# Safe and Economical UAV Trajectory Planning in Low-Altitude Airspace: A Hybrid DRL-LLM Approach with Compliance Awareness

arXiv: https://arxiv.org/abs/2506.08532 · HTML: https://mlsanity.com/paper/2506.08532/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 64 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 26 of 26)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Generative AI Agents with Large Language Model for Satellite Networks via a Mixture of Experts Transmission](https://mlsanity.com/paper/2404.09134/) (2024)
- [Large Language Models for Multi-Robot Systems: A Survey](https://mlsanity.com/paper/2502.03814/) (2025)
- [A Survey on Reinforcement Learning in Aviation Applications](https://mlsanity.com/paper/2211.02147/) (2022)
- [A Safer Vision-based Autonomous Planning System for Quadrotor UAVs with Dynamic Obstacle Trajectory Prediction and Its Application with LLMs](https://mlsanity.com/paper/2311.12893/) (2023)
- [Large Language Model Based Multi-Objective Optimization for Integrated Sensing and Communications in UAV Networks](https://mlsanity.com/paper/2410.05062/) (2024)
- [LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networks](https://mlsanity.com/paper/2504.14556/) (2025)
- [Toward Realization of Low-Altitude Economy Networks: Core Architecture, Integrated Technologies, and Future Directions](https://mlsanity.com/paper/2504.21583/) (2025)
- [UAV Assisted Data Collection for Internet of Things: A Survey](https://mlsanity.com/paper/2211.09555/) (2022)
- [Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays](https://mlsanity.com/paper/2308.01511/) (2023)
- [Generative AI for Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2311.06523/) (2023)
- [Toward Democratized Generative AI in Next-Generation Mobile Edge Networks](https://mlsanity.com/paper/2411.09148/) (2024)
- [LLM-Land: Large Language Models for Context-Aware Drone Landing](https://mlsanity.com/paper/2505.06399/) (2025)
- [Joint Power and 3D Trajectory Optimization for UAV-enabled Wireless Powered Communication Networks with Obstacles](https://mlsanity.com/paper/2310.00384/) (2023)
- [Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning](https://mlsanity.com/paper/2403.12856/) (2024)
- [Joint Optimization of Resource Allocation, Phase Shift and UAV Trajectory for Energy-Efficient RIS-Assisted UAV-Enabled MEC Systems](https://mlsanity.com/paper/2208.13958/) (2022)
- [Reliable and Efficient Data Collection in UAV-based IoT Networks](https://mlsanity.com/paper/2311.05303/) (2023)
- [Integrating Large Language Models for UAV Control in Simulated Environments: A Modular Interaction Approach](https://mlsanity.com/paper/2410.17602/) (2024)
- [Toward a Sustainable Low-Altitude Economy: A Survey of Energy-Efficient RIS-UAV Networks](https://mlsanity.com/paper/2504.02162/) (2025)
- [Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control](https://mlsanity.com/paper/2504.10831/) (2025)
- [On Correcting Errors in Existing Mathematical Approaches for UAV Trajectory Design Considering No-Fly-Zones](https://mlsanity.com/paper/2308.13001/) (2023)

## Abstract (arXiv)

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.