Two three-dimensional localization algorithms for a swarm of underwater vehicles are presented. The first is grounded on an extended Kalman filter (EKF) scheme used to fuse some proprioceptive data such as the vessel's speed and some exteroceptive measurements such as the time of flight (TOF) sonar distance of the companion vessels. The second is a Monte Carlo particle filter localization processing the same sensory data suite. The results of several simulations using the two approaches are presented, with comparison. The case of a supporting surface vessel is also considered. An analysis of the robustness of the two approaches against some system parameters is given. © 2015 Author(s). Licensee InTech.

Swarm underwater acoustic 3D localization: Kalman vs Monte Carlo

Taraglio, S.
2015

Abstract

Two three-dimensional localization algorithms for a swarm of underwater vehicles are presented. The first is grounded on an extended Kalman filter (EKF) scheme used to fuse some proprioceptive data such as the vessel's speed and some exteroceptive measurements such as the time of flight (TOF) sonar distance of the companion vessels. The second is a Monte Carlo particle filter localization processing the same sensory data suite. The results of several simulations using the two approaches are presented, with comparison. The case of a supporting surface vessel is also considered. An analysis of the robustness of the two approaches against some system parameters is given. © 2015 Author(s). Licensee InTech.
Underwater robots;Sensor fusion;Swarm localization;Mobile robots and intelligent autonomous systems;Distributed control systems
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12079/2216
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