Error mitigation algorithm based on bidirectional fitting method for collision avoidance of Unmanned Surface Vehicle

• Autorzy: Song L. , Chen Z. , Mao Y. , Dong Z. , Xiang Z.

Identyfikatory

DOI

10.2478/pomr-2018-0127

• Języki publikacji: EN

Abstrakty

EN

Radars and sensors are essential devices for an Unmanned Surface Vehicle (USV) to detect obstacles. Their precision has improved significantly in recent years with relatively accurate capability to locate obstacles. However, small detection errors in the estimation and prediction of trajectories of obstacles may cause serious problems in accuracy, thereby damaging the judgment of USV and affecting the effectiveness of collision avoidance. In this study, the effect of radar errors on the prediction accuracy of obstacle position is studied on the basis of the autoregressive prediction model. The cause of radar error is also analyzed. Subsequently, a bidirectional adaptive filtering algorithm based on polynomial fitting and particle swarm optimization is proposed to eliminate the observed errors in vertical and abscissa coordinates. Then, simulations of obstacle tracking and prediction are carried out, and the results show the validity of the algorithm. Finally, the method is used to simulate the collision avoidance of USV, and the results show the validity and reliability of the algorithm.

Słowa kluczowe

EN

Unmanned Surface Vehicle; position prediction; error mitigation; autoregressive model; particle swarm optimization (PSO)

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2018
• Tom: nr 4
• Strony: 13--20
• Opis fizyczny: Bibliogr. 23 poz., rys.

Twórcy

autor

Song L.

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

autor

Chen Z.

• Key Laboratory of High Performance Ship Technology (Wuhan University of Technology) Ministry of Education, Wuhan, China

autor

Mao Y.

• Key Laboratory of High Performance Ship Technology (Wuhan University of Technology) Ministry of Education, Wuhan, China

autor

Dong Z.

• Key Laboratory of High Performance Ship Technology (Wuhan University of Technology) Ministry of Education, Wuhan, China

autor

Xiang Z.

• Key Laboratory of High Performance Ship Technology (Wuhan University of Technology) Ministry of Education, 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).