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Related Experiment Video

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

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Deep Learning-Based Intelligent Forklift Cargo Accurate Transfer System.

Jie Ren¹, Yusu Pan², Pantao Yao¹

¹Intelligent Perception and Control Center, Huzhou Institute of Zhejiang University, Huzhou 313098, China.

Sensors (Basel, Switzerland)

|November 11, 2022

Summary

This summary is machine-generated.

This study introduces an intelligent forklift system for precise cargo transfer, significantly improving pallet recognition and docking accuracy using deep learning. The advanced system achieves over 99.5% recognition and 6mm docking accuracy, enhancing logistics efficiency.

Keywords:

computer vision and its practical applications deep learning intelligent systems and control theory robotics

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Area of Science:

Robotics
Artificial Intelligence
Computer Vision

Background:

Current forklift systems suffer from low pallet recognition rates and poor docking accuracy.
Manual cargo handling is labor-intensive and prone to errors, impacting operational efficiency.

Purpose of the Study:

To develop an intelligent forklift cargo precision transfer system.
To enhance pallet recognition and docking accuracy in automated logistics.

Main Methods:

Utilized a deep learning-based recognition algorithm, specifically Yolov5, for pallet target detection and positional calculation.
Integrated small target detection techniques to improve recognition of pallet targets.
Implemented a high-precision control algorithm for precise pallet insertion.

Main Results:

Achieved a pallet recognition rate exceeding 99.5% over 7 days of continuous trials.
Demonstrated pallet docking accuracy with a maximum inaccuracy of 6 mm over 1000 evaluations.
The system requires fewer sensors and indicators for deployment compared to traditional methods.

Conclusions:

The intelligent forklift system significantly improves pallet recognition and docking accuracy.
The integration of deep learning and small target detection enhances the performance of automated cargo transfer.
The developed system offers a viable and stable solution for precision cargo handling in logistics.