![]() ![]() Standard testing protocols, such as flight demos or manual simulations, are now replaced by a much less expensive and more exhaustive process. While our simulation currently lacks certain graphical detail, our product proves the novel concept of an automated and time-efficient way to generate real-life locations. This testbed can quickly transform a satellite image of any physical location into a 3D testing environment using image recognition algorithms and neural networks. In this paper, a UAV testbed that can procedurally generate simulated environments was proposed. Our result is nearly 1.6k road images that pair favorably with the Mask R-CNN training model. The entirety of the road is captured in the formatted JSON annotation files by applying the OpenCV flood fill algorithm to each image instance. One final consideration is the fact that only road center lines are given in the unaltered annotations. This is because the original annotations differentiate between various types of roads, lane numbers, and whether or not is paved, which are too specific for our current implementation. Additionally, the image annotations are also parsed into JSON files that include the relevant information for segmenting roads. The raw source geotiffs of RGB raster data are converted to 8-bit JPEGs using the Geospacial Data Abstraction Library that can then be processed by Mask-R-CNN. These regions were chosen for their variety in road type/layout and scalability. The data we used includes satellite imagery from the WorldView-3 Satellite Sensor over various parts of Vegas and Shanghai. The dataset used for our implementation comes from the Spacenet Road Network Detection Challenge. Our platform utilizes this protocol to efficiently send and process the satellite image, as well as manually control the UAV. The server maintains this connection for each client, using it to push data in real-time. After the server accepts this request, it replaces the HTTP connection by a WebSocket connection. This process is known as a WebSocket handshake. This procedure starts with the client sending an HTTP request to the server asking to connect to a WebSocket. Melnikov described, WebSocket allows a long-held, bi-directional and full-duplex TCP socket connection established between client and server. Thus the idea of a new protocol, identified as WebSocket, was born. When the connection times out, the client is notified, and a new request is made repeatedly. The latency between client and server increased since each connection is kept open for as long as possible. However, as more client requests reached the server, a problem became more apparent. Until a decade ago, this way of getting real-time data was as effective. A common technique used in many web-platforms is long-polling, where the client continually checks the server for new data. Since the platform will handle real-time information coming from the frontend, backend, and UAV, it was necessary to address this issue. Such a testbed could allow for preliminary operational planning and testing worldwide, without the need for on-site evaluation or data collection in the future. Although certain graphical improvements could be made, this paper serves as a proof of concept for an novel autonomous and relatively-large scale environment generator. These environments are additionally based on satellite images, thus providing users with a more robust example of real-world UAV deployment. In this paper, we propose a new testbed that utilizes machine learning algorithms to procedurally generate, scale, and place 3D models to create a realistic environment. However, the use of generic environments and manually-created custom scenarios leaves more to be desired. The current use of simulated environments has been shown to be a relatively inexpensive, safe, and repeatable way to evaluate UAVs before real-world use. The increased demand for Unmanned Aerial Vehicles (UAV) has also led to higher demand for realistic and efficient UAV testing environments. ![]()
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