论文标题
自主无人机导航：基于DDPG的深入强化学习方法
Autonomous UAV Navigation: A DDPG-based Deep Reinforcement Learning Approach
论文作者
论文摘要
在本文中，我们建议使用深入的强化学习方法提出一个自主无人机路径计划框架。目的是利用自训练的无人机作为飞行移动单元，在给定的三维市区中达到空间分布的移动或静态目标。在这种方法中，具有连续动作空间的深层确定性政策梯度（DDPG）旨在训练无人机以贯穿或跨越障碍物，以达到其指定的目标。开发了定制的奖励功能，以最大程度地减少分离无人机及其目的地的距离，同时惩罚碰撞。数值模拟研究了无人机在学习环境方面的行为，并自主确定不同选定场景的轨迹。
In this paper, we propose an autonomous UAV path planning framework using deep reinforcement learning approach. The objective is to employ a self-trained UAV as a flying mobile unit to reach spatially distributed moving or static targets in a given three dimensional urban area. In this approach, a Deep Deterministic Policy Gradient (DDPG) with continuous action space is designed to train the UAV to navigate through or over the obstacles to reach its assigned target. A customized reward function is developed to minimize the distance separating the UAV and its destination while penalizing collisions. Numerical simulations investigate the behavior of the UAV in learning the environment and autonomously determining trajectories for different selected scenarios.
