Collective perception is a foundational problem in swarm robotics, in which the swarm must reach consensus on a coherent representation of the environment. An important variant of collective perception casts it as a best-of-$n$ decision-making process, in which the swarm must identify the most likely representation out of a set of alternatives. Past work on this variant primarily focused on characterizing how different algorithms navigate the speed-vs-accuracy tradeoff in a scenario where the swarm must decide on the most frequent environmental feature. Crucially, past work on best-of-$n$ decision-making assumes the robot sensors to be perfect (noise- and fault-less), limiting the real-world applicability of these algorithms. In this paper, we derive from first principles an optimal, probabilistic framework for minimalistic swarm robots equipped with flawed sensors. Then, we validate our approach in a scenario where the swarm collectively decides the frequency of a certain environmental feature. We study the speed and accuracy of the decision-making process with respect to several parameters of interest. Our approach can provide timely and accurate frequency estimates even in presence of severe sensory noise.
翻译:集体认识是群温机器人的一个基本问题,在这种情况下,群温必须就一致代表环境的问题达成共识。集体认知的一个重要变体将它描绘成一个最佳的美元决策程序,在这种进程中,群温必须确定一套替代方法中最可能的代表性。过去关于这一变体的工作主要侧重于说明不同算法如何在速度-速度-准确性权衡中操作速度-速度-速度-准确性权衡,在这种假设中,群温必须就最经常的环境特征作出决定。至关重要的是,过去关于美元最佳决策的工作假定机器人传感器是完美的(无噪音和错误的),限制了这些算法在现实世界中的适用性。在本文中,我们从第一个原则中为配备有缺陷传感器的微小的温温机器人提供了一个最佳的概率框架。然后,我们验证了我们的方法,在这种假设中,群温集体决定某种环境特征的频率。我们研究了有关若干利益参数的决策过程的速度和准确性。我们的方法可以提供及时和准确的频率估计,即使是在有严重传感器的噪音的情况下。