Multi-uav time-varying formation control method, program product, device and storage medium

Abstract

Embodiments of the present application disclose a kind of multi-unmanned aerial vehicle time-varying formation control method, program product, equipment and storage medium, comprising: obtaining the flight data of current time, and based on flight data and predetermined time-varying formation offset, actual comprehensive formation error is calculated;Time-varying formation offset is used to describe the expected offset of the follower unmanned aerial vehicle relative to the leader unmanned aerial vehicle at each time;Based on the error between actual comprehensive formation error and predetermined historical comprehensive formation error, dynamic internal variable is calculated;The historical comprehensive formation error is the actual comprehensive formation error corresponding to the last control update trigger result when trigger;Based on the preset weight information, dynamic internal variable and actual comprehensive formation error are weighted and summed, to obtain dynamic trigger threshold;Based on dynamic trigger threshold, current time and predetermined nearest threshold trigger time, unmanned aerial vehicle control parameter is generated, and follower unmanned aerial vehicle is controlled based on unmanned aerial vehicle control parameter.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a method, program product, device, and storage medium for controlling time-varying formations of multiple UAVs. Background Technology

[0002] With the large-scale application of unmanned aerial vehicle (UAV) technology in reconnaissance and surveillance, power line inspection, formation flying, and logistics delivery, multi-UAV cooperative control has become an inevitable trend in complex mission execution. Time-varying formation control is one of the core technologies of multi-UAV cooperation, requiring multiple UAVs to not only maintain a preset formation geometry during mission execution but also adjust their formation according to mission requirements, achieving formation reconfiguration and maneuvering. For example, scenarios such as maintaining a rotating formation while circling a target for reconnaissance, and formation contraction and expansion when traversing narrow passages, all require the formation shape or relative position to change over time. Therefore, researching multi-UAV time-varying formation control technology is of great significance for improving the mission adaptability, environmental adaptability, and cooperative efficiency of UAV swarms.

[0003] In time-varying formation control of multiple UAVs, event-triggered mechanisms have been widely adopted to reduce communication resource consumption. These mechanisms involve each following UAV exchanging state information and updating control inputs only when trigger conditions are met. However, most existing event-triggered mechanisms use static thresholds or fixed percentage thresholds, which cannot be dynamically adjusted based on the actual flight status of the UAVs during mission execution. For example, in the initial stage of the system or when errors change rapidly, static thresholds can lead to overly frequent triggers, wasting communication resources. Conversely, when the system stabilizes, static thresholds struggle to adaptively adjust trigger sensitivity, potentially causing communication deadlock or sparse triggering. This results in low communication resource utilization efficiency for multiple UAVs and a lack of adaptive adjustment capabilities based on actual flight conditions. Summary of the Invention

[0004] This invention provides a method, program product, device, and storage medium for controlling multiple UAVs in a time-varying formation. It can dynamically determine whether to perform UAV communication and control based on the actual state of the UAVs during flight, thereby improving the efficiency of communication resource utilization and adaptive adjustment capabilities of multiple UAVs.

[0005] In a first aspect, embodiments of the present invention provide a multi-UAV time-varying formation control method, comprising: The system acquires flight data at the current moment and calculates the actual comprehensive formation error based on the flight data and a predetermined time-varying formation offset. The flight data includes the flight data of the leader drone and its own flight data. The time-varying formation offset describes the expected offset of the following drone relative to the leader drone at each moment. The actual comprehensive formation error includes the actual position formation error and the actual speed formation error. Dynamic internal variables are calculated based on the error between the actual integrated formation error and the predetermined historical integrated formation error; the historical integrated formation error is the actual integrated formation error corresponding to the previous control update trigger result. The dynamic internal variables and the actual integrated formation error are weighted and summed based on preset weight information to obtain the dynamic trigger threshold. The drone control parameters are generated based on the dynamic trigger threshold, the current time, and the pre-determined nearest threshold trigger time, and the follower drone is controlled based on the drone control parameters.

[0006] In a second aspect, embodiments of the present invention provide a multi-UAV time-varying formation control device, the device comprising: The error calculation module is used to acquire the flight data at the current moment and calculate the actual comprehensive formation error based on the flight data and the predetermined time-varying formation offset; the flight data includes the flight data of the leader drone and its own flight data; the time-varying formation offset is used to describe the expected offset of the following drone relative to the leader drone at each moment; the actual comprehensive formation error includes the actual position formation error and the actual speed formation error. The first threshold determination module is used to calculate dynamic internal variables based on the error between the actual comprehensive formation error and the pre-determined historical comprehensive formation error; the historical comprehensive formation error is the actual comprehensive formation error corresponding to the previous control update triggering result when it was triggered. The second threshold determination module is used to perform a weighted summation of the dynamic internal variables and the actual integrated formation error based on preset weight information to obtain the dynamic trigger threshold. The drone control module is used to generate drone control parameters based on the dynamic trigger threshold, the current time, and the pre-determined nearest threshold trigger time, and to control the following drone based on the drone control parameters.

[0007] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-UAV time-varying formation control method as described in any of the embodiments of the present invention.

[0008] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-UAV time-varying formation control method as described in any of the embodiments of the present invention.

[0009] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements a multi-UAV time-varying formation control method as described in any of the embodiments of the present invention.

[0010] In this embodiment of the invention, a dynamic internal variable reflecting the degree of information aging is calculated by combining the actual comprehensive formation error and the historical comprehensive formation error. The dynamic trigger threshold is adaptively adjusted based on this dynamic internal variable and the actual comprehensive formation error, allowing the trigger threshold to be adjusted according to the actual formation error state, internal dynamic requirements, and information aging degree during UAV flight. Furthermore, based on the dynamic trigger threshold, the current moment, and the most recent threshold trigger moment, the following UAV is controlled, enabling the following UAV to dynamically determine UAV communication and control updates according to the actual flight state. This ensures the accuracy of formation tracking and the dynamic adaptability of time-varying formations. In other words, this embodiment of the invention can dynamically determine whether to perform UAV communication and control based on the actual state of the UAV during flight, improving the efficiency of communication resource utilization and adaptive adjustment capabilities of multiple UAVs. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart of a multi-UAV time-varying formation control method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a multi-UAV time-varying formation control device provided in an embodiment of the present invention. Detailed Implementation

[0013] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the invention and not all structures. The acquisition, storage, use, and processing of data in the technical solutions of this application comply with relevant national laws and regulations. It should be noted that, in the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solutions of this application, and do not imply that the applicant has already used or necessarily used the relevant content of such solutions.

[0014] Figure 1 This is a flowchart illustrating a multi-UAV time-varying formation control method provided by an embodiment of the present invention. The method dynamically determines whether to perform UAV communication and control based on the actual state of the UAVs during flight, improving the efficiency of communication resource utilization and adaptive adjustment capabilities of multiple UAVs. This method can be executed by a multi-UAV time-varying formation control device provided by an embodiment of the present invention, which can be implemented in software and / or hardware. The following embodiments will illustrate this using the integration of this device into an electronic device as an example. The electronic device in this solution can be a follower UAV, as shown in the reference... Figure 1 The method may specifically include the following steps:

[0015] Step 101: Obtain the flight data at the current moment, and calculate the actual comprehensive formation error based on the flight data and the predetermined time-varying formation offset.

[0016] Flight data is a collection of data acquired in real time by the follower drone during flight through onboard sensors, describing its own motion state and external environmental reference information. Flight data includes the leader drone's flight data and the follower drone's own flight data. The follower drone's own flight data is motion state information directly measured by its onboard sensors, including its actual position and speed. The actual position is the follower drone's current coordinates in a two-dimensional plane, and the actual speed is its current velocity vector in the same plane. The leader drone's flight data is the status information received by the follower drone from the leader drone via a wireless communication channel. The leader drone's flight data includes its actual position, speed, and control parameters. These control parameters include the control inputs calculated and executed by the leader drone at the current moment, such as the two-dimensional acceleration commands applied by the leader drone to track a preset reference trajectory.

[0017] Time-varying formation offset describes the expected offset of a follower drone relative to the leader drone at any given time. The expected offset includes the initial offset vector and the formation rotation angular velocity. The initial offset vector is the expected position offset of the follower drone relative to the leader drone at the initial moment (i.e., takeoff). For example, the initial offset vector of a follower drone in a square formation can be set to 0.8 meters east and 0.8 meters north. The formation rotation angular velocity describes the angular velocity of the formation as a whole rotating around the leader drone. For example, setting it to 0.15 radians per second indicates that the formation rotates counterclockwise around the leader at this angular velocity. Based on the initial offset vector and the formation rotation angular velocity, the follower drone can calculate its expected position, expected velocity, and expected acceleration relative to the leader drone at any given time.

[0018] The actual comprehensive formation error is used to fully describe the degree of formation tracking deviation of the following UAV at the current moment. The actual comprehensive formation error includes the actual position formation error and the actual speed formation error. The actual position formation error reflects the geometric distance of the following UAV from the desired formation in space, while the actual speed formation error reflects the rate at which the following UAV deviates from the desired formation in terms of motion trend. In this scheme, optionally, the actual comprehensive formation error is calculated based on flight data and time-varying formation offset, including: determining the current desired offset at the current moment in the time-varying formation offset; subtracting the desired position offset from the difference between the following actual position and the leader's actual position to obtain the actual position formation error; and subtracting the desired speed offset from the difference between the following actual speed and the leader's actual speed to obtain the actual speed formation error.

[0019] The current expected offset includes the current expected position offset and the current expected velocity offset. The current expected position offset represents the ideal spatial position that the follower drones should be relative to the leader drone at the current moment. Specifically, based on the leader drone completing reference trajectory tracking, each follower drone is required to form a preset rotational time-varying formation relative to the leader drone. Let the... i The expected position offset of each following drone at the initial moment is: To describe the change in formation shape over time, a rotation matrix is ​​introduced: ;in, Let be the formation rotation angular velocity, and t be the time t. The initial offset vector is transformed using this rotation matrix to obtain the desired position offset at the current time: The current desired velocity offset is the first-order time reciprocal of the current desired position offset. .

[0020] Furthermore, the difference between the actual position of the follower and the actual position of the leader is subtracted from the expected position offset to obtain the actual position formation error: ;in, This represents the actual position formation error. To follow the actual location, This refers to the actual position of the leader drone. The actual position formation error represents the deviation between the current actual position of the following drone and the ideal relative position it should be in, relative to the leader drone. If this error is zero, the following drone is exactly in the ideal position required for formation; if it is not zero, it indicates a positional deviation.

[0021] Subtracting the difference between the actual following speed and the leader's actual speed from the expected speed deviation yields the actual speed formation error: ;in, This represents the actual speed formation error. To follow the actual speed, This refers to the leader drone's actual speed. Actual speed formation error represents the deviation between the follower drone's current actual speed relative to the leader drone's speed and the ideal relative speed it should have. If this error is zero, the follower drone is moving precisely at the ideal speed required for formation; if it is not zero, it indicates a speed deviation.

[0022] Step 102: Calculate the dynamic internal variables based on the error between the actual comprehensive formation error and the predetermined historical comprehensive formation error.

[0023] The historical integrated formation error is the actual integrated formation error at the time of the last control update trigger. The dynamic internal variable is an auxiliary state variable maintained within the UAV, used to dynamically adjust the tightness of the dynamic trigger threshold. The dynamic internal variable evolves in real time based on the magnitude of the actual integrated formation error and the degree of information aging, possessing both memory and adaptive capabilities. When the actual integrated formation error is large, the dynamic internal variable increases, raising the dynamic trigger threshold to avoid excessively frequent communication; when information aging is severe, the dynamic internal variable is suppressed and decreased, causing the dynamic trigger threshold to decrease, ensuring timely updates. The dynamic trigger threshold is used to determine whether a control update is necessary.

[0024] In one optional implementation, the dynamic internal variable is calculated based on the actual integrated formation error, the predetermined historical integrated formation error, and the preset internal variable parameters, including: subtracting the historical integrated formation error from the actual integrated formation error to obtain the trigger-hold error; and determining the dynamic internal variable based on the predetermined dynamic variable update formula, the actual integrated formation error, the trigger-hold error, and the internal variable parameters. Specifically, the trigger-and-hold error quantifies the deviation between the historical error information currently used by the controller and the current actual error information. At the trigger moment, this value is zero; at non-trigger moments, this value gradually increases as the system state evolves. Subtracting the historical integrated formation error from the actual integrated formation error yields the trigger-and-hold error: ;in, To trigger the hold error, This refers to the historical comprehensive formation error, which is the actual comprehensive formation error saved when the drone was last triggered. This represents the actual overall formation error at the current time (time t).

[0025] The internal variable parameters include the attenuation coefficient, the error driving coefficient, and the hold-up error suppression coefficient. Further, the dynamic internal variables are determined based on the dynamic variable update formula, the actual integrated formation error, the trigger hold-up error, and the internal variable parameters: ;in, For dynamic internal variables, A value greater than 0 represents the decay coefficient, which controls the natural decay rate of dynamic internal variables. >0 represents the error driving coefficient, reflecting the positive driving effect of formation error on dynamic internal variables; >0 represents the error suppression coefficient, reflecting the suppressive effect of information aging on dynamic internal variables; The norm square of the actual integrated formation error; The norm squared is used to trigger the hold error.

[0026] The dynamic internal variables gradually decay over time and are influenced by both the actual integrated formation error and the trigger-hold error. When the system error is large, the internal variables can appropriately increase the trigger threshold, thereby avoiding overly dense triggering during the transition process; when the hold error continues to increase, the internal variables are suppressed, thereby prompting new triggers to occur in a timely manner.

[0027] Step 103: Based on the preset weight information, perform a weighted summation of the dynamic internal variables and the actual integrated formation error to obtain the dynamic trigger threshold.

[0028] The weighting information includes error weighting coefficients and dynamic weighting coefficients. Based on the preset weighting information, dynamic internal variables, and actual integrated formation error, the dynamic triggering threshold is calculated as follows:

[0029] ;in, For dynamic trigger threshold, >0 represents the error weighting coefficient, which adjusts the proportion of the formation error term's contribution to the threshold. >0 represents the dynamic term weight coefficient, which adjusts the contribution ratio of dynamic internal variables to the threshold.

[0030] The dynamic trigger threshold is composed of two parts: an error adaptive term (composed of the actual integrated formation error) and a dynamic adjustment term (composed of dynamic internal variables). The error adaptive term causes the threshold to change with the magnitude of the current formation error; the dynamic adjustment term achieves intelligent adjustment of the threshold based on the historical state of the system and real-time requirements through the adaptive evolution of dynamic internal variables.

[0031] Step 104: Generate UAV control parameters based on the dynamic trigger threshold, the current time, and the pre-determined nearest threshold trigger time, and control the following UAV based on the UAV control parameters.

[0032] Specifically, after determining the dynamic trigger threshold, the control update trigger result at the current time is determined based on the dynamic trigger threshold, the current time, and the pre-determined nearest threshold trigger time. When the control update trigger result is triggered, control update parameters are generated.

[0033] The most recent threshold trigger time is the specific time point at which the UAV last met the trigger conditions and performed a control update. In this scheme, optionally, the control update trigger result for the current time is determined based on the dynamic trigger threshold, the current time, and the pre-determined most recent threshold trigger time, including: generating UAV control parameters based on the dynamic trigger threshold, the current time, and the pre-determined most recent threshold trigger time, including:

[0034] Specifically, when the square of the norm of the trigger hold error is greater than or equal to the dynamic trigger threshold, the first trigger result is determined to be a trigger. When the historical information currently used by the drone has become unacceptably outdated, continued use will lead to a significant decline in control performance, the first trigger result is determined to be the trigger.

[0035] When the squared norm of the trigger hold error is less than the dynamic trigger threshold, the first trigger result is determined to be no trigger. If This indicates that the deviation between historical information and the actual state is still within a tolerable range, and continuing to use the old information for control will not significantly affect the formation tracking performance. In this case, the first trigger result is determined to be no trigger.

[0036] Furthermore, the time interval between the current moment and the most recent threshold trigger moment is determined; if the time interval is greater than or equal to the preset minimum trigger interval, the second trigger result is determined to be triggered; if the time interval is less than the minimum trigger interval, the second trigger result is determined to be not triggered.

[0037] The most recent threshold trigger time is the time point saved when the last control update trigger result was a trigger. Subtracting the most recent threshold trigger time from the current time gives the time interval between the two time points. The minimum trigger interval is a fixed constant preset during the task planning phase, such as 0.05 seconds, representing the shortest allowed duration between two adjacent triggers. Comparing the calculated time interval with the preset minimum trigger interval, if the time interval is greater than or equal to the minimum trigger interval, it means that sufficient time has passed since the last trigger, and the system has the time condition to execute the control update again. In this case, the second trigger result is determined to be a trigger. If the time interval is less than the minimum trigger interval, it means that the time since the last trigger is too close. If a trigger is immediately performed again, it may lead to communication congestion, frequent operation of the actuator, or infinite triggering problems. In this case, the second trigger result is determined to be no trigger. The second trigger result is a hard constraint to ensure the feasibility of the project. Even if the first trigger result is a trigger, if the time interval does not meet the requirements, triggering is still forcibly prohibited, ensuring that the triggering behavior is within the tolerance range of the physical and communication systems, fundamentally eliminating the risk of excessive triggering in extreme cases.

[0038] When both the first and second trigger results are triggered, the control update trigger result at the current moment is determined to be triggered. When the first trigger result is not triggered, or the second trigger result is not triggered, or both the first and second trigger results are not triggered, the control update trigger result at the current moment is determined to be not triggered. The first trigger result is based on adaptive judgment of the system state, providing the flexibility of intelligent triggering; the second trigger result is based on fixed time constraints, providing the safety of engineering implementation. Combining the first and second trigger results enables the event triggering mechanism to dynamically adjust the communication frequency according to actual needs, while ensuring the stable and reliable operation of the system under extreme conditions, achieving an optimal balance between control performance and resource consumption.

[0039] Furthermore, if the control update trigger result is triggered, the UAV control parameters are generated based on the control update trigger result, and the UAV following the UAV is controlled based on the UAV control parameters.

[0040] The flight data also includes the neighboring drones' comprehensive formation error; the leader drone's flight data also includes the leader's control parameters; the leader's control parameters include reference acceleration, position tracking feedback, and velocity tracking feedback.

[0041] Specifically, the team leader control parameters can be expressed as: ;in, For reference acceleration, For location tracking feedback items, As the desired reference trajectory, The actual location of the leader's drone. This is the position feedback gain matrix. For speed tracking feedback, For the desired speed, The actual speed of the leader's drone. This is the velocity feedback gain matrix.

[0042] The UAV control parameters include a neighbor consistency term, a leader compensation term, and a time-varying formation compensation term. The neighbor consistency term coordinates the relative motion among multiple follower UAVs, ensuring that the formation errors of each follower UAV are consistent. When the error of a follower UAV differs from that of its neighbors, this term acts as a control mechanism, driving the follower UAV to converge towards the error level of its neighbors, achieving distributed coordination. The leader compensation term ensures that follower UAVs can effectively track the movement trend of the leader. The leader compensation term includes a leader UAV anchoring term and direct compensation for the leader's control input. The leader UAV anchoring term provides feedback correction, suppressing the accumulation of formation errors; direct compensation for the leader's control input provides a feedforward reference, enabling follower UAVs to respond promptly to the leader's acceleration, deceleration, or turning actions, avoiding lag. The time-varying formation compensation term counteracts the dynamic effects of formation rotation. Since follower UAVs need to perform circular motion around the leader, a centripetal acceleration pointing towards the leader must be continuously applied. The time-varying formation compensation term directly provides this centripetal acceleration compensation, allowing follower UAVs to smoothly maintain rotating formation without being misinterpreted as positional deviations by the control system's feedback loop.

[0043] In one optional implementation, UAV control parameters are generated based on the control update triggering result, including: determining a neighbor consistency term based on the actual integrated formation error and the neighbor's integrated formation error; determining a leader compensation term based on the actual integrated formation error and the leader control parameters; and determining a time-varying formation compensation term based on the time-varying formation offset and the current time.

[0044] Specifically, for each neighboring UAV that has established a communication connection, the difference between that neighbor's historical integrated formation error and its own historical integrated formation error is calculated. The error differences of each neighbor are then summed using weighted summations based on the weight elements of the communication topology adjacency matrix. Each element of the adjacency matrix represents the weight of the communication connection between neighbors; a positive number indicates a connection, while a zero number indicates no connection. The weighted summation result is then linearly transformed using a feedback gain matrix to obtain the neighbor consistency term. ;in, For elements of the adjacency matrix, This is the historical comprehensive formation error (at this point, its value is already the actual comprehensive formation error at the current moment, because the control update trigger result at the current moment is triggered, so the most recent comprehensive formation error is the actual comprehensive formation error at the current moment). This is the historical comprehensive formation error of neighboring drones.

[0045] Taking the second-order time derivative of the desired position offset yields the time-varying formation compensation term. The leader's compensation includes the leader's drone anchoring item and the leader's direct control input compensation: The leader's drone anchoring item is as follows: ; Indicates the first The connection relationship between the follower drone and the leader drone. (Using...) This indicates that the leader's control input is directly compensated to generate the final drone control parameters:

[0046] The UAV control parameters are a two-dimensional vector, corresponding to the target acceleration commands in the east and north directions, respectively. The UAV control parameters can be used to drive the following UAV to produce the desired translational motion.

[0047] The technical solution of this embodiment acquires the flight data at the current moment and calculates the actual comprehensive formation error based on the flight data and a predetermined time-varying formation offset. The flight data includes the flight data of the leader drone and its own flight data. The time-varying formation offset is used to describe the expected offset of the following drone relative to the leader drone at each moment. A dynamic trigger threshold is calculated based on the actual comprehensive formation error, the predetermined historical comprehensive formation error, and preset internal variable parameters. The control update trigger result at the current moment is determined based on the dynamic trigger threshold, the current moment, and the predetermined most recent threshold trigger moment. The control update trigger result is either triggered or not triggered. If the control update trigger result is triggered, drone control parameters are generated according to the control update trigger result, and the following drone is controlled based on the drone control parameters. The technical solution of this embodiment calculates a dynamic internal variable reflecting the degree of information aging by using the actual comprehensive formation error and the historical comprehensive formation error. Based on this dynamic internal variable and the actual comprehensive formation error, the dynamic trigger threshold is adaptively adjusted, allowing the trigger threshold to be adjusted according to the actual formation error state, internal dynamic needs, and information aging degree during UAV flight. Furthermore, based on the dynamic trigger threshold, the current moment, and the most recent threshold trigger moment, the following UAV is controlled, enabling the following UAV to dynamically determine UAV communication and control updates according to the actual flight state. This ensures the accuracy of formation tracking and the dynamic adaptability of time-varying formations. In other words, this embodiment of the invention can dynamically determine whether to perform UAV communication and control based on the actual state of the UAV during flight, improving the efficiency of communication resource utilization and adaptive adjustment capabilities of multiple UAVs.

[0048] Figure 2 This is a schematic diagram of the structure of a multi-UAV time-varying formation control device provided in an embodiment of the present invention. This device is suitable for executing the multi-UAV time-varying formation control method provided in an embodiment of the present invention. Figure 2 As shown, the device may specifically include:

[0049] Error calculation module 201 is used to acquire flight data at the current moment and calculate the actual comprehensive formation error based on the flight data and a predetermined time-varying formation offset; the flight data includes flight data of the leader drone and its own flight data; the time-varying formation offset is used to describe the expected offset of the following drone relative to the leader drone at each moment; the actual comprehensive formation error includes actual position formation error and actual speed formation error. The first threshold determination module 202 is used to calculate dynamic internal variables based on the error between the actual comprehensive formation error and the pre-determined historical comprehensive formation error; the historical comprehensive formation error is the actual comprehensive formation error corresponding to the previous control update triggering result when it was triggered. The second threshold determination module 203 is used to perform a weighted summation of the dynamic internal variables and the actual integrated formation error based on preset weight information to obtain the dynamic trigger threshold. The drone control module 204 is used to generate drone control parameters based on the dynamic trigger threshold, the current time, and the pre-determined nearest threshold trigger time, and to control the following drone based on the drone control parameters.

[0050] Optionally, the flight data of the leader drone includes the actual position and speed of the leader drone; the flight data of the leader drone includes the actual position and speed of the following drone; the actual comprehensive formation error includes the actual position formation error and the actual speed formation error; the error calculation module 201 is specifically used to: determine the current expected offset at the current moment in the time-varying formation offset; the current expected offset includes the current expected position offset and the current expected speed offset; The difference between the actual position of the follower and the actual position of the leader is subtracted from the expected position offset to obtain the formation error of the actual position. The difference between the actual following speed and the actual leader speed is subtracted from the expected speed offset to obtain the actual speed formation error.

[0051] Optionally, the historical integrated formation error is the actual integrated formation error corresponding to the previous control update trigger result when it was triggered; the trigger threshold determination module 202 is specifically used to: subtract the historical integrated formation error from the actual integrated formation error to obtain the trigger hold error; The dynamic internal variables are determined based on the predetermined dynamic variable update formula, the actual integrated formation error, the trigger-hold error, and the internal variable parameters; the internal variable parameters include the attenuation coefficient, the error driving coefficient, and the hold-incorrect suppression coefficient.

[0052] Optionally, the trigger result determination module 203 is specifically used to: determine a first trigger result based on the trigger hold error and the dynamic trigger threshold; the first trigger result is either triggering or not triggering; A second triggering result is determined based on the current time and the most recent threshold triggering time; the second triggering result is either triggered or not triggered. The control update trigger result is determined based on the first trigger result and the second trigger result; when the control update trigger result is triggered, the control update parameters are generated.

[0053] Optionally, the trigger result determination module 203 is further configured to: determine the first trigger result as a trigger when the norm square of the trigger holding error is greater than or equal to the dynamic trigger threshold; When the square of the norm of the trigger hold error is less than the dynamic trigger threshold, the first trigger result is determined to be no trigger.

[0054] Optionally, the trigger result determination module 203 is further configured to: determine the time interval between the current time and the most recent threshold trigger time; If the time interval is greater than or equal to the preset minimum trigger interval, then the second trigger result is determined to be a trigger. If the time interval is less than the minimum trigger interval, then the second trigger result is determined to be no trigger.

[0055] Optionally, the flight data also includes the neighbor comprehensive formation error of each neighboring UAV; the flight data of the leader UAV also includes leader control parameters; the leader control parameters include reference acceleration, position tracking feedback, and velocity tracking feedback; the UAV control module 204 is specifically used to: determine the neighbor consistency term based on the actual comprehensive formation error and the neighbor comprehensive formation error; The leader compensation item is determined based on the actual comprehensive formation error and the leader control parameters. The time-varying formation compensation term is determined based on the time-varying formation offset and the current time.

[0056] The multi-UAV time-varying formation control device provided in this embodiment of the invention can execute the multi-UAV time-varying formation control method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Content not described in detail in this embodiment can be referred to the description in any method embodiment of the invention.

[0057] This invention provides a multi-UAV time-varying formation control system, characterized in that the system includes a leader UAV and multiple follower UAVs, wherein: The leader drone is used to: generate flight data of the leader drone based on a preset reference trajectory, and broadcast the flight data of the leader drone to each follower drone. The follower drone is used to: acquire flight data at the current moment, and calculate the actual comprehensive formation error based on the flight data and a predetermined time-varying formation offset; the flight data includes the flight data of the leader drone and its own flight data; the time-varying formation offset is used to describe the expected offset of the follower drone relative to the leader drone at each moment; the actual comprehensive formation error includes the actual position formation error and the actual speed formation error. Dynamic internal variables are calculated based on the error between the actual integrated formation error and the predetermined historical integrated formation error; the historical integrated formation error is the actual integrated formation error corresponding to the previous control update trigger result. The dynamic internal variables and the actual integrated formation error are weighted and summed based on preset weight information to obtain the dynamic trigger threshold. The drone control parameters are generated based on the dynamic trigger threshold, the current time, and the pre-determined nearest threshold trigger time, and the follower drone is controlled based on the drone control parameters.

[0058] The system provided in this embodiment calculates dynamic internal variables that reflect the degree of information aging by using actual and historical comprehensive formation errors. Based on these dynamic internal variables and the actual comprehensive formation error, the system adaptively adjusts the dynamic trigger threshold, allowing it to be adjusted according to the actual formation error state, internal dynamic needs, and information aging degree during UAV flight. Furthermore, based on the dynamic trigger threshold, the current moment, and the most recent threshold trigger moment, the system controls the following UAV, enabling it to dynamically determine UAV communication and control updates according to the actual flight state. This ensures the accuracy of formation tracking and the dynamic adaptability of time-varying formations. In other words, this embodiment of the invention can dynamically determine whether to perform UAV communication and control based on the actual state of the UAV during flight, improving the efficiency of communication resource utilization and adaptive adjustment capabilities of multiple UAVs.

[0059] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a multi-UAV time-varying formation control method as provided in all embodiments of this invention: acquiring flight data at the current moment, and calculating an actual comprehensive formation error based on the flight data and a predetermined time-varying formation offset; the flight data includes flight data of the leader UAV and its own flight data; the time-varying formation offset describes the expected offset of the following UAV relative to the leader UAV at each moment; the actual comprehensive formation error includes actual position formation error and actual speed formation error; calculating a dynamic internal variable based on the error between the actual comprehensive formation error and a predetermined historical comprehensive formation error; the historical comprehensive formation error is the actual comprehensive formation error corresponding to the last time the control update was triggered; weighting and summing the dynamic internal variable and the actual comprehensive formation error based on preset weight information to obtain a dynamic trigger threshold; generating UAV control parameters based on the dynamic trigger threshold, the current moment, and a predetermined most recent threshold trigger moment, and controlling the following UAV based on the UAV control parameters. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor electronic devices, apparatuses, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires; portable computer disks; hard disks; random access memory (RAM); read-only memory (ROM); erasable programmable read-only memory (EPROM or flash memory); optical fiber; portable compact disk read-only memory (CD-ROM); optical storage devices; magnetic storage devices; or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an electronic device, apparatus, or apparatus that can be executed.

[0060] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in conjunction with an electronic device, apparatus, or device that executes instructions.

[0061] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0062] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0063] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for time-varying formation control of multiple unmanned aerial vehicles (UAVs), characterized in that, Applied to following drones, the method includes: The system acquires flight data at the current moment and calculates the actual comprehensive formation error based on the flight data and a predetermined time-varying formation offset. The flight data includes the flight data of the leader drone and its own flight data. The time-varying formation offset describes the expected offset of the following drone relative to the leader drone at each moment. The actual comprehensive formation error includes the actual position formation error and the actual speed formation error. Dynamic internal variables are calculated based on the error between the actual integrated formation error and the predetermined historical integrated formation error; the historical integrated formation error is the actual integrated formation error corresponding to the previous control update trigger result. The dynamic internal variables and the actual integrated formation error are weighted and summed based on preset weight information to obtain the dynamic trigger threshold. The drone control parameters are generated based on the dynamic trigger threshold, the current time, and the pre-determined nearest threshold trigger time, and the follower drone is controlled based on the drone control parameters.

2. The method according to claim 1, characterized in that, The flight data of the leader drone includes the actual position and speed of the leader drone; the flight data of the drone itself includes the actual position and speed of the following drone. The actual integrated formation error is calculated based on the flight data and the predetermined time-varying formation offset, including: The current expected offset at the current moment is determined in the time-varying formation offset; the current expected offset includes the current expected position offset and the current expected velocity offset; The difference between the actual position of the follower and the actual position of the leader is subtracted from the expected position offset to obtain the formation error of the actual position; The difference between the actual following speed and the actual leader speed is subtracted from the expected speed offset to obtain the actual speed formation error.

3. The method according to claim 1, characterized in that, The dynamic internal variables are calculated based on the error between the actual integrated formation error and the predetermined historical integrated formation error, including: Subtracting the historical integrated formation error from the actual integrated formation error yields the trigger-hold error; Dynamic internal variables are determined based on the actual integrated formation error, the trigger-hold error, and the internal variable parameters; the internal variable parameters include the attenuation coefficient, the error driving coefficient, and the hold-incorrect suppression coefficient.

4. The method according to claim 3, characterized in that, Based on the dynamic trigger threshold, the current time, and the pre-determined nearest threshold trigger time, UAV control parameters are generated, including: A first triggering result is determined based on the trigger hold error and the dynamic trigger threshold; the first triggering result is either triggering or not triggering. A second triggering result is determined based on the current time and the most recent threshold triggering time; the second triggering result is either triggered or not triggered. The control update trigger result is determined based on the first trigger result and the second trigger result; when the control update trigger result is triggered, the control update parameters are generated.

5. The method according to claim 4, characterized in that, Determining the first trigger result based on the trigger hold error and the dynamic trigger threshold includes: When the square of the norm of the trigger hold error is greater than or equal to the dynamic trigger threshold, the first trigger result is determined to be a trigger. When the square of the norm of the trigger hold error is less than the dynamic trigger threshold, the first trigger result is determined to be no trigger.

6. The method according to claim 4, characterized in that, Determining the second triggering result based on the current time and the most recent threshold triggering time includes: Determine the time interval between the current time and the most recent threshold trigger time; If the time interval is greater than or equal to the preset minimum trigger interval, then the second trigger result is determined to be a trigger. If the time interval is less than the minimum trigger interval, then the second trigger result is determined to be no trigger.

7. The method according to claim 1, characterized in that, The flight data also includes the neighbor comprehensive formation error of each neighboring UAV; the flight data of the leader UAV also includes leader control parameters; the leader control parameters include reference acceleration, position tracking feedback, and velocity tracking feedback; the UAV control parameters include neighbor consistency, leader compensation, and time-varying formation compensation. Based on the control update trigger result, UAV control parameters are generated, including: The neighbor consistency term is determined based on the actual composite formation error and the neighbor composite formation error. The leader compensation item is determined based on the actual comprehensive formation error and the leader control parameters. The time-varying formation compensation term is determined based on the time-varying formation offset and the current time.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-UAV time-varying formation control method as described in any one of claims 1-7.

9. A multi-UAV time-varying formation control system, characterized in that, The system includes a leader drone and multiple follower drones, wherein: The leader drone is used to: generate leader drone flight data according to a preset reference trajectory, and broadcast the leader drone flight data to each follower drone; The following drone is used to: acquire flight data at the current moment, and calculate the actual comprehensive formation error based on the flight data and a predetermined time-varying formation offset; the flight data includes flight data of the leader drone and its own flight data; the time-varying formation offset is used to describe the expected offset of the following drone relative to the leader drone at each moment. The dynamic trigger threshold is calculated based on the actual comprehensive formation error, the predetermined historical comprehensive formation error, and the preset internal variable parameters. The control update triggering result at the current moment is determined based on the dynamic triggering threshold, the current moment, and the pre-determined nearest threshold triggering moment; the control update triggering result is either triggered or not triggered. If the control update trigger result is the trigger, generate UAV control parameters based on the control update trigger result, and control the following UAV based on the UAV control parameters.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the multi-UAV time-varying formation control method as described in any one of claims 1 to 7.

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