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[The Role of Recruitment in Robot Foraging]

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

Robots foraging DOWNLOAD PAPERGET JAVA EXECUTABLEDOWNLOAD CODEDOWNLOAD TALK SLIDES
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.

Poster

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).

Outcome:

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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Designing robot swarms

In software engineering, a design pattern associates a particular class of known problem with a particular class of effective solution. Analogously, swarm robot engineers would benefit from design patterns that each associate specific robot control schemes with desired collective performance. In this project, we characterise such design patterns for robot swarms in the context of collective foraging and task allocation.

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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Creeper is a Java MVC framework for those who want to create multi-agent simulations (or games) and need something to build on. Creeper takes care of effective updating and rendering. You only need to specify the world objects and how they should look like.

Controlling Ant-Based Construction

Stigmergy allows insect colonies to collectively build structures that no single individual is fully aware of. Since relatively minimal sensory and reasoning capabilities are required of the agents, such building activity could be utilised by robotic swarms if we could learn how to control the shape of the final structures.

Does Communication Make a Difference?

This paper compares different animal groups from eusocial insect colonies to human society and discusses their mechanics and behaviour as agent systems. The main focus is on interaction between the agents and on how properties of a system like effectiveness or predictability are affected by these interactions.

Robot Flocking: Sensors and Control

This paper discusses various kinds of robot sensory input, approaches to motor control and ways they could be used for flocking. Focus is put on vision and Gibsonian optic flow that could be utilised by robots with advanced behaviour.