The article describes a novel approach for deployment of a swarm of heterogeneous autonomous vehicles. Each vehicle is treated as the agent of a network, which cooperates in order to cover a given area according to its own capabilities. A general framework is introduced, which aims at providing tools for solving a large class of coordination problems. The capabilities of each agent are modeled with Descriptor Functions; the sum of these functions constitutes the swarm's descriptor. The goal of the swarm is to match a desired descriptor, by minimizing an appropriate cost functional. A control law is proposed, which is capable of driving the agents towards the achievement of the goal. The existence of local minima and of a global minimum is discussed. Theoretical results on the existence of the global minimum are given. The Area Coverage problem is selected as a preliminary test for the algorithm. Simulation results show the effectiveness of the proposed approach.

Near Optimal Swarm Deployment using Descriptor Functions

INNOCENTI, MARIO;POLLINI, LORENZO
2010

Abstract

The article describes a novel approach for deployment of a swarm of heterogeneous autonomous vehicles. Each vehicle is treated as the agent of a network, which cooperates in order to cover a given area according to its own capabilities. A general framework is introduced, which aims at providing tools for solving a large class of coordination problems. The capabilities of each agent are modeled with Descriptor Functions; the sum of these functions constitutes the swarm's descriptor. The goal of the swarm is to match a desired descriptor, by minimizing an appropriate cost functional. A control law is proposed, which is capable of driving the agents towards the achievement of the goal. The existence of local minima and of a global minimum is discussed. Theoretical results on the existence of the global minimum are given. The Area Coverage problem is selected as a preliminary test for the algorithm. Simulation results show the effectiveness of the proposed approach.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/202096
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