ETD system

Electronic theses and dissertations repository


Tesi etd-04092011-101435

Thesis type
Tesi di dottorato di ricerca
Swarm Abstractions for Distributed Estimation and Control
Settore scientifico disciplinare
Corso di studi
tutor Innocenti, Mario
tutor Pollini, Lorenzo
Parole chiave
  • robotic networks
  • multi-robot
  • deployment
  • descriptor functions
  • decentralized estimation
  • decentralized control
  • coverage
  • consensus algorithms
  • coordination
  • swarm
  • task-assignment
Data inizio appello
Riassunto analitico
Swarm of vehicles, instead of single or small groups of vehicles, are becoming <br>of large interest in a variety of applications for their intrinsic robustness and flexibility. <br>Probably, the most difficult issue is coordination and control of a large<br>number of small vehicles with limited processing, communication and power capabilities. <br>A large number of solutions for practical problems have been studied and proposed <br>by researchers worldwide; they are often specific solutions to specific problems <br>that are difficult to generalize. Most of them cannot manage the natural <br>heterogeneity of a large group of vehicles: it is often desired that <br>a swarm performs a complex mission in different phases <br>that may require different specialized vehicles instead of a multi role one. <br><br>The aim of this thesis is to develop a framework for swarm modeling, decentralized estimation <br>and control capable of managing many of the the application fields <br>proposed and studied in the literature. The environment and mission(s) under consideration<br>are as general as possible, as well as the characteristics of the agents<br>which may be all identical or heterogeneous. <br><br>The Descriptor Functions Framework, the main topic and contribution of this thesis,<br>associates a specific function to each agent, and one or more functions to the mission and uses <br>an analytical framework for generation of the agents control law.<br>Decentralized control and estimation of the relevant variables needed<br>as feedback are cast into an optimization problem; analysis of equilibria <br>and formal proof of convergence of the estimation and control law proposed are derived.<br>Then, a bio-inspired method for task self-assignment is applied to the Framework;<br>swarm vehicles are given the capability to select the best task to perform, <br>swap task with other agents during the mission, and assess the degree of completeness<br>of the various tasks in order to balance the swarm capabilities among them.<br>Finally an hardware test bed, together with experimental results, is be presented. <br>