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Design patterns for robot swarms

Design patterns for robot swarms
Design patterns for robot swarms
Demand for autonomous multi-robot systems, where robots can cooperate with each other without human intervention, is set to grow rapidly in the next decade. Today, technologies such as self-driving cars and fleets of robotic assistants in hospitals and warehouses are being developed and used. In the future, robot swarms could be deployed in retrieval, reconnaissance and construction missions.

Distributed collective systems have desirable properties, such as low cost of individual robots, robustness, fault tolerance and scalability. One of the main challenges in swarm robotics is that "bottom-up" approach to behaviour design is required. While the swarm performance is specified on the collective level of the swarm, robot designers need to program control algorithms of individual robots, while taking into account complex robot-robot interactions that allow emergence of collective intelligence. In order to be able to develop such systems, we need a methodology that aligns bottom-up design decisions with top-down design specifications.

In this thesis, a novel approach to understanding and designing robot swarms that perform foraging and task allocation is proposed. Based on thousands of different simulation experiments, the Information-Cost-Reward framework is formulated, that relates the way in which a swarm obtains and uses information, to its ability to use that information in order to obtain reward efficiently. Secondly, based on the information-based understanding of swarm performance, design patterns for robot swarms are formalised. The design patterns are modular aspects of robot behaviour that define when and how information should be obtained, exchanged or updated by robots, given particular swarm mission characteristics. Multiple design patterns can be unambiguously combined together in order to create a suitable robot control strategy.

The design patterns specify robot behaviour in a newly developed Behaviour-Data Relations Modeling Language, where relationships between robot behaviour and data stored in and outside of robots are explicitly defined. This allows the design patterns to define behaviour of robots that cooperate and share information.
University of Southampton
Pitonakova, Lenka
ef806152-a9c0-4075-806d-c75f0d3f7bbb
Pitonakova, Lenka
ef806152-a9c0-4075-806d-c75f0d3f7bbb
Crowder, Richard
ddeb646d-cc9e-487b-bd84-e1726d3ac023

Pitonakova, Lenka (2017) Design patterns for robot swarms University of Southampton Doctoral Thesis , 409pp.

Record type: Thesis (Doctoral)

Abstract

Demand for autonomous multi-robot systems, where robots can cooperate with each other without human intervention, is set to grow rapidly in the next decade. Today, technologies such as self-driving cars and fleets of robotic assistants in hospitals and warehouses are being developed and used. In the future, robot swarms could be deployed in retrieval, reconnaissance and construction missions.

Distributed collective systems have desirable properties, such as low cost of individual robots, robustness, fault tolerance and scalability. One of the main challenges in swarm robotics is that "bottom-up" approach to behaviour design is required. While the swarm performance is specified on the collective level of the swarm, robot designers need to program control algorithms of individual robots, while taking into account complex robot-robot interactions that allow emergence of collective intelligence. In order to be able to develop such systems, we need a methodology that aligns bottom-up design decisions with top-down design specifications.

In this thesis, a novel approach to understanding and designing robot swarms that perform foraging and task allocation is proposed. Based on thousands of different simulation experiments, the Information-Cost-Reward framework is formulated, that relates the way in which a swarm obtains and uses information, to its ability to use that information in order to obtain reward efficiently. Secondly, based on the information-based understanding of swarm performance, design patterns for robot swarms are formalised. The design patterns are modular aspects of robot behaviour that define when and how information should be obtained, exchanged or updated by robots, given particular swarm mission characteristics. Multiple design patterns can be unambiguously combined together in order to create a suitable robot control strategy.

The design patterns specify robot behaviour in a newly developed Behaviour-Data Relations Modeling Language, where relationships between robot behaviour and data stored in and outside of robots are explicitly defined. This allows the design patterns to define behaviour of robots that cooperate and share information.

Text Final thesis - Version of Record
Available under License University of Southampton Thesis Licence.
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More information

Published date: February 2017
Organisations: University of Southampton, Electronics & Computer Science

Identifiers

Local EPrints ID: 410360
URI: http://eprints.soton.ac.uk/id/eprint/410360
PURE UUID: 16dfdc73-0699-4e05-a519-1b0509652c1f
ORCID for Lenka Pitonakova: ORCID iD orcid.org/0000-0003-3633-7302

Catalogue record

Date deposited: 07 Jun 2017 16:30
Last modified: 09 Dec 2017 17:30

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Contributors

Author: Lenka Pitonakova ORCID iD
Thesis advisor: Richard Crowder

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