A cesium-based UAV swarm performance simulation system

Abstract

The application discloses a kind of unmanned aerial vehicle group performance simulation systems based on Cesium, comprising: environment building module is used to build unmanned aerial vehicle group performance environment, environment building is mainly divided into the import of real GIS space data and the import of physical environment parameter, route planning module is used for route planning, route planning includes formation scheme, task assignment, area planning, path drawing, simulation analysis module is used to provide simulation analysis module provides the whole process simulation of group performance, whole process simulation includes formation take-off, entry, performance, exit, recovery;Result export module is used to export the simulation playback of performance whole process as structured data, video or report, structured data can be stored in database or text file, and simulation result can be played back in system.The present application uses Cesium as the underlying framework, combines its GIS data expression ability and the performance parameters of unmanned aerial vehicle group, provides a kind of unmanned aerial vehicle group performance simulation system, can better satisfy user demand.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) technology and relates to a Cesium-based UAV swarm performance simulation system. Background Technology

[0002] With the increasing popularity of drones, especially the expanding market for consumer drones, there is a growing demand for drone performances catering to organizations and individuals. However, for real performances, the communication, positioning, formation, and trajectory planning of drone swarms all require pre-programmed control. Directly programming and controlling real drone swarms struggles to handle the complexities and unforeseen circumstances of real-world environments. Using large-scale drone swarms for rehearsals significantly increases performance costs and consumes administrative resources, while frequent on-site rehearsals and adjustments can diminish the expected performance effect. Therefore, simulation systems capable of combining virtual and real elements and realistically simulating drone performances at the software level can provide low-latency, low-cost, highly reliable, and highly realistic simulation support for drone performances. Summary of the Invention

[0003] The purpose of this invention is to provide a Cesium-based drone swarm performance simulation system. This invention uses Cesium as the underlying framework, combines its GIS data representation capabilities with drone swarm performance parameters, and provides a drone swarm performance simulation system that can better meet user needs.

[0004] The technical solution adopted in this invention is a Cesium-based drone swarm performance simulation system, comprising:

[0005] The environment setup module is used to build the drone swarm performance environment. Environment setup is mainly divided into importing real GIS spatial data and importing physical environment parameters. Real GIS data includes the terrain data and satellite image data of the performance venue, oblique photography data or 3D model tiles of surrounding buildings and public facilities, and GPS navigation satellite ephemeris parameters. The imported GIS data and entity model data are used by Cesium to simulate and restore real geographic information. Physical environment parameters cover the amount of fog, the location, direction and intensity of light sources, and the imported physical environment parameters are used by Cesium to simulate visible light imaging.

[0006] The route planning module is used for route planning, which includes formation planning, task assignment, area planning, and path drawing. Formation planning is responsible for dividing the drone swarm into different groups, each of which is mapped to a different set in Cesium. Task assignment is to assign the exported results of multiple dot matrix patterns to the corresponding drone formations. Area planning is used to specify the performance area and restrict the no-fly zone near the performance airspace. Path drawing is used by users to obtain the latitude and longitude of the corresponding geodetic coordinates of the points through a geographic model, and then connect the points sequentially in the 3D scene of Cesium to form the planned flight path, which is then associated with the drone formation as a coordinate array.

[0007] The simulation analysis module is used to provide a full-process simulation of the aircraft formation performance, including formation takeoff, entry, performance, exit, and recovery.

[0008] The results export module is used to export the simulation playback of the entire performance as structured data, video, or reports. The structured data can be stored in a database or text file, and the simulation results can be played back in the system.

[0009] It also includes a pattern customization module, which allows for the import of text, bitmaps and vector images, the import of sequence frames, and the export of actual performance dot matrix images.

[0010] The invention is further characterized by:

[0011] The pattern customization module uses a clustering simplification method to export the UAV swarm target dot matrix image. The UAV swarm target dot matrix image is the exported result of the actual performance dot matrix. If the number of sequence frames exceeds 1, the exported result is a dot matrix image tuple with the same sequence frame order.

[0012] The method for exporting raster image tuples per frame includes the following steps:

[0013] Step 1: Render the imported text and images into a raster image with a resolution matching the number of drones in the swarm using canvas.

[0014] Step 2: Project the colors of the bitmap onto the feature space of the RGBA channel. RGBA represents red, green, blue, and transparency, respectively. Transparency only supports two values: 0 and 1, which represent whether the corresponding bitmap does not need a drone and whether it does.

[0015] Step 3: In the feature space of the RGBA channel, using the RGBA reference values ​​of common LED light sources (white, blue, green, red, yellow) and several commonly used drone color schemes as centers, perform clustering of the matrix colors based on Euclidean distance;

[0016] Step 4: Compress and simplify the high-precision colors of the complete dot matrix into several colors that can actually be expressed by the drone based on the clustering results;

[0017] Step 5: Export the simplified drone swarm performance dot matrix data as a bitmap format supported by MIME type, and provide feedback on the drone colors and number required to perform the pattern based on the number of valid dots in the dot matrix.

[0018] It also includes a cluster configuration module, which is used for model selection, accessory selection, and quantity setting.

[0019] The video is shared as a file and simulates the recording process of the performance.

[0020] The reports can provide visual analysis of various characteristics of the simulated performance, offering decision support for further simulation and optimization.

[0021] The main accessory selection options are the color choices for the aircraft's surface paint and the mounted LED light sources. The aircraft's surface paint color selection supports custom paint simulation for aircraft group performances, and the LED light source colors can be specified as single-color or color-changing light sources, providing various possible combinations of aircraft group lighting for nighttime performances.

[0022] The quantity setting specifies the number of each type of drone, allowing for adjustments to drone combination details during simulated swarm performances.

[0023] The beneficial effects of this invention are:

[0024] 1. This invention can simulate and recreate the real geographical environment and visible light imaging in Cesium based on real GIS data and physical environment parameters, making the simulation of drone swarm performances closer to reality and improving the accuracy and credibility of the simulation.

[0025] 2. This invention allows for drone model selection, accessory selection, and quantity setting via a drone cluster configuration module, enabling the system to better meet the needs of different users and simulate various possible drone combination details.

[0026] 3. This invention can provide powerful support and tools for the simulation and optimization of drone swarm performances, which helps to improve the effect and reliability of drone swarm performances, while also greatly reducing the cost and risk of actual performances. Attached Figure Description

[0027] Figure 1 This is a flowchart of a Cesium-based drone swarm performance simulation system according to the present invention;

[0028] Figure 2 This invention relates to a Cesium-based drone swarm performance simulation system. Figure 1 ;

[0029] Figure 3This invention relates to a Cesium-based drone swarm performance simulation system. Figure 2 ;

[0030] Figure 4 This invention relates to a Cesium-based drone swarm performance simulation system. Figure 3 . Detailed Implementation

[0031] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0032] Cesium is an open-source and cross-platform JavaScript library focused on GIS data visualization. It allows the creation of 3D globes and 2D maps directly in the browser without any plugins, uses WebGL for graphics rendering, boasts excellent hardware compatibility, supports dynamic data visualization, and is cross-browser compatible. Cesium is licensed under the Apache 2.0 license and can be used freely for both commercial and non-commercial purposes. This invention uses Cesium as its underlying framework, combining its GIS data representation capabilities with the performance parameters of drone swarms, to provide a Cesium-based drone swarm performance simulation system that effectively meets user needs.

[0033] Example 1

[0034] The system designed in Example 1 includes an environment setup module, a route planning module, a simulation analysis module, and a results export module. Its working process is as follows: Figure 1 As shown.

[0035] The environment setup module is used to construct the drone swarm performance environment. Environment setup mainly involves importing real GIS spatial data and physical environment parameters. The real GIS data includes terrain data and satellite imagery of the performance venue, oblique photography data or 3D model tiles of surrounding buildings and public facilities, and GPS navigation satellite ephemeris parameters. The imported GIS data and entity model data are used by Cesium to simulate and recreate the real geographic information. Its key feature is its ability to fully utilize the spatial and visual information of GIS to recreate the performance venue and its surrounding features at the software level. Physical environment parameters cover weather conditions such as fog levels, the location, direction, and intensity of light sources (including sunlight and other lighting sources). The imported physical environment parameters are used by Cesium to simulate visible light imaging. Its key feature is its ability to intuitively recreate the visual effects of the drone swarm performance on the simulated performance venue, combining the physical characteristics of the environment. For example, when the sunlight source is below the ground level, the visual effects of a nighttime performance can be recreated; and when the absolute value of fog is set very high, the Tyndall effect of sudden heavy fog on the performance effect can be recreated.

[0036] The route planning module is used for route planning, which includes formation scheme, task assignment, area planning, and path drawing. The formation scheme is responsible for dividing the UAV swarm into different groups, and each group will be mapped to different sets of Cesium. This is the basis for Cesium to control a group of UAVs in batches and control different groups of UAVs to perform different operations.

[0037] Task assignment involves exporting multiple dot matrix results of a customized pattern and assigning them to the corresponding drone formation, which can simulate the division of labor among drone groups in actual performances.

[0038] The zoning is used to designate performance areas and restrict no-fly zones near the performance airspace. Cesium can use the zoning provided here to create visible virtual walls in the simulation scene, providing an intuitive display of the performance space and supporting evidence for administrative resource allocation requests.

[0039] After the formation plan is completed, the path is drawn. Relying on Cesium's response to mouse interaction events and geospatial calculation and analysis capabilities, users can obtain the latitude and longitude of the corresponding geodetic coordinate system by clicking on the earth model on the system software screen. The points are then connected sequentially in the Cesium 3D scene to form the planned flight path, which is then associated with the drone formation as a coordinate array, thereby completing the coarse planning of the formation's flight path.

[0040] The simulation analysis module is used to provide a full-process simulation of the aircraft formation performance, including formation takeoff, entry, performance, exit, and recovery.

[0041] The system also includes analysis tools, including:

[0042] Based on the drone's endurance, the effectiveness of route planning is analyzed;

[0043] Based on dot matrix export and drone swarm configuration, the matching degree between the number and ratio of drones and the pattern required to complete the performance is analyzed.

[0044] Based on environmental parameters and Cesium's camera position configuration, the visibility of the audience at various positions in the performance scene is analyzed.

[0045] Based on the shape and mass data of the 3D model, the risk of collision and the danger of accidental fall are analyzed.

[0046] The results export module is used to export the entire simulation playback as structured data, video, or reports. Structured data can be stored in a database or text file and the simulation results can be replayed within the system. Videos are shared as files and simulate the recording process of the performance. Reports provide visual analysis of various characteristics of the simulated performance, offering decision support for further simulation and optimization.

[0047] Example 2

[0048] Based on Example 1, the system adds a pattern customization module, which provides import of text, bitmap and vector images, import of sequence frames, and export of actual performance dot matrix.

[0049] The imported text content supports all UTF-8 character sets that can be displayed in the browser canvas object. Colors, fonts, and font sizes are configurable. Bitmap and vector images are imported in image formats supported by MIME types.

[0050] Importing sequence frames allows multiple patterns to be arranged in a given order, thus constraining the temporal order of multiple performance patterns.

[0051] The pattern customization module uses a clustering simplification method to export the target dot matrix image of the UAV swarm. The target dot matrix image of the UAV swarm is the export result of the actual performance dot matrix. If the number of sequence frames exceeds 1, the export result is a dot matrix image tuple with the same sequence frame order.

[0052] The method for exporting raster image tuples per frame includes the following steps:

[0053] Step 1: Render the imported text and images into a raster image with a resolution matching the number of drones in the swarm using canvas.

[0054] Step 2: Project the colors of the dot matrix image onto the feature space of the RGBA channel. RGBA represents Red, Green, Blue, and Alpha (transparency), respectively. The transparency only supports two state values: 0 and 1, which indicate that the corresponding dot matrix does not need or does need the drone, respectively.

[0055] Step 3: Within the feature space of the RGBA channel, clustering of the matrix colors is performed based on Euclidean distance, using the RGBA reference values ​​of common LED light sources (white, blue, green, red, yellow) and several commonly used drone color schemes as the center.

[0056] Step 4: Compress and simplify the high-precision colors of the complete dot matrix into several colors that can actually be expressed by the drone based on the clustering results;

[0057] Step 5: Export the simplified drone swarm performance dot matrix data as a bitmap format supported by MIME type to support the simulation effect of drone swarm performance to be close to reality, and provide feedback on the color and number of drones required to perform the pattern based on the number of valid dots in the dot matrix.

[0058] Example 3

[0059] Building upon Example 2, the system adds a fleet configuration module. This module works in conjunction with the pattern customization module to ensure the drones can complete customized patterns. The functions of the fleet configuration module include drone model selection, accessory selection, and quantity setting.

[0060] The main accessory selection options are the color choices for the aircraft's surface paint and the mounted LED light sources. The aircraft's surface paint color selection supports custom paint simulation for aircraft group performances, and the LED light source colors can be specified as single-color or color-changing light sources, providing various possible combinations of aircraft group lighting for nighttime performances.

[0061] The quantity setting specifies the number of each type of drone, allowing for adjustments to drone combination details during simulated swarm performances.

[0062] The drone selection function provides a database of flight parameters and 3D models for various drone models available on the market, tailored to the hardware requirements of actual drone swarm performances. It also allows users to import custom model parameters and 3D models into the database, creating a unified drone hardware list for selection. Multiple selections are supported to accommodate users' needs for various drone combinations in drone swarm simulations. The selected models are compiled into a list, generating a matching list of parameters. This provides the necessary 3D shape and material information for Cesium's drone model rendering, as well as data supporting drone flight time during Cesium simulations.

[0063] Figure 2 , 3 Figures 4 and 5 show the generated effect diagrams of Embodiment 3 of the present invention.

Claims

1. A Cesium-based drone swarm performance simulation system, characterized in that, Including: The environment setup module is used to build the drone swarm performance environment. Environment setup is mainly divided into importing real GIS spatial data and importing physical environment parameters. The real GIS spatial data includes the terrain data and satellite image data of the performance venue, oblique photography data or 3D model tiles of surrounding buildings and public facilities, and GPS navigation satellite ephemeris parameters. The imported GIS data and entity model data are used by Cesium to simulate and restore real geographic information. The physical environment parameters cover the amount of fog, the position and direction of the light source, and the lighting intensity. The imported physical environment parameters are used by Cesium to simulate visible light imaging. The route planning module is used for route planning. The route planning includes formation scheme, mission assignment, area planning, and path drawing. The formation scheme is responsible for dividing the UAV swarm into different groups, and each group will be mapped to different sets of Cesium. Task assignment involves exporting multiple dot matrix results of a customized pattern and assigning them to the corresponding drone formation; area planning is used to specify the performance area and restrict no-fly zones near the performance airspace. Path drawing involves users obtaining the latitude and longitude coordinates of points in the geodetic coordinate system through a geographic model, and then connecting the points sequentially in the 3D scene of Cesium to form a planned flight path, which is then associated with the drone formation as a coordinate array. The simulation analysis module is used to provide a full-process simulation of the aircraft formation performance, including formation takeoff, entry, performance, exit, and recovery. The results export module is used to export the simulation playback of the entire performance as structured data, video, or reports. The structured data can be stored in a database or text file, and the simulation results can be played back in the system. The pattern customization module provides the import of text, bitmap and vector images, the import of sequence frames, and the export of actual performance dot matrix. The pattern customization module uses a clustering simplification method to export the UAV swarm target dot matrix. The UAV swarm target dot matrix is ​​the exported result of the actual performance dot matrix. If the number of sequence frames exceeds 1, the exported result is a dot matrix tuple with the same sequence frame order. The method for exporting the bitmap tuple for each frame includes the following steps: Step 1: Render the imported text and images into a raster image with a resolution that matches the number of drones in the swarm using canvas. Step 2: Project the colors of the dot matrix onto the feature space of the RGBA channel, where RGBA represents red, green, blue, and transparency, respectively. The transparency only supports two states, 0 and 1, which represent whether the corresponding dot matrix does not need or does need the drone, respectively. Step 3: Within the feature space of the RGBA channel, clustering of the matrix colors is performed based on Euclidean distance, using the RGBA reference values ​​of common LED light sources (white, blue, green, red, yellow) and several commonly used drone color schemes as the center. Step 4: Compress and simplify the high-precision colors of the complete dot matrix into several colors that can actually be expressed by the drone based on the clustering results; Step 5: Export the simplified drone swarm performance dot matrix data as a bitmap format supported by MIME type, and provide feedback on the drone colors and number required to perform the pattern based on the number of valid dots in the dot matrix.

2. The Cesium-based drone swarm performance simulation system according to claim 1, characterized in that, It also includes a cluster configuration module, which is used for model selection, accessory selection, and quantity setting.

3. The Cesium-based drone swarm performance simulation system according to claim 1, characterized in that, The video is shared as a file and simulates the recording process of the performance.

4. The Cesium-based drone swarm performance simulation system according to claim 1, characterized in that, The reports can provide visual analysis of various characteristics of the simulated performance, offering decision support for further simulation and optimization.

5. A Cesium-based drone swarm performance simulation system according to claim 2, characterized in that, The main accessory selection options are the color choices for the aircraft's surface paint and the mounted LED light sources. The aircraft's surface paint color selection supports custom paint simulation for aircraft group performances, and the LED light source colors can be specified as single-color or color-changing light sources, providing various possible combinations of aircraft group lighting for nighttime performances.

6. The Cesium-based drone swarm performance simulation system according to claim 2, characterized in that, The quantity setting specifies the number of each type of drone, allowing for adjustments to drone combination details during simulated swarm performances.

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