LLM-drone: aerial additive manufacturing with drones planned using large language models

Abstract 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 by allowing construction in previously inaccessible areas. 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 feedback-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. We propose a novel methodology that leverages LLMs to design a construction plan for drone-based manufacturing, and adjust the plan in real time to address any errors that may occur. 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.