用知识图谱把任务目标翻译成机器人路径，让规划更智能可解释。
A Knowledge-Graph Translation Layer for Mission-Aware Multi-Agent Path Planning in Spatiotemporal Dynamics
- 构建双层知识图谱，将任务语义转化为各机器人的感知视角与行动规则。
- 在墨西哥湾水下无人机案例中，不同规则生成了性能优异的协同路径。
- 适合需要动态适应与可解释性的多智能体系统研究者使用。
在动态环境中，自主智能体的协调受到高层任务目标与底层规划输入之间语义鸿沟的阻碍。为此，我们提出一种以知识图谱（KG）为核心的框架，作为智能翻译层。该KG采用双平面架构，将陈述性事实转化为每个智能体的、任务感知的“世界观”及物理感知的通行规则，从而将任务语义与无领域依赖的规划器解耦。只需修改知识图谱中的事实，即可简便地调整复杂协同路径。在墨西哥湾的自主水下航行器（AUVs）案例研究中，该方法可视化展示了端到端流程，并定量证明不同陈述性策略可产生各异且高性能的结果。本工作将知识图谱不仅视为数据存储，更确立为具备状态记忆能力的强大编排器，用于构建自适应且可解释的自主系统。
原文摘要 · Abstract (English)
The coordination of autonomous agents in dynamic environments is hampered by the semantic gap between high-level mission objectives and low-level planner inputs. To address this, we introduce a framework centered on a Knowledge Graph (KG) that functions as an intelligent translation layer. The KG's two-plane architecture compiles declarative facts into per-agent, mission-aware ``worldviews" and physics-aware traversal rules, decoupling mission semantics from a domain-agnostic planner. This allows complex, coordinated paths to be modified simply by changing facts in the KG. A case study involving Autonomous Underwater Vehicles (AUVs) in the Gulf of Mexico visually demonstrates the end-to-end process and quantitatively proves that different declarative policies produce distinct, high-performing outcomes. This work establishes the KG not merely as a data repository, but as a powerful, stateful orchestrator for creating adaptive and explainable autonomous systems.
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