This study aims to enhance physical human−robot interaction (PHRI) in virtual fixture applications by proposing a variable damping admittance control method. The goal is to balance compliance and stability while incorporating human motion intention, thereby reducing oscillations and improving interaction performance in constrained environments.
A variable damping admittance control method is proposed, which dynamically adjusts damping based on estimated human motion intention. The interaction dynamics between the human and virtual fixtures are modeled, and the approach is experimentally validated using a UR5 robot in boundary-restriction scenarios.
Experimental results show that the proposed method effectively suppresses undesired motion oscillations while preserving compliance. This improves the smoothness and safety of human−robot interaction, and enhances task performance in virtual fixture applications.
This work innovatively integrates human motion intention into admittance control, enabling adaptive damping adjustment for optimized compliance-stability trade-offs. The method offers practical value for robotic applications requiring precise and stable human−robot collaboration, such as rehabilitation robot and industrial co-manipulation.
