Improved Swarm Engineering: Aligning Intuition and Analysis
John Harwell, Maria Gini

TL;DR
This paper introduces new metrics to assist in designing and analyzing swarm-robotic systems, improving prediction accuracy of swarm behavior and reducing design costs through quantitative analysis of control algorithms.
Contribution
It develops novel metrics for swarm self-organization, scalability, flexibility, and robustness, validated through analysis of multiple control algorithms in different scenarios.
Findings
Metrics accurately predict algorithm performance in different scenarios.
Quantitative results support intuitive assessments of swarm behaviors.
Metrics can be combined to improve behavior prediction.
Abstract
We present a set of metrics intended to supplement designer intuitions when designing swarm-robotic systems, increase accuracy in extrapolating swarm behavior from algorithmic descriptions and small test experiments, and lead to faster and less costly design cycles. We build on previous works studying self-organizing behaviors in autonomous systems to derive a metric for swarm emergent self-organization. We utilize techniques from high performance computing, time series analysis, and queueing theory to derive metrics for swarm scalability, flexibility to changing external environments, and robustness to internal system stimuli such as sensor and actuator noise and robot failures. We demonstrate the utility of our metrics by analyzing four different control algorithms in two scenarios: an indoor warehouse object transport scenario with static objects and a spatially unconstrained outdoor…
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Taxonomy
TopicsTransportation and Mobility Innovations · Modular Robots and Swarm Intelligence · Mobile Crowdsensing and Crowdsourcing
