Opportunistic localization of underwater robots using drifters and boats

Abstract

Abstract-The paper characterizes the performance of Autonomous Underwater Vehicle (AUV) localization when the AUV moves in environments where floating drifters or surface vessels are present and can be used for relative localization. In particular, we study how localization performance is affected by parameters e.g. surface object density, their visibility range and their motion. We present a discrete-time nonlinear model for an AUV equipped with onboard sensors and relative positioning information from surface objects (e.g., ranging and bearing) and derive the associated relative Posterior Cramér-Rao Lower Bound. We introduce a probabilistic motion model for the AUV, based on a random direction mobility model, to analyze the expected performance of the localization algorithm in terms of hitting time between the AUV and the surface objects. Finally, an extensive simulation analysis is performed using a discrete time Extended Kalman Filter with Maximum-Likelihood Data Association. As a proof of concept, an AUV equipped with an upward looking sonar is shown to detect a surface vessel and improve its localization estimate.

AUV dead-reckoning navigation based on neural network using a single accelerometer

“…In the simulation, we use the wave model to generate data, assuming that the AUV starts from the origin of the reference coordinate system and sails along the surface of the sea. Suppose that the acceleration of AUV's forward direction , n x a is fixed every 0.01 second, that means the motion of AUV is evenly accelerated during every 0.01 second, then we simulate the movement of the AUV by setting the value of a n,x , concretely, during [1][2][3] For the calculation of the pitch angle of AUV at time t, we need to know the function of the wave at time t, the heading angle α, and also need to know the coordinate of AUV at this time. Suppose that, at time t, the coordinate of AUV is A( , , )…”

Path Planning in Localization Uncertaining Environment Based on Dijkstra Method

“…The accumulation of drift by noise measurement is unbounded. The lower bound can be estimated by Cramer2Rao bound (Arrichiello et al, 2012 ), but the upper bound cannot be estimated by traditional methods, especially when there are no basic facts. However, the error distribution properties of multiple statistics can be used for the statistical estimation of errors.…”

ISME activity on the use of Autonomous Surface and Underwater Vehicles for acoustic surveys at sea

“…To the purpose, the ISME-node Cassino developed a localization strategy for the AUV-streamer system that, beyond using the data from navigation sensors of the AUV, as gyroscope, compass, Inertial Measurement Unit, USBL (as in [10]), it also makes use of information gathered from acoustic signals generated for the seismic survey. Indeed, considering that the sparkers and AUVs are equipped with acoustic modems with atomic clocks that allow precise synchronization, it is possible to use the direct time of arrival of the acoustic signals from the sparker to the hydrophones to compute range measurements.…”