Dynamic Obstacle Avoidance for UAV in Logistics Warehouses Based on Unified Obstacle State Representation

To address the challenge of low computational efficiency in UAV navigation within dynamic logistics warehouse environments, this paper presents a deep reinforcement learning (DRL) framework based on unified obstacle state representation. By integrating static and dynamic obstacles into a single "obstacle state" vector and eliminating the traditional voxel map maintenance mechanism, the proposed method significantly reduces computational complexity. The framework models the navigation task as a Markov Decision Process (MDP), using a lightweight neural network with Proximal Policy Optimization (PPO) to achieve real-time obstacle avoidance. Experimental results in simulated high-dynamic environments show a 95% collision-free success rate. This framework is particularly suitable for resource-constrained scenarios such as indoor logistics, where real-time adaptability to dynamic obstacles is critical.