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Updated: Jul 9, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Object Recognition and Grasping for Collaborative Robots Based on Vision.

Ruohuai Sun^1,2,3, Chengdong Wu^1,3, Xue Zhao⁴

¹College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.

Sensors (Basel, Switzerland)

|January 11, 2024

Summary

This summary is machine-generated.

This study presents a parallel YOLO-GG deep learning network for collaborative robot target recognition and grasping. This enhances robotic grasping efficiency and precision with improved detection speed and accuracy.

Keywords:

collaborative robots deep learning grasping detection parallel networks

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Last Updated: Jul 9, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Published on: October 14, 2017

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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Published on: April 21, 2023

Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Collaborative robots require efficient and precise target recognition and grasping for automation.
Existing methods often face limitations in real-time performance and accuracy for diverse objects.

Purpose of the Study:

To introduce a parallel YOLO-GG deep learning network for enhanced collaborative robot target recognition and grasping.
To improve the efficiency, precision, and real-time capabilities of visual classification and grasping tasks.

Main Methods:

A parallel deep vision network, YOLO-GG, integrating YOLOv3 and GG-CNN was proposed.
YOLOv3, pre-trained on COCO, handles object category and position detection.
GG-CNN, trained on Cornell Grasping dataset, predicts grasping pose and scale.

Main Results:

The YOLO-GG network achieved a 14.1% improvement in detection speed.
An accuracy of 94% was reached, surpassing YOLOv3 by 4.0%.
Experimental validation demonstrated successful real-world object grasping.

Conclusions:

The parallel YOLO-GG network effectively enhances collaborative robot target recognition and grasping.
The proposed method offers a significant advancement in robotic perception and manipulation tasks.