Robotic assembly systems either make use of expensive fixtures to hold components in predefined locations, or the poses of the components are determined using various machine vision techniques. Vision-guided assembly robots can handle subtle variations in geometries and poses of parts. Therefore, they provide greater flexibility than the use of fixtures. However, the currently established vision-guided assembly systems use 2D vision, which is limited to three degrees of freedom. The work reported in this paper is focused on flexible automated assembly of clearance fit machine components using 3D vision. The recognition and the estimation of the poses of the components are achieved by matching their CAD models with the acquired point cloud data of the scene. Experimental results obtained from a robot demonstrating the assembly of a set of rings on a shaft show that the developed system is not only reliable and accurate, but also fast enough for industrial deployment.
|Title of host publication||ICMV 2015|
|Publication status||Published - 2015|
|Event||International Conference on Machine Vision - Barcelona, Spain, Barcelona, Spain|
Duration: 19 Nov 2015 → 21 Nov 2015
|Conference||International Conference on Machine Vision|
|Period||19/11/15 → 21/11/15|
Bibliographical noteThe full text is currently unavailable on the repository.
- 3D vision
- automated assembly
- object recognition
- point cloud matching