Particle swarm optimization-based collision avoidance

Authors

TİMUR İNAN
AHMET FEVZİ BABA

DOI

10.3906/elk-1808-63

Abstract

Collision risk assessment and collision avoidance of vessels have always been an important topic in ocean engineering. Decision support systems are increasingly becoming the focus of many studies in the maritime industry today as vessel accidents are often caused by human error. This study proposes an anticollision decision support system that can determine surrounding obstacles by using the information received from radar systems, obtain the position and speed of obstacles within a certain time period, and suggest possible routes to prevent collisions. In this study we use a neural network to predict the subsequent positions of surrounding vessels, a fuzzy logic system to obtain the risk of collision, and a particle swarm optimization algorithm to find the safe and shortest path for collision avoidance.

Keywords

Particle swarm optimization, collision avoidance, collision risk assessment, neural network, fuzzy

First Page

2137

Last Page

2155

Recommended Citation

İNAN, TİMUR and BABA, AHMET FEVZİ (2019) "Particle swarm optimization-based collision avoidance," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 27: No. 3, Article 41. https://doi.org/10.3906/elk-1808-63
Available at: https://journals.tubitak.gov.tr/elektrik/vol27/iss3/41