A robot trajectory and posture collaborative planning method for complex component porous high-efficiency drilling and boring

Abstract

This invention discloses a robot trajectory and posture collaborative planning method for efficient drilling and boring of complex components, belonging to the field of industrial robot automated machining technology. The invention first collects data on hole positions, workpieces, and various models, generates candidate end-effector postures and completes reachability screening. It then performs station clustering by combining hole spatial distances and hole axis normal angles, constructs a weighted directed graph of hole movement, and simultaneously solves for the hole machining sequence and corresponding end-effector posture. Trajectory planning and smoothing are then performed; if the trajectory does not meet requirements, edge weights are updated and local replanning is conducted. This method is applicable to multi-hole machining of complex components such as large die-cast parts and ship cylinders, and can also be used for robot offline programming and machining cycle optimization. This invention can reduce the number of station switching times and non-cutting idle distances, while keeping the computational load within a reasonable range, adapting to various on-site operating conditions.

Description

Technical Field

[0001] This invention belongs to the field of industrial robot automated processing technology, specifically involving robot trajectory planning and attitude collaborative control technology for drilling and boring complex components. Background Technology

[0002] Complex components such as ultra-large die-cast parts, ship cylinders, aerospace structural parts, and energy equipment shells typically have numerous holes with large spans and significant normal variations. When robots perform drilling and boring operations, they need to perform non-cutting idle movement between different holes, while satisfying constraints such as the alignment of the tool axis with the hole axis normal, reachability of the end effector, no joint overstepping of limits, and avoidance of interference with the workpiece and fixtures at each hole. If the hole machining sequence and end effector posture are planned separately, problems such as long idle movement paths, frequent station switching, unreachable local hole postures, joint reversal between adjacent holes, and uneven trajectories can easily occur.

[0003] Existing research includes ideas for task space clustering and task ranking, which first divide the workstations or task clusters based on the reachability of the target point, and then solve for the target access order to reduce base or workstation movement. There are also robot drilling posture optimization and trajectory smoothing methods to reduce joint rollover, shorten paths, or satisfy velocity and acceleration constraints. However, for multi-workstation robot drilling and boring scenarios, there is still a lack of a method that unifies the modeling of hole position spatial distribution, hole axis normal, workstation switching, idle distance, posture reachability, and trajectory smoothness, and achieves engineering-usable planning results through coupling constraint decoupling. Summary of the Invention

[0004] The purpose of this invention is to provide a robot trajectory and attitude collaborative planning method for efficient drilling and boring of complex components, which solves the problem of the separation between hole position sequence and attitude planning in existing robot drilling and boring operations, resulting in long idle movement paths and unreachable attitudes of some holes.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A robot trajectory and attitude collaborative planning method for efficient drilling and boring of complex components includes the following steps:

[0007] By acquiring the data of the holes to be processed, the workpiece and fixture models, the robot kinematics model, and the drill and boring end model, the basic data for planning is obtained;

[0008] Based on the planning data, multiple end-position candidates are generated for each hole location, and the safe approach point, processing point and exit point for each hole location are determined.

[0009] The reachability screening of the end-effector attitude candidates is performed to obtain the reachable attitude set for each aperture location;

[0010] Based on the spatial distance between holes, the included angle of the hole axis normal, and the overlapping relationship of the reachable posture set, the holes to be processed are clustered to obtain one or more workstation clusters;

[0011] Based on the workstation cluster and the set of reachable postures, a weighted directed graph of hole position movement is constructed;

[0012] The weighted directed graph of hole position movement is solved to obtain the hole position processing sequence and the end posture corresponding to each hole position.

[0013] Based on the hole machining sequence and end posture, the trajectory planning and smoothing of adjacent holes are performed to generate a robot drilling and boring machining program.

[0014] In one possible implementation, the steps to obtain the planning basis data by acquiring the hole location data to be processed, the workpiece and fixture model, the robot kinematics model, and the drill and boring end model include:

[0015] Read the three-dimensional coordinates of the hole position, the normal direction of the hole axis, the hole diameter, and the machining type to obtain the original hole position data;

[0016] Based on the calibration relationship between the workpiece coordinate system, the robot base coordinate system, and the station coordinate system, the original hole position data is transformed to obtain the hole position data in the robot planning coordinate system.

[0017] The hole axis normal is normalized to obtain standardized planning basis data.

[0018] In one possible implementation, the step of generating multiple end-effector attitude candidates for each aperture location based on the planning baseline data includes:

[0019] Align the tool axis with the hole axis normal, and determine the safe approach point, machining point, and exit point along the hole axis direction;

[0020] Multiple redundant rotation angles are generated around the hole axis according to a preset step size, and each redundant rotation angle corresponds to a candidate end attitude.

[0021] In one possible implementation, the step of performing reachability screening on the end-effector attitude candidates to obtain a set of reachable attitudes for each aperture location includes:

[0022] For each candidate end-effector pose, inverse kinematics is solved to obtain the corresponding joint solution;

[0023] The joint solution is subjected to joint limitation checks, unusual pose checks, and collision checks;

[0024] Retain the end-position candidates that pass all checks to form the set of reachable positions for each aperture.

[0025] In one possible implementation, the step of clustering the holes to be processed based on the spatial distance between holes, the included angle of the hole axis normal, and the overlap relationship of the reachable posture set to obtain one or more workstation clusters includes:

[0026] The clustering distance between holes is calculated based on the spatial distance between holes, the included angle of the hole axis normal, and the accessibility similarity.

[0027] Clustering algorithms are used to group all holes into one or more workstation clusters.

[0028] In one possible implementation, the step of constructing a weighted directed graph of hole position movement based on the workstation cluster and the set of reachable postures includes:

[0029] Each aperture location and its reachable orientation are treated as nodes in a weighted directed graph;

[0030] Determine the edge weight between any two nodes based on the non-cutting idle distance, station switching cost, end-effector posture change cost, joint motion cost, and unreachability penalty.

[0031] Based on the nodes and edge weights, construct a weighted directed graph for hole position movement.

[0032] In one possible implementation, the steps of solving the weighted directed graph of hole position movement to obtain the hole position processing sequence and the end pose corresponding to each hole position include:

[0033] With the optimization objectives of reducing the number of workstation changes, compressing idle movement distance, and reducing attitude changes, an objective function is constructed.

[0034] A heuristic algorithm is used to solve the objective function to obtain the workstation cluster access order;

[0035] Within each workstation cluster, the hole access order is solved to obtain the overall hole processing order and the end posture corresponding to each hole.

[0036] In one possible implementation, the steps of planning and smoothing the trajectories of adjacent holes to generate a robotic drilling and boring program, based on the hole machining sequence and end-effector posture, include:

[0037] A sampling-based path planning algorithm is used to search for paths between adjacent holes to obtain initial trajectory nodes;

[0038] The initial trajectory nodes are subjected to continuous curvature smoothing and time parameterization to obtain a smooth trajectory;

[0039] Collision verification and motion constraint checks are performed on the smooth trajectory to generate a robotic drilling and boring program.

[0040] In one possible implementation, the steps of performing collision verification and motion constraint checks on the smooth trajectory to generate the robotic drilling and boring program include:

[0041] If the trajectory is not feasible or the cost exceeds the limit, update the edge weight of the corresponding edge in the weighted directed graph of hole position movement;

[0042] The affected workstation clusters are locally reprogrammed to generate a robotic drilling and boring program that satisfies the constraints.

[0043] Compared with the prior art, the beneficial effects of this invention are as follows: This invention incorporates hole position sequence optimization and end-effector posture planning into the same weighted directed graph and objective function for joint solution. Compared with the prior art method of planning hole position sequence and posture separately, it can avoid problems such as local hole position unreachability, collision or large joint reversal caused by first determining the processing sequence and then passively matching the posture, and ensure that the processing posture of all holes meets the robot motion constraints.

[0044] This invention clusters workstations based on the spatial distance between holes, the included angle of the hole axis normal, and the overlapping relationship of the reachable posture set. Compared with the method of dividing workstations only based on spatial location, it can more reasonably divide the processing area of ​​large-span complex components, effectively reduce the number of workstation switching, reduce the total distance of non-cutting robot movement, and improve the overall efficiency of mass hole processing.

[0045] This invention employs a decoupled solution strategy involving attitude reachability pre-screening, sequence-level search, trajectory-level verification, and edge weight back-substitution update. Compared to directly solving high-dimensional mixed-integer nonlinear problems, this significantly reduces computational complexity, keeping the computational load within an engineering-acceptable range. It also supports edge weight updates and local replanning after trajectory planning failures, adapting to real-world engineering conditions such as hole position deviations and fixture interference, thus enhancing the practicality of the planning method.

[0046] The processing results output by this invention not only include the hole processing sequence, but also cover the end pose corresponding to each hole, the feasible trajectory nodes of adjacent holes, and the time parameterized trajectory after continuous curvature smoothing. Compared with the planning method that only outputs the hole sequence, it can be directly converted into an offline processing program that can be executed by the robot, reducing the workload of subsequent programming adjustments and improving the quality and efficiency of offline programming. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a flowchart of the robot multi-station drilling and boring hole position sequence optimization and attitude cooperative planning according to an embodiment of the present invention;

[0049] Figure 2 This is a schematic diagram of the overall planning of the multi-station drilling and boring positions of the robot according to an embodiment of the present invention;

[0050] Figure 3 This is a schematic diagram of hole location clustering, workstation switching, and idle movement path according to an embodiment of the present invention;

[0051] Figure 4 This is a flowchart illustrating the optimization algorithm for decoupling coupling constraints according to an embodiment of the present invention.

[0052] Figure 5 This is a flowchart illustrating the robot trajectory and posture collaborative planning method for efficient drilling and boring of complex components according to an embodiment of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0054] Example:

[0055] It should be noted that the terms "comprising" and "having" and any variations thereof in the embodiments of the present invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0056] See Figure 5 This embodiment of a robot trajectory and attitude collaborative planning method for efficient drilling and boring of complex components includes the following steps:

[0057] Step 101: Obtain the basic planning data by acquiring the hole position data to be processed, the workpiece and fixture model, the robot kinematics model and the drilling and boring end model.

[0058] Specifically, the planning base data can be all the original models and parameter data required for robot motion planning.

[0059] The steps for obtaining the basic planning data by acquiring the data of the holes to be processed, the workpiece and fixture model, the robot kinematics model, and the drill and boring end model include:

[0060] Read the three-dimensional coordinates of the hole position, the normal direction of the hole axis, the hole diameter, and the machining type to obtain the original hole position data;

[0061] Based on the calibration relationship between the workpiece coordinate system, the robot base coordinate system, and the station coordinate system, the original hole position data is transformed to obtain the hole position data in the robot planning coordinate system.

[0062] The hole axis normal is normalized to obtain standardized planning basis data.

[0063] Specifically, the original borehole location data can be the original borehole location parameters read from design documents or measuring equipment; coordinate transformation can be the mapping of points and directions between different coordinate systems; normalization can be the process of converting the normal vector into a standard unit vector; and standardized planning base data can be planning raw data with unified format and parameters.

[0064] Step 102: Based on the planning data, generate multiple end-position candidates for each hole position, and determine the safe approach point, processing point, and exit point for each hole position.

[0065] Specifically, the end-effector posture candidate can be multiple robot end-effector posture schemes generated around the hole axis; the safe approach point can be a buffer point before the tool approaches the hole; the machining point can be the working point where the tool performs drilling and boring operations; and the exit point can be the point where the tool leaves the hole after completing machining.

[0066] The step of generating multiple end-effector attitude candidates for each aperture location based on the planning baseline data includes:

[0067] Align the tool axis with the hole axis normal, and determine the safe approach point, machining point, and exit point along the hole axis direction;

[0068] Multiple redundant rotation angles are generated around the hole axis according to a preset step size, and each redundant rotation angle corresponds to a candidate end attitude.

[0069] Specifically, the tool axis can be the central axis of the drilling and boring tool; the hole axis normal can be the central extension direction of the hole to be machined; the redundant rotation angle can be the rotation angle arranged around the hole axis; and the preset step size can be the angular interval between adjacent rotation angles.

[0070] Step 103: Perform reachability screening on the candidate end poses to obtain a set of reachable poses for each aperture.

[0071] Specifically, the reachable posture set can be a set of valid end postures that meet motion and safety constraints after screening.

[0072] The step of performing reachability screening on the end-effector attitude candidates to obtain the reachable attitude set for each aperture location includes:

[0073] For each candidate end-effector pose, inverse kinematics is solved to obtain the corresponding joint solution;

[0074] The joint solution is subjected to joint limitation checks, unusual pose checks, and collision checks;

[0075] Retain the end-position candidates that pass all checks to form the set of reachable positions for each aperture.

[0076] Specifically, inverse kinematics can be calculated by back-calculating the joint angles from the robot's end-effector posture; joint solutions can be the rotation angles corresponding to each joint of the robot; joint limit checks can be used to determine whether the joint angles are within the allowable range; singular pose checks can be used to determine whether the robot is in a singular motion region; and collision checks can be used to determine whether the robot, the cutting tool, and surrounding objects interfere with each other.

[0077] Step 104: Based on the spatial distance between holes, the included angle of the hole axis normal, and the overlapping relationship of the reachable posture set, cluster the holes to be processed to obtain one or more workstation clusters.

[0078] Specifically, a workstation cluster can be a grouping of holes to be processed that have similar spatial and orientation conditions.

[0079] The step of clustering the holes to be processed based on the spatial distance between holes, the included angle between the hole axis and the normal direction, and the overlap relationship of the reachable posture set to obtain one or more workstation clusters includes:

[0080] The clustering distance between holes is calculated based on the spatial distance between holes, the included angle of the hole axis normal, and the accessibility similarity.

[0081] Clustering algorithms are used to group all holes into one or more workstation clusters.

[0082] Specifically, clustering distance can be a hole location correlation metric calculated by integrating multiple features; spatial distance can be the straight-line distance between the three-dimensional coordinates of two hole locations; the hole axis normal angle can be the angle formed by two sets of hole axis normal vectors; reachability similarity can be the degree of overlap of the reachability posture sets of two hole locations; and the clustering algorithm can be a computational algorithm for data grouping. For example, the clustering algorithm can be the K-means algorithm or the DBSCAN algorithm; the number of hole locations within a single workstation cluster can be flexibly set according to the component size.

[0083] Step 105: Construct a weighted directed graph of hole position movement based on the workstation cluster and the set of reachable postures.

[0084] Specifically, the hole position movement weighted directed graph can be a topological graph with hole position and attitude as nodes and motion cost as edges.

[0085] The step of constructing a weighted directed graph of hole position movement based on the workstation cluster and the set of reachable postures includes:

[0086] Each aperture location and its reachable orientation are treated as nodes in a weighted directed graph;

[0087] Determine the edge weight between any two nodes based on the non-cutting idle distance, station switching cost, end-effector posture change cost, joint motion cost, and unreachability penalty.

[0088] Based on the nodes and edge weights, construct a weighted directed graph for hole position movement.

[0089] Specifically, a node can be a basic unit in a weighted directed graph representing a single hole location paired with a corresponding reachable posture; the non-cutting idle distance can be the path length of the robot between two processing points without cutting motion; the station switching cost can be the comprehensive loss value caused by the robot changing its work station; the end effector posture change cost can be the loss value corresponding to the robot's forward and backward posture transitions; the joint motion cost can be the comprehensive loss value caused by the rotation of each joint of the robot; the unreachable penalty can be the penalty value set for invalid motion paths; and the edge weight can be the weight value connecting two nodes in the directed graph. For example, the station switching cost is assigned a value based on the station movement time; the unreachable penalty is uniformly set to a maximum value to shield invalid paths; and the edge weight is obtained by weighted summation of multiple types of costs.

[0090] Step 106: Solve the weighted directed graph of the hole position movement to obtain the hole position processing sequence and the end posture corresponding to each hole position.

[0091] The steps of solving the weighted directed graph of hole position movement to obtain the hole position processing sequence and the end pose corresponding to each hole position include:

[0092] With the optimization objectives of reducing the number of workstation changes, compressing idle movement distance, and reducing attitude changes, an objective function is constructed.

[0093] A heuristic algorithm is used to solve the objective function to obtain the workstation cluster access order;

[0094] Within each workstation cluster, the hole access order is solved to obtain the overall hole processing order and the end posture corresponding to each hole.

[0095] Specifically, the optimization objective can be the performance index to be achieved in the planning process; the objective function can be a computational function that quantifies various losses and is used for optimization; the heuristic algorithm can be an intelligent solution algorithm for combinatorial optimization; the access order of workstation clusters can be the processing order between different workstation clusters; the access order of holes can be the processing order of each hole within a single workstation cluster. For example, the heuristic algorithm can be a genetic algorithm, an ant colony algorithm, or a 2-opt algorithm; the objective function assigns independent weights to workstation switching, idle movement distance, and posture changes; for holes with process sequence requirements, sequence constraints can be added during the solution process.

[0096] Step 107: Based on the hole machining sequence and end posture, perform trajectory planning and smoothing of adjacent holes to generate a robot drilling and boring machining program.

[0097] Specifically, trajectory planning and smoothing can generate collision-free and continuous robot motion paths; robot drilling and boring programs can be offline operation code that can be directly imported into the equipment for execution.

[0098] The steps of generating a robot drilling and boring program by planning and smoothing the trajectories of adjacent holes based on the hole machining sequence and end-effector posture include:

[0099] A sampling-based path planning algorithm is used to search for paths between adjacent holes to obtain initial trajectory nodes;

[0100] The initial trajectory nodes are subjected to continuous curvature smoothing and time parameterization to obtain a smooth trajectory;

[0101] Collision verification and motion constraint checks are performed on the smooth trajectory to generate a robotic drilling and boring program.

[0102] Specifically, a sampling-based path planning algorithm can be an algorithm based on spatial sampling to search for feasible paths; initial trajectory nodes can be discrete path points obtained from path search; continuous curvature smoothing can be a method of adjusting the path shape to make the curvature continuous; time parameterization can be a method of matching motion duration and velocity parameters for the trajectory; smooth trajectory can be a robot motion path with smooth shape and continuous motion; collision verification can be a check item to detect whether there is spatial interference in the path; motion constraint checking can be a check item to verify whether the joint position, velocity, and acceleration are compliant. For example, a sampling-based path planning algorithm can be an improved RRT algorithm; continuous curvature smoothing can be implemented using B-splines or fifth-order polynomials; motion constraints include robot joint limits, maximum motion velocity, and maximum motion acceleration.

[0103] Furthermore, the steps of performing collision verification and motion constraint checks on the smooth trajectory to generate the robot drilling and boring machining program include:

[0104] If the trajectory is not feasible or the cost exceeds the limit, update the edge weight of the corresponding edge in the weighted directed graph of hole position movement;

[0105] The affected workstation clusters are locally reprogrammed to generate a robotic drilling and boring program that satisfies the constraints.

[0106] Specifically, trajectory infeasibility can be due to issues such as collisions or joint overruns preventing execution; cost overruns can be due to the overall path loss exceeding a preset allowable range; edge weight updates can modify the weight values ​​of abnormal paths in a directed graph; and local replanning can be an operation that recalculates the planning only for abnormal regions. For example, the criteria for determining trajectory infeasibility is that the minimum safe distance is less than a preset safe threshold; the criteria for determining cost overruns is that the total loss of a single path segment is greater than a set upper limit; and during edge weight updates, the penalty value for abnormal paths can be increased or they can be directly marked as disabled edges.

[0107] This embodiment provides a robot trajectory and attitude collaborative planning method for efficient drilling and boring of complex components, applicable to multi-station drilling, boring, reaming, and boring operations on complex components such as ultra-large die-cast parts, ship cylinders, aerospace structural parts, and energy equipment shells. The overall process of this method is as follows: Figure 1 As shown, through a process of hole position data preprocessing, attitude candidate generation, reachability screening, station clustering, weighted directed graph construction, sequence and attitude synchronous solution, and trajectory planning smoothing, a robot drilling and boring machining program with fewer station switching times, shorter idle paths, and smoother attitude changes is output. The overall process of robot multi-station drilling and boring hole position planning is as follows: Figure 2 As shown, the hole position and normal input, station area clustering, hole position sequence optimization, posture and trajectory smoothing are completed in sequence to finally obtain the machining program that meets the requirements.

[0108] Import the hole position list provided by the CAD or measurement system, and read the coordinates, hole axis normal, hole diameter, and machining type of each hole position to obtain the original hole position data. Based on the calibration relationship between the workpiece coordinate system, robot base coordinate system, and station coordinate system, uniformly transform the hole positions to the robot planning coordinate system. For hole positions with missing normals or high noise, obtain local normals by fitting surrounding surfaces, and then normalize the normals to obtain standardized planning base data. The hole position data is represented as follows:

[0109] In the formula, This represents the complete data set for the i-th hole to be processed; Represents the three-dimensional coordinates of the i-th hole position; This represents the unit normal to the axis of the i-th hole; This represents the diameter of the i-th hole; Indicates the machining type of the i-th hole; This indicates the total number of holes to be processed; i represents the hole number.

[0110] For each hole, align the tool axis with the hole axis normal, and set a safe approach point, machining point, and exit point along the hole axis. Generate redundant rotation angles around the hole axis at preset step sizes, sampling at equal intervals from -180° to 180°. Each sampled rotation angle corresponds to an end-effector posture to be screened. The end-effector posture is represented as follows:

[0111] In the formula, This represents the rotation angle of the i-th hole position around the hole axis. The corresponding end-effector attitude; Indicates the direction of the hole axis normal For the axis of rotation, rotation angle The resulting attitude rotation matrix; Represents the three-dimensional coordinates of the i-th hole position; Indicates the redundant rotation angle around the hole shaft; This represents the preset set of rotation angle values ​​for the i-th hole position; the end posture is determined by the alignment condition of the hole axis normal and the redundant rotation angle around the hole axis.

[0112] For each end effector pose, the inverse kinematics solver is invoked to obtain one or more joint solutions. These solutions are then sequentially checked for joint constraints, singularities, robot body, spindle, and spatial envelope collisions between the tool and the workpiece or fixture. Only the end effectors that pass these checks are retained, forming the set of reachable poses for that hole location. The criteria for determining the reachable pose set are as follows:

[0113] In the formula, Let i represent the set of reachable postures for the i-th aperture. Indicates end attitude The corresponding inverse kinematics solution; Indicates the position of the robot's joints; , These represent the lower and upper limits of the robot's joint positions, respectively. Indicates the singularity of joint position; This represents the minimum threshold for singularity. This represents the robot's collision-free state. The set of reachable poses is determined by the existence of inverse kinematics, joint constraints, singularity threshold, and collision-free condition.

[0114] The diagram illustrates hole location clustering, workstation switching, and idle movement path as follows: Figure 3As shown, the clustering is based on the three-dimensional distance between holes, the included angle of the normal axis, the robot's posture accessibility, and the station coverage capability. The optimization objective is to reduce the number of station changes while decreasing the intra-cluster spatial distance and the amount of end-effector posture change. First, the clustering distance between holes is calculated based on the spatial distance between holes, the included angle of the hole axis normal axis, and the accessibility similarity. The calculation method for the clustering distance is as follows:

[0115] In the formula, This represents the clustering distance between the i-th hole and the j-th hole; 2 represents the Euclidean distance between the two apertures in three-dimensional space; This represents the inner product of the two holes along their axial normal. Used to calculate the included angle of the hole axis normal; This indicates the similarity of the sets of reachable poses between two aperture locations; , , These are the weight coefficients corresponding to spatial distance, normal angle, and accessibility similarity, respectively. The clustering distance simultaneously considers the spatial distance of the hole positions, the normal angle of the hole axis, and accessibility similarity; the three weights are used to match different workpieces and workstation constraints.

[0116] Then, K-means, hierarchical clustering, DBSCAN, or set-coverage-based clustering methods are used to divide the holes into several workstation clusters. The number of clusters can be determined by a combination of the maximum workstation radius, the maximum normal angle, the robot's reachable coverage, and the preset cycle time. The definition of a workstation cluster is as follows:

[0117] In the formula, This represents the m-th workstation cluster; This represents the reference position of the m-th workstation; This represents the distance from the i-th hole position to the reference position of the workstation; This represents the maximum coverage radius of the m-th workstation; This represents the set of reachable postures corresponding to the m-th workstation; This indicates that there is an intersection between the reachable orientation of the hole and the orientation of the workstation. The workstation cluster is jointly defined by the workstation coverage radius and the set of feasible orientations under that workstation.

[0118] For any two hole positions and their candidate postures, a directed edge is established if both satisfy the basic reachability and safety constraints. The edge weight consists of the idle distance, end-effector posture change, joint movement distance, station switching time, and trajectory planning success rate. If there are unreachable, collision, singular, or posture error exceeding the limit, the edge is not established or a large penalty value is set. The optimization objective function is as follows:

[0119] In the formula, This represents the comprehensive optimization objective function, and the objective is to find the minimum value of the function. This indicates the workstation switching indicator quantity corresponding to the k-th processing path; This represents the non-cutting idle distance between adjacent holes in the k-th segment; This represents the end-point attitude rotation angle difference corresponding to the k-th path segment; This represents the change in robot joint motion corresponding to the k-th path segment; This represents the unreachability / collision penalty value of the k-th path segment; ··· These are the weighting coefficients for each optimization indicator. The objective function evaluates workstation switching, idle distance, end-effector posture change, joint motion change, and unreachable or collision penalties in sequence.

[0120] At the workstation cluster level, the cluster access order is first determined to minimize the number of workstation switches and the distance between clusters. Then, within each cluster, the hole access order is determined to minimize the intra-cluster idle distance and end-effector posture change. The actual solution can be constructed using nearest neighbor to create an initial solution, which is then improved using 2-opt, 3-opt, genetic algorithms, tabu search, ant colony optimization, or mixed integer programming. For holes that must be completed according to the process sequence, pre- and post-order constraints can be added at the sequence level. The calculation methods for the idle distance and end-effector posture change of adjacent holes are as follows:

[0121] In the formula, This represents the non-cutting idle distance between adjacent holes within the k-th machining interval; , They represent the first , The serial number of each machining hole; The rotational angle difference representing the attitude of the ends of adjacent holes; , These are the attitude rotation matrices for two adjacent aperture positions; This represents matrix trace operation; the first term calculates the idle distance between adjacent machined holes, and the second term calculates the rotation angle difference between adjacent end postures.

[0122] The workstation switching indicator is defined as follows:

[0123] In the formula, This represents the workstation switching indication value for the k-th path segment; Indicates the workstation number to which the hole position belongs; This indicates that adjacent hole positions belong to different workstations, resulting in a workstation switch; 0 indicates that adjacent holes belong to the same workstation, and there is no workstation switch. The workstation switch indicator is used to count whether a workstation switch has occurred between adjacent holes.

[0124] For the approach, processing, and exit points between adjacent holes, an improved RRT or other sampling-based path planning algorithm is used to search for collision-free paths in joint space. After the path nodes are generated, B-spline, fifth-order polynomial, or S-curve time parameterization methods are used for smoothing to ensure continuity of position, velocity, and acceleration, and to ensure that the trajectory curvature changes meet the requirements of the robot controller. The constraints on hole axis attitude error and hole position positioning error are as follows:

[0125] In the formula, This represents the tool axis direction vector corresponding to the end-effector posture. Indicates the maximum permissible angle error of the hole shaft attitude; Indicates the actual positioning coordinates of the end effector; This indicates the maximum permissible error for hole positioning. The two constraints limit the hole shaft attitude error and the hole positioning error, respectively.

[0126] In the formula, Indicates the position of the robot's joints at time t; Indicates the speed of joint movement; Indicates the acceleration of joint movement; Indicates the maximum permissible speed of the joint; This represents the maximum permissible acceleration of the joint. The three constraints limit the joint position, joint velocity, and joint acceleration, respectively.

[0127] In the formula, This represents the set of spatial positions of all links of the robot at time t; This represents the overall spatial area formed by the workpiece and the fixture; This represents the shortest distance between two sets of spatial geometric shapes. This represents the minimum safe distance between the robot and the workpiece / fixture. Safe distance constraints are used to prevent interference between the robot, spindle, tool, and workpiece or fixture.

[0128] If trajectory planning fails, the speed or acceleration exceeds the limit after smoothing, or the edge becomes infeasible due to changes in local fixture constraints, the edge weights of the relevant edges in the weighted directed graph of hole position movement are updated, and local replanning is performed only on the affected workstation clusters to generate a robot drilling and boring machining program that satisfies the constraints.

[0129] This implementation adopts a decoupled solution strategy of attitude reachability pre-screening, sequence layer search, trajectory layer verification, and edge weight back-substitution update. The idea of ​​the coupled constraint decoupled solution optimization algorithm is as follows: Figure 4 As shown, the strategy first establishes a candidate posture set locally for each hole location and eliminates unreachable postures. Then, the remaining candidate postures are transformed into reachability and posture costs in the graph edge weights. Next, the approximate optimal hole location sequence is solved, and trajectory planning is performed on adjacent holes for verification. Failed edges are replaced with high-penalty or disabled edges, thus gradually converging to an engineering-executable solution. This strategy first obtains the work station cluster and candidate work stations through spatial normal clustering, then eliminates unreachable, collision, and singular postures through posture layer optimization, then constructs a graph model of hole location posture nodes and multi-cost edges, and optimizes the sequence layer to prioritize reducing work station switching and compressing idle movement distance. Finally, it connects adjacent holes through trajectory layer planning and satisfies motion constraints. If the cost exceeds the limit, the edge weights and postures are updated back-substituted until a processing procedure that meets the requirements is obtained.

[0130] This method can be extended to high-volume hole machining scenarios such as large die castings, ship cylinders, aerospace structural parts, energy equipment shells, and acoustic liner hole arrays. It is suitable for offline programming, station planning, and robot machining cycle optimization.

[0131] 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 the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. 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.

[0132] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the content of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for collaborative planning of robot trajectory and attitude for efficient drilling and boring of complex components with multiple holes, characterized in that, Includes the following steps: By acquiring the data of the holes to be processed, the workpiece and fixture models, the robot kinematics model, and the drill and boring end model, the basic data for planning is obtained; Based on the planning data, multiple end-position candidates are generated for each hole location, and the safe approach point, processing point and exit point for each hole location are determined. The reachability screening of the end-effector attitude candidates is performed to obtain the reachable attitude set for each aperture location; Based on the spatial distance between holes, the included angle of the hole axis normal, and the overlapping relationship of the reachable posture set, the holes to be processed are clustered to obtain one or more workstation clusters; Based on the workstation cluster and the set of reachable postures, a weighted directed graph of hole position movement is constructed; The weighted directed graph of hole position movement is solved to obtain the hole position processing sequence and the end posture corresponding to each hole position; Based on the hole machining sequence and end posture, the trajectory planning and smoothing of adjacent holes are performed to generate a robot drilling and boring machining program.

2. The robot trajectory and attitude cooperative planning method for high-efficiency drilling and boring of complex components with multiple holes according to claim 1, characterized in that, The steps to obtain the basic planning data by acquiring the data of the holes to be processed, the workpiece and fixture models, the robot kinematics model, and the drill and boring end model include: Read the three-dimensional coordinates of the hole position, the normal direction of the hole axis, the hole diameter, and the machining type to obtain the original hole position data; Based on the calibration relationship between the workpiece coordinate system, the robot base coordinate system, and the station coordinate system, the original hole position data is transformed to obtain the hole position data in the robot planning coordinate system. The hole axis normal is normalized to obtain standardized planning basis data.

3. The robot trajectory and attitude collaborative planning method for high-efficiency drilling and boring of complex components with multiple holes as described in claim 1, characterized in that, Based on the planning baseline data, the steps for generating multiple end-effector attitude candidates for each aperture location include: Align the tool axis with the hole axis normal, and determine the safe approach point, machining point, and exit point along the hole axis direction; Multiple redundant rotation angles are generated around the hole axis according to a preset step size, and each redundant rotation angle corresponds to a candidate end attitude.

4. The robot trajectory and attitude cooperative planning method for high-efficiency drilling and boring of complex components with multiple holes according to claim 1, characterized in that, The steps of performing reachability screening on the end-effector attitude candidates to obtain the reachable attitude set for each aperture location include: For each candidate end-effector pose, inverse kinematics is solved to obtain the corresponding joint solution; The joint solution is subjected to joint limitation checks, unusual pose checks, and collision checks; Retain the end-position candidates that pass all checks to form the set of reachable positions for each aperture.

5. The robot trajectory and attitude collaborative planning method for high-efficiency drilling and boring of complex components with multiple holes according to claim 1, characterized in that, The steps for clustering the holes to be machined into one or more workstation clusters based on the spatial distance between hole positions, the included angle between the hole axis and the normal direction, and the overlap relationship of the reachable posture sets include: The clustering distance between holes is calculated based on the spatial distance between holes, the included angle of the hole axis normal, and the accessibility similarity. Clustering algorithms are used to group all holes into one or more workstation clusters.

6. The robot trajectory and attitude cooperative planning method for high-efficiency drilling and boring of complex components with multiple holes according to claim 1, characterized in that, The steps for constructing a weighted directed graph of hole position movement based on the workstation cluster and the set of reachable postures include: Each aperture location and its reachable orientation are treated as nodes in a weighted directed graph; Determine the edge weight between any two nodes based on the non-cutting idle distance, station switching cost, end-effector posture change cost, joint motion cost, and unreachability penalty. Based on the nodes and edge weights, construct a weighted directed graph for hole position movement.

7. The robot trajectory and attitude cooperative planning method for high-efficiency drilling and boring of complex components with multiple holes according to claim 1, characterized in that, The steps for solving the weighted directed graph of hole position movement to obtain the hole position processing sequence and the end effector posture corresponding to each hole position include: With the optimization objectives of reducing the number of workstation changes, compressing idle movement distance, and reducing attitude changes, an objective function is constructed. A heuristic algorithm is used to solve the objective function to obtain the workstation cluster access order; Within each workstation cluster, the hole access order is solved to obtain the overall hole processing order and the end posture corresponding to each hole.

8. The robot trajectory and attitude cooperative planning method for high-efficiency drilling and boring of complex components with multiple holes according to claim 1, characterized in that, The steps for generating a robot drilling and boring program by planning and smoothing the trajectories of adjacent holes based on the hole machining sequence and end-effector posture include: A sampling-based path planning algorithm is used to search for paths between adjacent holes to obtain initial trajectory nodes; The initial trajectory nodes are subjected to continuous curvature smoothing and time parameterization to obtain a smooth trajectory; Collision verification and motion constraint checks are performed on the smooth trajectory to generate a robotic drilling and boring program.

9. The robot trajectory and attitude cooperative planning method for high-efficiency drilling and boring of complex components with multiple holes according to claim 8, characterized in that, The steps for performing collision verification and motion constraint checks on the smooth trajectory to generate a robot drilling and boring machining program include: If the trajectory is not feasible or the cost exceeds the limit, update the edge weight of the corresponding edge in the weighted directed graph of hole position movement; The affected workstation clusters are locally reprogrammed to generate a robotic drilling and boring program that satisfies the constraints.

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