Université Libre de Bruxelles.
Artif Life. 2011 Summer;17(3):183-202. doi: 10.1162/artl_a_00031. Epub 2011 May 9.
Evolutionary robotics (ER) is a powerful approach for the automatic synthesis of robot controllers, as it requires little a priori knowledge about the problem to be solved in order to obtain good solutions. This is particularly true for collective and swarm robotics, in which the desired behavior of the group is an indirect result of the control and communication rules followed by each individual. However, the experimenter must make several arbitrary choices in setting up the evolutionary process, in order to define the correct selective pressures that can lead to the desired results. In some cases, only a deep understanding of the obtained results can point to the critical aspects that constrain the system, which can be later modified in order to re-engineer the evolutionary process towards better solutions. In this article, we discuss the problem of engineering the evolutionary machinery that can lead to the desired result in the swarm robotics context. We also present a case study about self-organizing synchronization in a swarm of robots, in which some arbitrarily chosen properties of the communication system hinder the scalability of the behavior to large groups. We show that by modifying the communication system, artificial evolution can synthesize behaviors that scale properly with the group size.
进化机器人学（Evolutionary Robotics，ER）是一种自动合成机器人控制器的强大方法，因为它几乎不需要有关要解决的问题的先验知识，就可以获得良好的解决方案。对于集体和群体机器人学来说尤其如此，因为群体的期望行为是每个个体遵循的控制和通信规则的间接结果。然而，实验者在设置进化过程时必须做出一些任意的选择，以便定义可以导致期望结果的正确选择压力。在某些情况下，只有深入了解所获得的结果，才能指出限制系统的关键方面，然后可以对其进行修改，以便重新设计进化过程以获得更好的解决方案。在本文中，我们讨论了在群体机器人学上下文中，设计可以导致期望结果的进化机制的问题。我们还介绍了一个关于机器人群体自组织同步的案例研究，其中通信系统中任意选择的一些属性会阻碍行为在大群体中的可扩展性。我们表明，通过修改通信系统，人工进化可以综合出与群体规模适当扩展的行为。
