Communication Model-Task Pairing in Artificial Swarm Design

TL;DR

This study investigates how different biological-inspired communication models affect the performance of artificial swarms across various tasks, highlighting the importance of model-task pairing in swarm design.

Contribution

It demonstrates that the choice of communication model significantly influences swarm performance, emphasizing the need for careful model selection in artificial swarm systems.

Findings

Communication model significantly impacts task performance.

Pairing of communication model and task is crucial for optimal results.

Performance varies notably across different models and tasks.

Abstract

Unraveling the nature of the communication model that governs which two individuals in a swarm interact with each other is an important line of inquiry in the collective behavior sciences. A number of models have been proposed in the biological swarm literature, with the leading models being the metric, topological, and visual models. The hypothesis evaluated in this manuscript is whether the choice of a communication model impacts the performance of a tasked artificial swarm. The biological models are used to design coordination algorithms for a simulated swarm, which are evaluated over a range of six swarm robotics tasks. Each task has an associated set of performance metrics that are used to evaluate how the communication models fare against each other. The general findings demonstrate that the communication model significantly affects the swarm's performance for individual tasks, and this result implies that the communication model-task pairing is an important consideration when designing artificial swarms. Further analysis of each tasks' performance metrics reveal instances in which pairwise considerations of model and one of the various experimental factors becomes relevant. The reported research demonstrates that the artificial swarm's task performance can be increased through the careful selection of a communications model.