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Bin-Picking for Planar Objects Based on a Deep Learning Network: A Case Study of USB Packs.

Tuan-Tang Le¹, Chyi-Yeu Lin^2,3,4

¹Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan.

Sensors (Basel, Switzerland)

|August 22, 2019

Summary

This study introduces a novel deep learning method for random bin-picking of planar objects. The approach achieves 100% classification accuracy and over 99% pick success rate, enabling efficient industrial robotics.

Keywords:

artificial intelligence instance segmentation neural network object detection pose estimation random bin-picking

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Traditional random bin-picking methods struggle with planar objects due to insufficient geometric data.
Existing approaches often rely heavily on 3D information, limiting their applicability to certain object types.

Purpose of the Study:

To develop a robust solution for random bin-picking of planar objects in cluttered environments.
To leverage 2D image data for improved object classification, localization, and pose estimation.

Main Methods:

An instance segmentation-based deep learning approach using 2D image data for object classification and localization.
A novel method for extracting 3D point cloud data from 2D pixel values to establish a coordinate system on planar objects.

Main Results:

Achieved 100% accuracy in classifying two-sided objects in an unseen dataset.
Demonstrated highly effective 3D pose prediction with average translation and rotation errors below 0.23 cm and 2.26°, respectively.
Attained a system success rate exceeding 99% for object picking at an average processing time of 0.9 seconds per step.

Conclusions:

The proposed method offers a promising solution for random bin-picking of planar objects, surpassing previous approaches in success rate and efficiency.
Successful implementation on USB packs suggests broad applicability to other planar objects in industrial settings.
The approach exhibits significant commercialization potential due to its precision and speed.