Publications related to Flocking

Predicting the long-term collective behaviour of fish pairs with deep learning

Francesco Mondada, Alexandre Massoud Alahi, Vaios Papaspyros

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social in ...

2024

Vision-based Flocking in Aerial Robot Swarms

Fabian Maximilian Schilling

Aerial robot swarms have the potential to perform time-critical and dangerous tasks such as disaster response without compromising human safety. However, their reliance on external infrastructure such as global positioning for localization and wireless net ...

EPFL2022

On the Scalability of Vision-based Drone Swarms in the Presence of Occlusions

Dario Floreano, Fabian Maximilian Schilling, Enrica Soria

Vision-based drone swarms have recently emerged as a promising alternative to address the fault-tolerance and flexibility limitations of centralized and communication-based aerial collective systems. Although most vision-based control algorithms rely on th ...

2022

Model Predictive Control of Aerial Swarms

Enrica Soria

Aerial robot swarms can have a large socio-economic impact. They can perform time-critical missions faster than a single robot and access dangerous environments without compromising human safety. However, swarm deployment is often limited to free environme ...

EPFL2022

Vision-based Drone Flocking in Outdoor Environments

Dario Floreano, Fabrizio Schiano, Fabian Maximilian Schilling

Decentralized deployment of drone swarms usually relies on inter-agent communication or visual markers that are mounted on the vehicles to simplify their mutual detection. This letter proposes a vision-based detection and tracking algorithm that enables gr ...

2021

Flocking-enhanced social contagion

Ignacio Pagonabarraga Mora

Populations of mobile agents-animal groups, robot swarms, or crowds of people-self-organize into a large diversity of states as a result of information exchanges with their surroundings. While in many situations of interest the motion of the agents is driv ...

AMER PHYSICAL SOC2020

The influence of limited visual sensing on the Reynolds ﬂocking algorithm

Dario Floreano, Fabrizio Schiano, Enrica Soria

The interest in multi-drone systems flourished in the last decade and their application is promising in many fields. We believe that in order to make drone swarms flying smoothly and reliably in real-world scenarios we need a first intermediate step which ...

2019

Learning Vision-based Flight in Drone Swarms by Imitation

Dario Floreano, Fabrizio Schiano, Julien Jean Denis Nicolas Lecoeur, Fabian Maximilian Schilling

Decentralized drone swarms deployed today either rely on sharing of positions among agents or detecting swarm members with the help of visual markers. This work proposes an entirely visual approach to coordinate markerless drone swarms based on imitation l ...

2019

Fixed-wing drones for communication networks

Maja Varga

In the last decade, drones became frequently used to provide eye-in-the-sky overview in the outdoor environment. Their main advantage compared to the other types of robots is that they can fly above obstacles and rough terrains and they can quickly cover l ...

EPFL2016

Intearcting Brownian Swarms: Some Analytical Results

Max-Olivier Hongler, Guillaume Adrien Sartoretti

We consider the dynamics of swarms of scalar Brownian agents subject to local imitation mechanisms implemented using mutual rank-based interactions. For appropriate values of the underlying control parameters, the swarm propagates tightly and the distances ...

MDPI AG2016