Method for generating movement path of flexible member in robot maintenance of high-speed rail catenary

Abstract

This application relates to the field of robot operation planning technology, and in particular to a method for generating motion paths for robots repairing flexible components in high-speed railway overhead contact lines. The method includes: acquiring the initial and final poses of the end effector of the robot repairing the flexible component; determining the initial and final positions of the end effector based on the initial and final poses; generating a position trajectory of the end effector based on the initial and final positions; determining feasible poses for multiple points within the position trajectory; generating an attitude trajectory of the end effector based on the feasible poses; and finally generating a motion path for the robot repairing the flexible component based on the position trajectory and attitude trajectory. This solves the problem that related path planning methods fail to effectively prune the planning space, resulting in low planning efficiency and difficulty in achieving safe, efficient, and stable automated replacement of flexible components.

Description

Technical Field

[0001] This application relates to the field of robot operation planning technology, and in particular to a method for generating motion paths for flexible components in high-speed railway overhead contact lines using robots. Background Technology

[0002] The overhead contact system contains numerous flexible linear components (referred to as flexible parts) such as catenary wires, contact wires, and droppers. These components are highly susceptible to fatigue, deformation, and even breakage under long-term harsh outdoor environments (such as wind vibration, temperature differences, salt spray, and electrical spark corrosion), necessitating maintenance and replacement during designated maintenance windows. To reduce reliance on manual labor, experts and scholars have conducted in-depth research on the automated replacement of flexible parts using collaborative robots.

[0003] In related technologies, for the multi-degree-of-freedom, weakly constrained, and underactuated operation task of flexible component replacement, it is necessary to solve the attitude of the flexible component to determine the motion path of the robot arm. Related methods are based on solving the initial value problem of the flexible component. That is, taking the starting end of the flexible component as the fixed end, the pose information of the starting end is directly obtained, and the internal forces and torques at the starting end are collected by force sensors as initial conditions. Then, the pose information of the entire flexible component is obtained by integrating along the arc length of the centerline of the flexible component in the positive direction.

[0004] However, in related technologies, the initial value problem requires the position, orientation, force, torque, and distributed force of the starting end segment. But in actual operation, when a robot grasps the starting and ending ends of the dropper, it can only determine the position and orientation of the starting end and the position and orientation of the ending end. This is a boundary value problem and cannot be solved using the initial value problem in related methods. In addition, the related path planning methods do not effectively prune the planning space, resulting in low planning efficiency and difficulty in achieving safe, efficient, and stable automated replacement of flexible parts. This problem urgently needs to be solved. Summary of the Invention

[0005] This application provides a method for generating motion paths for flexible components in high-speed railway overhead contact lines using robots. This method addresses the problem that related path planning methods fail to effectively prune the planning space, resulting in low planning efficiency and making it difficult to achieve safe, efficient, and stable automated replacement of flexible components.

[0006] The first aspect of this application provides a method for generating a motion path for a robot repairing a flexible component in a high-speed railway overhead contact line, comprising the following steps: obtaining the initial pose and the final pose of the end of the robot repairing the flexible component; determining the initial position and the final position of the end of the robot based on the initial pose and the final pose, so as to generate a position trajectory of the end of the robot based on the initial position and the final position; determining feasible poses for multiple position points in the position trajectory, and generating an attitude trajectory of the end of the robot based on the feasible poses, so as to generate a motion path for the robot repairing the flexible component based on the position trajectory and the attitude trajectory.

[0007] Based on the above technical means, this application embodiment generates a position trajectory, combines the feasible postures of multiple position points on the position trajectory to generate a posture trajectory, and then combines the position trajectory and posture trajectory to generate the motion path of the robot repair flexible parts. This realizes the collaborative planning of position and posture, and can incorporate the obstacle avoidance constraints and balance constraints of the operation object, effectively avoiding the risk of deformation of the flexible parts during operation and interference with environmental obstacles, as well as operational instability. In addition, by reducing invalid sampling and computational consumption through hierarchical planning, the success rate, reliability and real-time performance of automated maintenance operations are improved, ensuring that the robot repairs the flexible parts smoothly, safely and executably. Furthermore, by introducing terminal operability when determining feasible postures, the planning space can be effectively pruned, improving planning efficiency.

[0008] Optionally, in one embodiment of this application, before determining the feasible poses of multiple location points in the location trajectory, the method further includes: calculating terminal operability estimates of the multiple location points; determining whether the terminal operability estimates are greater than or equal to a preset terminal operability threshold; if the terminal operability estimates are greater than or equal to the preset terminal operability threshold, determining that the multiple location points belong to a first region; if the terminal operability estimates are less than the preset terminal operability threshold, determining that the multiple location points belong to a second region, wherein the flexibility of the first region is higher than that of the second region.

[0009] Based on the above technical means, this application embodiment calculates the terminal operability estimate of the location point and combines it with the preset terminal operability threshold to divide the location point into a first region and a second region with different flexibility, clarifying the difference in attitude adjustment capability, and providing corresponding guidance for subsequent candidate attitude sampling. In the first region with higher flexibility, a more flexible sampling method is adopted to improve efficiency and the smoothness and continuity of attitude trajectory, while in the second region with lower flexibility, a more targeted sampling method is adopted to ensure feasibility.

[0010] Optionally, in one embodiment of this application, determining the feasible postures of multiple location points in the position trajectory includes: if the multiple location points belong to the first region, determining a first sampling center based on the start and end postures of the terminal, sampling candidate postures of the location points based on the first sampling center, and determining the feasible posture based on the candidate postures; if the multiple location points belong to the second region, determining a second sampling center based on the relative positional relationship between the multiple location points and the start end of the robot repair flexible component, sampling candidate postures based on the second sampling center, and determining the feasible posture based on the candidate postures.

[0011] Based on the above technical means, the embodiments of this application distinguish between the first region and the second region to which the location point belongs, and specifically determine the first sampling center and the second sampling center of the corresponding region. Candidate postures are sampled based on different sampling centers, and then feasible postures are determined based on the candidate postures. This achieves the targeting and rationality of feasible posture sampling, effectively avoids invalid sampling caused by indiscriminate sampling, and improves the efficiency and accuracy of feasible posture screening.

[0012] Optionally, in one embodiment of this application, determining the feasible posture based on the candidate posture includes: determining a first boundary condition for the starting end, and generating a second boundary condition for the terminal end based on the candidate posture and the corresponding position point; mapping the first boundary condition and the second boundary condition to a preset normalized coordinate system to generate corresponding third boundary conditions and fourth boundary conditions; and determining the feasible posture based on the third boundary conditions and the fourth boundary conditions.

[0013] Based on the above technical means, the embodiments of this application clearly define the start and end constraints of the feasible posture determination by determining the first boundary condition and the second boundary condition, ensuring the pertinence and directionality of mechanical verification. By mapping the first boundary condition and the second boundary condition to a preset normalized coordinate system respectively, the corresponding third boundary condition and fourth boundary condition are generated, eliminating the calculation deviation under different coordinate systems, unifying the solution benchmark of the mechanical model, reducing the computational complexity, and determining the feasible posture according to the third boundary condition and the fourth boundary condition, it is possible to accurately verify whether the candidate posture can meet the mechanical balance requirements of the flexible component.

[0014] Optionally, in one embodiment of this application, before determining the feasible posture, the method further includes: determining the centerline position, cross-sectional posture quaternion, cross-sectional internal force, cross-sectional Dabu vector, and distributed force of the robot repair flexible component; establishing the variation relationship between the centerline position, the cross-sectional posture quaternion, the cross-sectional internal force, the cross-sectional Dabu vector, the distributed force, and the arc length coordinate; and establishing a mechanical model of the robot repair flexible component based on the variation relationship, so as to determine the feasible posture using the mechanical model.

[0015] Based on the above technical means, the embodiments of this application establish the relationship between the centerline position, cross-sectional orientation quaternion, cross-sectional internal force, cross-sectional Dabu vector, distributed force and arc length coordinate, and construct a mechanical model, which can accurately calculate the shape of the centerline, so that the robot's operation can meet the balance constraint and obstacle avoidance constraint, thereby ensuring the stability, safety and feasibility of the robot's operation of repairing flexible parts, and improving the accuracy and reliability of trajectory generation.

[0016] A second aspect of this application provides a motion path generation device for a robot repairing a flexible component in a high-speed railway overhead contact line, comprising: an acquisition module for acquiring the initial pose and the final pose of the end of the robot repairing the flexible component; a first generation module for determining the initial position and the final position of the end of the robot based on the initial pose and the final pose, so as to generate a position trajectory of the end of the robot based on the initial position and the final position; and a second generation module for determining feasible poses of multiple position points in the position trajectory, and generating an attitude trajectory of the end of the robot based on the feasible poses, so as to generate a motion path for the robot repairing the flexible component based on the position trajectory and the attitude trajectory.

[0017] Based on the above technical means, this application embodiment generates a position trajectory, combines the feasible postures of multiple position points on the position trajectory to generate a posture trajectory, and then combines the position trajectory and posture trajectory to generate the motion path of the robot repair flexible parts. This realizes the collaborative planning of position and posture, incorporates obstacle avoidance constraints and balance constraints of the operation object, effectively avoids the risk of deformation of the flexible parts during operation and interference with environmental obstacles, and operational instability. At the same time, by reducing invalid sampling and computation consumption through hierarchical planning, the success rate, reliability and real-time performance of automated maintenance operations are improved, ensuring that the robot repairs flexible parts smoothly, safely and executably. In addition, by introducing terminal operability when determining feasible postures, the planning space can be effectively pruned, improving planning efficiency.

[0018] Optionally, in one embodiment of this application, it further includes: a calculation module, configured to calculate the terminal operability estimate of the plurality of location points; a judgment module, configured to determine whether the terminal operability estimate is greater than or equal to a preset terminal operability threshold; a first judgment module, configured to determine that the plurality of location points belong to a first region if the terminal operability estimate is greater than or equal to the preset terminal operability threshold; and a second judgment module, configured to determine that the plurality of location points belong to a second region if the terminal operability estimate is less than the preset terminal operability threshold, wherein the flexibility of the first region is higher than that of the second region.

[0019] Based on the above technical means, this application embodiment calculates the terminal operability estimate of the location point and combines it with the preset terminal operability threshold to divide the location point into a first region and a second region with different flexibility, clarifying the difference in attitude adjustment capability, and providing corresponding guidance for subsequent candidate attitude sampling. In the first region with higher flexibility, a more flexible sampling method is adopted to improve efficiency and the smoothness and continuity of attitude trajectory, while in the second region with lower flexibility, a more targeted sampling method is adopted to ensure feasibility.

[0020] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The processor executes the program to implement the motion path generation method for robot maintenance of flexible components in high-speed rail catenary as described in the above embodiments.

[0021] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for generating motion paths for robot maintenance of flexible components in a high-speed railway overhead contact line.

[0022] The fifth aspect of this application provides a computer program product, including a computer program that, when executed, implements the above-described method for generating motion paths for robot maintenance of flexible components in a high-speed railway overhead contact line.

[0023] This application's embodiments generate a position trajectory, combine it with feasible poses of multiple points on the position trajectory to generate a pose trajectory, and then combine the position trajectory and pose trajectory to generate the motion path of the robot's repair flexible component. This achieves collaborative planning of position and pose, incorporates obstacle avoidance and balance constraints of the manipulated object, effectively avoiding the risk of deformation of the flexible component during operation, interference with environmental obstacles, and operational instability. Simultaneously, hierarchical planning reduces invalid sampling and computational costs, improving the success rate, reliability, and real-time performance of automated maintenance operations, ensuring smooth, safe, and executable operation of the robot's repair flexible component. Furthermore, by introducing terminal operability when determining feasible poses, the planning space can be effectively pruned, improving planning efficiency. Therefore, this solves the problem of related path planning methods not processing the planning space and having low efficiency in equal-weighted sampling.

[0024] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0025] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a method for generating motion paths for robot maintenance of flexible components in a high-speed railway overhead contact system, according to an embodiment of this application. Figure 2 This is a schematic diagram illustrating the pose definition of points on a flexible member according to an embodiment of this application; Figure 3 This is a schematic diagram illustrating the initial value problem and boundary value problem of a flexible component according to an embodiment of this application; Figure 4 This is a schematic diagram showing the normalized boundary conditions of a flexible component according to an embodiment of this application. Figure 5 This is a schematic diagram illustrating the definition of operational flexibility of the end position of a flexible component according to an embodiment of this application; Figure 6(a) is a distribution map of the operational flexibility of each point in the flexible component task space according to an embodiment of this application; Figure 6(b) is an association map of points in the flexible component task space according to an embodiment of this application; Figure 7 This is a block diagram of a motion path generation device for robot maintenance of flexible components in high-speed railway overhead contact lines, provided according to an embodiment of this application. Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0026] Figure label: 70 - Motion path generation device for robot maintenance of flexible components in high-speed rail catenary; 100 - Acquisition module, 200 - First generation module, 300 - Second generation module; 801 - Memory, 802 - Processor and 803 - Communication interface. Detailed Implementation

[0027] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0028] Research has revealed that the high failure rate of related methods is not due to a single factor, but rather to multiple defects in modeling assumptions, control logic, and solution strategies, specifically in the following three aspects.

[0029] First, from the perspective of physical modeling assumptions, the relevant planning methods are generally based on the assumption of rigid or quasi-rigid bodies, which makes it difficult to effectively handle the large deformation and nonlinear mechanical behavior of flexible parts during dual-arm gripping, transportation, and installation. Because it is impossible to accurately predict the follow-up deformation of the line caused by the robot's movement, the flexible parts are prone to overstretching or collision with surrounding equipment during actual execution, resulting in extremely poor operational reliability.

[0030] Secondly, from the perspective of control logic coupling, the relevant planning methods simply decouple position planning from attitude planning. In reality, the spatial configuration of flexible components is highly dependent on the attitude of the gripping points at both ends, while the relevant methods usually rely on empirical settings or simple interpolation to determine the attitude, which leads to unexpected deformation and internal stress concentration of the line during movement, potentially causing instability, abrupt changes in shape, or excessive distortion.

[0031] Finally, from the perspective of solution strategy efficiency, the relevant planning methods lack a quantitative representation of the system's operational dexterity space. Flexible components have a clearly defined feasible attitude range under specific poses, but most methods employ full-space random search or rigid body kinematic constraints, failing to set pre-guidance conditions to effectively prune the planning space. This results in a blind and time-consuming planning process, making it difficult to complete real-time trajectory generation within a limited window of opportunity, thus limiting the practicality of the technology.

[0032] To address the aforementioned shortcomings, this application provides a method and apparatus for generating motion paths for robots to repair flexible components in high-speed rail overhead contact lines.

[0033] The following describes, with reference to the accompanying drawings, a method and apparatus for generating motion paths for robot maintenance of flexible components in high-speed rail overhead contact lines, according to embodiments of this application.

[0034] Figure 1This is a flowchart of a method for generating motion paths for robot maintenance of flexible components in a high-speed railway overhead contact system, according to an embodiment of this application.

[0035] like Figure 1 As shown, the motion path generation method for the robot to repair flexible components in the high-speed rail overhead contact line includes the following steps: In step S101, the starting pose and ending pose of the end of the robot repair flexible component are obtained.

[0036] It should be noted that the deformation of the flexible component is determined solely by the relative pose of the end effectors of the robot's two robotic arms, and is independent of their absolute spatial position. To simplify the mechanical analysis, during the motion path generation process, the coordinate system of the starting end of the flexible component is treated as a fixed reference, and only the relative pose change of the end effector relative to the starting end is planned. In actual operation, the two robotic arms can move synchronously; as long as the relative pose of the two ends is consistent with the generated result, the deformation state of the flexible component can be guaranteed. Therefore, in terms of mathematical description, it is equivalent to the motion of the end effector relative to the starting end.

[0037] like Figure 2 As shown, for a length of L For flexible components, in this embodiment, arc length coordinates can be established along its centerline. s ,in, s ∈[0, L At the starting end of the flexible component, s =0, at the end of the flexible component s = L .

[0038] Therefore, the embodiments of this application can have a length of L Establish arc length coordinates along the centerline of the flexible component. s The mechanical state is defined as a state vector, which contains the centerline position r( s ), cross-sectional attitude quaternion q( s ), cross-sectional internal force F( s ), cross-sectional Dabu vector ω ( s ), distributed force f( s ), denoted as Y( s )=[r( s ),q( s ),F( s ), ω ( s ),f( s )] T .

[0039] In actual execution, the user can specify the starting and ending poses of the robot's end effector for repairing the flexible component within the task space. The robot then receives these user-specified starting and ending poses. The starting pose refers to the position and orientation of the flexible component's end effector at the start of the task, corresponding to the starting point of the motion path generation. The ending pose refers to the position and orientation of the flexible component's end effector at the end of the task, corresponding to the ending point of the motion path generation.

[0040] In step S102, the starting position and ending position of the terminal are determined based on the starting pose and ending pose, so as to generate the position trajectory of the terminal based on the starting position and ending position.

[0041] In actual execution, after receiving the user-specified start and end poses, the robot first extracts the corresponding position components from the start and end poses to obtain the start position r. s ( L ) and termination position r g ( L Then, using a fast-exploration random tree or related algorithm, the terminal location trajectory pos_traj is generated.

[0042] In step S103, feasible postures of multiple position points in the position trajectory are determined, and the posture trajectory of the terminal is generated based on the feasible postures, so as to generate the motion path of the robot repair flexible component based on the position trajectory and the posture trajectory.

[0043] In the embodiments of this application, multiple location points can be understood as one or more spatial coordinate points selected from the position trajectory of the terminal end, which serve as reference points for attitude screening.

[0044] A feasible posture can be understood as the posture that the terminal can adopt at multiple location points after posture sampling and feasibility verification, satisfying the obstacle avoidance and balance constraints of the manipulated object. Specifically, in this embodiment, the location point and the candidate posture are first used as the boundary conditions of the terminal. Then, combined with the boundary conditions of the starting point, the flexible component is verified to be in a balanced configuration by a target shooting method, and the flexible component is verified to pass the collision detection. If the flexible component is in a balanced configuration and can pass the collision detection, the feasible posture is determined.

[0045] An attitude trajectory can be understood as an attitude change curve formed by connecting the feasible attitudes of multiple position points in sequence according to the order of the position trajectories, reflecting the spatial orientation change of the terminal from the initial attitude to the final attitude.

[0046] In actual execution, after generating the position trajectory pos_traj of the end effector, the robot first searches for a feasible pose ori_node for each position point pos_node in the position trajectory pos_traj, then combines all feasible poses into a pose trajectory ori_traj, and finally combines the position trajectory pos_traj and the pose trajectory ori_traj to form a complete motion path of the robot end effector, which is used for gripping, moving and installing flexible parts.

[0047] Optionally, in one embodiment of this application, before determining a feasible posture, the method further includes: determining the centerline position, cross-sectional posture quaternion, cross-sectional internal force, cross-sectional Dabu vector, and distributed force of the robot repair flexible component; establishing the variation relationship between the centerline position, cross-sectional posture quaternion, cross-sectional internal force, cross-sectional Dabu vector, distributed force, and arc length coordinates; and establishing a mechanical model of the robot repair flexible component based on the variation relationship, so as to determine the feasible posture using the mechanical model.

[0048] In actual implementation, the embodiment of this application can first determine the centerline position r( s ), cross-sectional attitude quaternion q( s ), cross-sectional internal force F( s ), cross-sectional Dabu vector ω ( s ), distributed force f( s Then, based on the static equilibrium-deformation constitutive relationship of the flexible component, the relationship between the centerline position, the quaternion of the cross-section orientation, the internal force of the cross-section, the Dabu vector of the cross-section, the distributed force and the arc length coordinate is established to construct a mechanical model.

[0049] The mechanical model is represented by ordinary differential equations, the expression of which can be, but is not limited to, the following: = , Specifically, = = , = = , = = , = = , = = 0, in, s Let the coordinates be arc length. The centerline position, For the quaternion of the cross-section attitude, For the internal forces of the cross section, The cross-section Darbu vector, For distributed force, The first component of the cross-sectional attitude quaternion. The second component of the cross-sectional attitude quaternion. The third component of the cross-sectional attitude quaternion. The fourth component of the cross-sectional attitude quaternion. Let be the component of the Darbu vector of the cross section on the normal vector. Let be the component of the Darbu vector of the cross section on the binormal vector. Let be the tangential component of the Dabu vector of the cross section. These are the components of the internal forces on the cross section along the normal vector. These are the components of the internal forces of the cross section along the secondary normal vector. This represents the tangential component of the internal forces within the cross section. The components of the Darbu vector of the cross section on the normal vector when no operation is performed. The components of the cross section Darbu vector on the binormal vector when no operation is performed. Let be the tangential component of the Dabu vector of the cross section when no operation is performed. The first stiffness coefficient, This is the second stiffness coefficient. The third stiffness coefficient, The components of the distributed force on the normal vector. The component of the distributed force on the binormal vector. This represents the tangential component of the distributed force. g Reflecting the relationship between the state vector and the arc length coordinates, The relationship between the centerline tangent and the quaternion of the cross-sectional orientation (i.e., dr( s ) / d s =d3(q( s ), where d3 is the unit vector representing the tangent of the centerline in the local coordinate system of the cross section. This reflects the relationship between the quaternion of the cross-section attitude and the Darbu vector of the cross-section. Reflects the equilibrium relationship of internal forces within the cross section. Constitutive relations reflecting the Darbu vector of the cross section.

[0050] This application embodiment establishes the relationship between the centerline position, cross-sectional orientation quaternion, cross-sectional internal force, cross-sectional Dabu vector, distributed force and arc length coordinate, and constructs a mechanical model. This enables the accurate calculation of the centerline shape, allowing the robot's manipulation to meet balance constraints and obstacle avoidance constraints. This, in turn, ensures the stability, safety and feasibility of the robot's operation on flexible parts, and improves the accuracy and reliability of trajectory generation.

[0051] Therefore, as Figure 3 As shown, the embodiments of this application can transform the solution of the equilibrium configuration of the flexible component into a two-point boundary value problem for the operation scenario of a robot's two robotic arms grasping a flexible component.

[0052] Specifically, in this embodiment, the position and orientation of the robot's gripping point on the flexible component are first used as boundary conditions. To simplify calculations, the boundary conditions at the starting end are set to fixed values; the boundary conditions at the terminal end are set to [r( L ),q( L The specific details are specified by the task. In some cases, such as... Figure 4 As shown, for the case where the boundary conditions are not normalized, we can first find the solution when the boundary conditions are normalized, and then perform rotation and translation operations on the solution through homogeneous transformation to obtain the target solution when the boundary conditions are not normalized.

[0053] Then, this embodiment of the application uses a target-shooting method for iterative solution to determine whether the flexible component has an equilibrium configuration (including shape and internal forces) under given boundary conditions. Specifically, this embodiment of the application first determines the boundary conditions (rL, qL) of the given terminal and sets an error tolerance for screening the test values. a Convergence tolerance Maximum number of iterations k Furthermore, a set of candidate test solutions {G} is obtained. The candidate test solution set {G} is generated by orthogonally sampling the normalized initial boundary conditions. Since r(0), q(0), and f(0) are fixed values ​​under normalization conditions, for example, r(0) = [0,0,0] T , q(0)=[0, 0.7071,0, 0.7071] T f(0) = [0,0,0], only for F(0), ω (0) (reflecting the initial force distribution and the variable cross-sectional Darbu vector) is orthogonally sampled, and for the sampled F(0), ω (0), solve the initial value problem to calculate the state of each point in the middle of the corresponding flexible part, and combine them as the alternative test firing solution set {G}.

[0054] Furthermore, in this embodiment of the application, the function SEEK_INIT({G},rL,qL) is called. This function filters out all possible solutions that satisfy ||rL|| from the generated set of candidate test solutions {G}. calc1 ( L ) rL||< a The trial firing solutions G0 are used to form the trial firing solution set {G0}, where r calc1 ( L ) represents the terminal position of the centerline in the set of candidate test firing solutions, rL is the given terminal position of the centerline (corresponding position point), and qL is the given quaternion of the cross-sectional attitude (corresponding candidate attitude).

[0055] Furthermore, in this embodiment of the application, for each test solution G0 in the test solution set, the following iterative optimization steps are performed: Determine the initial conditions for the test launch: Y(0) = [r(0), q(0), F(0), ω (0),f(0)] T Considering that r(0), q(0), and f(0) are fixed values ​​under normalization conditions, they are denoted as [F(0), ω [0]∈G0, and solve the initial value problem based on the initial conditions of the test firing, and numerically integrate to s = L The corresponding terminal state Y is obtained. calc ( L )=[r calc ( L ),q calc ( L ),F calc ( L ), ω calc ( L ),f calc ( L )] T ; Take r from the terminal state. calc ( L ), q calc ( L To calculate the boundary condition error E=||r calc ( L ) rL|| +||1-q calc ( L )] T qL||; If the boundary condition error E < Then return the current solution state set {Y( s This reflects the overall state of the flexible component; If the boundary condition error E≥ Then calculate the Jacobian matrix J of the boundary condition error with respect to the normalized initial boundary conditions. E / [F(0) T , ω (0) T ] T And update the initial conditions for the test launch [F(0), ω (0)] k =[F(0), ω (0)] k 1 J 1 E; If all initial conditions for the test firing in the solution set cannot be met within the maximum number of iterations... k If the solution converges inward, it means there is no solution.

[0056] In this embodiment, the iterative solution process of the above-mentioned shooting method can be defined as a solver. The input is the boundary condition (rL, qL) of the terminal, which corresponds to the position point and candidate posture of the terminal. In the actual execution process, by calling the solver, the test solution corresponding to the position point and candidate posture of the terminal is found in the test solution set. If it cannot be found, it is reported that there is no solution.

[0057] Optionally, in one embodiment of this application, before determining the feasible attitudes of multiple location points in the location trajectory, the method further includes: calculating terminal operability estimates of the multiple location points; determining whether the terminal operability estimates are greater than or equal to a preset terminal operability threshold; if the terminal operability estimates are greater than or equal to the preset terminal operability threshold, then determining that the multiple location points belong to a first region; if the terminal operability estimates are less than the preset terminal operability threshold, then determining that the multiple location points belong to a second region, wherein the flexibility of the first region is higher than that of the second region.

[0058] It should be noted that, as Figure 5 As shown, in this embodiment of the application, the terminal operability can be defined for the position t of the terminal in the task space. μ (t) represents the ratio of the size of the set of end attitudes that enables the flexible component to achieve a stable configuration when the normalized end position is at point t, to the size of all possible attitude sets. Its definition can be, but is not limited to, the following: μ (t)= N sori / N d , in, μ (t) represents the terminal operability.N sori To ensure that the flexible component achieves a stable end-effector attitude set size, N d Size of the set of all possible poses.

[0059] Terminal operability was numerically calculated using the Monte Carlo method combined with a numerical solver for boundary value problems of flexible components. Specifically, a large number of random samples of the terminal attitude q were taken at position t. L ), call the solver to solve for the overall equilibrium state of the flexible component, given t and q ( L As the terminal boundary condition, the set of stable solution states {Y( s If the calculation result does not converge (i.e. there is no solution), it is recorded as a success. If the calculation result does not converge (i.e. there is no solution), it is recorded as a failure. The proportion of successful solutions (i.e. there is a stable equilibrium configuration) can be counted.

[0060] Furthermore, embodiments of this application can discretize the task space of the flexible component and calculate the value of each sampling point. μ (t) values ​​are used to generate a dense operational flexibility distribution map, and based on a preset terminal operability threshold... μ (like μ =0.5) divides the task space into highly flexible regions ( μ (t)≥0.5) and low flexibility region ( μ (t)<0.5).

[0061] Furthermore, embodiments of this application can utilize cluster analysis and GMM (Gaussian Mixture Model) to process the discrete samples obtained above. μ The t-values ​​are fitted using Gaussian distributions on multiple two-dimensional planes, and the discrete distributions in the task space are approximated. μ (t) is approximated to obtain the functional relationship between terminal operability and workspace coordinates, forming a continuous and rapidly evaluable approximate model. The expression of the approximate model can be, but is not limited to, as follows: = , in, This is an estimate of the terminal operability. The number of Gaussian components (at least 2). For the first The amplitude of a Gaussian distribution For the first The center of a Gaussian distribution For the first The covariance matrix is ​​a Gaussian distribution. The approximate model serves as prior knowledge to guide subsequent motion planning.

[0062] Furthermore, such as Figure 5 As shown, statistical analysis of the embodiments of this application reveals that, in low flexibility regions, the terminal boundary posture q(…) can maintain a stable shape for the flexible component. L The median direction tends to align with the direction of the line connecting the two endpoints of the flexible component; therefore, the deviation angle between this median direction and the direction of the line connecting the endpoints is defined as... θ and establish θ The correlation map with position t. As shown in Figure 6, the similarity between the operational flexibility distribution map and the correlation map can serve as a geometric guidance criterion for attitude sampling in low flexibility regions.

[0063] Based on the above explanation, in actual execution, after the robot generates the terminal position trajectory pos_traj, for each position point pos_node in the position trajectory pos_traj, it first calculates the estimated terminal operability value of the position point pos_node using an approximate model, and then determines whether the estimated terminal operability value is greater than or equal to a preset terminal operability threshold (e.g., ...). μ =0.5), when the estimated terminal operability value is greater than or equal to the preset terminal operability threshold, the location point pos_node is determined to belong to the first region (i.e., the high flexibility region), and when the estimated terminal operability value is less than the preset terminal operability threshold, the location point pos_node is determined to belong to the second region (i.e., the low flexibility region).

[0064] This application embodiment calculates the terminal operability estimate of the location point and combines it with a preset terminal operability threshold to divide the location point into a first region and a second region with different flexibility, clarifying the difference in attitude adjustment capability and providing corresponding guidance for subsequent candidate attitude sampling. In the first region with higher flexibility, a more flexible sampling method is used to improve efficiency and the smoothness and continuity of the attitude trajectory, while in the second region with lower flexibility, a more targeted sampling method is used to ensure feasibility.

[0065] Optionally, in one embodiment of this application, determining the feasible postures of multiple location points in the position trajectory includes: if the multiple location points belong to a first region, determining a first sampling center based on the start posture and end posture of the terminal, sampling candidate postures of the location points based on the first sampling center, and determining feasible postures based on the candidate postures; if the multiple location points belong to a second region, determining a second sampling center based on the relative positional relationship between the multiple location points and the start end of the robot repair flexible component, sampling candidate postures based on the second sampling center, and determining feasible postures based on the candidate postures.

[0066] In the embodiments of this application, candidate poses can be understood as a set of poses obtained at multiple location points in the position trajectory according to the corresponding sampling center and through preset sampling rules (such as Gaussian distribution sampling), used to screen feasible poses at the end point.

[0067] In actual implementation, the robot performs no more than a Next, random sampling of the attitude, let the current sampling number be . att ,in, att It is a positive integer, initially set to 1. After each sampling, att Increment by 1 until... att = a Sampling will stop when the time is right.

[0068] When the position point pos_node belongs to the first region, the robot randomly samples the pose of position point pos_node to obtain candidate poses. Specifically, the sampling process follows a Gaussian distribution, with the sampling center being the first sampling center smooth_ori1, and the adaptive sampling variance is... σ + att / a (π- σ ),in, σ The initial variance is given. The first sampling center, smooth_ori1, is set based on the initial orientation q of the flexible component's end effector. s and the termination posture q of the terminal g The pose is obtained by interpolating the path parameters at the location point pos_node. The expression for the first sampling center smooth_ori1 can be, but is not limited to, as follows: smooth_ori1=q(pos_node) smooth =q s (q s -1 q g )^( d t,start / d traj ), Where smooth_ori1 is the first sampling center, and q(pos_node) smooth To perform interpolation at position point pos_node, q s The initial attitude of the terminal, q g This refers to the termination posture of the terminal. d t,start This represents the path length between the current position pos_node and the starting position. d traj This represents the path length between the starting and ending positions.

[0069] When the position point pos_node belongs to the second region, the robot randomly samples the pose of position point pos_node to obtain candidate poses. Specifically, the sampling process follows a Gaussian distribution, with the sampling center being the second sampling center smooth_ori2, and the adaptive sampling variance is... σ + att / a (π- σ The second sampling center, smooth_ori2, is set to align primarily with the pose of the line vector connecting the position point pos_node and the starting end. It should be noted that adaptive sampling variance... σ + att / a (π- σ The initial variance is used during the first sampling. σ As the number of samplings increases, the adaptive sampling variance gradually expands until it covers the entire attitude space.

[0070] This application embodiment distinguishes between the first region and the second region to which the location point belongs, and specifically determines the first sampling center and the second sampling center of the corresponding region. Candidate postures are sampled based on different sampling centers, and then feasible postures are determined based on the candidate postures. This achieves the targeting and rationality of feasible posture sampling, effectively avoids invalid sampling caused by indiscriminate sampling, and improves the efficiency and accuracy of feasible posture screening.

[0071] Optionally, in one embodiment of this application, determining a feasible posture based on a candidate posture includes: determining a first boundary condition at the starting end, and generating a second boundary condition at the ending end based on the candidate posture and the corresponding position point; mapping the first boundary condition and the second boundary condition to a preset normalized coordinate system respectively to generate corresponding third boundary conditions and fourth boundary conditions; and determining a feasible posture based on the third boundary conditions and the fourth boundary conditions.

[0072] In the embodiments of this application, the first boundary condition refers to the boundary condition of the flexible component's starting end before normalization, including position and orientation; the second boundary condition refers to the boundary condition of the flexible component's ending end before normalization, including position and orientation; the third boundary condition refers to the boundary condition of the flexible component's starting end after normalization, for example, r(0)=[0,0,0], q(s)=[0, 0.7071,0, 0.7071]; The fourth boundary condition refers to the normalized boundary condition of the flexible component's end.

[0073] A preset normalized coordinate system refers to a coordinate system that is pre-defined and used to unify boundary conditions. For example, a preset normalized coordinate system is a standard coordinate system with the starting point located at the origin and the attitude fixed.

[0074] In actual execution, after obtaining the terminal position point pos_node and the corresponding candidate pose ori_sample, the robot can normalize them to obtain the normalized terminal position point and the corresponding candidate pose. Combining the normalized position and pose of the starting end, the solver of the aforementioned target shooting method iterative solution is called to solve the overall state of the flexible part.

[0075] If the solution is successful, it proves that the position point pos_node of the terminal end has a balanced configuration with the flexible part under the corresponding candidate pose ori_sample. Furthermore, the collision detection function CHECK_COLLISION can be called (i.e., for the solved set of flexible rod states {Y( s Extract the state vector Y( )} for each location point. s The position component r() in ) s ), and sequentially check two adjacent r( s The connection between the two objects will not enter the envelope of the obstacle obs_set. If it enters the envelope, a collision is verified. If it does not enter the envelope, no collision is verified. Verify whether the flexible part collides with environmental obstacles or the robot body. If no collision is verified, the candidate posture ori_sample can be determined as a feasible posture.

[0076] If the solution fails, it proves that the position point pos_node of the terminal end does not have a balanced configuration with the flexible part under the corresponding candidate attitude ori_sample. In this case, attitude sampling can be performed again for resolving and collision verification. Alternatively, if a collision is verified, attitude sampling can be performed again for resolving and collision verification. If it continues to fail (i.e.) a If a feasible attitude is still not determined after random sampling of the attitude, return to step S102, offset the position point pos_node on the position trajectory, and resample the attitude to solve and verify the collision again.

[0077] This application embodiment clearly defines the start and end constraints of feasible posture determination by determining the first and second boundary conditions, ensuring the pertinence and directionality of mechanical verification. By mapping the first and second boundary conditions to a preset normalized coordinate system, corresponding third and fourth boundary conditions are generated, eliminating calculation deviations under different coordinate systems, unifying the solution benchmark of the mechanical model, and reducing computational complexity. By determining feasible postures based on the third and fourth boundary conditions, it is possible to accurately verify whether candidate postures can meet the mechanical balance requirements of flexible components.

[0078] The following is an illustrative description of a specific embodiment of the motion path generation method for robot maintenance of flexible components in high-speed rail overhead contact lines proposed in this application.

[0079] Users can specify the start and end poses of the robot's end effector for repairing flexible parts in the task space, and then the robot receives the start and end poses specified by the user.

[0080] After receiving the user-specified start and end poses, the robot first extracts the corresponding position components from the start and end poses to obtain the start position r. s ( L ) and termination position r g ( L Then, using a fast-exploration random tree or related algorithm, the terminal location trajectory pos_traj is generated.

[0081] After generating the terminal position trajectory pos_traj, the robot first calculates the estimated terminal operability value of each position point pos_node in the trajectory pos_traj using an approximate model, and then determines whether the estimated terminal operability value is greater than or equal to a preset terminal operability threshold (e.g., ...). μ =0.5). When the estimated terminal operability value is greater than or equal to the preset terminal operability threshold, the position point pos_node is determined to belong to the first region (i.e., the high flexibility region). When the estimated terminal operability value is less than the preset terminal operability threshold, the position point pos_node is determined to belong to the second region (i.e., the low flexibility region). When the position point pos_node belongs to the first region, the robot randomly samples the posture of the position point pos_node to obtain candidate postures. The sampling center is the first sampling center smooth_ori1, which is set based on the initial posture q of the flexible end effector. s and the termination posture q of the terminal g The pose is obtained by interpolating the path parameters at the position point pos_node. When the position point pos_node belongs to the second region, the robot randomly samples the pose of the position point pos_node to obtain candidate poses. The sampling center is the second sampling center smooth_ori2, which is set in a way that prioritizes aligning with the pose of the line vector connecting the position point pos_node and the starting end.

[0082] After obtaining the end effector's position point pos_node and the corresponding candidate pose ori_sample, the robot normalizes them to obtain the normalized end effector position point and corresponding candidate pose. Combining the normalized start-end position and pose, a solver using the target-shooting iterative method is invoked to solve for the overall state of the flexible component. If the solution is successful and collision verification is passed, the candidate pose ori_sample is determined as the feasible pose ori_node. Then, all feasible poses are combined into an attitude trajectory ori_traj. Finally, the position trajectory pos_traj and the attitude trajectory ori_traj are combined to form the complete motion path of the robot's end effector, used for grasping, transporting, and installing the flexible component.

[0083] The motion path generation method for robot maintenance of flexible components in high-speed railway catenary proposed in this application generates a position trajectory, combines the feasible postures of multiple position points on the position trajectory to generate a posture trajectory, and then combines the position trajectory and posture trajectory to generate the motion path for robot maintenance of flexible components. This achieves collaborative planning of position and posture, incorporates obstacle avoidance and balance constraints of the operated object, effectively avoids the risk of deformation of flexible components during operation and interference with environmental obstacles, and operational instability. At the same time, hierarchical planning reduces invalid sampling and computational consumption, improving the success rate, reliability, and real-time performance of automated maintenance operations, ensuring smooth, safe, and executable robot maintenance of flexible components. Furthermore, by introducing terminal operability when determining feasible postures, the planning space can be effectively pruned, improving planning efficiency. Thus, it solves the problems of related path planning methods that do not process the planning space and have low efficiency of equal-weighted sampling.

[0084] Next, referring to the accompanying drawings, a motion path generation device for robot maintenance of flexible components in high-speed rail overhead contact lines, according to an embodiment of this application, is described.

[0085] Figure 7 This is a block diagram of a motion path generation device for robot maintenance of flexible components in high-speed rail overhead contact lines, provided according to an embodiment of this application.

[0086] like Figure 7 As shown, the motion path generation device 70 for robot maintenance of flexible components in high-speed rail catenary includes: an acquisition module 100, a first generation module 200, and a second generation module 300.

[0087] The acquisition module 100 is used to acquire the starting pose and ending pose of the end of the flexible component being repaired by the robot.

[0088] The first generation module 200 is used to determine the start position and end position of the terminal based on the start pose and end pose, so as to generate the position trajectory of the terminal based on the start position and end position.

[0089] The second generation module 300 is used to determine the feasible postures of multiple position points in the position trajectory, and generate the posture trajectory of the terminal based on the feasible postures, so as to generate the motion path of the robot repair flexible parts based on the position trajectory and the posture trajectory.

[0090] Optionally, in one embodiment of this application, it further includes: a calculation module, a judgment module, a first judgment module, and a second judgment module.

[0091] The calculation module is used to calculate the estimated terminal operability values ​​for multiple location points.

[0092] The judgment module is used to determine whether the estimated value of terminal operability is greater than or equal to the preset terminal operability threshold.

[0093] The first determination module is used to determine that multiple location points belong to the first region when the estimated value of terminal operability is greater than or equal to the preset terminal operability threshold.

[0094] The second determination module is used to determine that multiple location points belong to the second region when the estimated value of terminal operability is less than the preset terminal operability threshold. The first region has higher flexibility than the second region.

[0095] Optionally, in one embodiment of this application, the second generation module 300 includes: a first determining unit and a second determining unit.

[0096] The first determining unit is used to determine a first sampling center based on the starting and ending postures of the terminal when multiple location points belong to a first region, and to sample the candidate postures of the location points based on the first sampling center, and to determine a feasible posture based on the candidate postures.

[0097] The second determining unit is used to determine a second sampling center based on the relative positional relationship between the multiple location points and the starting end of the robot repair flexible component when multiple location points belong to the second region, and to sample candidate postures based on the second sampling center, and to determine feasible postures based on the candidate postures.

[0098] Optionally, in one embodiment of this application, the first determining unit includes: a first generating subunit, a second generating subunit, and a determining subunit.

[0099] The first generation sub-unit is used to determine the first boundary conditions at the starting end and generate the second boundary conditions at the ending end based on the candidate pose and the corresponding position point.

[0100] The second generation sub-unit is used to map the first boundary condition and the second boundary condition to a preset normalized coordinate system, respectively, to generate the corresponding third boundary condition and fourth boundary condition.

[0101] Determine the sub-units to determine feasible attitudes based on the third and fourth boundary conditions.

[0102] Optionally, in one embodiment of this application, it further includes: a determination module, a first establishment module, and a second establishment module.

[0103] The determination module is used to determine the centerline position, cross-sectional orientation quaternion, cross-sectional internal force, cross-sectional Dabu vector, and distributed force of the flexible component being repaired by the robot.

[0104] The first module is used to establish the relationship between the centerline position, the quaternion of the cross-section orientation, the internal forces of the cross-section, the cross-section distribution vector, the distributed force, and the arc length coordinates.

[0105] The second module is used to establish a mechanical model of the robot's flexible components based on the changing relationships, so as to determine feasible postures using the mechanical model.

[0106] It should be noted that the explanation of the above-described embodiment of the motion path generation method for robot maintenance of flexible components in high-speed rail catenary also applies to the motion path generation device for robot maintenance of flexible components in high-speed rail catenary in this embodiment, and will not be repeated here.

[0107] The motion path generation device for robot maintenance of flexible components in high-speed rail catenary proposed in this application generates a position trajectory, combines the feasible postures of multiple position points on the position trajectory to generate an attitude trajectory, and then combines the position trajectory and attitude trajectory to generate the motion path for robot maintenance of flexible components. This achieves collaborative planning of position and posture, incorporates obstacle avoidance and balance constraints of the operated object, effectively avoids the risk of deformation of flexible components during operation and interference with environmental obstacles, and operational instability. At the same time, hierarchical planning reduces invalid sampling and computational consumption, improving the success rate, reliability, and real-time performance of automated maintenance operations, ensuring smooth, safe, and executable robot maintenance of flexible components. Furthermore, by introducing terminal operability when determining feasible postures, the planning space can be effectively pruned, improving planning efficiency. Thus, it solves the problems of related path planning methods that do not process the planning space and have low efficiency of equal-weighted sampling.

[0108] Figure 8 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. The electronic device may include: The memory 801, the processor 802, and the computer program stored on the memory 801 and capable of running on the processor 802.

[0109] When the processor 802 executes the program, it implements the motion path generation method for robot maintenance of flexible components in high-speed rail catenary provided in the above embodiments.

[0110] Furthermore, electronic devices also include: Communication interface 803 is used for communication between memory 801 and processor 802.

[0111] The memory 801 is used to store computer programs that can run on the processor 802.

[0112] The memory 801 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0113] If the memory 801, processor 802, and communication interface 803 are implemented independently, then the communication interface 803, memory 801, and processor 802 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 8 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0114] Optionally, in a specific implementation, if the memory 801, processor 802, and communication interface 803 are integrated on a single chip, then the memory 801, processor 802, and communication interface 803 can communicate with each other through an internal interface.

[0115] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0116] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for generating motion paths for robot maintenance of flexible components in high-speed rail overhead contact lines.

[0117] This application also provides a computer program product, including a computer program that, when executed, implements the above-mentioned method for generating motion paths for robot maintenance of flexible components in high-speed rail overhead contact lines.

[0118] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0119] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0120] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0121] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0122] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0123] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0124] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0125] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for generating motion paths for robots repairing flexible components in high-speed railway overhead contact lines, characterized in that, Includes the following steps: Obtain the initial and final poses of the end effector of the flexible component being repaired by the robot. The starting position and ending position of the terminal are determined based on the starting pose and the ending pose, so as to generate the position trajectory of the terminal based on the starting position and the ending position; The feasible postures of multiple position points in the position trajectory are determined, and the posture trajectory of the terminal is generated according to the feasible postures, so as to generate the motion path of the robot maintenance flexible component according to the position trajectory and the posture trajectory.

2. The method according to claim 1, characterized in that, Before determining the feasible poses of multiple location points in the location trajectory, the method further includes: Calculate the terminal operability estimate for the plurality of location points; Determine whether the estimated value of the terminal operability is greater than or equal to a preset terminal operability threshold; If the estimated value of the terminal operability is greater than or equal to the preset terminal operability threshold, then the plurality of location points are determined to belong to the first region; If the estimated terminal operability value is less than the preset terminal operability threshold, then the plurality of location points are determined to belong to the second region, wherein the flexibility of the first region is higher than that of the second region.

3. The method according to claim 2, characterized in that, Determining the feasible poses of multiple location points in the location trajectory includes: If the plurality of location points belong to the first region, then a first sampling center is determined based on the starting and ending postures of the terminal, and candidate postures of the location points are sampled based on the first sampling center, and feasible postures are determined based on the candidate postures. If the plurality of location points belong to the second region, a second sampling center is determined based on the relative positional relationship between the plurality of location points and the starting end of the robot repair flexible component, and the candidate posture is sampled based on the second sampling center, and the feasible posture is determined based on the candidate posture.

4. The method according to claim 3, characterized in that, The step of determining the feasible pose based on the candidate poses includes: Determine the first boundary condition of the starting end, and generate the second boundary condition of the terminal end based on the candidate pose and the corresponding position point; The first boundary condition and the second boundary condition are respectively mapped to a preset normalized coordinate system to generate the corresponding third boundary condition and fourth boundary condition; The feasible posture is determined based on the third boundary condition and the fourth boundary condition.

5. The method according to claim 4, characterized in that, Before determining the feasible posture, the method further includes: Determine the centerline position, cross-sectional orientation quaternion, cross-sectional internal forces, cross-sectional Dabu vector, and distributed forces of the flexible component being repaired by the robot. Establish the relationships between the centerline position, the cross-sectional orientation quaternion, the cross-sectional internal forces, the cross-sectional Dabu vector, the distributed forces, and the arc length coordinates; A mechanical model of the robot's repair flexible component is established based on the described changing relationship, and the feasible posture is determined using the mechanical model.

6. A motion path generation device for robot maintenance of flexible components in high-speed railway overhead contact lines, characterized in that, include: The acquisition module is used to acquire the start and end poses of the end effector of the flexible component being repaired by the robot. The first generation module is used to determine the start position and end position of the terminal end based on the start pose and the end pose, so as to generate the position trajectory of the terminal end based on the start position and the end position. The second generation module is used to determine the feasible postures of multiple position points in the position trajectory, and generate the posture trajectory of the terminal based on the feasible postures, so as to generate the motion path of the robot maintenance flexible component based on the position trajectory and the posture trajectory.

7. The apparatus according to claim 6, characterized in that, Also includes: The calculation module is used to calculate the terminal operability estimate of the multiple location points; The judgment module is used to determine whether the estimated value of the terminal operability is greater than or equal to a preset terminal operability threshold. The first determination module is used to determine that the plurality of location points belong to the first region if the estimated value of the terminal operability is greater than or equal to the preset terminal operability threshold. The second determination module is used to determine that the plurality of location points belong to the second region if the estimated value of the terminal operability is less than the preset terminal operability threshold, wherein the flexibility of the first region is higher than that of the second region.

8. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the motion path generation method for robot maintenance of flexible components in high-speed rail overhead contact lines as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the motion path generation method for robot maintenance of flexible components in high-speed railway catenary as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the motion path generation method for robot maintenance of flexible components in high-speed rail catenary as described in any one of claims 1-5.

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