Flapping-fin unmanned underwater vehicle (UUV) propulsion systems enable high maneuverability for tasks ranging from station-keeping to surveillance but are often constrained by their limited computational power and battery capacity. Previous research has demonstrated that time-series neural network models can accurately predict the thrust and power of certain fin kinematics based on the specified gait coupled with the fin configuration, but can not fit an inverse neural network that takes a thrust request and tunes the kinematics by weighting thrust generation, smooth movement transitions, and power attributes. We study various combinations of the three weights and fin materials to create different ‘modes’ of movement for a multi-objective UUV, based on controller intent using an inverse neural network. Finally, we implement and validate an enhanced power-aware inverse model by benchmarking on the Raspberry Pi Model 4B system and testing through generated simulated movements.

Flapping-fin unmanned underwater vehicle (UUV) propulsion systems provide high maneuverability for naval tasks such as surveillance and terrain exploration. Recent work has explored the use of time-series neural network surrogate models to predict thrust from vehicle design and fin kinematics. We develop a search-based inverse model that leverages a kinematics-to-thrust neural network model for control system design. Our inverse model finds a set of fin kinematics with the multi-objective goal of reaching a target thrust and creating a smooth kinematic transition between flapping cycles. We demonstrate how a control system integrating this inverse model can make online, cycle-to-cycle adjustments to prioritize different system objectives.

In this paper we propose an algorithm to actively control the distance of a motor-propeller system (MPS) to a large obstacle using data from a single microphone. The method is based upon a broadband constructive/destructive interference pattern across the audible frequency band that is present when the MPS is near an obstacle. By taking the difference between the power spectrum in the obstacle-free case and the spectrum when recording near an obstacle, a broadband oscillation with respect to frequency is revealed. The frequency of this oscillation is linearly-related to the distance from the microphone to the wall. We present both static and dynamic experiments showcasing the ability of the proposed method to estimate the distance to a wall as well as actively control it.