Robot manipulator self-identification for surrounding obstacle detection

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

doi:10.1007/s11042-016-3275-8

Robot manipulator self-identification for surrounding obstacle detection

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

School of Computing

Research output: Contribution to journal › Article › peer-review

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Abstract

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 language	English
Article number	5
Pages (from-to)	6495-6520
Number of pages	26
Journal	Multimedia Tools and Applications
Volume	76
Issue number	5
Early online date	13 Feb 2016
DOIs	https://doi.org/10.1007/s11042-016-3275-8
Publication status	Published - Mar 2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

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

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10.1007/s11042-016-3275-8

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title = "Robot manipulator self-identification for surrounding obstacle detection",

abstract = "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.",

keywords = "Manipulator self-identification, Superpixel, Collision prediction, Point cloud, RCUK, EPSRC, EP/L026856/1, EP/J004561/1 ",

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AU - Wang, Xinyu

AU - Yang, Chenguang

AU - Ju, Zhaojie

AU - Ma, Hongbin

AU - Fu, Mengyin

PY - 2017/3

Y1 - 2017/3

N2 - 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.

AB - 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.

KW - Manipulator self-identification

KW - Superpixel

KW - Collision prediction

KW - Point cloud

KW - RCUK

KW - EPSRC

KW - EP/L026856/1

KW - EP/J004561/1

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M3 - Article

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JO - Multimedia Tools and Applications

JF - Multimedia Tools and Applications

IS - 5

M1 - 5

ER -

Robot manipulator self-identification for surrounding obstacle detection

Abstract

UN SDGs

Keywords

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