发表机构
Karlsruhe University of Applied Sciences (HKA)（卡尔斯鲁厄应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对CAD数据不完善导致的装配序列规划难题，提出混合框架，结合神经网络关系提取、人在回路验证与几何符号规划，在ASAP数据集上实现85.83%成功率并大幅缩短规划时间。
AI 中文摘要
装配序列规划（ASP）因其组合性质仍然是一个具有挑战性的问题，使得穷举规划方法对于复杂的工业装配体不切实际。此外，许多CAD模型缺乏可靠的语义接触信息，或需要大量的人工预处理，限制了现有方法的适用性。本文提出了一种混合ASP框架，将基于学习的关系提取与几何符号推理相结合，以从不完美的CAD数据生成可行的机器人拆卸序列。一个神经网络从点云中预测语义几何关系，而人在回路中的验证能够纠正不确定的预测和规划失败。提取的关系被转换为符号装配图，使几何符号规划器能够高效地计算局部有效的机器人操作原语集合。一种基于可见性的射线投射策略引导搜索可行的拆卸方向，无需穷举组合搜索，而局部解空间则实现了高效的序列优化。该框架在一个引入的装配数据集和ASAP测试数据集上进行了评估。在ASAP测试数据集上，所提出的规划器实现了85.83%的规划成功率，同时将所有装配规模的中位规划时间减少了超过一个数量级，并且对于超过30个组件的装配体，与基线相比减少了超过50倍。结果表明，所提出的混合框架能够从不完美的CAD数据实现高效的机器人装配序列规划，同时大幅减少规划时间。通过结合基于学习的特征分割、人在回路中的验证和几何符号推理，该框架为可扩展和适应性强的机器人装配与拆卸规划提供了实用基础。
英文摘要
Assembly Sequence Planning (ASP) remains a challenging problem due to its combinatorial nature, making exhaustive planning approaches impractical for complex industrial assemblies. Furthermore, many CAD models lack reliable semantic contact information or require extensive manual preprocessing, limiting the applicability of existing methods. This paper presents a hybrid ASP framework combining learning-based relation extraction with geometric-symbolic reasoning to generate feasible robotic disassembly sequences from imperfect CAD data. A neural network predicts semantic geometric relations from point clouds, while human-in-the-loop verification enables correction of uncertain predictions and planning failures. Extracted relations are transformed into a symbolic assembly graph, enabling a geometric-symbolic planner to efficiently compute locally valid sets of robotic manipulation primitives. A visibility-based ray-casting strategy guides the search for feasible disassembly directions without requiring an exhaustive combinatorial search, while the local solution space enables efficient sequence optimization. The framework is evaluated on an introduced assembly dataset and on the ASAP test dataset. On the ASAP test dataset, the proposed planner achieves an 85.83% planning success rate while reducing the median planning time by more than one order of magnitude across all assembly sizes and by more than a factor of 50 for assemblies with more than 30 components compared to the baseline. The results demonstrate that the proposed hybrid framework enables efficient robotic assembly sequence planning from imperfect CAD data while substantially reducing planning time. By combining learning-based feature segmentation, human-in-the-loop verification, and geometric-symbolic reasoning, the framework provides a practical foundation for scalable and adaptable robotic assembly and disassembly planning.
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