Prioritized foraging strategies for an ant colony-inspired swarm system

Hari R. Iyer, Manish Kumar, Subramanian Ramakrishnan

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

Abstract

We investigate ant-colony-inspired foraging strategies for enhancing the efficiency of a swarm of artificial agents engaged in a search-and-retrieval application. First, we extend a mathematical model of ant foraging to account for the evolution of the information collected during search-and-retrieval over time. We then use the extended model to numerically investigate the efficiency of search-and-retrieval under the two distinct cases of non-depleting information and depleting information at the sources. In the former case, we obtain optimal ranges of parameter values of the ant foraging model that enhance efficiency. In the latter case, we find that appropriately designed deposition functions in the model can induce self-organization in the swarm and therefore prioritize the collection of quickly depleting information. The ability to prioritize is highly desirable in a swarm for search-and-retrieval applications and, to our knowledge, induced emergent behavior resulting in prioritization capabilities has not been reported in swarms inspired by ant foraging. The results are expected to be broadly significant for swarm robotics as well as in applications such as the Travelling Salesman Problem (TSP) with time-varying profit.

Original languageEnglish (US)
Title of host publicationAdaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791884270
DOIs
StatePublished - 2020
Externally publishedYes
EventASME 2020 Dynamic Systems and Control Conference, DSCC 2020 - Virtual, Online
Duration: Oct 5 2020Oct 7 2020

Publication series

NameASME 2020 Dynamic Systems and Control Conference, DSCC 2020
Volume1

Conference

ConferenceASME 2020 Dynamic Systems and Control Conference, DSCC 2020
CityVirtual, Online
Period10/5/2010/7/20

Bibliographical note

Publisher Copyright:
Copyright © 2020 ASME

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Ant colony optimization
  • Search problems
  • Swarm robotics

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