A flexible and controllable brush system and method for treating aerospace surface

Abstract

The present invention relates to the field of brush cleaning control technology, and in particular to a flexible and controllable brush system and method for treating the surface of a spacecraft. The method comprises: obtaining spacecraft surface data, constructing point cloud data, and generating three-dimensional point cloud data; preprocessing the three-dimensional point cloud data, converting the preprocessed three-dimensional point cloud data into a three-dimensional model, identifying the cleaning area and extracting regional feature information; based on the three-dimensional model and regional feature information, using a dynamic geometric feedback iterative path planning algorithm to calculate the cleaning path with the highest coverage rate, generating a preliminary cleaning path, using a nonlinear multi-feedback flexible brush optimization algorithm to dynamically adjust and optimize the cleaning configuration parameters of the brush, and obtaining the optimized cleaning configuration parameters; and treating the surface of the spacecraft according to the preliminary cleaning path and the optimized configuration parameters. The method solves the technical problems of not being able to accurately adapt to and fully cover the surface of the spacecraft and having poor dynamic control capabilities when cleaning the surface of the spacecraft.

Description

Technical Field

[0001] The invention relates to the technical field of brush cleaning control, and in particular to a flexible and controllable spacecraft surface treatment brush system and method. Background Art

[0002] Surface cleaning is a crucial link in the operation and maintenance of spacecraft. Spacecraft surfaces are exposed to the harsh space environment and are susceptible to factors such as particle contamination, dust accumulation, cold welding materials, and space radiation. These contaminants will not only reduce the performance of the spacecraft, but may also affect its life and function. Traditional spacecraft surface cleaning methods usually rely on manual operation or rigid mechanical equipment. This method often shows insufficient adaptability when faced with spacecraft shells with complex curvatures, multi-material distributions, and fragile surfaces. Existing cleaning technologies are well adapted to planar surfaces and simple materials, but there is a lack of effective solutions for comprehensive cleaning of complex surfaces and precise processing of multi-material distributions.

[0003] At the same time, the existing brush method for treating the surface of a spacecraft has the following technical problems: when cleaning the surface of a spacecraft, it cannot accurately adapt to and fully cover the surface of the spacecraft and has poor dynamic control capabilities. Summary of the invention

[0004] The present invention provides a flexible and controllable spacecraft surface treatment brush system and method to solve the technical problems of being unable to accurately adapt to and fully cover the spacecraft surface and having poor dynamic control capability when cleaning the spacecraft surface.

[0005] A flexible and controllable spacecraft surface treatment brush system and method of the present invention specifically includes the following technical solutions:

[0006] A flexible and controllable brush method for treating a spacecraft surface comprises the following steps:

[0007] S1. Scan the spacecraft surface to obtain spacecraft surface data, construct point cloud data, and generate three-dimensional point cloud data; pre-process the three-dimensional point cloud data and convert the pre-processed three-dimensional point cloud data into a three-dimensional model of the spacecraft surface; process and analyze the three-dimensional point cloud data to identify the cleaning area and extract regional feature information; based on the three-dimensional model of the spacecraft surface and the regional feature information, use the dynamic geometry feedback iterative path planning algorithm to generate a preliminary cleaning path;

[0008] S2. Based on the preliminary cleaning path, the cleaning configuration parameters of the brush are dynamically adjusted and optimized through the nonlinear multi-feedback flexible brush optimization algorithm to obtain the optimized cleaning configuration parameters; and according to the preliminary cleaning path and the optimized cleaning configuration parameters, the brush is driven to perform cleaning operations on the spacecraft surface.

[0009] Preferably, the S1 specifically includes:

[0010] The reflection intensity changes and material information of the preprocessed point cloud data are combined to identify the clean area, and the features of the clean area are extracted. The priority of the clean area is determined according to the extracted features, and finally the regional feature information is obtained, including priority information, regional curvature data and surface material information.

[0011] Preferably, the S1 specifically includes:

[0012] In the implementation process of the dynamic geometry feedback iterative path planning algorithm, the initial triangular mesh units of the three-dimensional model of the spacecraft surface are used to form an initial path node set according to the three-dimensional model of the spacecraft surface and the regional feature information. After the initial node set is generated, the priority of the initial path node is adjusted through the dynamic geometry feedback mechanism to obtain the dynamic priority of the node.

[0013] Preferably, the S1 specifically includes:

[0014] In the implementation process of the dynamic geometry feedback iterative path planning algorithm, a nested optimization mechanism is used to generate the first path based on the dynamic priority of the node; and a local path cost function is introduced to optimize the spatial change rate of the dynamic priority of the node, the local curvature and the surface material information to obtain the local path cost value. The formula of the local path cost function is:

[0015]

[0016] in, It is The local path cost of each node; It is The spatial change rate of the dynamic priority of each node; , , is the weight parameter of the local path cost function; It is The local curvature value of each node; is the path differential length; Indicates the length of the local path; It is The surface material information of each node.

[0017] Preferably, the S1 specifically includes:

[0018] In the specific implementation process of the nested optimization mechanism, starting from the starting node, the next node with the smallest local path cost value is selected according to the local path cost function, and the local path cost value and the dynamic priority of the node are updated to output the first path sequence; and the first path is optimized through the global path iterative reconstruction mechanism. The global path cost function combines the local path cost value, path length and path smoothness, and is defined as:

[0019]

[0020] in, is the global path cost function; For the The length of the path where the node is located; is the second-order gradient term of the node’s dynamic priority; , is the adjustment coefficient of the global cost function; It is The path length ratio item where the nodes are located; is the maximum path length; is the total number of domain nodes of the current node; It is The gradient of the regional curvature data of each node.

[0021] Preferably, the S1 specifically includes:

[0022] In the specific implementation process of the global path iterative reconstruction mechanism, the path nodes are reconnected and optimized to minimize the local path cost and obtain the updated local path cost. The priority distribution is recalculated and the smoothness of the new path sequence is adjusted to obtain a preliminary clean path.

[0023] Preferably, the S2 specifically includes:

[0024] In the process of implementing the nonlinear multi-feedback flexible brush optimization algorithm, the cleaning configuration parameters of the brush are initialized based on the preliminary cleaning path. The cleaning configuration parameters include: brush stiffness, movement speed, length, material, and pressure application method; after the parameter initialization is completed, multiple feedback signals are introduced, and the feedback signals are coupled to form a total feedback signal :

[0025]

[0026] in, is the pressure deviation; is the rate of change of curvature; is the material hardness gradient; is in position Material hardness parameters at ; Is the default material hardness value.

[0027] Preferably, the S2 specifically includes:

[0028] In the implementation process of the nonlinear multi-feedback flexible brush optimization algorithm, the cleaning configuration parameters of the brush are dynamically optimized based on the total feedback signal to obtain the optimized brush stiffness parameters, brush length parameters, brush material hardness parameters and brush movement speed parameters; and the pressure application method is dynamically adjusted by optimizing the pressure parameters in real time to obtain the optimized target pressure parameters and optimized pressure parameters.

[0029] A flexible and controllable brush system for treating a spacecraft surface, comprising the following parts:

[0030] Environmental perception module, cleaning path planning module, flexible brush control module, pressure control module, cleaning execution module;

[0031] The environmental perception module uses a laser scanner and an ultrasonic sensor to scan the surface of the spacecraft, generate three-dimensional point cloud data, and construct a three-dimensional model of the spacecraft surface. At the same time, it processes and analyzes the three-dimensional point cloud data, identifies the cleaning area and extracts the regional feature information, and sends the three-dimensional model and regional feature information of the spacecraft surface to the cleaning path planning module;

[0032] The cleaning path planning module calculates the cleaning path with the highest coverage rate based on the three-dimensional model and regional feature information of the spacecraft surface using a dynamic geometric feedback iterative path planning algorithm, generates a preliminary cleaning path, and transmits the preliminary cleaning path to the flexible brush control module and the cleaning execution module;

[0033] The flexible brush control module dynamically adjusts the cleaning configuration parameters of the brush based on the preliminary cleaning path, obtains the dynamically adjusted cleaning configuration parameters, and sends the dynamically adjusted cleaning configuration parameters to the pressure control module; receives the pressure deviation of the pressure control module, and adjusts the stiffness and movement speed of the brush and the pressure application method in real time according to the pressure deviation to obtain the optimized cleaning configuration parameters, and sends the optimized cleaning configuration parameters to the cleaning execution module;

[0034] The pressure control module monitors the contact pressure between the brush and the surface during the cleaning process, collects real-time pressure data through the pressure sensor, compares it with the preset pressure range, obtains the pressure deviation, and feeds the pressure deviation back to the flexible brush control module;

[0035] The cleaning execution module drives the brush to clean the spacecraft surface along the planned path based on the preliminary cleaning path and the optimized cleaning configuration parameters.

[0036] The beneficial effects of the technical solution of the present invention are:

[0037] 1. The dynamic geometry feedback iterative path planning algorithm is used to achieve dynamic generation and optimization of the cleaning path with the highest coverage. The algorithm can optimize the cleaning path in real time to adapt to the complex geometric characteristics of the surface, including curvature changes, material distribution and contamination level, through dynamic priority adjustment formula and nested optimization mechanism. This dynamic adaptability significantly improves the cleaning efficiency and coverage of the spacecraft surface, especially in the cleaning task of complex curved surfaces of spacecraft.

[0038] 2. A nonlinear multi-feedback flexible brush optimization algorithm is introduced. A multidimensional nonlinear optimization process is constructed by dynamically adjusting the brush stiffness, length, movement speed, material selection and pressure application method. By coupling feedback signals (such as pressure deviation, curvature change rate, material hardness gradient), comprehensive optimization of brush configuration parameters is achieved, which can accurately match local requirements in different cleaning areas. This optimization mechanism ensures the efficiency and safety of cleaning tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a structural diagram of a flexible and controllable brush system for treating a spacecraft surface according to the present invention;

[0040] Figure 2 The present invention is a flow chart of a flexible and controllable spacecraft surface treatment brush method. DETAILED DESCRIPTION

[0041] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0043] The specific scheme of a flexible and controllable spacecraft surface treatment brush system and method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0044] See attached Figure 1 , which shows a structural diagram of a flexible and controllable spacecraft surface treatment brush system provided by an embodiment of the present invention, the system includes the following parts:

[0045] Environmental perception module, cleaning path planning module, flexible brush control module, pressure control module, cleaning execution module;

[0046] The environmental perception module uses a laser scanner and an ultrasonic sensor to scan the surface of the spacecraft, generate three-dimensional point cloud data, and construct a three-dimensional model of the spacecraft surface. At the same time, it processes and analyzes the three-dimensional point cloud data, identifies the cleaning area, and extracts regional feature information, including priority information, surface material information, and regional curvature data, and sends the three-dimensional model of the spacecraft surface and regional feature information to the cleaning path planning module;

[0047] The cleaning path planning module calculates the cleaning path with the highest coverage rate using a dynamic geometry feedback iterative path planning algorithm based on the three-dimensional model of the spacecraft surface and regional feature information, generates a preliminary cleaning path including path nodes, the curvature of each path segment, and regional material information, and transmits the preliminary cleaning path to the flexible brush control module and the cleaning execution module;

[0048] The flexible brush control module dynamically adjusts the cleaning configuration parameters of the brush based on the preliminary cleaning path, including stiffness, movement speed, length, material and pressure application method, obtains the dynamically adjusted cleaning configuration parameters, and sends the dynamically adjusted cleaning configuration parameters to the pressure control module; receives the pressure deviation of the pressure control module, and adjusts the stiffness and movement speed of the brush and the pressure application method in real time according to the pressure deviation to obtain the optimized cleaning configuration parameters, and sends the optimized cleaning configuration parameters to the cleaning execution module;

[0049] The pressure control module monitors the contact pressure between the brush and the surface during the cleaning process, collects real-time pressure data through the pressure sensor, compares it with the preset pressure range, obtains the pressure deviation, and feeds the pressure deviation back to the flexible brush control module;

[0050] The cleaning execution module drives the brush to clean the spacecraft surface according to the planned path based on the preliminary cleaning path and the optimized cleaning configuration parameters, thereby achieving flexible and controllable spacecraft surface treatment.

[0051] See attached Figure 2 , which shows a flow chart of a flexible and controllable spacecraft surface treatment brush method provided by an embodiment of the present invention, the method comprising the following steps:

[0052] S1. Scan the spacecraft surface to obtain spacecraft surface data, construct point cloud data, and generate three-dimensional point cloud data; pre-process the three-dimensional point cloud data and convert the pre-processed three-dimensional point cloud data into a three-dimensional model of the spacecraft surface; process and analyze the three-dimensional point cloud data to identify the cleaning area and extract regional feature information; based on the three-dimensional model of the spacecraft surface and the regional feature information, use the dynamic geometry feedback iterative path planning algorithm to generate a preliminary cleaning path;

[0053] First, the surface of the spacecraft is scanned by a laser scanner and an ultrasonic sensor to obtain the surface data of the spacecraft. The laser scanner measures the reflection time of the spacecraft surface point by point according to the resolution and scanning density preset by the expert experience method, calculates the spatial coordinates of each target point, and uses the formula Construct point cloud data, where is the distance from the target point to the laser scanner measured by the laser scanner (radial distance); is the scanning angle, which represents the deflection angle of the laser beam relative to the reference plane of the laser scanner; It is the three-dimensional coordinate of the target point in the laser scanner coordinate system. At the same time, the ultrasonic sensor emits a beam to the surface of the spacecraft, and records the material information of the spacecraft surface, such as hardness and roughness, through the echo time and intensity. The point cloud data and material information are then synchronized with the timestamp and unified to the global coordinate system of the spacecraft surface to form three-dimensional point cloud data.

[0054] Furthermore, the existing preprocessing technology is used to preprocess the 3D point cloud data to obtain preprocessed 3D point cloud data, and then the preprocessed 3D point cloud data is converted into a 3D model of the spacecraft surface through the existing triangular mesh algorithm. , is the initial triangular mesh element; , It is the number of initial triangular mesh units, which contains the geometric shape of the spacecraft surface and can also reflect the material information of the spacecraft surface. The regional growing algorithm is used to divide the spacecraft surface into blocks to achieve regional division, and then the regional curvature data is extracted according to the curvature calculation formula. The above algorithms and formulas are all existing technologies and will not be described here.

[0055] Furthermore, the existing grayscale value change threshold method is used to identify the clean area based on the reflection intensity change and material information of the preprocessed three-dimensional point cloud data. Feature engineering technology is then used to extract features of the clean area, including information such as the degree of contamination, surface curvature complexity, and material hardness. The priority of the clean area is determined based on the extracted features using expert experience, and finally the regional feature information is obtained, including priority information, regional curvature data, and surface material information.

[0056] Furthermore, according to the three-dimensional model and regional feature information of the spacecraft surface, the dynamic geometry feedback iterative path planning algorithm is used to calculate the cleaning path with the highest coverage rate and generate a preliminary cleaning path. The dynamic geometry feedback iterative path planning algorithm is based on the dynamic geometry feedback mechanism and realizes the planning of the cleaning path with the highest coverage rate through multiple nested optimizations and global path iterative reconstruction. The specific implementation process is as follows:

[0057] First, based on the 3D model of the spacecraft surface and regional characteristic information, wherein the regional characteristic information includes priority information , regional curvature data , surface material information , reflecting the urgency of cleaning the area, geometric complexity and material cleaning difficulty; in addition, setting the path length threshold Minimum distance between nodes As planning constraints. Based on the 3D model of the spacecraft surface and regional feature information, and convert the initial triangular mesh units of the three-dimensional model of the spacecraft surface into Construct the initial path node set After generating the initial path node set, the priority of the initial path node is adjusted through the dynamic geometric feedback mechanism so that it can be dynamically optimized according to local features during the path planning process. The priority adjustment formula is:

[0058]

[0059] in, It is The dynamic priority of each node, that is, the node priority updated by the dynamic geometry feedback mechanism; It is The initial priority of each node reflects the importance and urgency of the nodes in the cleaning area; is the feedback adjustment coefficient, which is used to control the intensity of dynamic adjustment of priority and is determined according to the expert experience method; It is The gradient of the regional curvature data of each node represents the rate of change of the regional curvature data in space, which is calculated by the curvature difference of adjacent triangular mesh units (regions); It is The gradient of the surface material information of each node represents the spatial rate of change of the material hardness or roughness; Is the current node With neighboring nodes The Euclidean distance of is the total number of domain nodes of the current node; It is The local curvature value of each node is used to describe the curvature of the spacecraft surface.

[0060] Furthermore, based on the dynamic priority of the node , a nested optimization mechanism is used to generate the first path. The local path cost function is introduced, and the spatial change rate of the node's dynamic priority, local curvature and surface material characteristics are combined for optimization to balance the importance of the local path and the difficulty of cleaning. The formula of the local path cost function is:

[0061]

[0062] in, It is The local path cost of each node indicates the importance and cleaning difficulty of the local path; It is The spatial change rate of the dynamic priority of each node reflects the importance gradient of the local path; , , It is the weight parameter of the local path cost function, which is used to control the spatial change rate of the dynamic priority of the node, the relative influence of the local curvature and the surface material characteristics, and is determined according to the expert experience method; is the differential length of the path, reflecting the microelement segment of the path planning, representing the integral variable, defined as a small increment of the path length; Represents the length of the local path, that is, the cumulative path length from the start node to the current local end point.

[0063] The specific implementation process of the nested optimization mechanism is as follows: starting from the starting node, select the next node with the smallest local path cost value according to the local path cost function, and update the local path cost value at the same time and the dynamic priority of nodes Each iteration is based on the path output of the previous stage and records the area covered by the current path and the local path cost. When the path length of the node reaches the path length threshold Or when all high-priority areas have been covered, the nested optimization process is terminated and the first path sequence, i.e., the first path, is output.

[0064] To further improve the global performance of the path, the first path is optimized through the global path iterative reconstruction mechanism. Based on the first path, the efficiency and clean coverage of the path are improved from a global perspective by optimizing the node connection method, recalculating the priority distribution and the clean coverage status. The global path cost function comprehensively considers the local path cost value, path length and path smoothness, and is defined as follows:

[0065]

[0066] in, is the global path cost function, which represents the importance and cleaning efficiency of the complete path; For the The length of the path where the node is located; It is The second-order gradient term of the dynamic priority of the node represents Spatial smoothness of the dynamic priorities of nodes; , are the adjustment coefficients of the global cost function, which control the impact of path length and the smoothness of the dynamic priority of nodes respectively; It is The path length ratio term of the nodes is used to control the total path length within a reasonable range; the second-order gradient term of the dynamic priority of the node By constraining the path smoothness, the paths with frequent turning points are reduced, and the gradient of regional curvature data complexity is introduced. , limiting the extreme variations in global path costs.

[0067] The specific implementation process of the global path iterative reconstruction mechanism is as follows: First, based on the first path sequence, the initial global path cost is calculated using the above formula Then, we reconnect and optimize the path nodes, that is, check the node connection mode in the first path and find the connection that can be optimized. Assume that the node in the current path and Connected, test whether there is an optimal connection node in the node neighborhood Alternative The goal of the path node reconnection optimization is to minimize the local path cost:

[0068]

[0069] in, is the updated local path cost; node neighborhood Defined as satisfying The set of all nodes of ; It is Dynamic priority of nodes; It is The local curvature value of each node; It is Spacecraft surface material information of each node; , , is the weight parameter of the local path cost function.

[0070] If a new optimal connection node is found , replace the original path segment for , and update the first path sequence.

[0071] Further, the priority distribution is recalculated; the priority of the path coverage area is re-evaluated and a new dynamic priority is calculated. ; The updated dynamic priority will further affect node selection and connection optimization in subsequent iterations.

[0072] Finally, the new path sequence is smoothed to minimize sharp turns in the path. The node positions are adjusted by the existing smooth cost function constraints.

[0073] After completing the above optimization, recalculate the The global path cost and the last global path cost If the global path cost value converges (that is, the absolute difference between two adjacent global path cost values ​​is less than the preset convergence threshold) or the maximum number of iterations is reached, the iteration is stopped.

[0074] Finally, a complete cleaning path sequence, namely the preliminary cleaning path, is output.

[0075] S2. Based on the preliminary cleaning path, the cleaning configuration parameters of the brush are dynamically adjusted and optimized through the nonlinear multi-feedback flexible brush optimization algorithm to obtain the optimized cleaning configuration parameters; and according to the preliminary cleaning path and the optimized cleaning configuration parameters, the brush is driven to perform cleaning operations on the surface of the spacecraft.

[0076] Based on the preliminary cleaning path, the nonlinear multi-feedback flexible brush optimization algorithm is used to dynamically adjust and optimize the cleaning configuration parameters of the brush to obtain the optimized cleaning configuration parameters; the nonlinear multi-feedback flexible brush optimization algorithm establishes a nonlinear optimization process through a multi-feedback coupling mechanism, emphasizing the multi-dimensional nonlinear adjustment of the cleaning configuration parameters of the brush. The cleaning configuration parameters of the brush include: brush stiffness, movement speed, length, material, and pressure application method; and introduces a complex coupling feedback control relationship to ensure cleaning accuracy and effect, and avoid damage to the surface of the spacecraft or incomplete cleaning. The specific implementation process is as follows:

[0077] In order to dynamically adjust the cleaning configuration parameters of the flexible brush, the path points and their physical characteristics are first extracted from the preliminary cleaning path, including the coordinates of the path points, local curvature data and surface material information, and used to initialize the initial parameters of the brush's stiffness, movement speed, length, material selection and pressure application method.

[0078] As a specific embodiment, the initial brush stiffness parameters are constructed by parameter initialization formula: , Brush length parameters , Brush movement speed parameters , the formula is:

[0079]

[0080]

[0081]

[0082] in, is the stiffness parameter of the brush, which is used to describe the flexibility of the brush when it applies pressure on the surface of the spacecraft; is the initial stiffness value of the brush, setting the default stiffness; is in position The local curvature parameter at ; is the maximum value of the local curvature parameter; is in position Material hardness parameters at ; It is the default material hardness value, used to initialize the material selection benchmark; It is the maximum value of the material hardness, which is used to normalize the influence range of hardness in the formula; is the parameter adjustment sensitivity factor used to control the response strength of stiffness to path characteristics; is in position The length parameter of the brush at is used to describe the extension range of the brush; is the initial length of the brush, indicating the default length of the brush before optimization; It is the average value of the local curvature, which is used to describe the curvature of a small area (local path segment) on the spacecraft surface. It reflects the geometric complexity of the local area and determines how the stiffness and length of the brush dynamically adapt to the shape changes of the spacecraft surface. is the sum of all curvature values ​​on the cleaning path of the entire spacecraft surface, indicating the overall curvature complexity of the cleaning path; is the parameter adjustment sensitivity factor used to control the strength of the length response to path characteristics; is the basic movement speed of the brush, the default setting; is the material hardness gradient; It is the material hardness gradient diffusion parameter, which is used to control the effect of material hardness on movement speed; is the target pressure parameter, i.e., the preset pressure parameter, which is used to define the desired surface pressure during the cleaning process; is the maximum value of the target pressure.

[0083] After the parameters are initialized, multiple feedback signals are introduced to achieve dynamic optimization, including pressure deviation. , curvature change rate and material hardness gradient The pressure deviation is defined by the difference between the preset pressure and the actual pressure; the curvature change rate is calculated by the local curvature difference of the path point; the material hardness gradient is defined by the local gradient of the material hardness parameter;

[0084] The feedback signal is processed by the following coupling to form the total feedback signal :

[0085]

[0086] Furthermore, the cleaning configuration parameters of the brush are dynamically optimized according to the total feedback signal, and the brush stiffness parameter adjustment formula is:

[0087]

[0088] in, It is the optimized brush stiffness parameter, which is used to adjust the brush flexibility characteristics in real time; is the stiffness adjustment sensitivity factor, which is used to control the response strength of the total feedback signal to the brush stiffness adjustment;

[0089] The brush length parameter adjustment is combined with the square decreasing law of the total feedback signal, and the formula is:

[0090]

[0091] in, is the optimized brush length parameter, which is used to dynamically adjust the working range of the brush; It is the diffusion parameter of the brush length, which is used to control the variation of the brush length.

[0092] In order to meet the requirements of different brush materials, the hardness parameters of the brush material are selected and optimized according to the total feedback signal. The formula is:

[0093]

[0094] in, is the optimized hardness parameter of the brush material, which is used to dynamically select the best material at the end of the brush; is the rounding function; is the material selection sensitivity factor, which is used to control the impact of the total feedback signal on material optimization.

[0095] Optimized brush movement speed parameters Dynamically adjusted to:

[0096]

[0097] in, is the maximum value of the total feedback signal, used for normalization; It is the speed adjustment sensitivity factor, which is used to control the adjustment amplitude of the total feedback signal on the brush movement speed; It is an adjustment response to the local curvature. The larger the local curvature, the more significant the adjustment of the brush movement speed.

[0098] Finally, the dynamic adjustment of the pressure application method is achieved by optimizing the pressure parameters in real time. The target pressure parameter adjustment formula is:

[0099]

[0100] in, is the optimized target pressure parameter, which is used to dynamically adjust the pressure distribution during the cleaning process; is the target pressure parameter, i.e., the preset pressure parameter, which is used to define the desired surface pressure during the cleaning process;

[0101] The pressure parameter optimization formula is:

[0102]

[0103] in, is the optimized target pressure parameter, which is used to dynamically adjust the pressure distribution during the cleaning process; is the optimized pressure parameter used to describe the pressure pattern applied by the brush during the cleaning process; is the initial pressure parameter, which is used to indicate the baseline value of the pressure applied by the brush when it is not optimized; is the actual pressure parameter, which is used to describe the real-time pressure value applied by the brush to the surface of the spacecraft; It is the pressure adjustment control parameter, which is used to control the adjustment range of the optimized pressure parameter.

[0104] After the above processing, the optimized cleaning configuration parameters are obtained. Further, according to the preliminary cleaning path and the optimized cleaning configuration parameters, the brush is driven to clean the spacecraft surface according to the planned path, thereby realizing flexible and controllable spacecraft surface treatment.

[0105] In summary, a flexible and controllable spacecraft surface treatment brush system and method have been completed.

[0106] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0107] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0108] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A flexible and controllable brush method for treating the surface of a spacecraft, characterized in that: The following steps are involved: S1. Scan the spacecraft surface to obtain spacecraft surface data, construct point cloud data, and generate three-dimensional point cloud data; Preprocess the 3D point cloud data and convert the preprocessed 3D point cloud data into a 3D model of the spacecraft surface; process and analyze the 3D point cloud data to identify the clean area and extract the regional feature information; Based on the three-dimensional model of the spacecraft surface and regional feature information, a preliminary cleaning path is generated using a dynamic geometry feedback iterative path planning algorithm; In the process of implementing the dynamic geometry feedback iterative path planning algorithm, according to the three-dimensional model of the spacecraft surface and the regional feature information, the initial triangular mesh units of the three-dimensional model of the spacecraft surface are used to form an initial path node set. After the initial node set is generated, the priority of the initial path node is adjusted through the dynamic geometry feedback mechanism to obtain the dynamic priority of the node. Based on the dynamic priority of nodes, a nested optimization mechanism is used to generate the first path; The local path cost function is introduced, and the spatial change rate of the node's dynamic priority, local curvature and surface material information are optimized to obtain the local path cost value. The formula of the local path cost function is: in, It is The local path cost of each node; It is The spatial change rate of the dynamic priority of each node; , , is the weight parameter of the local path cost function; It is The local curvature value of each node; is the path differential length; Indicates the length of the local path; It is Surface material information of each node; In the specific implementation process of the nested optimization mechanism, starting from the starting node, the next node with the smallest local path cost value is selected according to the local path cost function, and the local path cost value and the dynamic priority of the node are updated to output the first path sequence; and the first path is optimized through the global path iterative reconstruction mechanism. The global path cost function combines the local path cost value, path length and path smoothness, and is defined as: in, is the global path cost function; For the The length of the path where the node is located; is the second-order gradient term of the node’s dynamic priority; , is the adjustment coefficient of the global cost function; It is The path length ratio item where the nodes are located; is the maximum path length; is the total number of domain nodes of the current node; It is The gradient of the regional curvature data of each node; In the specific implementation process of the global path iterative reconstruction mechanism, the path nodes are reconnected and optimized to minimize the local path cost value, and the updated local path cost value is obtained; the priority distribution is recalculated, and the smoothness of the new path sequence is adjusted to obtain a preliminary clean path; S2. Based on the preliminary cleaning path, the cleaning configuration parameters of the brush are dynamically adjusted and optimized through the nonlinear multi-feedback flexible brush optimization algorithm to obtain the optimized cleaning configuration parameters; and according to the preliminary cleaning path and the optimized cleaning configuration parameters, the brush is driven to perform cleaning operations on the spacecraft surface.

2. A flexible and controllable brush method for treating a spacecraft surface according to claim 1, characterized in that: The S1 specifically includes: The reflection intensity changes and material information of the preprocessed point cloud data are combined to identify the clean area, and the features of the clean area are extracted. The priority of the clean area is determined according to the extracted features, and finally the regional feature information is obtained, including priority information, regional curvature data and surface material information.

3. A flexible and controllable brush method for treating a spacecraft surface according to claim 1, characterized in that: The S2 specifically includes: In the process of implementing the nonlinear multi-feedback flexible brush optimization algorithm, the cleaning configuration parameters of the brush are initialized based on the preliminary cleaning path. The cleaning configuration parameters include: brush stiffness, movement speed, length, material, and pressure application method; after the parameter initialization is completed, multiple feedback signals are introduced, and the feedback signals are coupled to form a total feedback signal : in, is the pressure deviation; is the rate of change of curvature; is the material hardness gradient; is in position Material hardness parameters at ; Is the default material hardness value.

4. A flexible and controllable brush method for treating a spacecraft surface according to claim 3, characterized in that: The S2 specifically includes: In the implementation process of the nonlinear multi-feedback flexible brush optimization algorithm, the cleaning configuration parameters of the brush are dynamically optimized based on the total feedback signal to obtain the optimized brush stiffness parameters, brush length parameters, brush material hardness parameters and brush movement speed parameters; and the pressure application method is dynamically adjusted by optimizing the pressure parameters in real time to obtain the optimized target pressure parameters and optimized pressure parameters.

5. A flexible and controllable spacecraft surface treatment brush system, applied to a flexible and controllable spacecraft surface treatment brush method as claimed in claim 1, characterized in that: Includes the following parts: Environmental perception module, cleaning path planning module, flexible brush control module, pressure control module, cleaning execution module; The environmental perception module uses a laser scanner and an ultrasonic sensor to scan the surface of the spacecraft, generate three-dimensional point cloud data, and construct a three-dimensional model of the spacecraft surface. At the same time, it processes and analyzes the three-dimensional point cloud data, identifies the cleaning area and extracts the regional feature information, and sends the three-dimensional model and regional feature information of the spacecraft surface to the cleaning path planning module; The cleaning path planning module calculates the cleaning path with the highest coverage rate based on the three-dimensional model and regional feature information of the spacecraft surface using a dynamic geometric feedback iterative path planning algorithm, generates a preliminary cleaning path, and transmits the preliminary cleaning path to the flexible brush control module and the cleaning execution module; The flexible brush control module dynamically adjusts the cleaning configuration parameters of the brush based on the preliminary cleaning path, obtains the dynamically adjusted cleaning configuration parameters, and sends the dynamically adjusted cleaning configuration parameters to the pressure control module; Receive the pressure deviation from the pressure control module, adjust the stiffness and movement speed of the brush and the pressure application method in real time according to the pressure deviation, obtain the optimized cleaning configuration parameters, and send the optimized cleaning configuration parameters to the cleaning execution module; The pressure control module monitors the contact pressure between the brush and the surface during the cleaning process, collects real-time pressure data through the pressure sensor, compares it with the preset pressure range, obtains the pressure deviation, and feeds the pressure deviation back to the flexible brush control module; The cleaning execution module drives the brush to clean the spacecraft surface along the planned path based on the preliminary cleaning path and the optimized cleaning configuration parameters.

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