Swarm shape manipulation through connection control

Giuliano Punzo, Derek J. Bennet, M. Macdonald

Research output: Contribution to conferencePaper

Abstract

The control of a large swarm of distributed agents is a well known challenge within the study of unmanned autonomous systems. However, it also presents many new opportunities. The advantages of operating a swarm through distributed means has been assessed in the literature for efficiency from both operational and economical aspects; practically as the number of agents increases, distributed control is favoured over centralised control, as it can reduce agent computational costs and increase robustness on the swarm. Distributed architectures, however, can present the drawback of requiring knowledge of the whole swarm state, therefore limiting the scalability of the swarm. In this paper a strategy is presented to address the challenges of distributed architectures, changing the way in which the swarm shape is controlled and providing a step towards verifiable swarm behaviour, achieving new configurations, while saving communication and computation resources. Instead of applying change at agent level (e.g. modify its guidance law), the sensing of the agents is addressed to a portion of agents, differentially driving their behaviour. This strategy is applied for swarms controlled by artificial potential functions which would ordinarily require global knowledge and all-to-all interactions. Limiting the agents' knowledge is proposed for the first time in this work as a methodology rather than obstacle to obtain desired swarm behaviour.

Conference

Conference12th Conference Towards Autonomous Robotic Systems 2011
CountryUnited Kingdom
CitySheffield
Period31/08/112/09/11

Fingerprint

Swarm
Manipulation
Distributed Architecture
Limiting
Guidance Law
Scalability
Distributed Control
Potential Function
Autonomous Systems
Computational Cost
Sensing
Communication
Robustness
Configuration
Resources
Methodology
Costs

Keywords

  • Unmanned autonomous systems
  • distributed control
  • verifiable swarm behaviour
  • artificial potential functions

Cite this

Punzo, G., Bennet, D. J., & Macdonald, M. (2010). Swarm shape manipulation through connection control. Paper presented at 12th Conference Towards Autonomous Robotic Systems 2011, Sheffield , United Kingdom.
Punzo, Giuliano ; Bennet, Derek J. ; Macdonald, M. / Swarm shape manipulation through connection control. Paper presented at 12th Conference Towards Autonomous Robotic Systems 2011, Sheffield , United Kingdom.
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Punzo, G, Bennet, DJ & Macdonald, M 2010, 'Swarm shape manipulation through connection control' Paper presented at 12th Conference Towards Autonomous Robotic Systems 2011, Sheffield , United Kingdom, 31/08/11 - 2/09/11, .

Swarm shape manipulation through connection control. / Punzo, Giuliano; Bennet, Derek J.; Macdonald, M.

2010. Paper presented at 12th Conference Towards Autonomous Robotic Systems 2011, Sheffield , United Kingdom.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Swarm shape manipulation through connection control

AU - Punzo, Giuliano

AU - Bennet, Derek J.

AU - Macdonald, M.

PY - 2010/8/31

Y1 - 2010/8/31

N2 - The control of a large swarm of distributed agents is a well known challenge within the study of unmanned autonomous systems. However, it also presents many new opportunities. The advantages of operating a swarm through distributed means has been assessed in the literature for efficiency from both operational and economical aspects; practically as the number of agents increases, distributed control is favoured over centralised control, as it can reduce agent computational costs and increase robustness on the swarm. Distributed architectures, however, can present the drawback of requiring knowledge of the whole swarm state, therefore limiting the scalability of the swarm. In this paper a strategy is presented to address the challenges of distributed architectures, changing the way in which the swarm shape is controlled and providing a step towards verifiable swarm behaviour, achieving new configurations, while saving communication and computation resources. Instead of applying change at agent level (e.g. modify its guidance law), the sensing of the agents is addressed to a portion of agents, differentially driving their behaviour. This strategy is applied for swarms controlled by artificial potential functions which would ordinarily require global knowledge and all-to-all interactions. Limiting the agents' knowledge is proposed for the first time in this work as a methodology rather than obstacle to obtain desired swarm behaviour.

AB - The control of a large swarm of distributed agents is a well known challenge within the study of unmanned autonomous systems. However, it also presents many new opportunities. The advantages of operating a swarm through distributed means has been assessed in the literature for efficiency from both operational and economical aspects; practically as the number of agents increases, distributed control is favoured over centralised control, as it can reduce agent computational costs and increase robustness on the swarm. Distributed architectures, however, can present the drawback of requiring knowledge of the whole swarm state, therefore limiting the scalability of the swarm. In this paper a strategy is presented to address the challenges of distributed architectures, changing the way in which the swarm shape is controlled and providing a step towards verifiable swarm behaviour, achieving new configurations, while saving communication and computation resources. Instead of applying change at agent level (e.g. modify its guidance law), the sensing of the agents is addressed to a portion of agents, differentially driving their behaviour. This strategy is applied for swarms controlled by artificial potential functions which would ordinarily require global knowledge and all-to-all interactions. Limiting the agents' knowledge is proposed for the first time in this work as a methodology rather than obstacle to obtain desired swarm behaviour.

KW - Unmanned autonomous systems

KW - distributed control

KW - verifiable swarm behaviour

KW - artificial potential functions

UR - http://www.taros.org.uk/

M3 - Paper

ER -

Punzo G, Bennet DJ, Macdonald M. Swarm shape manipulation through connection control. 2010. Paper presented at 12th Conference Towards Autonomous Robotic Systems 2011, Sheffield , United Kingdom.