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Docking assessment algorithm for autonomous underwater vehicles
Highlights A new docking assessment algorithm comprising three phases: depth tracking, docking-feasibility region analysis, and docking-success probability evaluation is proposed. An algorithm to evaluate the probability of docking success is presented based on the probability of sensor data. An algorithm to decide docking turning or turning left or right as well as predict the docking success was presented. Computer simulations for successful and unsuccessful cases of docking situations were performed and several comparisons have been made for discussion.
Abstract This paper presents an algorithm for docking a torpedo-shaped autonomous underwater vehicle (AUV). We propose a new docking assessment algorithm comprising three phases: depth tracking, docking-feasibility region analysis, and docking-success probability evaluation. For depth-tracking analysis, a neural network-generated path is used to satisfy constrained docking conditions of depth and distance. With regard to docking feasibility region analysis, the working space of the AUV can provide a possibility region of successful docking. In the analysis, working space is expressed by a turning ellipsoid, which is the numerical solution of the maximum yawing motion. An algorithm is presented to evaluate the probability of docking success, based on the probability of sensor data. A good contribution of this approach is that a criterion for assessing the feasibility of the desired path for docking is given through the proposed docking assessment algorithm.
Docking assessment algorithm for autonomous underwater vehicles
Highlights A new docking assessment algorithm comprising three phases: depth tracking, docking-feasibility region analysis, and docking-success probability evaluation is proposed. An algorithm to evaluate the probability of docking success is presented based on the probability of sensor data. An algorithm to decide docking turning or turning left or right as well as predict the docking success was presented. Computer simulations for successful and unsuccessful cases of docking situations were performed and several comparisons have been made for discussion.
Abstract This paper presents an algorithm for docking a torpedo-shaped autonomous underwater vehicle (AUV). We propose a new docking assessment algorithm comprising three phases: depth tracking, docking-feasibility region analysis, and docking-success probability evaluation. For depth-tracking analysis, a neural network-generated path is used to satisfy constrained docking conditions of depth and distance. With regard to docking feasibility region analysis, the working space of the AUV can provide a possibility region of successful docking. In the analysis, working space is expressed by a turning ellipsoid, which is the numerical solution of the maximum yawing motion. An algorithm is presented to evaluate the probability of docking success, based on the probability of sensor data. A good contribution of this approach is that a criterion for assessing the feasibility of the desired path for docking is given through the proposed docking assessment algorithm.
Docking assessment algorithm for autonomous underwater vehicles
Vu, Mai The (Autor:in) / Choi, Hyeung-Sik (Autor:in) / Nhat, Thieu Quang Minh (Autor:in) / Nguyen, Ngoc Duc (Autor:in) / Lee, Sang-Do (Autor:in) / Le, Tat-Hien (Autor:in) / Sur, Joono (Autor:in)
Applied Ocean Research ; 100
20.04.2020
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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