SwarmLab: a Matlab Drone Swarm Simulator

Among the available solutions for drone swarm simulations, we identified a gap in simulation frameworks that allow easy algorithms prototyping, tuning, debugging and performance analysis, and do not require the user to interface with multiple programming languages. We present SwarmLab, a software entirely written in Matlab, that aims at the creation of standardized processes and metrics to quantify the performance and robustness of swarm algorithms, and in particular, it focuses on drones. We showcase the functionalities of SwarmLab by comparing two state-of-the-art algorithms for the navigation of aerial swarms in cluttered environments, Olfati-Saber's and Vasarhelyi's. We analyze the variability of the inter-agent distances and agents' speeds during flight. We also study some of the performance metrics presented, i.e. order, inter and extra-agent safety, union, and connectivity. While Olfati-Saber's approach results in a faster crossing of the obstacle field, Vasarhelyi's approach allows the agents to fly smoother trajectories, without oscillations. We believe that SwarmLab is relevant for both the biological and robotics research communities, and for education, since it allows fast algorithm development, the automatic collection of simulated data, the systematic analysis of swarming behaviors with performance metrics inherited from the state of the art.Comment: Accepted to the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Online Flocking Control of UAVs with Mean-Field Approximation

We present a novel approach to the formation controlling of aerial robot swarms that demonstrates the flocking behavior. The proposed method stems from the Unmanned Aerial Vehicle (UAV) dynamics; thus, it prevents any unattainable control inputs from being produced and subsequently leads to feasible trajectories. By modeling the inter-agent relationships using a pairwise energy function, we show that interacting robot swarms constitute a Markov Random Field. Our algorithm builds on the Mean-Field Approximation and incorporates the collective behavioral rules: cohesion, separation, and velocity alignment. We follow a distributed control scheme and show that our method can control a swarm of UAVs to a formation and velocity consensus with real-time collision avoidance. We validate the proposed method with physical and high-fidelity simulation experiments.Comment: To appear in the proceedings of IEEE International Conference on Robotics and Automation (ICRA), 202

Artificial Intelligence Applications for Drones Navigation in GPS-denied or degraded Environments

L'abstract è presente nell'allegato / the abstract is in the attachmen

UPPLIED: UAV Path Planning for Inspection through Demonstration

In this paper, a new demonstration-based path-planning framework for the visual inspection of large structures using UAVs is proposed. We introduce UPPLIED: UAV Path PLanning for InspEction through Demonstration, which utilizes a demonstrated trajectory to generate a new trajectory to inspect other structures of the same kind. The demonstrated trajectory can inspect specific regions of the structure and the new trajectory generated by UPPLIED inspects similar regions in the other structure. The proposed method generates inspection points from the demonstrated trajectory and uses standardization to translate those inspection points to inspect the new structure. Finally, the position of these inspection points is optimized to refine their view. Numerous experiments were conducted with various structures and the proposed framework was able to generate inspection trajectories of various kinds for different structures based on the demonstration. The trajectories generated match with the demonstrated trajectory in geometry and at the same time inspect the regions inspected by the demonstration trajectory with minimum deviation. The experimental video of the work can be found at https://youtu.be/YqPx-cLkv04.Comment: Accepted for publication in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023), Detroit, Michigan, US

Selection of critical nodes in drone airways graphs via graph neural networks

This Master Thesis has two distinct parts. The first one mod- els an application of Graph Neural Networks (GNN) for the identifica- tion of critical nodes in graphs that correspond to traffic networks. We call critical nodes those that can compromise the traffic flow in some subgraphs of the network. Specifically, the example data for the demon- stration corresponds to the Vienna subway network, hence the linear subgraphs correspond to the subway lines with intersections at some key subway stations. Those critical nodes relative to a subway line compro- mise the traffic flow at this line, therefore, we propose three GNN based approaches for the identification of such critical nodes, reporting encour- aging results. The second part of the Master Thesis illustrates the back- ground research work on drone airspace management and a discussion of how the reported results may have some relevance for this emerging dif- ficult problem. The main idea is that the urban airspace for drones, that may be carrying out delivery of either persons (aerotaxis) or goods, can be structured along airways that mimic the existing network of streets. The computational example explored in part one of the Master Thesis, thus, becomes relevant for the development of intelligent drone airspace management