arXiv cs.AI· Shermin Sherkat, Thomas Wortmann, Andreas Wortmann·· 3 天前AI 评分18
APTree：面向建筑机器人的自适应可扩展自动化任务规划行为树框架
APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction
AI 导读
APTree 是一个模型驱动框架，将自动化任务规划与基于行为树的模块化任务执行相结合，通过协调多个异构符号规划器在运行时自动生成并组合模块化任务计划。研究团队为其开发了领域特定语言及配套执行引擎，并在真实 livMatS Biomimetic Shell 展馆上验证，可检测设计时与运行时的规格和规划错误，并跨多个规划模块协调三种符号规划器生成大规模任务计划。
正文 · 原文
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.
| Comments: | Accepted for publication in The Journal of Systems and Software. 46 pages, 17 figures, 16 listings, 6 tables |
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) |
| Cite as: | arXiv:2610.18523 [cs.RO] |
| (or arXiv:2610.18523v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18523 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shermin Sherkat [view email]
[v1]
Tue, 13 Oct 2026 15:12:13 UTC (4,121 KB)
来源：arXiv cs.AI · arxiv.org