自主无人机系统的深度学习和强化学习：理论到部署的路线图
Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment
摘要：
无人机系统（UAS）越来越多地用于商业，民用和军事应用。目前最先进的UAS仍然依赖于具有强大无线链路的远程人类控制器来执行其中的几个应用。缺乏自主性限制了可以部署UAS的应用和任务的领域。为无人机系统实现自主性和智能化将有助于克服这一障碍，并扩大其使用范围，提高安全性和效率。在这个数字时代，计算资源的指数级增长和大量数据的可用性导致了机器学习从去年冬天开始复苏。因此，在本章中，我们将讨论如何利用机器学习的一些进展，特别是深度学习和强化学习来开发下一代自主UAS。我们首先在引言部分讨论当前无人机系统的应用、挑战和机遇，以此作为本章的出发点。然后，我们将概述本章讨论的一些关键深度学习和强化学习技术。计算机视觉是实现无人机自主性必不可少的一个关键领域。因此，我们讨论了如何使用深度学习方法来完成一些有助于提供UAS自主性的基本任务。然后，我们讨论了如何强化学习探索使用这些信息提供自主控制和导航的无人机。接下来，我们为读者提供了选择合适的仿真套件和硬件平台的方向，这将有助于快速原型化基于UAS的新型机器学习解决方案。我们还讨论了与开发自主UAS解决方案的各个方面有关的开放性问题和挑战，以揭示潜在的研究领域。
英文摘要：
Unmanned Aerial Systems (UAS) are being increasingly deployed for commercial, civilian, and military applications. The current UAS state-of-the-art still depends on a remote human controller with robust wireless links to perform several of these applications. The lack of autonomy restricts the domains of application and tasks for which a UAS can be deployed. Enabling autonomy and intelligence to the UAS will help overcome this hurdle and expand its use improving safety and efficiency. The exponential increase in computing resources and the availability of large amount of data in this digital era has led to the resurgence of machine learning from its last winter. Therefore, in this chapter, we discuss how some of the advances in machine learning, specifically deep learning and reinforcement learning can be leveraged to develop next-generation autonomous UAS. We first begin motivating this chapter by discussing the application, challenges, and opportunities of the current UAS in the introductory section. We then provide an overview of some of the key deep learning and reinforcement learning techniques discussed throughout this chapter. A key area of focus that will be essential to enable autonomy to UAS is computer vision. Accordingly, we discuss how deep learning approaches have been used to accomplish some of the basic tasks that contribute to providing UAS autonomy. Then we discuss how reinforcement learning is explored for using this information to provide autonomous control and navigation for UAS. Next, we provide the reader with directions to choose appropriate simulation suites and hardware platforms that will help to rapidly prototype novel machine learning based solutions for UAS. We additionally discuss the open problems and challenges pertaining to each aspect of developing autonomous UAS solutions to shine light on potential research areas.
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。
