发表机构
University of Connecticut; University of Illinois Chicago（康涅狄格大学; 伊利诺伊大学芝加哥分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出A-RAM智能体框架，结合LLM与领域工具，实现机器人增材制造工艺规划的执行前评估，显著降低关节抖动并缩短规划时间。
AI 中文摘要
机器人增材制造（AM）将材料挤出打印扩展到龙门运动学之外，但使工艺规划依赖于机器人。切片器生成的计划在零件坐标中看似有利，但在机械臂上可能变得不可行或对机器人不利，因为切片器工艺决策和零件方向决定了生成的路径，而零件方向和工位放置影响其运动学实现。现有的增材制造工具、基于大语言模型（LLM）的决策支持方法和数字孪生系统未提供对这些耦合决策的集成执行前评估。本文提出智能体机器人增材制造（A-RAM），一种智能体-专家-工具框架，将用户意图和零件文件转换为可追溯、可执行的计划。LLM解释制造目标和约束，识别规定和可搜索的规划变量，并将此推理编码在模式约束的请求中；确定性规划智能体实例化相应的搜索工作流，而领域工具计算切片、放置、逆运动学、轨迹时序、关节6加加速度和挤出的定量证据。该框架在六轴机器人臂增材制造单元上通过三个案例研究进行评估，涵盖专家指定规划、仅目标规划、目标依赖的填充筛选和几何依赖的方向-放置选择。在评估的候选集中，所选计划相比最不利的有效候选者，最大关节6加加速度降低高达53.5%，平均绝对关节6加加速度降低48.3%，而目标特定的填充筛选产生的运动规划完成时间比相应最不利的筛选模式缩短高达40.1%，挤出路径缩短高达12.7%。
英文摘要
Robotic additive manufacturing (AM) extends material-extrusion printing beyond gantry kinematics but makes process planning robot-dependent. A slicer-generated plan that appears favorable in part coordinates can become infeasible or robotically unfavorable on a manipulator because slicer-process decisions and part orientation determine the generated path, while part orientation and workspace placement affect its kinematic realization. Existing AM tools, large language model (LLM)-based decision-support methods, and digital-shadow systems do not provide integrated pre-execution evaluation of these coupled decisions. This paper presents agentic robotic additive manufacturing (A-RAM), an agent-specialist-tool framework that converts user intent and a part file into traceable, execution-ready plans. The LLM interprets manufacturing objectives and constraints, identifies prescribed and searchable planning variables, and encodes this reasoning in a schema-constrained request; a deterministic Planning Agent instantiates the corresponding search workflow, while domain tools compute quantitative evidence for slicing, placement, inverse kinematics, trajectory timing, Joint-6 jerk, and extrusion. The framework is evaluated on a six-axis robotic-arm AM cell through three case studies covering expert-specified planning, goal-only planning, objective-dependent infill screening, and geometry-dependent orientation-placement selection. Across the evaluated candidate sets, selected plans achieve up to 53.5% lower maximum Joint-6 jerk and 48.3% lower mean absolute Joint-6 jerk than the least favorable valid candidates, while objective-specific infill screening yields motion-plan completion times up to 40.1% shorter and extrusion paths up to 12.7% shorter than the corresponding least favorable screened patterns.
Comments25 pages, 17 figures
