Swarm Operations

The Smart Move Lab at the University of the Aegean explores autonomous Unmanned Surface Vessels (USVs) for real-time marine monitoring and conservation. Our research focuses on swarm coordination, coverage path planning, and AI-driven mission management, integrating IoT, MQTT, and Kafka for optimized control and telemetry. In this page we demonstrate sample videos related to our latest research activities in this field.

Motivation 

As ocean conservation gains priority, efficient monitoring of marine protected areas is essential to combat threats such as illegal fishing and unauthorized vessel entry. Autonomous systems, particularly Unmanned Surface Vessels (USVs), offer a promising, low-cost solution for long-range, sustainable operations.  

  • Adaptive and autonomous systems are currently being explored for real-time monitoring of large marine areas as a source for reliable monitoring. 
  • These provide a low cost and reusable solution that can be easily deployed in different areas and conditions, capable of performing a variety of functions.  
  • USVs are more suitable for long range endurance operations as they can stay at sea for extended lengths of time and operate in bad weather conditions, without the need of refueling or recharging often by making use of wind propulsion and solar technology with low environmental impact (less noise) and low or no carbon footprint. 
  • USVs can be fitted with small, lightweight, and inexpensive sensors capable of detecting and observing targets over a very limited area. 

Research Activities 

Our lab is actively working on complex swarms’ operation. We combine autonomous/unmanned surface vehicles control data with real world live (AIS) maritime data to design complex USV & swarm missions in real world settings.  

  • Area patrolling  
  • Larger scale exhibitions  
  • Swarm formations  
  • USV COLREG compliance scenarios 

Swarm based coverage path planning


Coverage path planning is a common task for autonomous vehicles, it is the task of determining a path that passes over all points within an area of interest. Typical applications of such algorithms are patrolling, exploration and SAR operations.  Utilizing swarms of autonomous surface vehicle for such operations at can significantly increase detection capabilities, as unmanned surface vehicle can have typically low power consumption and operate in area for extended periods detecting objects at sea level, air and underwater. 

Unlike aerial drones that have huge optical and sensing footprint and avoid obstacles by increasing the flight altitude, for unmanned surface vehicles (USV) at sea it is important to determine beforehand both the mission plan as well as the outer boundaries of the operational area. A collaborative mission plan can effectively split areas of interest into sub-regions and effectively increase temporal coverage of each area. 

Swarm coverage path planning consists of the generation of distinct mission plans for each USV so the whole area of interest is visited by a USV exactly one time on each iteration.  Typically for sea drones, a safe buffer zone along the coastline defines the operation area. 

In this video we use the Software in the loop (SITL) to emulate the controller of a swarm of 10 USVs that patrol a small marina. In our setup each USV is equipped with a small IoT companion device that awaits its mission plan from its command center through web. The communication uses the MQTT protocol commands for ingesting commands and Kafka to capture and retain the USV telemetry stream. Some basic control functionality is exposed to external applications through REST API. 

AIS Toolbox

AIS Toolbox

The AIS Toolbox is an open-source Python package developed by SmartMove Lab to process and analyze Automatic Identification System (AIS) data. It provides a streamlined workflow for handling AIS messages, generating vessel density maps, and supporting maritime data analysis.

Key Features

  • AIS Data Processing – Integrates AIS positional and static reports to enrich vessel tracking information.
  • Data Cleaning & Filtering – Removes erroneous or incomplete messages to ensure accurate results.
  • Density Map Generation – Creates maps based on vessel distribution, either by counting unique vessels per grid cell or calculating time spent in specific areas.

Applications

The AIS Toolbox is designed for researchers, maritime analysts, and policymakers who require efficient tools for processing AIS data. It can be used for marine traffic monitoring, route analysis, and environmental impact studies.

The project builds upon previous open-source initiatives and benefits from contributions within the maritime data community. For more details and source code, visit the AIS Toolbox repository.