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Volume 12
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10.3390/machines12110786
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Article

Robotic Grasping Technology Integrating Large Kernel Convolution and Residual Connections

by
Liang Li
,
Nan Li
,
Rui Nan
,
Yangfei He
,
Chunlei Li
*,
Weiliang Zhang
and
Pan Fan
School of Mechanical Engineering, Baoji University of Arts and Sciences, Baoji 721016, China
*
Author to whom correspondence should be addressed.
Machines 2024, 12(11), 786; https://doi.org/10.3390/machines12110786
Submission received: 20 October 2024 / Revised: 3 November 2024 / Accepted: 4 November 2024 / Published: 7 November 2024
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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Abstract

To meet real-time grasping demands in complex environments, this paper proposes a lightweight yet high-performance robotic grasping model. The model integrates large kernel convolution and residual connections to generate grasping information for unknown objects from RGB and depth images, enabling real-time generation of stable grasping plans from the images. The proposed model achieved favorable accuracy on both the Cornell and Jacquard standard grasping datasets. Compared to other methods, the proposed model significantly reduces the number of parameters while achieving comparable performance, making it a lightweight model. Additionally, real-world experiments were conducted using a six-axis collaborative robot on a set of previously unseen household objects with diverse and adversarial shapes, achieving a comprehensive grasping success rate of 93.7%. Experimental results demonstrate that the proposed model not only improves grasping accuracy but also has strong potential for practical applications, particularly in resource-constrained robotic systems.
Keywords: large kernel convolution; residual connections; lightweight model; robotic grasping large kernel convolution; residual connections; lightweight model; robotic grasping

Share and Cite

MDPI and ACS Style

Li, L.; Li, N.; Nan, R.; He, Y.; Li, C.; Zhang, W.; Fan, P. Robotic Grasping Technology Integrating Large Kernel Convolution and Residual Connections. Machines 2024, 12, 786. https://doi.org/10.3390/machines12110786

AMA Style

Li L, Li N, Nan R, He Y, Li C, Zhang W, Fan P. Robotic Grasping Technology Integrating Large Kernel Convolution and Residual Connections. Machines. 2024; 12(11):786. https://doi.org/10.3390/machines12110786

Chicago/Turabian Style

Li, Liang, Nan Li, Rui Nan, Yangfei He, Chunlei Li, Weiliang Zhang, and Pan Fan. 2024. "Robotic Grasping Technology Integrating Large Kernel Convolution and Residual Connections" Machines 12, no. 11: 786. https://doi.org/10.3390/machines12110786

APA Style

Li, L., Li, N., Nan, R., He, Y., Li, C., Zhang, W., & Fan, P. (2024). Robotic Grasping Technology Integrating Large Kernel Convolution and Residual Connections. Machines, 12(11), 786. https://doi.org/10.3390/machines12110786

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