{"title":"Review of vision-based reinforcement learning for drone navigation","authors":"Anas Aburaya, Hazlina Selamat, Mohd Taufiq Muslim","doi":"10.1007/s41315-024-00356-9","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":44563,"journal":{"name":"International Journal of Intelligent Robotics and Applications","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Intelligent Robotics and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s41315-024-00356-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
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.
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