Simulated and Real-World Evolution of Predator Robots

Gongjin Lan, Jiunhan Chen, A. E. Eiben

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


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 languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728124858
Publication statusPublished - Dec 2019
Event2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, China
Duration: 6 Dec 20199 Dec 2019


Conference2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019


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


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