AGENT: An Aerial Vehicle Generation and Design Tool Using Large Language Models
This work addresses the problem of slow design iteration for engineers in aerial vehicle development by providing an AI-powered tool that accelerates the pipeline, though it is incremental as it builds on existing LLM methods applied to a new dataset.
The paper tackles the bottleneck of computationally expensive physics simulators in computer-aided design workflows for cyberphysical systems by proposing AGENT, a tool built on the CodeT5+ large language model that generates and evaluates UAV designs conditioned on flight dynamics properties, achieving strong performance with a 220M-parameter model executable on a single GPU.
Computer-aided design (CAD) is a promising application area for emerging artificial intelligence methods. Traditional workflows for cyberphysical systems create detailed digital models which can be evaluated by physics simulators in order to narrow the search space before creating physical prototypes. A major bottleneck of this approach is that the simulators are often computationally expensive and slow. Recent advancements in AI methods offer the possibility to accelerate these pipelines. We use the recently released AircraftVerse dataset, which is especially suited for developing and evaluating large language models for designs. AircraftVerse contains a diverse set of UAV designs represented via textual design trees together with detailed physics simulation results. Following the recent success of large language models (LLMs), we propose AGENT (Aircraft GENeraTor). AGENT is a comprehensive design tool built on the CodeT5+ LLM which learns powerful representations of aircraft textual designs directly from JSON files. We develop a curriculum of training tasks which imbues a single model with a suite of useful features. AGENT is able to generate designs conditioned on properties of flight dynamics (hover time, maximum speed, etc.). Additionally, AGENT can issue evaluations of designs allowing it to act as a surrogate model of the physics simulation that underlies the AircraftVerse dataset. We present a series of experiments which demonstrate our system's abilities. We are able to achieve strong performance using the smallest member of the CodeT5+ family (220M parameters). This allows for a flexible and powerful system which can be executed on a single GPU enabling a clear path toward future deployment.