Second-order Kinematics for Floating-base Robots using the Redundant Acceleration Feedback of an Artificial Sensory Skin

In this work, we propose a new estimation method for second-order kinematics for floating-base robots, based on highly redundant distributed inertial feedback. The linear acceleration of each robot link is measured at multiple points using a multimodal, self-configuring and self-calibrating artificial skin. The proposed algorithm is two-fold: i) the skin acceleration data is fused at the link level for state dimensionality reduction; ii) the estimated values are then fused limb-wise with data from the joint encoders and the main inertial measurement unit (IMU), using a Sigma-point Kalman filter. In this manner, it is possible to estimate the joint velocities and accelerations while avoiding the lag and noise amplification phenomena associated with conventional numerical derivation approaches. Experiments performed on the right arm and torso of a REEM-C humanoid robot, demonstrate the consistency of the proposed estimation method.