How to safely plan trajectories with fewer agents?
Limiting Computation Levels in Prioritized Trajectory Planning with Safety Guarantees*
September 10, 2024
https://arxiv.org/pdf/2409.05029This paper tackles the challenge of planning collision-free trajectories for multiple autonomous vehicles with varying priorities. Instead of relying on the traditional sequential planning that is slow with many vehicles, the authors propose using reachability analysis to allow parallel planning. To avoid overly cautious behavior from parallel planning, they introduce a grouping method that selectively sequentializes a subset of computations, striking a balance between safety, speed, and solution quality.
For LLM-based multi-agent systems, this research is relevant in:
- Scalability: Using reachable sets for collision avoidance and selective sequentialization offers a path to scaling multi-agent systems, as purely sequential planning becomes computationally infeasible with many agents.
- Safety & Performance: This approach provides a method for guaranteeing safety (no collisions) while optimizing for performance (less conservative actions) - important considerations when deploying LLMs in real-world applications.
- Decentralized Control: The focus on P-DMPC (Prioritized Distributed Model Predictive Control) aligns with the decentralized nature of multi-agent systems, allowing for more robust and flexible control compared to centralized approaches.