Dynamic task partitioning for foraging robot swarms

Edgar Buchanan*, Andrew Pomfret, Jon Timmis

*Corresponding author for this work

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

Abstract

Dead reckoning error is a common problem in robotics that can be caused by multiple factors related to sensors or actuators. These errors potentially cause landmarks recorded by a robot to appear in a different location with respect to the actual position of the object. In a foraging scenario with a swarm of robots, this error will ultimately lead to the robots being unable to return successfully to the food source. In order to address this issue, we propose a computationally low-cost finite state machine strategy with which robots divide the total travelling distance into a variable number of segments, thus decreasing accumulated dead-reckoning error. The distance travelled by each robot changes according to the success and failure of exploration. Our approach is more flexible than using a previously used fixed size approach for the travel distance, thus allowing swarms greater flexibility and scaling to larger areas of operation.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Pages113-124
Number of pages12
Volume9882 LNCS
ISBN (Print)9783319444260
DOIs
Publication statusPublished - 2016
Event10th International Conference on Swarm Intelligence, ANTS 2016 - Brussels, Belgium
Duration: 7 Sept 20169 Sept 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9882 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference10th International Conference on Swarm Intelligence, ANTS 2016
Country/TerritoryBelgium
CityBrussels
Period7/09/169/09/16

Keywords

  • Fault tolerance
  • Foraging
  • Swarm robotics
  • Task partitioning

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