Skill Assistance with Robot for Manual Welding

-SkillAssist-

by Mustafa Suphi Erden

Marie Curie Intra-European Fellowship, Project No: 297857

 

 

Host Institute:

Learning Algorithms and Systems Laboratory (LASA), École Polytechnique Fédérale de Lausanne

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Potential Impact

The impact of the results of this project will be substantial for assistive robotics. This project provided an answer to the questions of "what, when, and how to assist with robot" during a fine and industrially relevant manipulation task, manual welding: the impedance level is to be compensated with virtual dynamics, when the performer generates large variation movements. Furthermore, the project also demonstrated that providing real-time feedback alarm results in immediate improvement of welding performance by decreasing the position variations, and potentially helps the novice welders to learn suppression of such variations in the long run. The developed assistance and training schemes and the method based on impedance measurements may inspire the design and development of assistive control systems for a variety of manipulation tasks ranging from painting, polishing, scrubbing in industry, to micro and minimally invasive surgery and physiological rehabilitation in medicine. The method also exemplified a bio-inspired methodology, by which one first analyzes the human behavior, identifies human hand impedance and position variations to determine skill levels in manipulation, and finally uses these to determine control characteristics for robotic assistance and training purposes.