Joint Velocity and Acceleration Estimation in Serial Chain Rigid Body and Flexible Joint Manipulators

This paper deals with the problem of accurately computing and estimating joint velocity and acceleration in robotic manipulators. Generally, it is well known that numerical differentiation of noisy position signals even with significant filtering is no viable solution. This is especially true for computing joint acceleration. Specifically, our solution to this problem fuses joint position measurement with link accelerometers, which are affordable and easy to install. Since the sensor readings are affected by noise, drift and bias, suitable data fusion and filtering methods are proposed for improving the estimation for practical use. Simulation results based on a realistic dynamics model of a 7-DoF robot including various parasitic effects and experimental results with a 7-DoF robot demonstrate the effectiveness of our approach. This method would have multiple use, e.g., in monitoring external joint torques and handle possibly unforeseen collisions. Furthermore, other applications such as load identification and compensation as well as state feedback linearization for flexible joint robots could finally become possible also practical.