Title :
Location-Aware, Flexible Task Management for Collaborating Unmanned Autonomous Vehicles
Author :
Wang, Meng ; Zhao, Yang ; Doboli, Alex
Author_Institution :
Dept. of Electr. & Comput. Eng., State Univ. of New York at Stony Brook, Stony Brook, NY, USA
fDate :
July 29 2009-Aug. 1 2009
Abstract :
Unmanned autonomous vehicles (UAVs) are emerging as a breakthrough concept in technology. A main challenge related to UAV control is devising flexible strategies with predictable performance in hard-to-predict conditions. This paper proposes an approach to performance predictive collaborative control of UAVs operating in environments with fixed targets. The paper offers detailed experimental insight on the quality, scalability and computational complexity of the proposed method.
Keywords :
computational complexity; mobile robots; predictive control; remotely operated vehicles; computational complexity; flexible task management; location-awareness; performance predictive collaborative control; unmanned autonomous vehicle; Adaptive systems; Collaboration; Communication system control; Decision making; Mobile robots; NASA; Processor scheduling; Remotely operated vehicles; Unmanned aerial vehicles; Vehicle dynamics;
Conference_Titel :
Adaptive Hardware and Systems, 2009. AHS 2009. NASA/ESA Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
978-0-7695-3714-6
DOI :
10.1109/AHS.2009.67