Computational Modelling Group

Understanding the Role of Recruitment in Robot Foraging

1st June 2013
24th August 2013
Research Team
Lenka Pitonakova
Seth Bullock, Richard Crowder

Robots foraging

When is it profitable for robots to forage collectively? Here we compare the ability of swarms of simulated bio-inspired robots to forage either collectively or individually. The conditions under which recruitment (where one robot alerts another to the location of a resource) is profitable are characterised, and explained in terms of the impact of three types of interference between robots (physical, environmental, and informational). Key factors determining swarm performance include resource abundance, the reliability of shared informa- tion, time limits on foraging, and the ability of robots to cope with congestion around discovered resources and around the base location. Additional experiments introducing odometry noise indicate that collective foragers are more susceptible to odometry error.


Life sciences simulation: Swarm Behaviour

Algorithms and computational methods: Agents

Software Engineering Tools: Eclipse, Git

Programming languages and libraries: Java

Computational platforms: Mac OS X

Transdisciplinary tags: Complex Systems, Computer Science, Software Engineering