Error Mitigation Algorithm Based on Bidirectional Fitting Method for Collision Avoidance of Unmanned Surface Vehicle

Polish maritime research

Zhuo Chen^2¹  Lifei Song^1,2² 

[1]Key Laboratory of High Performance Ship Technology (Wuhan University of Technology) Ministry of Education,, Wuhan, China^2

[2]School of Transportation, Wuhan University of Technology,, Wuhan, China^1

关键词: Unmanned Surface Vehicle;    Position prediction;    Error mitigation;    Autoregressive model;    Particle Swarm Optimization;   

DOI  :  10.2478/pomr-2018-0127

学科分类：工程和技术（综合）

来源: Politechnika Gdanska * Wydzial Oceanotechniki i Okretownictwa

【 摘 要 】

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.

【 授权许可】

Unknown