DocumentCode :
1284955
Title :
Robust Adaptive Markov Decision Processes: Planning with Model Uncertainty
Author :
Bertuccelli, Luca F. ; Wu, Albert ; How, Jonathan P.
Volume :
32
Issue :
5
fYear :
2012
Firstpage :
96
Lastpage :
109
Abstract :
The ability of autonomous systems to make complex decisions is becoming an increasingly commonplace requirement for many cooperative control operations, including the management of teams of robots such as unmanned aerial vehicles (UAV). Central to this research is the requirement to optimize the vehicle decisions, such as route planning and allocation of team resources, while operating in a dynamic and uncertain environment. Even with the advent of increasingly sophisticated vehicle sensors that can improve the information about the surroundings, uncertainty remains a ubiquitous feature of UAV applications and a key issue in UAV research.
Keywords :
Markov processes; adaptive control; autonomous aerial vehicles; cooperative systems; mobile robots; multi-robot systems; optimal control; path planning; resource allocation; robot vision; robust control; uncertain systems; MDP; UAV; autonomous systems; complex decision making; cooperative control operations; dynamic environment; information improvement; robotic team management; robust adaptive Markov decision processes; route planning; team resource allocation; uncertain environment; unmanned aerial vehicles; vehicle decision optimization; vehicle sensors; Dynamic scheduling; Path planning; Resource management; Robot sensing systems; Robustness; Unmanned aerial vehicles;
fLanguage :
English
Journal_Title :
Control Systems, IEEE
Publisher :
ieee
ISSN :
1066-033X
Type :
jour
DOI :
10.1109/MCS.2012.2205478
Filename :
6302309
Link To Document :
بازگشت