Assessing Seaglider® Model-based Position Accuracy on an Acoustic Tracking Range

Seagliders® are buoyancy-driven autonomous underwater vehicles whose sub-surface position estimates are typically derived from velocities inferred using a flight model. We present a method for computing velocities and positions during the different phases typically encountered during a dive-climb profile based on a buoyancy-driven flight model. We compare these predictions to observations gathered from a Seaglider deployment on the acoustic tracking range in Dabob Bay (200 m depth, mean vehicle speeds ~30 cm s-1), permitting us to bound the position accuracy estimates and understand sources of various errors. We improve position accuracy estimates during long vehicle accelerations by numerically integrating the flight-model's fundamental momentum-balance equations. Overall, based on an automated estimation of flight-model parameters, we confirm previous work that predicted vehicle velocities in the dominant dive and climb phases are accurate to < 1 cm s-1, which bounds the accumulated position error in time. However, in this energetic tidal basin, position error also accumulates due to unresolved depth-dependent flow superimposed upon an inferred depth-averaged current.

A system of coordinated autonomous robots for Lagrangian studies of microbes in the oceanic deep chlorophyll maximum

The deep chlorophyll maximum (DCM) layer is an ecologically important feature of the open ocean. The DCM cannot be observed using aerial or satellite remote sensing; thus, in situ observations are essential. Further, understanding the responses of microbes to the environmental processes driving their metabolism and interactions requires observing in a reference frame that moves with a plankton population drifting in ocean currents, i.e., Lagrangian. Here, we report the development and application of a system of coordinated robots for studying planktonic biological communities drifting within the ocean. The presented Lagrangian system uses three coordinated autonomous robotic platforms. The focal platform consists of an autonomous underwater vehicle (AUV) fitted with a robotic water sampler. This platform localizes and drifts within a DCM community, periodically acquiring samples while continuously monitoring the local environment. The second platform is an AUV equipped with environmental sensing and acoustic tracking capabilities. This platform characterizes environmental conditions by tracking the focal platform and vertically profiling in its vicinity. The third platform is an autonomous surface vehicle equipped with satellite communications and subsea acoustic tracking capabilities. While also acoustically tracking the focal platform, this vehicle serves as a communication relay that connects the subsea robot to human operators, thereby providing situational awareness and enabling intervention if needed. Deployed in the North Pacific Ocean within the core of a cyclonic eddy, this coordinated system autonomously captured fundamental characteristics of the in situ DCM microbial community in a manner not possible previously.

tacost: Testing and simulating the performance of acoustic tracking systems

Abstracttacost is a Python package to allow the testing of acoustic tracking systems. While many microphone array systems have been characterised analytically and experimentally – these are time-intensive methods. tacost provides a simulation based framework to rapidly assess the tracking behaviour of multiple array geometries, and the dissection of other relevant parameters. This paper explains briefly the design of the package and highlights two example use cases in which the tracking accuracy of different microphone geometries are characterised.