Autonomous UAV Flight Control for GPS-Based Navigation

Kwak, Jeonghoon; Sung, Yunsick

doi:10.1109/access.2018.2854712

Cited by 73 publications

(40 citation statements)

References 25 publications

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“…In [10], the authors discuss design considerations and present a system that allows a drone to fly to remote locations, and to wireless charge difficult to access sensors. Since the GPS does not provide sufficient accuracy [11], they develop a relative localisation algorithm based on sensing the magnetic field of the power transfer system with an average error of 15 cm and a maximum power transfer of 4.2 W. In this work, we combine elements of the relative localisation system based on sensing the magnetic field presented in [10] with results from [12], [13], which describe a high efficiency (70%) inductive power transfer system (IPT), to create a system capable of 150 W instantaneous WPT (Wireless Power Transfer) with real-time feedback for the UAV's precision landing algorithm. Our work focuses on charging static terrestrial devices, although charging of drones has been considered in the literature ranging from similar IPT techniques to harvesting from power lines [14].…”

Section: Related Workmentioning

confidence: 99%

A Flexible, Low-Power Platform for UAV-Based Data Collection From Remote Sensors

et al. 2020

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This paper presents the design and characterisation of a new low-power hardware platform to integrate unmanned aerial vehicle and wireless sensor technologies. In combination, these technologies can overcome data collection and maintenance problems of in situ monitoring in remote and extreme environments. Precision localisation in support of maximum efficiency mid-range inductive power transfer when recharging devices and increased throughput between drone and device are needed for data intensive monitoring applications, and to balance proximity time for devices powered by supercapacitors that recharge in seconds. The platform described in this paper incorporates ultra-wideband technology to achieve highperformance ranging and high data throughput. It enables the development of a new localisation system that is experimentally shown to improve accuracy by around two orders of magnitude to 10 cm with respect to GNSS and achieves almost 6 Mbps throughput in both lab and field conditions. These results are supported by extensive modelling and analysis. The platform is designed for application flexibility, and therefore includes a wide range of sensors and expansion possibilities, with source code for two applications made immediately available as part of a open source project to support research and development in this new area.

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Section: Related Workmentioning

confidence: 99%

A Flexible, Low-Power Platform for UAV-Based Data Collection From Remote Sensors

et al. 2020

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“…To capture fixed objects, a method to designate the waypoints for the micro UAS flight path is used [9][10][11]. The object is photographed through a camera mounted on the micro UAS, which flies autonomously based on the waypoints.…”

Section: Autonomous Micro Uas For Survaillencementioning

confidence: 99%

“…To perform a task, the micro UAS flies autonomously based on the pilot's control or the waypoint set by the pilot [9][10][11]. During the flight, the camera mounted on the micro UAS captures its surrounding environment.…”

Section: Introductionmentioning

confidence: 99%

End-To-End Controls Using K-Means Algorithm for 360-Degree Video Control Method on Omnidirectional Camera-Equipped Autonomous Micro Unmanned Aircraft Systems

Kwak¹,

Sung²

2019

Applied Sciences

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Micro unmanned aircraft systems (micro UAS)-related technical research is important because micro UAS has the advantage of being able to perform missions remotely. When an omnidirectional camera is mounted, it captures all surrounding areas of the micro UAS. Normal field of view (NFoV) refers to a view presented as an image to a user in a 360-degree video. The 360-degree video is controlled using an end-to-end controls method to automatically provide the user with NFoVs without the user controlling the 360-degree video. When using the end-to-end controls method that controls 360-degree video, if there are various signals that control the 360-degree video, the training of the deep learning model requires a considerable amount of training data. Therefore, there is a need for a method of autonomously determining the signals to reduce the number of signals for controlling the 360-degree video. This paper proposes a method to autonomously determine the output to be used for end-to-end control-based deep learning model to control 360-degree video for micro UAS controllers. The output of the deep learning model to control 360-degree video is automatically determined using the K-means algorithm. Using a trained deep learning model, the user is presented with NFoVs in a 360-degree video. The proposed method was experimentally verified by providing NFoVs wherein the signals that control the 360-degree video were set by the proposed method and by user definition. The results of training the convolution neural network (CNN) model using the signals to provide NFoVs were compared, and the proposed method provided NFoVs similar to NFoVs of existing user with 24.4% more similarity compared to a user-defined approach.

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“…Three-dimensional (3D) digital environments are provided to take advantage of various platforms such as remotely controlled unmanned aerial vehicles and autonomous vehicles [1][2][3][4][5][6]. To generate a 3D digital world with realistic models of urban and natural environments, light detection and ranging (LiDAR) or vision-based point clouds are used to recognize motions of objects such as users in the real environment.…”

Section: Introductionmentioning

confidence: 99%

Automatic 3D Landmark Extraction System Based on an Encoder–Decoder Using Fusion of Vision and LiDAR

Kwak¹,

Sung²

2020

Remote Sensing

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To provide a realistic environment for remote sensing applications, point clouds are used to realize a three-dimensional (3D) digital world for the user. Motion recognition of objects, e.g., humans, is required to provide realistic experiences in the 3D digital world. To recognize a user’s motions, 3D landmarks are provided by analyzing a 3D point cloud collected through a light detection and ranging (LiDAR) system or a red green blue (RGB) image collected visually. However, manual supervision is required to extract 3D landmarks as to whether they originate from the RGB image or the 3D point cloud. Thus, there is a need for a method for extracting 3D landmarks without manual supervision. Herein, an RGB image and a 3D point cloud are used to extract 3D landmarks. The 3D point cloud is utilized as the relative distance between a LiDAR and a user. Because it cannot contain all information the user’s entire body due to disparities, it cannot generate a dense depth image that provides the boundary of user’s body. Therefore, up-sampling is performed to increase the density of the depth image generated based on the 3D point cloud; the density depends on the 3D point cloud. This paper proposes a system for extracting 3D landmarks using 3D point clouds and RGB images without manual supervision. A depth image provides the boundary of a user’s motion and is generated by using 3D point cloud and RGB image collected by a LiDAR and an RGB camera, respectively. To extract 3D landmarks automatically, an encoder–decoder model is trained with the generated depth images, and the RGB images and 3D landmarks are extracted from these images with the trained encoder model. The method of extracting 3D landmarks using RGB depth (RGBD) images was verified experimentally, and 3D landmarks were extracted to evaluate the user’s motions with RGBD images. In this manner, landmarks could be extracted according to the user’s motions, rather than by extracting them using the RGB images. The depth images generated by the proposed method were 1.832 times denser than the up-sampling-based depth images generated with bilateral filtering.

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Autonomous UAV Flight Control for GPS-Based Navigation

Cited by 73 publications

References 25 publications

A Flexible, Low-Power Platform for UAV-Based Data Collection From Remote Sensors

A Flexible, Low-Power Platform for UAV-Based Data Collection From Remote Sensors

End-To-End Controls Using K-Means Algorithm for 360-Degree Video Control Method on Omnidirectional Camera-Equipped Autonomous Micro Unmanned Aircraft Systems

Automatic 3D Landmark Extraction System Based on an Encoder–Decoder Using Fusion of Vision and LiDAR

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