ESARBench: 一种用于代理无人机体感知搜索与救援的基准
ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue
AI总结：
本文提出ESARBench，首个用于评估基于MLLM的无人机在高真实感搜索与救援场景中的基准，通过构建高保真环境和600个任务数据集，揭示了ESAR中的关键瓶颈。
摘要：
多模态大语言模型（MLLM）的快速发展使无人机在空间推理、语义理解和复杂决策方面具备了卓越能力，使其天然适合无人机搜索与救援（SAR）。然而，现有无人机SAR研究主要依赖传统视觉和路径规划方法，缺乏统一的体感代理基准。为此，我们首先提出新的任务——体感搜索与救援（ESAR），要求空中代理自主探索复杂环境、识别救援线索并推理受害者位置以执行知情决策。此外，我们提出ESARBench，首个综合性的基准，旨在评估MLLM驱动的无人机代理在高度真实的SAR场景中的表现。利用Unreal Engine 5和AirSim，我们构建了四个高保真、大规模的开放环境，直接映射自真实世界地理信息系统（GIS）数据，以确保逼真的景观。为了严格模拟实际救援操作，该基准集成了动态变量，包括天气状况、时间、以及随机线索放置。此外，我们创建了600个任务数据集，基于真实世界救援案例，并提出了稳健的评估指标。我们评估了从传统启发式方法到先进地面和空中MLLM基于ObjectNav代理的多种基线。实验结果突显了ESAR中的挑战，揭示了空间记忆、空中适应以及搜索效率与飞行安全之间的权衡问题。我们希望ESARBench能成为推动体感搜索与救援领域研究的重要资源。源代码和项目页面：https://4amgodvzx.github.io/ESAR.github.io。
英文摘要：
The rapid advancement of Multimodal Large Language Models (MLLMs) has empowered Unmanned Aerial Vehicle (UAV) with exceptional capabilities in spatial reasoning, semantic understanding, and complex decision-making, making them inherently suited for UAV Search and Rescue (SAR). However, existing UAV SAR research is dominated by traditional vision and path-planning methods and lacks a comprehensive and unified benchmark for embodied agents. To bridge this gap, we first propose the novel task of \textbf{Embodied Search and Rescue (ESAR)}, which requires aerial agents to autonomously explore complex environments, identify rescue clues, and reason about victim locations to execute informed decision-making. Additionally, we present \textbf{ESARBench}, the first comprehensive benchmark designed to evaluate MLLM-driven UAV agents in highly realistic SAR scenarios. Leveraging Unreal Engine 5 and AirSim, we construct four high-fidelity, large-scale open environments mapped directly from real-world Geographic Information System (GIS) data to ensure photorealistic landscapes. To rigorously simulate actual rescue operations, our benchmark incorporates dynamic variables including weather conditions, time of day, and stochastic clue placement. Furthermore, we create a dataset of 600 tasks modeled after real-world rescue cases and propose a robust set of evaluation metrics. We evaluate diverse baselines, ranging from traditional heuristics to advanced ground and aerial MLLM-based ObjectNav agents. Experimental results highlight the challenges in ESAR, revealing critical bottlenecks in spatial memory, aerial adaptation, and the trade-off between search efficiency and flight safety. We hope ESARBench serves as a valuable resource to advance research on Embodied Search and Rescue domain. Source code and project page: https://4amgodvzx.github.io/ESAR.github.io.
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。
