This paper applies a form of Distributed Model Predictive Control (DMPC) to the problem of multi-vehicle search of an area. The objective of the team of vehicles is to thoroughly search an area of unknown content, collecting rewards while avoiding collision and duplication of effort. The DMPC algorithm employed, which offers guaranteed fea- sibility and flexible communications, has recently been extended to a cooperative form, where agents consider a greater portion of the global objective. It is shown by simulation that use of this cooperative cost leads to an improvement in system-wide performance over that of a simple greedy implementation.
|Translated title of the contribution||Multi-Vehicle Cooperative Search using Distributed Model Predictive Control|
|Title of host publication||AIAA Guidance, Navigation and Control Conference, Honolulu, USA|
|Publisher||American Institute of Aeronautics and Astronautics Inc. (AIAA)|
|Publication status||Published - Aug 2008|
Bibliographical noteConference Organiser: AIAA
Other identifier: AIAA 2008-7138