An Admittance-based Control Strategy for Human-Robot Collaboration Task Quality Management

Today's dynamic industrial environments are characterized by continuous process changes, which demand diverse expertise among workers. However, this diversity can inadvertently impact production quality and productivity, since inexperienced workers require significant time to learn and adapt to complex processes. Consequently, the need for innovative approaches such as Human-Robot Collaboration (HRC) to manage task quality effectively arises. This paper explores an Admittance-based control strategy approach for HRC task quality management, where two modes were implemented: Free-drive mode and Guided-drive mode. Both modes implement an admittance control with adaptable admittance parameters. Free-drive mode allows easy and effortless hand-drive of a tool attached to a UR10e end-effector. Guided-drive mode is the main mode of this study, which allow a corrective and intuitive assistance aiming to minimize errors occurred in a hand-drive task towards a task quality management. The performance of both controllers is evaluated regarding metrics such as trajectory error, exerted force, time execution, and muscle activation percentage.