3D Deep Reinforcement Learning Based UAV Trajectory Planning in Dynamic and Partially Observable Environments
Trajectory planning for unmanned aerial vehicles (UAVs) in dynamic and partially observable environments becomes more complex when extended from two-dimensional to three-dimensional navigation. Although Deep Reinforcement Learning (DRL) methods have shown strong performance in 2D scenarios, their application to 3D spaces requires redesigned observation models, action representations, and safety mechanisms. This paper extends a 2D DRL-based trajectory planning framework to 3D environments using Proximal Policy Optimization (PPO), Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG). UAV agents are trained to reach randomly placed 3D targets while avoiding static and dynamic obstacles using only local sensory information. The observation space combines a local 3D occupancy representation with a relative 3D goal vector, preserving partial observability and avoiding reliance on a global map. This article proposes that the simulation results demonstrate robust, collision-aware navigation and improved safety and trajectory efficiency in each one of the DRL algorithms implemented, each having positive and negative specifics.