Editorial for Vol. 25 No. 1

The beginning of another publishing year – and this would be our jubilee 25th – usually provides the opportunity to perform changes in the organization of a scientific journal. In this respect I am pleased to present you a recent reshape of our editorial team, which has been enhanced in order to better cope with the increasing number of submission. Namely, our former Associate Editors Siniša Šegvić and Jan Šnajder have been co-opted into the Editorial Board, while Bruno Blašković, Goran Delač, Domagoj Jakobović, Alan Jović, Miljenko Mikuc and Stjepan Picek took over the positions of Associate Editors. I am fully confident that with such a strengthened editorial team CIT. Journal of Computing and Information Technology will succeed in better accomplishing its publishing mission, targeting the improvement of its publishing effort as well as the shortening of submissions\u27 processing times

Aeolus Ocean -- A simulation environment for the autonomous COLREG-compliant navigation of Unmanned Surface Vehicles using Deep Reinforcement Learning and Maritime Object Detection

Heading towards navigational autonomy in unmanned surface vehicles (USVs) in the maritime sector can fundamentally lead towards safer waters as well as reduced operating costs, while also providing a range of exciting new capabilities for oceanic research, exploration and monitoring. However, achieving such a goal is challenging. USV control systems must, safely and reliably, be able to adhere to the international regulations for preventing collisions at sea (COLREGs) in encounters with other vessels as they navigate to a given waypoint while being affected by realistic weather conditions, either during the day or at night. To deal with the multitude of possible scenarios, it is critical to have a virtual environment that is able to replicate the realistic operating conditions USVs will encounter, before they can be implemented in the real world. Such "digital twins" form the foundations upon which Deep Reinforcement Learning (DRL) and Computer Vision (CV) algorithms can be used to develop and guide USV control systems. In this paper we describe the novel development of a COLREG-compliant DRL-based collision avoidant navigational system with CV-based awareness in a realistic ocean simulation environment. The performance of the trained autonomous Agents resulting from this approach is evaluated in several successful navigations to set waypoints in both open sea and coastal encounters with other vessels. A binary executable version of the simulator with trained agents is available at https://github.com/aavek/Aeolus-OceanComment: 22 pages, last blank page, 17 figures, 1 table, color, high resolution figure