Variable Admittance Control Using Velocity-Curvature Patterns to Enhance Physical Human-Robot Interaction

This letter introduces a variable admittance control approach aimed at enhancing intuitive human-robot interaction by considering both direct and indirect human intentions. The magnitude of force serves as a representation of direct intentions, delineating preferences for rapid or precise motions. Drawing from the movement sciences and motor control field, the minimum-jerk model is employed to mirror human motor system control policies and movement behaviors. From this model, velocity-curvature patterns are derived, enabling an intuitive estimation of indirect intentions indicating long-term objectives like trajectory and turning direction. We propose an innovative guidance method, rooted in the estimation of indirect human intentions, enabling the robot to concurrently follow and guide the operator. To assess the efficacy of this approach, both offline simulations and real-time human experiments are conducted on a six-DOF robot and a force/torque sensor. Our comprehensive experiments demonstrate substantial enhancements in both accuracy ( ${\geq {{12.1}\%}}$ ) and smoothness ( ${\geq {{18.3}\%}}$ ) over fixed admittance control and state-of-the-art variable admittance methods.