Fault-tolerant decentralized multi-sensor link velocity and acceleration estimation for elastic-joint robots

In robot manipulators position sensing has been well established as a core kinematics measurement, while velocity is typically numerically deduced from it via differentiation. However, various algorithms, whether they be control, collision monitoring and detection, friction compensation, learning-based methods, or model identification, all would highly benefit from accurate and high-bandwidth link-side velocity and acceleration information. In this work, we address the problem of decentralized multi-sensor fusion for estimating high-accuracy and high-bandwidth link-side velocity and acceleration for both rigid-body as well as flexible-joint robot manipulators. This is done via fusing the joint torque and motor position measurements with inertial measurement unit s (IMUs) mounted on the robot structure. Moreover, we discuss how to optimally distribute these IMUs along the robot structure. One goal is to maximize signal-to-noise ratio in the sense of Fisher and consequently allow for more accurate estimation results. A secondary goal may be to increase the probability of detection of unwanted and unpredictable collisions. Furthermore, since the proposed setup relies on different sensor types—which can be further exploited—a practical yet simple sensor failure detection and isolation scheme is introduced. We evaluate our claims and methods in simulations and experiments based on a state-of-the-art 7 degrees of freedom (DoF) manipulator.