Unmanned aerial vehicle cluster collaborative navigation algorithm test method and system

Abstract

The invention relates to the technical field of unmanned aerial vehicle cluster collaborative navigation, in particular to an unmanned aerial vehicle cluster collaborative navigation algorithm test method and system, and the method comprises the steps: setting initial parameters of a simulation unmanned aerial vehicle cluster, and setting flight environment parameters; loading a collaborative navigation algorithm, and checking the effectiveness of the collaborative navigation algorithm; configuring an operation mode and error injection parameters of a collaborative navigation algorithm; unmanned aerial vehicle cluster flight simulation is started based on the initial parameters and the flight environment parameters, and flight state information of unmanned aerial vehicle cluster nodes is acquired and recorded; errors are added to the flight state information, a collaborative navigation algorithm is operated based on the obtained simulation operation data, and simulation unmanned aerial vehicle cluster node movement is adjusted according to a navigation operation result; obtaining a navigation performance test result, and displaying the result on a user interface; the method can be used for large-scale node verification of the unmanned aerial vehicle cluster.

Description

Technical Field

[0001] The present invention relates to the technical field of cooperative navigation of unmanned aerial vehicle clusters, and in particular to a method and system for testing an algorithm for cooperative navigation of unmanned aerial vehicle clusters. Background Art

[0002] As a low-cost intelligent unmanned system, drone swarms can carry multiple mission payloads for saturated autonomous operations, which makes it difficult to effectively defend and intercept them. In actual application, cooperative navigation technology is one of the core technologies of drone swarms. Relying on cooperative navigation technology, large-scale cluster systems can deploy high-precision autonomous navigation equipment on a few drones and low-precision autonomous navigation equipment on most drones, and use radio ranging and data sharing to perform fusion navigation, so that drone swarms can obtain high-precision navigation accuracy at a low cost. When technical developers design collaborative navigation algorithms, the verification process is a very important link. There are currently two main methods: one is numerical simulation calculation, which inputs data into the algorithm program in the computer and verifies the algorithm under ideal conditions. This method is difficult to verify the impact of actual errors on the algorithm; the other method is to conduct experimental verification, install the equipment running the algorithm on a motion platform for dynamic testing. This method depends on the site environment and hardware conditions, consumes manpower and material resources, and the implementation process is complicated and inefficient. Therefore, it is necessary to use efficient testing methods to test the drone swarm collaborative navigation algorithm to provide a guarantee for the rapid iteration of the algorithm.

[0003] At present, the relevant achievements in cooperative navigation technology of drone swarms are mainly concentrated in the data fusion technology of cooperative navigation, which solves the problem of fusion calculation of multi-source information. However, the test results of related algorithms are mainly simulated and verified under relatively ideal conditions, without considering the constraints and influences of sensor errors and interference conditions on the algorithms. Therefore, the test results are difficult to reflect the performance in actual application.

[0004] The Chinese invention patent with the announcement number CN117852316B discloses a cluster collaborative navigation simulation test system and method under communication constraints, which relates to the field of cluster control technology. The system includes: a user interaction module, which is used to provide an interactive window for setting simulation parameters, receive user input, display simulation scenes, and display the status information of smart units in the cluster; a collaborative planning module, which is used to display the trajectory-related information of smart units; a database module, which is used to store data; a cluster collaborative navigation analysis module, which is provided with a collaborative navigation algorithm model, which is used to generate simulation scenes according to simulation parameters, calculate and update the status information and trajectory-related information of smart units according to simulation parameters and collaborative navigation algorithm models, send the status information to the user interaction module, and send the trajectory-related information to the collaborative planning module; an information business interaction module, which is used to realize communication interaction between modules. The invention can realize simulation test analysis of clusters and visualize the status and trajectory information of smart units in clusters.

[0005] The existing solutions for testing technology are mainly aimed at the motion planning of multiple intelligent units, using collaborative navigation algorithms to plan collaborative trajectories and verify the planning algorithms. They mainly focus on the method design and application verification of collaborative navigation algorithms, but fail to effectively and quickly test the designed algorithms. Therefore, more efficient testing methods for drone cluster collaborative navigation algorithms are needed. Summary of the invention

[0006] In view of the above problems, the present invention provides a method and system for testing a drone cluster collaborative navigation algorithm, which solves the technical problem in the prior art that it is difficult to carry out large-scale node verification in the testing of drone cluster collaborative navigation algorithms.

[0007] On the one hand, the present invention provides a method for testing a drone cluster collaborative navigation algorithm, comprising the following steps: Step S1, setting the initial parameters of the simulated drone cluster and the flight environment parameters; the initial parameters include the number of drone nodes and the initial configuration of the drone nodes; Step S2, loading the designed collaborative navigation algorithm, and verifying the validity of the collaborative navigation algorithm; configuring the operation mode of the collaborative navigation algorithm, and configuring error injection parameters, the operation mode including: using the navigation operation result of the collaborative navigation algorithm for navigation and not using the navigation operation result of the collaborative navigation algorithm for navigation; Step S3: starting the UAV cluster flight simulation based on the initial parameters of the simulated UAV cluster and the flight environment parameters, and acquiring and recording the flight status information of the simulated UAV cluster nodes in real time; Step S4: during the UAV cluster flight simulation process, using the error injection parameter to add errors to the flight state information of the simulated UAV cluster node to obtain simulated operation data; based on the simulated operation data, using the collaborative navigation algorithm to obtain and record navigation operation results; adjusting the movement of the simulated UAV cluster node according to the operation mode and the navigation operation results; Step S5: perform data comparison and analysis on the recorded flight status information of the drone cluster node and the navigation calculation result, obtain the navigation performance test result, and display the navigation performance test result on the user interface.

[0008] Preferably, in step S1, the initial configuration of the UAV node includes flight parameters, sensor parameters, and initial position and attitude of the UAV node; the flight parameters include platform type and flight indicators, the sensor parameters include sensor type and sensor performance parameters; the flight environment parameters include climate condition parameters of the simulated environment.

[0009] Preferably, the step S2 specifically includes: Step S2-1, loading a corresponding collaborative navigation algorithm for each drone node according to the number of drone nodes; and checking the interface compatibility of the collaborative navigation algorithm and whether it can be run; Step S2-2: configuring the operation mode of the collaborative navigation algorithm and configuring error injection parameters, wherein the error injection parameters include error parameters of multiple different types of sensors.

[0010] Preferably, the step S3 specifically includes: Step S3-1, based on the flight parameters of the drone node, the sensor parameters, the initial position and attitude, and the flight environment parameters, the flight status information of the drone cluster node is generated in real time, the flight status information of the drone cluster node includes the position, speed, attitude and sensor data of the drone node, and the sensor data includes observation data of various sensors generated based on the sensor parameters; Step S3-2: Record the flight status information of the drone cluster nodes in real time.

[0011] Preferably, the step S4 specifically includes: Step S4-1, respectively configuring corresponding error models according to the error parameters of the multiple different types of sensors, generating errors according to the error models, and superimposing the errors on the flight status information of the drone cluster nodes to form the simulation operation data; Step S4-2, inputting the simulation operation data into the collaborative navigation algorithm, obtaining and recording navigation operation results, the navigation operation results including: the position, speed and attitude of the drone node; Step S4-3, determine the current operating mode. If the current operating mode is to navigate using the navigation calculation results of the collaborative navigation algorithm, adjust the flight status information of the drone cluster node according to the navigation calculation results; otherwise, do not adjust the flight status information of the drone cluster node.

[0012] Preferably, the step S5 specifically includes: Step S5-1, establishing a flight simulation scene interface, a test management interactive interface, and a data analysis management interface, wherein the flight simulation scene interface displays a virtual environment in which the drone flies, and the test management interactive interface enables the user to set test parameters and start a test; Step S5-2, comparing and analyzing the recorded flight status information of the drone cluster node and the navigation calculation result, and outputting the obtained navigation performance test result to the data analysis management interface; Step S5-3: displaying the flight simulation scene interface, the test management interaction interface and the data analysis management interface on a display device respectively.

[0013] Preferably, step S5-3 specifically includes: displaying the flight simulation scene interface, test management interaction interface and data analysis management interface on three displays respectively; the three displays are respectively connected to simulation computers, and the drone cluster collaborative navigation algorithm testing method is run on the simulation computers.

[0014] On the one hand, the present invention provides a UAV cluster collaborative navigation algorithm testing system, comprising the following modules: A configuration module is used to set the initial parameters of the simulated drone cluster and the flight environment parameters; the initial parameters include the number of drone nodes and the initial configuration of the drone nodes; A loading module is used to load the designed collaborative navigation algorithm and verify the validity of the collaborative navigation algorithm; configure the operation mode of the collaborative navigation algorithm and configure error injection parameters, wherein the operation mode includes: navigating using the navigation operation result of the collaborative navigation algorithm and navigating without using the navigation operation result of the collaborative navigation algorithm; A simulation module, used to start the UAV cluster flight simulation based on the initial parameters of the simulated UAV cluster and the flight environment parameters, and to obtain and record the flight status information of the UAV cluster nodes in real time; A computing module is used to add errors to the flight status information of the drone cluster nodes using the error injection parameters during the drone cluster flight simulation to obtain simulated operation data; based on the simulated operation data, use the collaborative navigation algorithm to obtain and record navigation operation results; and adjust the motion of the simulated drone cluster nodes according to the operation mode and the navigation operation results; The display module is used to perform data comparison and analysis on the recorded flight status information of the drone cluster nodes and the navigation calculation results, obtain the navigation performance test results, and display the navigation performance test results on the user interface.

[0015] Compared with the prior art, the present invention has at least the following beneficial effects: (1) The present invention provides a simulation test environment for building a collaborative navigation algorithm based on virtual scenes and data simulation, which significantly reduces the test cost and improves the test efficiency compared to the traditional actual UAV-mounted test method. The system does not need to actually purchase and maintain a large number of UAV equipment, avoiding the risk of equipment damage that may occur during the test. At the same time, it also overcomes the limitations of external conditions such as weather and site, and can carry out testing at any time.

[0016] (2) The test system of the present invention has good scalability and flexibility. It can quickly create and configure different numbers of drone nodes with different performance according to actual needs, and support large-scale cluster simulation testing. The system can easily adjust the platform parameters, sensor configuration and other properties of the drone through software definition, so that the test process can cover more application scenarios and usage conditions, providing strong support for the comprehensive verification of the algorithm.

[0017] (3) The present invention designs a perfect sensor data generation and error injection mechanism in the simulation environment, which can accurately simulate the data characteristics of various sensors in the actual operating environment. It can truly reflect the data uncertainty in the actual environment, thereby ensuring the credibility and effectiveness of the test results. This simulation test method that simulates the characteristics of real data provides a reliable basis for the performance evaluation and optimization of the collaborative navigation algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings are only for the purpose of illustrating particular embodiments and are not to be construed as limiting the invention.

[0019] Figure 1 A schematic diagram of the hardware structure of the UAV cluster collaborative navigation algorithm testing system provided by the present invention.

[0020] Figure 2 This is a schematic diagram of the software architecture of the UAV cluster collaborative navigation algorithm testing system provided by the present invention.

[0021] Figure 3 This is a schematic diagram of the functions and information interaction relationships of each module in the drone cluster collaborative navigation algorithm test system software provided by the present invention.

[0022] Figure 4 This is a software operation flow chart of the UAV cluster collaborative navigation algorithm testing system provided by the present invention.

[0023] Figure 5 This is a flow chart of the UAV cluster collaborative navigation algorithm testing method provided by the present invention.

[0024] Figure 6 This is a structural diagram of the UAV cluster collaborative navigation algorithm testing system provided by the present invention.

[0025] Figure numerals: 10 - configuration module, 20 - loading module, 30 - simulation module, 40 - operation module, 50 - display module. DETAILED DESCRIPTION

[0026] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. In addition, the present invention can also be implemented in other ways different from those described herein, and therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0027] The present invention aims to provide an efficient and reliable testing platform for evaluating and optimizing the cooperative navigation algorithm of UAV clusters, and establishes a software system for testing the cooperative navigation algorithm of UAV clusters. Figure 2 As shown, the software system consists of three layers: cluster operation, algorithm operation, and test operation. Specifically, it includes software modules such as flight environment simulation, cluster node management, cluster control management, multi-source data generation, error injection management, algorithm loading management, operation mode management, and data analysis and processing.

[0028] Through this software system, a drone cluster is built in a virtual scene, multi-source data signals are generated and errors are simulated as the data source for collaborative navigation. After loading the test algorithm and setting the initial state, the algorithm operation is performed and visualized using the data injected with errors. In a virtual environment, the drone cluster is controlled to perform collaborative formation flight, and the simulation data and algorithm calculation results are analyzed and compared, thereby realizing efficient verification of the collaborative navigation algorithm of large-scale drone clusters.

[0029] In order to illustrate the effectiveness of the method proposed by the present invention, the above technical solution of the present invention is described in detail below through a specific embodiment. Figure 5 As shown, a method for testing a drone cluster collaborative navigation algorithm is disclosed, and the specific implementation steps are as follows: Step S1, setting the initial parameters of the simulated drone cluster and the flight environment parameters; the initial parameters include the number of drone nodes and the initial configuration of the drone nodes; In this step, first enable the cluster operation layer function and establish a simulation environment for the simulated drone cluster. Set the number of drone nodes in the drone cluster and determine the initial configuration of all drone nodes.

[0030] In some embodiments, the initial configuration of the drone node includes flight parameters, sensor parameters, and initial position and attitude of the drone node.

[0031] In some embodiments, the flight parameters of the UAV node may include performance indicators such as flight platform type, flight speed, take-off weight, endurance time, maximum ceiling, turning radius, take-off and landing methods; the sensor parameters of the UAV node may include readings, sampling frequency, accuracy, noise characteristics and other parameters of sensors such as inertial measurement unit (IMU), global positioning system (GPS) receiver, altimeter, inter-machine radio ranging, visual measurement, etc.; in addition, the initial three-dimensional coordinate position and attitude angle information of each UAV node can also be set as the initial position and attitude.

[0032] In some embodiments, the flight environment parameters are used to represent the climatic conditions when the drone is flying. The flight environment parameters may include: weather, temperature, humidity, wind speed, wind direction, visibility, etc. These parameters can be configured according to actual test requirements.

[0033] Step S2, loading the designed collaborative navigation algorithm to verify the effectiveness of the collaborative navigation algorithm; configuring the operating mode of the collaborative navigation algorithm and configuring error injection parameters, the operating mode including: navigating using the navigation operation result of the collaborative navigation algorithm and navigating not using the navigation operation result of the collaborative navigation algorithm.

[0034] In this step, the algorithm operation layer function is first turned on to start the algorithm operation environment, and then the designed collaborative navigation algorithm is loaded, and the collaborative navigation algorithm program is loaded into the algorithm operation environment.

[0035] In some embodiments, the algorithm operation layer can load the corresponding collaborative navigation algorithm instance for each node according to the number of drone nodes in the initial parameters. Each algorithm instance can contain independent data cache space and computing threads, ensuring the parallel processing capability of the algorithm in multi-node scenarios.

[0036] In some embodiments, the process of verifying the validity of the collaborative navigation algorithm may include: loading the collaborative navigation algorithm through a standardized interface and performing interface compatibility verification to ensure that the algorithm can run normally, correctly receive input data and output navigation results that meet format requirements.

[0037] When running the collaborative navigation algorithm test, it can be set to two different operation modes: algorithm independent operation mode and algorithm in-loop operation mode. When the algorithm independent operation mode is adopted, the simulated UAV node in the system simulation operation does not use the output results of the collaborative navigation algorithm for navigation, and only performs data analysis and recording in the data analysis and processing module. This mode has a small amount of calculation and a fast processing speed, but it cannot display the navigation effect of the algorithm in the UAV cluster flight in real time, and can only provide error analysis. When the algorithm in-loop operation mode is adopted, the simulated UAV node in the system simulation operation uses the output results of the collaborative navigation algorithm as the data input of the control loop for navigation. This mode has a large amount of calculation and a slow processing speed, but it can display the navigation effect of the algorithm in the UAV cluster flight in real time, which is convenient for intuitive understanding of the effectiveness of the algorithm.

[0038] When running the collaborative navigation algorithm test, you can configure error injection parameters. The error injection parameters are used to add errors to the sensor data in the flight status information of the drone cluster nodes during the subsequent flight simulation process, thereby simulating the error interference to the drone during flight.

[0039] In some embodiments, different error models can be configured for different types of sensors. For example, error injection parameters may include: setting random walk error for GPS receiver, setting zero bias and scale factor error for IMU, setting Gaussian white noise for altimeter, error and delay for inter-machine radio ranging, aberration and distortion for visual acquisition, etc. The parameters of these error models can be flexibly adjusted through configuration files.

[0040] Step S3: start the drone cluster flight simulation based on the initial parameters of the simulated drone cluster and the flight environment parameters, and obtain and record the flight status information of the drone cluster nodes in real time.

[0041] In this step, the operation logic of all UAV simulation nodes is started in the cluster operation layer to perform UAV cluster flight simulation. During the flight simulation, an event-driven mechanism can be used to generate real-time status information including position, speed, attitude, etc., as well as observation data from various sensors based on the flight parameters, sensor parameters, initial position and attitude of the UAV nodes, and flight environment parameters.

[0042] In some embodiments, the flight status information of the drone cluster node may include the position, speed, posture and sensor data of the drone node, and the sensor data includes observation data of various sensors generated based on the sensor parameters in the initial configuration of the drone node.

[0043] At the same time, in this step, the function of the test operation layer of the software system is turned on to record the flight status information of the drone cluster nodes in real time.

[0044] Step S4: During the UAV cluster flight simulation process, the error injection parameters are used to add errors to the flight status information of the UAV cluster nodes to obtain simulated operation data; based on the simulated operation data, the collaborative navigation algorithm is used to obtain and record navigation calculation results; and the movement of the simulated UAV cluster nodes is adjusted according to the operation mode and the navigation calculation results.

[0045] For the flight status information generated during the UAV cluster flight simulation, the present invention adds errors according to the error injection parameters.

[0046] In some embodiments, the algorithm operation layer can form errors according to the error models configured for different types of sensors, and superimpose the errors on the flight status information of the drone cluster nodes to form the simulation operation data, thereby simulating the error interference encountered by the drone during flight.

[0047] During the UAV swarm flight simulation process, the simulation operation data is input into the collaborative navigation algorithm to obtain and record navigation operation results, which may include state estimation values ​​such as position, speed, and attitude.

[0048] After obtaining the navigation calculation result, determining whether the current operation mode is the algorithm-in-the-loop operation mode, if the current operation mode is the algorithm-in-the-loop operation mode, sending the navigation calculation result to the cluster operation layer, and the cluster operation layer adjusts the flight status information of the drone cluster node according to the navigation calculation result; If the current operating mode is not the algorithm-in-the-loop operating mode, the flight status information of the drone cluster node is not adjusted.

[0049] Step S5: perform data comparison and analysis on the recorded flight status information of the drone cluster node and the navigation calculation result, obtain the navigation performance test result, and display the navigation performance test result on the user interface.

[0050] The test operation layer of the software system of the present invention records the flight status information of the drone cluster node and the navigation operation result. The flight status information of the drone cluster node and the navigation operation result are compared and analyzed to obtain the navigation performance test result, and the navigation performance test result is displayed on the user interface. In some embodiments, a distributed database may be used to store all recorded data, and data test results such as navigation error statistical analysis and trajectory comparison analysis may be plotted in real time to form a data analysis management interface and displayed on the user interface.

[0051] In some embodiments, the present invention can establish a flight simulation scene interface and a test management interaction interface during the flight simulation process, wherein the flight simulation scene interface displays the virtual environment of the drone flight, and the test management interaction interface enables the user to set test parameters and start the test, and the flight simulation scene and test management interaction interface are displayed on the user interface.

[0052] In some embodiments, the hardware environment in which the drone cluster collaborative navigation algorithm testing method of the present invention runs is composed of a simulation computer and three displays, such as Figure 1 As shown. The simulation computer is the carrier for running the software system and provides high-performance simulation computing hardware resources; the three monitors are used to display the simulation scene interface, the test management interaction interface, and the data analysis management interface.

[0053] like Figure 3 As shown, the specific functions of each software module in the software system of the UAV cluster collaborative navigation algorithm test of the present invention are as follows.

[0054] (1) Flight environment simulation, which can simulate various climate conditions, geographical and space environments, drone swarms and their flight behaviors, etc., to build a flight test environment for verifying collaborative navigation algorithms; (2) Cluster node management: to manage the parameters and scale of drone nodes in the drone cluster. The type, index, sensor, initial state, etc. of the drone nodes can be configured, and the corresponding scale can be configured according to the needs. The drone nodes are created in the simulated flight environment module to form a drone cluster. (3) Cluster control management, which enables flight behavior management and control of virtual drone clusters. It can operate the cluster as a whole or operate a single drone independently. (4) Multi-source data generation: Generate simulation data based on the position, attitude, sensor, environment and other status of each UAV node in the simulated flight environment, and provide original simulation input data for cluster simulation operation; (5) Error injection management: generating drone state errors according to specific rules and superimposing the errors on multi-source data to provide simulated sensor data that approximates the actual situation for collaborative navigation algorithm operations; (6) Algorithm loading management, which is used to load and run the collaborative navigation algorithm. It creates an algorithm object for each simulated UAV. Each algorithm object receives the corresponding simulated sensor data after error injection and performs independent operation and calculation, and gives the calculation results of each UAV node. (7) Operation mode management, which is used to configure the test mode and select different operation modes based on whether the algorithm output results need to be fed back to the control end; (8) Data analysis and processing, which is used to record and compare the original simulation data and algorithm calculation results during the simulation process, and to analyze the accuracy of the calculation results.

[0055] like Figure 4 The figure shows the software operation process of the UAV cluster collaborative navigation algorithm test system. The hardware environment, software system and operation process of the aforementioned UAV cluster collaborative navigation algorithm test are used to build a virtual flight scene and UAV cluster in a simulation environment. By generating simulated data and injecting errors, multi-source data is provided for the collaborative navigation algorithm to perform navigation solutions, thereby realizing the collaborative navigation performance of large-scale UAV clusters under the simulation system.

[0056] like Figure 6 As shown, the present invention discloses a UAV cluster collaborative navigation algorithm testing system, comprising the following modules: Configuration module 10, used to set the initial parameters of the simulated drone cluster and the flight environment parameters; the initial parameters include the number of drone nodes and the initial configuration of the drone nodes; The loading module 20 is used to load the designed collaborative navigation algorithm and verify the validity of the collaborative navigation algorithm; configure the operation mode of the collaborative navigation algorithm and configure the error injection parameters, wherein the operation mode includes: navigating using the navigation operation result of the collaborative navigation algorithm and navigating without using the navigation operation result of the collaborative navigation algorithm; The simulation module 30 is used to start the UAV cluster flight simulation based on the initial parameters of the simulated UAV cluster and the flight environment parameters, and to obtain and record the flight status information of the UAV cluster nodes in real time; The operation module 40 is used to add errors to the flight state information of the drone cluster nodes using the error injection parameters during the drone cluster flight simulation to obtain simulated operation data; based on the simulated operation data, use the collaborative navigation algorithm to obtain and record navigation operation results; and adjust the simulated drone cluster node movement according to the operation mode and the navigation operation results; The display module 50 is used to perform data comparison and analysis on the recorded flight status information of the drone cluster nodes and the navigation calculation results, obtain navigation performance test results, and display the navigation performance test results on the user interface.

[0057] Although the specific embodiments of the present invention depict various actions or steps in a specific order, this should be understood as requiring such actions or steps to be performed in the specific order shown or in a sequential order, or requiring all illustrated actions or steps to be performed to obtain the desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Certain features described in the context of a separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination. The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

[0058] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for testing a drone cluster collaborative navigation algorithm, characterized in that: The following steps are involved: Step S1, setting the initial parameters of the simulated drone cluster and the flight environment parameters; the initial parameters include the number of drone nodes and the initial configuration of the drone nodes; Step S2: loading the designed collaborative navigation algorithm to verify the effectiveness of the collaborative navigation algorithm; Configuring an operation mode of the collaborative navigation algorithm and configuring error injection parameters, the operation mode including: navigating using the navigation operation result of the collaborative navigation algorithm and navigating not using the navigation operation result of the collaborative navigation algorithm; Step S3: starting the UAV cluster flight simulation based on the initial parameters of the simulated UAV cluster and the flight environment parameters, and acquiring and recording the flight status information of the simulated UAV cluster nodes in real time; Step S4: during the UAV cluster flight simulation process, using the error injection parameter to add errors to the flight state information of the simulated UAV cluster node to obtain simulated operation data; based on the simulated operation data, using the collaborative navigation algorithm to obtain and record navigation operation results; adjusting the movement of the simulated UAV cluster node according to the operation mode and the navigation operation results; Step S5: perform data comparison and analysis on the recorded flight status information of the drone cluster node and the navigation calculation result, obtain the navigation performance test result, and display the navigation performance test result on the user interface.

2. The method for testing the cooperative navigation algorithm of a drone swarm according to claim 1, characterized in that: In step S1, the initial configuration of the UAV node includes the flight parameters, sensor parameters, and initial position and attitude of the UAV node; the flight parameters include platform type and flight indicators, the sensor parameters include sensor type and sensor performance parameters; the flight environment parameters include climate condition parameters of the simulated environment.

3. The method for testing the cooperative navigation algorithm of the drone cluster according to claim 2 is characterized in that: The step S2 specifically includes: Step S2-1, loading a corresponding collaborative navigation algorithm for each drone node according to the number of drone nodes; and checking the interface compatibility of the collaborative navigation algorithm and whether it can be run; Step S2-2: configuring the operation mode of the collaborative navigation algorithm and configuring error injection parameters, wherein the error injection parameters include error parameters of multiple different types of sensors.

4. The method for testing the UAV cluster collaborative navigation algorithm according to claim 3 is characterized in that: The step S3 specifically includes: Step S3-1, based on the flight parameters of the drone node, the sensor parameters, the initial position and attitude, and the flight environment parameters, the flight status information of the drone cluster node is generated in real time, wherein the flight status information of the drone cluster node includes the position, speed, attitude, and sensor data of the drone node, and the sensor data includes observation data of various sensors generated based on the sensor parameters; Step S3-2: Record the flight status information of the drone cluster nodes in real time.

5. The method for testing the UAV cluster collaborative navigation algorithm according to claim 4, characterized in that: The step S4 specifically includes: Step S4-1, respectively configuring corresponding error models according to the error parameters of the multiple different types of sensors, generating errors according to the error models, and superimposing the errors on the flight status information of the drone cluster nodes to form the simulation operation data; Step S4-2, inputting the simulation operation data into the collaborative navigation algorithm, obtaining and recording navigation operation results, the navigation operation results including: the position, speed and attitude of the drone node; Step S4-3, determine the current operating mode. If the current operating mode is to navigate using the navigation calculation results of the collaborative navigation algorithm, adjust the flight status information of the drone cluster node according to the navigation calculation results; otherwise, do not adjust the flight status information of the drone cluster node.

6. The method for testing the UAV cluster collaborative navigation algorithm according to claim 5, characterized in that: The step S5 specifically includes: Step S5-1, establishing a flight simulation scene interface, a test management interactive interface, and a data analysis management interface, wherein the flight simulation scene interface displays a virtual environment in which the drone flies, and the test management interactive interface enables the user to set test parameters and start a test; Step S5-2, comparing and analyzing the recorded flight status information of the drone cluster node and the navigation calculation result, and outputting the obtained navigation performance test result to the data analysis management interface; Step S5-3: displaying the flight simulation scene interface, the test management interaction interface and the data analysis management interface on a display device respectively.

7. The method for testing the cooperative navigation algorithm of a drone swarm according to claim 6, characterized in that: Step S5-3 specifically includes: displaying the flight simulation scene interface, test management interaction interface and data analysis management interface on three displays respectively; the three displays are connected to simulation computers respectively, and the drone cluster collaborative navigation algorithm test method is run on the simulation computers.

8. A UAV swarm collaborative navigation algorithm testing system, characterized in that: Includes the following modules: A configuration module is used to set the initial parameters of the simulated drone cluster and the flight environment parameters; the initial parameters include the number of drone nodes and the initial configuration of the drone nodes; A loading module, used to load the designed collaborative navigation algorithm and verify the effectiveness of the collaborative navigation algorithm; Configuring an operation mode of the collaborative navigation algorithm and configuring error injection parameters, the operation mode including: navigating using the navigation operation result of the collaborative navigation algorithm and navigating not using the navigation operation result of the collaborative navigation algorithm; A simulation module, used to start the UAV cluster flight simulation based on the initial parameters of the simulated UAV cluster and the flight environment parameters, and to obtain and record the flight status information of the UAV cluster nodes in real time; A computing module is used to add errors to the flight status information of the drone cluster nodes using the error injection parameters during the drone cluster flight simulation to obtain simulated operation data; based on the simulated operation data, use the collaborative navigation algorithm to obtain and record navigation operation results; and adjust the motion of the simulated drone cluster nodes according to the operation mode and the navigation operation results; The display module is used to perform data comparison and analysis on the recorded flight status information of the drone cluster nodes and the navigation calculation results, obtain the navigation performance test results, and display the navigation performance test results on the user interface.

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