A Lightweight Vision-Guided Framework for Multi-Object Robotic Grasping and Polynomial Spatial Mapping

Abstract

Robotic grasping based on monocular vision remains challenging due to perception uncertainty, pixel-to-world mapping errors, and a tight per-frame budget. This paper presents a lightweight vision-based multi-object robotic grasping framework that integrates object detection, polynomial pixel-to-world mapping, and rule-based grasp execution into a unified pipeline. Object detection is carried out with a You Only Look Once version 13 (YOLOv13) detector, into which lightweight structural modules are inserted for the tabletop grasping task. To enable accurate perception-to-action mapping, a polynomial calibration model is introduced to transform image-space detections into planar world-space coordinates, effectively correcting geometric distortions caused by preprocessing. Based on the mapped targets, a rule-based grasp planning strategy is developed to generate physically feasible grasp poses for a Universal Robots 5 (UR5) robotic arm without relying on depth sensors or learning-based grasp prediction. The proposed framework is implemented and validated in CoppeliaSim. Quantitative results show end-to-end grasp success rising from 86% with the center-point baseline to 94% with the proposed strategy on the same 50 paired placements, while mapping and planning together add about 1 ms per frame. The gain comes from enforcing execution feasibility on an already sufficient map, not from a more accurate mapping model.