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

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Direct Multi-Target Teaching Interface for Autonomous Handling of Multi-Stack Logistics in a Warehouse.

Haegyeom Choi¹, Jaehyun Jeong², Taezoon Park³

¹Department of Mechanical Engineering, Soongsil University, Seoul 06978, Republic of Korea.

Sensors (Basel, Switzerland)

|September 14, 2024

Summary

This study introduces an autonomous pick-place system for warehouses. The user-friendly interface enhances operational efficiency and achieves high user satisfaction in complex logistics environments.

Keywords:

autonomous pick–place direct teaching interface target product recognition

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Area of Science:

Robotics and Automation
Human-Computer Interaction
Logistics and Supply Chain Management

Background:

Complex logistics environments require efficient pick-place operations.
Existing systems may lack intuitive interfaces for autonomous task selection.
Real-time remote monitoring is crucial for warehouse management.

Purpose of the Study:

To develop and evaluate a framework for autonomous pick-place operations.
To create a direct multi-target teaching interface for product selection.
To enhance efficiency and user satisfaction in warehouse tasks.

Main Methods:

Combined tag and segmentation information for product recognition.
Utilized a rack-mounted camera for real-time remote viewing via tablet.
Implemented a direct teaching interface for product selection and task planning.
Performed autonomous pick-place operations based on generated optimal paths.

Main Results:

Achieved high user satisfaction with an average score of 77.5 on the System Usability Scale (SUS).
Demonstrated the framework's capability in enabling autonomous pick-place operations.
Validated the effectiveness of the direct multi-target teaching interface.

Conclusions:

The proposed framework significantly enhances operational efficiency in complex warehouses.
The user-friendly interface provides an effective solution for autonomous logistics tasks.
The system offers a promising approach to improving warehouse automation and worker experience.