Morphological computing, the use of the physical design of a robot to ease the realization of a given task has been proven to be a relevant concept in the context of swarm robotics. Here we demonstrate both experimentally and numerically, that the success of such a strategy may heavily rely on the type of policy adopted by the robots, as well as on the details of the physical design. To do so, we consider a swarm of robots, composed of Kilobots embedded in an exoskeleton, the design of which controls the propensity of the robots to align or anti-align with the direction of the external force they experience. We find experimentally that the contrast that was observed between the two morphologies in the success rate of a simple phototactic task, where the robots were programmed to stop when entering a light region, becomes dramatic, if the robots are not allowed to stop, and can only slow down. Building on a faithful physical model of the self-aligning dynamics of the robots, we perform numerical simulations and demonstrate on one hand that a precise tuning of the self-aligning strength around a sweet spot is required to achieve an efficient phototactic behavior, on the other hand that exploring a range of self-alignment strength allows for a rich expressivity of collective behaviors.

翻译：形态计算——利用机器人的物理设计来简化特定任务的实现——已被证明是集群机器人学中的一个重要概念。本文通过实验和数值模拟证明，此类策略的成功与否在很大程度上取决于机器人所采用的策略类型以及物理设计的细节。为此，我们研究了一个由嵌入外骨骼的Kilobot机器人组成的集群，其设计控制着机器人与所受外力方向趋于对齐或反向对齐的倾向性。实验发现，在一个简单的趋光任务中（机器人被编程在进入光照区域时停止），两种形态在成功率上原本存在的差异会变得极为显著——如果机器人不被允许停止，而只能减速。基于对机器人自对齐动力学的精确物理建模，我们进行了数值模拟。结果表明：一方面，需要将自对齐强度精确调整至最佳点附近才能实现高效的趋光行为；另一方面，探索自对齐强度的变化范围能够实现集体行为的丰富表达能力。