任意三维环境中基于人在回路强化学习的无人机避障
UAV Obstacle Avoidance by Human-in-the-Loop Reinforcement in Arbitrary 3D Environment
AI总结：
本文提出一种结合人在回路的深度强化学习方法，通过动态调整奖励函数实现无人机在任意三维复杂环境中的自动避障，并在城市、乡村和森林场景中验证了其能缩短训练收敛时间、提升导航效率与准确性。
摘要：
本文聚焦于基于深度强化学习方法的大规模三维复杂环境中无人机的连续控制。其目的是使无人机从某一初始点到达任意目标点，且飞行高度和速度在导航过程中可变。在本工作中，我们提出了一种结合人在回路的深度强化学习方法，使无人机在飞行过程中能够自动避开障碍物。我们基于相关领域知识设计了多种奖励函数来引导无人机导航。人在回路的作用是根据不同情况动态改变无人机的奖励函数，以更好地适应无人机避障需求。我们在城市、乡村和森林场景下验证了成功率和平均步长，实验结果表明，所提方法能够缩短训练收敛时间，并提高导航任务的效率和准确性。代码可在 https://github.com/Monnalo/UAV_navigation 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
