用拓扑神经网络与语义织网框架，实现机器人在动态环境中的认知与鲁棒控制统一。
Ontology Neural Network and ORTSF: A Framework for Topological Reasoning and Delay-Robust Control
- 将语义关系建模为动态拓扑过程，融合曲率与同调结构保持关系完整性。
- 在显著延迟下仍能保持控制相位裕度与信号连续性，实测验证鲁棒性。
- 适合需高透明度与协作能力的智能机器人系统，如人机共融场景。
自主机器人系统在感知、定位、建图和控制方面取得了显著进展，但现有框架在表征和保持关系语义、上下文推理及认知透明性方面仍存在根本缺陷，难以适应动态的人类中心环境。本文提出统一架构：本体神经网络（ONN）与本体实时语义织网（ORTSF）。ONN将关系语义推理形式化为动态拓扑过程，通过在统一损失函数中嵌入Forman-Ricci曲率、持久同调与语义张量结构，确保场景演化过程中关系完整性和拓扑一致性。ORTSF将推理轨迹转化为可执行控制指令，并补偿系统延迟，集成预测与延迟感知算子，即使在显著延迟条件下仍能保持相位裕度与控制信号连续性。实证研究证明该框架能有效统一语义认知与鲁棒控制，为认知机器人提供数学严谨且实际可行的解决方案。
原文摘要 · Abstract (English)
The advancement of autonomous robotic systems has led to impressive capabilities in perception, localization, mapping, and control. Yet, a fundamental gap remains: existing frameworks excel at geometric reasoning and dynamic stability but fall short in representing and preserving relational semantics, contextual reasoning, and cognitive transparency essential for collaboration in dynamic, human-centric environments. This paper introduces a unified architecture comprising the Ontology Neural Network (ONN) and the Ontological Real-Time Semantic Fabric (ORTSF) to address this gap. The ONN formalizes relational semantic reasoning as a dynamic topological process. By embedding Forman-Ricci curvature, persistent homology, and semantic tensor structures within a unified loss formulation, ONN ensures that relational integrity and topological coherence are preserved as scenes evolve over time. The ORTSF transforms reasoning traces into actionable control commands while compensating for system delays. It integrates predictive and delay-aware operators that ensure phase margin preservation and continuity of control signals, even under significant latency conditions. Empirical studies demonstrate the ONN + ORTSF framework's ability to unify semantic cognition and robust control, providing a mathematically principled and practically viable solution for cognitive robotics.
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