用语义图替代传统流程，让机器人行为更灵活可追溯
Behavior Trees vs Executable Ontologies: a Comparative Analysis of Robot Control Paradigms
- 用事件驱动代替轮询执行，实现状态自动传播
- 支持运行时修改模型，全程追踪时间轨迹
- 适合动态变化场景，尤其适合复杂系统设计
本文对比了两种机器人行为建模方法：基于控制流的命令式行为树（BTs）与基于数据流规则的声明式可执行本体（EO），后者通过boldsea框架实现。BTs采用分层结构进行行为组织，而EO将领域表示为时间性、事件驱动的语义图。实验表明，EO通过事件驱动的状态传播机制，在反应性和模块化方面达到与BTs相当的水平。该方法将机器人控制从过程编程转向语义领域建模，弥合了语义与过程之间的鸿沟。EO支持运行时模型修改、完整的时间轨迹追踪，以及数据、逻辑和接口的统一表达，这些特性在BTs中难以实现或无法实现。尽管在已知、确定性场景中BTs仍具优势，但此对比凸显了两种方法在动态演化系统中的适用差异。
原文摘要 · Abstract (English)
This paper compares two distinct approaches to modeling robotic behavior: imperative Behavior Trees (BTs) and declarative Executable Ontologies (EO), implemented through the boldsea framework. BTs structure behavior hierarchically using control-flow, whereas EO represents the domain as a temporal, event-based semantic graph driven by dataflow rules. We demonstrate that EO achieves comparable reactivity and modularity to BTs through a fundamentally different architecture: replacing polling-based tick execution with event-driven state propagation. We propose that EO offers an alternative framework, moving from procedural programming to semantic domain modeling, to address the semantic-process gap in traditional robotic control. EO supports runtime model modification, full temporal traceability, and a unified representation of data, logic, and interface - features that are difficult or sometimes impossible to achieve with BTs, although BTs excel in established, predictable scenarios. The comparison is grounded in a practical mobile manipulation task. This comparison highlights the respective operational strengths of each approach in dynamic, evolving robotic systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。