UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment
This work addresses UAV navigation challenges for autonomous flight applications, but it is incremental as it builds on existing DRL methods with human-in-the-loop modifications.
This paper tackled UAV obstacle avoidance in 3D environments by combining deep reinforcement learning with human-in-the-loop reward adjustments, resulting in reduced training convergence time and improved navigation efficiency and accuracy across urban, rural, and forest scenarios.
This paper focuses on the continuous control of the unmanned aerial vehicle (UAV) based on a deep reinforcement learning method for a large-scale 3D complex environment. The purpose is to make the UAV reach any target point from a certain starting point, and the flying height and speed are variable during navigation. In this work, we propose a deep reinforcement learning (DRL)-based method combined with human-in-the-loop, which allows the UAV to avoid obstacles automatically during flying. We design multiple reward functions based on the relevant domain knowledge to guide UAV navigation. The role of human-in-the-loop is to dynamically change the reward function of the UAV in different situations to suit the obstacle avoidance of the UAV better. We verify the success rate and average step size on urban, rural, and forest scenarios, and the experimental results show that the proposed method can reduce the training convergence time and improve the efficiency and accuracy of navigation tasks. The code is available on the website https://github.com/Monnalo/UAV_navigation.