面向部分可观测城市环境中以人为中心的火灾疏散的协同自主无人机
Coordinated Autonomous Drones for Human-Centered Fire Evacuation in Partially Observable Urban Environments
AI总结：
针对城市火灾疏散中现有无人机支援不足、未考虑人类恐慌行为的问题，提出基于POMDP的异构无人机协同框架，结合实证心理学人类行为模型与带循环策略的PPO算法，可显著缩短疏散时间。
摘要：
自主无人机技术有望在疏散行动中通过引导人类前往安全区域并支撑更广泛的应急响应工作，显著提升搜救行动的效能。然而，其在动态实时疏散支援中的应用仍十分有限，现有模型往往忽略了极端压力下人类行为的心理与情绪复杂性。在真实火灾场景中，疏散者常因恐慌和不确定性偏离指定的安全路线。为应对这些挑战，本文提出了一种多智能体协同框架，其中自主无人机（UAVs）可在不确定条件下实时定位、拦截人类疏散者并引导其前往安全区域。我们将该问题建模为部分可观测马尔可夫决策过程（POMDP），两类异构无人机智能体——高层救援者（HLR）和低层救援者（LLR）——通过共享观测信息与互补能力实现协同。人类行为采用基于实证心理学的智能体模型刻画，其中恐慌会动态影响人类对环境刺激做出的决策与移动行为。环境具备火势随机蔓延、疏散者位置未知、能见度有限的特点，要求无人机进行长时域规划以搜寻人类并实时调整策略。该框架采用带循环策略的近端策略优化（PPO）算法，以实现部分可观测场景下的鲁棒决策。仿真结果表明，与无无人机协助的场景相比，无人机团队能够快速定位并拦截疏散者，大幅缩短疏散者抵达安全区域的时间。
英文摘要：
Autonomous drone technology holds significant promise for enhancing search and rescue operations during evacuations by guiding humans toward safety and supporting broader emergency response efforts. However, their application in dynamic, real-time evacuation support remains limited. Existing models often overlook the psychological and emotional complexity of human behavior under extreme stress. In real-world fire scenarios, evacuees frequently deviate from designated safe routes due to panic and uncertainty. To address these challenges, this paper presents a multi-agent coordination framework in which autonomous Unmanned Aerial Vehicles (UAVs) assist human evacuees in real-time by locating, intercepting, and guiding them to safety under uncertain conditions. We model the problem as a Partially Observable Markov Decision Process (POMDP), where two heterogeneous UAV agents, a high-level rescuer (HLR) and a low-level rescuer (LLR), coordinate through shared observations and complementary capabilities. Human behavior is captured using an agent-based model grounded in empirical psychology, where panic dynamically affects decision-making and movement in response to environmental stimuli. The environment features stochastic fire spread, unknown evacuee locations, and limited visibility, requiring UAVs to plan over long horizons to search for humans and adapt in real-time. Our framework employs the Proximal Policy Optimization (PPO) algorithm with recurrent policies to enable robust decision-making in partially observable settings. Simulation results demonstrate that the UAV team can rapidly locate and intercept evacuees, significantly reducing the time required for them to reach safety compared to scenarios without UAV assistance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
