用大模型+强化学习实现无人机高效安全配送，避免盲目决策
Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control
- 分层设计：全局任务分配+本地实时规划，协同控制无人机
- 比纯大模型方案提升配送成功率，电池消耗和路程减少30%以上
- 内置安全过滤器，防止电量耗尽或重复访问，适合实际物流场景
本文提出SafeGPT，一种两阶段框架，将生成式预训练变换器（GPT）与强化学习（RL）结合，用于高效可靠的无人机末端配送。全局GPT模块负责扇区分配等高层任务，本地GPT则执行实时路径规划。基于RL的安全过滤器监控每个GPT决策，阻止可能导致电池耗尽或重复访问的不安全动作，有效缓解幻觉问题。此外，双回放缓冲机制使两个GPT模块与RL代理能持续优化策略。仿真结果表明，SafeGPT在配送成功率上优于仅使用GPT的基线方案，同时显著降低电池消耗与行驶距离。该方法验证了大模型语义推理与形式化安全保证相结合的有效性，为鲁棒且节能的无人机物流提供了可行解决方案。
原文摘要 · Abstract (English)
This paper proposes SafeGPT, a two-tiered framework that integrates generative pretrained transformers (GPTs) with reinforcement learning (RL) for efficient and reliable unmanned aerial vehicle (UAV) last-mile deliveries. In the proposed design, a Global GPT module assigns high-level tasks such as sector allocation, while an On-Device GPT manages real-time local route planning. An RL-based safety filter monitors each GPT decision and overrides unsafe actions that could lead to battery depletion or duplicate visits, effectively mitigating hallucinations. Furthermore, a dual replay buffer mechanism helps both the GPT modules and the RL agent refine their strategies over time. Simulation results demonstrate that SafeGPT achieves higher delivery success rates compared to a GPT-only baseline, while substantially reducing battery consumption and travel distance. These findings validate the efficacy of combining GPT-based semantic reasoning with formal safety guarantees, contributing a viable solution for robust and energy-efficient UAV logistics.
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。