Communication Issues

Analysis of the performance of existing communication technologies, on sea-port scenarios under several weather conditions.

Operations and Planning

Coordination of drones to perform sensing tasks, with heterogeneous requirements, priorities and sampling rates.

Data Analysis and Visualization

Development of tools and software for visualization and analysis of gathered data, employing machine learning tecniques.

Latest Project Publications


TaMaRA: a Task Management and Routing Algorithm for FANETs

Existing long-range communication technologies are often inadequate to meet the data rate requirements and delay constraints of safety-critical applications. To face this challenge, we formulated the connected deployment problem, where we require the FANET to dynamically create connected coverage formations to ensure multi-hop low-latency communications while performing the monitoring task. We proposed a joint Task Management and Routing Algorithm, called TaMaRA.


SIDE : Self drIving DronEs embrace uncertainty (December 2021)

We propose SIDE in the context of critical scenarios where a squad of drones is required to autonomously inspect an area of interest under uncertainty, of time and location of target events. The main goal of SIDE is to ensure maximum coverage of event monitoring with minimum average inspection delay. With no initial knowledge, the drones share their local observations of the environment and apply the Parzen-Rosenblatt approach to manage a dynamic probabilistic map of ongoing events. This map is integrated into a virtual force approach for a joint solution to distributed dynamic trajectory planning and collision avoidance.

Genetic Algorithm

GenPath - A Genetic Multi Round Path Planning Algorithm for Aerial Vehicles (May 2021)

We develop Gen-Path, a genetic algorithm for efficient scheduling of multi-round UAV missions, under several objective functions. The algorithm selects the most efficient paths from a set of feasible trajectories.


MAD for FANETs: Movement Assisted Delivery for Flying Ad-hoc Networks (July 2021)

We propose MAD (Movement Assisted Delivery): a packet routing protocol specifically tailored for networks of aerial vehicles. MAD enables adaptive selection of the most suitable relay nodes for packet delivery, resorting to movement-assisted delivery upon need, which is supported by a reinforcement learning approach.