A complete grocery pick-and-pack application using a computationally lightweight vision-based mobile manipulator - Mansakul, Thanavin; Tang, Gilbert; Webb, Phil; Rice, Jamie; Oakley, Daniel; Fowler, James (MDPI, 2026)
Mobile manipulators have become essential platforms for autonomous tasks that demand high-quality performance and efficient operational processes. This paper presents a complete grocery pick-and-pack system for a mobile manipulator, integrating a graphical user interface (GUI) with an end-to-end vision-based grasp detection pipeline designed for lightweight computation. The system is evaluated on the Grocery Pick-and-Pack Benchmark (Level-3), the most challenging level due to deformable objects, dimensional constraints, and strict grasp-point requirements. Experimental results demonstrate an average success rate of 92% across five item classes, with the deformable sweet bag the most challenging at 60% and an average execution time of 7.5 s on an edge device. The system achieves strong computational efficiency, reflected by a compute-to-speed ratio (CSR) of 0.008, with a total model size of only 30.9 MB. Performance is further validated across multiple hardware platforms and under real competition scenarios in the European Robotics League 2025. The findings highlight the practical impact of lightweight, vision-based mobile manipulation and provide insights into current challenges and future research directions for autonomous robotic applications.
This article belongs to the Special Issue Advanced Sensors and AI Integration for Human–Robot Teaming
Keywords: Analytical Chemistry; 3103 Ecology; 4008 Electrical engineering; 4009 Electronics, sensors and digital hardware; 4104 Environmental management; 4606 Distributed computing and systems software; vision-based grasping system; end-to-end grasp detection; mobile manipulator; lightweight computation; object detection; object pose estimation; machine vision