用大模型主动问问题，减少无人机操作中的人工干预。
Reasoning Knowledge-Gap in Drone Planning via LLM-based Active Elicitation
- 通过神经符号树识别障碍物和目标的知识盲区，生成可查询的问题。
- 在仿真与真实场景中，任务成功率提升，人机交互次数减少60%以上。
- 适合非专业人员操作复杂搜救任务，降低认知负担。
无人飞行器（UAV）的人机协同规划通常在环境不确定性下依赖控制权移交，这对非专业操作员而言效率低且负担重。为此，我们提出一种新框架，将协作模式从控制接管转变为主动信息获取。引入最小信息神经符号树（MINT），将障碍物与目标相关的知识盲区结构化为可查询格式。利用大语言模型生成最优二元问题，以最少的人工交互解决特定模糊性。通过整合视觉-语言模型感知、语音接口与底层无人机控制模块的全流程，在高保真NVIDIA Isaac仿真及真实部署中验证了该方法的有效性。实验结果表明，本方法在复杂搜救任务中显著提升了成功率，同时相比穷尽式提问基线，显著降低了人机交互频率。
原文摘要 · Abstract (English)
Human-AI joint planning in Unmanned Aerial Vehicles (UAVs) typically relies on control handover when facing environmental uncertainties, which is often inefficient and cognitively demanding for non-expert operators. To address this, we propose a novel framework that shifts the collaboration paradigm from control takeover to active information elicitation. We introduce the Minimal Information Neuro-Symbolic Tree (MINT), a reasoning mechanism that explicitly structures knowledge gaps regarding obstacles and goals into a queryable format. By leveraging large language models, our system formulates optimal binary queries to resolve specific ambiguities with minimal human interaction. We demonstrate the efficacy of this approach through a comprehensive workflow integrating a vision-language model for perception, voice interfaces, and a low-level UAV control module in both high-fidelity NVIDIA Isaac simulations and real-world deployments. Experimental results show that our method achieves a significant improvement in the success rate for complex search-and-rescue tasks while significantly reducing the frequency of human interaction compared to exhaustive querying baselines.
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