Scaling and Trade-offs in Multi-agent Autonomous Systems

TL;DR

This paper introduces a scalable approach using physical science-inspired data-scaling techniques to analyze and optimize multi-agent autonomous systems, revealing performance boundaries and trade-offs in drone swarm scenarios.

Contribution

It applies dimensional-analysis and data-scaling to large-scale simulations, providing a new framework for understanding and optimizing autonomous swarm performance.

Findings

Scaling laws reveal success-failure boundaries.

Trade-offs between agent count and platform parameters quantified.

Embedding path planning improves scaling outcomes.

Abstract

Designing autonomous drone swarms is hampered by a vast design space spanning platform, algorithmic, and numerical-strength choices. We perform large-scale agent-based simulations in three canonical scenarios: swarm-on-swarm battle, cooperative area search with attrition, and pursuit of scattering targets. We demonstrate that dimensional-analysis and data-scaling, established techniques in physical sciences, can be leveraged to collapse performance data onto scaling functions that are mathematically simple, yet counterintuitive and therefore difficult to predict a priori. These scaling laws reveal success-failure boundaries, including sharp break points. Additionally, we show how this technique can be used to quantify trade-offs between agent count and platform parameters such as velocity, sensing or weapon range, and attrition rate. Furthermore, we show the benefits of embedding an optimal path planning loop within this framework, which can qualitatively improve the scaling laws that govern the outcome. The methods we demonstrate are highly flexible and would enable rapid, budget-aware sizing and algorithm selection for large autonomous swarms.