机器人系统中的行为树：关于实践与经验的实证研究
Behavior Trees for Robotic Systems: An Empirical Study on Practices and Experiences
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中文总结 AI 辅助
本研究通过汽车公司行动研究和34名从业者调查，发现行为树能提升团队沟通和决策可理解性，但面临设计集成挑战、库文档不足及节点排序困难等问题。
中文摘要 AI 辅助
在过去十年中，行为树（BT）已成为协调机器人系统任务的主导行为模型之一。然而，关于行为树在现实世界环境中采用的实证证据仍然有限，尤其是关于从业者的经验和实践。如果没有来自学术界和工业界的基于从业者的证据，研究社区就有可能开发出原则上合理但仅部分符合实际挑战的指南和工具。这种证据的缺乏导致了临时性的实践，阻碍了软件的重用、维护和演化。本文报告了一项混合方法研究，结合了在一家汽车公司进行的技术行动研究调查和对34名机器人从业者的问卷调查。我们的结果表明，行为树改善了团队沟通和机器人决策逻辑的可理解性，反映了行为树在任务协调之外的实用价值。与此同时，从业者面临着多个非平凡的设计和集成决策，这些决策因缺乏适当的指南和工具支持而变得复杂。这些决策涵盖了在实现行为树并将其集成到ROS中时的架构和语言选择，我们报告了观察到的模式。此外，从业者面临着多因素的粒度决策，以及对当前库的混合体验。对于最常用的行为树库，他们报告了实现挑战和文档缺口。关于规划算法，从业者发现确定最优行为树节点排序具有挑战性。关于可扩展性的观点仍然没有定论，当前库和实践中的局限性阻碍了更广泛的采用。我们最后提出了跨领域的观察和对在机器人系统中采用行为树的从业者以及旨在推进行为树采用的实证理解的研究人员的启示。
英文摘要
Over the last decade, behavior trees (BT) have become one of the dominant behavior models for coordinating missions of robotic systems. Yet empirical evidence on BT adoption in real-world contexts remains limited, especially regarding practitioners experiences and practices. Without practitioner-grounded evidence from both academic and industrial settings, the research community risks developing guidelines and tools that are plausible in principle but only partly aligned with real challenges. This scarcity of evidence leads to ad-hoc practices, impeding software reuse, maintenance, and evolution. This paper reports a mixed-methods study combining a technical action research investigation at an automotive company with a survey of 34 robotics practitioners. Our results indicate that BTs improve team communication and the understandability of robotic decision-making logic, reflecting BTs practical value beyond mission coordination. At the same time, practitioners face multiple non-trivial design and integration decisions, complicated by the lack of adequate guidelines and tool support. These decisions span architectural and language choices when implementing BTs and integrating them within ROS, for which we report observed patterns. Additionally, practitioners faced multi-factor granularity decisions and mixed experiences with current libraries. For the most used BT libraries, they reported implementation challenges and documentation gaps. Regarding planning algorithms, practitioners find deciding optimal BT node ordering challenging. Views on scalability remained inconclusive, and limitations in current libraries and practices hinder broader adoption. We conclude with cross-cutting observations and implications for both practitioners adopting BTs in robotic systems and researchers aiming to advance empirical understanding of BT adoption.
发表机构
- Chalmers University of Technology（查尔姆斯理工大学）
- University of Gothenburg（哥德堡大学）
- Radboud University（拉德堡德大学）
- Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。
