Giannis Spiliopoulos

As the need for persistent ocean monitoring grows, fleets of Unmanned Surface Vessels (USVs) are a promising way to survey large marine protected areas with low operational cost. Their effectiveness, however, depends on solving a challenging multi-robot Coverage Path Planning (CPP) problem over complex coastal geometries while strictly respecting safety boundaries. This paper presents H3CPP, a CPP framework that leverages the H3 hierarchical hexagonal grid to discretize arbitrarily shaped marine areas, cluster them into connected subregions, and construct boundary-aware graphs on which Traveling Salesman Problem (TSP) tours are computed. Each H3 cell is sized to match the nominal sensing footprint of a USV, and edges that intersect land or forbidden zones are exponentially penalized, encouraging safe paths that remain within the operational polygon. We evaluate H3CPP on seven benchmark polygonal maps and on a real-world coastal marina using 1–10 USVs. For a single USV in complex, non-convex environments, H3CPP achieves near-complete coverage (coverage ratio ≈ 1.0 of the target area) while avoiding boundary violations that occur with swath and spiral baselines, and reduces path length by up to 10% compared to these methods in the most irregular shapes. In the Syros marina scenario, multi-USV experiments show that H3CPP decreases the total traveled distance by 4–34% (≈ 20% on average) relative to a decomposition-plus-swaths baseline, while maintaining similar coverage and systematically reducing mission makespan as the fleet size increases from 1 to 10 USVs. The proposed framework therefore combines geodesic hexagonal indexing, connectivity-aware clustering, and penalized graph-based TSP planning into a practical CPP solution for USV swarms operating in realistic coastal environments.

Ships are often considered as the backbone of the global economy. A fundamental unresolved problem is how to best operate fleets, given a sudden increase in demand, such as that reported following the first months of the COVID19 pandemic. Advancing our knowledge of the supply chain's delicate equilibrium between demand and supply, requires analyzing huge amounts of ship-related positional data, thus revealing which areas should be avoided due to potential congestion buildup and where cargoes should be rerouted to. Herein, we analyze a large-scale high-resolution mobility data set of more than 7,000 container ships, collected over an extended period of 36 months, covering the entire globe, so as to measure quantitatively the effects of the pandemic post-hoc on the supply chain. To further understand these fine-grained mobility patterns, we introduce a mobility model for calculating ship presence times (or waiting times) at a global scale. We then reveal the congestion points, which strongly correlate with port waiting areas and anchorages. We analyse the data to reveal how times at port areas were affected by the rising number of ships waiting to load or unload cargo. Following this, we transform this data into a ‘port to port’ graph mapping the international flow of containerised trade. We apply methods of graph theory, complex networks, and multivariate statistics to unravel the hidden relationships between global maritime structure and ship time distribution. This analysis is novel in respect to the size of the data analyzed, the algorithmic approach and the impact of the results which reveal some affinities between pre-COVID and post-COVID shipping patterns.

In this paper, we introduce a publicly available real-world dataset collected during the Aegean Ro-Boat Race 2023, which took place at the University of the Aegean in Syros, Greece. The Aegean Ro-boat Race represents an international competition at the university level, challenging teams to innovate and develop autonomous marine robotic systems capable of performing in unknown dynamic maritime environments under real-world conditions. The 2023 competition featured three primary mission tasks, each designed to test different aspects of the robotic systems: (1) High-Speed Performance for evaluating the speed and agility of the autonomous vessels, (2) Collision Avoidance for assessing the systems’ ability to detect and avoid obstacles in real-time and (3) Endurance for testing the operational longevity and efficiency of the robotic systems over extended periods. In total, seven teams registered for the competition, with five of them being from Greece and two from the countries of Portugal and Latvia. Due to several technical difficulties, three vessels were able to complete all races and data was recorded during their entire participation. The spatiotemporal data for the “Aegean Ro-boat Race” was gathered through an onboard data logging system that continuously monitored various sensors, including GPS, for all vessels during the entire competition. The dataset includes positional reports from the vessels during all three races (totaling in over 6500 records), the positions of the external track and obstacle buoys, together with a file regarding the weather conditions during the race day.

The “Aegean Ro-boat Race” is an international university level competition, challenging teams to design and develop innovative autonomous robotic systems that can perform at sea in real world conditions. This is an innovation race amongst universities, but with the support of industry, that merges visionary thinking from different fields to solve problems in marine robotics and autonomous systems. Topics to be judged include autonomous behavior, sensor fusion, object detection, path planning, reliability, and overall innovation. This year's competition took place in Syros, Greece on the 12th of July 2023, and consisted of three mission tasks, focused on i) high speed and performance ii) collision avoidance and iii) endurance. This work details what happened during the first “Aegean Ro-boat Race”, while presenting the results of this unique learning experience and discussing advances achieved in terms of innovation.

This paper presents a recently developed Operational Forecast Platform (OFP) for prevailing sea conditions at very important ports worldwide (Accu-Waves). The OFP produces reliable high-resolution predictions of wave characteristics in and around ocean ports. Its goal is to support safer navigation, predict possible port downtime, assist vessel approaching, enhance management of towing services, and bolster secure ship maneuvering in busy ports around the globe. Accu-Waves OFP is based on integrated, high-resolution wave modelling over the continental shelf and in coastal areas that incorporates data from global- and regional-scale, open-sea wave and ocean circulation forecasts as boundary conditions. The coupling, nesting, calibration, and implementation of the models are reported and discussed in this paper, concerning 50 selected areas near and inside significant port basins. The detailed setup of the Accu-Waves OFP and its sub-system services is also provided regarding three-day forecasts at three-hourly intervals. The validation of the wave forecast system against in situ observations from wave buoys in coastal areas of the USA, Belgium, and Spain, as well as other model predictions by established OFPs, seems very promising, with performance skill scores ranging from adequate to very good. An exceptional case of stormy seas under severe marine weather conditions with very high wave maxima (>10 m) in the port of Algeciras is further discussed, confirming the good performance of the Accu-Waves OFP.

Density maps support a bird’s eye view of vessel traffic, through providing an overview of vessel behavior, either at a regional or global scale in a given timeframe. However, any inaccuracies in the underlying data, due to sensor noise or other factors, evidently lead to erroneous interpretations and misleading visualizations. In this work, we propose a novel algorithmic framework for generating highly accurate density maps of shipping activities, from incomplete data collected by the Automatic Identification System (AIS). The complete framework involves a number of computational steps for (1) cleaning and filtering AIS data, (2) improving the quality of the input dataset (through trajectory reconstruction and satellite image analysis) and (3) computing and visualizing the subsequent vessel traffic as density maps. The framework describes an end-to-end implementation pipeline for a real world system, capable of addressing several of the underlying issues of AIS datasets. Real-world data are used to demonstrate the effectiveness of our framework. These experiments show that our trajectory reconstruction method results in significant improvements up to 15% and 26% for temporal gaps of 3–6 and 6–24 h, respectively, in comparison to the baseline methodology. Additionally, a use case in European waters highlights our capability of detecting “dark vessels”, i.e., vessel positions not present in the AIS data.

The article “MongoDB Vs PostgreSQL: A comparative study on performance aspects”, written by Antonios Makris, Konstantinos Tserpes, Giannis Spiliopoulos, Dimitrios Zissis, Dimosthenis Anagnostopoulos, was originally published electronically on the publisher’s internet portal on 05 June 2020 without open access.

Several modern day problems need to deal with large amounts of spatio-temporal data. As such, in order to meet the application requirements, more and more systems are adapting to the specificities of those data. The most prominent case is perhaps the data storage systems, that have developed a large number of functionalities to efficiently support spatio-temporal data operations. This work is motivated by the question of which of those data storage systems is better suited to address the needs of industrial applications. In particular, the work conducted, set to identify the most efficient data store system in terms of response times, comparing two of the most representative of the two categories (NoSQL and relational), i.e. MongoDB and PostgreSQL. The evaluation is based upon real, business scenarios and their subsequent queries as well as their underlying infrastructures and concludes in confirming the superiority of PostgreSQL in almost all cases with the exception of the polygon intersection queries. Furthermore, the average response time is radically reduced with the use of indexes, especially in the case of MongoDB.