用概率化方法让无人机在复杂环境中合法自主飞行。
Probabilistic Mission Design for Neuro-Symbolic Unmanned Aircraft Systems
- 结合神经网络与逻辑程序，处理地理数据和感知噪声
- 生成概率任务景观图，量化飞行合法性置信度
- 支持多模态输入，适合物流与应急场景
先进空中交通（AAM）需要精确可信的法律概念与限制模型来指导无人航空器（UAS）的导航。在有人居住的动态不确定环境中实现鲁棒运行是关键挑战。本文提出概率任务设计（ProMis），一种新型神经符号框架，用于在法律约束下导航UAS。ProMis通过将不确定的地理空间数据与噪声感知信息，与声明式混合概率逻辑程序（HPLP）相结合，推理智能体状态空间及其合法性。为使规划兼顾法律限制与不确定性，ProMis生成概率任务景观（PML），即标量场，量化整个状态空间中HPLP被满足的信念程度。本文扩展了ProMis的推理能力与计算特性，并展示其与大型语言模型（LLM）及基于Transformer的视觉模型的集成。实验验证了ProMis在多模态输入下的有效性，适用于多种AAM场景。
原文摘要 · Abstract (English)
Advanced Air Mobility (AAM) is a growing field that demands accurate and trustworthy models of legal concepts and restrictions for navigating Unmanned Aircraft Systems (UAS). In addition, any implementation of AAM needs to face the challenges posed by inherently dynamic and uncertain human-inhabited spaces robustly. Nevertheless, the employment of UAS beyond visual line of sight (BVLOS) is an endearing task that promises to significantly enhance today's logistics and emergency response capabilities. Hence, we propose Probabilistic Mission Design (ProMis), a novel neuro-symbolic approach to navigating UAS within legal frameworks. ProMis is an interpretable and adaptable system architecture that links uncertain geospatial data and noisy perception with declarative, Hybrid Probabilistic Logic Programs (HPLP) to reason over the agent's state space and its legality. To inform planning with legal restrictions and uncertainty in mind, ProMis yields Probabilistic Mission Landscapes (PML). These scalar fields quantify the belief that the HPLP is satisfied across the agent's state space. Extending prior work on ProMis' reasoning capabilities and computational characteristics, we show its integration with potent machine learning models such as Large Language Models (LLM) and Transformer-based vision models. Hence, our experiments underpin the application of ProMis with multi-modal input data and how our method applies to many AAM scenarios.
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