A Hybrid Deep Learning Model for UAV Path Planning in Dynamic Environments

Integrating Unmanned Aerial Vehicles (UAVs) into military operations has become increasingly significant in recent years. In practical missions, UAVs are usually operated in unpredictable conditions, requiring robust planning to adapt to real-time changes. Thus, an effective path-planning algorithm is essential for navigating UAVs through complex terrains. However, the commonly used Rapidly-exploring Random Tree (RRT) algorithm for UAV path planning often generates suboptimal paths that require extensive post-processing to improve. While its variants can converge to the optimal solution, they suffer from more memory and computation consumption. On the other hand, purely learning-based methods could result in planning failures or excessively long paths generated for obstacle avoidance. In this work, we introduce a UAV path planning model that integrates diverse sensor data through a neural network architecture featuring CNN, LSTM, and an attention mechanism. Then, the deep learning-enhanced RRT algorithm combines the neural module with the informed sampling methods for adaptive and efficient pathfinding in dynamic environments. After carefully considering the trade-offs among path optimality, computational efficiency, and fewer turning angles, our approach exhibits superior overall performance compared to Informed RRT*, Batch Informed Trees (BIT*) algorithms, and the state-of-the-art Layered RQN.