Abstract: Faced with unstructured and unknown environments,such as exploring in mountains and jungles,UAVs must simultaneously perform environment sensing and trajectory planning in the absence of a priori conditions.Traditional methods are constrained by multiple factors such as algorithms and sensors,resulting in limited exploration range,low efficiency,and susceptibility to interference from environmental changes.To solve this problem,this study proposes an autonomous exploration method for UAVs based on deep reinforcement learning.The method is based on the normalized advantage functions(NAF) algorithm and introduces three algorithmic enhancement mechanisms to improve the exploration range and efficiency of UAVs in unstructured and unknown environments.By conducting experiments in a self-designed simulation environment,the results of simulation expe-riments and analysis show that the improved NAF algorithm has a larger exploration range and higher efficiency compared to the original version,while exhibiting superior convergence and robustness.

Key words: Autonomous UAV exploration, Intelligent decision making, Deep reinforcement learning, NAF algorithm, Augmentation mechanism