Surface Following using Direct Adaptive Admittance Control

Many robotic tasks, such as polishing or grinding, involve maintaining contact with and applying a force against the environment while following a given trajectory. In this paper, we present an adaptive admittance controller that aligns its control parameters online to be in the direction of an estimate of the surface normal vector. This essentially allows a robot to follow an unknown surface, as is the case in uncalibrated setups or quick changeover production. We present and compare three different surface normal estimation algorithms: the integral adaptive law and two Riemannian manifold based algorithms. Our experimental results show that the adaptive controller using the simple Riemannian gradient descent yields the lowest tracking error of the three. It has 73% decrease in positional error and 43% decrease in angular error compared with the controller with the integral adaptive law, and overall is effective at aligning the robot tool online against surface moving in an a priori unknown pattern.