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Real-time grasping strategies using event camera

Author

Listed:
  • Xiaoqian Huang

    (Khalifa University)

  • Mohamad Halwani

    (Khalifa University)

  • Rajkumar Muthusamy

    (Dubai Future Labs)

  • Abdulla Ayyad

    (Khalifa University
    Khalifa University of Science and Technology)

  • Dewald Swart

    (Strata Manufacturing PJSC)

  • Lakmal Seneviratne

    (Khalifa University)

  • Dongming Gan

    (Purdue University)

  • Yahya Zweiri

    (Khalifa University
    Khalifa University)

Abstract

Robotic vision plays a key role for perceiving the environment in grasping applications. However, the conventional framed-based robotic vision, suffering from motion blur and low sampling rate, may not meet the automation needs of evolving industrial requirements. This paper, for the first time, proposes an event-based robotic grasping framework for multiple known and unknown objects in a cluttered scene. With advantages of microsecond-level sampling rate and no motion blur of event camera, the model-based and model-free approaches are developed for known and unknown objects’ grasping respectively. The event-based multi-view approach is used to localize the objects in the scene in the model-based approach, and then point cloud processing is utilized to cluster and register the objects. The proposed model-free approach, on the other hand, utilizes the developed event-based object segmentation, visual servoing and grasp planning to localize, align to, and grasp the targeting object. Using a UR10 robot with an eye-in-hand neuromorphic camera and a Barrett hand gripper, the proposed approaches are experimentally validated with objects of different sizes. Furthermore, it demonstrates robustness and a significant advantage over grasping with a traditional frame-based camera in low-light conditions.

Suggested Citation

  • Xiaoqian Huang & Mohamad Halwani & Rajkumar Muthusamy & Abdulla Ayyad & Dewald Swart & Lakmal Seneviratne & Dongming Gan & Yahya Zweiri, 2022. "Real-time grasping strategies using event camera," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 593-615, February.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:2:d:10.1007_s10845-021-01887-9
    DOI: 10.1007/s10845-021-01887-9
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    Cited by:

    1. Mohammed Salah & Abdulla Ayyad & Mohammed Ramadan & Yusra Abdulrahman & Dewald Swart & Abdelqader Abusafieh & Lakmal Seneviratne & Yahya Zweiri, 2024. "High speed neuromorphic vision-based inspection of countersinks in automated manufacturing processes," Journal of Intelligent Manufacturing, Springer, vol. 35(7), pages 3067-3081, October.

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