Foraging theory for autonomous vehicle speed choice

Theodore P. Pavlic; Kevin M. Passino

doi:10.1016/j.engappai.2008.10.017

Foraging theory for autonomous vehicle speed choice

Theodore P. Pavlic, Kevin M. Passino

Research output: Contribution to journal › Article › peer-review

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 language	English (US)
Pages (from-to)	482-489
Number of pages	8
Journal	Engineering Applications of Artificial Intelligence
Volume	22
Issue number	3
DOIs	https://doi.org/10.1016/j.engappai.2008.10.017
State	Published - Apr 1 2009
Externally published	Yes

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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10.1016/j.engappai.2008.10.017

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Foraging theory for autonomous vehicle speed choice

Abstract

Keywords

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