Paper Info
Title
Path planning for optimal classification
Abstract
As stated in the Office of the Secretary of Defense's Unmanned Aircraft Systems Roadmap 2005-2030, reconnaissance is the number one priority mission for Unmanned Air Vehicles (UAVs) of all sizes. During reconnaissance missions, classification of objects of interest (e.g, as friend or foe) is key to mission performance. Classification is based on information collection, and it has generally been assumed that the more information collected, the better the classification decision. Although this is a correct general trend, a recent study has shown it does not hold in all cases. This paper focuses on presenting methods to plan paths for unmanned vehicles that optimize classification decisions (as opposed to the amount of information collected). We consider an unmanned vehicle (agent) classifying an object of interest in a given area. The agent plans its path to collect the information most relevant to optimizing its classification performance, based on the maximum likelihood ratio. In addition, a classification performance measure for multiple measurements is analytically derived.
Year
DOI
Venue
2012
10.1109/CDC.2012.6427090
Decision and Control
Keywords
Field
DocType
aerospace control,autonomous aerial vehicles,military aircraft,mobile robots,path planning,pattern classification,telerobotics,UAV,decision classification,information collection,maximum likelihood ratio,object classification,optimal classification,path planning,reconnaissance missions,secretary of defense unmanned aircraft systems roadmap,unmanned vehicle agent
Motion planning,Mathematical optimization,Simulation,Computer science,Maximum likelihood,Operations research,Telerobotics,Mobile robot
Conference
ISSN
ISBN
Citations 
0743-1546 E-ISBN : 978-1-4673-2064-1
978-1-4673-2064-1
2
PageRank 
References 
Authors
0.39
9
4
Name
Order
Citations
PageRank
Mariam Faied121.74
Pierre T. Kabamba240.81
Baro Hyun3183.84
Anouck R. Girard420.39