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Research on Key Technology of Logistics Sorting Robot

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Intelligent Robotics and Applications (ICIRA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12595))

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Abstract

It is an important direction to reduce the cost and improve the efficiency in the field of logistics to use automatic equipment such as mechanical arm to complete logistics sorting efficiently and accurately. This paper introduces a kind of mechanical arm sorting system. It improves two technologies—instance segmentation and poses estimation by using instance segmentation technology based on deep learning and calculating the point cloud vector of the depth camera. By using the Rapid-exploration Random Tree-Connect method (RRT-Connect) and Probabilistic Roadmap Method (PRM) algorithm, it can complete motion planning and select sucker to absorb and hold objects according to commodity size and weight. It is proved that the model recognition rate of the training system is high, the working efficiency of the system is 500 pieces/h, and the success rate of grasping is more than 99%.

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Acknowledgement

This research was supported by the National Key R&D Program of China 2018YFB1309300. The authors would like to personally thank all the team members.

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Beihang University, Beijing, China

    Hongwei Xiang, Zhe Liu & Diansheng Chen

  2. Zhejiang Cainiao Supply Chain Management Co., Ltd., Hangzhou, Zhejiang, China

    Yong Wang & Yang Gao

  3. Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100191, People’s Republic of China

    Diansheng Chen

Authors
  1. Hongwei Xiang
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  2. Yong Wang
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  3. Yang Gao
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  4. Zhe Liu
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  5. Diansheng Chen
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Corresponding author

Correspondence to Diansheng Chen .

Editor information

Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Chee Seng Chan

  2. Harbin Institute of Technology, Harbin, China

    Hong Liu

  3. Shanghai Jiao Tong University, Shanghai, China

    Xiangyang Zhu

  4. Monash University, Selangor, Malaysia

    Chern Hong Lim

  5. Tsinghua University, Beijing, China

    Xinjun Liu

  6. Shenyang Institute of Automation, Shenyang, China

    Lianqing Liu

  7. Tunku Abdul Rahman University College, Kuala Lumpur, Malaysia

    Kam Meng Goh

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Xiang, H., Wang, Y., Gao, Y., Liu, Z., Chen, D. (2020). Research on Key Technology of Logistics Sorting Robot. In: Chan, C.S., et al. Intelligent Robotics and Applications. ICIRA 2020. Lecture Notes in Computer Science(), vol 12595. Springer, Cham. https://doi.org/10.1007/978-3-030-66645-3_11

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  • DOI: https://doi.org/10.1007/978-3-030-66645-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66644-6

  • Online ISBN: 978-3-030-66645-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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