Skip to main content

Isolating sub-populations to exploit locality in discounted robot foraging

Resource type
Thesis type
((Thesis)) M.Sc.
Date created
We examine a canonical multi-robot foraging task, in which multiple objects must be located, collected and delivered. Each type of object must go to a unique delivery location. The value of each delivery is discounted over time. We describe a system in which a population of robots are eff ectively allocated to local (and thus high-reward-rate) foraging tasks, by keeping them ignorant of distant (thus poor-reward-rate) tasks. Robots learn about available tasks by local communication, with a fi xed communication range that controls the rate at which task knowledge propagates. Our empirical data suggests that there is an optimal communication radius for our setting. Our system is eff ective at allocating robots to tasks, performing better than fully-informed robots. Interesting emergent group behaviour dynamics are described.
Copyright statement
Copyright is held by the author.
The author granted permission for the file to be printed and for the text to be copied and pasted.
Scholarly level
Supervisor or Senior Supervisor
Thesis advisor: Vaughan, Richard
Member of collection
Download file Size
etd6431_PJabbariTaleghani.pdf 1.63 MB

Views & downloads - as of June 2023

Views: 0
Downloads: 0