Calibration of Error Distributions in Robot Kinematics for Increased Precision in Manipulation Tasks

Kurzfassung

The accuracy of robotic forward kinematics is commonly improved by calibration. However, most calibration methods only take deterministic errors, such as inaccurate geometry and unknown stiffnesses, into account and neglect errors with stochastic characteristics, including joint friction, gear backlash and component wear. In order to incorporate these effects, this paper presents a calibration procedure for a probabilistic forward kinematics model which identifies both systematic errors and error sources whose combined effects are modeled as distributions within a kinematic chain. Additionally, we present a method for using the identified distributions to enhance task-relevant accuracy by minimizing the end-effector uncertainty resulting from such distributions in a task-specific way. Finally, this idea is demonstrated in an experiment with a research rover tasked with picking up a payload box from a lander. Here, we show that end-effector error in task-relevant directions can be reduced by 40\% by choosing low-uncertainty over high-uncertainty configurations.