arXiv：cs.AI· Shermin Sherkat, Thomas Wortmann, Andreas Wortmann·· 20 小时前
APTree：面向建筑机器人任务规划的自适应可扩展行为树框架
APTree: Extended Behavior Trees for Adaptive and Scalable Automated Robotic Task Planning in Construction
AI 导读
APTree 是一个模型驱动的框架，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，可在运行时反复协调多种符号规划器自动生成并组合模块化任务计划。该研究还提出用于任务计划形式化描述与验证的领域特定语言及配套执行引擎，并在真实 livMatS Biomimetic Shell 展亭上完成评估。机器可检验的形式化分析表明，在明确的假设条件下，组合执行与局部重规划是安全的。
正文
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