Lifelong LaCAM with Local Guidance for Lifelong MAPF

2026-05-19 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A new multi-agent pathfinding (MAPF) solver, Lifelong LaCAM with Local Guidance (LLLG), significantly improves throughput and runtime in real-time, lifelong MAPF (LMAPF) scenarios. LLLG integrates local guidance into a receding-horizon, windowed planning framework, warm-starting guidance from previous solutions to mitigate congestion. Evaluated on a Mac Studio with M1 Ultra 64 GB RAM, LLLG consistently outperforms existing planners like RHCR, PIBT, LaCAM, Guided-PIBT, and WPPL across various dense and sparse environments. For instance, on ht_chantry maps with bottlenecks, LLLG achieves 81% higher throughput while reducing runtime by 96% compared to RHCR. It also demonstrates scalability up to 10,000 agents, maintaining sub-second planning times per step and a 30% throughput gain over PIBT in large warehouse maps. The method's effectiveness stems from its ability to provide agent-centric spatiotemporal cues for imminent conflicts, smoothing traffic and suppressing repeated stops.

Key takeaway

For research scientists developing multi-robot coordination systems, LLLG offers a robust solution for real-time, lifelong multi-agent pathfinding in dense environments. You should consider implementing LLLG's receding-horizon, warm-started local guidance approach to achieve superior throughput and computational efficiency, especially when dealing with hundreds to thousands of agents and continuous task arrivals. This method significantly outperforms prior techniques in both speed and solution quality, making it a critical advancement for practical LMAPF deployments.

Key insights

LLLG uses warm-started local guidance within a receding-horizon framework to boost lifelong multi-agent pathfinding throughput.

Principles

• Local guidance mitigates congestion effectively.
• Warm-starting guidance from prior solutions is crucial.
• Real-time planning requires efficient, scalable methods.

Method

LLLG employs a receding-horizon windowed planning framework, warm-starting local guidance from the previous solution's suffix at each timestep, and refines guidance iteratively to reduce bias.

In practice

• Use LLLG for high-density, real-time LMAPF.
• Warm-start guidance from previous solution suffixes.
• Consider hindrance for lightweight throughput gains.

Topics

• Lifelong Multi-Agent Pathfinding
• Local Guidance
• LaCAM Solver
• Receding Horizon Planning
• Multi-robot Coordination

Code references

• allegorywrite/lllg
• Jiaoyang-Li/RHCR
• HirokiNagai-39/pibt-tiebreaking
• nobodyczcz/guided-pibt
• DiligentPanda/MAPF-LRR2023

Best for: Research Scientist, AI Scientist, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.