Foraging theory for autonomous vehicle speed choice

Theodore P. Pavlic, Kevin M. Passino

Research output: Contribution to journalArticle

7 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)482-489
Number of pages8
JournalEngineering Applications of Artificial Intelligence
Volume22
Issue number3
DOIs
StatePublished - Apr 1 2009
Externally publishedYes

Keywords

  • Decision-making algorithms
  • Intelligent control
  • Optimal control
  • Speed-accuracy trade-off
  • Speed-cost trade-off
  • Task-type choice

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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