An End-to-End Computationally Lightweight Vision-Based Grasping System for Grocery Items
Year: 2025
Type: article
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.
Source: Sensors
DOI: 10.3390/s25175309
Institution: Cranfield University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4413684049