About me

Welcome to my personal website. My name is Andreas Anastasiou and I am currently a Ph.D. candidate at the Department of Electrical and Computer Engineering at the University of Cyprus. I am also working as a Research Engineer at KIOS Research and Innovation Center of Excellence in Cyprus. Prior to that, I received both my B.Sc. and M.Sc. from the Department of Electrical and Computer Engineering at the University of Cyprus in 2018 and 2020 respectively. My current research interests are concerned with multi-UAV systems, UAV path-planning, and embedded systems.




SWIFTERS leverages UAV swarms to study, design, develop, and test, UAV cooperation strategies to assist civil protection operations, such as emergency evacuation. This action has come together after seeing the great potentials of UAVs in emergency management during the DG ECHO PREDICATE project. In particular, PREDICATE demonstrated the use of standalone UAVs for watch-keeping and patrolling regions of interest in order to enhance the disaster prevention capabilities of civil protection and other relevant authorities. Building upon these previous action results, SWIFTERS is a continuation of the work conducted by the KIOS Research Center at the University of Cyprus (UCY-KIOS), the Center for Security Studies of the Ministry of Interior in the Hellenic Republic (KEMEA), and the Cyprus Civil Defence (CCD), i.e., all members of the PREDICATE project consortium. 

ICARUS: Power DIstribution Network InspeCtion PlAtfoRm Using UAVS

The ICARUS project utilizes a drone and develops an autonomous vision-based artificial intelligence toolkit, to detect, track and identify power infrastructure components, and gathers reliable spatial/time data associated to these components in a safe and fast manner.
This project was submitted for the My Galileo Drone contest which ending up being in the semi-finals.

Publication: 10.1109/ICUAS51884.2021.9476742

The AIDERS project aims at developing application-specific algorithms and novel mapping platform that will harness the large volume of data that first responders are now able to collect through heterogeneous sensors (including visual, thermal and multispectral cameras, LIDAR, CBRN sensors, etc.) on-board RPAS units, and converting that data into actionable decisions for improved emergency response. I contributed to the project by implementing computer vision and deep learning algorithms  to detect and visualize exact gps location of moving targets into the platform. Introducing video is in Greek.
You can find more information on the project’s website.

Hyperion: A Robust Drone-Based Target Tracking System

Hyperion is a robust detect, track and follow algorithm for UASs, leveraging and integrating various computer vision techniques and a combination of two proportional integral derivative (PID) controllers for following a moving vehicle. The Hyperion system is evaluated under real settings using off-the-shelf hardware and an elaborated comparison was made with a variety of state-of-the-art trackers available in the OpenCV library

Publication: 10.1109/ICUAS51884.2021.9476687.

This work dives into the challenging task of multiple castaway tracking using an autonomous UAV agent. Leveraging on the computing power of the modern embedded devices, we propose a Model Predictive Control (MPC) framework for tracking multiple castaways assumed to drift afloat in the aftermath of a maritime accident. We consider a stationary radar sensor that is responsible or signaling the search mission by providing noisy measurements of each castaway’s initial state. The UAV agent aims at detecting and tracking the moving targets with its equipped onboard camera sensor that has limited sensing range. In this work, we also experimentally determine the probability of target detection from real-world data by training and evaluating various Convolutional Neural Networks (CNNs).

Where to find me