A Knowledge-Driven Meta-Learning Method for Ultra-Fast Path Planning in Lightweight UAVs

Unmanned Aerial Vehicles (UAVs) face significant challenges in autonomous navigation due to their limited energy and computational resources. This paper introduces a knowledge-driven meta-learning framework specifically designed for ultra-fast path planning in lightweight UAVs. The proposed approach integrates domain-specific knowledge across three core domains-environment, network, and behavior-with visual data to enable adaptive learning from unlabeled data and rapid model retraining in various scenarios. To evaluate this framework, we created the Meta-UAV Optimal Path Dataset, a unique dataset tailored for complex, multi-domain path planning tasks. Additionally, a knowledge-driven loss function incorporating physics-based constraints ensures that the model's predictions align with real-world conditions. Experimental results demonstrate that our model achieves superior path efficiency, cross-domain adaptability, and lower resource consumption compared to traditional models, making it a suitable choice for real-world UAV applications.