Minimalistic Collective Perception with Imperfect Sensors

TL;DR

This paper develops an optimal probabilistic framework for collective perception in swarm robotics with imperfect sensors, enabling accurate environmental feature frequency estimation despite sensor noise.

Contribution

It introduces a novel probabilistic approach tailored for minimalistic robots with flawed sensors, extending previous perfect-sensor models to real-world noisy conditions.

Findings

Effective frequency estimation under severe sensor noise

Balances speed and accuracy in decision-making

Validates approach through simulation experiments

Abstract

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.