Calibration-free IMU-based Kinematic State Estimation for Robotic Manipulators

Abstract

The precise knowledge of a robot manipulator’s kinematic state including position, velocity, and acceleration is one of the base requirements for the application of advanced control algorithms. To obtain this information, encoder data could be differentiated numerically. However, the resulting velocity and acceleration estimates are either noisy or delayed as a result of low-pass filtering. Numerical differentiation can be circumvented by the utilization of gyroscopes and accelerometers, but these suffer from a variety of measurement errors and nonlinearity regarding the desired quantities. Therefore, we present a novel, real-time capable kinematic state estimator based on the Extended Kalman filter with states for the effective sensor biases. This way, the handling of arbitrary inertial sensor setups is made possible without calibration on manipulators composed of revolute and prismatic joints. Simulation experiments show that the proposed estimator is robust towards various error sources and that it outperforms competing approaches. Moreover, the practical relevance is demonstrated using a real manipulator with two joints.