The Probabilistic Robot Kinematics Model and its Application to Sensor Fusion

Robots with elasticity in structural components can suffer from undesired end-effector positioning imprecision, which exceeds the accuracy requirements for successful manipulation. We present the Probabilistic-Product-Of-Exponentials robot model, a novel approach for kinematic modeling of robots. It does not only consider the robot's deterministic geometry but additionally models time-varying and configuration-dependent errors in a probabilistic way. Our robot model allows to propagate the errors along the kinematic chain and to compute their influence on the end-effector pose. We apply this model in the context of sensor fusion for manipulator pose correction for two different robotic systems. The results of a simulation study, as well as of an experiment, demonstrate that probabilistic configuration-dependent error modeling of the robot kinematics is crucial in improving pose estimation results.