Bioinspired morphing fins to provide optimal maneuverability, stability, and response to turbulence in rigid hull AUVs.

By adopting bioinspired morphing fins, we demonstrate how to achieve good directional stability, exceptional maneuverability, and minimal adverse response to turbulent flow, properties that are highly desirable for rigid hull AUVs, but are presently difficult to achieve because they impose contradictory requirements. We outline the theory and design for switching between operating with sufficient stability that ensures a steady course in the presence of disturbances, with low corrective control action; reverting to high maneuverability to execute very rapid course and depth changes, improving turning rate by 25% up to 50%; and ensuring at all times that angular responses to external turbulence are minimized. We then demonstrate the developments through tests on a 1 m long autonomous underwater vehicle, namedMorpheus. The vehicle is capable of dynamically changing its stability-maneuverability qualities by using tuna-inspired morphing fins, which can be deployed, deflected and retracted, as needed. A series of free-swimming experiments and maneuvering simulations, combined with mathematical analysis, led to the design of optimal retractable morphing fins.

Passive acoustic tracking of an unmanned underwater vehicle using bearing-Doppler-speed measurements.

Tracking unmanned underwater vehicles (UUVs) in the presence of shipping traffic is a critical task for passive acoustic harbor security systems. In general, the vessels can be tracked by their unique acoustic signature caused by machinery vibration and cavitation noise. However, cavitation noise of UUVs is quiet relative to that of ships. Furthermore, tracking a target with bearing-only measurements requires the observing platform to maneuver. In this work, it is demonstrated that it is possible to passively track an UUV from its high-frequency motor noise using a stationary array in a shallow-water experiment with passing boats. The motor noise provides high signal-to-noise ratio measurements of the bearing, range rate, and speed, which we combined in an unscented Kalman filter to track the target. First, beamforming is applied to estimate the bearing. Next, the range rate is calculated from the Doppler effect on the motor noise. The propeller rotation rate can be estimated from the motor signature and converted to the speed using a pre-identified model of the robot. The bearing-Doppler-speed measurements outperformed the traditional bearing-Doppler target motion analysis: the bearing, bearing rate, range, and range rate accuracy improved by a factor of 2×, 16×, 3×, and 6×, respectively. Finally, the robustness of the tracking solution to an unknown vehicle model is evaluated.