Implementation of a Flexible and Lightweight Depth-Based Visual Servoing Solution for Feature Detection and Tracing of Large, Spatially-Varying Manufacturing Workpieces

Robotics

Lloyd Tinkler¹  Chris Scraggs¹  Lee Clift²  Divya Tiwari²  Windo Hutabarat²  Jonathan M. Aitken²  Ashutosh Tiwari² 

[1] Advanced Manufacturing Research Centre, Sheffield S9 1ZA, UK;Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK;

关键词: kinematics;    visual servoing;    edge detection;    robotic vision;    inverse kinematics;    edge tracing;   

DOI  :  10.3390/robotics11010025

来源: DOAJ

【 摘 要 】

This work proposes a novel solution for detecting and tracing spatially varying edges of large manufacturing workpieces, using a consumer grade RGB depth camera, with only a partial view of the workpiece and without prior knowledge. The proposed system can visually detect and trace various edges, with a wide array of degrees, to an accuracy of 15 mm or less, without the need for any previous information, setup or planning. A combination of physical experiments on the setup and more complex simulated experiments were conducted. The effectiveness of the system is demonstrated via simulated and physical experiments carried out on both acute and obtuse edges, as well as typical aerospace structures, made from a variety of materials, with dimensions ranging from 400 mm to 600 mm. Simulated results show that, with artificial noise added, the solution presented can detect aerospace structures to an accuracy of 40 mm or less, depending on the amount of noise present, while physical aerospace inspired structures can be traced with a consistent accuracy of 5 mm regardless of the cardinal direction. Compared to current industrial solutions, the lack of required planning and robustness of edge detection means it should be able to complete tasks more quickly and easily than the current standard, with a lower financial and computational cost than the current techniques being used within.

【 授权许可】

Unknown