Physics-Informed Modeling and Control of Emergent Behaviors in Robot Swarms

2026-05-31

AI Summary

This work addresses the challenge of uniformly modeling and controlling multi-stage robotic swarm behaviors under conditions of local perception, limited communication, and decentralization. The authors propose PhySwarm, a novel framework that integrates physical priors into swarm modeling through a micro-macro coupling mechanism to achieve coherent regulation. At the macroscopic level, a multi-phase advection-diffusion-reaction (Macro-ADR) density field model captures collective dynamics, while an equivalent deterministic motion model (Micro-EDM) characterizes individual agent behavior at the microscopic scale. A neural physics controller (NPC) is then designed, combining reinforcement learning with physics-informed neural networks for joint optimization. Evaluated on tasks including path-guided foraging, reconfigurable formation, and role-adaptive search-and-rescue, the framework successfully generates interpretable, multi-stage emergent behaviors, demonstrating its effectiveness and innovation in orchestrating cooperative swarm organization.

Abstract

Robot swarms can exhibit coherent collective behaviors through local perception, limited communication and decentralized decision-making, yet modeling and controlling such emergence remains challenging when behaviors unfold over multiple phases. Here we introduce PhySwarm, a physics-informed micro--macro framework that represents multi-stage swarm emergence as physically constrained density-field evolution coupled to executable robot motion. At the macroscopic level, a multi-phase advection--diffusion--reaction model (Macro-ADR) describes phase-dependent swarm-density evolution through directed transport, diffusion-based spatial regulation and behavioral phase transitions. At the microscopic level, an equivalent deterministic motion model (Micro-EDM) realizes these mechanisms through potential-field advection, density-gradient compensation and rate- or event-gated phase switching. A neural-physics controller (NPC) maps local observations and temporal memory to bounded physical parameters, and is trained with a reinforcement learning--PINN objective that combines task rewards with macro-scale density residuals and micro-scale motion-consistency constraints. In several proof-of-concept swarm missions -- including trail-guided foraging, formation-reconfigurable navigation and role-adaptive search and rescue -- we demonstrate that PhySwarm can generate distinct multi-stage emergent behaviors within a unified physics-informed modeling framework. The learned density fields and physical parameters provide interpretable evidence of how advection, diffusion and reaction jointly regulate multi-stage swarm organization. These results establish a physics-informed route for learning, interpreting and controlling emergent behaviors in robot swarms.

Problem

• robot swarms
• emergent behaviors
• multi-stage modeling
• physics-informed control
• collective dynamics

Innovation

• physics-informed modeling
• multi-phase swarm emergence
• advection-diffusion-reaction
• neural-physics controller
• micro-macro framework