Open AccessError Mitigation Algorithm Based on Bidirectional Fitting Method for Collision Avoidance of Unmanned Surface Vehicle
Author(s) -
Lifei Song,
Zhuo Chen,
Yunsheng Mao,
Zaopeng Dong,
Zuquan Xiang
Publication year - 2018
Publication title -
polish maritime research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.374
H-Index - 21
eISSN - 2083-7429
pISSN - 1233-2585
DOI - 10.2478/pomr-2018-0127
Subject(s) - computer science , collision avoidance , particle swarm optimization , unmanned surface vehicle , position (finance) , reliability (semiconductor) , radar , algorithm , obstacle avoidance , autoregressive model , obstacle , control theory (sociology) , polynomial , collision , simulation , artificial intelligence , mathematics , engineering , mobile robot , robot , marine engineering , statistics , computer security , law , telecommunications , power (physics) , control (management) , quantum mechanics , political science , physics , finance , economics , mathematical analysis
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.