Using Robots to Collect Data for Machine Vision Tasks
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Systems, control and mechatronics (MPSYS), MSc
Complex adaptive systems (MPCAS), MSc
Complex adaptive systems (MPCAS), MSc
Publicerad
Författare
El-Nahass, Karim
Urbanos, Gonzalo
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The current final assembly process of automotive wire harnesses into vehicles predominantly
relies on manual labor and skill. This reliance leads to safety and ergonomic
issues when lifting heavy wire harnesses and applying high-pressure manipulations
to components for 8 hours a day. This thesis combines collaborative
robotics and artificial intelligence to collect connector data for machine vision tasks
in the automotive industry, addressing the problem of insufficient data. The research
investigates an approach using a robotic setup for automated data collection.
The framework includes data acquisition utilizing a UR5 robot and an Intel RealSense
D435 camera; robot-camera communication using a Raspberry Pi 4b as a
bridge; and an automatic labeling tool. The collected dataset comprises 8 different
connectors commonly used in automotive wire harnesses. The resultant datasets
(first the manually annotated dataset and second the automatic annotated dataset)
are evaluated using YOLOv8, a deep-learning based object detection model. The
evaluation results present a higher accuracy (mAP50 = 93.5%) for the manually
annotated dataset compared to the automatic labeling approach (mAP50 = 74.4%)
which suggests that there is still room for improvement on the automatic labeling
tool used. This accuracy difference is concluded to be due to the inability to control
lighting conditions in the workspace in the lab.
Beskrivning
Ämne/nyckelord
accuracy , automatic annotation , connector , dataset , ergonomics , object detection , robotic system , wire harness