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 Proceeding (Non-Journal item)

10 Citations (SciVal)


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 publicationSwarm Intelligence - 10th International Conference, ANTS 2016, Proceedings
EditorsXiaodong Li, Manuel López-Ibáñez, Carlo Pinciroli, Marco Dorigo, Mauro Birattari, Thomas Stützle, Kazuhiro Ohkura
PublisherSpringer Nature
Number of pages12
ISBN (Print)9783319444260
Publication statusPublished - 2016
Externally publishedYes
Event10th International Conference on Swarm Intelligence, ANTS 2016 - Brussels, Belgium
Duration: 07 Sept 201609 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)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Swarm Intelligence, ANTS 2016
Period07 Sept 201609 Sept 2016


  • Fault tolerance
  • Foraging
  • Swarm robotics
  • Task partitioning

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