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Target Tracking using an Autonomous Underwater Vehicle: A Moving Path Following Approach
This paper demonstrates a possible application of the Moving Path Following (MPF) motion control approach applied to the marine environment. Specifically, the problem of target estimation and tracking using Autonomous Underwater Vehicle (AUV) is considered, wherein the robotic vehicle is equipped wi...
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creator | Praveen Jain, R. Pedro Aguiar, A. Sousa, Joao Borges de |
description | This paper demonstrates a possible application of the Moving Path Following (MPF) motion control approach applied to the marine environment. Specifically, the problem of target estimation and tracking using Autonomous Underwater Vehicle (AUV) is considered, wherein the robotic vehicle is equipped with a ranging sensor in order to localize a possibly moving target. Such a problem bears a strong resemblance to the single beacon navigation problem, in which the AUV is expected to execute state and input trajectories that renders the target states observable. To this end, control laws are proposed for an AUV to follow a geometric path, that is stationary with respect to the moving target, leading to the MPF problem. The geometric path is chosen such that the target states are observable. An extended Kalman filter is utilized to estimate the target position and velocity using range-only measurements. The efficacy of the MPF approach is illustrated through a source localization and tracking scenario in simulations. |
doi_str_mv | 10.1109/AUV.2018.8729784 |
format | conference_proceeding |
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Specifically, the problem of target estimation and tracking using Autonomous Underwater Vehicle (AUV) is considered, wherein the robotic vehicle is equipped with a ranging sensor in order to localize a possibly moving target. Such a problem bears a strong resemblance to the single beacon navigation problem, in which the AUV is expected to execute state and input trajectories that renders the target states observable. To this end, control laws are proposed for an AUV to follow a geometric path, that is stationary with respect to the moving target, leading to the MPF problem. The geometric path is chosen such that the target states are observable. An extended Kalman filter is utilized to estimate the target position and velocity using range-only measurements. 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Specifically, the problem of target estimation and tracking using Autonomous Underwater Vehicle (AUV) is considered, wherein the robotic vehicle is equipped with a ranging sensor in order to localize a possibly moving target. Such a problem bears a strong resemblance to the single beacon navigation problem, in which the AUV is expected to execute state and input trajectories that renders the target states observable. To this end, control laws are proposed for an AUV to follow a geometric path, that is stationary with respect to the moving target, leading to the MPF problem. The geometric path is chosen such that the target states are observable. An extended Kalman filter is utilized to estimate the target position and velocity using range-only measurements. 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Specifically, the problem of target estimation and tracking using Autonomous Underwater Vehicle (AUV) is considered, wherein the robotic vehicle is equipped with a ranging sensor in order to localize a possibly moving target. Such a problem bears a strong resemblance to the single beacon navigation problem, in which the AUV is expected to execute state and input trajectories that renders the target states observable. To this end, control laws are proposed for an AUV to follow a geometric path, that is stationary with respect to the moving target, leading to the MPF problem. The geometric path is chosen such that the target states are observable. An extended Kalman filter is utilized to estimate the target position and velocity using range-only measurements. The efficacy of the MPF approach is illustrated through a source localization and tracking scenario in simulations.</abstract><pub>IEEE</pub><doi>10.1109/AUV.2018.8729784</doi><tpages>6</tpages></addata></record> |
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ispartof | 2018 IEEE/OES Autonomous Underwater Vehicle Workshop (AUV), 2018, p.1-6 |
issn | 2377-6536 |
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source | IEEE Xplore All Conference Series |
subjects | Angular velocity Attitude control Estimation Kinematics Motion control Robot sensing systems Target tracking |
title | Target Tracking using an Autonomous Underwater Vehicle: A Moving Path Following Approach |
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