用大模型规划无人机打印,实现语义指令到建筑的自动建造。
LLM-Drone: Aerial Additive Manufacturing with Drones Planned Using Large Language Models
- 用大语言模型动态生成和调整无人机打印路径
- 在受限环境下实现90%的建造准确率
- 支持错误自修正,适合复杂环境快速建模
增材制造(AM)已通过精确构建复杂结构重塑生产方式。然而,在高处或偏远区域等挑战性环境中,传统增材制造面临局限。无人机辅助的空中增材制造为此提供解决方案。尽管无人机路径规划、控制与定位技术不断进步,但现有方法精度仍不足以支持基于前馈挤出的传统工艺(如熔融沉积成型)。近期大语言模型(LLMs)的出现,凭借其强大的语义理解与实时规划能力,推动多个领域变革。本文提出将大语言模型融入空中增材制造,用于辅助施工任务的规划与执行,提升灵活性并实现反馈式设计与建造系统。利用大模型的语义理解与自适应能力,可动态生成并调整现场建造方案,确保在受限环境下的高效与精准施工。系统仅需语义提示即可完成结构设计与建造,并在严格规划约束下成功理解空间环境。其反馈机制可在制造过程遭遇意外误差时,通过大语言模型重新规划,无需复杂启发式规则或评估函数。结合语义规划与自动纠错,本系统实现了90%的建造准确率,将简单文本提示转化为实际结构。
原文摘要 · Abstract (English)
Additive manufacturing (AM) has transformed the production landscape by enabling the precision creation of complex geometries. However, AM faces limitations when applied to challenging environments, such as elevated surfaces and remote locations. Aerial additive manufacturing, facilitated by drones, presents a solution to these challenges. However, despite advances in methods for the planning, control, and localization of drones, the accuracy of these methods is insufficient to run traditional feedforward extrusion-based additive manufacturing processes (such as Fused Deposition Manufacturing). Recently, the emergence of LLMs has revolutionized various fields by introducing advanced semantic reasoning and real-time planning capabilities. This paper proposes the integration of LLMs with aerial additive manufacturing to assist with the planning and execution of construction tasks, granting greater flexibility and enabling a feed-back based design and construction system. Using the semantic understanding and adaptability of LLMs, we can overcome the limitations of drone based systems by dynamically generating and adapting building plans on site, ensuring efficient and accurate construction even in constrained environments. Our system is able to design and build structures given only a semantic prompt and has shown success in understanding the spatial environment despite tight planning constraints. Our method's feedback system enables replanning using the LLM if the manufacturing process encounters unforeseen errors, without requiring complicated heuristics or evaluation functions. Combining the semantic planning with automatic error correction, our system achieved a 90% build accuracy, converting simple text prompts to build structures.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。