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[Task Allocation in Foraging Robot Swarms]

Multi-agent systems C++ A-Life Project: Designing Robot Swarms
Date: Apr 2016

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Publication:
Pitonakova, L., Crowder R. & Bullock, S. (2016). Task allocation in foraging robot swarms: The Role of Information Sharing. In Gershenson, C. et al. (eds.), Proceedings of the Fifteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE XV), MIT Press, 306-313.

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items. When there are many robots in the swarm, or when collected items accumulate quickly in a drop-off location, congestion can prevent the swarm from working effectively. In such scenarios, self-regulation of workforce can prevent unnecessary energy consumption.

In this paper, we analyse bee-inspired self-regulation algorithms for robot swarms that deliver items into a single drop-off location.

We explore two types of self-regulation:
  • Non-social, where robots go to rest when they experience congestion
  • Social, where robots broadcast information about congestion to their team mates to tell them that they should rest

Robots foragingPerformance of the swarms in various environments

Outcome:
We show that both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected. More importantly, the rate at which information about congestion spreads through a swarm affects the scalability of the explored robot control strategies.

A slow information flow, characteristic for non-social self-regulation, leads to behaviour suitable for a larger number of experimental scenarios. On the other hand, fast information flow, achieved by social self-regulation, causes more extreme difference in performance across scenarios. Using swarms with faster information flow thus requires us to be more certain about the environmental conditions we employ our swarms in.



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