用大模型增强强化学习，让无人机更安全省电地规划城市飞行路径。
Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness
- 融合强化学习与大模型推理，提升路径规划智能性。
- 在碰撞避免、合规飞行和能耗效率上全面优于现有方法。
- 适合关注低空经济中无人机安全高效调度的从业者。
低空经济的快速发展推动了无人机（UAV）的广泛应用。然而，在复杂城市环境中，现有轨迹规划研究常忽视空域约束与经济效率等关键因素。深度强化学习（DRL）虽被视为潜在解决方案，但受限于学习效率低，实际应用受限。为此，本文提出一种结合深度强化学习与大语言模型（LLM）推理的混合框架，实现安全、合规且经济高效的无人机路径规划。实验结果表明，该方法在数据采集率、避障能力、成功着陆率、法规符合度及能效等方面均显著优于多个基线方法，验证了其在低空经济网络约束下应对无人机轨迹规划挑战的有效性。
原文摘要 · Abstract (English)
The rapid growth of the low-altitude economy has driven the widespread adoption of unmanned aerial vehicles (UAVs). This growing deployment presents new challenges for UAV trajectory planning in complex urban environments. However, existing studies often overlook key factors, such as urban airspace constraints and economic efficiency, which are essential in low-altitude economy contexts. Deep reinforcement learning (DRL) is regarded as a promising solution to these issues, while its practical adoption remains limited by low learning efficiency. To overcome this limitation, we propose a novel UAV trajectory planning framework that combines DRL with large language model (LLM) reasoning to enable safe, compliant, and economically viable path planning. Experimental results demonstrate that our method significantly outperforms existing baselines across multiple metrics, including data collection rate, collision avoidance, successful landing, regulatory compliance, and energy efficiency. These results validate the effectiveness of our approach in addressing UAV trajectory planning key challenges under constraints of the low-altitude economy networking.
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