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Model Predictive Control of Aerial Swarms

Publications associées (55)

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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

A Robust Relative Positioning System for Multi-Robot Formations Leveraging an Extended GM-PHD Filter

Alcherio Martinoli

We propose a multi-robot tracking method to provide state estimates that allow a group of robots to maintain a formation even when the communication fails. We extend a Gaussian Mixture Probability Hypothesis Density filter to incorporate, firstly, absolute ...

2017

Electroencephalography as implicit communication channel for proximal interaction between humans and robot swarms

Francesco Mondada, Mohammad Ehsanul Karim

Search and rescue, autonomous construction, and many other semi-autonomous multi-robot applications can benefit from proximal interactions between an operator and a swarm of robots. Most research on proximal interaction is based on explicit communication t ...

Springer Verlag2016

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Although the technology for fully autonomous swarms of robots is rapidly progressing, the human operator will continue to play an important role during any swarming mission due to safety, monitoring and control constraints. In this paper, we present the se ...

Springer2016

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

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Population-based learning techniques have been proven to be effective in dealing with noise in numerical benchmark functions and are thus promising tools for the high-dimensional optimization of controllers for multiple robots with limited sensing capabili ...

2015

Distributed vs. Centralized Particle Swarm Optimization for Learning Flocking Behaviors

Alcherio Martinoli, Inaki Navarro Oiza, Ezequiel Leonardo Di Mario

In this paper we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots. We use a noise-resistant version of Particle Swarm Optimization to learn in simulation a set of 50 weights of a plastic artificial neura ...

The MIT Press2015

Extension of a Ground Control Interface for Swarms of Small Drones

Dario Floreano, Grégoire Hilaire Marie Heitz, Nicolas Dousse

2015

Distributed Learning of Cooperative Robotic Behaviors using Particle Swarm Optimization

Alcherio Martinoli, Inaki Navarro Oiza, Ezequiel Leonardo Di Mario

In this paper we study the automatic synthesis of robotic controllers for the coordinated movement of multiple mobile robots. The algorithm used to learn the controllers is a noise-resistant version of Particle Swarm Optimization, which is applied in two d ...

2014

In current robotics research there is a vast body of work on algorithms and control methods for groups of decentralized cooperating robots, called a swarm or collective. These algorithms are generally meant to control collectives of hundreds or even thousa ...

Elsevier Science Bv2014