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Volume 14
Issue 6
10.3390/act14060259
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Optimized Design and Deep Vision-Based Operation Control of a Multi-Functional Robotic Gripper for an Automatic Loading System

by
Yaohui Wang
1,2,†,
Sheng Guo
1,†,
Jinliang Zhang
3,
Hongbo Ding
1,
Bo Zhang
3,
Ao Cao
1,
Xiaohu Sun
2,
Guangxin Zhang
1,
Shihe Tian
3,
Yongxu Chen
1,
Jixuan Ma
3 and
Guangrong Chen
1,4,*
1
Robotics Research Center, Beijing Jiaotong University, Beijing 100044, China
2
Huaneng Coal Technology Research Co., Ltd., Beijing 100070, China
3
Zhalainuoer Coal Industry Co., Ltd., Hulunbuir 021410, China
4
Tangshan Research Institute, Beijing Jiaotong University, Tangshan 063000, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Actuators 2025, 14(6), 259; https://doi.org/10.3390/act14060259
Submission received: 9 April 2025 / Revised: 15 May 2025 / Accepted: 20 May 2025 / Published: 23 May 2025
(This article belongs to the Special Issue Advancement in the Design and Control of Robotic Grippers—Second Edition)
Versions Notes

Abstract

This study presents an optimized design and vision-guided control strategy for a multi-functional robotic gripper integrated into an automatic loading system for warehouse environments. The system adopts a modular architecture, including standardized platforms, transport containers, four collaborative 6-DOF robotic arms, and a multi-sensor vision module. Methodologically, we first developed three gripper prototypes, selecting the optimal design (30° angle between the gripper and container side) through workspace and interference analysis. A deep vision-based recognition system, enhanced by an improved YOLOv5 algorithm and multi-feature fusion, was employed for real-time object detection and pose estimation. Kinematic modeling and seventh-order polynomial trajectory planning ensured smooth and precise robotic arm movements. Key results from simulations and experiments demonstrated a 95.72% success rate in twist lock operations, with a positioning accuracy of 1.2 mm. The system achieved a control cycle of 35 ms, ensuring efficiency compared with non-vision-based methods. Practical implications include enabling fully autonomous container handling in logistics, reducing labor costs, and enhancing operational safety. Limitations include dependency on fixed camera setups and sensitivity to extreme lighting conditions.
Keywords: optimized design; deep vision-based control; multi-functional robotic gripper; automatic loading system; standardized transport optimized design; deep vision-based control; multi-functional robotic gripper; automatic loading system; standardized transport

Share and Cite

MDPI and ACS Style

Wang, Y.; Guo, S.; Zhang, J.; Ding, H.; Zhang, B.; Cao, A.; Sun, X.; Zhang, G.; Tian, S.; Chen, Y.; et al. Optimized Design and Deep Vision-Based Operation Control of a Multi-Functional Robotic Gripper for an Automatic Loading System. Actuators 2025, 14, 259. https://doi.org/10.3390/act14060259

AMA Style

Wang Y, Guo S, Zhang J, Ding H, Zhang B, Cao A, Sun X, Zhang G, Tian S, Chen Y, et al. Optimized Design and Deep Vision-Based Operation Control of a Multi-Functional Robotic Gripper for an Automatic Loading System. Actuators. 2025; 14(6):259. https://doi.org/10.3390/act14060259

Chicago/Turabian Style

Wang, Yaohui, Sheng Guo, Jinliang Zhang, Hongbo Ding, Bo Zhang, Ao Cao, Xiaohu Sun, Guangxin Zhang, Shihe Tian, Yongxu Chen, and et al. 2025. "Optimized Design and Deep Vision-Based Operation Control of a Multi-Functional Robotic Gripper for an Automatic Loading System" Actuators 14, no. 6: 259. https://doi.org/10.3390/act14060259

APA Style

Wang, Y., Guo, S., Zhang, J., Ding, H., Zhang, B., Cao, A., Sun, X., Zhang, G., Tian, S., Chen, Y., Ma, J., & Chen, G. (2025). Optimized Design and Deep Vision-Based Operation Control of a Multi-Functional Robotic Gripper for an Automatic Loading System. Actuators, 14(6), 259. https://doi.org/10.3390/act14060259

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