DRL-Based UAV Autonomous Navigation and Obstacle Avoidance with LiDAR and Depth Camera Fusion

Abstract

With the growing application of unmanned aerial vehicles (UAVs) in complex, stochastic environments, autonomous navigation and obstacle avoidance represent critical technical challenges requiring urgent solutions. This study proposes an innovative deep reinforcement learning (DRL) framework that leverages multimodal perception through the fusion of LiDAR and depth camera data. A sophisticated multi-sensor data preprocessing mechanism is designed to extract multimodal features, significantly enhancing the UAV’s situational awareness and adaptability in intricate, stochastic environments. In the high-level decision-maker of the framework, to overcome the intrinsic limitation of low sample efficiency in DRL algorithms, this study introduces an advanced decision-making algorithm, Soft Actor-Critic with Prioritization (SAC-P), which markedly accelerates model convergence and enhances training stability through optimized sample selection and utilization strategies. Validated within a high-fidelity Robot Operating System (ROS) and Gazebo simulation environment, the proposed framework achieved a task success rate of 81.23% in comparative evaluations, surpassing all baseline methods. Notably, in generalization tests conducted in previously unseen and highly complex environments, it maintained a success rate of 72.08%, confirming its robust and efficient navigation and obstacle avoidance capabilities in complex, densely cluttered environments with stochastic obstacle distributions.