基于GraspNet的物体平铺场景下类别导向抓取算法

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中图分类号：TP391 文献标识码：A

GraspNet-based Category-oriented Grasping Method for Object Planar Scenes

SONG Shimiao，GU Feifan，GE Jiashang，YANG Jie

Abstract： To solve the problem class-based grasping in multicategory tiled scenes，this paper adopts different feature fusion methods proposes a joint optimization algorithm MC-GSNet （Multi-Class GraspNet） that fuses category semantics grasping posture an optimization algorithm MT-GSNet （Multi-Task GraspNet） that builds a multitask learning model. The improved methods explicitly incorporate category information， optimize the generation logic grasp poses enhance the algorithm's adaptability success rate in multi-category object planar scenes. Experimental results on the public dataset GraspNet-lBillion demonstrate that the proposed methods significantly improve task adaptability grasping success rates in multi-category planar scenes. MC-GSNet MT-GSNet achieve 32.6% 43.9% average accuracy improvements in grasp detection， respectively； MT-GSNet exhibits superior adaptability to unseen objects due to its integration segmentation features. The experimental results in the simulation environment show that the grasp successful rates （GSR） MC-GSNet MT-GSNet reached 88.3% 95.0% respectively，which can meet the needs actual engineering deployment.

Keywords： grasping detection; category-oriented; feature fusion; GraspNet-lBillion

机器人抓取技术在结构化场景中已取得显著进展，但在复杂平铺场景，如平面上随机的多个类别物体中仍面临诸多挑战[1]。（剩余10151字）