@article{Ayob_Jalal_Hassri_Rahman_Jamaludin_2020,
	author = {Ayob, Ahmad Faisal Mohamad and Jalal, N.I. and Hassri, M.H. and Rahman, S.A. and Jamaludin, S.},
	title = {Neuroevolutionary Autonomous Surface Vehicle Simulation in Restricted Waters},
	journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation},
	volume = {14},
	number = {4},
	pages = {865-873},
	year = {2020},
	url = {./Article_Neuroevolutionary_Autonomous_Surface_Ayob,56,1071.html},
	abstract = {Safe, accurate, and predictable autonomous systems in marine vehicles are paramount. An understanding of an intelligent system fitted inside a ship is critical to ensure an autonomous ship is safe to be operated. Although the use of artificial intelligence in the design of the road-based vehicle has arrived at the self-driving level, there exists a significant gap within the research of autonomous ship to operate in restricted water (riverine and ports). Hence, this article shall discuss the relevant works of literature to set a preliminary guiding principle for the design of an autonomous ship. We present a simple illustrative framework as a starting point for ship designers to begin working in a simulated environment, which can be used as a foundation before the physical autonomous-ships are constructed and tested in a real-world situation. The framework consists of a virtual 3D environment and a surface vehicle with distance sensors, controlled by a neuroevolution-based autonomous piloting system. In this work, two scenarios will be presented: navigation in restricted waters, and obstacle avoidance capability of an autonomous ship. Results show that the resulting autonomous surface vehicle (ASV) is also capable of performing obstacle avoidance in the test track, albeit not being trained to do so in the training track. The work demonstrated in this paper is useful to the ship designers and can be extended for scenario-based planning for autonomous ship design.},
	doi = {10.12716/1001.14.04.11},
	issn = {2083-6473},
	publisher = {Gdynia Maritime University, Faculty of Navigation},
	keywords = {Restricted Waters, Artificial Neural Network (ANN), Maritime Autonomous Surface Ships (MASS), Autonomous Vehicles, Autonomous Surface Vehicle (ASV), Neuroevolutionary Simulation, Neuroevolutionary Autonomous Surface Vehicle Simulation, Neural Network Allocation (NNA)}
}