Efficient foraging strategies in multi-agent systems through curve evolutions

Musad Haque; Amir Rahmani; Magnus Egerstedt; Anthony Yezzi

doi:10.1109/TAC.2013.2281877

Efficient foraging strategies in multi-agent systems through curve evolutions

Musad Haque, Amir Rahmani, Magnus Egerstedt, Anthony Yezzi

Mechanical & Aerospace Engineering

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

8 Scopus citations

Abstract

In nature, communal hunting is often performed by predators charging through an aggregation of prey. Variations exist in the geometric shape of the charging front depending on the particulars of the feeding strategy. Inspired by biology, this technical note investigates these geometric variations, and we model the predator front as a curve moving through a prey density. Using variational arguments for evolving the curve shape, we optimize the shape of the front.

Original language	English (US)
Article number	2281877
Pages (from-to)	1036-1041
Number of pages	6
Journal	IEEE Transactions on Automatic Control
Volume	59
Issue number	4
DOIs	https://doi.org/10.1109/TAC.2013.2281877
State	Published - Apr 2014

Keywords

Bio-inspired methods
curve evolutions
multi-agent foraging

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Systems Engineering
Computer Science Applications

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10.1109/TAC.2013.2281877

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AB - In nature, communal hunting is often performed by predators charging through an aggregation of prey. Variations exist in the geometric shape of the charging front depending on the particulars of the feeding strategy. Inspired by biology, this technical note investigates these geometric variations, and we model the predator front as a curve moving through a prey density. Using variational arguments for evolving the curve shape, we optimize the shape of the front.

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Efficient foraging strategies in multi-agent systems through curve evolutions

Abstract

Keywords

ASJC Scopus subject areas

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