assembly systems.

Limitations exist due to connector exterior design constraints.

Abstract

The shift towards electrification and autonomous driving in the automotive industry results in more and more automotive wire harnesses being installed in modern automobiles, which stresses the great significance of guaranteeing the quality of automotive wire harness assembly. The mating of connectors is essential in the final assembly of automotive wire harnesses due to the importance of connectors on wire harness connection and signal transmission. However, the current manual operation of mating connectors leads to severe problems regarding assembly quality and ergonomics, where the robotized assembly has been considered, and different vision-based solutions have been proposed to facilitate a better perception of the robot control system on connectors. Nonetheless, there has been a lack of deep learning-based solutions for detecting automotive wire harness connectors in previous literature. This paper presents a deep learning-based connector detection for robotized automotive wire harness assembly. A dataset of twenty automotive wire harness connectors was created to train and evaluate a two-stage and a one-stage object detection model, respectively. The experiment results indicate the effectiveness of deep learning-based connector detection for automotive wire harness assembly but are limited by the design of the exteriors of connectors.