AutoFPDesigner: Automated Flight Procedure Design Based on Multi-Agent Large Language Model
On the map: LLM agents and tool use → Multimodal and embodied agents
Abstract (arXiv)
Current flight procedure design methods heavily rely on human-led design process, which is not only low auto-mation but also suffer from complex algorithm modelling and poor generalization. To address these challenges, this paper proposes an agent-driven flight procedure design method based on large language model, named Au-toFPDesigner, which utilizes multi-agent collaboration to complete procedure design. The method enables end-to-end automated design of performance-based navigation (PBN) procedures. In this process, the user input the design requirements in natural language, AutoFPDesigner models the flight procedure design by loading the design speci-fications and utilizing tool libraries complete the design. AutoFPDesigner allows users to oversee and seamlessly participate in the design process. Experimental results show that AutoFPDesigner ensures nearly 100% safety in the designed flight procedures and achieves 75% task completion rate, with good adaptability across different design tasks. AutoFPDesigner introduces a new paradigm for flight procedure design and represents a key step towards the automation of this process. Keywords: Flight Procedure Design; Large Language Model; Performance-Based Navigation (PBN); Multi Agent;
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use. Builds on 4 of its 6 references that are in the map. First posted on arXiv 19 Oct 2024.
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4 of 6 references.
References · 4
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2022Training language models to follow instructions with human feedbackcited by 15,712
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2023Language Agents with Reinforcement Learning for Strategic Play in the Werewolf Gamecited by 148
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