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A Method for Object Recognition and Robot Grasping Detection in Multi-object Scenes

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Intelligent Robotics and Applications (ICIRA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13457))

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Abstract

Due to robot grasping has always been an open challenge and a difficult problem, it has attracted many researches. With the wide application of deep learning methods in robot grasping, the grasping performance of robots has been greatly improved. Traditionally, many robot grasp detection approaches focus on how to find the best grasp. However, it is very important for robots to have an object recognition function during the grasping process to meet more industrial requirements, such as industrial assembly tasks and sorting tasks. In addition, the problem of missed detection has always existed in the current multi-object grasping detection. To solve the above problems, this paper proposes a two-stage robot grasping method to recognize objects and detect the most likely grasp for every object in multi-object scenes. Our approach achieved a detection accuracy of 65.7% on the VMRD dataset and outperformed the benchmark algorithm by 11.2%. The simulation experimental results showed that our approach achieved a recognition success rate of 96.7% and a grasp success rate of 85% for robotic grasp detection in multi-object scenes.

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Author information

Authors and Affiliations

  1. School of Mechanical Engineering, Shenyang Jianzhu University, No. 25, Hunnan Middle Road, Shenyang, 110168, China

    Jiajun Zheng, Yuanyuan Zou & Jie Xu

  2. National-Local Joint Engineering Laboratory of NC Machining Equipment and Technology of High-Grade Stone, Shenyang, 110168, China

    Jiajun Zheng, Yuanyuan Zou & Jie Xu

  3. Kunshan Intelligent Equipment Research Institute, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Lingshen Fang

Authors
  1. Jiajun Zheng
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  2. Yuanyuan Zou
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  3. Jie Xu
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  4. Lingshen Fang
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Corresponding author

Correspondence to Yuanyuan Zou .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Honghai Liu

  2. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  3. Shenyang Institute of Automation, Shenyang, Liaoning, China

    Lianqing Liu

  4. Harbin Institute of Technology, Harbin, China

    Li Jiang

  5. Shanghai Jiao Tong University, Shanghai, China

    Guoying Gu

  6. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Xinyu Wu

  7. Harbin Institute of Technology, Harbin, China

    Weihong Ren

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Zheng, J., Zou, Y., Xu, J., Fang, L. (2022). A Method for Object Recognition and Robot Grasping Detection in Multi-object Scenes. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13457. Springer, Cham. https://doi.org/10.1007/978-3-031-13835-5_17

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  • DOI: https://doi.org/10.1007/978-3-031-13835-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-13834-8

  • Online ISBN: 978-3-031-13835-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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