FORMULA: FORmation MPC with neUral barrier Learning for safety Assurance
This addresses safety and scalability issues in multi-robot systems for applications like disaster response and logistics, representing a novel method for a known bottleneck.
The paper tackles the challenge of ensuring robust, safety-aware formation control for multi-robot systems in cluttered and dynamic environments by presenting FORMULA, a framework that integrates MPC with neural network-based control barrier functions, resulting in scalable, safety-aware navigation with reduced computational load.
Multi-robot systems (MRS) are essential for large-scale applications such as disaster response, material transport, and warehouse logistics, yet ensuring robust, safety-aware formation control in cluttered and dynamic environments remains a major challenge. Existing model predictive control (MPC) approaches suffer from limitations in scalability and provable safety, while control barrier functions (CBFs), though principled for safety enforcement, are difficult to handcraft for large-scale nonlinear systems. This paper presents FORMULA, a safe distributed, learning-enhanced predictive control framework that integrates MPC with Control Lyapunov Functions (CLFs) for stability and neural network-based CBFs for decentralized safety, eliminating manual safety constraint design. This scheme maintains formation integrity during obstacle avoidance, resolves deadlocks in dense configurations, and reduces online computational load. Simulation results demonstrate that FORMULA enables scalable, safety-aware, formation-preserving navigation for multi-robot teams in complex environments.