Instrumented tools and algorithms for estimation of human joint mechanical impedance during tooling tasks

• 主要作者: Phan, GIa Hoang
• 其他作者: Choo Kok Fah
• 格式: Theses and Dissertations
• 語言: English
• 出版: 2017
• 主題: DRNTU::Engineering::Mechanical engineering::Robots
• 在線閱讀: http://hdl.handle.net/10356/70591

In recent years, robots have been successfully applied to automate repetitive, structured and non-contact tasks such as painting and welding. However, when it comes to contact tasks such as fine finishing tasks, these still cannot be performed by robots and often require the intervention of skilled human operators. Control strategies currently adopted for industrial robots are based on position/force control and do not clearly capture the skills developed by experienced human operators. The main objective of this work is to develop tools and algorithms that capture human motor skills for the purpose of transfer to industrial robots. Human motor skills are here broadly intended as the coordination of multiple and redundant degrees of freedom (e.g. due to the kinematics of the human arm) and the regulation of forces and torques as well as the regulation of mechanical impedance (e.g. stiffness of the human arm). This study uses available kinematic/dynamic information and applies stiffness estimation methods to infer the (neuro-)mechanical impedance of the human arm/wrist during motion, in particular during an industrial tooling task. This approach to stiffness estimation is validated on an impedance-controlled robot. The stiffness values estimated for human subjects, at different force levels, correlated positively with the muscular activity. The novelty of our method is that the perturbations originate from the stochastic nature of the task itself rather than from a dedicated, external system as in all previous works.