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Coalition Formation for Unmanned Quadrotors

[+] Author Affiliations
A. Mancini, A. Benini, E. Frontoni, P. Zingaretti, S. Longhi

Università Politecnica delle Marche, Ancona, Italy

Paper No. DETC2011-48904, pp. 745-752; 8 pages
  • ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3: 2011 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications, Parts A and B
  • Washington, DC, USA, August 28–31, 2011
  • Conference Sponsors: Design Engineering Division and Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5480-8
  • Copyright © 2011 by ASME


Today Unmanned Aerial Vehicles (UAVs) and in particular quad-rotors represent novel platforms to accomplish a wide set of missions as surveillance, Search & Rescue, inspection, photogrammetry. The main limitation of these vehicles is represented by the restricted operating area. The area is mainly limited by power supplies (batteries or fuel). A strategy to overcame this limitation is to increase the number of vehicles forming a coalition. The main benefit of coalition formation are the extended mission range and the capability to increase the sensorial set. Each vehicles is a part of a dynamic network that must be properly coordinated in order to optimize all the available resources. In this paper a new framework for simulation of unmanned vehicles in cooperative scenarios is first presented. The framework is based on the interaction of a physics-engine, which simulates the dynamics of vehicles and their interaction with world increasing the realism of simulation, and a simulation environment where the high-level strategy is designed/developed. A Model Predictive Control (MPC) is then introduced to solve the problem of leader-follower applied to quad-rotors. Using the developed framework and the MPC technique is possible to easily instantiate the coalition minimizing also a cost function. The obtained results from the control strategy point of view show that positioning error at steady state is equal to zero. The MPC allows also the modelling of different conflicting constraints as the control actions, positioning error, and fuel/energy consumption.

Copyright © 2011 by ASME



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