ResearchResearch paperSafety & Alignment · Reinforcement Learning · Robotics & Embodied AI1 source · Oct 6, 2026

No Need to Stop the Fleet: Localized Anomaly Isolation via Dead Zones in AGV Fleets

Automated Guided Vehicle (AGV) fleets in production and logistics environments rely on a system-wide emergency stop to handle local anomalies such as malfunctions, hazards, or contaminated areas.

Key points

  • This paper proposes dead zones --- a formally defined localized isolation approach that serves as an additional fail-operational mechanism for situations where a full system-wide halt is not strictly needed.
  • We introduce two operational strategies: basic dead zone handling, in which affected robots enter a dead zone and stop, and dead zone handling with escaping, in which robots that are sufficiently far from a dead zone reroute around it.
  • For escaping on counterclockwise trajectories we prove collision-freedom with a bounded arrival delay for all escaping robots, provided a minimum pairwise separation is kept between robots.
  • Experimental evaluation confirms the theoretical guarantees and characterizes the throughput tradeoffs between all three approaches: full emergency stop, basic dead zone handling, and dead zone handling with escaping.

Sources (1)

  • [1]No Need to Stop the Fleet: Localized Anomaly Isolation via Dead Zones in AGV Fleets
    arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 03:20 PM
    Automated Guided Vehicle (AGV) fleets in production and logistics environments rely on a system-wide emergency stop to handle local anomalies such as malfunctions, hazards, or contaminated areas.
    This paper proposes dead zones --- a formally defined localized isolation approach that serves as an additional fail-operational mechanism for situations where a full system-wide halt is not strictly needed.

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