A Geometry-Sensitive Quorum Sensing Algorithm for the Best-of-N Site Selection Problem

TL;DR

This paper introduces a geometry-sensitive quorum sensing algorithm inspired by ant behavior, improving the accuracy of site selection in environments with varied site distances and arrangements.

Contribution

The paper presents a novel geometry-aware model for the best-of-N site selection problem, addressing limitations of previous models in non-uniform geographic scenarios.

Findings

Enhanced accuracy in selecting higher quality, distant sites

Robustness to geographic challenges in site distribution

Better decision-making in realistic, complex environments

Abstract

The house hunting behavior of the Temnothorax albipennis ant allows the colony to explore several nest choices and agree on the best one. Their behavior serves as the basis for many bio-inspired swarm models to solve the same problem. However, many of the existing site selection models in both insect colony and swarm literature test the model's accuracy and decision time only on setups where all potential site choices are equidistant from the swarm's starting location. These models do not account for the geographic challenges that result from site choices with different geometry. For example, although actual ant colonies are capable of consistently choosing a higher quality, further site instead of a lower quality, closer site, existing models are much less accurate in this scenario. Existing models are also more prone to committing to a low quality site if it is on the path between the agents' starting site and a higher quality site. We present a new model for the site selection problem and verify via simulation that is able to better handle these geographic challenges. Our results provide insight into the types of challenges site selection models face when distance is taken into account. Our work will allow swarms to be robust to more realistic situations where sites could be distributed in the environment in many different ways.