Contact Force Estimation for Robotic Manipulators based on Disturbance Kalman Filter

Abstract

Accurate contact force estimation is essential for achieving safe and stable physical interaction between a robot and its environment. Currently, contact force estimation methods based on joint force signals present challenges such as poor accuracy and significant noise. This paper proposes the disturbance Kalman filter to improve the performance of contact force estimation. This algorithm estimates contact force using a joint force sensor based on a generalized momentum-based robot dynamic model. By employing polynomial functions to model disturbance dynamics in the system state, we achieve accurate contact force estimation through a Kalman filter. Furthermore, a parameter tuning method for the disturbance Kalman filter is proposed to enhance the robustness of the algorithm to process noise and model uncertainty. Finally, this method is compared with the Generalized Momentum Observer applied to 5-DoF collaborative manipulators. Simulation and experimental results demonstrate that the disturbance Kalman filter can provide a robust and accurate estimation of contact force.