Autonomous underwater vehicles for scientific and naval operations

Bovio, Edoardo; Cecchi, Daniele; Baralli, Francesco

doi:10.1016/j.arcontrol.2006.08.003

Cited by 92 publications

(21 citation statements)

References 11 publications

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“…The scenario is based on an unknown underwater environment with fixed obstacles and targets, where each robot has to move at a constant depth. A 2D scenario is typically considered in a large number of missions, as described in [7]; for this reason, we limit our model to work in a 2D underwater scenario. In the following, dynamics of a real AUV will be disregarded, and the study will be focused on kinematic simulations, which are extremely helpful in verifying the method efficiency; the test on a realistic AUV model, characterized by nonlinear dynamics, will be presented in the following section.…”

Section: Proposed Bb Approach For Target Detectionmentioning

confidence: 99%

A Behavior-Based Mission Planner for Cooperative Autonomous Underwater Vehicles^†

Sorbi¹,

Capua²,

Fontaine³

et al. 2012

mar technol soc j

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Due to its applications in marine research, oceanographic, and undersea exploration, Autonomous Underwater Vehicles (AUVs) and the related control algorithms has been recently under intense investigation. In this work, we address target detection and tracking issues, proposing a control strategy which is able to benefit from the cooperation among robots within the fleet. In particular, we introduce a behavior-based planner for cooperative AUVs, proposing an algorithm able to search and recognize targets, in both static and dynamic scenarios. With no a priori information about the surrounding environment, robots cover an unknown area with the goal of finding objects of interest. When a target is found, the AUVs' goal become to classify it (fixed target) or track it (mobile target), with no information about target trajectory and with the constraint on maintaining the formation. Results demonstrate the good overall performance of the proposed algorithm in both scenarios.

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Section: Proposed Bb Approach For Target Detectionmentioning

confidence: 99%

A Behavior-Based Mission Planner for Cooperative Autonomous Underwater Vehicles^†

Sorbi¹,

Capua²,

Fontaine³

et al. 2012

mar technol soc j

View full text Add to dashboard Cite

show abstract

“…There are many different types of missions that single or multiple AUVs could conceivably be assigned to accomplish in underwater environment. Civil applications include ocean scientific investigation, mine hunting, pipeline laying and monitoring, maritime searching and rescue, and so on [3] [4] . for military application, "U.S. Navy's 2004 UUV Master Plan" defines nine mission categories for UUVs, including: inspection/identification, time critical strike, mine countermeasures, anti-submarine warfare, and so on [5] .…”

Section: Introductionmentioning

confidence: 99%

Depth control of AUV using active disturbance rejection controller

Chen

Zhang

et al. 2014

Proceedings of the 33rd Chinese Control Conference

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The intelligent motion control technology of Autonomous Underwater Vehicle (AUV) is the basis of long-distance voyage for AUV. The motion control capability in vertical plane is an important technical for AUV to complete multifarious missions. So, it is important and realistic significance to research on motion control in vertical plane of AUV. Motion control problem of AUV in vertical plane, especially depth control problems is researched based on active disturbance rejection control (ADRC) method in this paper. The kinetic and dynamic model in vertical plane of AUV is established and simplified. The active disturbance rejection controller design method is introduced. Detailed algorithms of each part for active disturbance rejection controller are discussed according to separability principle of ADRC. Based on ADRC, the depth controller is designed and applied in AUV. At last, simulation results indicate that the method is feasibility and effectiveness for AUV depth control.

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“…The increasing demands of underwater operations and scientific research lead the development of the underwater survey technology [1], [2], [3]. The optical vision systems are a common method for survey, but their visibility is limited in turbid water [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Feasibility study of noise reduction for imaging sonar based on time-domain filtering

Cho

Pyo

et al. 2014

Oceans 2014 - Taipei

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Underwater sonar image can be obtained by the multi-beam imaging sonar. Because of its high frame rate, the imaging sonar can be used for underwater survey especially in turbid water condition. However, the sonar image is generally degraded by the speckle noise. In this paper, we propose a realtime noise reduction algorithm by using the recursive least square algorithm, which is one of the adaptive filters. Through experiments, the performance of the proposed algorithm is analyzed and its feasibility is determined. Also the difference between the proposed algorithm and the conventional noise reduction algorithms that are based on spatial-domain filters are discussed.

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Autonomous underwater vehicles for scientific and naval operations

Cited by 92 publications

References 11 publications

A Behavior-Based Mission Planner for Cooperative Autonomous Underwater Vehicles^†

A Behavior-Based Mission Planner for Cooperative Autonomous Underwater Vehicles^†

Depth control of AUV using active disturbance rejection controller

Feasibility study of noise reduction for imaging sonar based on time-domain filtering

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Autonomous underwater vehicles for scientific and naval operations

Cited by 92 publications

References 11 publications

A Behavior-Based Mission Planner for Cooperative Autonomous Underwater Vehicles†

A Behavior-Based Mission Planner for Cooperative Autonomous Underwater Vehicles†

Depth control of AUV using active disturbance rejection controller

Feasibility study of noise reduction for imaging sonar based on time-domain filtering

Contact Info

Product

Resources

About

A Behavior-Based Mission Planner for Cooperative Autonomous Underwater Vehicles^†

A Behavior-Based Mission Planner for Cooperative Autonomous Underwater Vehicles^†