• 대한조선학회논문집
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• 제61권3호
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• pp.170-184
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• 2024

With the advancement of autonomous navigation technology in maritime domain, there is an active research on swarming Unmanned Surface Vehicles (USVs) that can fulfill missions with low cost and high efficiency. In this study, we propose a formation control algorithm that maintains a certain shape when multiple unmanned surface vehicles operate in a swarm. In the case of swarming, individual USVs need to be able to accurately follow the target state and avoid collisions with obstacles or other vessels in the swarm. In order to generate guidance commands for swarm formation control, the potential field method has been a major focus of swarm control research, but the method using the potential field only uses the position information of obstacles or other ships, so it cannot effectively respond to moving targets and obstacles. In situations such as the formation change of a swarm of ships, the formation control is performed in a dense environment, so the position and velocity information of the target and nearby obstacles must be considered to effectively change the formation. In order to overcome these limitations, this paper applies a method that considers relative velocity to the potential field-based guidance law to improve target following and collision avoidance performance. Considering the relative velocity of the moving target, the potential field for nearby obstacles is newly defined by utilizing the concept of Velocity Obstacle (VO), and the effectiveness and efficiency of the proposed method is verified through swarm control simulation, and swarm control experiments using a small scaled unmanned surface vehicle platform.

• Advances in robotics research
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• 제2권2호
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• pp.161-182
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• 2018

This paper proposes a novel receding horizon control (RHC) algorithm for formation control of a swarm of unmanned surface vehicles (USVs) using particle swarm optimization (PSO). The proposed control algorithm provides the coordinated path tracking of multi-agent USVs while preventing collisions and considering external disturbances such as ocean currents. A three degrees-of-freedom kinematic model of the USV is used for the RHC with guaranteed stability and convergence by incorporating a sequential Monte Carlo (SMC)-based particle initialization. An ocean current model-based estimator is designed to compensate for the effect of ocean currents on the USVs. This method is compared with the PSO-based RHC algorithms to demonstrate the performance of the formation control and the collision avoidance in the presence of ocean currents through numerical simulations.

• 한국시뮬레이션학회논문지
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• 제31권3호
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• pp.1-10
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• 2022

국방 분야는 아군 피해를 최소화하고 전투 효과를 향상하기 위해 무인 시스템을 운용한다. 해군의 무인수상정(USV, Swarm Unmanned Surface Vehicle)은 통신범위 내 군집 형성과 USV 간의 상황인식 정보공유를 통해 임무를 수행한다. 본 논문은 무인수상정의 내부 연동 및 외부 연동을 위한 인터페이스 연동어댑터 시스템(IAS, Interface Adapter System)을 제안한다. IAS는 USV의 자율운항 경로생성과 무장할당을 계획하는 임무계획 부체계(MPS, Mission Planning Subsystem)의 타 부체계 간 연동을 담당하며, 주요 기능은 MPS와 타 부체계 간 인터페이스 데이터를 실시간으로 교환하는 것이다. 이를 위해, MPS의 기능 요구사항과 연동 메시지를 식별 및 분석하였고, 실시간 분산처리 미들웨어를 활용하여 IAS를 구현하였다. 실험은 군집 USV 시뮬레이션 환경을 통한 다수의 통합시험을 수행하였으며, 지연시간과 연동 메시지 손실률을 측정하였다. 그 결과, 제안한 IAS는 MPS와 타 부체계 간 성공적인 가교역할을 수행할 것으로 기대한다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제8권5호
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• pp.475-486
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• 2016

Four-rotor dish-shaped unmanned underwater vehicles (FRDS UUVs) are new type underwater vehicles. The main goal of this paper is to develop a quick method to optimize the design of hydraulic support landing platform for the new UUV. In this paper, the geometry configuration and instability type of the platform are defined. Computational investigations are carried out to study the hydrodynamic performance of the landing platform using the Computational Fluid Dynamics (CFD) method. Then, the response surface model of the optimization objective is established. The intelligent particle swarm optimization (PSO) is applied to finding the optimal solution. The result demonstrates that the stability of landing platform is significantly improved with the global objective index increasing from 1.045 to 1.158 (10.86% higher) after the optimization process.