Heterogeneous multi-unmanned aerial vehicle collaborative planning method and system for complex environment

Abstract

The invention discloses a heterogeneous multi-unmanned aerial vehicle cooperative planning method and system for a complex environment. The method comprises the following steps: mapping surrounding obstacle information to a three-dimensional space based on prior information, generating a local environment map, performing heterogeneous multi-unmanned aerial vehicle position marking in the local environment map, and performing identification and information processing on dynamic obstacles; performing path planning of a single unmanned aerial vehicle by using an improved artificial potential field method considering distance gradient change; and obtaining size information and relative distances of different unmanned aerial vehicles by using the obtained position marks among the heterogeneous unmanned aerial vehicles and the relative distance information provided by the UWB beacons, and performing dynamic obstacle avoidance among the heterogeneous unmanned aerial vehicles. According to the invention, distance information provided by UWB is utilized, and a path planning algorithm and a collision avoidance mechanism are established based on an improved artificial potential field method, so that multiple heterogeneous unmanned aerial vehicles can safely and efficiently pass through a narrow multi-obstacle area, mutual collision is avoided, and tasks are completed.

Description

Technical Field

[0001] The present invention belongs to the technical field of multi-UAV collaboration, and in particular relates to a heterogeneous multi-UAV collaborative planning method and system for complex environments. Background Art

[0002] In recent years, drone technology has been widely used, especially in multi-drone collaborative flight missions in complex environments, such as disaster relief, environmental monitoring, military reconnaissance, etc. The collaborative flight of multiple drones can improve mission efficiency and coverage. However, in real applications, the safe passage of multiple drones in narrow and multi-obstacle areas and other similar restricted environments is still a problem that needs to be solved. Drone clusters often find it difficult to adapt to sudden environmental changes and collide with obstacles or each other.

[0003] The patent with publication number CN118655914A discloses a method for realizing collaborative task allocation and path planning of a multi-UAV system. After assigning tasks to multiple UAVs, the method uses the optimized artificial potential field algorithm (OAPF) to plan a flight path for each UAV, introduces dynamically adjusted potential field parameters and heuristic search strategies, and improves the real-time performance and obstacle avoidance capabilities of path planning. This scheme further considers "finding a negative gradient of the gravitational potential field along the gravitational direction of the basic potential field" and "finding a negative gradient of the repulsive potential field along the gravitational direction and the repulsive direction of the basic potential field". Although the dynamic adjustment of the potential field parameters solves the local optimal problem in most scenarios, it fails to consider the complex obstacle conditions (such as when passing through a narrow pipe or a narrow pass from a wide place). At this time, although the obstacle is not directly in the direction of the UAV's advance, because the direction of the repulsive field is opposite to the direction of the gravitational field at this time, the UAV's combined force is 0 when the UAV approaches the pass, and it cannot pass through the pass. And this scheme does not consider the problem of heterogeneous multi-UAV collaborative planning.

[0004] The paper "Path Planning for Dense Drone Formation Based on Modified Artificial Potential Fields" proposes a dense drone formation path planning algorithm based on artificial potential field (APF), which uses a "target exchange algorithm" to avoid the local optimum of multiple drones. The principle is that when the drones around a drone target point reach their respective targets, under the influence of multiple repulsive forces, the drones cannot reach the target point and are blocked by multiple obstacle drones. At this time, the drone at the local optimum exchanges its target point with the nearby drones. This method can solve the local optimum problem caused by the drone cluster, but it cannot solve the local optimum problem in a complex obstacle environment. Moreover, the collaborative planning problem between heterogeneous drones is not considered.

[0005] The artificial potential field method is a local path planning method commonly used in distributed UAV cluster path planning methods. This method is simple and efficient, but it has obvious limitations when passing through narrow areas with many obstacles. These shortcomings are mainly reflected in the following aspects: 1) Local optimal solution problem The artificial potential field method relies on local gravitational and repulsive fields to calculate the path, while the drone only plans the route based on its own perception and communication information with other drones. In complex environments such as narrow areas with multiple obstacles, the artificial potential field algorithm is prone to fall into local optimal solutions. For example, when drones are affected by mutual repulsion and the repulsion of obstacles, they may stop at a certain position and cannot move forward, resulting in "oscillation" or "stagnation". This is particularly evident in narrow areas with multiple obstacles, because the space is limited and the impact of the repulsive field on the drone becomes more significant.

[0006] 2) Inadequate conflict avoidance mechanism In the existing distributed artificial potential field scheme, drones rely on relative position information between each other to avoid collisions. However, the repulsive field design in the traditional artificial potential field cannot fully solve the collision problem when multiple drones pass through a narrow area with multiple obstacles at the same time. When multiple drones approach a narrow space at the same time, the distance between each other will become very close, and the repulsive field may not be enough to effectively coordinate the movement of the drones, which may easily cause local oscillations in the route and even path conflicts or collisions. In addition, by calculating the relative distance of drones with the help of SLAM (Simultaneous Localization and Mapping) or GNSS (Global Navigation Satellite System) methods, communication delays and calculation delays will also cause drones to be unable to adjust their paths in time.

[0007] 3) Path planning is not smooth enough Since the artificial potential field method is a path planning algorithm based on force fields, it relies on simple repulsion and gravity models to guide the drone. For complex tasks passing through narrow areas with multiple obstacles, the artificial potential field method lacks sufficient precision and cannot dynamically adjust and optimize the path. In narrow areas with multiple obstacles, the drone's path may be too simple and fail to fully consider the smoothness of the flight and the change in distance, which can easily lead to path incoherence and affect the efficiency and safety of the passage. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a heterogeneous multi-UAV collaborative planning method and system for complex environments in response to the deficiencies in the above-mentioned prior art, which is used to solve the technical problem that when UAVs pass through a narrow environment with multiple obstacles, multiple heterogeneous UAVs cannot pass through a narrow area with multiple obstacles safely and orderly, effectively avoiding mutual collisions while ensuring path smoothness and stability.

[0009] The present invention adopts the following technical solutions: A heterogeneous multi-UAV collaborative planning method for complex environments includes the following steps: Acquire prior information including size classification of heterogeneous UAVs, location of global target points, environmental perception information, relative distance information provided by UWB beacons, and the UWB beacon number corresponding to each UAV; Based on the obtained prior information, the surrounding obstacle information is mapped into three-dimensional space to generate a local environment map, in which the positions of heterogeneous multi-UAVs are marked, and dynamic obstacles are identified and information processed; The improved artificial potential field method considering the distance gradient change is used to carry out the path planning of a single UAV; The position marks between heterogeneous UAVs and the relative distance information provided by UWB beacons are used to obtain the size information and relative distances of different UAVs, and dynamic obstacle avoidance is performed between heterogeneous multiple UAVs.

[0010] Preferably, the prior information including the size classification of heterogeneous UAVs, the location of the global target point, the environmental perception information, the relative distance information provided by the UWB beacon, and the UWB beacon number corresponding to each UAV is obtained as follows: At the beginning of the mission, the global position coordinates of the drone target point are preset; The drone perceives the surrounding environment in real time, captures the location information of obstacles, and simultaneously obtains its own position; and measures the relative distance between adjacent drones.

[0011] Preferably, a local environment map is generated, the positions of heterogeneous multi-UAVs are marked in the local environment map, and dynamic obstacles are identified and information processed as follows: Based on the sensor data of the drone, the drone maps the surrounding obstacle information onto a three-dimensional map to generate a local environment map, and updates the obstacle positions in the local environment map according to the actual situation. The local environment map not only contains the positions of static obstacles, but also detects and updates the status of dynamic obstacles based on sensor data. Drones share their relative positions in real time based on global coordinates provided by GNSS or SLAM; Get the relative distance between drones and UWB beacon numbers; The drone detects and marks dynamic obstacles, which are then integrated into a local environment map that is updated in real time.

[0012] Preferably, the path planning of a single UAV is performed using the improved artificial potential field method that takes into account the distance gradient change as follows: Set the repulsive field of the obstacle and the gravitational field of the target point; The distance between the drone and the target point creates an attractive force, ensuring that the drone moves toward the target point; The drone detects the location of obstacles and calculates the distance to the obstacles. When approaching an obstacle, the repulsive field is dynamically corrected by detecting the distance between the drone and the obstacle. Use the one-dimensional distance information from the UAV to the target point provided by the UWB beacon to dynamically correct the gravitational field; When the drone approaches an area with multiple obstacles, it calculates the distance gradient information from the target point in real time through UWB information and updates the drone's speed by setting a smoothing factor. , smooth the motion trajectory of the drone.

[0013] Preferably, the repulsive field The calculation is as follows:

[0014] in, is the repulsion coefficient, d i is the distance between the drone and the obstacle, d safe It's a safe distance.

[0015] Preferably, the gravitational field is adjusted based on the distance gradient change , when the distance gradient changes <0, the speed of the drone approaching the target point slows down, increasing the gravity; when the distance gradient changes >0, the speed of the drone approaching the target point increases, the gravity is reduced, and the change degree of the distance ladder as follows:

[0016] in, is the distance between the UAV and the target point at time t.

[0017] Preferably, the gravity coefficient is dynamically adjusted based on the distance gradient change as follows:

[0018] Among them, α>1 means increasing the gravitational coefficient, and β<1 means decreasing the gravitational coefficient.

[0019] Preferably, the drone's speed is updated each time as follows:

[0020] in, is the smoothing factor, It is the total force at the current moment.

[0021] Preferably, the dynamic obstacle avoidance between heterogeneous multi-UAVs is specifically as follows: When multiple drones approach a multi-obstacle area at the same time, the relative distance between each drone and the adjacent drone is measured, and the size of the drone is confirmed by the UWB beacon number. The repulsion between drones is calculated based on the relative distance between drones. The repulsion of drone i on drone j in a multi-drone cluster is:

[0022] in, is the repulsion coefficient, s i and j is the size factor of the drone obtained by the UWB number, d is the distance between the two drones, is the relative position vector of UAV i pointing to UAV j; Use SLAM or GNSS to determine the position of the drone in the global coordinate system, and combine UWB data to calculate the repulsive direction of each drone relative to other drones; According to the motion state of the drone, the repulsion and gravitational field are updated in real time, and the repulsion is adjusted dynamically; Each drone performs collaborative planning through shared global information and UWB relative distance information to pass through multiple obstacle areas in sequence and without conflict.

[0023] In a second aspect, an embodiment of the present invention provides a heterogeneous multi-UAV collaborative planning system for complex environments, including: The information input module obtains the size classification of heterogeneous UAVs, the location of global target points, environmental perception information, relative distance information provided by UWB beacons, and prior information of the UWB beacon number corresponding to each UAV; The map building module maps the surrounding obstacle information into three-dimensional space based on the obtained prior information, generates a local environment map, marks the positions of heterogeneous multi-UAVs in the local environment map, and identifies and processes dynamic obstacles; The single UAV path planning module uses an improved artificial potential field method that takes into account the distance gradient change to plan the path of a single UAV; The heterogeneous multi-UAV collaboration module uses the position marks between heterogeneous UAVs and the relative distance information provided by UWB beacons to obtain the size information and relative distances of different UAVs, and performs dynamic obstacle avoidance between heterogeneous multi-UAVs.

[0024] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned heterogeneous multi-UAV collaborative planning method for complex environments when executing the computer program.

[0025] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned heterogeneous multi-UAV collaborative planning method for complex environments.

[0026] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned heterogeneous multi-UAV collaborative planning method for complex environments when executing the computer program.

[0027] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned heterogeneous multi-UAV collaborative planning method for complex environments.

[0028] Compared with the prior art, the present invention has at least the following beneficial effects: A collaborative planning method for heterogeneous multi-UAVs in complex environments. The improved artificial potential field method considering gradient changes can prevent the UAV from falling into local optimality when encountering obstacles. If there are obstacles in the forward direction, the repulsive force will affect the speed of the UAV, and then the distance gradient between the UAV and the target point will change; according to the change of distance gradient, the gravitational field of the end point is dynamically adjusted to ensure that the obstacle is bypassed at a certain speed. It can handle the problem of different types of UAVs working together in the same environment and is suitable for a wide range of UAV systems and mission types. By using environmental perception information and relative distance information provided by UWB beacons, UAVs can effectively identify and avoid static and dynamic obstacles, improving flight safety and reliability. An improved artificial potential field method that takes into account distance gradient changes is used for path planning to generate a smoother and more natural flight path, reducing the occurrence of sudden changes or non-optimal paths. By integrating the UWB beacon number and relative distance information of each UAV, accurate relative positioning and formation control can be achieved, enabling multi-UAV systems to perform complex collaborative tasks. Real-time updates of environmental maps are suitable for operation in complex and unknown environments. By optimizing path planning and collaboration between multiple UAVs, the time and resources required to complete tasks can be reduced, improving overall operational efficiency. Since UAVs of different sizes and capabilities can be handled, the UAV fleet can be flexibly configured according to mission requirements, enhancing the adaptability and versatility of the system. Through accurate location markings and relative distance information, UAVs can maintain a safe distance, effectively reducing the risk of collision during flight.

[0029] Furthermore, the UWB beacon number corresponds to the drone, which is convenient for corresponding the size factors between different drones. It can be directly used when calculating the repulsive field. The UWB beacon is used to directly provide the relative distance between drones, avoiding repeated calculations and reducing calculation delays.

[0030] Furthermore, by converting sensor data into a three-dimensional map representation, the drone can quickly build a model of the surrounding environment, which helps the drone to perform spatial navigation and obstacle avoidance; the drones share their relative positions in real time based on the global coordinates provided by GNSS or SLAM, and use the distance information provided by UWB beacons to determine the relative distance between drones. This can ensure that the drones maintain relative distances and avoid collisions when perception degrades or some sensors fail. Multiple drones can observe dynamic obstacles at different angles, facilitating comprehensive obstacle marking.

[0031] Furthermore, by setting up a UWB (ultra-wideband) base station at the end point, the distance change rate between the drone and the target point is monitored in real time, and the gravity exerted on the drone is dynamically adjusted to ensure that the drone can overcome the local optimal solution problem and pass through the narrow multi-obstacle area smoothly; the UWB (ultra-wideband) beacon is used to provide accurate one-dimensional distance information for the drone, so that each drone can accurately perceive the relative distance to other drones with low latency, and adjust the strength of the repulsive field according to the distance information. At the same time, according to the characteristics of heterogeneous drones of different sizes, the size factor is introduced to adjust the repulsive field parameters. Through this improved repulsive field design, the collision problem of drones in narrow multi-obstacle areas can be effectively avoided; by introducing a smoothing processing mechanism in path planning, it is ensured that the drone's path not only considers gravity and repulsion, but also has flight smoothness. In the narrow multi-obstacle area, the drone can pass smoothly according to the optimized path, avoiding the incoherence and sharp turns of the path, and improving the efficiency and safety of the flight.

[0032] Furthermore, when drone swarms perform collaborative planning tasks, if the task is located in a complex environment, the drones cannot be collaboratively planned according to the pre-set route. It is necessary to ensure that the drones have the ability to avoid dynamic obstacles and the drones have the ability to avoid each other, so dynamic obstacle avoidance is required.

[0033] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0034] In summary, the method of the present invention can optimize and improve the path planning and collaborative passing capabilities of distributed UAV clusters in narrow and multi-obstacle areas using the artificial potential field method, and improve the passing efficiency, path accuracy and conflict avoidance capabilities of UAV clusters.

[0035] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is the road map of the present invention; Figure 2This is a schematic diagram of a single UAV path planning module of the present invention; Figure 3 This is a schematic diagram of a heterogeneous multi-UAV path planning module of the present invention; Figure 4 A schematic diagram of a computer device provided by an embodiment of the present invention.

[0037] Figure 5 The block diagram of a chip provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0040] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0041] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0042] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0043] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0044] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0045] The present invention provides a collaborative planning method for heterogeneous multiple UAVs in complex environments. The method utilizes the distance information provided by UWB (ultra-wideband) and establishes a path planning algorithm and a collision avoidance mechanism based on an improved artificial potential field method, so that heterogeneous multiple UAVs can safely and efficiently traverse narrow areas with multiple obstacles, avoid mutual collisions and complete their tasks.

[0046] The present invention aims to solve the following problems: 1) Mutual obstacle avoidance and coordination between heterogeneous UAVs: When multiple UAVs are performing tasks simultaneously, how to use relative position and distance information to avoid collisions between UAVs and ensure that multiple UAVs can pass through narrow areas with multiple obstacles in sequence; 2) Obstacle avoidance and path planning in complex environments: In complex environments, such as narrow passages or areas with dense obstacles, how to use improved path planning algorithms to ensure that the drone can pass smoothly and avoid collisions with obstacles; 3) Jitter and sharp turn problems in path planning: Traditional path planning methods (such as artificial potential field method) may produce uneven paths or sharp turns, resulting in unstable UAV motion. This invention solves the problem of path jitter or sudden changes in the path planning process through path smoothing, ensuring that the UAV motion trajectory is smooth and natural; 4) Local optimal problem in path planning: When using the artificial potential field method for local path planning, local optimal problems will occur when passing through narrow areas, making it impossible for the drone to move forward.

[0047] The present invention provides a heterogeneous multi-UAV collaborative planning method for complex environments, comprising the following steps: S1, input prior information; It includes the size classification of heterogeneous UAVs, the location of global target points, environmental perception information, relative distance information provided by UWB beacons, and the UWB beacon number corresponding to each UAV.

[0048] Each drone will preset the global position coordinates of the target point at the beginning of the mission, and obtain the target point position through GNSS (Global Navigation Satellite System) or external input; Through laser radar or depth camera, the drone perceives the three-dimensional information of the surrounding environment in real time, including the location, shape and size of obstacles, captures the location information of obstacles, and simultaneously obtains its own position; Each drone is equipped with a UWB (ultra-wideband) module to measure the relative distance between adjacent drones and ensure a safe distance between multiple drones.

[0049] UWB (Ultra-Wideband) ranging has centimeter-level accuracy, which can ensure safe distance control between drones.

[0050] S2, collect information and generate maps; Map the surrounding obstacle information into three-dimensional space to generate a local environment map. Mark the positions of heterogeneous multi-UAVs in the map, including the marking of UWB beacon numbers. Identify dynamic obstacles and process information.

[0051] S201, based on the drone's sensor data (lidar or depth camera), the drone maps surrounding obstacle information onto a three-dimensional map to generate a local environment map; The map not only contains the locations of static obstacles, but also detects and updates the status (e.g., position, speed) of dynamic obstacles based on sensor data.

[0052] S202, the current position of each drone and the position of the target point will be marked on the map; Share relative positions in real time via global coordinates provided by GNSS (Global Navigation Satellite System) or SLAM (Simultaneous Localization and Mapping), and relative distances directly via UWB (Ultra-Wideband) beacons; S203, obtaining the relative distance between the drones and the UWB (ultra-wideband) beacon numbers between the drones through the UWB (ultra-wideband) module, so as to ensure the coordinated operation of the drones in a narrow space; S204. The drone detects and marks dynamic obstacles (such as other drones, pedestrians, or vehicles) through sensor data and integrates them into the map for real-time update.

[0053] S3, path planning of a single UAV, using an improved artificial potential field method that considers distance gradient changes; Each drone performs path planning based on the artificial potential field method; The distance between the drone and the target point generates an attractive force, ensuring that the drone moves toward the target point. At the same time, the drone detects the position of obstacles through sensors and calculates the distance to the obstacles. The obstacles generate repulsive forces, which increase as the distance decreases, preventing the drone from colliding with obstacles.

[0054] S301, by detecting the distance between the UAV and the obstacle, correcting the repulsive field so that the closer the distance to the obstacle is, the greater the repulsive force is, thereby ensuring that the UAV will not collide with the obstacle; When the drone approaches an obstacle, the repulsive field is dynamically adjusted according to the distance between the drone and the obstacle; The repulsive force calculation formula is:

[0055] in, is the repulsion coefficient, d i is the distance between the drone and the obstacle, d safe It's a safe distance.

[0056] As the drone approaches an obstacle, the repulsive force increases significantly, ensuring it stays away from the obstacle.

[0057] S302, based on the distance gradient change between the UAV and the target point, the gravity field is dynamically adjusted to prevent the UAV from falling into the local optimal solution, ensuring that the UAV can smoothly pass through the narrow area with many obstacles; the adjustment of the gravity field is based on the direction of the distance gradient change. When the speed of the UAV approaching the target point slows down, the gravity coefficient is increased, and when the speed increases, the gravity coefficient is reduced, which can ensure that the UAV is out of the local optimal solution; In order to more precisely control the path of the UAV in a narrow area with multiple obstacles, the one-dimensional distance information from the UAV to the target point provided by the UWB (ultra-wideband) beacon is used to correct the gravitational field.

[0058] When the drone approaches a narrow area with many obstacles, the surrounding environment changes dramatically and the space on the side of the drone is rapidly compressed, resulting in a significant increase in the repulsive force in the narrow area with many obstacles. It is necessary to use UWB (ultra-wideband) information to correct the gravitational field. The drone uses UWB (ultra-wideband) information to calculate the distance gradient information from the target point in real time. If the distance gradient decreases, the system will dynamically increase the gravity to ensure that the drone always stays close to the target point.

[0059] Gravitational field correction requires the following steps:

[0060] in, is the distance between the drone and the target point at time t, is the time step.

[0061]

[0062] Variation of distance gradient Reflects the speed change of the drone approaching the target point: when <0, indicating that the speed of the UAV approaching the target point slows down, and the gravity is increased to speed up the approach to the target; when >0, it means that the UAV is approaching the target point faster, reducing gravity, avoiding rushing to the target too quickly, and preventing system instability.

[0063] Based on the change of distance gradient, the gravitational coefficient Dynamically adjusted to:

[0064] Among them, α>1 means increasing the gravity coefficient, which is suitable for the situation where the UAV approaches at a slower speed; β<1 means reducing the gravity coefficient, which is suitable for the situation where the UAV approaches at a too fast speed.

[0065] S303, by setting a smoothing factor, suppressing the local oscillation of the UAV during the movement, smoothing the movement trajectory of the UAV, and ensuring the path stability.

[0066] Based on the artificial potential field method, a smoothing factor is introduced to prevent the drone from vibrating violently due to changes in gravity and repulsion. Smoothing optimizes the curvature of the path to make the drone's movement smoother and adapt to actual flight scenarios.

[0067] In order to avoid sudden jitters in the drone path due to changes in the combined force, a smoothing factor is introduced to prevent abrupt changes in the drone speed. The drone updates its speed at a certain frequency. Calculated by the following formula:

[0068] in, is the smoothing factor, is the total force at the current moment (the combined force of gravity and repulsion).

[0069] Through the above methods, it can be ensured that a single drone can smoothly pass through narrow areas with multiple obstacles.

[0070] S4. Use the UWB beacon numbers and distance information provided between heterogeneous UAVs to obtain the size information and relative distances of different UAVs, and perform dynamic obstacle avoidance between heterogeneous UAVs.

[0071] S401, measuring the relative distance between each drone and the adjacent drones, and confirming the size of the drones through the UWB beacon number, where different numbers correspond to different size factors, and calculating the repulsive force between the drones based on the measurement results; When multiple drones approach a narrow area with multiple obstacles at the same time, the relative distance between drones provided by the UWB (ultra-wideband) module is used to calculate the repulsion between drones; The relative positions of the drones are determined using SLAM (Simultaneous Localization and Mapping) or GNSS (Global Navigation Satellite System), which in turn determines the direction of the repulsion. Similar to the repulsion of obstacles, the repulsion between drones ensures that they do not collide with each other. The calculation of the repulsion is dynamically adjusted based on the distance between the drones. The closer the distance, the greater the repulsion.

[0072] Assume that drone i is in a multi-drone cluster and the repulsive force it receives from drone j is:

[0073] in, is the repulsion coefficient, s i and j is the size factor of the drone obtained by UWB (Ultra Wideband) number, d is the distance between the two drones, is the relative position vector of UAV i pointing to UAV j.

[0074] S402, using SLAM (simultaneous localization and mapping) or GNSS (global navigation satellite system) system to determine the position of the drone in the global coordinate system, combined with UWB (ultra-wideband) data, to calculate the repulsive direction of each drone relative to other drones, to avoid collisions between multiple drones during movement; Drones know each other's positions through GNSS (Global Navigation Satellite System) or SLAM (Simultaneous Positioning and Mapping), and directly obtain each other's high-precision distance information through UWB (Ultra-Wideband) information, which can avoid calculation delays and ensure the real-time nature of distance information; thus dynamically adjusting their own flight direction and speed. If two drones are too close, the repulsive force increases, forcing them to adjust their flight trajectories so that they can pass through narrow and multi-obstacle areas in sequence.

[0075] S403. In narrow areas with multiple obstacles, the relative position and distance of each drone is measured through UWB (ultra-wideband), and the repulsion force is dynamically adjusted so that the drones can pass through in sequence to avoid obstruction or collision between drones; S404. Each UAV performs collaborative planning through shared global information and UWB (ultra-wideband) relative distance information to ensure that each UAV passes through a narrow area in sequence and without conflict.

[0076] According to the movement status of the drone, the repulsion and gravitational fields are updated in real time to ensure that multiple drones can pass through a narrow space in sequence and avoid conflicts.

[0077] S5. Execution and feedback.

[0078] S501, each UAV flies according to the calculated path and fine-tunes the path according to the sensor data updated in real time; S503. The system continuously monitors the motion state of the drone through sensors. When it detects that the drone's path deviates or the environment changes (such as the appearance of new obstacles), the system automatically adjusts the gravity and repulsion parameters to correct the path.

[0079] In another embodiment of the present invention, a heterogeneous multi-UAV collaborative planning system for complex environments is provided. The system can be used to implement the above-mentioned heterogeneous multi-UAV collaborative planning method for complex environments. Specifically, the heterogeneous multi-UAV collaborative planning system for complex environments includes an information input module, a map building module, a single UAV path planning module and a heterogeneous multi-UAV collaborative planning module.

[0080] Among them, the information input module obtains the size classification of heterogeneous UAVs, the location of global target points, environmental perception information, relative distance information provided by UWB beacons, and prior information of the UWB beacon number corresponding to each UAV; The map building module maps the surrounding obstacle information into three-dimensional space based on the obtained prior information, generates a local environment map, marks the positions of heterogeneous multi-UAVs in the local environment map, and identifies and processes dynamic obstacles; The single UAV path planning module uses an improved artificial potential field method that takes into account the distance gradient change to plan the path of a single UAV; The heterogeneous multi-UAV collaboration module uses the position marks between heterogeneous UAVs and the relative distance information provided by UWB beacons to obtain the size information and relative distances of different UAVs, and performs dynamic obstacle avoidance between heterogeneous multi-UAVs.

[0081] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a heterogeneous multi-UAV collaborative planning method for complex environments, including: The prior information including the size classification of heterogeneous UAVs, the location of global target points, environmental perception information, relative distance information provided by UWB beacons, and the UWB beacon number corresponding to each UAV is obtained; based on the obtained prior information, the surrounding obstacle information is mapped into three-dimensional space to generate a local environment map, and the positions of heterogeneous multi-UAVs are marked in the local environment map, and dynamic obstacles are identified and information processed; the path planning of a single UAV is performed using the improved artificial potential field method that considers the change of distance gradient; the size information and relative distance of different UAVs are obtained by using the position marks between heterogeneous UAVs and the relative distance information provided by UWB beacons, and dynamic obstacle avoidance is performed between heterogeneous multi-UAVs.

[0082] See also Figure 4 , the terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for heterogeneous multi-UAV collaborative planning for complex environments in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the heterogeneous multi-UAV collaborative planning system for complex environments in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0083] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 4 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0084] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0085] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0086] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0087] See also Figure 5 , the terminal device is a chip, and the chip 600 of this embodiment includes a processor 622, which may be one or more, and a memory 632 for storing a computer program executable by the processor 622. The computer program stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. In addition, the processor 622 may be configured to execute the computer program to execute the above-mentioned heterogeneous multi-UAV collaborative planning method for complex environments.

[0088] In addition, the chip 600 may further include a power supply component 626 and a communication component 650, wherein the power supply component 626 may be configured to perform power management of the chip 600, and the communication component 650 may be configured to implement communication, for example, wired or wireless communication, of the chip 600. In addition, the chip 600 may further include an input / output interface 658. The chip 600 may operate based on an operating system stored in the memory 632.

[0089] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory.

[0090] The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the heterogeneous multi-UAV collaborative planning method for complex environments in the above embodiment; the processor may load and execute the following steps: The prior information including the size classification of heterogeneous UAVs, the location of global target points, environmental perception information, relative distance information provided by UWB beacons, and the UWB beacon number corresponding to each UAV is obtained; based on the obtained prior information, the surrounding obstacle information is mapped into three-dimensional space to generate a local environment map, and the positions of heterogeneous multi-UAVs are marked in the local environment map, and dynamic obstacles are identified and information processed; the path planning of a single UAV is performed using the improved artificial potential field method that considers the change of distance gradient; the size information and relative distance of different UAVs are obtained by using the position marks between heterogeneous UAVs and the relative distance information provided by UWB beacons, and dynamic obstacle avoidance is performed between heterogeneous multi-UAVs.

[0091] In order to make the purpose, 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 in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0092] System hardware deployment This system is mainly composed of the following hardware devices: Drone platform: equipped with LiDAR (or depth camera), UWB (ultra-wideband) module, IMU (inertial measurement unit), GNSS (global navigation satellite system) receiver (or SLAM (simultaneous localization and mapping) module).

[0093] Controller: Each drone carries an autonomous flight controller that processes sensor data, calculates path planning, and executes flight control commands.

[0094] Communication module: Drones share and coordinate data through wireless communication modules (such as Wi-Fi, 4G / 5G).

[0095] UWB (Ultra-Wideband) module: used for accurate distance measurement between drones and between drones and target points, ensuring that multiple drones can avoid each other in a narrow space.

[0096] Use 3D matrices or other suitable data structures in MATLAB to represent the flight environment, including static and dynamic obstacles.

[0097] Simulate the working principle and data output of sensors such as LiDAR or depth camera, UWB module, IMU, GNSS (or SLAM) to test algorithms without actual sensors.

[0098] Build a physical model of the drone, including its dynamics, so that the simulation accurately reflects its real-world behavior.

[0099] Write code to implement path planning, obstacle avoidance, attitude control, and other necessary algorithms.

[0100] Simulate the process of data exchange between drones through wireless communication modules, which includes relative position information, obstacle detection data, and other information required for collaborative flight.

[0101] Run simulation experiments in MATLAB to observe how the multi-UAV system performs under various preset scenarios.

[0102] The simulation results are used to evaluate the performance of the system, such as the efficiency of path planning, the success rate of obstacle avoidance, and the coordination between multiple drones.

[0103] The algorithm parameters are adjusted according to the simulation results to achieve the best flight effect.

[0104] Use MATLAB's graphics processing capabilities to visualize the UAV's flight trajectory, obstacle avoidance, and group collaboration.

[0105] Finally, we used MATLAB’s code generation tools to convert it into code that can run on actual drone hardware.

[0106] In summary, the heterogeneous multi-UAV collaborative planning method and system for complex environments of the present invention has advantages in the following aspects: 1) Wider application scenarios In the existing methods, when a drone suddenly passes through a narrow pass, or passes through a narrow pipe from a wider position, the repulsive force provided by the obstacles on both sides in front can still cause the drone to enter a local optimum and be unable to move forward. The method of the present invention has a wider application scenario and can prevent the drone from entering the local optimum in more scenarios.

[0107] 2) Improve the efficiency of drones passing through narrow and multi-obstacle areas The existing artificial potential field method is prone to fall into local optimality, resulting in the inability of drones to effectively pass through narrow areas. However, the present invention dynamically corrects the gravitational field based on the gradient change of the distance between the drone and the target point to ensure that the drone can adjust the gravity in time when approaching the target to avoid slow speed or stagnation. Therefore, the present invention can improve the efficiency of drones passing through narrow and multi-obstacle areas in complex environments and avoid path pauses or detours.

[0108] 3) Improved repulsive field correction to enhance obstacle avoidance The traditional artificial potential field method often has insufficient design of the repulsive field when the distance to the obstacle is close, resulting in the risk of collision of the drone. The present invention optimizes the repulsive field design, especially introduces UWB (ultra-wideband) ranging technology to accurately measure the distance between the obstacle and the adjacent drone, making the repulsive force calculation more accurate. The drone can generate obstacle avoidance behavior in time to avoid collision with static or dynamic obstacles, thereby enhancing the safety of obstacle avoidance.

[0109] 4) Conflict avoidance mechanism for heterogeneous multi-UAV collaboration In the prior art, the path planning and conflict avoidance of multiple drones often rely on centralized computing, which has computing delays and single-point failure problems. The present invention introduces a method combining UWB (ultra-wideband) ranging technology and SLAM (simultaneous localization and mapping) or GNSS (global navigation satellite system) to enable each drone to autonomously calculate the repulsive force between other drones, determine the drone size factor through the UWB (ultra-wideband) beacon number, and realize distributed path planning. The drone can autonomously adjust the flight path locally to avoid collisions with other drones, thereby improving the stability and safety of the system, especially the coordinated passage of multiple drones in complex environments such as narrow and multi-obstacle areas.

[0110] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0111] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0112] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0113] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0114] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0116] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0117] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0118] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0119] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0120] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A heterogeneous multi-UAV collaborative planning method for complex environments, characterized in that: The following steps are involved: Acquire the prior information including the size classification of heterogeneous UAVs, the location of global target points, environmental perception information, relative distance information provided by UWB beacons, and the UWB beacon number corresponding to each UAV; Based on the obtained prior information, the surrounding obstacle information is mapped into three-dimensional space to generate a local environment map, in which the positions of heterogeneous multi-UAVs are marked, and dynamic obstacles are identified and information processed; The improved artificial potential field method considering the distance gradient change is used to carry out the path planning of a single UAV; The position marks between heterogeneous UAVs and the relative distance information provided by UWB beacons are used to obtain the size information and relative distances of different UAVs, and dynamic obstacle avoidance is performed between heterogeneous multiple UAVs.

2. The method for heterogeneous multi-UAV collaborative planning in complex environments according to claim 1 is characterized in that: The prior information including the size classification of heterogeneous UAVs, the location of global target points, environmental perception information, relative distance information provided by UWB beacons, and the UWB beacon number corresponding to each UAV is obtained as follows: At the beginning of the mission, the global position coordinates of the drone target point are preset; The drone perceives the surrounding environment in real time, captures the location information of obstacles, and simultaneously obtains its own position; and measures the relative distance between adjacent drones.

3. The method for heterogeneous multi-UAV collaborative planning in complex environments according to claim 1 is characterized in that: Generate a local environment map, mark the positions of heterogeneous multi-UAVs in the local environment map, and identify and process dynamic obstacles. Specifically: Based on the sensor data of the drone, the drone maps the surrounding obstacle information onto a three-dimensional map to generate a local environment map, and updates the obstacle positions in the local environment map according to the actual situation. The local environment map not only contains the positions of static obstacles, but also detects and updates the status of dynamic obstacles based on sensor data. Drones share their relative positions in real time based on global coordinates provided by GNSS or SLAM; Get the relative distance between drones and UWB beacon numbers; The drone detects and marks dynamic obstacles, which are then integrated into a local environment map that is updated in real time.

4. The method for heterogeneous multi-UAV collaborative planning in complex environments according to claim 1 is characterized in that: The path planning of a single UAV using the improved artificial potential field method considering the distance gradient change is as follows: Set the repulsive field of the obstacle and the gravitational field of the target point; The distance between the drone and the target point creates an attractive force, ensuring that the drone moves toward the target point; The drone detects the location of obstacles and calculates the distance to the obstacles. When approaching an obstacle, the repulsive field is dynamically corrected by detecting the distance between the drone and the obstacle. Use the one-dimensional distance information from the UAV to the target point provided by the UWB beacon to dynamically correct the gravitational field; When the drone approaches an area with multiple obstacles, it calculates the distance gradient information from the target point in real time through UWB information and updates the drone's speed by setting a smoothing factor. , smooth the motion trajectory of the drone.

5. The method for heterogeneous multi-UAV collaborative planning in complex environments according to claim 4 is characterized in that: Repulsive field The calculation is as follows: in, is the repulsion coefficient, d i is the distance between the drone and the obstacle, d safe It's a safe distance.

6. The method for heterogeneous multi-UAV collaborative planning in complex environments according to claim 4 is characterized in that: Gravitational field adjustment based on distance gradient changes , when the distance gradient changes <0, the speed of the drone approaching the target point slows down, increasing the gravity; when the distance gradient changes >0, the speed of the drone approaching the target point increases, the gravity is reduced, and the change degree of the distance ladder as follows: in, is the distance between the UAV and the target point at time t.

7. The method for heterogeneous multi-UAV collaborative planning in complex environments according to claim 6 is characterized in that: Dynamically adjust gravity coefficient based on distance gradient changes as follows: Among them, α>1 means increasing the gravitational coefficient, and β<1 means decreasing the gravitational coefficient.

8. The method for heterogeneous multi-UAV collaborative planning in complex environments according to claim 4 is characterized in that: The speed of the drone at each update as follows: in, is the smoothing factor, It is the total force at the current moment.

9. The method for heterogeneous multi-UAV collaborative planning in complex environments according to claim 1 is characterized in that: Dynamic obstacle avoidance between heterogeneous multi-UAVs is as follows: When multiple drones approach a multi-obstacle area at the same time, the relative distance between each drone and the adjacent drone is measured, and the size of the drone is confirmed by the UWB beacon number. The repulsion between drones is calculated based on the relative distance between drones. The repulsion of drone i on drone j in a multi-drone cluster is: in, is the repulsion coefficient, s i and j is the size factor of the drone obtained by the UWB number, d is the distance between the two drones, is the relative position vector of UAV i pointing to UAV j; Use SLAM or GNSS to determine the position of the drone in the global coordinate system, and combine UWB data to calculate the repulsive direction of each drone relative to other drones; According to the motion state of the drone, the repulsion and gravitational field are updated in real time, and the repulsion is adjusted dynamically; Each drone performs collaborative planning through shared global information and UWB relative distance information to pass through multiple obstacle areas in sequence and without conflict.

10. A heterogeneous multi-UAV collaborative planning system for complex environments, characterized in that: include: The information input module obtains the size classification of heterogeneous UAVs, the location of global target points, environmental perception information, relative distance information provided by UWB beacons, and prior information of the UWB beacon number corresponding to each UAV; The map building module maps the surrounding obstacle information into three-dimensional space based on the obtained prior information, generates a local environment map, marks the positions of heterogeneous multi-UAVs in the local environment map, and identifies and processes dynamic obstacles; The single UAV path planning module uses an improved artificial potential field method that takes into account the distance gradient change to plan the path of a single UAV; The heterogeneous multi-UAV collaboration module uses the position marks between heterogeneous UAVs and the relative distance information provided by UWB beacons to obtain the size information and relative distances of different UAVs, and performs dynamic obstacle avoidance between heterogeneous multi-UAVs.

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