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Cooperative coevolution of real predator robots and virtual robots in the pursuit domain

机译：追求域中真正捕食机器人和虚拟机器人的合作协作

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摘要

The pursuit domain, or predator-prey problem is a standard testbed for the study of coordination techniques. In spite that its problem setup is apparently simple, it is challenging for the research of the emerged swarm intelligence. This paper presents a particle swarm optimization (PSO) based cooperative coevolutionary algorithm for the (predator) robots, called CCPSO-R, where real and virtual robots coexist in an evolutionary algorithm (EA). Virtual robots sample and explore the vicinity of the corresponding real robots and act as their action spaces, while the real robots consist of the real predators who actually pursue the prey robot without fixed behavior rules under the immediate guidance of the fitness function, which is designed in a modular manner with very limited domain knowledge. In addition, kinematic limits and collision avoidance considerations are integrated into the update rules of robots. Experiments are conducted on a scalable swarm of predator robots with 4 types of preys, the results of which show the reliability, generality, and scalability of the proposed CCPSO-R. Comparison with a representative dynamic path planning based algorithm Multi-Agent Real-Time Pursuit (MAPS) further shows the effectiveness of CCPSO-R. Finally, the codes of this paper are public available at: https://github.com/LijunSun90/pursuitCCPSOR. (C) 2020 Elsevier B.V. All rights reserved.
机译：追求域或捕食者 - 猎物问题是研究协调技术的标准测试。尽管它的问题设置显然很简单，但它对出现的群体智能研究有挑战性。本文介绍了一种基于粒子群优化（PSO）的合作共同算法，用于（捕食者）机器人，称为CCPSO-R，其中真实和虚拟机器人以进化算法（EA）共存。虚拟机器人采样并探索相应的真实机器人附近并充当其动作空间，而实际机器人则由实际追求猎物机器人的实际捕食者组成，而在没有固定的行为规则的情况下，设计以非常有限的域知识的模块化方式。此外，运动限制和碰撞避免考虑因素被整合到机器人的更新规则中。实验在可伸缩的捕食者机器人中进行，具有4种类型的捕食器，结果显示了所提出的CCPSO-R的可靠性，一般性和可扩展性。与基于代表性的动态路径规划的算法的比较多代理实时追踪（MAPS）进一步显示了CCPSO-R的有效性。最后，本文的代码是公开的：https://github.com/lijunsun90/pursuiticcpsor。 （c）2020 Elsevier B.V.保留所有权利。

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