Formica ex machina: ant swarm foraging from physical to virtual and back again

  • Authors:

  • Joshua P. Hecker;Kenneth Letendre;Karl Stolleis;Daniel Washington;Melanie E. Moses

  • Affiliations:

  • Department of Computer Science, University of New Mexico, Albuquerque;Department of Computer Science, University of New Mexico, Albuquerque, USA, Department of Biology, University of New Mexico, Albuquerque;Department of Computer Science, University of New Mexico, Albuquerque;Department of Computer Science, University of New Mexico, Albuquerque;Department of Computer Science, University of New Mexico, Albuquerque, USA, Department of Biology, University of New Mexico, Albuquerque

  • Venue:
  • ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
  • Year:
  • 2012

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Abstract

Ants use individual memory and pheromone communication to forage efficiently. We implement these strategies as distributed search algorithms in robotic swarms. Swarms of simple robots are robust, scalable and capable of exploring for resources in unmapped environments. We test the ability of individual robots and teams of three robots to collect tags distributed in random and clustered distributions in simulated and real environments. Teams of three real robots that forage based on individual memory without communication collect RFID tags approximately twice as fast as a single robot using the same strategy. Our simulation system mimics the foraging behaviors of the robots and replicates our results. Simulated swarms of 30 and 100 robots collect tags 8 and 22 times faster than teams of three robots. This work demonstrates the feasibility of programming large robot teams for collective tasks such as retrieval of dispersed resources, mapping, and environmental monitoring. It also lays a foundation for evolving collective search algorithms in silico and then implementing those algorithms in machina in robust and scalable robotic swarms.