对比四种机器人任务建模形式,帮工程师选最适合的方案。
Formalisms for Robotic Mission Specification and Execution: A Comparative Analysis
- 用行为树、状态机等四类形式化方法建模任务流程
- 评估各方法在表达力和工具支持上的优劣
- 适合机器人系统设计者快速匹配合适建模方式
机器人正广泛应用于多样化场景并承担多任务。随着系统复杂度提升及环境动态变化,结构化、可扩展的任务定义方法变得至关重要,通常由领域专家而非机器人专业人员制定任务规范。然而,目前尚无统一标准的单/多机器人任务形式化表达方式。已有多种形式化方法被不同程度采用,包括行为树、状态机、层次化任务网络以及业务流程模型与标注(BPMN),它们在抽象层级、表达能力及与人工工作流和外部设备的集成支持上各有差异。本文对这四种形式化方法进行了系统性分析,聚焦于任务级描述而非软件开发。研究考察其底层控制结构与任务概念,评估在真实任务建模中的表达力与局限,并分析现有工具支持程度。通过与专家验证结果,揭示各类方法在机器人系统建模中的适用性、优势与不足,旨在为研究人员与实践者在设计鲁棒、可适应的机器人及多机器人任务时提供选择依据。
原文摘要 · Abstract (English)
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.
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