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Grasping Novel Objects with Real-Time Obstacle Avoidance

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Social Robotics (ICSR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11357))

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Abstract

This paper proposes a new approach to grasp novel objects while avoiding real-time obstacles. The general idea is to perform grasping of novel objects and do collision avoidance at the same time. There are two main contributions. Firstly, a fast and robust method of real-time grasp detection is presented based on morphological image processing and machine learning. Secondly, we integrate our robotic grasping algorithms with some existing collision prediction strategies. It is really helpful to grasp objects on the condition that a robot is surrounded by obstacles. Additionally, it is very practical, runs in real-time and can be easily adaptable with respect to different robots and working conditions. We demonstrate our approaches using the Kinect sensor and the Baxter robot with a series of experiments.

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Acknowledgement

This work was partially supported by National Nature Science Foundation (NSFC) under Grants 61473120, 61811530281 and 51705371, Science and Technology Planning Project of Guangzhou 201607010006, State Key Laboratory of Robotics and System (HIT) Grant SKLRS-2017-KF-13, and the Fundamental Research Funds for the Central Universities 2017ZD057.

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Authors and Affiliations

  1. Key Laboratory of Autonomous Systems and Networked Control, College of Automation Science and Engineering, South China University of Technology, Guangzhou, China

    Jiahao Zhang, Chenguang Yang & Ying Feng

  2. The Institute of Technological Sciences, Wuhan University, Wuhan, China

    Miao Li

Authors
  1. Jiahao Zhang
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  2. Chenguang Yang
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  3. Miao Li
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  4. Ying Feng
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Corresponding authors

Correspondence to Chenguang Yang or Miao Li .

Editor information

Editors and Affiliations

  1. The National University of Singapore, Singapore, Singapore

    Shuzhi Sam Ge

  2. Qatar University, Doha, Qatar

    John-John Cabibihan

  3. University Carlos III de Madrid, Madrid, Spain

    Miguel A. Salichs

  4. Canterbury University, Auckland, New Zealand

    Elizabeth Broadbent

  5. Wichita State University, Wichita, KS, USA

    Hongsheng He

  6. The Pennsylvania State University, University Park, PA, USA

    Alan R. Wagner

  7. Universidad Carlos III de Madrid, Madrid, Spain

    Álvaro Castro-González

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Zhang, J., Yang, C., Li, M., Feng, Y. (2018). Grasping Novel Objects with Real-Time Obstacle Avoidance. In: Ge, S., et al. Social Robotics. ICSR 2018. Lecture Notes in Computer Science(), vol 11357. Springer, Cham. https://doi.org/10.1007/978-3-030-05204-1_16

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  • DOI: https://doi.org/10.1007/978-3-030-05204-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05203-4

  • Online ISBN: 978-3-030-05204-1

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