AUV (Redermor) Obstacle Detection and Avoidance Experimental Evaluation

Quidu, Isabelle; Hétet, A.; Dupas, Yann; Lefèvre, Sébastien

doi:10.1109/oceanse.2007.4302304

Cited by 14 publications

(4 citation statements)

References 5 publications

(9 reference statements)

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“…The approach was based on determining the trajectory by using the graph method combined with the A* method. The Redermor test platform equipped with forward-looking sonar (FLS), ten echosounder, and side scan sonar in the [93] study was used to collect sonar data during underwater tests. The authors have been able to classify the obstacles and prove that it is possible to define the boundary level around the obstacle.…”

Section: Until 2010mentioning

confidence: 99%

Review of Collision Avoidance and Path Planning Algorithms Used in Autonomous Underwater Vehicles

Kot

2022

Electronics

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The rapid technological development of computing power and system operations today allows for increasingly advanced algorithm implementation, as well as path planning in real time. The objective of this article is to provide a structured review of simulations and practical implementations of collision-avoidance and path-planning algorithms in autonomous underwater vehicles (AUVs). The novelty of the review paper is to consider not only the results of numerical research but also the newest results of verifying collision-avoidance and path-planning algorithms in real applications together with a comparison of the difficulties encountered during simulations and their practical implementation. Analysing the last 20 years of AUV development, it can be seen that experiments in a real environment are dominated by classical methods. In the case of simulation studies, artificial intelligence (AI) methods are used as often as classical methods. In simulation studies, the APF approach is most often used among classical methods, whereas among AI algorithms reinforcement learning and fuzzy logic methods are used. For real applications, the most used approach is reactive behaviors, and AI algorithms are rarely used in real implementations. Finally, this article provides a general summary, future works, and a discussion of the limitations that inhibit the further development in this field.

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Section: Until 2010mentioning

confidence: 99%

Review of Collision Avoidance and Path Planning Algorithms Used in Autonomous Underwater Vehicles

Kot

2022

Electronics

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“…Thereby, development of an intelligent 3D obstacle recognition system becomes necessary for AUVs safe navigation. Some notable researches recently have been executed for obstacle detection of AUVs using SVM and other techniques [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

An Obstacle Recognizing Mechanism for Autonomous Underwater Vehicles Powered by Fuzzy Domain Ontology and Support Vector Machine

Bukhari

Kim

2014

Mathematical Problems in Engineering

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The autonomous underwater vehicle (AUV) and the problems associated with its safe navigation have been studied for the last two decades. The real-time underwater obstacle recognition procedure still has many complications associated with it and the issue becomes worse with vague sensor data. These problems can be coped with the merger of a robust classification mechanism and a domain knowledge acquisition technique. In this paper, we introduce a hybrid mechanism to recognize underwater obstacles for AUV based on fuzzy domain ontology and support vector machine (SVM). SVM is an efficient algorithm that was developed for recognizing 3D object in recent years and is a new generation learning system based on recent advances in statistical learning theory. The amalgamation of fuzzy domain ontology with SVM boosts the performance of the obstacle recognition module by providing the timely semantic domain information of the surrounding circumstances. Also the reasoning ability of the fuzzy domain ontology can expedite the obstacle avoidance process. In order to evaluate the performance of the system, we developed a prototype simulator based on OpenGL and VC++. We compared the outcomes of our proposed technique with backpropagation algorithm and classic SVM based techniques.

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“…As one of the key technology, the acoustic imaging simulation is paid more attention at present. It not only plays an important role in the closed-loop simulation system, but also its simulation levels are of great significance to improve the whole performance of AUV and deepen the study of underwater perception system (Cufi et al, 2002;Horner, 2005;Quidu and Hetet, 2007) .…”

Section: Introductionmentioning

confidence: 99%

Imaging Simulation of Looking Forward Sonar

Zhang¹,

Wan²,

Ma³

et al. 2013

RJASET

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In this study, we propose a new method for the imaging simulation of looking forward sonar. According to the theory of acoustics, the Kerchief's integral formula is used to solve the scattering sound field of objects in the water and the models of objects are set up. Secondly, the surface of objects is divided into some parts and the formulas are proposed to gain echo data according to the reflecting condition of those parts. These data are used to generate sonar images of objects. At last, the simulation results of echo data and sonar images are contrasted with those gained from the water tank test. The results verified the feasibility of the new method.

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AUV (Redermor) Obstacle Detection and Avoidance Experimental Evaluation

Cited by 14 publications

References 5 publications

Review of Collision Avoidance and Path Planning Algorithms Used in Autonomous Underwater Vehicles

Review of Collision Avoidance and Path Planning Algorithms Used in Autonomous Underwater Vehicles

An Obstacle Recognizing Mechanism for Autonomous Underwater Vehicles Powered by Fuzzy Domain Ontology and Support Vector Machine

Imaging Simulation of Looking Forward Sonar

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