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[The Role of Recruitment in Robot Foraging]

Multi-agent systems Java AI Project: Designing Robot Swarms
Date: Sep 2013

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Publication:
Pitonakova, L., Crowder R. & Bullock, S. (2014). Understanding the role of recruitment in collective robot foraging. In Lipson, H. et al. (eds.), ALIFE 14: The Fourteenth International Conference on the Synthesis and Simulation of Living Systems, MIT Press, 264-271.

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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.

Robots foraging

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).

We show that key factors determining swarm performance include resource abundance, the reliability of shared information, 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.



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Impressions from ALIFE 14 New York

This summer, I attended the Artificial Life conference in New York. There were some interesting and not-so-interesting talks, but generally I am very glad I went. I had a chance to meet some great people and more importantly, to get much needed feedback on my own research. I also got offered to try out real robots in my research.

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