Method of emergency collision avoidance for unmanned surface vehicle (USV) based on motion ability database

• Autorzy: Song Lifei , Chen Houjing , Xiong Wenhao , Dong Zaopeng , Mao Puxiu , Xiang Zuquan , Hu Kai

Identyfikatory

DOI

10.2478/pomr-2019-0025

• Języki publikacji: EN

Abstrakty

EN

The unmanned surface vehicles (USV) are required to perform a dynamic obstacle avoidance during fulfilling a task. This is essential for USV safety in case of an emergency and such action has been proved to be difficult. However, little research has been done in this area. This study proposes an emergency collision avoidance algorithm for unmanned surface vehicles (USVs) based on a motion ability database. The algorithm is aimed to address the inconsistency of the existing algorithm. It is proposed to avoid collision in emergency situations by sharp turning and treating the collision avoidance process as a part of the turning movement of USV. In addition, the rolling safety and effect of speed reduction during the collision avoidance process are considered. First, a USV motion ability database is established by numerical simulation. The database includes maximum rolling angle, velocity vector, position scalar, and steering time data during the turning process. In emergency collision avoidance planning, the expected steering angle is obtained based on the International Regulations for Preventing Collisions at Sea (COLREGs), and the solution space, with initial velocity and rudder angle taken as independent variables, is determined by combining the steering time and rolling angle data. On the basis of this solution space, the objective function is solved by the particle swarm optimization (PSO) algorithm, and the optimal initial velocity and rudder angle are obtained. The position data corresponding to this solution is the emergency collision avoidance trajectory. Then, the collision avoidance parameters were calculated based on the afore mentioned model of motion. With the use of MATLAB and Unity software, a semi-physical simulation platform was established to perform the avoidance simulation experiment under emergency situation. Results show the validity of the algorithm. Hence results of this research can be useful for performing intelligent collision avoidance operations of USV and other autonomous ships.

Słowa kluczowe

EN

unmanned surface vehicle; emergency collision avoidance; velocity obstacle method; motion ability

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2019
• Tom: nr 2
• Strony: 55--67
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Song Lifei

• Key Laboratory of High Performance Ship Technology (Wuhan University of Technology) Ministry of Education Heping Avenue, 430063 Wuhan
• School of Transportation Wuhan University of Technology Heping Avenue, 430063 Wuhan

autor

Chen Houjing

• Key Laboratory of High Performance Ship Technology (Wuhan University of Technology) Ministry of Education Heping Avenue, 430063 Wuhan
• China Ship Development and Design Center Zhangzhidong Road, Wuhan China

autor

Xiong Wenhao

• School of Transportation Wuhan University of Technology Heping Avenue, 430063 Wuhan China

autor

Dong Zaopeng

• Key Laboratory of High Performance Ship Technology (Wuhan University of Technology) Ministry of Education Heping Avenue, 430063 Wuhan
• School of Transportation Wuhan University of Technology Heping Avenue, 430063 Wuhan China

autor

Mao Puxiu

• University of Southern California University Park Los Angeles 740–2311 Los Angeles USA

autor

Xiang Zuquan

• Key Laboratory of High Performance Ship Technology (Wuhan University of Technology) Ministry of Education Heping Avenue, 430063 Wuhan
• School of Transportation Wuhan University of Technology Heping Avenue, 430063 Wuhan China

autor

Hu Kai

• China Ship Development and Design Center, Wuhan, China

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).