Designing controllers for robot swarms is challenging, because human
developers have typically no good understanding of the link between the details
of a controller that governs individual robots and the swarm behavior that is
an indirect result of the interactions between swarm members and the
environment. In this paper we investigate whether an evolutionary approach can
mitigate this problem. We consider a very challenging task where robots with
limited sensing and communication abilities must follow the gradient of an
environmental feature and use Differential Evolution to evolve a neural network
controller for simulated robots. We conduct a systematic study to measure the
flexibility and scalability of the method by varying the size of the arena and
number of robots in the swarm. The experiments confirm the feasibility of our
approach, the evolved robot controllers induced swarm behavior that solved the
task. We found that solutions evolved under the harshest conditions (where the
environmental clues were the weakest) were the most flexible and that there is
a sweet spot regarding the swarm size. Furthermore, we observed collective
motion of the swarm, showcasing truly emergent behavior that was not
represented in- and selected for during evolution.

本文研究了机器人群体控制器的设计，并探索了一种进化方法来缓解人类开发者对控制器细节和群体行为之间联系的困难。使用微分进化来发展神经网络控制器，实现机器人遵循环境特征的梯度并解决任务。研究表明，进化所得解决方案在控制较差的情况下具有最大的灵活性，而有一个群体规模的 “甜蜜点”，同时观察到群体的集体运动，展示了真正的新兴行为，这是进化过程中未被代表和选择的。