Robots are increasingly deployed across diverse domains and designed for multi-purpose operation. As robotic systems grow in complexity and operate in dynamic environments, the need for structured, expressive, and scalable mission-specification approaches becomes critical, with mission specifications often defined in the field by domain experts rather than robotics specialists. However, there is no standard or widely accepted formalism for specifying missions in single- or multi-robot systems. A variety of formalisms, such as Behavior Trees, State Machines, Hierarchical Task Networks, and Business Process Model and Notation, have been adopted in robotics to varying degrees, each providing different levels of abstraction, expressiveness, and support for integration with human workflows and external devices. This paper presents a systematic analysis of these four formalisms with respect to their suitability for robot mission specification. Our study focuses on mission-level descriptions rather than robot software development. We analyze their underlying control structures and mission concepts, evaluate their expressiveness and limitations in modeling real-world missions, and assess the extent of available tool support. By comparing the formalisms and validating our findings with experts, we provide insights into their applicability, strengths, and shortcomings in robotic system modeling. The results aim to support practitioners and researchers in selecting appropriate modeling approaches for designing robust and adaptable robot and multi-robot missions.

翻译：随着机器人在不同领域的部署日益广泛，并被设计用于多用途操作，机器人系统的复杂性不断增加，且需在动态环境中运行，因此对结构化、表达性强且可扩展的任务规范方法的需求变得至关重要。这些任务规范通常由领域专家（而非机器人专家）在现场定义。然而，目前尚无标准或广泛接受的形式化方法用于单机器人或多机器人系统中的任务规范。在机器人学中，已有多种形式化方法被不同程度地采用，例如行为树、状态机、分层任务网络以及业务流程模型与标记法，它们各自提供了不同层次的抽象、表达能力，以及对与人工工作流和外部设备集成的支持。本文针对这四种形式化方法在机器人任务规范适用性方面进行了系统分析。我们的研究聚焦于任务层面的描述，而非机器人软件开发。我们分析了它们的基础控制结构和任务概念，评估了它们在建模现实世界任务时的表达能力和局限性，并评估了现有工具支持的程度。通过比较这些形式化方法并与专家验证我们的发现，我们深入探讨了它们在机器人系统建模中的适用性、优势和不足。研究结果旨在为从业者和研究人员在选择合适的建模方法以设计稳健且适应性强的单机器人及多机器人任务时提供参考。