The accuracy of autonomous navigation and obstacle avoidance of unmanned aerial vehicles (UAVs) in complex environments has become one challenging task. In this paper, an autonomous navigation and obstacle avoidance of the UAV (ANOAU) algorithm based on deep reinforcement learning (DRL) has been proposed to achieve accurate path planning in complex environments. In our work, we use an actor–critic‐based DRL framework to achieve autonomous UAV control from sensor input to the output of the UAV’s action and design a set of reward functions that can be adapted to autonomous navigation and obstacle avoidance for the UAV in the complex environment. Meanwhile, to alleviate the decision‐making bias caused by the incomplete observables of the UAV, we use a gate recurrent unit network to enhance the ability to perceive the uncertain environment, enhance the perception representation and improve the accuracy of UAV real‐time decision‐making. Experimental simulation results verify that the ANOAU algorithm achieves good UAV flight attitude adaptive adjustment in navigation and obstacle avoidance tasks and significantly improves the generalization ability and training efficiency of the UAV navigation controller in a complex environment.

Path planning and obstacle avoidance are pivotal for intelligent unmanned aerial vehicle (UAV) systems in various domains, such as postdisaster rescue, target detection, and wildlife conservation. Currently, reinforcement learning (RL) has become increasingly popular in UAV decision-making. However, the RL approaches confront the challenges of partial observation and large state space when searching for random targets through continuous actions. This paper proposes a representation enhancement-based proximal policy optimization (RE-PPO) framework to address these issues. The representation enhancement (RE) module consists of observation memory improvement (OMI) and dynamic relative position-attitude reshaping (DRPAR). OMI reduces collision under partially observable conditions by separately extracting perception features and state features through an embedding network and feeding the extracted features to a gated recurrent unit (GRU) to enhance observation memory. DRPAR compresses the state space when modeling continuous actions by transforming movement trajectories of different episodes from an absolute coordinate system into different local coordinate systems to utilize similarity. In addition, three step-wise reward functions are formulated to avoid sparsity and facilitate model convergence. We evaluate the proposed method in three 3D scenarios to demonstrate its effectiveness. Compared to other methods, our method achieves a faster convergence during training and demonstrates a higher success rate and a lower rate of timeout and collision during inference. Our method can significantly enhance the autonomy and intelligence of UAV systems under partially observable conditions and provide a reasonable solution for UAV decision-making under uncertainties.