arXiv subjects

Marco Fabris

Publications and source records attributed to Marco Fabris.

24 records · Page 2Linked to original sources

arXiv2022

Distributed Strategies for Dynamic Coverage with Limited Sensing Capabilities

In this work, it is presented the development of a novel distributed algorithm performing robotic coverage, clustering and dispatch around an event in static-obstacle structured environments without relying on metric information. Specifically, the aim is to account for the trade-off between local communication given by bearing visibility sensors installed on each agent involved, optimal deployment in closed unknown scenarios and focus of a group of agents on one point of interest. The particular targets of this study can be summarized as 1. the computation, under certain topological assumptions, of a lower bound for the number of required agents, which are provided by a realistic geometric model (e.g. a round shape) to emphasize physical limitations; 2. the minimization of the number of nodes and links maintaining a distributed approach over a connected communication graph; 3. the identification of an activation cluster around an event with a radial decreasing intensity, sensed by each agent; 4. the attempt to send the agents belonging to the cluster towards the most intense point in the scenario by minimizing a weighted isoperimetric functional.

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arXiv2022

On the Distributed Estimation from Relative Measurements: a Graph-Based Convergence Analysis

For a multi-agent system state estimation resting upon noisy measurements constitutes a problem related to several application scenarios. Adopting the standard least-squares approach, in this work we derive both the (centralized) analytic solution to this issue and two distributed iterative schemes, which allow to establish a connection between the convergence behavior of consensus algorithm toward the optimal estimate and the theory of the stochastic matrices that describe the network system dynamics. This study on the one hand highlights the role of the topological links that define the neighborhood of agent nodes, while on the other allows to optimize the convergence rate by easy parameter tuning. The theoretical findings are validated considering different network topologies by means of numerical simulations.

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arXiv2022

Optimal Time-Invariant Formation Tracking for a Second-Order Multi-Agent System

Given a multi-agent linear system, we formalize and solve a trajectory optimization problem that encapsulates trajectory tracking, distance-based formation control and input energy minimization. To this end, a numerical projection operator Newton's method is developed to find a solution by the minimization of a cost functional able to capture all these different tasks. To stabilize the formation, a particular potential function has been designed, allowing to obtain specified geometrical configurations while the barycenter position and velocity of the system follows a desired trajectory.

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arXiv2022

Bearing-based Autonomous Communication Relay Positioning under Field-of-View Constraints

This paper investigates the problem of communication relay establishment for multiple agent-based mobile units using a relay vehicle. The objective is to drive autonomously the relay vehicle to attain a position for communication relay establishment while maintaining the other vehicles inside of its field-of-view. A bearing-based control law is proposed for the relay drone and designed for both single and multiple agents. We also provide a collision avoidance scheme that ensures no collisions between the relay and other agents. Numerical simulations and experimental results are reported as well to show the efficacy of the proposed approach.

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arXiv2021

A General Regularized Distributed Solution for System State Estimation from Relative Measurements

This work presents a novel general regularized distributed solution for the state estimation problem in networked systems. Resting on the graph-based representation of sensor networks and adopting a multivariate least-squares approach, the designed solution exploits the set of the available inter-sensor relative measurements and leverages a general regularization framework, whose parameter selection is shown to control the estimation procedure convergence performance. As confirmed by the numerical results, this new estimation scheme allows (i) the extension of other approaches investigated in the literature and (ii) the convergence optimization in correspondence to any (undirected) graph modeling the given sensor network.

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arXiv2021

A Proximal Point Approach for Distributed System State Estimation

System state estimation constitutes a key problem in several applications involving multi-agent system architectures. This rests upon the estimation of the state of each agent in the group, which is supposed to access only relative measurements w.r.t. some neighbors state. Exploiting the standard least-squares paradigm, the system state estimation task is faced in this work by deriving a distributed Proximal Point-based iterative scheme. This solution entails the emergence of interesting connections between the structural properties of the stochastic matrices describing the system dynamics and the convergence behavior toward the optimal estimate. A deep analysis of such relations is provided, jointly with a further discussion on the penalty parameter that characterizes the Proximal Point approach.

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