Multi-flight manipulator contact force self-adaptive cooperative impedance control method and related equipment

Abstract

The application provides a kind of multi-flight manipulator contact force self-adapting collaborative impedance control method and related equipment, it is related to flight manipulator control technical field.The steps of the method include: according to the contact force information of each manipulator, the contact force error and collaborative factor of each manipulator are calculated;According to the contact force error of each manipulator, the adjusted impedance parameters of each manipulator are obtained;According to the collaborative factor, the collaborative error of all manipulators is calculated;According to the collaborative error, the adjusted expected position of each manipulator is obtained;According to the adjusted expected position and the adjusted impedance parameters, the control command of each manipulator is generated, and the movement of each manipulator is controlled through the control command.The method of the application aims to solve the problem that the contact force is difficult to accurately control when multiple flight manipulators work collaboratively in the prior art, lacks effective collaborative control strategy and has poor adaptability to complex environment, and realizes accurate control of multiple flight manipulators working collaboratively.

Description

Adaptive Cooperative Impedance Control Method and Related Equipment for Contact Force of Multi-Flying Robotic Arms Technical Field

[0001] This invention relates to the field of flight robotic arm control technology, and more specifically, to a method and related equipment for adaptive cooperative impedance control of contact force of multiple flight robotic arms. Background Technology

[0002] With the development of robotics technology, flying robotic arm systems, composed of multi-rotor aircraft equipped with robotic arms, have shown great potential in complex environments, such as disaster relief and high-altitude building maintenance. In these scenarios, multiple flying robotic arms often need to work together to complete the task of manipulating objects. Existing multi-flying robotic arm collaborative control technologies mainly suffer from the following defects and shortcomings:

[0003] 1. Inaccurate contact force control: During collaborative operations, the contact force between each flying robotic arm and the object is difficult to control precisely. Because the flying robotic arms are in the air, they are affected by factors such as airflow disturbances and changes in the aircraft's attitude, which can easily cause fluctuations in contact force, leading to unstable operations and potentially damaging the object or the robotic arm.

[0004] 2. Poor coordination: When multiple robotic arms operate in coordination, the lack of an effective coordination control strategy makes it difficult to ensure the synchronization of movements and force balance among the arms. This may lead to problems such as shaking and deviation of the object during operation, affecting the accuracy and efficiency of the operation.

[0005] 3. Insufficient adaptability: Existing technologies lack the ability to adaptively adjust to objects of different shapes, weights, and materials, as well as complex and ever-changing environmental conditions. When the characteristics of the object or the environment change, the performance of the control system will significantly degrade, failing to guarantee the smooth operation of collaborative work.

[0006] There is currently no effective technical solution to the above problems. Summary of the Invention

[0007] The purpose of this invention is to provide a method and related equipment for adaptive cooperative impedance control of contact force of multiple flying robotic arms, which aims to solve the problems of difficulty in accurately controlling contact force, lack of effective cooperative control strategies, and poor adaptability to complex environments when multiple flying robotic arms are working together in the prior art, and to achieve precise control of the cooperative operation of multiple flying robotic arms.

[0008] In a first aspect, the present invention provides a multi-flying robotic arm contact force adaptive cooperative impedance control method, applied to the control system of an aircraft equipped with multiple robotic arms, comprising the following steps:

[0009] S1. Acquire sensor data using sensors pre-positioned on the robotic arm; the sensor data includes contact force information for each robotic arm.

[0010] S2. Calculate the contact force error of each robotic arm based on the contact force information of each robotic arm;

[0011] S3. Based on the contact force error of each robotic arm, dynamically adjust the impedance parameters of each robotic arm to obtain the adjusted impedance parameters of each robotic arm;

[0012] S4. Calculate the coordination factor of each robotic arm based on the contact force information of each robotic arm;

[0013] S5. Calculate the coordination error of all robotic arms based on the coordination factor;

[0014] S6. Based on the coordination error, adjust the desired position of each robotic arm to obtain the adjusted desired position of each robotic arm;

[0015] S7. Based on the adjusted desired position and the adjusted impedance parameters, generate control commands for each robotic arm, and control the movement of each robotic arm through the control commands.

[0016] The present invention provides an adaptive cooperative impedance control method for contact force of multiple flying robotic arms. Through this technical solution, this application addresses a flying robotic arm system composed of multiple flying robotic arms mounted on a multi-rotor aircraft. During the process of multiple flying robotic arms collaboratively grasping and manipulating objects in the air (such as moving, transporting, ascending, descending, rotating, etc.), the contact force between each robotic arm and the object is sensed in real time. The control parameters are dynamically adjusted using an adaptive cooperative impedance control algorithm to achieve precise cooperative control of the contact force of each flying robotic arm, ensuring the smooth completion of the operation and improving the stability, safety, and adaptability of multi-flying robotic arm collaborative operations.

[0017] Furthermore, the specific steps in step S2 include:

[0018] Calculate the contact force error using the following formula:

[0019] ;

[0020] in, For contact force error, This represents the actual contact force vector within the contact force information. Let be the desired contact force vector.

[0021] Furthermore, the impedance parameters include the inertia matrix, damping matrix, and stiffness matrix.

[0022] Furthermore, the specific steps in step S3 include:

[0023] S31. Adjust the inertia matrix according to the following formula:

[0024] ;

[0025] in, Let the inertia matrix be the matrix at the (k+1)th adjustment. Let be the inertia matrix at the k-th adjustment. The preset learning rate for the inertia matrix, The contact force error during the k-th adjustment. Let be the transpose matrix of the contact force error at the k-th adjustment. The sampling time interval;

[0026] S32. Adjust the damping matrix according to the following formula:

[0027] ;

[0028] in, This is the damping matrix at the (k+1)th adjustment. Let be the damping matrix at the k-th adjustment. The learning rate is the preset damping matrix;

[0029] S33. Adjust the stiffness matrix according to the following formula:

[0030] ;

[0031] in, This is the stiffness matrix at the (k+1)th adjustment. Let be the stiffness matrix at the k-th adjustment. The learning rate is the preset stiffness matrix.

[0032] Furthermore, the specific steps in step S4 include:

[0033] The coordination factor of each robotic arm is calculated using the following formula:

[0034] ;

[0035] in, Let i be the coordination factor of the i-th robotic arm. The actual contact force of the i-th robotic arm. This represents the total number of robotic arms on the aircraft.

[0036] Furthermore, the specific steps in step S5 include:

[0037] Calculate the cooperative error using the following formula:

[0038] ;

[0039] in, For cooperative error, This refers to the total number of robotic arms on the aircraft. Let i be the coordination factor of the i-th robotic arm. The actual contact force of the i-th robotic arm. This represents the average of the actual contact forces of all robotic arms.

[0040] Furthermore, the specific steps in step S6 include:

[0041] Adjust the desired position of each robotic arm according to the following formula:

[0042] ;

[0043] in, Let i be the desired position of the i-th robotic arm during the (k+1)-th adjustment. Let i be the desired position of the i-th robotic arm during the k-th adjustment. For coordinated position adjustment coefficient, Let be the cooperative error at the k-th adjustment.

[0044] Secondly, the present invention provides a multi-flying robotic arm contact force adaptive cooperative impedance control device, applied to the control system of an aircraft equipped with multiple robotic arms, comprising:

[0045] The first acquisition module is used to acquire sensor data through sensors pre-arranged on the robotic arm; the sensor data includes contact force information of each robotic arm.

[0046] The first calculation module is used to calculate the contact force error of each robotic arm based on the contact force information of each robotic arm.

[0047] The first adjustment module is used to dynamically adjust the impedance parameters of each robotic arm based on the contact force error of each robotic arm, so as to obtain the adjusted impedance parameters of each robotic arm.

[0048] The second acquisition module is used to calculate the cooperation factor of each robotic arm based on the contact force information of each robotic arm.

[0049] The second calculation module is used to calculate the coordination error of all robotic arms based on the coordination factor.

[0050] The second adjustment module is used to adjust the desired position of each robotic arm according to the coordination error, so as to obtain the adjusted desired position of each robotic arm.

[0051] The generation control module is used to generate control commands for each robotic arm based on the adjusted desired position and adjusted impedance parameters, and to control the movement of each robotic arm through the control commands.

[0052] The multi-flying robotic arm contact force adaptive cooperative impedance control device provided by this invention uses an adaptive cooperative impedance control algorithm to dynamically adjust control parameters, thereby achieving precise cooperative control of the contact force of each flying robotic arm, ensuring the smooth completion of the operation process, and significantly improving the stability, safety, and adaptability to different operating environments and object characteristics of multi-flying robotic arm cooperative operations.

[0053] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory storing computer-readable instructions, which, when executed by the processor, perform the steps of the multi-flying robotic arm contact force adaptive cooperative impedance control method provided in the first aspect above.

[0054] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the multi-flying robotic arm contact force adaptive cooperative impedance control method provided in the first aspect above.

[0055] As can be seen from the above, the adaptive cooperative impedance control method for contact force of multiple flying robotic arms provided by this invention effectively solves the problems of difficult precise control of contact force, lack of effective cooperative control strategies, and poor adaptability to complex environments in the prior art when multiple flying robotic arms are working together, by sensing the contact force between each robotic arm and the object in real time and dynamically adjusting the control parameters using an adaptive cooperative impedance control algorithm. Specifically, this method can dynamically adjust the inertia matrix, damping matrix, and stiffness matrix according to the real-time changes in contact force through adaptive impedance parameter adjustment, enabling the control system to quickly adapt to different working environments and object characteristics, significantly improving the accuracy and stability of contact force control. At the same time, by introducing cooperative factors and cooperative errors, the balance of contact force and synchronization of actions of multiple flying robotic arms are achieved, ensuring the stable operation of the object during cooperative operation and improving the efficiency and reliability of multi-flying robotic arm cooperative operation. In addition, this method has strong adaptability to objects of different shapes, weights, and materials, as well as complex and changing environmental conditions. By adaptively adjusting the control parameters, it can ensure the smooth operation of multi-flying robotic arm cooperative operation under various conditions, thereby expanding the application range of multi-flying robotic arm systems.

[0056] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0057] Figure 1 is a flowchart of a multi-flying robotic arm contact force adaptive cooperative impedance control method provided in an embodiment of the present invention.

[0058] Figure 2 is a schematic diagram of a multi-flying robotic arm contact force adaptive cooperative impedance control device provided in an embodiment of the present invention.

[0059] Figure 3 is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0060] Label Explanation:

[0061] 100. First acquisition module; 200. First calculation module; 300. First adjustment module; 400. Second acquisition module; 500. Second calculation module; 600. Second adjustment module; 700. Generation control module; 13. Electronic device; 1301. Processor; 1302. Memory; 1303. Communication bus. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0063] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0064] Please refer to Figure 1, which is a flowchart of a multi-flying robotic arm contact force adaptive cooperative impedance control method. This multi-flying robotic arm contact force adaptive cooperative impedance control method is applied to the control system of an aircraft equipped with multiple robotic arms, and includes the following steps:

[0065] S1. Acquire sensor data through sensors pre-positioned on the robotic arms; the sensors positioned on the aircraft include force sensors for each robotic arm; the sensor data includes contact force information for each robotic arm.

[0066] S2. Calculate the contact force error of each robotic arm based on the contact force information of each robotic arm;

[0067] S3. Based on the contact force error of each robotic arm, dynamically adjust the impedance parameters of each robotic arm to obtain the adjusted impedance parameters of each robotic arm;

[0068] S4. Calculate the coordination factor of each robotic arm based on the contact force information of each robotic arm;

[0069] S5. Calculate the coordination error of all robotic arms based on the coordination factor;

[0070] S6. Based on the coordination error, adjust the desired position of each robotic arm to obtain the adjusted desired position of each robotic arm;

[0071] S7. Based on the adjusted desired position and the adjusted impedance parameters, generate control commands for each robotic arm, and control the movement of each robotic arm through the control commands;

[0072] This invention relates to a flying robotic arm system consisting of a multi-rotor aircraft equipped with a working robotic arm. During the process of multiple flying robotic arms collaboratively grasping and manipulating objects in the air (such as moving, transporting, lifting, lowering, rotating, etc.), the system achieves precise collaborative control of the contact force between each robotic arm and the object by sensing the contact force between each robotic arm and the object in real time and dynamically adjusting the control parameters using an adaptive collaborative impedance control algorithm. This ensures the smooth completion of the operation and improves the stability, safety, and adaptability of multi-flying robotic arm collaborative operations.

[0073] Compared with the prior art, the present invention has the following innovations:

[0074] 1. Adaptive Impedance Parameter Adjustment: The adaptive impedance parameter adjustment method proposed in this invention can dynamically adjust the inertia matrix, damping matrix, and stiffness matrix according to the real-time changes in contact force, enabling the control system to quickly adapt to different working environments and object characteristics, thereby improving the accuracy and stability of contact force control.

[0075] 2. Effective Cooperative Control Strategy: By introducing a cooperative factor and cooperative error, the contact force balance and motion synchronization of multiple flying robotic arms are achieved. This strategy can automatically adjust control parameters based on the contact force information of each robotic arm, ensuring stable operation of the object during cooperative work and improving the efficiency and reliability of multi-flying robotic arm cooperative operations.

[0076] 3. Strong adaptability: The method of this invention has strong adaptability to objects of different shapes, weights, and materials, as well as complex and ever-changing environmental conditions. By adaptively adjusting the control parameters, it can ensure the smooth operation of multi-flying robotic arms in various situations, thus expanding the application range of multi-flying robotic arm systems.

[0077] This application achieves precise coordinated control of the contact force between each robotic arm and the object by real-time sensing of the contact force between each robotic arm and the object, and dynamically adjusts the control parameters using an adaptive cooperative impedance control algorithm. This ensures the smooth completion of the operation and significantly improves the stability, safety, and adaptability to different operating environments and object characteristics of multi-flying robotic arm collaborative operations.

[0078] To better understand the technical solution proposed in this application, it is necessary to explain some key terms and implementation environments involved. The "aircraft" referred to in this application typically refers to a multi-rotor aircraft equipped with multiple "robotic arms." These robotic arms work together to perform tasks involving objects, such as grasping, carrying, moving, ascending, descending, or rotating. "Sensor" refers to force sensors pre-positioned on the robotic arms to acquire real-time "contact force information" between the robotic arms and the object. "Impedance parameters" are key parameters in impedance control, typically including the inertia matrix, damping matrix, and stiffness matrix, which together determine the dynamic response characteristics of the robotic arm when interacting with the environment. The control method of this application aims to enable the robotic arm to better adapt to changes in the external environment through adaptive adjustment of these parameters, achieving precise force control and collaborative operation.

[0079] The core of the multi-flying robotic arm contact force adaptive cooperative impedance control method proposed in this application lies in achieving precise control of the cooperative operation of multiple flying robotic arms through a series of steps.

[0080] First, in step S1, sensor data is acquired using sensors pre-positioned on the robotic arms. These sensors are typically force sensors, capable of sensing the contact forces between each robotic arm and the external environment or object in real time. For example, a three-axis or six-axis force sensor can be installed on the end effector of each robotic arm to obtain a precise contact force vector. This sensor data, especially the contact force information of each robotic arm, serves as the fundamental input for subsequent control algorithms.

[0081] Next, in step S2, the contact force error of each robotic arm is calculated based on the contact force information of each robotic arm. Calculating the contact force error is crucial for assessing the difference between the current contact force and the desired contact force. For example, the contact force error can be obtained by comparing the actually measured contact force vector with a preset desired contact force vector.

[0082] Subsequently, in step S3, the impedance parameters of each robotic arm are dynamically adjusted based on the contact force error of each arm, resulting in the adjusted impedance parameters for each arm. This dynamic adjustment of impedance parameters is a key aspect of the adaptive capability of this application. For example, the inertia matrix, damping matrix, and stiffness matrix can be updated in real time using a certain learning rate or adaptive law, based on the magnitude and direction of the contact force error. This adjustment allows the compliance or stiffness of the robotic arm to be optimized according to the actual contact conditions, thereby improving the accuracy and stability of contact force control.

[0083] In step S4, the collaboration factor of each robotic arm is calculated based on the contact force information of each robotic arm. The collaboration factor is introduced to quantify the contribution or force ratio of each robotic arm in the collaborative operation. For example, the collaboration factor of a robotic arm can be obtained by calculating the ratio of the actual contact force of a single robotic arm to the sum of the total contact forces of all its robotic arms.

[0084] Further, in step S5, the coordination error of all robotic arms is calculated based on the coordination factor. The coordination error reflects the balance of the contact force distribution and the synchronization of the movements among the robotic arms. For example, the coordination error can be calculated by weighting and summing the coordination factor of each robotic arm with its contact force information and comparing it with the average value of the actual contact force of all robotic arms.

[0085] Then, in step S6, the desired positions of each robotic arm are adjusted according to the coordination error to obtain the adjusted desired positions of each robotic arm. The adjustment of the desired positions is to correct the problems of incoordination or uneven force distribution among the robotic arms in collaborative operations. For example, the desired positions of each robotic arm can be fine-tuned by using a coordination position adjustment coefficient based on the magnitude and direction of the coordination error, so as to make the movements of each robotic arm more synchronized and the contact force more balanced.

[0086] Finally, in step S7, control commands for each robotic arm are generated based on the adjusted desired position and the adjusted impedance parameters, and the movement of each robotic arm is controlled by these commands. The generation of control commands serves as a bridge between the aforementioned adjustments and the actual movement of the robotic arms. For example, the adjusted desired position and impedance parameters can be used, combined with inverse dynamics or inverse kinematics models, to calculate the torque or velocity commands required for each robotic arm joint, thereby achieving precise control of the robotic arms.

[0087] The adaptive cooperative impedance control method for contact force of multiple flying robotic arms proposed in this application works by constructing a closed-loop feedback control system to achieve precise control of contact force and synchronization of movements during collaborative operations. When multiple flying robotic arms cooperate to grasp and manipulate an object, firstly, force sensors pre-placed on each robotic arm acquire real-time contact force information between each arm and the object. This contact force information is then fed into the control system to calculate the contact force error of each robotic arm, i.e., the deviation between the actual contact force and the desired contact force.

[0088] Based on these contact force errors, the control system dynamically adjusts the impedance parameters of each robotic arm, including the inertia matrix, damping matrix, and stiffness matrix. This adaptive adjustment allows the compliance or stiffness of each robotic arm to be optimized according to the actual contact conditions, thereby ensuring stable contact force when interacting with objects and adapting to different working environments and object characteristics.

[0089] Simultaneously, the system calculates a coordination factor based on the contact force information of each robotic arm, and further calculates the coordination error of all robotic arms. The coordination factor reflects the contribution ratio of each robotic arm to the overall coordinated force, while the coordination error quantifies the balance of contact force distribution and the synchronization of movements among the robotic arms. The presence of coordination error indicates that there may be uneven force distribution or uncoordinated movements among the robotic arms.

[0090] To address coordination errors, the control system dynamically adjusts the desired positions of each robotic arm based on these errors. This adjustment aims to achieve better force balance and motion synchronization during collaborative operations by fine-tuning the motion trajectories of each arm. Finally, combining the adjusted desired positions and adjusted impedance parameters, the system generates control commands for each robotic arm. These commands are sent to the actuators of the robotic arms, precisely controlling their movement to ensure object stability during operation and successful completion of the collaborative task. In this way, this application achieves precise coordinated control of contact forces across multiple flying robotic arms, significantly improving the stability, safety, and adaptability of collaborative operations.

[0091] The adaptive cooperative impedance control method for contact force of multi-flying robotic arms proposed in this application demonstrates significant progress and innovation in several aspects compared to existing technologies. Traditional multi-flying robotic arm cooperative control technologies often struggle to achieve precise control of contact force and effective cooperative operation when facing complex and variable operating environments and object characteristics. For example, when grasping and transporting objects in the air, the contact force between each robotic arm and the object is prone to fluctuation due to external factors such as airflow disturbances and changes in aircraft attitude, leading to instability in the operation and potentially damaging the object or the robotic arm. Furthermore, existing technologies lack effective strategies to ensure synchronized movements and force balance among the robotic arms during cooperative operation, easily resulting in problems such as object swaying, displacement, or even detachment.

[0092] This application introduces an adaptive impedance parameter adjustment mechanism, which dynamically adjusts the inertia matrix, damping matrix, and stiffness matrix based on real-time changes in contact force. This adaptive capability allows the control system to quickly adapt to different operating environments and object characteristics, thereby significantly improving the accuracy and stability of contact force control. For example, when the robotic arm contacts objects of different hardness or shape, the impedance parameters can be automatically adjusted to provide a more compliant or more rigid response, ensuring a smooth transition and precise maintenance of the contact force.

[0093] Furthermore, this application constructs an effective cooperative control strategy by introducing a cooperative factor and cooperative error. This strategy can automatically adjust control parameters based on the contact force information of each robotic arm, achieving balanced contact forces and synchronized movements among multiple flying robotic arms. For example, when the contact force of a certain robotic arm is too large or too small, the cooperative error will prompt the system to adjust the desired position of that robotic arm, making its force more balanced with that of other robotic arms, thereby ensuring stable operation of the object during cooperative work. This cooperative control strategy significantly improves the efficiency and reliability of multi-flying robotic arm cooperative operations.

[0094] In summary, the method of this application exhibits greater adaptability to objects of different shapes, weights, and materials, as well as complex and variable environmental conditions. Through adaptive adjustment of control parameters and effective collaborative control strategies, this application ensures smooth collaborative operation of multiple flying robotic arms under various conditions, greatly expanding the application scope of multi-flying robotic arm systems and providing a more reliable and efficient solution for complex environment operations such as high-altitude building maintenance and disaster relief.

[0095] In some embodiments, the specific steps in step S2 include:

[0096] Calculate the contact force error using the following formula:

[0097] ;

[0098] in, For contact force error, This represents the actual contact force vector within the contact force information. Let be the desired contact force vector.

[0099] Specifically, contact force error refers to the difference between the actual contact force vector and the desired contact force vector. Its purpose is to quantify the deviation between the current contact force and the target contact force, providing a basis for subsequent impedance parameter adjustment and coordinated control. In practical applications, the actual contact force vector can be acquired in real time through force sensors pre-positioned on the robotic arm, while the desired contact force vector can be preset or dynamically planned according to specific task requirements. For example, when multiple robotic arms collaboratively grasp and transport an object, the desired contact force vector can be set to the force value required to stably support the object, and adjusted according to the object's weight, shape, and dynamic changes during the operation.

[0100] The proposed solution obtains the contact force error by directly calculating the difference between the actual contact force vector and the desired contact force vector. This provides a direct and accurate reflection of the deviation between the force experienced by each robotic arm when in contact with an object and the preset target. This explicit error quantification method provides accurate input for the adaptive adjustment of impedance parameters and the calculation of coordination factors in subsequent steps, ensuring that the control system can make effective decisions and adjustments based on real-time force feedback. In this way, the system can promptly detect and correct any deviation between the contact force and the desired value, thereby maintaining mechanical balance and stability during operation.

[0101] The above technical solution clarifies the calculation method for contact force error, enabling the control system to accurately sense and quantify the mechanical state of each robotic arm when in contact with an object. This helps improve the accuracy and response speed of subsequent adaptive adjustment of impedance parameters and provides a reliable error signal for collaborative control among multiple robotic arms, thereby enhancing the contact force control accuracy and stability of the entire multi-flying robotic arm system in complex operating environments. This precise error calculation method is the foundation for realizing adaptive collaborative impedance control of contact force among multiple flying robotic arms, ensuring that the system can respond quickly and appropriately to changes in the external environment.

[0102] In some embodiments, the impedance parameters include the inertia matrix, the damping matrix, and the stiffness matrix;

[0103] The specific steps in step S3 include:

[0104] S31. Adjust the inertia matrix according to the following formula:

[0105] ;

[0106] in, Let the inertia matrix be the matrix at the (k+1)th adjustment. Let be the inertia matrix at the k-th adjustment. The preset learning rate for the inertia matrix, The contact force error during the k-th adjustment. Let be the transpose matrix of the contact force error at the k-th adjustment. The sampling time interval;

[0107] S32. Adjust the damping matrix according to the following formula:

[0108] ;

[0109] in, This is the damping matrix at the (k+1)th adjustment. Let be the damping matrix at the k-th adjustment. The learning rate is the preset damping matrix;

[0110] S33. Adjust the stiffness matrix according to the following formula:

[0111] ;

[0112] in, This is the stiffness matrix at the (k+1)th adjustment. Let be the stiffness matrix at the k-th adjustment. The learning rate is the preset stiffness matrix.

[0113] Specifically, the inertia matrix, damping matrix, and stiffness matrix mentioned above are key parameters describing the interaction characteristics of the robotic arm with its environment. The inertia matrix reflects the robotic arm's response speed and inertia magnitude to forces during contact; the damping matrix determines the robotic arm's energy dissipation and resistance to velocity changes during movement; and the stiffness matrix represents the robotic arm's ability to resist deformation when subjected to external forces. Dynamic adjustment of these matrices is crucial for achieving precise contact force control. Among these, The transpose matrix represents the contact force error at the k-th adjustment. and The product provides a direction and magnitude to indicate how the impedance parameter should be adjusted based on the current error. , and These are respectively used as preset learning rates for the inertia matrix, damping matrix, and stiffness matrix to control the step size and speed of each adjustment, ensuring the stability and convergence of the adjustment process. The sampling time interval is used to synchronize the cumulative effect of the error with time, so that the adjustment amount is proportional to the duration of the error.

[0114] The proposed solution uses contact force error as a feedback signal to iteratively update the inertia matrix, damping matrix, and stiffness matrix. When the system detects an error between the actual and desired contact force, this error is used to calculate an adjustment term and added to the current impedance parameters. For example, if the actual contact force is consistently higher than the desired value, the system adjusts the inertia matrix, damping matrix, and stiffness matrix to make the robotic arm exhibit greater compliance during contact, thereby reducing the contact force. Conversely, if the contact force is too low, the parameters are adjusted to increase stiffness. This error-based adaptive adjustment mechanism allows the robotic arm to "learn" and adapt to its interaction with the environment in real time, maintaining precise contact force control even in uncertain or dynamically changing operating environments. The introduction of a learning rate and sampling time interval further ensures the stability and effectiveness of the adjustment process, avoiding excessive parameter oscillations or under-adjustment.

[0115] Through the above technical solution, the impedance parameters (inertia matrix, damping matrix, and stiffness matrix) can be adaptively adjusted according to real-time contact force errors, enabling the control system to respond more accurately to changes in the external environment and object characteristics. This adaptive adjustment mechanism significantly improves the accuracy and stability of contact force control, especially when multiple robotic arms collaboratively grasp and manipulate objects of different shapes, weights, and materials. The system no longer relies on preset fixed parameters but can dynamically optimize its interaction characteristics with the environment, effectively avoiding excessive or insufficient contact force due to parameter mismatch, thus improving the safety, reliability, and adaptability to complex working environments.

[0116] In some preferred embodiments, it is assumed that a multi-flying robotic arm system is collaboratively grasping and transporting an object of unknown weight. During the initial contact phase, the impedance parameters of each robotic arm are set to a set of default values. Once the robotic arm contacts the object, force sensors acquire the actual contact force information of each robotic arm in real time. If there is a significant error between the actual contact force and the expected contact force of a particular robotic arm—for example, if the actual contact force is too large—this error is used to update the impedance parameters of that robotic arm. Specifically, within each sampling time interval, the inertia matrix, damping matrix, and stiffness matrix of the robotic arm are updated according to the aforementioned formulas. Through this iterative update, the impedance characteristics of the robotic arm are gradually adjusted, making it more compliant during contact, thereby reducing excessive contact force. As the transport process progresses, even if the object's center of gravity changes or environmental disturbances occur, these adaptive adjustment mechanisms ensure that the contact force of each robotic arm remains within the expected range, thus guaranteeing the stability and safety of the collaborative operation.

[0117] In some embodiments, the specific steps in step S4 include:

[0118] The coordination factor of each robotic arm is calculated using the following formula:

[0119] ;

[0120] in, Let i be the coordination factor of the i-th robotic arm. The actual contact force of the i-th robotic arm. This represents the total number of robotic arms on the aircraft.

[0121] Specifically, the synergy factor aims to quantify the proportion of the actual contact force of a single robotic arm in the total actual contact force of all robotic arms. This refers to force information acquired through force sensors pre-positioned on the robotic arm, reflecting the interaction between the robotic arm and the object. N represents the total number of robotic arms participating in the collaborative operation; for example, if the aircraft carries two robotic arms, then N is 2. The formula obtains a dimensionless proportionality by dividing the actual contact force of a single robotic arm by the sum of the actual contact forces of all robotic arms. This value reflects the relative contribution of each robotic arm to the overall contact force distribution.

[0122] The proposed solution calculates the coordination factor using the aforementioned formula, providing a quantitative basis for subsequent coordination error calculation. When multiple robotic arms collaboratively grasp and manipulate objects, the contact forces between each arm and the object may differ. By calculating the coordination factor for each arm, the relative weight of each arm in the overall contact force can be clearly understood. This quantification allows the system to accurately assess whether the force distribution among the arms is balanced, thus laying the foundation for adjusting the desired positions of each arm to achieve precise coordinated control of the contact force. It is precisely because of the accurate calculation of the coordination factor that the assessment of coordination error is more accurate, thereby ensuring the effectiveness of the entire coordinated control strategy.

[0123] Through the above technical solution, this application provides a clear and standardized method to quantify the force contribution of each robotic arm in collaborative operations. This precise method for calculating the collaboration factor enables the system to more accurately identify uneven force distribution among the robotic arms, thereby providing reliable data support for subsequent calculation of collaboration errors and adjustment of desired positions. This effectively improves the balance and accuracy of contact force control during collaborative operations of multiple flying robotic arms, further enhancing the system's collaborative operation stability, safety, and adaptability.

[0124] In some embodiments, the specific steps in step S5 include:

[0125] Calculate the cooperative error using the following formula:

[0126] ;

[0127] in, For cooperative error, This refers to the total number of robotic arms on the aircraft. Let i be the coordination factor of the i-th robotic arm. The actual contact force of the i-th robotic arm. This represents the average of the actual contact forces of all robotic arms.

[0128] Specifically, coordination error is defined as an indicator of the balance and synchronization of contact force distribution among multiple robotic arms during collaborative operation. N represents the total number of robotic arms on the aircraft, used to determine the range of robotic arms participating in collaborative control. Let be the collaboration factor of the i-th robotic arm. Its value reflects the relative weight or importance of the robotic arm in the overall collaborative force distribution. This collaboration factor is usually calculated based on the actual contact force of each robotic arm to reflect its contribution in the current working state. This refers to the actual contact force generated when the i-th robotic arm comes into contact with an object. This force is obtained through force sensors pre-positioned on the robotic arm. This represents the average actual contact force of all robotic arms, serving as a benchmark for measuring the overall contact force level. By comparing the actual contact force of each robotic arm with its average value, multiplying it by the corresponding coordination factor, and then summing the results, a comprehensive coordination error can be obtained. This error quantifies the degree to which the contact force of each robotic arm deviates from the ideal coordination state.

[0129] The proposed solution calculates the coordination error using the aforementioned formula, enabling precise quantification of the uneven distribution of contact force during collaborative operations of multiple robotic arms. This calculation method compares the actual contact force of each robotic arm with the average contact force of all robotic arms, thereby identifying the degree to which the contact force of a single robotic arm deviates from the overall average level. Furthermore, by introducing a coordination factor to weight the deviation of each robotic arm, the calculation of the coordination error more accurately reflects the relative importance of different robotic arms in the collaborative task or the impact of their contact force deviations on the overall coordination effect. Thus, this coordination error provides crucial feedback information for subsequent adjustments to the desired positions of each robotic arm, ensuring that the system can make fine-tuned adjustments based on the actual force distribution, effectively driving the contact forces of each robotic arm towards equilibrium and synchronization, thereby improving the stability and accuracy of multi-robotic arm collaborative operations.

[0130] Through the above technical solution, this application provides a precise method for quantifying the contact force coordination state of multiple flying robotic arms. This method, by comprehensively considering the actual contact force of each robotic arm, the overall average contact force, and the coordination factors of each robotic arm, can generate a representative coordination error. This coordination error not only reflects the overall balance of the contact forces of each robotic arm but also, through the weighting effect of the coordination factors, highlights the deviations of robotic arms that have a greater impact on the overall coordination effect, thus providing a more accurate and effective basis for subsequent control adjustments. Therefore, the solution of this application significantly improves the precision and response speed of contact force control when multiple flying robotic arms perform coordinated grasping and operation in complex working environments, ensuring the smooth progress of the operation and further enhancing the adaptability and reliability of the system.

[0131] In some embodiments, the specific steps in step S6 include:

[0132] Adjust the desired position of each robotic arm according to the following formula:

[0133] ;

[0134] in, Let i be the desired position of the i-th robotic arm during the (k+1)-th adjustment. Let i be the desired position of the i-th robotic arm during the k-th adjustment. For coordinated position adjustment coefficient, Let be the cooperative error at the k-th adjustment.

[0135] Specifically, the desired position refers to the target position or posture of the robotic arm in the operating space during adjustment. This desired position serves as the reference point for the control system to guide the robotic arm's movement. The cooperative position adjustment coefficient is a preset positive value used to adjust the sensitivity and step size of the cooperative error to the desired position adjustment. Its magnitude determines the system's response speed and adjustment range to the cooperative error. The cooperative error is the difference between the actual contact force of all robotic arms and the average contact force, reflecting the uneven distribution of contact force among the robotic arms.

[0136] This application's solution introduces a coordination error and combines it with a coordination position adjustment coefficient to dynamically adjust the desired positions of each robotic arm in the form of negative feedback. When a coordination error exists, it indicates an uneven distribution of contact forces among the robotic arms, requiring system adjustment to restore coordination. By multiplying the coordination error by a negative coordination position adjustment coefficient and adding it to the current desired position, the desired position can be adjusted in the direction of reducing the coordination error. For example, if the contact force of a certain robotic arm is too large, causing the coordination error to deviate from the desired value, its desired position will be adjusted to reduce the contact force of that robotic arm, thereby promoting a more balanced contact force across all robotic arms. This desired position adjustment mechanism based on coordination error effectively guides each robotic arm to maintain a balance of contact forces during collaborative operation, preventing any single robotic arm from bearing excessively large or small loads, thus improving the overall system stability.

[0137] Through the above technical solution, the desired position of each robotic arm can be adaptively adjusted according to real-time coordination errors, thereby achieving precise collaborative control of the contact force distribution of multiple flying robotic arms. This dynamic adjustment mechanism effectively solves the problems of unstable collaborative operation and uneven contact force distribution caused by fixed desired positions or untimely adjustments in traditional methods. Therefore, when multiple flying robotic arms collaboratively grasp and manipulate objects, it ensures a more balanced contact force among the robotic arms, significantly improving the stability, safety, and adaptability of collaborative operations. Especially when facing complex and changing environments and objects with different characteristics, it can better maintain the smooth operation of the task.

[0138] As a specific implementation, suppose an aircraft system equipped with two robotic arms is collaboratively transporting an irregular object. At a certain moment, contact force information acquired by sensors shows that the actual contact force of the first robotic arm is significantly greater than that of the second robotic arm, resulting in a positive coordination error. According to the adjustment formula of this application, the desired positions of both robotic arms will be adjusted based on the coordination error. Specifically, due to... It is a positive value, and If it is positive, then Less than This means the system adjusts the desired positions of the two robotic arms to move them in a direction that reduces contact force, or more precisely, in a direction that reduces the imbalance of contact force. Through this adjustment, the desired position of the first robotic arm is fine-tuned to reduce its contact force in subsequent control, while the desired position of the second robotic arm may be fine-tuned to slightly "tighten" its grip, thus achieving a redistribution and balance of contact force. For example, if the contact force of the first robotic arm is too large, its desired position may be fine-tuned to slightly "loosen" its grip on the object, while the desired position of the second robotic arm may be fine-tuned to slightly "tighten" its grip, thereby achieving a redistribution and balance of contact force. This dynamic, adaptive adjustment of desired position ensures that the two robotic arms maintain stable cooperative gripping throughout the entire handling process, avoiding object tilting, slippage, or system instability caused by uneven contact force.

[0139] Please refer to Figure 2, which illustrates a multi-flying robotic arm contact force adaptive cooperative impedance control device according to some embodiments of the present invention. This device is applied to the control system of an aircraft equipped with multiple robotic arms. The multi-flying robotic arm contact force adaptive cooperative impedance control device is integrated into a back-end control device in the form of a computer program, including:

[0140] The first acquisition module 100 is used to acquire sensor data through sensors pre-arranged on the robotic arm; the sensor data includes contact force information of each robotic arm.

[0141] The first calculation module 200 is used to calculate the contact force error of each robotic arm based on the contact force information of each robotic arm.

[0142] The first adjustment module 300 is used to dynamically adjust the impedance parameters of each robotic arm according to the contact force error of each robotic arm, so as to obtain the adjusted impedance parameters of each robotic arm.

[0143] The second acquisition module 400 is used to calculate the cooperation factor of each robotic arm based on the contact force information of each robotic arm.

[0144] The second calculation module 500 is used to calculate the coordination error of all robotic arms based on the coordination factor.

[0145] The second adjustment module 600 is used to adjust the desired position of each robotic arm according to the coordination error, so as to obtain the adjusted desired position of each robotic arm.

[0146] The generation control module 700 is used to generate control commands for each robotic arm based on the adjusted desired position and the adjusted impedance parameters, and to control the movement of each robotic arm through the control commands.

[0147] In some embodiments, the first calculation module 200 performs the following when calculating the contact force error of each robotic arm based on the contact force information of each robotic arm:

[0148] Calculate the contact force error using the following formula:

[0149] ;

[0150] in, For contact force error, This represents the actual contact force vector within the contact force information. Let be the desired contact force vector.

[0151] In some embodiments, the first adjustment module 300 performs the following steps when dynamically adjusting the impedance parameters of each robotic arm based on the contact force error of each robotic arm to obtain the adjusted impedance parameters of each robotic arm:

[0152] S31. Adjust the inertia matrix according to the following formula:

[0153] ;

[0154] in, Let the inertia matrix be the matrix at the (k+1)th adjustment. Let be the inertia matrix at the k-th adjustment. The preset learning rate for the inertia matrix, The contact force error during the k-th adjustment. Let be the transpose matrix of the contact force error at the k-th adjustment. The sampling time interval;

[0155] S32. Adjust the damping matrix according to the following formula:

[0156] ;

[0157] in, This is the damping matrix at the (k+1)th adjustment. Let be the damping matrix at the k-th adjustment. The learning rate is the preset damping matrix;

[0158] S33. Adjust the stiffness matrix according to the following formula:

[0159] ;

[0160] in, This is the stiffness matrix at the (k+1)th adjustment. Let be the stiffness matrix at the k-th adjustment. The learning rate is the preset stiffness matrix.

[0161] In some embodiments, the second acquisition module 400 performs the following when calculating the cooperation factor of each robotic arm based on the contact force information of each robotic arm:

[0162] The coordination factor of each robotic arm is calculated using the following formula:

[0163] ;

[0164] in, Let i be the coordination factor of the i-th robotic arm. The actual contact force of the i-th robotic arm. This represents the total number of robotic arms on the aircraft.

[0165] In some embodiments, the second calculation module 500 performs the following when calculating the coordination error of all robotic arms based on the coordination factor:

[0166] Calculate the cooperative error using the following formula:

[0167] ;

[0168] in, For cooperative error, This refers to the total number of robotic arms on the aircraft. Let i be the coordination factor of the i-th robotic arm. The actual contact force of the i-th robotic arm. This represents the average of the actual contact forces of all robotic arms.

[0169] In some embodiments, the second adjustment module 600 is executed when adjusting the desired position of each robotic arm based on the coordination error to obtain the adjusted desired position of each robotic arm:

[0170] Adjust the desired position of each robotic arm according to the following formula:

[0171] ;

[0172] in, Let i be the desired position of the i-th robotic arm during the (k+1)-th adjustment. Let i be the desired position of the i-th robotic arm during the k-th adjustment. For coordinated position adjustment coefficient, Let be the cooperative error at the k-th adjustment.

[0173] Please refer to Figure 3, which is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The present invention provides an electronic device 13, including: a processor 1301 and a memory 1302. The processor 1301 and the memory 1302 are interconnected and communicate with each other via a communication bus 1303 and / or other forms of connection mechanisms (not shown). The memory 1302 stores computer-readable instructions executable by the processor 1301. When the electronic device is running, the processor 1301 executes these computer-readable instructions to perform the multi-flying robotic arm contact force adaptive cooperative impedance control method in any optional implementation of the above embodiments, to achieve the following functions: through pre-arrangement... Sensors on the robotic arms acquire sensor data, including contact force information for each arm. Based on this data, the contact force error of each arm is calculated. The impedance parameters of each arm are dynamically adjusted based on these errors to obtain the adjusted impedance parameters. The coordination factor of each arm is calculated based on its contact force information. The coordination error of all arms is calculated based on the coordination factor. The desired position of each arm is adjusted based on the coordination error to obtain the adjusted desired position. Control commands are generated for each arm based on the adjusted desired position and the adjusted impedance parameters, and the movement of each arm is controlled by these commands.

[0174] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the multi-flying robotic arm contact force adaptive cooperative impedance control method in any optional implementation of the above embodiments to achieve the following functions: acquiring sensor data through sensors pre-arranged on the robotic arms; the sensor data includes contact force information of each robotic arm; calculating the contact force error of each robotic arm based on the contact force information of each robotic arm; dynamically adjusting the impedance parameters of each robotic arm based on the contact force error of each robotic arm to obtain the adjusted impedance parameters of each robotic arm; calculating the cooperative factor of each robotic arm based on the contact force information of each robotic arm; calculating the cooperative error of all robotic arms based on the cooperative factor; adjusting the desired position of each robotic arm based on the cooperative error to obtain the adjusted desired position of each robotic arm; generating control commands for each robotic arm based on the adjusted desired position and the adjusted impedance parameters, and controlling the movement of each robotic arm through the control commands.

[0175] The computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0176] In the embodiments provided by this invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.

[0177] Furthermore, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0178] Furthermore, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0179] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0180] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for adaptive cooperative impedance control of contact force of multiple flying robotic arms, applied to the control system of an aircraft equipped with multiple robotic arms, characterized in that, Includes the following steps: S1. Acquire sensor data using sensors pre-positioned on the robotic arm; the sensor data includes contact force information for each robotic arm. S2. Calculate the contact force error of each robotic arm based on the contact force information of each robotic arm; the specific steps in step S2 include: calculating the contact force error according to the following formula: ;in, For contact force error, This represents the actual contact force vector within the contact force information. S3. Based on the contact force error of each robotic arm, dynamically adjust the impedance parameters of each robotic arm to obtain the adjusted impedance parameters of each robotic arm; the impedance parameters include the inertia matrix, damping matrix, and stiffness matrix; the specific steps in step S3 include: S31. Adjust the inertia matrix according to the following formula: ;in, Let the inertia matrix be the matrix at the (k+1)th adjustment. Let be the inertia matrix at the k-th adjustment. The preset learning rate for the inertia matrix, The contact force error during the k-th adjustment. Let be the transpose matrix of the contact force error at the k-th adjustment. S32. The sampling time interval is given; adjust the damping matrix according to the following formula: ;in, This is the damping matrix at the (k+1)th adjustment. Let be the damping matrix at the k-th adjustment. S33. Set the learning rate for the damping matrix; Adjust the stiffness matrix according to the following formula: ;in, This is the stiffness matrix at the (k+1)th adjustment. Let be the stiffness matrix at the k-th adjustment. S4. Calculate the coordination factor of each robotic arm based on the contact force information of each robotic arm. The specific steps in step S4 include: calculating the coordination factor of each robotic arm according to the following formula: ;in, Let i be the coordination factor of the i-th robotic arm. The actual contact force of the i-th robotic arm. S5. Calculate the coordination error of all robotic arms based on the coordination factor. The specific steps in step S5 include: calculating the coordination error according to the following formula: ;in, For cooperative error, This refers to the total number of robotic arms on the aircraft. Let i be the coordination factor of the i-th robotic arm. The actual contact force of the i-th robotic arm. S6. The average of the actual contact forces of all robotic arms; Based on the coordination error, adjust the desired position of each robotic arm to obtain the adjusted desired position of each robotic arm; The specific steps in step S6 include: adjusting the desired position of each robotic arm according to the following formula: ;in, Let i be the desired position of the i-th robotic arm during the (k+1)-th adjustment. Let i be the desired position of the i-th robotic arm during the k-th adjustment. For coordinated position adjustment coefficient, S7. Based on the adjusted desired position and the adjusted impedance parameters, generate control commands for each robotic arm and control the movement of each robotic arm through the control commands.

2. A multi-flying robotic arm contact force adaptive cooperative impedance control device, applied to the control system of an aircraft equipped with multiple robotic arms, characterized in that, include: The first acquisition module is used to acquire sensor data through sensors pre-arranged on the robotic arm; The sensor data includes contact force information for each robotic arm; The first calculation module is used to calculate the contact force error of each robotic arm based on the contact force information of each robotic arm; the specific steps include: calculating the contact force error according to the following formula: ;in, For contact force error, This represents the actual contact force vector within the contact force information. The desired contact force vector is defined in the first adjustment module, which dynamically adjusts the impedance parameters of each robotic arm based on the contact force error, to obtain the adjusted impedance parameters of each robotic arm. The impedance parameters include the inertia matrix, damping matrix, and stiffness matrix. Specific steps include: S31. Adjusting the inertia matrix according to the following formula: ;in, Let the inertia matrix be the matrix at the (k+1)th adjustment. Let be the inertia matrix at the k-th adjustment. The preset learning rate for the inertia matrix, The contact force error during the k-th adjustment. Let be the transpose matrix of the contact force error at the k-th adjustment. S32. The sampling time interval is given; adjust the damping matrix according to the following formula: ;in, This is the damping matrix at the (k+1)th adjustment. Let be the damping matrix at the k-th adjustment. S33. Set the learning rate for the damping matrix; Adjust the stiffness matrix according to the following formula: ;in, This is the stiffness matrix at the (k+1)th adjustment. Let be the stiffness matrix at the k-th adjustment. The learning rate is a preset stiffness matrix; the second acquisition module is used to calculate the coordination factor of each robotic arm based on the contact force information of each robotic arm; the specific steps include: calculating the coordination factor of each robotic arm according to the following formula: ;in, Let i be the coordination factor of the i-th robotic arm. The actual contact force of the i-th robotic arm. The first module represents the total number of robotic arms on the aircraft; the second module calculates the coordination error of all robotic arms based on the coordination factor; the specific steps include calculating the coordination error according to the following formula: ;in, For cooperative error, This refers to the total number of robotic arms on the aircraft. Let i be the coordination factor of the i-th robotic arm. The actual contact force of the i-th robotic arm. The first module represents the average actual contact force of all robotic arms; the second module is used to adjust the desired position of each robotic arm based on the coordination error, thus obtaining the adjusted desired position of each robotic arm; the specific steps include: adjusting the desired position of each robotic arm according to the following formula: ;in, Let i be the desired position of the i-th robotic arm during the (k+1)-th adjustment. Let i be the desired position of the i-th robotic arm during the k-th adjustment. For coordinated position adjustment coefficient, The coordination error at the k-th adjustment is defined; a control module is generated to generate control commands for each robotic arm based on the adjusted desired position and the adjusted impedance parameters, and to control the movement of each robotic arm through the control commands.

3. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer-readable instructions, which, when executed by the processor, perform the steps in the multi-flying robotic arm contact force adaptive cooperative impedance control method as described in claim 1.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it performs the steps in the multi-flying robotic arm contact force adaptive cooperative impedance control method as described in claim 1.

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