Trust-Aware Sequential Decision Making and Rollout Planning for Resilient Multi-Robot Systems

2026-08-26

AI Summary

研究针对多机器人系统中定位欺骗问题，提出结合概率定位信任与行为证据的信任感知监控方法，以移除被检测到的敌对者，恢复规划执行一致性。

Abstract

Sequential decision-making in multi-robot systems typically assumes that planning information is reliable and that agents execute the actions anticipated by the planner. Compromised agents can violate both assumptions, creating a mismatch between the planning model and physical execution. We study this problem in online multi-robot routing under localization spoofing. We introduce a distance-constrained spoofing model for monitor-aware adversaries, together with a tiered bipartite matching strategy that maximizes assignment influence while limiting spoofing magnitude. To mitigate such attacks, we develop a trust-aware monitor that combines probabilistic localization trust, calibrated using real GPS spoofing data, with behavioral evidence from task execution to classify agents and remove detected adversaries from subsequent planning. We further show that undetected adversaries can cause rollout to lose its expected cost-improvement behavior by violating planner-execution consistency. Trust-aware removal restores this consistency after detection, enabling stable routing and recovery of rollout's empirical advantage over the base policy. Experiments using real GPS spoofing datasets and San Francisco taxicab demand demonstrate effective detection and resilient routing across varying spoofing capabilities, adversarial fleet sizes, adaptive attacks, monitoring configurations, and rollout horizons.

Problem

• Sequential Decision Making
• Multi-Robot Systems
• Localization Spoofing
• Trust-Aware

Innovation

• trust-aware decision making
• localization spoofing
• tiered bipartite matching
• probabilistic localization trust
• resilient multi-robot systems