Review of vision-based reinforcement learning for drone navigation

Anas Aburaya, Hazlina Selamat, Mohd Taufiq Muslim
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Abstract

In recent years, Unmanned aerial vehicles (UAVs) have witnessed a surge in popularity and implementation for both civilian and military usage. UAVs can be utilized for a wide range of applications, including mapping, surveillance, and inspection. For many of these applications, a high level of autonomy is required. Autonomy refers to the ability to complete missions or tasks without human intervention. Autonomous navigation is an essential element of autonomy, especially in GPS-denied environments where GNSS-based navigation is not reliable. Due to size and weight limitations, many UAVs employ vision-based localization and navigation techniques for GPS-denied environments. Reinforcement Learning (RL) is also increasingly being implemented for robotic applications, including obstacle avoidance, battery management, and navigation. Existing reviews typically focus on either vision-based autonomous navigation of drones or RL navigation for drones in general, but none specifically concentrate on the use of vision-based methods and RL for drone navigation. Moreover, previous reviews have highlighted the use of reinforcement learning based on tasks such as takeoff, landing, and navigation, whereas this review categorizes the use of RL based on the navigation problem and image input types for the RL models as these define the needed hardware and processing capabilities of the system. We define the current challenges and limitations for vision based RL navigation to provide direction for future works. Finally we provide an analysis of the favorable conditions for each category and the possibility of combining multiple categories to overcome the disadvantages of each.

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基于视觉的无人机导航强化学习回顾
近年来,无人驾驶飞行器(UAVs)在民用和军用领域的普及和应用激增。无人飞行器的应用范围非常广泛,包括测绘、监视和检查。其中许多应用都需要高度的自主性。自主性是指在无人干预的情况下完成任务或工作的能力。自主导航是自主性的一个基本要素,尤其是在全球定位系统失效的环境中,基于全球导航卫星系统的导航并不可靠。由于尺寸和重量的限制,许多无人机在 GPS 信号缺失的环境中采用了基于视觉的定位和导航技术。强化学习(RL)也越来越多地应用于机器人领域,包括避障、电池管理和导航。现有的综述通常侧重于基于视觉的无人机自主导航或一般的无人机 RL 导航,但没有一篇专门讨论基于视觉的方法和 RL 在无人机导航中的应用。此外,以前的综述强调了基于起飞、着陆和导航等任务的强化学习的使用,而本综述则根据导航问题和 RL 模型的图像输入类型对 RL 的使用进行了分类,因为这些定义了系统所需的硬件和处理能力。我们定义了基于视觉的 RL 导航目前面临的挑战和限制,为未来的工作指明了方向。最后,我们分析了每个类别的有利条件,以及结合多个类别以克服每个类别的缺点的可能性。
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来源期刊
CiteScore
3.80
自引率
5.90%
发文量
50
期刊介绍: The International Journal of Intelligent Robotics and Applications (IJIRA) fosters the dissemination of new discoveries and novel technologies that advance developments in robotics and their broad applications. This journal provides a publication and communication platform for all robotics topics, from the theoretical fundamentals and technological advances to various applications including manufacturing, space vehicles, biomedical systems and automobiles, data-storage devices, healthcare systems, home appliances, and intelligent highways. IJIRA welcomes contributions from researchers, professionals and industrial practitioners. It publishes original, high-quality and previously unpublished research papers, brief reports, and critical reviews. Specific areas of interest include, but are not limited to:Advanced actuators and sensorsCollective and social robots Computing, communication and controlDesign, modeling and prototypingHuman and robot interactionMachine learning and intelligenceMobile robots and intelligent autonomous systemsMulti-sensor fusion and perceptionPlanning, navigation and localizationRobot intelligence, learning and linguisticsRobotic vision, recognition and reconstructionBio-mechatronics and roboticsCloud and Swarm roboticsCognitive and neuro roboticsExploration and security roboticsHealthcare, medical and assistive roboticsRobotics for intelligent manufacturingService, social and entertainment roboticsSpace and underwater robotsNovel and emerging applications
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