Mean-field models in swarm robotics: a survey

Area of Science:

• Robotics
• Control Theory
• Dynamical Systems

Background:

• Swarm robotics involves coordinating multiple autonomous agents.
• Designing scalable control strategies for large swarms is challenging.
• Microscopic models struggle with large numbers of agents.

Purpose of the Study:

• To survey the application of fluid approximations (mean-field models) in swarm robotics.
• To highlight the benefits of mean-field models for control strategy design.
• To showcase their use in various swarm robotics tasks.

Main Methods:

• Reviewing literature on mean-field models in swarm robotics.
• Analyzing models based on discrete/continuous states and time.
• Examining applications across different swarm behaviors.

Main Results:

• Mean-field models (ODEs, PDEs, difference equations) are effective for swarm robotics.
• These macroscopic models are scalable and independent of agent number.
• They enable rigorous analysis using control and dynamical systems theory.

Conclusions:

• Mean-field models provide a scalable and theoretically grounded approach to swarm robotics control.
• They facilitate provable guarantees for collective behaviors.
• Applications include coverage, task allocation, self-assembly, consensus, and mapping.