Robot manipulator self-identification for surrounding obstacle detection

被引：43
|
作者：
Wang, Xinyu [1 ,3 ]
Yang, Chenguang [2 ]
Ju, Zhaojie [4 ]
Ma, Hongbin [1 ,3 ]
Fu, Mengyin [1 ,3 ]
机构：
[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
[2] Swansea Univ, Coll Engn, Swansea SA1 8EN, W Glam, Wales
[3] Beijing Inst Technol, State Key Lab Intelligent Control & Decis Complex, Beijing 100081, Peoples R China
[4] Univ Portsmouth, Sch Comp, Portsmouth, Hants, England
来源：
MULTIMEDIA TOOLS AND APPLICATIONS | 2017年 / 76卷 / 05期
基金：
英国工程与自然科学研究理事会;
关键词：
Manipulator self-identification; Superpixel; Collision prediction; Point cloud;
D O I：
10.1007/s11042-016-3275-8
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 (R) sensor and the Baxter (R) robot have been performed to demonstrate the performance of the proposed algorithms.
引用
收藏
页码：6495 / 6520
页数：26
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