Unmanned aerial vehicle simulation method, device, equipment, medium and product

Abstract

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle simulation method, device and equipment, a medium and a product, and the method comprises the steps: simulating and generating at least one target unmanned aerial vehicle based on target unmanned aerial vehicle parameters; environment parameter information is received, and a task scene is simulated and generated according to the environment parameter information; controlling the target unmanned aerial vehicle to execute the target task in the task scene according to the target algorithm; the algorithm is selected after a corresponding triggering operation is received in the algorithm selection interface; the algorithm interface comprises an algorithm selection control of at least one algorithm type. According to the invention, the simulation effect of the unmanned aerial vehicle is more comprehensive and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and particularly to an unmanned aerial vehicle simulation method, device, equipment, medium and product. Background Art

[0002] With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicle systems have shown great application potential in multiple fields such as military, civilian, and scientific research. As an important means to support the research and development, testing, and optimization of unmanned aerial vehicle systems, unmanned aerial vehicle simulation technology has become increasingly important. Currently, there are a variety of unmanned aerial vehicle simulation platforms with different functions, which can accurately simulate the flight behavior, sensor data, and environmental interaction of unmanned aerial vehicles, and support the testing of complex mission scenarios and diverse application requirements. However, the simulation platforms have deficiencies in real-time performance, modularity, and scalability, and are difficult to adapt to rapidly changing mission environments and diverse user needs. Summary of the Invention

[0003] In view of this, the present invention provides an unmanned aerial vehicle simulation method, device, equipment, medium and product to make the effect of unmanned aerial vehicle simulation more comprehensive and reliable.

[0004] In a first aspect, the present invention provides an unmanned aerial vehicle simulation method, which includes: based on target unmanned aerial vehicle parameters, simulating and generating at least one target unmanned aerial vehicle; receiving environmental parameter information and simulating and generating a mission scenario according to the environmental parameter information; controlling the target unmanned aerial vehicle to execute a target mission in the mission scenario according to a target algorithm; the algorithm is selected after receiving a corresponding trigger operation in an algorithm selection interface; the algorithm interface includes algorithm selection controls of at least one algorithm type.

[0005] In an optional implementation manner, the algorithm type includes at least one of a trajectory control algorithm, a local mapping algorithm, a global path algorithm, and a multi-task algorithm.

[0006] In an optional implementation manner, when selecting a trajectory algorithm, controlling the target unmanned aerial vehicle to execute a target mission in the mission scenario according to the target algorithm includes: calculating a flight trajectory based on the current state and target position of the target unmanned aerial vehicle; controlling the target unmanned aerial vehicle to fly along the flight trajectory.

[0007] In an optional implementation manner, when selecting a local mapping algorithm, controlling the target unmanned aerial vehicle to execute a target mission in the mission scenario according to the target algorithm includes: converting the environmental parameter information into a map available for navigation based on the local mapping algorithm.

[0008] In an optional implementation manner, when selecting a global path algorithm, controlling the target unmanned aerial vehicle to execute a target mission in the mission scenario according to the target algorithm includes: planning a flight path from a starting position to a target position based on the environmental parameter information.

[0009] In an alternative embodiment, when selecting a task allocation algorithm, the target unmanned aerial vehicle is controlled to perform a target task in a task scenario according to the target algorithm, including: based on the requirements of the target task and environmental parameter information, allocating multiple targets to each unmanned aerial vehicle.

[0010] In a second aspect, the present invention provides a drone simulation device, the device includes: a drone simulation module for simulating and generating at least one target drone based on target drone parameters; a task scenario simulation module for receiving environmental parameter information and simulating and generating a task scenario according to the environmental parameter information; an algorithm application module for controlling the target drone to perform a target task in the task scenario according to the target algorithm; the algorithm is selected after receiving a corresponding trigger operation in an algorithm selection interface; the algorithm interface includes algorithm selection controls of at least one algorithm type.

[0011] In an alternative embodiment, the algorithm application module is further configured to: the algorithm type includes at least one of a trajectory control algorithm, a local mapping algorithm, a global path algorithm, and a multi-task algorithm.

[0012] In an alternative embodiment, when selecting a trajectory algorithm, the above algorithm application module includes: a first trajectory calculation unit for calculating a flight trajectory according to the current state and target position of the target drone; a second trajectory calculation unit for controlling the target drone to fly along the flight trajectory.

[0013] In an alternative embodiment, when selecting a local mapping algorithm, the above algorithm application module further includes: a local mapping unit for converting environmental parameter information into a map available for navigation based on the local mapping algorithm.

[0014] In an alternative embodiment, when selecting a global path algorithm, the above algorithm application module further includes: a path planning unit for planning a flight path from a starting position to a target position based on environmental parameter information.

[0015] In an alternative embodiment, when selecting a task allocation algorithm, the above algorithm application module further includes: a target allocation unit for allocating multiple targets to each unmanned aerial vehicle based on the requirements of the target task and environmental parameter information.

[0016] In a third aspect, the present invention provides a computer device, including: a memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the drone simulation method of the first aspect or any corresponding embodiment thereof.

[0017] Fourthly, the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the UAV simulation method according to the first aspect or any corresponding embodiment thereof as described above.

[0018] Fifthly, the present invention provides a computer program product, including computer instructions, and the computer instructions are used to cause a computer to execute the UAV simulation method according to the first aspect or any corresponding embodiment thereof as described above.

[0019] The technical solution provided by this application may include the following beneficial effects:

[0020] Based on the target UAV parameters, at least one target UAV is simulated and generated, and a UAV model that meets the requirements is created. The environmental parameter information is received, and a mission scenario is simulated according to the environmental parameter information, and a test environment highly similar to the real environment is constructed. Controlling the target UAV to execute the target mission in the mission scenario according to the target algorithm can demonstrate the effect of the algorithm. The algorithm is selected after receiving the corresponding trigger operation in the algorithm selection interface, and the algorithm interface includes algorithm selection controls of at least one algorithm type. The design of the algorithm selection interface enables the user to select the required algorithm. Through the establishment of the UAV model, scenario simulation, and selection and use of the algorithm, the effect of UAV simulation is more comprehensive and reliable. Description of the Drawings

[0021] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required to be used in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 It is a schematic diagram of a UAV swarm collaborative simulation research system based on ROS2 according to an embodiment of the present invention;

[0023] Figure 2 It is a flowchart of the UAV simulation method according to an embodiment of the present invention;

[0024] Figure 3 It is the interface of the UAV swarm collaborative simulation platform according to an embodiment of the present invention;

[0025] Figure 4 It is a structural block diagram of the UAV simulation device according to an embodiment of the present invention;

[0026] Figure 5 It is a schematic diagram of the hardware structure of the computer device according to an embodiment of the present invention. Detailed implementation manners

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] Figure 1 It is a schematic diagram of a collaborative simulation research system for a UAV swarm based on ROS2 according to an embodiment of the present invention. ROS2 (Robot Operating System 2) is the core framework of this platform, which provides a distributed and modular operating system, supporting multi-robot communication, real-time performance, scalability, and efficient robot control. ROS2 provides a flexible development environment for the UAV swarm, enabling coordination, state sharing, and task scheduling among UAVs.

[0029] As Figure 1 shown, the system architecture includes:

[0030] UAV simulation module: used to simulate the physical behaviors of a single UAV or multiple UAVs, such as flight control, sensor data acquisition, etc.

[0031] Environment simulation module: used to create task environments and task scenarios, such as obstacles, enemy targets, task areas, etc.

[0032] Swarm collaboration module: realizes the collaboration among UAVs, such as formation flight, task allocation, and information sharing.

[0033] Algorithm integration module: mainly focuses on algorithms based on UAV trajectory control, local mapping, global path, task allocation, etc., realizes the integration of algorithms, improves the automation degree of the system, optimizes the control performance, enhances the adaptability and flexibility of the system, and realizes efficient, accurate, and reliable flight control.

[0034] Flight control module: combines related technologies such as DDS and Mavros to improve the data distribution and communication capabilities of the system, allows ROS2 nodes to interact with the MAVLink protocol, and thus realizes functions such as UAV control, status monitoring, and task planning.

[0035] In this simulation system, the operation and control capabilities of the UAV swarm are the core of the research. The following are some main functions:

[0036] Formation flight: Multiple UAVs fly in a certain formation in space, maintaining a fixed relative position.

[0037] Task allocation and collaboration: The drones in the cluster can dynamically allocate tasks according to task requirements and current status, such as search, reconnaissance, strike, etc.

[0038] Information sharing and communication: The drones in the cluster exchange information through the communication mechanism (DDS) of ROS2 to ensure task collaboration.

[0039] Autonomous obstacle avoidance and decision-making: Each drone has a certain degree of autonomy, capable of avoiding obstacles based on environmental and sensor data and making decisions.

[0040] The simulation tools adopted in the present invention include:

[0041] Gazebo: ROS2 supports the combination with Gazebo. Gazebo provides a high-fidelity physical engine that can simulate the flight behavior, sensor data, and environmental interaction of drones. According to actual requirements, researchers can create specific task scenarios, such as urban environments, enemy targets, the interaction between drones and ground forces, etc.

[0042] RViz: Used to visualize information such as the flight state of drones, sensor data, and environmental models, facilitating debugging and analysis.

[0043] According to an embodiment of the present invention, an embodiment of a drone simulation method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0044] In this embodiment, a drone simulation method is provided, which is applied to a drone cluster collaborative simulation research system based on ROS2. Figure 2 It is a schematic flowchart of the drone simulation method according to an embodiment of the present invention, as Figure 2 shown, and this process includes the following steps:

[0045] Step S101, based on the target drone parameters, simulate and generate at least one target drone.

[0046] Simulate and generate a drone model based on the preset target drone parameters. The target drone parameters are the basis for constructing the drone model, including the size, weight, maximum flight speed, endurance, sensor type and performance, flight control system parameters, etc. of the drone.

[0047] In the UAV swarm collaborative simulation research system based on ROS2, the UAV simulation module uses these parameters to create virtual models of UAVs. These models can simulate the actual flight behaviors of UAVs, such as takeoff, landing, cruising, turning, etc., providing necessary support for subsequent mission scenario simulation and target mission execution.

[0048] Step S102: Receive environmental parameter information and simulate and generate a mission scenario according to the environmental parameter information.

[0049] After generating the UAV model, construct an environment similar to the real world so that the UAV can perform tasks in this environment. The environmental parameter information includes terrain features (such as mountains, rivers, urban buildings, etc.), weather conditions (such as wind speed, wind direction, rainfall, etc.), obstacle distribution (such as trees, buildings, other aircraft, etc.), and other factors that may affect UAV flight and mission execution.

[0050] In the ROS2 simulation system, the environment simulation module uses this environmental parameter information to construct a virtual mission or test environment. This environment will serve as the background for the UAV to perform tasks, and it can simulate various complex scenarios, such as urban street fighting, mountain reconnaissance, maritime patrol, etc.

[0051] Step S103: Control the target UAV to execute the target mission in the mission scenario according to the target algorithm; the algorithm is selected after receiving the corresponding trigger operation in the algorithm selection interface; the algorithm interface includes algorithm selection controls of at least one algorithm type.

[0052] Optionally, the algorithm type includes at least one of a trajectory control algorithm, a local mapping algorithm, a global path algorithm, and a multi-task algorithm.

[0053] After establishing the UAV model and the mission scenario, control the UAV to execute the mission according to the target algorithm selected by the user. Prompt the user to select a suitable algorithm through the algorithm selection interface. The algorithm selection interface provides selection controls for various algorithm types, such as the shortest path algorithm, the trajectory control algorithm, the local mapping algorithm, the global path algorithm, the multi-task algorithm, etc. The user can select one or more algorithms according to their own needs to control the flight and mission execution of the UAV.

[0054] When the user selects the target algorithm, the system will control the UAV to perform tasks in the mission scenario according to these algorithms. For example, if the global path algorithm is selected, the system will calculate the optimal flight path of the UAV from the starting point to the ending point and control the UAV to fly along this path; if the trajectory control algorithm is selected, the system will control the flight attitude, speed, etc. of the UAV according to the preset trajectory. Through this process, the user can intuitively see the effects of different algorithms in the UAV mission execution, thereby optimizing and improving the algorithm performance and enhancing the mission efficiency of the UAV.

[0055] A UAV simulation method provided in this embodiment makes the UAV simulation effect more comprehensive and reliable through UAV model establishment, scenario simulation, and algorithm selection and use.

[0056] In an optional implementation manner, when the trajectory algorithm is selected, the process of step S103 includes the following steps:

[0057] Step a11, calculate the flight trajectory based on the current state and target position of the target UAV.

[0058] The trajectory control algorithm is responsible for the flight trajectory planning and tracking of the UAV. It calculates the optimal flight trajectory based on the current state and target position of the UAV and ensures that the UAV can fly stably along this trajectory. The trajectory control algorithm usually needs to consider the dynamic characteristics of the UAV, environmental constraints, and various interference factors during the flight process.

[0059] Among them, the UAV current state information includes the position, speed, attitude (such as pitch angle, yaw angle, roll angle), remaining battery power, sensor data (such as camera, radar, etc.) of the UAV, etc. Based on the collected current state information and target position, the trajectory control algorithm will start to calculate the flight trajectory. After the calculation is completed, the trajectory control algorithm will output the flight trajectory, which can be a series of three-dimensional coordinate points representing the flight path of the UAV from the current position to the target position.

[0060] Step a12, control the target UAV to fly along the flight trajectory.

[0061] First, parse the flight trajectory and convert it into control instructions that can be understood by the UAV flight control system. The control instructions will be sent to the flight control system of the UAV. The flight control system will adjust components such as the engine and control surfaces of the UAV according to these instructions, thereby changing the flight state of the UAV and making it fly along the predetermined flight trajectory.

[0062] In an optional implementation manner, when the local mapping algorithm is selected, the process of step S103 further includes:

[0063] Convert the environmental parameter information into a map that can be used for navigation based on the local mapping algorithm.

[0064] The local mapping algorithm is used to construct a map of the surrounding environment in real time during the flight of the drone. It uses sensors carried by the drone (such as cameras, lidar, etc.) to obtain environmental information, and through algorithm processing, converts this information into a map that can be used for navigation. The local mapping algorithm can help the drone perform precise positioning and obstacle avoidance in complex environments.

[0065] Specifically, preprocess the collected sensor data. Extract useful feature information from the preprocessed data, such as edges, corners, planes, etc. These feature information will be used to construct a geometric representation of the environment. Based on the extracted feature information, use the local mapping algorithm (such as SLAM-based algorithms, feature matching algorithms, etc.) to construct a local map of the surrounding environment of the drone.

[0066] In an alternative embodiment, when selecting the global path algorithm, the process of step S103 further includes:

[0067] Plan a flight path from the starting position to the target position based on the environmental parameter information.

[0068] The global path planning algorithm is to plan an optimal or feasible flight path for the drone from the starting point to the target point in the case of known environmental information. It usually needs to consider multiple factors such as the flight ability of the drone, environmental constraints, task requirements, etc., to ensure that the drone can reach the target point safely and efficiently. The global path planning algorithm can adopt various strategies, such as heuristic search algorithms (such as the A* algorithm), graph theory algorithms (such as the Dijkstra algorithm), etc. Use the selected algorithm to calculate the optimal or feasible flight path from the starting position to the target position in the constructed environmental model, and output the calculated flight path to the flight control system of the drone.

[0069] In an alternative embodiment, when selecting the task allocation algorithm, the process of step S103 further includes:

[0070] Based on the requirements of the target task and the environmental parameter information, allocate multiple targets to each drone.

[0071] The task allocation algorithm is used in a multi-drone system to reasonably allocate multiple targets to each drone according to the task requirements and environmental conditions. Its aim is to achieve the overall optimal task execution effect and improve the task execution efficiency and resource utilization rate of the drone. The task allocation algorithm can adopt various methods, such as particle swarm, genetic algorithm, etc., to adapt to drone systems of different scales and complexities.

[0072] The requirements of the target task refer to the specific goals that the UAV swarm needs to achieve when performing tasks. The goals may include: Search and reconnaissance: Search a specific area to detect or confirm the existence of a target; Target strike: Strike or destroy the detected target; Material delivery: Deliver materials or equipment to a designated location; Information collection: Collect environmental or target information of a specific area. The task allocation algorithm refers to the algorithm that reasonably allocates multiple targets to each UAV according to the requirements of the target task and environmental parameter information. In the UAV swarm cooperative simulation research system based on ROS2, the task allocation algorithm needs to consider factors such as target priority, UAV capabilities, path planning, and cooperative tasks.

[0073] Based on the requirements of the target task and environmental parameter information, allocate multiple targets to each UAV. Input the task requirements and environmental parameters into the task allocation algorithm. Evaluate the possibility of each UAV to execute the task according to its current state and capabilities. Combine the environmental parameter information to plan the flight path for each UAV to the target location. Allocate multiple targets to each UAV according to the target priority, UAV capabilities, and flight path. Output the task allocation result to the flight control system of the UAV to guide the UAV to execute the task.

[0074] Integrating algorithms such as trajectory control, local mapping, global path planning, and task allocation can form a complete UAV autonomous flight system. This system has the following functions: Autonomous flight ability: Through the global path planning and local mapping algorithms, the UAV can perform precise navigation and obstacle avoidance in a complex environment to achieve autonomous flight; Task execution efficiency: Through the task allocation algorithm, the UAV system can reasonably allocate tasks to improve the overall task execution efficiency; Safety: The trajectory control algorithm can ensure that the UAV remains stable during flight and avoid safety accidents such as collisions; Adaptability: The integrated system can adapt to different flight environments and task requirements, with strong flexibility and scalability.

[0075] In summary, the UAV swarm cooperative simulation research system based on ROS2 can further improve the autonomous decision-making ability of the UAV swarm, enabling it to autonomously adjust strategies in a dynamically changing task environment. Through the research and simulation of this platform, researchers can verify different control and cooperation algorithms, conduct actual combat simulations of UAV swarms in complex task environments, and ultimately provide strong support for the practical application of UAV swarms.

[0076] Figure 3 It is the interface of the UAV swarm cooperative simulation platform according to the embodiments of the present invention. The UAV swarm cooperative simulation platform based on ROS2 mainly realizes functions such as cooperative tasks, path planning, and task allocation of the UAV swarm through the powerful communication ability, real-time support, and various modular tools of ROS2.

[0077] Among them, the function operation interface of the simulation platform is developed based on the secondary development of QGC (QGroundControl). As Figure 3 shown, in the software interface corresponding to this simulation platform, basic information such as the geographical environment, UAV type, and UAV model is displayed on the software interface. The leftmost box is the first-level selection control, and users can trigger various functions by operating it. For example, when the user triggers the control corresponding to the algorithm, an algorithm selection interface can be displayed above the scene, and then the user can select or change the algorithm used in the simulation process on the interface. A UAV status monitoring control is also set at the lower right of the software interface to monitor the real-time motion state of the UAV.

[0078] In this embodiment, a UAV simulation device is also provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0079] This embodiment provides a UAV simulation device. As Figure 4 shown, it includes:

[0080] A UAV simulation module 401, configured to simulate and generate at least one target UAV based on target UAV parameters;

[0081] A mission scenario simulation module 402, configured to receive environmental parameter information and simulate and generate a mission scenario according to the environmental parameter information;

[0082] An algorithm application module 403, configured to control the target UAV to execute a target mission in the mission scenario according to a target algorithm; the algorithm is selected after receiving a corresponding trigger operation in the algorithm selection interface; the algorithm interface includes algorithm selection controls of at least one algorithm type.

[0083] In an alternative embodiment, the algorithm application module 403 is further configured to:

[0084] The algorithm type includes at least one of a trajectory control algorithm, a local mapping algorithm, a global path algorithm, and a multi-task algorithm.

[0085] In an alternative embodiment, when selecting a trajectory algorithm, the above algorithm application module 403 includes:

[0086] A first trajectory calculation unit, configured to calculate a flight trajectory according to the current state and target position of the target UAV;

[0087] A second trajectory calculation unit, configured to control the target unmanned aerial vehicle to fly along a flight trajectory.

[0088] In an alternative embodiment, when selecting a local mapping algorithm, the above algorithm application module 403 further includes:

[0089] A local mapping unit, configured to convert the environmental parameter information into a map available for navigation based on the local mapping algorithm.

[0090] In an alternative embodiment, when selecting a global path algorithm, the above algorithm application module 403 further includes:

[0091] A path planning unit, configured to plan a flight path from a starting position to a target position based on the environmental parameter information.

[0092] In an alternative embodiment, when selecting a task allocation algorithm, the above algorithm application module 403 further includes:

[0093] A target allocation unit, configured to allocate multiple targets to each unmanned aerial vehicle based on the requirements of the target task and the environmental parameter information.

[0094] The further function descriptions of the above modules and units are the same as those in the corresponding embodiments above, and will not be elaborated here.

[0095] The unmanned aerial vehicle simulation device in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0096] An embodiment of the present invention further provides a computer device having the above Figure 4 shown unmanned aerial vehicle simulation device.

[0097] Please refer to Figure 5 , Figure 5 is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As Figure 5As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Each component communicates with each other using different buses and can be installed on a common motherboard or in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative embodiments, if needed, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as an array of servers, a set of blade servers, or a multi-processor system). Figure 5 In the figure, one processor 10 is taken as an example.

[0098] The processor 10 can be a central processing unit, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above programmable logic device can be a complex programmable logic device, a field programmable gate array, a generic array logic, or any combination thereof.

[0099] Among them, the memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiments.

[0100] The memory 20 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device, etc. In addition, the memory 20 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 can optionally include a memory remotely set relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0101] The memory 20 can include a volatile memory, such as a random access memory; the memory can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid-state drive; the memory 20 can also include a combination of the above types of memories.

[0102] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30, and the output device 40 can be connected through a bus or other means.Figure 5 Take the bus connection as an example.

[0103] The input device 30 can receive input digital or character information, and generate key signal inputs related to the user settings and function controls of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED), and a haptic feedback device (e.g., a vibration motor), etc. The above display device includes but is not limited to a liquid crystal display, a light emitting diode, a display, and a plasma display. In some alternative embodiments, the display device may be a touch screen.

[0104] The embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented by downloading through a network the original computer code stored in a remote storage medium or a non-transitory machine-readable storage medium and to be stored in a local storage medium, so that the methods described herein can be stored in such software processes on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above-mentioned types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0105] A part of the present invention can be applied as a computer program product, such as computer program instructions, which when executed by a computer, can call or provide the methods and / or technical solutions according to the present invention through the operations of the computer. Those skilled in the art should be able to understand that the forms of existence of computer program instructions in a computer-readable medium include but are not limited to source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include but are not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible by the computer.

[0106] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for simulating an unmanned aerial vehicle, characterized in that, The method includes: Based on target UAV parameters, at least one target UAV is simulated and generated; Receiving environmental parameter information and simulating and generating a mission scenario according to the environmental parameter information; Controlling the target UAV to execute a target mission in the mission scenario according to a target algorithm; the algorithm is selected after receiving a corresponding trigger operation in an algorithm selection interface; the algorithm interface includes algorithm selection controls of at least one algorithm type.

2. The method according to claim 1, characterized in that, The algorithm type includes at least one of a trajectory control algorithm, a local mapping algorithm, a global path algorithm, and a multi-task algorithm.

3. The method according to claim 2, wherein When the trajectory algorithm is selected, the controlling the target UAV to execute a target mission in the mission scenario according to a target algorithm includes: Calculating a flight trajectory according to the current state and the target position of the target UAV; Controlling the target UAV to fly along the flight trajectory.

4. The method according to claim 2, characterized in that When the local mapping algorithm is selected, the controlling the target UAV to execute a target mission in the mission scenario according to a target algorithm further includes: Converting the environmental parameter information into a map available for navigation based on the local mapping algorithm.

5. The method according to claim 2, wherein When the global path algorithm is selected, the controlling the target UAV to execute a target mission in the mission scenario according to a target algorithm further includes: Planning a flight path from a starting position to a target position based on the environmental parameter information.

6. The method according to claim 2, wherein When the task assignment algorithm is selected, the controlling the target UAV to execute a target mission in the mission scenario according to a target algorithm further includes: Based on the requirements of the target mission and the environmental parameter information, allocating multiple targets to each UAV.

7. A drone simulation device, characterized in that, The device includes: A UAV simulation module for simulating and generating at least one target UAV based on target UAV parameters; A mission scenario simulation module for receiving environmental parameter information and simulating and generating a mission scenario according to the environmental parameter information; An algorithm application module for controlling the target UAV to execute a target mission in the mission scenario according to a target algorithm; the algorithm is selected after receiving a corresponding trigger operation in an algorithm selection interface; the algorithm interface includes algorithm selection controls of at least one algorithm type.

8. A computer device, characterized in that, Including: A memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the UAV simulation method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, Computer instructions are stored on a computer-readable storage medium, and the computer instructions are used to cause a computer to execute the UAV simulation method according to any one of claims 1 to 6.

10. A computer program product, characterized in that, Including computer instructions, and the computer instructions are used to cause a computer to execute the UAV simulation method according to any one of claims 1 to 6.

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