Stability of On-Line and On-Board Evolving of Adaptive Collective Behavior

König, Lukas; Jebens, Kristof; Kernbach, Serge; Levi, Paul

doi:10.1007/978-3-540-78317-6_30

Cited by 18 publications

(22 citation statements)

References 5 publications

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“…Embedded is defined as both online (the adaptation/learning process never stops) and onboard (the optimization algorithm and evaluation process are part of the control loop). To date, only few, but promising, works have adressed this topic: [7,11,12,14,15,17,20,23,24,25,26]. Running an embedded EA within a single robot provides strong advantages regarding continuous adaptation and autonomy with regards to a human supervisor.…”

Section: Introductionmentioning

confidence: 99%

Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm

Montanier

Bredèche

2011

New Horizons in Evolutionary Robotics

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Section: Introductionmentioning

confidence: 99%

Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm

Montanier

Bredèche

2011

New Horizons in Evolutionary Robotics

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“…The dependence of our approach on the informative quality of the environment through robot behaviours may be similar to the boot-strapping problem highlighted by Konig et al [10], which links the distributed evolutionary development of robot behaviours to their spatial mobility. In future work we would like to vary the number of robots, as the number of robots constitutes the evolutionary population of on-board simulators, to investigate any gains of parallelism in evaluations towards evolutionary convergence.…”

Section: Discussionmentioning

confidence: 75%

“…There have been several recent investigations into online, on-board, distributed evolutionary robotics motivated by the vision of a multi-robot system capable of continuous unsupervised evolutionary adaptation [10][8] [20] [19] [4] [5]. Whilst the online on-board distributed approach is suitable for swarm robotics, three problematic issues are highlighted, and form part of the underlying motivation to develop our work:…”

Section: Introductionmentioning

confidence: 99%

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The distributed co-evolution of an on-board simulator and controller for swarm robot behaviours

2014

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The distributed co-evolution of an on-board simulator and controller for swarm robot behaviours. Evolutionary Intelligence, 7 (2). pp. 95-106. ISSN 1864-5909 Available from: http://eprints.uwe.ac.uk/23450We recommend you cite the published version. The publisher's URL is: http://dx.doi.org/10.1007/s12065-014-0112-8 Refereed: YesThe final publication is available at Springer via http://dx.doi.org/doi:10.1007/s12065?014?0112?8 Disclaimer UWE has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material. UWE makes no representation or warranties of commercial utility, title, or fitness for a particular purpose or any other warranty, express or implied in respect of any material deposited.UWE makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights. UWE accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pending investigation in the event of an allegation of any such infringement. PLEASE SCROLL DOWN FOR TEXT.Noname manuscript No. Abstract We investigate the reality gap, specifically the environmental correspondence of an on-board simulator. We describe a novel distributed co-evolutionary approach to improve the transference of controllers that co-evolve with an on-board simulator. A novelty of our approach is the the potential to improve transference between simulation and reality without an explicit measurement between the two domains. We hypothesise that a variation of on-board simulator environment models across many robots can be competitively exploited by comparison of the real controller fitness of many robots. We hypothesise that the real controller fitness values across many robots can be taken as indicative of the varied fitness in environmental correspondence of on-board simulators, and used to inform the distributed evolution an on-board simulator environment model without explicit measurement of the real environment. Our results demonstrate that our approach creates an adaptive relationship between the onboard simulator environment model, the real world behaviour of the robots, and the state of the real environment. The results indicate that our approach is sensitive to whether the real behavioural performance of the robot is informative on the state real environment.

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“…[3], [4]). In particular, a collective actuation of robotic organisms, being a complex function of many interacting individuals, can be obtained in the evolutionary way [5], [6]. Here, we think about several relevant long-term applications, where a continuous communication with a human operator is impossible, like deep space or ocean explorations, robotic Mars missions or other human-less environments.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary robotics: The next-generation-platform for on-line and on-board artificial evolution

Kernbach

Meister

Scholz

et al. 2009

2009 IEEE Congress on Evolutionary Computation

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Abstract-In this paper we present the development of a new self-reconfigurable robotic platform for performing on-line and on-board evolutionary experiments. The designed platform can work as an autonomous swarm robot and can undergo collective morphogenesis to actuate in different morphogenetic structures. The platform includes a dedicated power management, rich sensor mechanisms for on-board fitness measurement as well as very powerful distributed computational system to run learning and evolutionary algorithms. The whole development is performed within several large European projects and is open-hardware and open-software.

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Stability of On-Line and On-Board Evolving of Adaptive Collective Behavior

Cited by 18 publications

References 5 publications

Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm

Embedded Evolutionary Robotics: The (1+1)-Restart-Online Adaptation Algorithm

The distributed co-evolution of an on-board simulator and controller for swarm robot behaviours

Evolutionary robotics: The next-generation-platform for on-line and on-board artificial evolution

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