Cooperatively learning mobile agents for gradient climbing

Jongeun Choi, Songhwai Oh, Roberto Horowitz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Citations (Scopus)

Abstract

This paper presents a novel class of self-organizing autonomous sensing agents that form a swarm and learn the static field of interest through noisy measurements from neighbors for gradient climbing. In particular, each sensing agent maintains its own smooth map which estimates the field. It updates its map using measurements from itself and its neighbors and simultaneously moves toward a maximum of the field using the gradient of its map. The proposed cooperatively learning control consists of motion coordination based on the recursive spatial estimation of an unknown field of interest with measurement noise. The convergence properties of the proposed coordination algorithm are analyzed using the ODE approach and verified by a simulation study.

Original languageEnglish
Title of host publicationProceedings of the 46th IEEE Conference on Decision and Control 2007, CDC
Pages3139-3144
Number of pages6
DOIs
Publication statusPublished - 2007
Event46th IEEE Conference on Decision and Control 2007, CDC - New Orleans, LA, United States
Duration: 2007 Dec 122007 Dec 14

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other46th IEEE Conference on Decision and Control 2007, CDC
CountryUnited States
CityNew Orleans, LA
Period07/12/1207/12/14

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

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  • Cite this

    Choi, J., Oh, S., & Horowitz, R. (2007). Cooperatively learning mobile agents for gradient climbing. In Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC (pp. 3139-3144). [4434061] (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2007.4434061