Pose Estimation of a Thruster-Driven Bioinspired Multi-Link Robot

Abstract-- This work demonstrates pose (position and shape) estimation for a free-floating, bioinspired multi-link robot with unactuated joints, link-mounted thrusters for control, and a single gyroscope per link, resulting in an underactuated, minimally sensed platform. Through a proof-of-concept hardware experiment and offline Kalman filter analysis, we show that the robot's pose can be reliably estimated. State estimation is performed using an unscented Kalman filter augmented with Gaussian process residual learning to compensate for nonzero-mean, non-Gaussian noise. We further show that a filter trained on a multi-gait dataset (forward, backward, left, right, and turning) performs comparably to one trained on a larger forward-gait-only dataset when both are evaluated on the same forward-gait test trajectory. These results reveal overlap in the gait input space, which can be exploited to reduce training data requirements while enhancing the filter's generalizability across multiple gaits. I. Introduction The performance of dynamical systems such as underwater robots, autonomous vehicles, and aircraft autopilots critically depends on accurate knowledge of the system state to ensure robustness against disturbances and maintain safety guarantees. At the same time, size, weight, and power constraints limit the number and type of sensors and actuators that can be incorporated into many systems, leading to systems that are both underactuated and minimally sensed.