An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items

Mansakul, Thanavin; Tang, Gilbert; Webb, Phil; Rice, Jamie; Oakley, Daniel; Fowler, James

doi:10.3390/s25175309

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Open AccessArticle

An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items

by

Thanavin Mansakul

^*

,

Gilbert Tang

,

Phil Webb

,

Jamie Rice

,

Daniel Oakley

and

James Fowler

Centre for Robotics and Assembly, Faculty of Engineering and Applied Sciences, Cranfield University, Bedford MK43 0AL, UK

^*

Author to whom correspondence should be addressed.

Sensors 2025, 25(17), 5309; https://doi.org/10.3390/s25175309

Submission received: 14 July 2025 / Revised: 18 August 2025 / Accepted: 20 August 2025 / Published: 26 August 2025

(This article belongs to the Special Issue Computer Vision and Sensing Technologies for Industrial Quality Inspection: 2nd Edition)

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Abstract

Vision-based grasping for mobile manipulators poses significant challenges in machine perception, computational efficiency, and real-world deployment. This study presents a computationally lightweight, end-to-end grasp detection framework that integrates object detection, object pose estimation, and grasp point prediction for a mobile manipulator equipped with a parallel gripper. A transformation model is developed to map coordinates from the image frame to the robot frame, enabling accurate manipulation. To evaluate system performance, a benchmark and a dataset tailored to pick-and-pack grocery tasks are introduced. Experimental validation demonstrates an average execution time of under 5 s on an edge device, achieving a 100% success rate on Level 1 and 96% on Level 2 of the benchmark. Additionally, the system achieves an average compute-to-speed ratio of 0.0130, highlighting its energy efficiency. The proposed framework offers a practical, robust, and efficient solution for lightweight robotic applications in real-world environments.

Keywords: vision-based grasping system; end-to-end grasp detection; mobile manipulator; lightweight computation; object detection; object pose estimation; machine vision

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MDPI and ACS Style

Mansakul, T.; Tang, G.; Webb, P.; Rice, J.; Oakley, D.; Fowler, J. An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items. Sensors 2025, 25, 5309. https://doi.org/10.3390/s25175309

AMA Style

Mansakul T, Tang G, Webb P, Rice J, Oakley D, Fowler J. An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items. Sensors. 2025; 25(17):5309. https://doi.org/10.3390/s25175309

Chicago/Turabian Style

Mansakul, Thanavin, Gilbert Tang, Phil Webb, Jamie Rice, Daniel Oakley, and James Fowler. 2025. "An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items" Sensors 25, no. 17: 5309. https://doi.org/10.3390/s25175309

APA Style

Mansakul, T., Tang, G., Webb, P., Rice, J., Oakley, D., & Fowler, J. (2025). An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items. Sensors, 25(17), 5309. https://doi.org/10.3390/s25175309

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An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items

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