TD3-DLSAs: A Diffusion-Guided Low-Rank Transfer Reinforcement Learning Framework for 3D UAV Path Planning in Unknown Environments

Unmanned aerial vehicle (UAV) path planning in unknown 3D environments remains challenging due to missing prior maps, irregular terrain, and real-time obstacle avoidance requirements. This paper proposes TD3-DLSAs, a novel framework that integrates a conditional diffusion model with low-rank adaptation (LoRA) for transfer reinforcement learning. The diffusion model generates multiple feasible local subgoals based on the current UAV state, target, and local elevation map, thereby reducing the exploration burden on the reinforcement learning agent. LoRA enables rapid adaptation to new terrains by fine-tuning only a small set of low-rank matrices and scaling parameters. Extensive experiments on three increasingly difficult 3D mountain scenarios demonstrate that TD3-DLSAs achieve a success rate of 93% in the easiest and 84% in the hardest test set, outperforming standard TD3 by up to 24% and the state-of-the-art TD3-LSAs by 11%. Compared with standard RL baselines, TD3-DLSAs also tend to produce shorter and smoother trajectories in complex terrains, while maintaining a lower computational cost. These results confirm that the synergistic combination of diffusion guidance and low-rank transfer learning provides a robust and generalizable solution for autonomous UAV navigation in completely unknown environments.