Reconfiguration method of drone module, electronic device

Abstract

The application discloses a reconstruction method of a UAV module, and an electronic device. The method comprises the following steps: in each iteration period, selecting a first quantity limit of UAV formation structures with higher performance scores from a current UAV formation structure category, and recording the UAV formation structures as competitive UAV formation structures; performing a deformation operation on each competitive UAV formation structure according to a deformation probability, and obtaining a second quantity limit of deformed UAV formation structures; calculating the performance scores of the deformed UAV formation structures according to the mass, rotational inertia and docking surface connection of each UAV module in each deformed UAV formation structure; updating the current UAV formation structure category by using the deformed UAV formation structures, so as to perform iteration in the next period, until an iteration termination condition is met, wherein the iteration termination condition is obtained according to a maximum iteration number and a convergence number; and taking the updated UAV formation structure category obtained in the iteration period that meets the iteration termination condition as a flight configuration of the reconstructed UAV module. The method can improve the efficiency and effectiveness of the reconstruction of the UAV module.

Description

Technical Field

[0001] This invention relates to the field of aircraft technology, and more particularly to a method for reconfiguring an unmanned aerial vehicle (UAV) module and an electronic device. Background Technology

[0002] For the structural optimization problem of aircraft, the enumeration algorithm first finds all non-isomorphic configurations and then selects the optimal configuration from them, which is extremely inefficient. Moreover, as the number of modular UAVs increases, the set of all possible configurations grows exponentially, making it impractical to find the optimal configuration through exhaustive search. Summary of the Invention

[0003] This invention aims to at least partially address one of the technical problems in related technologies. Therefore, the object of this invention is to propose a method and electronic device for reconfiguring a drone module, thereby improving the efficiency and effectiveness of drone module reconfiguration.

[0004] To achieve the above objectives, an embodiment of the first aspect of the present invention proposes a method for reconstructing unmanned aerial vehicle (UAV) modules. The method includes: obtaining the number of UAV modules and the mass and moment of inertia of each UAV module, wherein each UAV module has multiple docking surfaces; initializing UAV formation structure categories and iteration parameters based on the number of UAV modules, wherein the iteration parameters include the number of UAV formation structure categories, a maximum number of iterations, a first quantity limit, a second quantity limit, a deformation probability, and a convergence count; for each UAV formation structure in the initial UAV formation structure categories, calculating the performance score of the UAV formation structure based on the mass, moment of inertia, and connection status of the docking surfaces of each UAV module in the UAV formation structure; and in each iteration cycle, reconstructing the UAV formation structure from the current UAV module... The top 100 drone formations with the highest performance scores in the formation structure category are selected as competitive drone formations. For each competitive drone formation, a deformation operation is performed according to the deformation probability to obtain 200 deformed drone formations. The performance score of each deformed drone formation is calculated, and the current drone formation structure category is updated using the deformed drone formations for the next iteration cycle until the iteration termination condition is met. The iteration termination condition is obtained based on the maximum number of iterations and the number of convergences. The updated drone formation structure category obtained in the iteration cycle that meets the iteration termination condition is used as the flight configuration of the reconstructed drone module.

[0005] In addition, the reconfiguration method of the UAV module in the above embodiments of the present invention may also have the following additional technical features:

[0006] According to one embodiment of the present invention, calculating the performance score of a UAV array structure includes: obtaining the coordinate positions of each UAV module in the UAV array structure based on the connection status of each UAV module in the UAV array structure and the coordinate positions of the UAV module at the root node; calculating the geometric center position of the UAV array structure based on the coordinate positions and masses of each UAV module in the UAV array structure, and calculating the relative positions between the coordinate positions of each UAV module in the structure tree and the geometric center position; constructing an objective function based on the relative positions and the moment of inertia of each UAV module; solving for the maximum value of the objective function, and using the maximum value as the performance score of the UAV array structure.

[0007] According to one embodiment of the present invention, the geometric center position of the UAV array structure is calculated by the following formula:

[0008]

[0009] Wherein, d0 represents the geometric center position. The mass of the UAV array structure is represented by n, and the number of UAV modules is represented by m. i Let d represent the mass of the i-th UAV module. i This represents the position coordinates of the i-th UAV module.

[0010] According to one embodiment of the present invention, constructing the objective function based on the relative positions and the rotational inertia of each UAV module includes: obtaining the total rotational inertia of the UAV formation structure based on the relative positions and the rotational inertia of each UAV module; constructing an objective matrix based on the total rotational inertia and the relative positions; and constructing the objective function based on the objective matrix.

[0011] According to an embodiment of the present invention, the objective function is expressed by the following formula:

[0012]

[0013] in, , represents the target matrix, J represents the total moment of inertia. i Let x represent the moment of inertia of the i-th UAV module. i y i Let x and y represent the x and y coordinates of the i-th UAV module relative to the geometric center, respectively, and cond(·) represent the condition number of the matrix. σ max (·) denotes the largest singular value of the matrix.

[0014] According to one embodiment of the present invention, the step of performing a deformation operation on the competitive UAV formation structure according to the deformation probability to obtain a second limited number of deformable UAV formation structures includes: generating a random number within a preset range, wherein the value of the deformation probability is within the preset range; when the random number is less than the deformation probability, performing a deformation operation on the competitive UAV formation structure to obtain the second limited number of deformable UAV formation structures.

[0015] According to one embodiment of the present invention, the deformation operation of the competitive UAV formation structure includes: randomly selecting two connected docking surfaces in the competitive UAV formation structure and disconnecting them to obtain a first sub-formation group and a second sub-formation group; randomly selecting an idle docking surface on the first sub-formation group, denoted as the first idle docking surface, and randomly selecting an idle docking surface on the second sub-formation group, denoted as the second idle docking surface; and connecting the first idle docking surface and the second idle docking surface to obtain a deformed UAV formation structure.

[0016] According to one embodiment of the present invention, after obtaining the deformable UAV formation structure, the method further includes: rotating the smaller sub-formation group in the first sub-formation group and the second sub-formation group to obtain a rotated UAV formation structure; determining whether there is module position overlap in the rotated UAV formation structure; if not, then using the rotated UAV formation structure as the final deformable UAV formation structure.

[0017] According to one embodiment of the present invention, the method further includes: when the random number is greater than or equal to the deformation probability, updating the current tree structure category using the corresponding competitive UAV formation structure.

[0018] To achieve the above objectives, an embodiment of the second aspect of the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory, wherein when the computer program is executed by the processor, it implements the above-described method for reconfiguring a drone module.

[0019] The UAV module reconstruction method and electronic device of this invention, in each iteration cycle, selects the top 100 UAV formation structures with higher performance scores from the current UAV formation structure category, denoted as competing UAV formation structures. For each competing UAV formation structure, a deformation operation is performed on the competing UAV formation structure according to the deformation probability to obtain a second 1000 deformable UAV formation structures. The performance score of each deformable UAV formation structure is calculated based on the mass, moment of inertia, and connection status of the docking surfaces of each UAV module in each deformable UAV formation structure. The current UAV formation structure category is updated using the deformable UAV formation structures for the next iteration cycle, until the iteration termination condition is met. The iteration termination condition is obtained based on the maximum number of iterations and the number of convergences. The updated UAV formation structure category obtained in the iteration cycle that meets the iteration termination condition is used as the flight configuration of the reconstructed UAV module. This improves the efficiency and effectiveness of UAV module reconstruction.

[0020] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0021] Figure 1 This is a flowchart of the reconstructing method of the UAV module according to an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of the structure of an example drone module of the present invention;

[0023] Figure 3 This is a schematic diagram of an example flight configuration of the present invention;

[0024] Figure 4 This is a schematic diagram illustrating an example of the UAV formation structure categories and the performance scores of each UAV formation structure in this invention.

[0025] Figures 5(a)-5(d) This is a flowchart illustrating an example of the present invention of a deformation operation on a competing drone array structure;

[0026] Figure 6 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0027] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0028] The following description, with reference to the accompanying drawings, describes a method for reconstructing a drone module and an electronic device according to embodiments of the present invention.

[0029] Figure 1 This is a flowchart of the reconstructing method of the unmanned aerial vehicle module according to an embodiment of the present invention.

[0030] like Figure 1 As shown, the reconfiguration method for the UAV module includes:

[0031] S101, obtain the number of UAV modules and the mass and moment of inertia of each UAV module, wherein the UAV module has multiple docking surfaces.

[0032] In this embodiment, the drone module includes a drone and a frame. The drone can move with multiple degrees of freedom within the frame, which has multiple docking surfaces for connecting and reconfiguring the various drone modules. The structures of the drone modules can be the same or different, primarily differing in the payload they carry. For example, the payload could be a camera, a robotic arm, a leakage current detector, a gas detector, etc., resulting in variations in the mass and moment of inertia of each drone module.

[0033] For example, such as Figure 2 As shown, the drone can be connected to the central frame via a 2-DOF passive gimbal mechanism, with no rotation angle limitation, and can be used as an omnidirectional thrust generator. This quadcopter drone includes a flight control board, a quadcopter deck, a power distribution board, brushless motors (which can be four), propellers, etc. The brushless motors drive the propellers, and the motor speed is controlled by an electronic speed controller. It is powered by a battery (such as a 14.8V lithium battery).

[0034] See Figure 2 The frame may be equipped with a locking mechanism to lock the torsional degree of freedom (β) of the drone. The locking mechanism can lock the passive rotation support to prevent the drone from tilting (α). When the locking mechanism is activated, the drone module can be treated as a rigid body and fly as a normal quadcopter.

[0035] See Figure 2 The frame of the drone module is a cube with a side length of l. The frame includes a carbon fiber tube and a connecting structure. The connecting structure has grooves or protrusions. An electromagnet can be provided on the corresponding connecting structure to facilitate the connection between modules. The magnetism (including positive magnetism, negative magnetism, and non-magnetism) of the electromagnet can be controlled by the connecting controller, which is powered by a battery.

[0036] S102, based on the number of UAV modules, initialize the UAV formation structure category and iteration parameters, wherein the iteration parameters include the number of UAV formation structure categories, the maximum number of iterations, the first quantity limit, the second quantity limit, the deformation probability, and the number of convergences.

[0037] Specifically, a UAV module can connect to other UAV modules through its docking surface. Let the number of UAV modules be n. The larger the value of n, the more flight configurations (represented by UAV array structures) can be obtained, the larger the number of UAV array structure categories that can be initialized, and the larger the maximum number of iterations, the first quantity limit, the second quantity limit, the deformation probability, and the number of convergences can also be, so as to improve the effectiveness of the iteration results.

[0038] The drone formation structure can be a tree structure, such as... Figure 3 As shown in the image.

[0039] S103, for each UAV formation structure in the initial UAV formation structure category, calculate the performance score of the UAV formation structure based on the mass, moment of inertia and connection status of each UAV module in the UAV formation structure.

[0040] Specifically, after n UAV modules are docked, a flight configuration can be formed. Each UAV module can still act as an omnidirectional thrust generator after docking, giving the resulting multi-rotor flight configuration the potential for overdrive, thereby achieving better maneuverability in position and orientation tracking than individual UAV modules.

[0041] For example, to represent the overall flight configuration, the four docking surfaces of the UAV module are defined as f1, f2, f3, and f4, as follows: Figure 3 As shown. See also Figure 3 F w F represents the world coordinate system. S This represents the coordinate system in which the flight configuration is located. The coordinate position of the flight configuration is defined as X. S =[x S ,y S ,z S ] T Roll-pitch-yaw attitude is defined as Angular velocity is defined as Ω S =[p S ,q S ,r S ] T F iS d represents the center of the i-th UAV module. i =[x i ,y i [0] represents the position coordinates of the i-th UAV module.

[0042] For ease of description, see Figure 3After encoding each UAV module, the first UAV module is defined as the root node of the UAV array structure. Based on this root node, an assembly relationship matrix A(S) can be obtained to represent the UAV array structure. In the assembly relationship matrix A(S), each row represents a UAV module, and each column represents the mating surface of that module. A can be used to... i,j =k indicates that the j-th docking surface of the i-th UAV module is connected to the k-th UAV module.

[0043] S104. In each iteration cycle, select the top 100 drone formation structures with the highest performance scores from the current drone formation structure category. These are denoted as competitive drone formation structures. For each competitive drone formation structure, perform a deformation operation on it according to the deformation probability to obtain a second 1000 deformed drone formation structures.

[0044] S105, calculate the performance score of each deformable UAV formation structure, and update the current UAV formation structure category using the deformable UAV formation structure to proceed with the next iteration until the iteration termination condition is met. The iteration termination condition is obtained based on the maximum number of iterations and the number of convergences.

[0045] S106, the updated UAV array structure category obtained by the iteration cycle that satisfies the iteration termination condition is used as the flight configuration of the reconstructed UAV module.

[0046] In some embodiments of the present invention, calculating the performance score of the UAV array structure includes: obtaining the coordinate positions of each UAV module in the UAV array structure based on the connection status of each UAV module in the UAV array structure and the coordinate positions of the UAV module at the root node; calculating the geometric center position of the UAV array structure based on the coordinate positions and mass of each UAV module in the UAV array structure, and calculating the relative positions between the coordinate positions of each UAV module in the structure tree and the geometric center position; constructing an objective function based on the relative positions and the rotational inertia of each UAV module; solving for the maximum value of the objective function, and using the maximum value as the performance score of the UAV array structure.

[0047] For example, the geometric center of the UAV formation can be calculated using the following formula:

[0048]

[0049] Where d0 represents the geometric center position, The mass of the UAV array structure is represented by n, and the number of UAV modules is represented by m. i Let d represent the mass of the i-th UAV module. i This represents the position coordinates of the i-th UAV module.

[0050] Specifically, the coordinates of the UAV module at the root node (e.g., d1 = [0,0,0]) and the connection relationships between the UAV modules are known, as well as the step size matrix of the connected UAV modules. The coordinates d of other UAV modules can be calculated recursively. i Then, considering the quality of each drone module, the following method was used: The geometric center of the UAV formation can be obtained. By translating the geometric center to the origin, the position coordinates of each UAV module are correspondingly translated, yielding the relative position d. i =d i -d0. Afterwards, an objective function can be constructed based on the relative positions and the rotational inertia of each UAV module; the maximum value of the objective function is then calculated, and this maximum value is used as the performance score of the UAV formation structure.

[0051] In some embodiments of the present invention, an objective function is constructed based on the relative positions and the rotational inertia of each UAV module, including: obtaining the total rotational inertia of the UAV formation structure based on the relative positions and the rotational inertia of each UAV module; constructing an objective matrix based on the total rotational inertia and the relative positions; and constructing an objective function based on the objective matrix.

[0052] For example, the objective function can be expressed as follows:

[0053]

[0054] in, Represents the target matrix. Let be a skew-symmetric matrix representing the relative position of the i-th UAV module. J represents the total moment of inertia. i Let x represent the moment of inertia of the i-th UAV module. i y i Let x and y represent the x and y coordinates of the i-th UAV module relative to its geometric center, respectively, and cond(·) represent the condition number of the matrix. σ max (·) denotes the largest singular value of the matrix.

[0055] Specifically, for a flight configuration consisting of n UAV modules, its translational motion mechanics model can be described as follows:

[0056]

[0057] Where M represents the total mass of the flight configuration. This indicates that the flight configuration is linear acceleration, where g represents gravitational acceleration. The tilt angle α of the i-th UAV module represents... i and twist angle β i The function, Ti This represents the magnitude of the thrust generated by the i-th UAV module.

[0058] The rotational dynamics model of the flight configuration can be described as follows:

[0059]

[0060] in, Represents the total moment of inertia. It is the angular acceleration of the flight configuration.

[0061] Combining the above translational mechanics model and rotational dynamics model, we can obtain:

[0062]

[0063] in, This represents the 6-DOF matrix corresponding to the flight configuration.

[0064] Using the method of force decomposition, it is defined as an intermediate variable. in, Will The nonlinear relationship in the equation is transformed into a linear variable to obtain:

[0065]

[0066] in, This represents a constant allocation matrix with full row rank.

[0067] Therefore, F can be used as the system input to analyze the flight configuration from a linear dynamics perspective. The actual tilt angle α of each UAV module... i Twist angle β i and thrust T i It can be calculated using inverse kinematics through F.

[0068] To optimize flight configuration and generate a high-energy-efficiency over-actuated platform, dynamic equations (including the assignment matrix W and the total moment of inertia J) can be used. S From a control perspective, the objective function is designed. For the optimization problem of flight configuration, it can be represented by the following optimization structure:

[0069]

[0070] st rank(W) = 6

[0071]

[0072] S(d1,…,d n ) = 1

[0073] in, As the optimization objective, it is defined as minimizing the required thrust energy exponent ||T|| for all possible desired acceleration commands. 2 The constraint rank(W) = 6 is to implement overdrive, constraint To ensure that the geometric center of the flight configuration is located at the origin, the constraint S(d1,…,d n The setting is 1 to ensure that all drone modules are not connected in an overlapping manner.

[0074] Since the allocation matrix W can be expressed by the following formula:

[0075]

[0076] in, It is d i The skew-symmetric matrix, I3, represents an identity matrix of size 3, indicating that translational dynamics are independent of the configuration. Therefore, the optimization problem described above can focus only on the rotational dynamics, i.e., the total moment of inertia is related to the flight configuration-related structures. In this case, the objective matrix can be constructed. Therefore, the optimization objective in the above optimization structure can be simplified to:

[0077]

[0078] in, Representation matrix The Moore-Penrose inverse matrix, σ max (·) denotes the largest singular value of the matrix.

[0079] The optimization problem described above can then be transformed into a combinatorial optimization problem, as follows:

[0080]

[0081] That is, to maximize the objective function, in the objective function The full driving constraints were considered, including rank(W) = 6; Characterized thrust minimization, including

[0082] For each drone formation structure within the drone formation structure category, its objective function can be obtained. By maximizing the objective function, the performance score of the corresponding drone formation structure can be obtained. For example, such as... Figure 4 As shown, the performance scores of a flight configuration consisting of five UAV modules are presented. Figure 4 (a) has the lowest performance score due to insufficient drive. Figure 4 The middle (f) has a symmetrical structure and has the highest performance score.

[0083] In some embodiments of the present invention, the competitive drone formation structure is deformed according to the deformation probability to obtain a second number of deformable drone formation structures, including: generating a random number within a preset range, wherein the value of the deformation probability is within the preset range; when the random number is less than the deformation probability, the competitive drone formation structure is deformed to obtain a second number of deformable drone formation structures.

[0084] For example, the deformation probability can be set to a value within a preset range of 0 to 1, such as 0.6. When performing a deformation operation, a random number between 0 and 1 is first generated, and this random number is compared with the deformation probability to determine whether to perform the deformation operation. For example, if the generated random number is 0.4, which is less than 0.6, it can be assumed that the performance score after deformation may exceed that of the competing drone formation structure. Therefore, a deformation operation is performed to generate more drone formation structures for selection.

[0085] In some embodiments, the deformation operation of the competitive UAV formation structure includes: randomly selecting two connected docking surfaces in the competitive UAV formation structure and disconnecting them to obtain a first sub-formation group and a second sub-formation group; randomly selecting an idle docking surface on the first sub-formation group, denoted as the first idle docking surface, and randomly selecting an idle docking surface on the second sub-formation group, denoted as the second idle docking surface; and connecting the first idle docking surface and the second idle docking surface to obtain the deformed UAV formation structure.

[0086] For example, as shown in Figure 5(a), the competitive UAV formation structure includes 5 nodes (i.e., UAV modules), denoted as ①, ②, ③, ④, and ⑤. The 5 nodes are connected in a chain, and adjacent nodes are connected by docking surfaces f1 and f3. The two docking surfaces of this competitive UAV formation structure are randomly selected as docking surface f1 (denoted as 3,1) of node ③ and docking surface f3 (denoted as 4,3) of node ④. This can be represented by a matrix. As shown in Figure 5(b), the selected docking surfaces in Figure 5(a) are broken to obtain the first subarray group (including nodes ①, ②, and ③) and the second subarray group (including nodes ④ and ⑤). The free docking surfaces of the first subarray group can be represented by matrix LF, and the free docking surfaces of the second subarray group can be represented by matrix RF, as detailed below:

[0087]

[0088] As shown in Figure 5(c), an idle docking surface is randomly selected on the first sub-array group, denoted as the first idle docking surface L = [3,4], which is the docking surface f4 of node ③; and an idle docking surface is randomly selected on the second sub-array group, denoted as the second idle docking surface R = [4,3], which is the docking surface f3 of node ④. The first idle docking surface L = [3,4] and the second idle docking surface R = [4,3] are connected to obtain the deformable UAV array structure shown in Figure 5(c).

[0089] In some embodiments of the present invention, after obtaining the deformable UAV formation structure, the method further includes: rotating the smaller sub-formation group in the first sub-formation group and the second sub-formation group to obtain a rotated UAV formation structure; determining whether there is module position overlap in the rotated UAV formation structure; if not, then using the rotated UAV formation structure as the final deformable UAV formation structure.

[0090] For example, after obtaining the deformable UAV formation structure shown in Figure 5(c), if the degrees of freedom directions of the UAV modules in the first sub-formation group are different from those in the second sub-formation group, then the sub-formation groups can be rotated. To reduce control complexity, the smaller sub-formation groups in the first and second sub-formation groups can be rotated to obtain a rotated UAV formation structure. As shown in Figure 5(d), the second sub-formation group including nodes ④ and ⑤ is rotated. After rotation, the docking surface f4 of node ③ is connected to the docking surface f2 of node ④, and the docking surface f4 of node ④ is connected to the docking surface f2 of node ⑤, thereby making the degrees of freedom directions of each UAV module in the structure tree the same, which facilitates subsequent control.

[0091] It should be noted that if the rotated UAV formation structure has overlapping module positions, then this rotating UAV formation structure is unreasonable and should be discarded. If it does not exist, then the UAV formation structure category will be updated for the next iteration.

[0092] In some embodiments of the present invention, the reconstructing method of the UAV module further includes: when the random number is greater than or equal to the deformation probability, updating the current tree structure category using the corresponding competing UAV array structure.

[0093] Specifically, if the random number is greater than or equal to the deformation probability, it can be assumed that even if the competitive drone formation structure is deformed, the performance score after deformation will not exceed that of the competitive drone formation structure. Therefore, the competitive drone formation structure will not be deformed, and it will be directly used to update the current tree structure category.

[0094] The UAV module reconstruction method of this invention represents the flight configuration through the UAV array structure and uses an iterative algorithm to solve the reconstruction optimization problem. During the iteration process, custom deformation operations are performed and the performance score of the corresponding UAV array structure is calculated based on the mass, moment of inertia and other parameters of each UAV module. This method can achieve an effective flight configuration based on the reconstruction of a large number of UAV modules and can improve computational efficiency.

[0095] Figure 6 This is a structural block diagram of an electronic device according to an embodiment of the present invention.

[0096] like Figure 6 As shown, the electronic device 500 includes a processor 501 and a memory 503. The processor 501 and the memory 503 are connected, for example, via a bus 502. Optionally, the electronic device 500 may also include a transceiver 504. It should be noted that in practical applications, the transceiver 504 is not limited to one type, and the structure of this electronic device 500 does not constitute a limitation on the embodiments of the present invention.

[0097] Processor 501 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 501 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0098] Bus 502 may include a pathway for transmitting information between the aforementioned components. Bus 502 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 502 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0099] The memory 503 stores a computer program corresponding to the reconfiguration method of the UAV module in the above embodiments of the present invention. This computer program is executed under the control of the processor 501. The processor 501 executes the computer program stored in the memory 503 to implement the content shown in the aforementioned method embodiments.

[0100] Among them, electronic devices 500 include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6 The electronic device 500 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0101] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0102] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0103] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0104] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0105] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0106] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0107] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0108] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for reconstructing a UAV module, characterized in that, The method includes: The number of unmanned aerial vehicle (UAV) modules and the mass and moment of inertia of each UAV module are obtained, wherein each UAV module has multiple docking surfaces; Based on the number of UAV modules, initialize the UAV formation structure category and iteration parameters, wherein the iteration parameters include the number of UAV formation structure categories, the maximum number of iterations, a first quantity limit, a second quantity limit, the deformation probability, and the number of convergences; For each UAV formation structure in the initial UAV formation structure category, the performance score of the UAV formation structure is calculated based on the mass, moment of inertia, and connection status of the docking surfaces of each UAV module in the UAV formation structure. In each iteration cycle, select the top 100 drone formations with the highest performance scores from the current drone formation structure category, and denot them as competitive drone formations. For each competitive drone formation, perform a deformation operation on the competitive drone formation according to the deformation probability to obtain the second 1000 deformed drone formations. Calculate the performance score of each of the deformable UAV formation structures, and update the current UAV formation structure category using the deformable UAV formation structure to perform the next iteration until the iteration termination condition is met, wherein the iteration termination condition is obtained based on the maximum number of iterations and the number of convergences; The updated UAV array structure category obtained by the iteration cycle that satisfies the iteration termination condition is used as the flight configuration of the reconstructed UAV module. Calculate the performance score of the UAV formation structure, including: Based on the connection status of each UAV module in the UAV array structure and the coordinate position of the UAV module at the root node, the coordinate position of each UAV module in the UAV array structure is obtained. Based on the coordinate positions and masses of each UAV module in the UAV formation structure, calculate the geometric center position of the UAV formation structure, and calculate the relative position between the coordinate positions of each UAV module in the UAV formation structure and the geometric center position; Based on the relative positions and the moments of inertia of each of the UAV modules, an objective function is constructed; Find the maximum value of the objective function and use the maximum value as the performance score of the UAV array structure.

2. The method for reconstructing a UAV module according to claim 1, characterized in that, The geometric center position of the UAV array structure is calculated using the following formula: in, Indicates the location of the geometric center. , where represents the total mass of the UAV array structure, and n represents the number of UAV modules. This represents the mass of the i-th drone module. This represents the position coordinates of the i-th UAV module.

3. The method for reconstructing a UAV module according to claim 2, characterized in that, The step of constructing the objective function based on the relative positions and the moments of inertia of each UAV module includes: The total moment of inertia of the UAV array structure is obtained based on the relative positions and the moments of inertia of each UAV module. Construct a target matrix based on the total moment of inertia and the relative position; The objective function is constructed based on the objective matrix.

4. The method for reconstructing a UAV module according to claim 3, characterized in that, The objective function is expressed by the following formula: in, , represents the target matrix, Let be a skew-symmetric matrix representing the relative position of the i-th UAV module. , representing the total moment of inertia, This represents the moment of inertia of the i-th UAV module. , These represent the x and y coordinates of the i-th UAV module relative to the geometric center, respectively. The condition number represents the matrix. This represents the largest singular value of the matrix.

5. The method for reconstructing a UAV module according to claim 1, characterized in that, The step of performing a deformation operation on the competing UAV formation structure according to the deformation probability to obtain the second limit number of deformable UAV formation structures includes: Generate random numbers within a preset range, wherein the value of the deformation probability is within the preset range; When the random number is less than the deformation probability, the competitive UAV formation structure is deformed to obtain the second limit number of deformed UAV formation structures.

6. The method for reconstructing a UAV module according to claim 5, characterized in that, The deformation operation on the competing UAV array structure includes: Two connected docking surfaces in the competitive UAV formation structure are randomly selected and disconnected to obtain a first sub-formation group and a second sub-formation group; Randomly select an idle docking surface on the first subarray group and record it as the first idle docking surface; and randomly select an idle docking surface on the second subarray group and record it as the second idle docking surface. Connect the first idle docking surface with the second idle docking surface to obtain a deformable UAV array structure.

7. The method for reconstructing a UAV module according to claim 6, characterized in that, After obtaining the deformable UAV array structure, the method further includes: Rotate the smaller sub-array groups in the first and second sub-array groups to obtain a rotating UAV array structure; Determine whether there is any overlap in the positions of the modules in the rotating UAV array structure; If it does not exist, the rotating UAV formation structure will be used as the final deformable UAV formation structure.

8. The method for reconstructing a UAV module according to claim 5, characterized in that, The method further includes: When the random number is greater than or equal to the deformation probability, the current drone formation structure category is updated using the corresponding competing drone formation structure.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the computer program is executed by the processor, it implements the reconfiguration method of the UAV module according to any one of claims 1-8.

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