A minimal collision strategy of synergy between pushing and grasping for large clusters of objects

Chong Chen, ShiJun Yan, Miaolong Yuan, ChiatPin Tay, Dongkyu Choi, Quang Dan Le

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

Abstract

Grasping and moving objects in a large cluster is a common real scenario. In such scenarios, tens of objects are adjacent to each other, even stacked layer by layer, so that simple grasp would not work due to obstruction. In this paper, we propose a well-designed strategy to use synergy of pushing and grasping to automatically push and grasp objects in a large tightly packed cluster of objects. Our strategy is to detect and grasp isolated graspable objects first before other actions. We then use a smart strategy that pushes objects at the narrowest edge of the clusters. For push action, the robot pushes the edge at the perpendicular direction relative to the cluster, thus improving the performance of isolation and minimizing collisions. We have conducted experiments in both simulation and real-world environments with more than 20 cluttered objects and demonstrated that our solution outperforms existing deep learning based methods, especially in challenging cases, and achieves significantly higher completion rate, grasp success rate, picked rate and efficiency.
Original languageEnglish
Title of host publication2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages6817-6822
Number of pages6
ISBN (Electronic)9781665491907
ISBN (Print)9781665491914
DOIs
Publication statusPublished - 13 Dec 2023

Publication series

NameIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
PublisherIEEE
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Keywords

  • image edge detection
  • grasping
  • deep learning
  • collision avoidance
  • intelligent robots

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