A drone cooperative control system

Abstract

This invention discloses a collaborative control system for unmanned aerial vehicles (UAVs), relating to the field of UAV control technology. The system includes a perception module that uses a distributed sensor network to perceive the dynamic relationships between UAVs in real time; an identification module that automatically enters a collaborative adaptation state when it detects potential imbalances in the collaborative state of the UAV group and a persistent trend; and a control module that maintains the collaborative stability of the UAV group based on a self-organizing control method in the collaborative adaptation state, thereby avoiding the gradual decay of formation collaboration efficiency. This invention achieves proactive maintenance of the collaborative stability of the UAV group, effectively avoiding the gradual decay of formation collaboration efficiency and solving the problems of lag and passive control in traditional control systems.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) control technology, specifically a UAV collaborative control system. Background Technology

[0002] During drone swarm missions, the dynamic relationships between individual drones significantly impact collaborative effectiveness. Complex external environmental interferences, such as strong winds altering flight paths, electromagnetic interference affecting signal transmission, and varying drone equipment performance, can easily lead to potential imbalances in the drone swarm's collaborative state. If these imbalances persist and become a trend, the formation's collaborative effectiveness will gradually decline, severely reducing the accuracy and efficiency of mission execution.

[0003] Traditional UAV control systems, limited by technology, struggle to accurately perceive real-time changes in the dynamic relationships between UAVs. Furthermore, they are unable to implement timely and effective adjustments when signs of imbalance in the collaborative state emerge and persist. Remedial measures are often only taken after a significant decline in formation coordination efficiency, making it difficult to guarantee high-efficiency collaboration throughout the entire mission. Therefore, accurately and in real-time perceiving the dynamic relationships between UAVs, quickly identifying potential imbalances in the collaborative state, and effectively adjusting them has become a core issue in maintaining the stability of UAV group collaboration and preventing the decline in collaborative efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a collaborative control system for unmanned aerial vehicles (UAVs) that solves the technical problem in the prior art of being unable to identify potential signs of imbalance and persistent trends in the collaborative state of a group and to adaptively adjust accordingly.

[0005] A drone collaborative control system includes:

[0006] The perception module uses a distributed sensor network to perceive the dynamic relationships between various drones in real time.

[0007] The identification module automatically enters the collaborative adaptation state when it identifies potential signs of imbalance in the collaborative state of the drone group and forms a continuous trend.

[0008] The control module maintains the stability of the drone swarm's coordination based on a self-organizing control method under the collaborative adaptation state, so as to avoid the gradual decay of the formation's coordination efficiency.

[0009] Furthermore, in the distributed sensor network of the sensing module:

[0010] After capturing the position, flight speed and acceleration of other drones in the vicinity by the sensing units of each drone, the parameter information is obtained. This parameter information is incorporated into a self-growing dynamic evolution model, which transforms the position information into coordinate points whose dimensions are dynamically adjusted according to the size of the group, and the speed and acceleration into the dynamic values ​​that drive the coordinate points to move.

[0011] The delay and interruption of information transmission are converted into interference in the dynamic grid, and the interference decays non-linearly over time.

[0012] When the dynamic values ​​of multiple drones form an interactive cycle, a cooperative flow characteristic reflecting the inertia of the group's motion is automatically generated.

[0013] Furthermore, after the identification module receives the dynamic association information from the perception module:

[0014] When analyzing the distance variation and trajectory overlap between UAVs, the distance change rate and trajectory divergence rate are combined to construct a dynamic correlation grid that reflects the group's collaborative trend.

[0015] When the intensity of change in any local area within the associated grid exceeds the average level of the population, and the intensity difference between the local area and its adjacent areas shows an increasing trend and forms a continuous diffusion path, it is determined as the initial formation of a potential imbalance trend without waiting for the specific changes in distance or trajectory to reach a critical state.

[0016] Furthermore, the collaborative adaptation state includes small sliding adjustments for local imbalances and partition migration adjustments for overall imbalances;

[0017] During small-scale sliding adjustments, the unbalanced drone predicts its trajectory based on the motion parameters of neighboring drones, finely adjusts its position along the direction of force, and simultaneously monitors surrounding trajectories to avoid interference.

[0018] During the partition migration adjustment, the group first divides virtual blocks according to the distribution of real-time communication connections. Potential congestion paths are predicted by extrapolating the signal interaction strength and historical motion inertia of each block. Areas with weak signal connections are cleared first, and then the group gradually migrates from blocks with dense signal connections to sparse blocks, maintaining a dynamic buffer spacing at the edges of each block during the migration.

[0019] Furthermore, the organization and control adopts a distributed signal correlation, and the dynamic signal contains phase adjustment coding;

[0020] The relative layout of the group can be inferred by the signal phase difference between any two drones, and the phase correlation can be adjusted in combination with the imbalance trend. In the process of promoting the group to form a stable cooperative mode, potential conflict points in signal transmission are continuously monitored.

[0021] Based on the signal frequency overlap and transmission path intersection, the phase parameters of relevant UAVs can be adjusted in advance to avoid new imbalances caused by signal interference.

[0022] Furthermore, during the flight parameter adjustment phase, the unbalanced UAV first acquires the current flight parameters of the adjacent UAVs and delineates the adjustable range based on the remaining energy consumption capacity of both UAVs.

[0023] Within this range, the flight altitude and speed range of each aircraft are determined through real-time interaction, so that the parameters of adjacent aircraft maintain both differences and complementarity, avoiding mutual interference.

[0024] During the overall adaptation phase, the control module first summarizes the distribution of flight parameters of the group and categorizes them according to parameter similarity;

[0025] For categories with overly concentrated parameters, a differentiated adjustment command is automatically triggered to guide similar drones to disperse into complementary parameter ranges;

[0026] During the adjustment process, the dynamic correlation information transmitted from the reference sensing module is synchronized to ensure that the parameter adjustment range matches the group's collaborative state and maintains overall stability.

[0027] Furthermore, it also includes:

[0028] Convert the drone's motion state into virtual pheromone concentration;

[0029] The concentration accumulates along the flight path and decreases in a gradient along the airflow direction. By sensing the concentration distribution, the group can predict the movement tendency of its companions and avoid potential trajectory conflicts in advance.

[0030] Furthermore, it also includes:

[0031] The movement of drone swarms is viewed as a dynamic flow field;

[0032] When a rotating airflow with an abnormal velocity gradient appears in a local area, and the angle between the direction of this movement and the mainstream direction of the population shows an increasing trend, it is directly identified as a precursor to imbalance and triggers early intervention.

[0033] Furthermore, it also includes:

[0034] During partition migration and adjustment, the migration priority and time window of each block are preset so that the movement rhythm of adjacent blocks forms a complementary phase, avoiding path intersection congestion during the migration process.

[0035] Furthermore, it also includes:

[0036] Self-organizing regulation incorporates historical association memory characteristics;

[0037] When two drones have established a stable signal association, even if they temporarily leave the communication range, they still retain implicit regulatory weights based on historical associations, which accelerate collaborative adaptation when re-establishing the connection.

[0038] Compared with the prior art, the beneficial effects of the present invention are:

[0039] This invention uses a distributed sensor network to perceive the dynamic relationships between UAVs in real time, and combines this with an identification module to accurately identify potential imbalances and persistent trends in the group's collaborative state. This triggers a self-organizing control mode of the control module, forming a complete closed loop from perception and identification to control. This achieves the proactive maintenance of the collaborative stability of the UAV group, effectively avoids the gradual decay of formation collaboration efficiency, and solves the problems of slow response and passive control in traditional control systems. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of the system framework structure of the present invention. Detailed Implementation

[0041] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Please see Figure 1 This application provides a collaborative control system for unmanned aerial vehicles (UAVs), comprising:

[0043] The perception module uses a distributed sensor network to perceive the dynamic relationships between various drones in real time.

[0044] The identification module automatically enters the collaborative adaptation state when it identifies potential signs of imbalance in the collaborative state of the drone group and forms a continuous trend.

[0045] The control module maintains the stability of the drone swarm's coordination based on a self-organizing control method under the collaborative adaptation state, so as to avoid the gradual decay of the formation's coordination efficiency.

[0046] In this embodiment, the distributed sensor network refers to a network composed of various sensors deployed on each UAV, including millimeter-wave radar, lidar, etc., used to collect information such as the position, speed, attitude and relative distance of the UAV in real time, so as to achieve comprehensive perception of the dynamic relationship between the UAVs.

[0047] Dynamic correlation refers to the mutual relationships and influences among drones in a drone swarm in terms of position, speed, and heading. Specifically, it can be characterized by parameters such as the relative distance, speed difference, and heading angle of each drone, and is used to reflect the tightness and stability of swarm coordination.

[0048] Potential signs of imbalance refer to early signs that indicate a possible imbalance in the collaborative state of a drone group. These signs can be identified by analyzing the data collected by the perception module using the identification module, thus enabling early detection of anomalies in the collaborative state.

[0049] A persistent trend refers to a state in which potential imbalances persist or intensify over multiple consecutive sampling periods. For example, if a drone deviates from its preset flight path for 5 consecutive seconds and the deviation distance gradually increases, this can be determined by the time-series analysis of the data through the identification module. This is used to distinguish between occasional fluctuations and persistent changes that may lead to imbalances.

[0050] Among them, the collaborative adaptation state refers to a special working state that the system enters after the identification module determines that there are potential signs of imbalance in the collaborative state of the UAV group and that this imbalance is forming a continuous trend. At this time, the control module will activate the self-organizing control mechanism to prioritize the stability of the group's collaboration, and the adjustment threshold of relevant parameters will be appropriately relaxed to adapt to the control requirements.

[0051] Self-organizing control refers to the control module, under coordinated adaptation, using real-time status information of each UAV to autonomously adjust the behavior of the group through autonomous negotiation and information exchange between UAVs, thereby enabling rapid response to situations of coordination imbalance.

[0052] Formation coordination effectiveness refers to the ability and efficiency of a swarm of drones to perform tasks as a whole. It can be measured by indicators such as task completion time, formation formation maintenance accuracy, and information exchange success rate. For example, in reconnaissance missions, the coverage of the formation on the target and the clarity of image acquisition are used to evaluate the effectiveness of group coordination.

[0053] Progressive decay refers to the process by which formation coordination effectiveness gradually declines over time. It manifests as a gradual increase in task completion time, a gradual decrease in formation maintenance accuracy, and a gradual increase in information exchange delay. It is usually caused by the failure to correct imbalances in coordination in a timely manner and is used to describe the deteriorating trend of coordination effectiveness.

[0054] The core innovation of this application lies in the real-time perception of the dynamic relationship between UAVs through a distributed sensor network, combined with the identification module to accurately identify potential imbalances and persistent trends in the group's collaborative state, thereby activating the self-organizing control mode of the control module. This forms a complete working process from perception and identification to control, realizing the proactive maintenance of the collaborative stability of the UAV group, effectively avoiding the gradual decay of formation collaboration efficiency, and solving the problems of slow response and passive control in traditional control systems.

[0055] For example, the perception module collects information such as the position, speed, and attitude of each UAV in real time through a distributed sensor network, and calculates parameters such as the relative distance, speed difference, and heading angle between each UAV, thereby perceiving the dynamic relationship between them.

[0056] The identification module continuously monitors and analyzes the dynamic correlation parameters acquired by the sensing module. When the parameters are found to be outside the normal range, potential imbalance signs are identified. If such signs persist or worsen within five consecutive sampling periods, a continuous trend is determined, and the system automatically enters a collaborative adaptation state.

[0057] In the collaborative adaptation state, the control module adopts a self-organizing control method. Each UAV autonomously negotiates based on the real-time status information of neighboring UAVs and adjusts its own speed, heading and other parameters. For example, UAVs that deviate from the flight path actively decelerate and adjust their heading, while neighboring UAVs accelerate appropriately to maintain the collaborative stability of the group.

[0058] During the adjustment process, relevant indicators of formation coordination effectiveness are continuously monitored until potential signs of imbalance are eliminated, the coordination state returns to normal, and the system exits the coordination adaptation state, so as to avoid a gradual decline in formation coordination effectiveness.

[0059] As a preferred implementation, the UAV collaborative control system of this application is specifically implemented as follows:

[0060] First, in a formation of 10 drones, each drone is equipped with a millimeter-wave radar, a GPS module (for positioning accuracy), and an IMU, forming a distributed sensor network that collects and shares the status information of each drone in real time via wireless communication, with a sampling period of 1 second.

[0061] The perception module calculates dynamic correlation parameters based on the collected information: for example, the relative distance between UAV 1 and UAV 2 is 60 meters, their speeds are 15m / s and 14m / s respectively, the speed difference is 1m / s, and the heading angle is 8 degrees, all of which are within the normal range.

[0062] When encountering a sudden gust of wind, drone 3 was affected and began to deviate from the preset flight path. It deviated by 1.5 meters in the first second, 2.8 meters in the second second, and 3.2 meters in the third second (exceeding the 3-meter threshold). The rate of change of the relative distance with the adjacent drone 4 reached 6 m / s (exceeding the 5 m / s threshold), and the identification module determined that there were potential signs of imbalance. Subsequently, in the fourth and fifth seconds, the deviation distance of drone 3 continued to increase to 3.5 meters and 4 meters, respectively, and the rate of change of relative distance remained at 5.8 m / s. The identification module confirmed that a continuous trend had formed, and the system automatically entered the cooperative adaptation state.

[0063] The control module initiates self-organizing control: UAV 3 obtains the position and speed information of neighboring UAVs 4 and 5 through the sensor network, autonomously calculates the adjustment plan, decelerates to 13m / s and adjusts its course towards the center of the formation (correction angle of 3 degrees); after receiving the status information of UAV 3, UAVs 4 and 5 decelerate to 14.5m / s and 14.2m / s respectively, appropriately increasing the relative distance with UAV 3 to avoid collision, while maintaining the overall formation.

[0064] After 30 seconds of adjustment, UAV 3 returned to its preset flight path, the rate of change of relative distance with neighboring UAVs decreased to 2 m / s, all dynamic correlation parameters returned to normal range, the identification module determined that the potential imbalance signs had been eliminated, and the system exited the cooperative adaptation state. Throughout the process, the formation's reconnaissance coverage of the target area remained above 95% without significant decline, effectively avoiding the gradual decay of cooperative effectiveness.

[0065] The beneficial effects of this application are mainly reflected in the following aspects: First, by using a distributed sensor network to perceive the dynamic correlation between UAVs in real time, it achieves accurate grasp of the group's collaborative state, solving the problem that traditional systems have difficulty monitoring dynamic changes in real time; Second, the identification module can promptly detect potential signs of imbalance and determine whether a continuous trend has formed, enabling the system to enter a control state before the collaborative efficiency declines, changing from passive response to proactive prevention; Third, the control module adopts a self-organizing control method, with each UAV autonomously negotiating and adjusting, resulting in a fast response speed and effectively maintaining the stability of group collaboration, avoiding the gradual decline of formation collaboration efficiency; Fourth, the system does not rely on external centralized control, has a high degree of automation, adapts to complex and ever-changing environments, and can ensure that UAV groups can efficiently perform various tasks such as reconnaissance, mapping, and search and rescue.

[0066] In one embodiment, in the distributed sensor network of the sensing module:

[0067] After capturing the position, flight speed and acceleration of other drones in the vicinity by the sensing units of each drone, the parameter information is obtained. This parameter information is incorporated into a self-growing dynamic evolution model, which transforms the position information into coordinate points whose dimensions are dynamically adjusted according to the size of the group, and the speed and acceleration into the dynamic values ​​that drive the coordinate points to move.

[0068] It's important to note that the self-growing dynamic evolution model is a type of model that can automatically adjust the data processing dimensions based on the size of the drone swarm. For example, when there are 6 drones in the formation, the model automatically constructs a 6-dimensional data space, with each drone's real-time position information corresponding to the coordinates of one dimension. If a new drone joins, increasing the total to 8, the model immediately expands to an 8-dimensional data space, with the two added dimensions corresponding to the position parameters of the newly added drone. This dynamic adjustment is achieved through a pre-set dimension mapping table, which stores the mapping relationship between different swarm sizes and the corresponding number of dimensions. When a change in the number of drones is detected, the system automatically calls the corresponding dimension configuration from the table.

[0069] The delay and interruption of information transmission are converted into interference in the dynamic grid, and the interference decays non-linearly over time.

[0070] When the dynamic values ​​of multiple drones form an interactive cycle, a cooperative flow characteristic reflecting the inertia of the group's motion is automatically generated.

[0071] It should be reiterated that the dynamic value is a physical quantity used to quantify the degree to which the drone's motion affects its surrounding companions. It is calculated using a weighted summation formula: Dynamic Value = Flight Speed ​​× 0.5 + Acceleration × 0.5 (the weights can be dynamically adjusted according to the flight mission type; for example, when performing precise formation missions, the acceleration weight can be increased to 0.6). A larger value indicates a stronger driving effect on the positional changes of surrounding drones.

[0072] Dynamic mesh is a virtual architecture that simulates the information interaction environment between unmanned aerial vehicles (UAVs). Each node in the mesh represents a potential information interaction point.

[0073] The specific rules for converting information transmission delays and interruptions into interference are as follows: for every 0.5-second increase in delay time, the initial value of the interference increases by 3 units; for every 0.5-second duration of communication interruption, the initial value of the interference increases by 8 units. The non-linear decay of the interference follows the function: , where t is time (unit: seconds).

[0074] Cooperative flow characteristics are a comprehensive indicator reflecting the correlation of UAV swarm movements. This characteristic is automatically generated when at least three UAVs are detected to form a cyclical interaction of power values ​​(e.g., the power value of UAV A affects the coordinate movement of UAV B, the power value of UAV B affects the coordinate movement of UAV C, and the power value of UAV C reacts back to UAV A). This characteristic includes three core parameters: cycle strength (the product of all power values ​​in the cycle), direction of action (the closed-loop path formed according to the order of power value transmission, such as A-B-C-A), and duration (the time interval from the formation of the cycle to its disintegration).

[0075] It should be understood that simple parameter acquisition and transmission are insufficient to accurately reflect the complex dynamic relationships of large-scale drone swarms. Therefore, this embodiment improves the perception accuracy of the sensing module in detecting the dynamic relationships of drone swarms by modifying the information processing methods in the distributed sensor network.

[0076] For example, converting location information into dynamic coordinate points enables the system to adapt to drone swarms of different sizes; converting velocity and acceleration into dynamic values ​​can more intuitively reflect the motion influence relationship between drones.

[0077] Specifically, by converting the delay and interruption of information transmission into nonlinearly decaying interference, the system can more realistically simulate the impact of the actual communication environment on group coordination.

[0078] The generation of collaborative flow characteristics allows the system to grasp the overall trend of group movement. This multi-dimensional and dynamic information processing method makes the group dynamic correlation information output by the perception module more comprehensive and accurate, providing high-quality basic data for the subsequent identification module to judge the group's collaborative state and the control module to maintain collaborative stability, effectively improving the reliability and adaptability of the entire UAV collaborative control system.

[0079] In one embodiment, after the identification module receives the dynamic association information from the sensing module:

[0080] When analyzing the distance variation and trajectory overlap between UAVs, the distance change rate and trajectory divergence rate are combined to construct a dynamic correlation grid that reflects the group's collaborative trend.

[0081] When the intensity of change in any local area within the associated grid exceeds the average level of the population, and the intensity difference between the local area and its adjacent areas shows an increasing trend and forms a continuous diffusion path, it is determined as the initial formation of a potential imbalance trend without waiting for the specific changes in distance or trajectory to reach a critical state.

[0082] In this embodiment, the dynamic association grid refers to a network structure constructed with individual UAVs as nodes and the degree of dynamic association between them as edge weights. It can divide the group's activity space into several sub-regions using a grid-based partitioning algorithm and calculate the collaborative association parameters within each region. The change intensity refers to the combined change in distance and trajectory overlap between UAVs within a region per unit time, which can be obtained by weighted summation of the distance change rate and the trajectory divergence rate. The continuous diffusion path refers to a continuous chain of regions formed by the spread of a local region with abnormal change intensity to its surroundings, which can be determined by tracing the direction vector of the intensity difference between regions.

[0083] It should be understood that the traditional judgment method based on critical values ​​requires waiting for the imbalance to fully manifest, which may cause the best time for regulation to be missed.

[0084] Therefore, this needs to be addressed by constructing a dynamic correlation grid that combines the rate of distance change with the trajectory divergence speed. This allows the system to capture the changing trends of the cooperative state in advance from the rate dimension, rather than focusing solely on static distance or trajectory parameters. By monitoring the intensity of changes in local areas and the difference trends and diffusion paths between local and adjacent areas, the system can make judgments as soon as signs of imbalance begin to show a sustained diffusion trend, achieving early identification of potential imbalances. This avoids the lag of traditional critical value judgments, allowing the system to enter a cooperative adaptation state before imbalances fully manifest, providing more response time for subsequent control modules, effectively reducing the risk of gradual decline in formation cooperative efficiency, and improving the stability of UAV swarm cooperation. For example, suppose 10 UAVs fly in formation, with a normal spacing of about 5 meters between them. If 3 of the UAVs begin to deviate from their original trajectories due to airflow, the rate of distance change between them accelerates (e.g., from 0.2 meters per second to 0.8 meters per second), and the trajectory divergence speed also increases significantly. At this point, in the dynamically correlated grid, the intensity of change in the local area where these three drones are located reaches 1.5 times the group average, and the intensity difference between this area and the area where the two adjacent drones are located increases from 0.3 to 0.7 within 10 seconds, forming a continuous diffusion path. The system does not need to wait for the distance between these drones to exceed the preset 8-meter threshold before determining that a potential imbalance trend has initially formed and promptly entering a collaborative adaptation state.

[0085] In one embodiment, the cooperative adaptation state includes small sliding adjustments for local imbalances and partition migration adjustments for overall imbalances.

[0086] During small-scale sliding adjustments, the unbalanced drone predicts its trajectory based on the motion parameters of neighboring drones, finely adjusts its position along the direction of force, and simultaneously monitors surrounding trajectories to avoid interference.

[0087] During the partition migration adjustment, the group first divides virtual blocks according to the distribution of real-time communication connections. Potential congestion paths are predicted by extrapolating the signal interaction strength and historical motion inertia of each block. Areas with weak signal connections are cleared first, and then the group gradually migrates from blocks with dense signal connections to sparse blocks, maintaining a dynamic buffer spacing at the edges of each block during the migration.

[0088] Here are some explanations of concepts: Local imbalance refers to a small-scale coordination deviation caused by a few drones due to momentary interference. Its characteristics include an imbalance affecting no more than 20% of the total group and a deviation within a preset safety threshold. Overall imbalance refers to a state where more than 50% of the drones experience coordination disorder or a break in the core communication link. In this case, the overall coordination of the group decreases significantly, severely affecting normal formation flight. Small-scale sliding adjustment refers to a small-scale position correction operation for local imbalance, with an adjustment range of 0.5-1 times the drone's own fuselage length and an adjustment time not exceeding 10 seconds, ensuring a smooth adjustment process without causing new interference to surrounding drones. Partition migration adjustment refers to a large-scale group position adjustment method for overall imbalance. By dividing the group into multiple virtual blocks, orderly migration is achieved to restore coordination. The migration range typically covers more than 50% of the group's activity area. Dynamic buffer spacing refers to the real-time adjustable safety distance maintained between drones at the edge of a block. Its value is 1.5-2 times the average spacing between drones within the block, and it can dynamically increase or decrease with the block's movement speed.

[0089] It should be understood that because local, small-scale imbalances and overall systemic imbalances have fundamentally different impacts and resolution needs, a uniform adjustment approach would lead to resource waste or regulatory failure. This invention, however, employs small-scale sliding adjustments to local imbalances, avoiding them through adjacent parameter deduction and fine-tuning, thus quickly correcting deviations without triggering systemic fluctuations.

[0090] Furthermore, for the regional migration and adjustment of overall imbalance, management is achieved by dividing the system into virtual blocks. Signal strength analysis is used to prioritize the clearing of weak links, and migration is then carried out according to connection density gradients, ensuring the orderly nature of large-scale adjustments. This hierarchical control mechanism, while ensuring the effectiveness of adjustments, minimizes interference with normal collaborative processes, effectively maintains the collaborative stability of the UAV swarm under different imbalance states, and slows down the rate of formation effectiveness decay.

[0091] In one embodiment, the self-organizing control employs distributed signal correlation, and the dynamic signal includes phase adjustment coding;

[0092] The relative layout of the group can be inferred by the signal phase difference between any two drones, and the phase correlation can be adjusted in combination with the imbalance trend. In the process of promoting the group to form a stable cooperative mode, potential conflict points in signal transmission are continuously monitored.

[0093] Based on the signal frequency overlap and transmission path intersection, the phase parameters of relevant UAVs can be adjusted in advance to avoid new imbalances caused by signal interference.

[0094] It should be understood that distributed signal association refers to a drone swarm without a unified signal control center. Each drone establishes association through direct signal interaction with its neighbors, and signal transmission does not rely on a fixed hierarchical structure. Phase adjustment coding is specific coded information embedded in dynamic signals to identify the phase characteristics of the signal, enabling drones to recognize and adjust phase relationships. Its coding length can be dynamically adjusted between 8 and 32 bits depending on the swarm size. Phase correlation is a parameter describing the degree of correlation between the signal phases of two drones, ranging from 0 to 1, with values ​​closer to 1 indicating a stronger correlation. Potential collision points refer to locations or times during signal transmission where signal interference may occur due to factors such as similar frequencies or intersecting paths.

[0095] This decentralized signal correlation allows for more flexible swarm control, preventing the overall impact of central node failures; phase adjustment coding provides a foundation for phase difference inference and adjustment. By inferring the layout through phase difference and adjusting correlation based on imbalance trends, stable swarm collaboration can be promoted; while early monitoring and avoidance of potential conflict points can reduce the occurrence of new imbalances. This control method enhances the swarm's ability to cope with complex environments and ensures the stability of collaboration.

[0096] For a simple example, in a swarm of drones, the signal phase difference between drones A and B is 30 degrees, suggesting their relative positions are close. Upon identifying a local imbalance trend, the system adjusts their phase correlation from 0.6 to 0.8. During coordination, if the signal frequencies of drones C and D overlap by 70% and their transmission paths are about to intersect, identifying a potential conflict point, the system preemptively adjusts the phase parameter of drone C by 5 degrees, preventing signal interference from causing a new imbalance.

[0097] In one embodiment, during the flight parameter adjustment, in the local adaptation phase, the unbalanced UAV first obtains the current flight parameters of the adjacent UAVs, and delineates the adjustable range based on the remaining energy consumption capacity of both UAVs.

[0098] Within this range, the flight altitude and speed range of each aircraft are determined through real-time interaction, so that the parameters of adjacent aircraft maintain both differences and complementarity, avoiding mutual interference.

[0099] In this embodiment, flight parameters refer to the set of physical quantities characterizing the flight state of the UAV, including but not limited to parameters such as flight altitude and flight speed. These parameters are collected and updated in real time by sensors onboard the UAV. Remaining energy consumption capacity refers to the effective flight time that the UAV can support with its current remaining battery power, and its value is calculated by the battery management system. Adjustable range refers to the numerical range within which the unbalanced UAV can adjust its flight parameters while ensuring the execution of basic tasks. This range changes dynamically according to the remaining energy consumption capacity; the higher the remaining energy consumption, the larger the adjustable range.

[0100] During the overall adaptation phase, the control module first summarizes the distribution of flight parameters of the group and categorizes them according to parameter similarity;

[0101] Among them, parameter similarity is a quantitative indicator used to measure the similarity of flight parameters of different UAVs. The value range is 0-1, which is obtained by calculating the cosine similarity between parameter vectors. When the value is 0.8 or above, it is determined that the parameters are highly similar.

[0102] For categories with overly concentrated parameters, a differentiated adjustment command is automatically triggered to guide similar drones to disperse into complementary parameter ranges;

[0103] Among them, the complementary parameter range refers to the range of parameters that function synergistically with the current parameter category.

[0104] During the adjustment process, the dynamic correlation information transmitted from the reference sensing module is synchronized to ensure that the parameter adjustment range matches the group's collaborative state and maintains overall stability.

[0105] Dynamic association information is the real-time association data of the UAV group output by the perception module, including inter-UAV distance, relative speed, communication signal strength, etc., which is used to reflect the current cooperative status of the group.

[0106] For example, because the local adaptation phase focuses on parameter coordination between adjacent drones, defining the range based on remaining energy consumption ensures the feasibility of adjustments, and parameter differences and complementarities effectively avoid interference; the overall adaptation phase, through classification and guided dispersion, solves the problem of low coordination efficiency caused by overly concentrated parameters, while referencing dynamic correlation information ensures the rationality of overall adjustments. This phased adjustment approach balances local coordination and overall stability, improving the adaptability of drone swarms in complex environments.

[0107] In this disclosure, a phased flight parameter adjustment design achieves an organic unity between local coordination and overall stability.

[0108] Furthermore, since a single parameter adjustment method is difficult to balance individual adaptability and group coordination, focusing only on the local may lead to an imbalance in the overall parameter distribution, while emphasizing only the whole may ignore the conflicts caused by individual differences.

[0109] Therefore, by defining the adjustable range in conjunction with the remaining energy consumption capacity during the local adaptation phase, the feasibility of adjustment is ensured, and direct interference from adjacent drones is avoided through parameter differences and complementarity.

[0110] In the overall adaptation phase, the parameters are categorized based on similarity and guided to disperse into complementary intervals, which effectively solves the problem of low collaborative efficiency caused by parameter concentration.

[0111] Furthermore, by referencing dynamic correlation information to control the adjustment range, the adaptability of parameter adjustments to the group's collaborative state is ensured, avoiding new imbalances caused by over-adjustment. This hierarchical, adaptive adjustment mechanism significantly improves the collaborative stability and mission execution efficiency of the drone swarm in complex environments.

[0112] In one embodiment, it further includes:

[0113] The motion state of the drone is converted into virtual pheromone concentration. It should be noted that virtual pheromone concentration is a virtual quantitative indicator used to characterize the motion traces and trends of drones. Its value ranges from 0 to 100. The higher the value, the greater the possibility that the area has been frequently passed by drones or that drones have been moving in this area recently. It can be calculated by weighting motion state parameters such as the drone's speed, dwell time, and trajectory repeatability.

[0114] The concentration accumulates along the flight path and decreases in a gradient along the airflow direction. By sensing the concentration distribution, the group can predict the movement tendency of its companions and avoid potential trajectory conflicts in advance.

[0115] Specifically, gradient decay refers to the gradual decrease in virtual pheromone concentration along the airflow direction with increasing distance. For example, in an environment with an airflow speed of 5 m / s, the concentration decays to 50% of its initial value at 10 meters from the pheromone generation point and to 30% at 20 meters. The specific decay coefficient can be dynamically adjusted according to the real-time airflow speed. Potential trajectory conflict refers to the situation where the predicted flight trajectories of two or more drones may overlap at a future time or location.

[0116] This embodiment converts motion state into virtual pheromone concentration, enabling the drone's motion traces to be perceived by the group in the form of concentration.

[0117] The characteristic of concentration accumulating along the trajectory and decreasing along the airflow gradient not only preserves the influence of historical motion trajectories but also reflects the potential influence of current airflow on motion trends, providing a reliable basis for the group to predict the motion direction of its companions.

[0118] It should be understood that by sensing the concentration distribution, a swarm can predict potential conflicts before their trajectories actually intersect, thus adjusting its own motion parameters in advance and transforming passive response into active avoidance. By combining the prediction method using virtual pheromones with the previous parameter adjustment strategy, the probability of trajectory conflicts is further reduced, improving the smoothness and safety of the drone swarm's coordinated movement. For example, when a drone swarm is performing an outdoor inspection mission, drone C flies eastward at a speed of 30 km / h. Its motion state is converted into a virtual pheromone concentration, initially 80 (due to its high speed and stable trajectory), which accumulates along its flight path. At this time, the ambient airflow direction is southeast at a speed of 3 m / s. Therefore, the virtual pheromone concentration generated by C decreases in a gradient along the southeast direction: at 5 meters southeast of C's current position, the concentration decreases to 60; at 10 meters, it decreases to 40; and at 15 meters, it decreases to 20. Drone D, positioned 10 meters southwest of C, detected a higher concentration of virtual pheromones (with a gradient change) at 5 meters southeast. Combining this with airflow direction, it predicted that C might be slightly adjusted southeastward due to airflow influence. D's current flight path was initially expected to intersect C's predicted path in 10 seconds (potential trajectory conflict). Based on this prediction, D adjusted its flight direction to northeast in advance, avoiding the potential conflict area and ensuring the coordination of their movements.

[0119] In one embodiment, it further includes:

[0120] The movement of drone swarms is viewed as a dynamic flow field;

[0121] When a rotating airflow with an abnormal velocity gradient appears in a local area, and the angle between the direction of this movement and the mainstream direction of the population shows an increasing trend, it is directly identified as a precursor to imbalance and triggers early intervention.

[0122] In this embodiment, the dynamic flow field refers to the field where the drone swarm is viewed as a whole, and its motion state exhibits continuous changes like fluid flow. This field can be constructed by comprehensively considering parameters such as the speed and direction of each drone in the swarm. Anomaly in speed gradient refers to a state where the rate of change of drone speed in a local area significantly deviates from the average rate of change of the swarm speed. This is typically determined by whether the difference between the speed gradient value and the average speed gradient value exceeds a preset threshold. Rotating airflow-like motion refers to drones exhibiting circular or spiral motion trajectories resembling rotating airflow in a local area. The mainstream direction of the swarm refers to the flight direction of the majority of drones in the swarm, which can be determined by statistically analyzing the flight directions of more than 50% of the drones in the swarm and taking their average direction. Premonitions of imbalance refer to early signs indicating a potential imbalance in the drone swarm. Early intervention refers to adjustment and control measures taken before the imbalance formally develops.

[0123] For example, because traditional imbalance judgments focus on discrete parameters of individuals or localities, it is difficult to detect signs of imbalance early from the overall trend of motion. To solve this problem;

[0124] Therefore, viewing swarm motion as a dynamic flow field allows for a better understanding of the overall motion characteristics of the swarm. Focusing on the abnormal velocity gradients and rotating airflow patterns in localized areas, along with the changing angle between these patterns and the mainstream direction, can keenly detect early signals that may lead to overall imbalance. By identifying this as a precursor to imbalance and triggering early intervention, measures can be taken before imbalance occurs, significantly reducing its negative impact and improving the stability and safety of swarm motion. For example, suppose a swarm of drones is performing an aerial photography mission, flying eastward along the mainstream direction, creating a dynamic flow field. In a localized area in the northern part of the swarm, five drones exhibit abnormal velocity gradients and rotating airflow patterns, with their velocity change rates significantly higher than the swarm average. Simultaneously, the angle between this rotating airflow pattern and the mainstream direction increases from 30 degrees to 60 degrees within one minute, showing an increasing trend. Based on this, the system directly identifies it as a precursor to imbalance, immediately triggering early intervention by sending adjustment commands to the drones in that area, guiding them to gradually return to a flight state consistent with the mainstream direction, thus preventing imbalance.

[0125] In one embodiment, it further includes:

[0126] During partition migration and adjustment, the migration priority and time window of each block are preset so that the movement rhythm of adjacent blocks forms a complementary phase, avoiding path intersection congestion during the migration process.

[0127] It should be noted that migration priority refers to the order in which virtual blocks are migrated. It is typically determined by a comprehensive assessment of factors such as the number of drones within a block, the importance of the mission, and signal connection strength, and is divided into three levels: high, medium, and low. Blocks with higher priority are migrated first. A time window refers to a specific period of time during which each block can be migrated. Its duration is determined by the block size and migration distance, generally ranging from 5 to 30 seconds. The time windows of adjacent blocks do not overlap or partially overlap but are staggered during critical migration phases. Complementary phase refers to the coordinated and alternating movement rhythms of adjacent blocks, like interlocking gears in a clock. When one block is moving, an adjacent block may be stationary or in a ready state. Path intersection congestion refers to the phenomenon where migration paths of different blocks intersect and drones pass by simultaneously, causing obstruction.

[0128] For example, if each block starts migrating at the same time without rhythm during partition migration, congestion is likely to occur due to path intersections, affecting migration efficiency and even causing collision risks.

[0129] This method, by pre-setting migration priorities, ensures that important blocks are migrated first, guaranteeing the progress of core tasks. The setting of time windows standardizes the migration time of each block. Combined with the movement rhythm of complementary phases, the migration of adjacent blocks is staggered in time, avoiding a large number of drones appearing on the cross paths at the same time, effectively reducing the possibility of path intersection congestion and improving the overall imbalance adjustment effect.

[0130] In one embodiment, it further includes:

[0131] Self-organizing regulation incorporates historical association memory characteristics; in this embodiment, historical association memory characteristics refer to the system's ability to store and retrieve stable signal association information formed between UAVs in the past, such as association duration, signal interaction strength, and coordination efficiency.

[0132] When two drones have established a stable signal association, even if they temporarily leave the communication range, they still retain implicit regulatory weights based on historical associations, which accelerate collaborative adaptation when re-establishing the connection.

[0133] It should be noted that stable signal association refers to a state where the signal interaction strength between two drones remains at a preset threshold for 30 seconds or more without communication interruption. Implicit control weight is a potential control factor assigned to drones based on historical stable associations. Its value ranges from 0 to 1; the more stable the historical association and the longer its duration, the higher the weight value. For example, for two drones continuously associated for 10 minutes, the implicit control weight can be set to 0.8. Re-establishing connection refers to the state where two drones that were previously out of communication range re-enter each other's communication coverage area, and signal interaction is restored. Accelerated collaborative adaptation means that, compared to drones without historical associations, drones with historical associations experience a 30% or more reduction in the time required to adjust collaborative parameters after reconnection.

[0134] In complex environments, drones may temporarily lose communication range due to various factors. Re-establishing a collaborative relationship from scratch when reconnecting takes considerable time and impacts the overall effectiveness of the swarm. However, by incorporating historical association memory characteristics into self-organizing regulation, the efficiency of drone swarm re-coordination is improved.

[0135] By incorporating historical association memory characteristics and retaining implicit regulatory weights, drones that have previously had stable associations can quickly adjust their coordination parameters based on historical data when reconnecting, reducing adaptation time and lowering the probability of conflicts during re-coordination, thereby improving the dynamic adaptability of the entire group.

[0136] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A collaborative control system for unmanned aerial vehicles (UAVs), characterized in that, include: The perception module uses a distributed sensor network to perceive the dynamic relationships between various drones in real time. The identification module automatically enters the collaborative adaptation state when it identifies potential signs of imbalance in the collaborative state of the drone group and forms a continuous trend. The control module maintains the stability of the drone swarm's coordination based on a self-organizing control method under the cooperative adaptation state, so as to avoid the gradual decay of the formation's coordination efficiency. In the distributed sensor network of the sensing module: After capturing the position, flight speed and acceleration of other drones in the vicinity by the sensing units of each drone, the parameter information is obtained. This parameter information is incorporated into a self-growing dynamic evolution model, which transforms the position information into coordinate points whose dimensions are dynamically adjusted according to the size of the group, and the speed and acceleration into the dynamic values ​​that drive the coordinate points to move. The delay and interruption of information transmission are converted into interference in the dynamic grid, and the interference decays non-linearly over time. When the dynamic values ​​of multiple drones form an interactive cycle, a cooperative flow characteristic reflecting the inertia of the group's motion is automatically generated. The cooperative flow characteristic is a comprehensive indicator reflecting the correlation of the drone group's motion. It is automatically generated when at least three drones are detected to form a dynamic value cycle. The cooperative flow characteristic includes three core parameters: cycle intensity, which is the product of all dynamic values ​​in the cycle; direction of action, which is the closed loop path formed according to the order of dynamic value transmission; and duration, which is the time interval from the formation of the cycle to its disintegration.

2. The UAV collaborative control system according to claim 1, characterized in that: After the recognition module receives the dynamic association information from the perception module: When analyzing the distance variation and trajectory overlap between UAVs, the distance change rate and trajectory divergence rate are combined to construct a dynamic correlation grid that reflects the group's collaborative trend. When the intensity of change in any local area within the associated grid exceeds the average level of the population, and the intensity difference between the local area and its adjacent areas shows an increasing trend and forms a continuous diffusion path, it is determined as the initial formation of a potential imbalance trend without waiting for the specific changes in distance or trajectory to reach a critical state.

3. The UAV collaborative control system according to claim 1, characterized in that, The collaborative adaptation state includes small sliding adjustments for local imbalances and partition migration adjustments for overall imbalances; During small-scale sliding adjustments, the unbalanced drone predicts its trajectory based on the motion parameters of neighboring drones, finely adjusts its position along the direction of force, and simultaneously monitors surrounding trajectories to avoid interference. During the partition migration adjustment, the group first divides virtual blocks according to the distribution of real-time communication connections. Potential congestion paths are predicted by extrapolating the signal interaction strength and historical motion inertia of each block. Areas with weak signal connections are cleared first, and then the group gradually migrates from blocks with dense signal connections to sparse blocks, maintaining a dynamic buffer spacing at the edges of each block during the migration.

4. The UAV collaborative control system according to claim 3, characterized in that, Self-organizing control employs distributed signal correlation, and the dynamic signal contains phase adjustment coding; The relative layout of the group can be inferred by the signal phase difference between any two drones, and the phase correlation can be adjusted in combination with the imbalance trend. In the process of promoting the group to form a stable cooperative mode, potential conflict points in signal transmission are continuously monitored. Based on the signal frequency overlap and transmission path intersection, the phase parameters of relevant UAVs can be adjusted in advance to avoid new imbalances caused by signal interference.

5. A UAV collaborative control system according to claim 4, characterized in that, During the flight parameter adjustment phase, the unbalanced UAV first acquires the current flight parameters of the adjacent UAVs and delineates the adjustable range based on the remaining energy consumption capacity of both UAVs. Within this range, the flight altitude and speed range of each aircraft are determined through real-time interaction, so that the parameters of adjacent aircraft are both different and complementary, avoiding mutual interference. During the overall adaptation phase, the control module first summarizes the distribution of flight parameters of the group and categorizes them according to parameter similarity; For categories with overly concentrated parameters, a differentiated adjustment command is automatically triggered to guide similar drones to disperse into complementary parameter ranges; During the adjustment process, the dynamic correlation information transmitted from the reference sensing module is synchronized to ensure that the parameter adjustment range matches the group's collaborative state and maintains overall stability.

6. The UAV collaborative control system according to claim 1, characterized in that: Also includes: Convert the drone's motion state into virtual pheromone concentration; The concentration accumulates along the flight path and decreases in a gradient along the airflow direction. By sensing the concentration distribution, the group can predict the movement tendency of its companions and avoid potential trajectory conflicts in advance.

7. A UAV cooperative control system according to claim 2, characterized in that, Also includes: The movement of drone swarms is viewed as a dynamic flow field; When a local area exhibits a rotating airflow-like motion with an abnormal velocity gradient, and the angle between the direction of this motion and the mainstream direction of the population shows an increasing trend, it is directly identified as a precursor to imbalance, triggering early intervention.

8. A UAV collaborative control system according to claim 3, characterized in that, Also includes: During partition migration and adjustment, the migration priority and time window of each block are preset so that the movement rhythm of adjacent blocks forms a complementary phase, avoiding path intersection congestion during the migration process.

9. A UAV collaborative control system according to claim 4, characterized in that, Also includes: Self-organizing regulation incorporates historical association memory characteristics; When two drones have established a stable signal association, even if they temporarily leave the communication range, they still retain implicit regulatory weights based on historical associations, which accelerate collaborative adaptation when re-establishing the connection.

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