An interpretable machine learning framework for modeling and analysis of swarm robot behaviors

Abstract

Swarm robotics aims to achieve robust collective behaviors through large numbers of relatively simple robots, but modeling and interpreting these emergent dynamics from real experimental data remains challenging. This work proposes an interpretable machine learning framework for modeling and analyzing swarm robot behaviors using a public IEEE DataPort dataset of swarm robotics experiments (eight robots, 200 time steps, and 1,600 labeled samples). We construct a feature-based representation of local interaction metrics (alignment, cohesion, separation, velocity, and position) and train a Random Forest classifier to recognize four behavioral phases: exploration, aggregation, formation, and foraging. The proposed classifier attains 98.12% overall accuracy and high per-class precision and recall, while feature importance and Shapley additive explanation analyses highlight alignment (31.44%) and cohesion (21.62%) as dominant behavioral drivers. Unsupervised clustering with KMeans and DBSCAN, supported by a Silhouette score of 0.2541 and an adjusted Rand index up to 0.69, reveals moderately separable latent structure consistent with the labeled phases. A Random Forest regressor further links local interaction features to global performance indicators, achieving high results on task-level outcomes. Our framework provides a unified, reproducible, and interpretable pipeline for real multi-robot data that combines classification, clustering, and regression. The results demonstrate that biologically inspired features can support accurate, explainable phase recognition and performance prediction, enabling data-driven design of swarm controllers for applications such as precision agriculture, search and rescue, and environmental monitoring.