Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning

Unmanned aerial vehicle (UAV) navigation plays a crucial role in its ability to perform autonomous missions in complex environments. Most of the existing reinforcement learning methods to solve the UAV navigation problem fix the flight altitude and velocity, which largely reduces the difficulty of the algorithm. But the methods without adaptive control are not suitable in low-altitude environments with complex situations, generally suffering from weak obstacle avoidance. Some UAV navigation studies with adaptive flight only have weak obstacle avoidance capabilities. To address the problem of UAV navigation in low-altitude environments, we construct autonomous UAV navigation in 3D environments with adaptive control as a Markov decision process and propose a deep reinforcement learning algorithm. To solve the problem of weak obstacle avoidance, we creatively propose the guide attention method to make a UAV’s decision focus shift between the navigation task and obstacle avoidance task according to changes in the obstacle. We raise a novel velocity-constrained loss function and add it to the original actor loss to improve the UAV’s velocity control capability. Simulation experiment results demonstrate that our algorithm outperforms some of the state-of-the-art deep reinforcement learning algorithms performing UAV navigation tasks in a 3D environment and has outstanding performance in algorithm effectiveness, with the average reward increasing by 9.35, the success rate of navigation tasks increasing by 14%, and the collision rate decreasing by 14%.