Paper Info
Title
Foraging theory for autonomous vehicle speed choice
Abstract
We consider the optimal control design of an abstract autonomous vehicle (AAV). The AAV searches an area for tasks that are detected with a probability that depends on vehicle speed, and each detected task can be processed or ignored. Both searching and processing are costly, but processing also returns rewards that quantify designer preferences. We generalize results from the analysis of animal foraging behavior to model the AAV. Then, using a performance metric common in behavioral ecology, we explicitly find the optimal speed and task processing choice policy for a version of the AAV problem. Finally, in simulation, we show how parameter estimation can be used to determine the optimal controller online when density of task types is unknown.
Year
DOI
Venue
2009
10.1016/j.engappai.2008.10.017
Eng. Appl. of AI
Keywords
Field
DocType
autonomous vehicle speed choice,task type,intelligent control optimal control task-type choice speed-accuracy trade-off speed-cost trade-off decision-making algorithms,optimal speed,optimal controller online,optimal control design,behavioral ecology,vehicle speed,abstract autonomous vehicle,animal foraging behavior,foraging theory,aav problem,task processing choice policy,intelligent control,parameter estimation,foraging behavior,optimal control
Intelligent control,Control theory,Mathematical optimization,Optimal control,Optimal control design,Computer science,Performance metric,Animal Foraging,Artificial intelligence,Estimation theory,Optimal foraging theory
Journal
Volume
Issue
ISSN
22
3
Engineering Applications of Artificial Intelligence
Citations 
PageRank 
References 
3
0.59
3
Authors
2
Name
Order
Citations
PageRank
Theodore P. Pavlic14210.50
Kevin M. Passino2419102.42