用神经网络自动学习安全约束，让多机器人团队在复杂环境里又快又安全地保持队形。
FORMULA: FORmation MPC with neUral barrier Learning for safety Assurance
- 用神经网络替代人工设计安全约束，实现分布式安全控制
- 在密集场景中避免死锁，保持队形完整，计算负担更低
- 适合需要高安全性与可扩展性的多机器人系统应用
多机器人系统在灾害救援、物资运输和仓储物流等大规模应用中至关重要，但在复杂动态环境中实现鲁棒、安全的编队控制仍面临重大挑战。现有模型预测控制（MPC）方法存在可扩展性差和难以保证安全的问题，而控制屏障函数（CBFs）虽能保证安全，却难以手动设计用于大规模非线性系统。本文提出FORMULA，一种融合模型预测控制（MPC）与控制李雅普诺夫函数（CLFs）以保障稳定性、并采用神经网络驱动的CBFs实现去中心化安全的分布式安全增强型预测控制框架，无需人工设计安全约束。该方法在避障过程中维持编队完整性，解决密集配置下的死锁问题，并降低在线计算负载。仿真结果表明，FORMULA可在复杂环境中实现可扩展、安全感知且保持编队的多机器人协同导航。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。