Real-Time Passive Acoustic Tracking of Underwater Vehicles

Com o crescente interesse na exploração oceânica, sistemas de localização subaquática têm sido largamente usados pela industria e comunidade cientifica. Neste trabalho foi desenvolvido um sistema de localização acústica passiva em tempo real, com uma topologia idêntica ao do ultra-short baseline. Este sistema calcula a posição a duas dimensões de uma fonte acústica submersa conhecida, com base na integração de medições da direção do som ao longo do tempo. O ângulo de chegada da onda sonora é estimado pelo atraso de fase entre os sinais adquiridos por dois hidrofones colocados perto um do outro. Esta configuração permite atenuar as diferenças nos sinais recebidos devidas a perturbações do canal acústico subaquático. Este algoritmo foi implementado em tempo real numa plataforma SoC reconfigurável (CPU ARM + FPGA), e validado com ensaios de campo realizados no mar

AUV SLAM and experiments using a mechanical scanning forward-looking sonar

Navigation technology is one of the most important challenges in the applications of autonomous underwater vehicles (AUVs) which navigate in the complex undersea environment. The ability of localizing a robot and accurately mapping its surroundings simultaneously, namely the simultaneous localization and mapping (SLAM) problem, is a key prerequisite of truly autonomous robots. In this paper, a modified-FastSLAM algorithm is proposed and used in the navigation for our C-Ranger research platform, an open-frame AUV. A mechanical scanning imaging sonar is chosen as the active sensor for the AUV. The modified-FastSLAM implements the update relying on the on-board sensors of C-Ranger. On the other hand, the algorithm employs the data association which combines the single particle maximum likelihood method with modified negative evidence method, and uses the rank-based resampling to overcome the particle depletion problem. In order to verify the feasibility of the proposed methods, both simulation experiments and sea trials for C-Ranger are conducted. The experimental results show the modified-FastSLAM employed for the navigation of the C-Ranger AUV is much more effective and accurate compared with the traditional methods

Development of an autonomous surface vehicle for monitoring underwater vehicles

JAMSTEC has developed and operated several AUVs (Autonomous Underwater Vehicle) as platform for scientific investigation and explorations of seabed mineral resources. Conventionally, a support vessel monitors only one AUV during its whole dive for safety and positioning. We propose an operation of multiple AUVs using an ASV (Autonomous Surface Vehicle) to improve survey efficiency. For this purpose, JAMSTEC has been developing an ASV “MAINAMI” with a length of 6 meters since 2013. It has a diesel engine, two thrusters and a rudder. The vehicle is equipped with an acoustic communication device and a satellite one, in order to relay information between an AUV and operators on a ship or on land.Date of Conference: 19-22 October 2015http://www.godac.jamstec.go.jp/darwin/cruise/kaiyo/ky15-e01/

Hausdorff Distance Applied On Real Data Experiment For Underwater Localization

International audience— This paper addresses the problem of localizing and tracking a surface or underwater vessel with the technique called as Hausdorff Distance. Two proposed approaches, based on TDOAs comparison, were used for 2-D localization, in range and depth, with one sensor only, and have been successfully applied to localize a motionless unknown target in a tank's experiment. Results in terms of the localization accuracy have been obtained with real signal and the performance of the proposed localization techniques have been demonstrated and confirmed by simulation with respect of signal-to-noise ratio and compared with the correlation techniques used nowadays for single hydrophones

Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling

We consider cooperative localization technique for mobile agents with communication and computation capabilities. We start by provide and overview of different decentralization strategies in the literature, with special focus on how these algorithms maintain an account of intrinsic correlations between state estimate of team members. Then, we present a novel decentralized cooperative localization algorithm that is a decentralized implementation of a centralized Extended Kalman Filter for cooperative localization. In this algorithm, instead of propagating cross-covariance terms, each agent propagates new intermediate local variables that can be used in an update stage to create the required propagated cross-covariance terms. Whenever there is a relative measurement in the network, the algorithm declares the agent making this measurement as the interim master. By acquiring information from the interim landmark, the agent the relative measurement is taken from, the interim master can calculate and broadcast a set of intermediate variables which each robot can then use to update its estimates to match that of a centralized Extended Kalman Filter for cooperative localization. Once an update is done, no further communication is needed until the next relative measurement