Unmanned aerial vehicle cluster control method and cluster system for urban street battle

Abstract

The invention provides an unmanned aerial vehicle cluster control method and cluster system for urban street battle. The method comprises the following steps: dividing an unmanned aerial vehicle cluster into an information detection unmanned aerial vehicle, an information processing unmanned aerial vehicle and a combat unmanned aerial vehicle according to a preset urban street battle demand; the information of the urban roadway is detected through the information detection unmanned aerial vehicle, and detection information is obtained; processing the detection information through the information processing unmanned aerial vehicle to generate a combat control signal; under the condition that the combat control signal is effective, combat is carried out in the city street combat environment through the unmanned aerial vehicle for combat; and under the condition that the combat control signal is invalid, the detection step of the information detection unmanned aerial vehicle and the processing step of the information processing unmanned aerial vehicle are repeatedly and iteratively executed until the combat control signal is valid, and smooth proceeding of the combat task is ensured. Through cooperative operation of the unmanned aerial vehicle cluster, the complex environment in the city street battle is effectively handled, the battle risk is reduced, the battle efficiency is improved, and an innovative solution is provided for modern city battle.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a UAV cluster control method and cluster system for urban street fighting. Background Art

[0002] Among various battlefield environments, urban street fighting is extremely complex, filled with narrow streets, tall buildings, and densely populated areas. Traditional tactics are often limited in this environment. Fighting within streets and buildings, with numerous buildings, walls, and obstacles available for concealment and defense, significantly hinders the transport of supplies and personnel for the attacking side.

[0003] The application of drone technology has brought convenience to urban street fighting, but ordinary drones still require personnel to operate them nearby. Moreover, the functions of drones used in urban fighting are relatively simple. Summary of the Invention

[0004] The present invention provides a UAV swarm control method and swarm system for urban street fighting. Through the collaborative operation of the UAV swarm, it can effectively cope with the complex environment in urban street fighting, reduce combat risks, improve combat efficiency, and provide an innovative solution for modern urban combat.

[0005] The present invention provides a method for controlling a swarm of unmanned aerial vehicles (UAVs) for urban street fighting, comprising: dividing a swarm of UAVs into information detection UAVs, information processing UAVs, and combat UAVs according to preset urban street fighting requirements; detecting information of urban alleys by the information detection UAVs to obtain detection information; processing the detection information by the information processing UAVs to generate a combat control signal; when the combat control signal is valid, conducting combat in an urban street fighting environment by the combat UAVs; and when the combat control signal is invalid, repeatedly iterating the detection step of the information detection UAV and the processing step of the information processing UAV until the combat control signal is valid.

[0006] According to a drone cluster control method for urban street fighting provided by the present invention, the information of urban alleys is detected by the information detection drone, and before the detection information is obtained, the method also includes: using an adaptive modulation and demodulation algorithm or an error correction coding algorithm to control the communication path between the information detection drone and the information processing drone; using a global positioning system and / or an inertial navigation system to obtain the position information of the information detection drone in real time; determining the distance between the information detection drone and the information processing drone based on the position information of the information detection drone; and according to the distance between the information detection drone and the information processing drone, performing flight path planning and flight status control on the information detection drone by the information processing drone, so that the information detection drone performs detection within a preset detection range; wherein the path planning algorithm includes at least one of a heuristic search algorithm, a Dijkstra algorithm and a rapid exploration random tree algorithm.

[0007] According to a drone cluster control method for urban street fighting provided by the present invention, the detection information is lidar information; the detection information is processed by the information processing drone to generate a combat control signal, including: preprocessing the lidar information by the information processing drone; the preprocessing method includes at least one of filtering, denoising and data fusion; converting the preprocessed lidar data into a global map; performing target detection on the global map based on a target detection algorithm; when a combat target is detected in the global map, the combat control signal is valid; when it is detected that the combat target does not exist in the global map, the combat control signal is invalid.

[0008] According to a drone cluster control method for urban street fighting provided by the present invention, the method also includes: monitoring the power level of the information detection drone through the information processing drone; when the power level of the information detection drone is lower than a preset power threshold, sending a return charging instruction to the information detection drone through the information processing drone; the return charging instruction is used to control the information detection drone to return for charging.

[0009] According to the present invention, a drone cluster control method for urban street fighting also includes: using a heartbeat detection algorithm or a fault diagnosis algorithm to monitor the working status of the information processing drone in real time; when a fault is detected in the information processing drone, using a backup takeover algorithm to start a backup drone to take over control and continue to perform the cluster control task.

[0010] According to a drone cluster control method for urban street fighting provided by the present invention, the combat in an urban street fighting environment is carried out by the combat drones, including: according to the requirements of the combat mission, the combat mission is decomposed into multiple subtasks by the information processing drone, and the subtasks are assigned to different combat drones; the collaborative actions between the combat drones are coordinated in real time by the communication module; according to the real-time feedback information during the combat process, the task allocation and action plan of each combat drone are dynamically adjusted by the information processing drone.

[0011] The present invention also provides a UAV cluster system for urban street fighting, comprising: an information detection UAV for detecting information in urban lanes and obtaining detection information; an information processing UAV for processing the detection information and generating a combat control signal; and a combat UAV for conducting combat in an urban street fighting environment when the combat control signal is valid.

[0012] According to a drone swarm system for urban street fighting provided by the present invention, the information detection drone is a rotor drone, and the information detection drone is equipped with a visible light camera, a laser radar and a communication module; the information detection drone is equipped with a mechanical fixing device on the fuselage, and the mechanical fixing device is used to fix the information detection drone at the detection position.

[0013] According to a drone swarm system for urban street fighting provided by the present invention, the information processing drone is a vertical take-off and landing drone; after the information detection drone completes the detection of the first area, the information processing drone automatically selects another landing point and controls the information detection drone to perform information detection in the second area; the information processing drone is also used to communicate with the rear control center.

[0014] According to the present invention, a drone swarm system for urban street fighting is provided, in which the combat drones are specifically used to carry corresponding combat equipment according to combat requirements and stay at preset locations to carry out combat in an urban street fighting environment.

[0015] The present invention provides a drone swarm control method and swarm system for urban street fighting. The method comprises: dividing a drone swarm into information detection drones, information processing drones, and combat drones according to preset urban street fighting requirements; using the information detection drones to detect information in urban lanes and obtain detection information; using the information processing drones to process the detection information and generate a combat control signal; when the combat control signal is valid, using the combat drones to conduct combat in the urban street fighting environment; and when the combat control signal is invalid, iteratively executing the detection steps of the information detection drones and the processing steps of the information processing drones until the combat control signal is valid, thereby ensuring the smooth progress of the combat mission. Through the collaborative operation of drone swarms, the present invention effectively copes with the complex environment of urban street fighting, reduces combat risks, improves combat efficiency, and provides an innovative solution for modern urban combat. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is a flow chart of a method for controlling a swarm of drones in urban street fighting, provided by the present invention.

[0018] Figure 2 This is a specific flow chart of a UAV cluster control method for urban street fighting provided by the present invention.

[0019] Figure 3 This is a structural diagram of a UAV swarm system for urban street fighting provided by the present invention.

[0020] Figure 4 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0021] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0022] Please refer to Figure 1 , Figure 1This is a flow chart of a method for controlling a swarm of unmanned aerial vehicles (UAVs) for urban street fighting provided by the present invention.

[0023] Please refer to Figure 2 , Figure 2 This is a schematic diagram of a specific flow chart of a drone swarm control method for urban street fighting provided by the present invention.

[0024] The present invention provides a method for controlling a swarm of unmanned aerial vehicles (UAVs) in urban street fighting, comprising: 101: Based on the preset urban street fighting requirements, the drone cluster is divided into information detection drones 301, information processing drones 302, and combat drones 303; 102: Detecting information of urban lanes using the information detection drone 301 to obtain detection information; 103: The detection information is processed by the information processing drone 302 to generate a combat control signal; 104: When the combat control signal is valid, conduct combat in urban street fighting environment using combat drone 303; 105: When the combat control signal is invalid, the detection step of the information detection drone 301 and the processing step of the information processing drone 302 are repeatedly executed until the combat control signal is valid.

[0025] Among various battlefield environments, urban street fighting is one of the most complex and challenging scenarios. To quickly address the challenges of urban street fighting, the present invention provides a drone swarm control method for urban street fighting. During an urban street fighting mission, based on pre-defined urban street fighting requirements, a drone swarm is divided into information detection drones 301, information processing drones 302, and combat drones 303. For example, based on the mission requirements, five information detection drones 301, two information processing drones 302, and three combat drones 303 are deployed. The information detection drones 301 are equipped with lightweight lidar and visible light cameras to detect urban lanes and obtain detection information, which can include terrain information and target location information. The information processing drones 302 are equipped with high-performance computing modules to process the detection information and generate combat control signals. The combat drones 303 are equipped with small arms systems to carry out specific combat missions.

[0026] Specifically, the drone cluster approaches the target area; the information processing drone 302 and the combat drone land in a safe area. During the information detection phase, the information processing drone 302 directs the information detection drone 301 to perform information detection on the urban lanes in the target area according to the preset flight path. The laser radar obtains the three-dimensional point cloud data of the lane at a scanning frequency of 10 times per second, and the visible light camera captures the lane image at a frequency of 30 frames per second. The detection information is transmitted to the information processing drone 302 in real time through the wireless communication module. Assuming the lane length is L , the flight speed of the detection drone is v , then the time to complete one detection is: t = L / v。

[0027] During the information processing phase, after receiving detection information, information processing drone 302 first uses lidar data to construct a three-dimensional map of the roadway. Using point cloud processing algorithms such as voxel filtering and ICP algorithms, it fuses data from multiple detection drones to generate a global map. Simultaneously, deep learning algorithms (such as YOLO) are used to perform target recognition on the image data, marking the locations of enemy personnel and weapons and equipment. Based on the global map and target information, information processing drone 302 generates combat control signals.

[0028] During the combat execution phase, if the combat control signal is valid, the combat drone 303, upon receiving the combat command, will fly to the target location along the planned path and execute the combat mission. The combat drone's flight path is planned using a heuristic search algorithm to ensure obstacle avoidance and rapid target arrival. If the combat control signal is invalid, the information detection drone 301 will re-execute the detection mission, and the information processing drone 302 will reprocess the data until the combat control signal is valid.

[0029] The method of the present invention enables automated or remotely controlled search of specific urban areas and the use of combat drones for processing. Optimizing drones for different purposes based on their specific roles reduces costs and improves overall system performance. Drone swarms can efficiently coordinate operations in complex urban combat environments, ensuring the smooth execution of combat missions.

[0030] As a preferred embodiment, the information of urban alleys is detected by the information detection drone 301, and before the detection information is obtained, it also includes: using an adaptive modulation and demodulation algorithm or an error correction coding algorithm to control the communication path between the information detection drone 301 and the information processing drone 302; using a global positioning system and / or an inertial navigation system to obtain the position information of the information detection drone 301 in real time; determining the distance between the information detection drone 301 and the information processing drone 302 based on the position information of the information detection drone 301; according to the distance between the information detection drone 301 and the information processing drone 302, the information processing drone 302 plans the flight path and controls the flight status of the information detection drone 301, so that the information detection drone 301 performs detection within a preset detection range; wherein the path planning algorithm includes at least one of a heuristic search algorithm, a Dijkstra algorithm and a rapid exploration random tree algorithm.

[0031] In order to ensure that the information detection drone 301 can efficiently perform detection tasks within a preset range during urban street fighting missions, in this embodiment, before the communication link between the information detection drone 301 and the information processing drone 302 is established, an adaptive modulation and demodulation algorithm or an error correction coding algorithm is used to optimize the communication path. The adaptive modulation and demodulation algorithm can dynamically adjust the modulation transmission rate according to the real-time quality of the communication channel to ensure communication reliability in complex electromagnetic environments. Error correction coding algorithms: such as convolutional codes and Turbo codes, improve the anti-interference ability of data transmission by adding redundant information to the data. In this way, the stability and efficiency of the communication link in a complex urban environment are ensured.

[0032] The location of the information detection drone 301 is acquired in real time via the Global Positioning System (GPS) and the Inertial Navigation System (INS). GPS provides high-precision global positioning data, while the INS provides short-term position compensation when GPS signals are blocked (e.g., in urban canyon environments). Based on this acquired location information, the information processing drone 302 calculates the distance to the information detection drone 301. Based on this calculated distance, the information processing drone 302 uses a flight path planning algorithm to plan a flight path for the information detection drone 301 and controls its flight state (speed, direction, altitude, etc.) to ensure that it remains within the preset detection range. For example, if the preset detection range is a circular area with a radius of 500 meters, the information processing drone 302 will monitor the position of the information detection drone 301 in real time. If it exceeds this range, it will immediately adjust its flight path to return it to the effective detection range.

[0033] During path planning, a heuristic search algorithm is used to find the optimal path by evaluating the cost from the starting point to the end point. The cost function typically consists of two components: the actual cost and the heuristic cost. In urban environments, the cost function is set based on buildings and obstacles, enabling the drone to avoid obstacles and find the shortest path. The Dijkstra algorithm is applicable to graphs without negatively weighted edges and finds the shortest path from the starting point to the end point by gradually expanding the path. In urban environments, buildings and streets can be treated as nodes and edges of the graph, and the Dijkstra algorithm can be used to plan the drone's path. The rapid exploration random tree algorithm uses random sampling and gradually constructs a tree structure to quickly find a feasible path from the starting point to the end point.

[0034] The flight control algorithm for the information detection drone 301 can use a PID (Proportional-Integral-Derivative) control algorithm, which uses three parameters: proportional (P), integral (I), and derivative (D) to adjust the control variable. In drone flight control, the PID controller can adjust the drone's flight attitude and speed in real time based on the deviation between the target position and the current position, ensuring that it flies along the planned path.

[0035] Through the above embodiments, the information detection drone 301 can efficiently and accurately perform detection tasks in complex urban environments, providing reliable data support for subsequent information processing and combat operations.

[0036] As a preferred embodiment, the detection information is lidar information; the detection information is processed by the information processing drone 302 to generate a combat control signal, including: preprocessing the lidar information by the information processing drone 302; the preprocessing method includes at least one of filtering, denoising and data fusion; converting the preprocessed lidar data into a global map; performing target detection on the global map based on a target detection algorithm; when a combat target is detected in the global map, the combat control signal is valid; when it is detected that no combat target is present in the global map, the combat control signal is invalid.

[0037] In this embodiment, an information detection drone 301 flies through an urban alleyway. Its onboard laser radar emits laser pulses at a frequency of 10 times per second, collecting 3D point cloud data of the surrounding environment. The laser radar has a 360-degree scanning range and a maximum detection range of 100 meters. Assuming the alleyway is 500 meters long and the drone flies at a speed of 5 meters per second, it takes 100 seconds to complete a single alleyway scan. In 100 seconds, the laser radar will collect a large amount of point cloud data for subsequent processing.

[0038] The collected lidar data is transmitted to the information processing drone 302 for preprocessing. Preprocessing includes filtering, denoising, and data fusion. Specifically, voxel filtering or statistical filtering algorithms are used. The voxel filtering algorithm divides the three-dimensional space into multiple small voxels, retaining a single point within each voxel. This reduces the amount of point cloud data and removes noise points. The statistical filtering algorithm identifies and removes outliers by calculating the statistical characteristics of the distance between each point and its neighbors. The lidar data from multiple information detection drones 301 are fused, and the ICP (Iterative Closest Point) algorithm is used to align the point cloud data through iterative optimization, eliminating errors caused by differences in sensor position and angle. The preprocessed lidar data is converted into a global map. The global map is represented in voxelized form, with each voxel marking a small area in space. Map information at different resolutions is dynamically represented using an octree data structure.

[0039] Based on a target detection algorithm, the information processing drone 302 analyzes the global map and identifies combat targets. Using a deep learning algorithm (such as YOLOv5), a pre-trained model is used to detect targets from the map's point cloud data. If a combat target (such as enemy personnel or weapons and equipment) is detected, a valid combat control signal is generated. If no combat target is detected, the combat control signal is invalid, and the information detection drone 301 will re-execute the detection mission.

[0040] Through the above embodiments, the information processing drone 302 can efficiently process lidar information, generate a global map and accurately detect combat targets, provide reliable combat control signals for the combat drone 303, and ensure the smooth execution of urban street fighting missions.

[0041] As a preferred embodiment, it also includes: monitoring the power of the information detection drone 301 through the information processing drone 302; when the power of the information detection drone 301 is lower than the preset power threshold, sending a return charging instruction to the information detection drone 301 through the information processing drone 302; the return charging instruction is used to control the information detection drone 301 to return for charging.

[0042] In this embodiment, the information processing drone 302 monitors the battery level of the information detection drone 301 in real time via a wireless communication module. The information detection drone 301 is equipped with a high-precision battery sensor that measures the remaining battery charge in real time and transmits this information to the information processing drone 302 once per second. When the information processing drone 302 detects that the battery level of the information detection drone 301 is below a preset threshold, it immediately sends a return charge instruction. Upon receiving the return charge instruction, the information detection drone 301 plans a return charge route based on the target location provided by the information processing drone 302. The information detection drone 301 flies to the charging point along the planned route and automatically docks with a charging module (which can be located on the signal processing drone) upon arrival. The charging module should provide efficient and safe charging performance, employing a constant current and constant voltage charging algorithm to fully charge the information detection drone 301 in a short period of time. Furthermore, the charging process must be safe to avoid dangerous situations such as overcharging and short circuits.

[0043] Of course, when the combat drone 303 is low on power, the above-mentioned charging strategy may also be adopted, which will not be described in detail in this embodiment.

[0044] Through the above embodiment, the information processing drone 302 can monitor the power of the information detection drone 301 in real time, and send a return charging instruction in time when the power is insufficient, ensuring that the information detection drone 301 can return safely for charging.

[0045] As a preferred embodiment, it also includes: using a heartbeat detection algorithm or a fault diagnosis algorithm to monitor the working status of the information processing drone 302 in real time; when a fault is detected in the information processing drone 302, using a backup takeover algorithm to start the standby drone to take over control and continue to execute the cluster control task.

[0046] In this embodiment, a heartbeat detection algorithm is used to monitor the operating status of the information processing drone 302 in real time. The information processing drone 302 sends a heartbeat signal to the system at regular intervals (e.g., 1 second) to indicate normal operation. The heartbeat signal contains basic drone status information, such as battery level, communication status, and task processing progress. Furthermore, a fault diagnosis algorithm is employed to further identify the fault type by analyzing the status information in the heartbeat signal. In standby mode, the backup drone maintains low-power operation, ready to take over control at any time. Upon receiving the takeover command, the backup drone immediately starts up and enters operational mode. The backup drone obtains the current mission status and the location information of each drone from the system via a communication module. Task synchronization time depends on task complexity and communication bandwidth. Based on the synchronized information, the backup drone continues to perform the cluster control task. The backup drone uses the same control algorithm as the original information processing drone 302 to ensure mission continuity and consistency. For example, the backup drone will continue to receive detection information from the information detection drone 301, process and generate combat control signals, and command the combat drone 303 to perform combat missions. At the same time, after taking over, the backup drone can adopt more radical strategies, such as increasing flight speed, expanding detection range, etc., to make up for the losses caused by the destroyed drone.

[0047] Through the above embodiment, the drone swarm can quickly activate the backup drone to take over control when the information processing drone 302 fails, ensuring mission continuity and reliability. This method effectively improves the survivability and combat effectiveness of the drone swarm in complex urban street fighting environments.

[0048] As a preferred embodiment, combat in an urban street fighting environment is carried out by using a combat drone 303, including: according to the requirements of the combat mission, the combat mission is decomposed into multiple sub-tasks by the information processing drone 302, and the sub-tasks are assigned to different combat drones 303; the collaborative actions between the combat drones are coordinated in real time by the communication module; according to the real-time feedback information during the combat process, the task allocation and action plan of each combat drone 303 are dynamically adjusted by the information processing drone 302.

[0049] In this embodiment, the information processing drone 302 breaks down a complex combat mission into multiple subtasks based on the mission requirements. For example, if the mission objective is to clear the enemy personnel in the tunnel and protect friendly forces, the information processing drone 302 breaks down the mission into the following subtasks: Subtask 1: Scout the alley entrance and mark the enemy's position.

[0050] Sub-task 2: Clear the enemy personnel in the alley.

[0051] Subtask 3: Protect friendly forces and provide fire support.

[0052] Information processing drone 302 assigns subtasks to different combat drones 303 based on their capabilities and status. For example, reconnaissance missions can be assigned to drones equipped with high-resolution cameras, while clearing missions can be assigned to drones equipped with small arms systems. Once assigned, information processing drone 302 coordinates the coordinated actions of the combat drones in real time through a communication module. The communication module utilizes high-speed wireless communication technology to ensure real-time transmission of commands.

[0053] During combat, the information processing drone 302 dynamically adjusts the mission assignments and action plans of each combat drone 303 based on real-time feedback. For example, if a reconnaissance drone detects enemy reinforcements, the information processing drone 302 can adjust the mission assignments, prioritize clearing missions, and replan the combat path. Dynamic adjustments are based on factors such as the real-time battlefield situation and the drones' battery levels and status.

[0054] Through the above-described embodiment, information processing drone 302 can efficiently decompose combat missions, coordinate the coordinated actions of various combat drones 303, and dynamically adjust task allocation and action plans based on real-time feedback. This approach effectively improves the combat effectiveness and flexibility of drone swarms in urban combat, ensuring the smooth execution of missions.

[0055] The following describes the drone swarm system for urban street fighting provided by the present invention. The drone swarm system for urban street fighting described below and the drone swarm control method for urban street fighting described above can be referenced to each other.

[0056] Please refer to Figure 3 , Figure 3 This is a structural schematic diagram of a drone swarm system for urban street fighting provided by the present invention.

[0057] The present invention also provides a drone swarm system for urban street fighting, comprising: an information detection drone 301, used to detect information in urban lanes and obtain detection information; an information processing drone 302, used to process the detection information and generate combat control signals; and a combat drone 303, used to conduct combat in an urban street fighting environment when the combat control signals are valid.

[0058] As a preferred embodiment, the information detection drone 301 is a rotor drone, which is equipped with a visible light camera, a laser radar and a communication module; the information detection drone 301 is equipped with a mechanical fixing device on the fuselage, which is used to fix the information detection drone 301 at the detection position.

[0059] In this embodiment, the information detection drone 301 is a small rotor drone with good maneuverability and flexibility, capable of flying freely in narrow urban lanes. The information detection drone 301 can be equipped with key equipment such as a visible light camera, a laser radar, and a communication module.

[0060] The visible light camera is used to acquire high-resolution image data, enabling clear capture of people, vehicles, and other important objects within the roadway. The lidar is used to acquire three-dimensional point cloud data, accurately measuring the geometry and distance information of the surrounding environment, assisting the drone in obstacle avoidance and environmental modeling. The communication module is used for real-time data transmission with the information processing drone 302, ensuring timely and accurate transmission of detection information to the information processing drone 302.

[0061] In addition, the information detection drone 301 is equipped with a mechanical fixing device for securing the drone at a specific detection location when needed. This mechanical fixing device can be either magnetic, hook, or mechanical clamping to accommodate different detection environments. The magnetic fixing method involves installing a strong magnet on the bottom of the information detection drone 301, which can adhere to metal surfaces of buildings (such as roofs and walls). The magnet's suction force is precisely calculated to ensure that the information detection drone 301 can withstand certain external forces and will not easily fall off after being fixed. The hook fixing method involves installing a retractable hook on the bottom of the information detection drone 301, which can be attached to structures such as window sills and awnings. The hook design incorporates ease of operation and a quick release function, allowing the drone to be quickly released when needed. The mechanical clamping method involves installing a mechanical clamping device on the bottom of the information detection drone 301, which can clamp to the edge of a building. The clamping device is designed to accommodate building edges of varying thicknesses, ensuring stable and reliable clamping. When sensors on the drone's underside detect contact with a building surface, they automatically activate the securing mechanism, securing the information detection drone 301 to the building surface or other structure. Once secured, the information detection drone 301 continues to collect environmental data but ceases flight operations, ensuring stable and accurate data collection. Sensors provide real-time feedback on the securing mechanism's status to the information processing drone 302, ensuring the reliability and safety of the securing process.

[0062] Through the above embodiments, the information detection drone 301 can efficiently and stably perform reconnaissance missions in complex urban environments. The design of the rotary-wing drone provides good maneuverability and flexibility, while the mechanical fixing device ensures the stability of data collection in a specific location.

[0063] As a preferred embodiment, the information processing drone 302 is a vertical take-off and landing drone; after the information detection drone 301 completes the detection of the first area, the information processing drone 302 automatically selects another landing point and controls the information detection drone 301 to perform information detection in the second area; the information processing drone 302 is also used to communicate with the rear control center.

[0064] To ensure efficient mission execution, in this embodiment, the information processing drone 302 is equipped with vertical takeoff and landing capabilities. This allows it to flexibly select takeoff and landing points in urban environments, regardless of terrain conditions. This provides excellent maneuverability and flexibility to adapt to different mission requirements. For example, it can land in narrow urban alleys, rooftops of buildings, squares, streets, and other open areas.

[0065] After the information detection drone 301 completes its survey of the first area, the information processing drone 302 automatically selects a new, safe, and suitable landing site based on the generated global map information. Selection criteria include the flatness of the terrain at the landing site, its distance from the target area, and its safety (e.g., avoiding enemy fire). After selecting the new landing site, the information processing drone 302 transmits new mission instructions to the information detection drone 301 via its communication module, directing it to conduct information surveys in the second area. During the mission, the information processing drone 302 communicates with the rear control center in real time via its wireless communication module (e.g., using TCP / IP (Transmission Control Protocol / Internet Protocol) or UDP (User Datagram Protocol)). This communication includes real-time transmission of global map information and combat control signals, as well as receiving instructions from the rear control center to adjust mission plans and combat strategies.

[0066] Communication and control between the rear control center and the forward drone swarm can be achieved through voice commands. Leveraging augmented reality technology, operators can visually visualize the drones' real-time status and operational environment through AR (augmented reality) devices, improving intuitive and efficient interaction. The interface layout and functionality are intelligently adjusted based on operator preferences and mission requirements, providing a personalized interactive experience.

[0067] Through the above-described embodiment, information processing drone 302 can efficiently perform missions in complex urban environments. Its vertical takeoff and landing (VTOL) function enables rapid takeoff and landing in confined spaces. Automatic landing point selection and mission adjustment ensure mission continuity and flexibility. Communication with a control center ensures real-time monitoring and adjustment of missions.

[0068] As a preferred embodiment, the combat drone 303 is specifically used to carry corresponding combat equipment according to combat needs, and stay at a preset location to conduct combat in an urban street fighting environment.

[0069] In this embodiment, combat drone 303 carries combat equipment based on mission requirements. This equipment includes, but is not limited to, small arms systems (e.g., light machine guns, grenade launchers), non-lethal weapons (e.g., tear gas launchers, rubber bullet guns), electronic warfare equipment (e.g., jammers, signal reconnaissance equipment), and cargo delivery devices. Small arms systems are used to eliminate enemy personnel, electronic warfare equipment is used to disrupt enemy communications, and cargo delivery devices are used to support friendly forces.

[0070] The Combat UAV 303 boasts stealth capabilities and high survivability, enabling it to operate covertly in hostile environments and reduce the risk of detection. The Combat UAV 303 utilizes stealth materials and a specialized design to minimize radar reflection and infrared signature, lowering the probability of enemy detection. The Combat UAV 303 can remain at a preset location, independent of the drone swarm, to perform specific combat missions. The selection of the preset location takes into account factors such as the mission objective, enemy location, and friendly force positions, ensuring the Combat UAV 303 is optimally positioned for mission execution. Small arms systems can be quickly installed on the Combat UAV 303 through standardized interfaces.

[0071] Information processing drone 302 assigns tasks to different combat drones 303 based on their capabilities and status. For example, one drone equipped with a small arms system is responsible for a clearance mission, while another equipped with electronic warfare equipment is responsible for a jamming mission. Combat drones 303 execute specific combat missions based on instructions from information processing drone 302. For example, the firing direction and timing of the small arms system should be precisely controlled by information processing drone 302 based on the target location and combat situation to achieve precise strikes. Combat drones 303 provide real-time feedback on mission execution, including operational progress, equipment status, and environmental information, via their communication modules. Based on this real-time feedback, information processing drone 302 dynamically adjusts the task assignments and action plans of combat drones 303.

[0072] Figure 4 An example of a physical structure diagram of an electronic device is shown below. Figure 4As shown, the electronic device may include: a processor 401, a communications interface 402, a memory 403, and a communications bus 404. The processor 401, communications interface 402, and memory 403 communicate with each other via the communications bus 404. The processor 401 can call logic instructions in the memory 403 to execute a method for controlling a swarm of drones for urban street fighting. The method includes: dividing the swarm of drones into an information detection drone 301, an information processing drone 302, and a combat drone 303 according to preset urban street fighting requirements; using the information detection drone 301 to detect information in urban streets and obtain detection information; using the information processing drone 302 to process the detection information and generate a combat control signal; and when the combat control signal is valid, using the combat drone 303 to conduct combat in an urban street fighting environment. If the combat control signal is invalid, iteratively executing the detection steps of the information detection drone 301 and the processing steps of the information processing drone 302 until the combat control signal is valid.

[0073] Furthermore, the logic instructions in the aforementioned memory 403 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0074] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the drone cluster control method for urban street fighting provided by the above methods, which includes: dividing the drone cluster into information detection drone 301, information processing drone 302 and combat drone 303 according to preset urban street fighting requirements; using the information detection drone 301 to detect information on urban alleys and obtain detection information; using the information processing drone 302 to process the detection information and generate a combat control signal; when the combat control signal is valid, using the combat drone 303 to fight in an urban street fighting environment; when the combat control signal is invalid, repeatedly iterating the detection step of the information detection drone 301 and the processing step of the information processing drone 302 until the combat control signal is valid.

[0075] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the drone cluster control method for urban street fighting provided by the above-mentioned methods, the method comprising: dividing the drone cluster into an information detection drone 301, an information processing drone 302 and a combat drone 303 according to preset urban street fighting requirements; detecting information of urban alleys by the information detection drone 301 to obtain detection information; processing the detection information by the information processing drone 302 to generate a combat control signal; when the combat control signal is valid, conducting combat in an urban street fighting environment by the combat drone 303; when the combat control signal is invalid, repeatedly iterating the detection step of the information detection drone 301 and the processing step of the information processing drone 302 until the combat control signal is valid.

[0076] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0077] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for controlling a swarm of drones in urban street fighting, characterized in that: include: Based on the preset urban street fighting requirements, drone clusters are divided into information detection drones, information processing drones, and combat drones; The information detection drone is used to detect information of urban lanes to obtain detection information; Processing the detection information by the information processing drone to generate a combat control signal; When the combat control signal is valid, the combat drone is used to conduct combat in an urban street fighting environment; When the combat control signal is invalid, the detection step of the information detection drone and the processing step of the information processing drone are repeatedly executed until the combat control signal is valid.

2. The method for controlling a swarm of drones in urban street fighting according to claim 1 is characterized in that: Before the information of the urban lanes is detected by the information detection drone and the detection information is obtained, the method further includes: Adopting an adaptive modulation and demodulation algorithm or an error correction coding algorithm to control the communication path between the information detection drone and the information processing drone; Using a global positioning system and / or an inertial navigation system to obtain the position information of the information detection drone in real time; determining the distance between the information detection drone and the information processing drone based on the position information of the information detection drone; According to the distance between the information detection drone and the information processing drone, the information processing drone performs flight path planning and flight state control on the information detection drone, so that the information detection drone performs detection within a preset detection range; The path planning algorithm includes at least one of a heuristic search algorithm, a Dijkstra algorithm, and a rapid exploration random tree algorithm.

3. The method for controlling drone swarms in urban street fighting according to claim 1 is characterized in that: The detection information is laser radar information; The processing of the detection information by the information processing drone to generate a combat control signal includes: Preprocessing the laser radar information by the information processing drone; the preprocessing method includes at least one of filtering, denoising and data fusion; Convert preprocessed lidar data into a global map; Performing target detection on the global map based on a target detection algorithm; In the case where a combat target is detected in the global map, the combat control signal is valid; When it is detected that the combat target does not exist in the global map, the combat control signal is invalid.

4. The method for controlling drone swarms in urban street fighting according to claim 1, characterized in that: Also includes: monitoring the power level of the information detection drone by the information processing drone; When the power level of the information detection drone is lower than a preset power threshold, a return charging instruction is sent to the information detection drone via the information processing drone; the return charging instruction is used to control the information detection drone to return for charging.

5. The method for controlling drone swarms in urban street fighting according to claim 1 is characterized in that: Also includes: Using a heartbeat detection algorithm or a fault diagnosis algorithm to monitor the working status of the information processing drone in real time; When a failure of the information processing drone is detected, a backup takeover algorithm is used to start the standby drone to take over control and continue to perform the cluster control task.

6. The method for controlling a swarm of drones in urban street fighting according to any one of claims 1 to 5, characterized in that: The method of using the combat drone to conduct combat in an urban street fighting environment includes: According to the requirements of the combat mission, the information processing drone decomposes the combat mission into multiple subtasks and assigns them to different combat drones; Real-time coordination of collaborative actions between combat drones through communication modules; According to the real-time feedback information during the combat process, the information processing drone dynamically adjusts the task allocation and action plan of each combat drone.

7. A UAV swarm system for urban street fighting, characterized by: include: Information detection drones are used to detect information in urban lanes and obtain detection information; An information processing drone, used to process the detection information and generate combat control signals; The combat drone is used to conduct combat in urban street fighting environments when the combat control signal is valid.

8. The UAV swarm system for urban street fighting according to claim 7 is characterized in that: The information detection drone is a rotor drone, which is equipped with a visible light camera, a laser radar and a communication module; the information detection drone is equipped with a mechanical fixing device on its fuselage, which is used to fix the information detection drone at the detection position.

9. The UAV swarm system for urban street fighting according to claim 7 is characterized in that: The information processing drone is a vertical take-off and landing drone; after the information detection drone completes the detection of the first area, the information processing drone automatically selects another landing point and controls the information detection drone to perform information detection in the second area; the information processing drone is also used to communicate with the rear control center.

10. The UAV swarm system for urban street fighting according to any one of claims 7 to 9, characterized in that: The combat drone is specifically used to carry corresponding combat equipment according to combat requirements, and stay at a preset location to carry out combat in an urban street fighting environment.

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