0000000001220498
AUTHOR
A. Fagiolini
showing 4 related works from this author
A scalable platform for safe and secure decentralized traffic management of multiagent mobile systems
2006
In this paper we describe the application of wireless sensor networking techniques to address the realization of a safe and secure decentralized traffic management system. We consider systems of many heterogeneous autonomous vehicles moving in a shared environment. Each vehicle is assumed to have different and possibly unspecified tasks, but they cooperate to avoid collisions. We are interested in designing a scalable architecture capable of accommodating a very large and dynamically changing number of vehicles, guaranteeing their safety, the achievement of their goals, and security against potential adversaries. By properly distributing and revoking cryptographic keys we are able to protec…
Co-simulation of bio-inspired multi-agent algorithms
2020
This paper reports on the co-simulation of a team of robots deployed in an exploration task, coordinated by a bio-inspired exploration algorithm. The co-simulation integrates the high-level exploration algorithm with detailed implementations of the robot controllers and kinematic models. Co-simulation results are used to find and correct mismatches between submodels.
Block-Based Models and Theorem Proving in Model-Based Development
2021
This paper presents a methodology to integrate computer-assisted theorem proving into a standard workflow for model-based development that uses a block-based language as a modeling and simulation tool. The theorem prover provides confidence in the results of the analysis as it guides the developers towards a correct formalization of the system under development.
Distributed and proximity-constrained C-means for discrete coverage control
2018
In this paper we present a novel distributed coverage control framework for a network of mobile agents, in charge of covering a finite set of points of interest (PoI), such as people in danger, geographically dispersed equipment or environmental landmarks. The proposed algorithm is inspired by C-Means, an unsupervised learning algorithm originally proposed for non-exclusive clustering and for identification of cluster centroids from a set of observations. To cope with the agents' limited sensing range and avoid infeasible coverage solutions, traditional C-Means needs to be enhanced with proximity constraints, ensuring that each agent takes into account only neighboring PoIs. The proposed co…