NEUSIS让无人机在复杂环境中自主搜寻目标，效果优于现有方法。
NEUSIS: A Compositional Neuro-Symbolic Framework for Autonomous Perception, Reasoning, and Planning in Complex UAV Search Missions
- 融合神经符号视觉与推理，构建可解释的感知-决策系统
- 在模拟城市任务中成功率、搜索效率和定位精度均超顶尖模型
- 适合需要可靠决策的无人机搜救场景，尤其注重安全与理解
本文针对无人机自主搜寻任务，要求在限定时间内基于简短描述于复杂危险环境（含禁飞区）中定位特定兴趣目标（EOI），同时处理有限且不确定的信息。提出一种组合式神经符号框架NEUSIS，集成神经符号视觉感知、推理与具身化（GRiD）模块以处理原始感官输入，维护概率世界模型表示环境，并采用分层规划组件（SNaC）实现高效路径规划。在AirSim与Unreal Engine构建的模拟城市搜救任务中，实验表明NEUSIS在成功率、搜索效率及3D定位精度上均优于当前最先进（SOTA）的视觉语言模型与搜索规划模型，验证了其在真实复杂场景中的有效性，为无人机自主搜寻系统提供了有前景的解决方案。
原文摘要 · Abstract (English)
This paper addresses the problem of autonomous UAV search missions, where a UAV must locate specific Entities of Interest (EOIs) within a time limit, based on brief descriptions in large, hazard-prone environments with keep-out zones. The UAV must perceive, reason, and make decisions with limited and uncertain information. We propose NEUSIS, a compositional neuro-symbolic system designed for interpretable UAV search and navigation in realistic scenarios. NEUSIS integrates neuro-symbolic visual perception, reasoning, and grounding (GRiD) to process raw sensory inputs, maintains a probabilistic world model for environment representation, and uses a hierarchical planning component (SNaC) for efficient path planning. Experimental results from simulated urban search missions using AirSim and Unreal Engine show that NEUSIS outperforms a state-of-the-art (SOTA) vision-language model and a SOTA search planning model in success rate, search efficiency, and 3D localization. These results demonstrate the effectiveness of our compositional neuro-symbolic approach in handling complex, real-world scenarios, making it a promising solution for autonomous UAV systems in search missions.
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