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Information processing in swarms of extremely simple robots


Department of Automatic Control and Systems Engineering

About the Project

Consider a swarm of a million robots. Such a swarm could simultaneously sample a million locations in the ocean, pollinate a million plants in a field, or target a million cancer cells within a human body. For such swarms to become universal, the robots would need to be extremely small (e.g. 1 cm cube), have ultra-low power consumption (e.g. 1 mW), and cost very little (e.g. 1 USD). How can such highly constrained robots effectively process information? What problems can they collectively solve? And what infrastructure can augment their abilities and facilitate deployment?

This project seeks to study radically minimal concepts (algorithmic and beyond) for information processing in massively distributed robotic systems. Examples include the "computation"-free swarming paradigm [1-4] and ensembles of self-propelling units that are interconnected via deformable links [5]. Although the project is expected to further theoretical understanding, experimental validation with real robots is encouraged.

The applicant would join a vibrant group with excellent facilities (http://naturalrobotics.group.shef.ac.uk).

Funding Notes

This is a self-funded research project. Prospective candidates should have a strong background in applied mathematics, computer science, engineering, physics or another related discipline. We require applicants to have either an undergraduate honours degree (2:1) or MSc (Merit or Distinction) from a reputable institution.

Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive: View Website.

References

[1] https://naturalrobotics.group.shef.ac.uk/publications/2014-ijrr-gauci.pdf
[2] https://www.youtube.com/watch?v=MtDPCIicmgo
[3] https://naturalrobotics.group.shef.ac.uk/publications/2019-mrs-ozdemir.pdf
[4] https://naturalrobotics.group.shef.ac.uk/publications/2019-iros-marques.pdf
[5] https://naturalrobotics.group.shef.ac.uk/publications/2019-mrs-pratissoli.pdf

Full details of how to apply can be found at the following link: https://www.sheffield.ac.uk/acse/research-degrees/applyphd

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