论文标题
使用深度学习和机器人操作系统使用移动机器人自动导航
Autonomous Navigation with Mobile Robots using Deep Learning and the Robot Operating System
论文作者
论文摘要
自主导航是一个长期存在的机器人研究领域，它为移动机器人在人类日常执行的相同环境上执行一系列任务提供了重要的能力。在本章中，我们提出了一组算法，用于使用机器人操作系统（ROS）训练和部署深层网络，以自动导航移动机器人。我们描述了解决此问题的三个主要步骤：i）使用ROS和凉亭在模拟环境中收集数据； ii）设计用于自动导航的深层网络，以及iii）在模拟和现实世界中将学习的策略部署在移动机器人上。从理论上讲，我们介绍了在正常环境（例如人造房屋，道路）和复杂环境（例如，倒塌的城市或自然洞穴）中进行强大导航的深度学习体系结构。我们进一步表明，使用RGB，LIDAR和Point Cloud等视觉方式对于提高移动机器人的自主权至关重要。我们的项目网站和演示视频可在https://sites.google.com/site/autonomousousnavigationros上找到。
Autonomous navigation is a long-standing field of robotics research, which provides an essential capability for mobile robots to execute a series of tasks on the same environments performed by human everyday. In this chapter, we present a set of algorithms to train and deploy deep networks for autonomous navigation of mobile robots using the Robot Operation System (ROS). We describe three main steps to tackle this problem: i) collecting data in simulation environments using ROS and Gazebo; ii) designing deep network for autonomous navigation, and iii) deploying the learned policy on mobile robots in both simulation and real-world. Theoretically, we present deep learning architectures for robust navigation in normal environments (e.g., man-made houses, roads) and complex environments (e.g., collapsed cities, or natural caves). We further show that the use of visual modalities such as RGB, Lidar, and point cloud is essential to improve the autonomy of mobile robots. Our project website and demonstration video can be found at https://sites.google.com/site/autonomousnavigationros.
