外观
论文arXiv 2610.1852315 天前
APTree：用于建筑中自适应且可扩展的自动化机器人任务规划的扩展行为树
原文链接：APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction（arXiv 2610.18523 · 发布日期：2026-10-13）
APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction
APTree用行为树整合自动规划与模块化执行，并在真实亭构建案例中验证建筑机器人任务规划。
核心要点
- 面向场外建筑预制的机器人任务规划主要依赖人工，效率低且阻碍机器人方案采用。
- APTree以行为树为执行模型，运行时协调异构符号规划器生成并组合模块化任务计划。
- 该框架提供领域专用语言和兼容执行引擎，用于任务计划的形式化规约与验证。
- 在livMatS Biomimetic Shell亭案例中，APTree检测注入错误并协调三个符号规划器生成大规模计划。
验证范围与局限：验证范围为真实livMatS Biomimetic Shell亭构建案例；安全性分析依赖规划器可靠性和计划非干扰等明确假设。
英文摘要 (Abstract)
Task planning for robots in off-site construction prefabrication is primarily manual, leading to planning inefficiencies and reluctance to adopt robotic solutions. Existing approaches provide limited support for scalable, modular automated task planning and localized runtime replanning. This paper proposes APTree, a model-driven framework that integrates automated task planning with modular task execution using behavior trees as the execution model. APTree repeatedly applies and coordinates heterogeneous symbolic planners to automatically generate and compose modular task plans at runtime. To support this approach, we develop a domain-specific language for the formal specification and validation of task plans, together with a compatible execution engine. We evaluate APTree on the real-world livMatS Biomimetic Shell pavilion, where it models the construction robotic fabrication domain, detects injected specification and planning errors at design time and runtime, and automatically generates large-scale task plans by coordinating three symbolic planners across numerous planning modules. A machine-checked formal analysis further shows that composed execution and local replanning are safe under explicitly stated assumptions, including planner soundness and non-interference of generated plans, providing practitioners with explicit conditions for dependable plan composition. APTree thus enables construction practitioners to leverage automated task planning without requiring detailed knowledge of symbolic planning techniques or APTree's internal implementation.
作者：Shermin Sherkat, Thomas Wortmann, Andreas Wortmann
来源
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