Robot manipulator self-identification for surrounding obstacle detection

Xinyu Wang, Chenguang Yang, Zhaojie Ju, Hongbin Ma, Mengyin Fu

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Obstacle detection plays an important role for robot collision avoidance and motion planning. This paper focuses on the study of the collision prediction of a dual-arm robot based on a 3D point cloud. Firstly, a self-identification method is presented based on the over-segmentation approach and the forward kinematic model of the robot. Secondly, a simplified 3D model of the robot is generated using the segmented point cloud. Finally, a collision prediction algorithm is proposed to estimate the collision parameters in real-time. Experimental studies using the KinectⓇ sensor and the BaxterⓇ robot have been performed to demonstrate the performance of the proposed algorithms.
Original languageEnglish
Article number5
Pages (from-to)6495-6520
Number of pages26
JournalMultimedia Tools and Applications
Issue number5
Early online date13 Feb 2016
Publication statusPublished - Mar 2017


  • Manipulator self-identification
  • Superpixel
  • Collision prediction
  • Point cloud
  • RCUK
  • EP/L026856/1
  • EP/J004561/1


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