Simulated and Real-World Evolution of Predator Robots

Gongjin Lan; Jiunhan Chen; A. E. Eiben

doi:10.1109/SSCI44817.2019.9002696

Simulated and Real-World Evolution of Predator Robots

Gongjin Lan, Jiunhan Chen, A. E. Eiben

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

This paper addresses the problem of designing behavioural strategies for a group of robots with a specific task, capturing another robot. Our proposed approach is to employ a smart prey with a pre-programmed strategy based on a novel Gaussian model of danger zones and use an evolutionary algorithm (EA) to optimize the predators' behavior. The EA is applied in two stages: first in simulation, then in hardware on the real robots. The best evolved robot controllers are then further inspected and compared by their robustness, i.e., performance under different conditions. The results show that our approach is successful, combining simulations, real-world evolution, and robustness analysis it is possible to develop good solutions for the predator-prey problem.

Original language	English
Title of host publication	2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1974-1981
Number of pages	8
ISBN (Electronic)	9781728124858
DOIs	https://doi.org/10.1109/SSCI44817.2019.9002696
Publication status	Published - Dec 2019
Event	2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, China Duration: 6 Dec 2019 → 9 Dec 2019

Conference

Conference	2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Country/Territory	China
City	Xiamen
Period	6/12/19 → 9/12/19

Keywords

bayesian optimization
CMA-ES
evolutionary algorithm
evolutionary robotics
predator prey
reality gap

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@inproceedings{fb0320429399484f9bb6d2464c5a04cf,

title = "Simulated and Real-World Evolution of Predator Robots",

abstract = "This paper addresses the problem of designing behavioural strategies for a group of robots with a specific task, capturing another robot. Our proposed approach is to employ a smart prey with a pre-programmed strategy based on a novel Gaussian model of danger zones and use an evolutionary algorithm (EA) to optimize the predators' behavior. The EA is applied in two stages: first in simulation, then in hardware on the real robots. The best evolved robot controllers are then further inspected and compared by their robustness, i.e., performance under different conditions. The results show that our approach is successful, combining simulations, real-world evolution, and robustness analysis it is possible to develop good solutions for the predator-prey problem.",

keywords = "bayesian optimization, CMA-ES, evolutionary algorithm, evolutionary robotics, predator prey, reality gap",

author = "Gongjin Lan and Jiunhan Chen and Eiben, {A. E.}",

year = "2019",

month = dec,

doi = "10.1109/SSCI44817.2019.9002696",

language = "English",

pages = "1974--1981",

booktitle = "2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 ; Conference date: 06-12-2019 Through 09-12-2019",

}

Lan, G, Chen, J & Eiben, AE 2019, Simulated and Real-World Evolution of Predator Robots. in 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019)., 9002696, Institute of Electrical and Electronics Engineers Inc., pp. 1974-1981, 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019, Xiamen, China, 6/12/19. https://doi.org/10.1109/SSCI44817.2019.9002696

Simulated and Real-World Evolution of Predator Robots. / Lan, Gongjin; Chen, Jiunhan; Eiben, A. E.

2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019). Institute of Electrical and Electronics Engineers Inc., 2019. p. 1974-1981 9002696.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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PY - 2019/12

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N2 - This paper addresses the problem of designing behavioural strategies for a group of robots with a specific task, capturing another robot. Our proposed approach is to employ a smart prey with a pre-programmed strategy based on a novel Gaussian model of danger zones and use an evolutionary algorithm (EA) to optimize the predators' behavior. The EA is applied in two stages: first in simulation, then in hardware on the real robots. The best evolved robot controllers are then further inspected and compared by their robustness, i.e., performance under different conditions. The results show that our approach is successful, combining simulations, real-world evolution, and robustness analysis it is possible to develop good solutions for the predator-prey problem.

AB - This paper addresses the problem of designing behavioural strategies for a group of robots with a specific task, capturing another robot. Our proposed approach is to employ a smart prey with a pre-programmed strategy based on a novel Gaussian model of danger zones and use an evolutionary algorithm (EA) to optimize the predators' behavior. The EA is applied in two stages: first in simulation, then in hardware on the real robots. The best evolved robot controllers are then further inspected and compared by their robustness, i.e., performance under different conditions. The results show that our approach is successful, combining simulations, real-world evolution, and robustness analysis it is possible to develop good solutions for the predator-prey problem.

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KW - evolutionary algorithm

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KW - reality gap

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BT - 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019)

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T2 - 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

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Simulated and Real-World Evolution of Predator Robots

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