How can CBFs ensure safe warehouse robot navigation?
Safe Human Robot Navigation in Warehouse Scenario
March 28, 2025
https://arxiv.org/pdf/2503.21141This paper proposes a safer navigation system for autonomous mobile robots (AMRs) in warehouses, using learned Control Barrier Functions (CBFs) to avoid collisions with static obstacles, dynamic obstacles (like human workers), and other robots. The CBFs are integrated with the Open Robotics Middleware Framework (OpenRMF) to coordinate tasks across multiple robots.
Key points for LLM-based multi-agent systems:
- Decentralized control: Each robot uses its own CBF for collision avoidance, treating other agents as uncontrollable. This simplifies coordination and scales well to multiple agents.
- Learned safety: CBFs are learned from real-world data, allowing them to adapt to specific robot dynamics and environments. This data-driven approach is relevant to training LLM agents in simulated environments.
- Safety guarantees: CBFs provide mathematical guarantees of safety, ensuring the robot avoids unsafe regions. This is crucial for real-world deployment of LLM-based agents.
- Integration with higher-level planning: The CBFs are integrated with OpenRMF for task allocation, demonstrating the feasibility of combining learned low-level controllers with symbolic planning systems. This modular approach is highly relevant to developing complex LLM-based multi-agent applications.
- Real-world experiments: The approach is validated in real-world experiments with multiple robots and dynamic obstacles, showcasing its practicality and robustness. This emphasis on real-world evaluation is important for bridging the gap between LLM research and practical applications.