Staffordshire University logo
STORE - Staffordshire Online Repository

Probabilistic Supervisory Control Theory (pSCT) Applied to Swarm Robotics

Lopes, Y.K., Trenkwalder, S.M., Leal, A.B., DODD, Tony and Gross, R. (2017) Probabilistic Supervisory Control Theory (pSCT) Applied to Swarm Robotics. In: Proceedings of AAMAS 2017. IFAAMAS, São Paulo, Brazil., pp. 1395-1403.

Full text not available from this repository. (Request a copy)

Abstract or description

Swarm robotics studies large groups of robots that work together to accomplish common tasks. Much of the used source code is developed in an ad-hoc manner, meaning that the correctness of the controller is not always verifiable. In previous work, supervisory control theory (SCT) and associated design tools have been used to address this problem. Given a formal description of the swarm?s agents capabilities and their desired behaviour, the control source code can be automatically generated. However, regular SCT cannot model probabilistic controllers (supervisors). In this paper, we propose a probabilistic supervisory control theory (pSCT) framework. It applies prior work on probabilistic generators in a way that allows controllers to be decomposed into multiple local modular supervisors. Local modular supervisors take advantage of the modularity of formal specifications to reduce the size required to store the control logic. To validate the pSCT framework, we model a distributed swarm robotic version of the graph colouring problem and automatically generate the control source code for the Kilobot swarm robotics platform. We report the results of systematic experiments with swarms of 25 and 100 physical robots.

Item Type: Book Chapter, Section or Conference Proceeding
Additional Information: © 2017 by IFAAMAS. Permission to make digital or hard copies of portions of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyright for components of this work owned by others than IFAAMAS must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
Faculty: School of Creative Arts and Engineering > Engineering
Depositing User: Library STORE team
Date Deposited: 15 Jul 2020 14:55
Last Modified: 01 Sep 2020 10:28
URI: http://eprints.staffs.ac.uk/id/eprint/6238

Actions (login required)

View Item View Item

DisabledGo Staffordshire University is a recognised   Investor in People. Sustain Staffs
Legal | Freedom of Information | Site Map | Job Vacancies
Staffordshire University, College Road, Stoke-on-Trent, Staffordshire ST4 2DE t: +44 (0)1782 294000