A control method and system for TC bearing spraying robot

Abstract

The present invention relates to the field of program-controlled spraying technology, and specifically to a control method and system for a TC bearing spraying robot. In the present invention, a non-dominated sorting genetic algorithm is used to calculate the path length, direction angle, and coating uniformity between nodes, thereby optimizing the selection of the spraying path. An ant colony algorithm is used to optimize the scheduling of spraying tasks, calculate the time intervals and angle changes between path segments, and sort them according to the dependencies between the paths, thereby further optimizing the parallelism in the spraying process, reducing the spraying time, and improving production efficiency. Through scheduling optimization, the spraying task can be completed in the shortest time, thereby improving the accuracy and efficiency of automated control. The close combination of path planning and task scheduling ensures process parameters, can accurately match each path segment, further improves the uniformity and consistency of the coating, reduces material waste, and improves the stability of the spraying quality.

Description

Technical Field

[0001] The present invention relates to the technical field of program-controlled spraying, and in particular to a control method and system for a TC bearing spraying robot. Background Art

[0002] The field of program-controlled spraying technology aims to precisely manage and automate the technical aspects of industrial spraying operations, including spray parameter control, spray path planning, and spray cycle scheduling, through a programmable approach. Its goals are to enhance the automation level of spraying operations, improve coating quality consistency, reduce material waste, and adapt to the spraying needs of complex curved surfaces or workpieces with diverse geometries.

[0003] A control method for a TC bearing spraying robot achieves uniform coating deposition on the outer or inner ring surface of a TC bearing through multi-axis coordinated control of the robot's motion path, spray gun opening and closing status, feed speed, spraying posture, and other process parameters. This method aims to address issues such as uneven spray thickness, corner spraying, and repeated spraying that exist in semi-automatic spraying methods, thereby achieving the technical goals of improving coating quality consistency, saving paint, reducing manual intervention, and enhancing the level of spraying automation.

[0004] In the existing technology, spray path planning and task scheduling are based on traditional semi-automatic control methods and lack intelligent algorithm support, which leads to uneven coating during the spraying process. Especially when dealing with complex surfaces and geometric structures, problems such as missed spraying in corners, repeated spraying and inconsistent coating thickness are prone to occur. The existing technology does not take into account the spatial and temporal dependencies between paths. Task scheduling is usually executed sequentially, and it is difficult to complete tasks efficiently within a limited time. Due to the lack of judgment and adjustment of parallel execution conditions between paths, the spraying efficiency is low, which easily causes waste in the spraying process. The static processing method in path planning fails to optimize the complex shape of the workpiece surface, which may lead to interference, omissions or uneven spraying during the spraying process. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a control method and system for a TC bearing spraying robot.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a control method of a TC bearing spraying robot, comprising the following steps:

[0007] Step 1: Based on the structural model of the TC bearing outer ring, inner ring, groove segment, and chamfer segment, the node distribution range is set, the path length and direction angle between nodes are calculated, the spatial distance between nodes, and the node normal angle are calculated, and the coating uniformity is calculated using a non-dominated sorting genetic algorithm. The path segments are screened, and the node selection weights are adjusted according to the target value to generate a path planning structure.

[0008] Step 2: Based on the path planning structure, extract the nodes in the path, calculate the spatial overlap between the nodes and the inner and outer circle grooves, the angled surfaces, and the curved areas, screen out the compliant path segments, and generate a path obstacle avoidance adjustment set;

[0009] Step 3: Based on the path obstacle avoidance adjustment set, the start and end node numbers of the path segment are extracted, the time intervals and angle changes between the nodes are calculated using the ant colony algorithm, the paths are sorted according to the dependencies between the paths, and the parallel execution conditions are detected. If they are met, the parallel path execution order is selected to generate a spraying task scheduling diagram;

[0010] Step 4: Based on the spraying task scheduling diagram, the posture angle, path curvature, and motion speed in the path segment are extracted, the angle change rate of the path is calculated, the path angle and speed are adjusted, and a posture optimization matrix is ​​generated;

[0011] Step 5: Based on the posture optimization matrix, extract the spray gun opening and closing time, gun distance, linear speed, and posture adjustment angle of the path node, match and bind them to the path control point in sequence, perform parameter injection, and generate a parameter injection control list.

[0012] As a further solution of the present invention, the path planning structure includes node position coordinates, path connection order, normal direction, path length and coating uniformity evaluation value; the path obstacle avoidance adjustment set includes path segment number, path node sequence, distance between nodes, obstacle area detection results and node connection relationship after path obstacle avoidance; the spraying task scheduling diagram includes path segment number, time sequence, parallel execution mark, node dependency; the posture optimization matrix includes path segment posture angle adjustment value, curvature correction value, speed optimization value; the parameter injection control list includes spray gun opening and closing time, gun distance, linear speed, posture adjustment angle, and path number.

[0013] As a further solution of the present invention, the specific steps of generating the path planning structure are:

[0014] Based on the structural model of the TC bearing outer ring, inner ring, groove section, and chamfer section, the spatial distribution range of the nodes is set. The spatial distance between each node is calculated according to the size, angle, and curvature of each structure. Based on the relative position relationship of the node positions in three-dimensional space, the spatial coordinates of each node are generated and a node distribution diagram is drawn.

[0015] Based on the node distribution graph, for each pair of nodes, the path length between the nodes is calculated, the directional angle between the nodes is measured, and the angle change on the plane and curved surface of the path is calculated based on the geometric relationship between the nodes. The path length data and directional change data of each path segment are recorded, and a path geometry dataset is constructed;

[0016] Based on the path geometry dataset, a non-dominated sorting genetic algorithm is used to calculate the coating uniformity of each node in the path segment. A coating thickness measurement tool is used to analyze the coating distribution of the path segment, evaluate the uniformity of the path, screen out path segments with coating uniformity that meets the standards, and generate a path planning structure.

[0017] As a further solution of the present invention, the non-dominated sorting genetic algorithm is according to the formula:

[0018]

[0019] in: Indicates coating uniformity, Indicates the first The coating thickness of each node, represents the average coating thickness of all nodes in the path segment, represents the total number of nodes in the path segment, Representation node The weight coefficient of Represents the sum of weight coefficients of all nodes.

[0020] As a further solution of the present invention, the specific steps of generating the path obstacle avoidance adjustment set are:

[0021] Based on the path planning structure, the coordinates and connection relationships of each node in the path are extracted, the spatial overlap between the node and the inner and outer ring grooves, the angled surface, and the curved area is calculated, and the overlap between each node position and the target area is compared to generate a node obstacle matching result;

[0022] Based on the node obstacle matching results, eligible path segments are screened out, path segments that overlap with obstacle areas are eliminated, the connection relationships of the path segments are readjusted, the spatial layout of the path segments is optimized, and a path obstacle avoidance adjustment set is generated.

[0023] As a further solution of the present invention, the specific steps of generating the spraying task scheduling diagram are:

[0024] Based on the path obstacle avoidance adjustment set, the start and end node numbers in the path segment are extracted, and the ant colony algorithm is used to calculate the time interval for each pair of nodes in the path. At the same time, based on the spatial coordinates of the nodes, the angle change of each path segment is calculated. By comparing the angle change amplitude of each node in the path segment, the time interval data and angle change data of each path segment are recorded to generate the inter-node time and angle data set;

[0025] Based on the inter-node time and angle dataset, all path segments are sorted. The priority of each path segment is calculated based on the time interval, angle change, and dependencies within the path segment. The execution order is adjusted based on the dependencies between the path segments to ensure that the path segments are executed in the appropriate order. A path execution order dataset is generated, and the parallelism conditions of the path are preliminarily verified.

[0026] Based on the path execution sequence data set, the parallel execution conditions between path segments are further detected. By analyzing the time window and angle changes of each path segment, if the parallel execution conditions are met, the path segments are merged and executed according to the time window and parallel strategy to generate a spraying task scheduling diagram.

[0027] As a further solution of the present invention, the ant colony algorithm is based on the formula:

[0028]

[0029] in: Heuristic information representing the path, Indicates that ants select slave nodes To Node The probability of Represents a slave node To Node The pheromone concentration, Representation node To Node The weight coefficient between Indicates the path To Path The comfort index, represents the influence coefficient of pheromone concentration on path selection, represents the influence coefficient of heuristic information on path selection, Represents the influence coefficient of weight coefficient on path selection, represents the influence coefficient of comfort index on route selection, Representation node The set of adjacent nodes.

[0030] As a further solution of the present invention, the specific steps of generating the posture optimization matrix are:

[0031] Based on the spraying task scheduling diagram, the posture angle, path curvature, and motion speed of each path segment are extracted, the angle change rate of each path segment is calculated, and the angle change trend is evaluated by calculating the angle deviation of each path segment to generate an angle change data set;

[0032] Based on the angle change data set, the speed change in the path segment is analyzed, and the speed transition of the path is optimized by adjusting the amplitude of the speed change in the path, adjusting the angle and speed of the path, and generating a posture optimization matrix.

[0033] As a further solution of the present invention, the specific steps of generating the parameter injection control list are:

[0034] Based on the posture optimization matrix, the spray gun opening and closing time, gun distance, linear speed, and posture adjustment angle of the path node are extracted. By calculating the time synchronization of each path node, the control parameters are matched according to the node order to ensure that the spray gun opening and closing time and posture adjustment angle of each path node are accurately bound to the path control point, and a control parameter matching data set is generated;

[0035] Based on the control parameter matching data set, the matching spray gun opening and closing time, gun distance, linear speed, and posture adjustment angle are injected into the corresponding path control points in sequence. Parameter injection is performed at the same time, and the spray gun operation data and speed adjustment parameters of each path point are recorded in the control system to generate a parameter injection control list.

[0036] A control system for a TC bearing spraying robot, the control system for the TC bearing spraying robot being used to execute the control method for the TC bearing spraying robot, the system comprising:

[0037] Path generation module: Based on the outer ring, inner ring, groove segment, and guide angle segment structural models, the node distribution range is set, the path length, direction angle, spatial distance, and node normal angle are calculated, and the coating uniformity is calculated using a non-dominated sorting genetic algorithm. Path segments are screened, node weights are adjusted, and a path planning structure is generated.

[0038] Path screening module: Based on the path planning structure, it extracts nodes, calculates the degree of spatial overlap with grooves, cut-angle surfaces, and curved areas, eliminates non-compliant path segments, and generates a path obstacle avoidance adjustment set;

[0039] Task scheduling module: Based on the path obstacle avoidance adjustment set, the start and end node numbers of the path segments are extracted, the ant colony algorithm is used to calculate the time interval and angle change, the paths are sorted, the parallel conditions are determined, and a spraying task scheduling diagram is generated;

[0040] Posture calculation module: Based on the spraying task scheduling diagram, it extracts the posture adjustment angle, curvature, and speed, calculates the angle change rate, adjusts the path angle and speed, and generates a posture optimization matrix;

[0041] Parameter binding module: Based on the posture optimization matrix, the spray gun opening and closing time, gun distance, linear speed, and posture angle are extracted and bound to the path control points to generate a parameter injection control list.

[0042] Compared with the prior art, the advantages and positive effects of the present invention are:

[0043] In the present invention, the path length, direction angle, and coating uniformity between nodes are calculated through a non-dominated sorting genetic algorithm, thereby optimizing the selection of the spray path and ensuring a more accurate and uniform spraying process. At the same time, the selection weights between nodes are adjusted, effectively improving the rationality of the path and ensuring that the spraying requirements of different geometric shapes and complex surfaces are better met.

[0044] In the present invention, the scheduling of spraying tasks is optimized through the ant colony algorithm, the time intervals and angle changes between path segments are calculated, and the paths are sorted according to their dependencies, which further optimizes the parallelism of the spraying process, reduces the spraying time, and improves production efficiency. Through scheduling optimization, the spraying task can be completed in the shortest time, improving the accuracy and efficiency of automated control.

[0045] In the present invention, the close combination of path planning and task scheduling ensures the process parameters and can accurately match each path segment, further improving the uniformity and consistency of the coating, reducing material waste and improving the stability of the spraying quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0047] Figure 2 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0048] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0049] Example 1

[0050] See also Figure 1 The present invention provides a technical solution: a control method for a TC bearing spraying robot, comprising the following steps:

[0051] Step 1: Based on the structural model of the TC bearing outer ring, inner ring, groove segment, and chamfer segment, the node distribution range is set, the path length and direction angle between nodes are calculated, the spatial distance between nodes, and the node normal angle are calculated, and the coating uniformity is calculated using a non-dominated sorting genetic algorithm. The path segments are screened, and the node selection weights are adjusted according to the target value to generate a path planning structure.

[0052] Step 2: Based on the path planning structure, extract the nodes in the path, calculate the spatial overlap between the nodes and the inner and outer circle grooves, angled surfaces, and curved areas, screen out the compliant path segments, and generate a path obstacle avoidance adjustment set;

[0053] Step 3: Based on the path obstacle avoidance adjustment set, the start and end node numbers of the path segment are extracted. The time intervals and angle changes between nodes are calculated using the ant colony algorithm. The paths are sorted according to their dependencies and the parallel execution conditions are checked. If they are met, the parallel path execution order is selected to generate the spraying task scheduling diagram.

[0054] Step 4: Based on the spraying task scheduling diagram, extract the posture angle, path curvature, and motion speed within the path segment, calculate the angle change rate of the path, adjust the path angle and speed, and generate the posture optimization matrix;

[0055] Step 5: Based on the posture optimization matrix, extract the gun opening and closing time, gun distance, linear speed, and posture adjustment angle of the path node, match and bind them to the path control points in sequence, perform parameter injection, and generate a parameter injection control list.

[0056] The path planning structure includes node position coordinates, path connection order, normal direction, path length and coating uniformity evaluation value. The path obstacle avoidance adjustment set includes path segment number, path node sequence, distance between nodes, obstacle area detection results and node connection relationship after path obstacle avoidance. The spraying task scheduling diagram includes path segment number, time sequence, parallel execution mark, node dependency relationship. The posture optimization matrix includes path segment posture angle adjustment value, curvature correction value, speed optimization value. The parameter injection control list includes spray gun opening and closing time, gun distance, linear speed, posture adjustment angle, and path number.

[0057] The specific steps to generate the path planning structure are:

[0058] Based on the structural model of the TC bearing outer ring, inner ring, groove section, and chamfer section, the spatial distribution range of the nodes is set. The spatial distance between each node is calculated according to the size, angle, and curvature of each structure. Based on the relative position relationship of the node positions in three-dimensional space, the spatial coordinates of each node are generated and a node distribution diagram is drawn.

[0059] Based on the node distribution graph, for each pair of nodes, the path length between the nodes is calculated, the directional angle between the nodes is measured, and the angle changes on the plane and curved surface of the path are calculated based on the geometric relationship between the nodes. The path length data and directional change data of each path segment are recorded, and a path geometry dataset is constructed.

[0060] Based on the path geometry dataset, a non-dominated sorting genetic algorithm is used to calculate the coating uniformity of each node in the path segment. A coating thickness measurement tool is used to analyze the coating distribution of the path segment, evaluate the path uniformity, select the path segments with coating uniformity that meets the standards, and generate a path planning structure.

[0061] Based on the TC bearing outer ring structure model, TC bearing inner ring structure model, groove segment structure model, and guide angle segment structure model, the spatial distribution range of the nodes is set. According to the size value, angle range parameter, and curvature control value of each type of structure, the distributable area within the structure boundary is delineated. Each area is discretized into a set of equidistant nodes with a fixed spacing, and each node is assigned a unique number. The nodes are arranged in sequence according to the structure type. The coordinate mapping method of the structural model is called. According to the radius value, tangent azimuth angle, and structure height value of each node, the three-dimensional spatial coordinate data of the node is generated. The node spatial coordinate matrix is ​​constructed, and a spatial index table is established according to the node number sequence. All nodes are visually drawn in the connection order to form a node distribution map.

[0062] Based on the node distribution graph, a path construction operation is performed on each pair of nodes. The coordinate values ​​of each pair of nodes are extracted and the distance between them is calculated. The structural direction information is called, and the angle between the direction of the connection between the nodes and the axial direction is measured. A direction angle set is constructed for each path segment. For the surface types of the outer circle, inner circle, groove segment, and chamfer segment, the node paths are grouped according to whether they fall on planar structures or curved structures. The structural type to which the path segments belong is grouped, and the length value and direction change angle of the path segments within each type of structure are recorded. A mapping table between path segments and structural surfaces is established. The numbers, structural types, path lengths, and direction angles of all path segments are summarized and integrated to construct a path geometry dataset.

[0063] Based on the path geometry dataset, a non-dominated sorting genetic algorithm is used to perform multi-objective evaluation of the coating uniformity of the nodes in the path segment. The input parameter settings are as follows: the population size is 200, the crossover probability is 0.9, the mutation probability is 0.1, the maximum number of iterations is set to 100, and the encoding method adopts the real number encoding mode. For each path segment, the node thickness value list generated by the coating thickness measurement tool is called, and all thickness value sequences in the node index order corresponding to the path segment are extracted. The thickness difference between adjacent nodes in each path segment is calculated, and the maximum difference and the standard deviation of all differences of each path segment are recorded. The two values ​​are used as the evaluation input of the objective function. The objective function set is constructed and the initial population generation, crossover operation, mutation processing and fitness sorting operations are performed. The set of path segments retained after non-dominated sorting and congestion evaluation is output and sorted. All path segment sets that meet the thickness uniformity screening criteria are numbered and archived to generate a path planning structure.

[0064] Non-dominated sorting genetic algorithm, according to the formula:

[0065]

[0066] in: Indicates coating uniformity, Indicates the first The coating thickness of each node, represents the average coating thickness of all nodes in the path segment, represents the total number of nodes in the path segment, Representation node The weight coefficient of Represents the sum of weight coefficients of all nodes;

[0067] Execution process: First, for each path node , use coating thickness measurement tools to measure the coating thickness , and calculate the average coating thickness of all nodes Next, the absolute difference between the coating thickness at each node and the average coating thickness is calculated. , to reflect the deviation of coating thickness at each node. In order to improve the accuracy of calculation, a weight coefficient is added. , which means that some nodes may contribute more to the overall uniformity. When calculating, the thickness difference of each node is multiplied by the node weight coefficient, that is, , and finally the sum of the weighted differences of all nodes divided by the total number of nodes And the sum of the weight coefficients of all nodes , normalize the result to get the final coating uniformity , thereby effectively evaluating the uniformity of coating spraying and ensuring that quality requirements are met.

[0068] The specific steps to generate a path obstacle avoidance adjustment set are:

[0069] Based on the path planning structure, the coordinates and connection relationships of each node in the path are extracted, and the spatial overlap between the node and the inner and outer ring grooves, angled surfaces, and curved areas is calculated. The overlap between each node position and the target area is compared to generate the node obstacle matching result.

[0070] Based on the node obstacle matching results, the qualified path segments are screened out, the path segments that overlap with the obstacle area are eliminated, the connection relationship of the path segments is readjusted, the spatial layout of the path segments is optimized, and the path obstacle avoidance adjustment set is generated;

[0071] Based on the path planning structure, the three-dimensional coordinate values ​​and node connection relationship table of each node in the path are extracted. The inner groove surface point set, outer groove surface point set, angled surface discrete point set, and curved area grid point set in the structural model are called to construct an obstacle area point set index matrix. The spatial coincidence calculation method is used to perform distance matching between the nodes and the obstacle point set. The calculation process calls the nearest neighbor distance between each node coordinate and the target point set, and the maximum matching threshold is set to 0.25mm. The minimum distance value is calculated for each node and the four types of obstacle point sets, and each type of distance value and the corresponding obstacle type number are recorded in the matching order. An obstacle type matching table is constructed for each node, and a Boolean value is used to mark whether there is any record with a distance value less than the matching threshold. After performing the above judgment processing on all nodes, the path segment to which the node belongs is remapped according to the path segment connection relationship, and the path segment numbers are summarized and marked to generate the node obstacle matching result.

[0072] Based on the node obstacle matching results, all path segment numbers and corresponding Boolean obstacle mark values ​​are extracted. Path removal operations are performed on path segments marked as True, and all path segments with obstacle coincidence marks are removed from the path segment set. A path segment adjacency matrix is ​​established for the remaining path segments according to the node connection relationship. A connection graph optimization method is used to perform connectivity judgment operations on broken path segments in the adjacency matrix. The maximum connection distance is set to 2 mm, and connection edges are added to path segment nodes with a distance less than the threshold. The connection graph structure update command is called to establish a number index for the newly generated path segment set. The connection structure table is regenerated for all retained path segments, and the structure information is output according to the path segment number to generate a path obstacle avoidance adjustment set.

[0073] The specific steps to generate the spraying task scheduling diagram are:

[0074] Based on the path obstacle avoidance adjustment set, the start and end node numbers in the path segment are extracted. The ant colony algorithm is used to calculate the time interval for each pair of nodes in the path. At the same time, the angle change of each path segment is calculated based on the spatial coordinates of the nodes. By comparing the angle change amplitude of each node in the path segment, the time interval data and angle change data of each path segment are recorded to generate the time and angle data set between nodes.

[0075] Based on the inter-node time and angle dataset, all path segments are sorted. The priority of each path segment is calculated based on the time interval, angle change, and dependencies within the path segment. The execution order is adjusted based on the dependencies between the path segments to ensure that the path segments are executed in the appropriate order. This generates a path execution order dataset and performs preliminary verification of the path parallelism conditions.

[0076] Based on the path execution sequence dataset, the parallel execution conditions between path segments are further detected. By analyzing the time window and angle change of each path segment, if the parallel execution conditions are met, the path segments are merged and executed according to the time window and parallel strategy to generate a spraying task scheduling diagram;

[0077] Based on the path obstacle avoidance adjustment set, the starting node number and the ending node number of each path segment are extracted, and the path segment index table is called to map the numbers of all path segments. The ant colony algorithm is used to calculate the time interval between node pairs in the path segment. The algorithm parameters are set as follows: pheromone initial value 0.1, pheromone volatility factor 0.5, heuristic function weight 2.0, pheromone weight 1.0, maximum number of iterations 200, number of ants per round 100, path length evaluation basis is node index sequence difference, the time interval is calculated for each pair of nodes according to the number difference, and the time spent on the path of all node pairs in the ant colony search is recorded. The three-dimensional space coordinate set of the path segment is called, and the three-point angle solution operation is performed on the coordinates of each node in the path. The angle change amplitude between the connecting lines of adjacent nodes in the path segment is calculated. The time interval value and the angle change value are matched and combined according to the path segment number to generate the time and angle dataset between nodes.

[0078] Based on the inter-node time and angle dataset, a path segment dependency set is established for all path segments. The path segment number difference, time interval value, and angle change amplitude are called to sort the path segment sequence relationship in the dependency set, and a path segment execution priority index table is constructed. All path segment numbers are rearranged in order of priority, and the path segment index is output in the rearranged order and recorded in the original position comparison table to generate a path execution sequence dataset.

[0079] Based on the path execution sequence dataset, the start and end numbers of the time window and the angle change range of each path segment are extracted. A path time intersection table is constructed for all path segments. It is determined whether there is overlap in number segments, coincidence in time intervals, and the angle change difference between two path segments is lower than the set parallel judgment threshold. The path intersection table is scanned in an ascending manner using a threshold range of 0.3 to 0.7. Path segments that meet all the conditions are grouped and a parallel mark is assigned to each group of path segments. After group encoding is performed on the path segments, a scheduling arrangement map is output to generate a spraying task scheduling diagram.

[0080] Ant colony algorithm, according to the formula:

[0081]

[0082] in: Heuristic information representing the path, Indicates that ants select slave nodes To Node The probability of Represents a slave node To Node The pheromone concentration, Representation node To Node The weight coefficient between Indicates the path To Path The comfort index, represents the influence coefficient of pheromone concentration on path selection, represents the influence coefficient of heuristic information on path selection, Represents the influence coefficient of weight coefficient on path selection, represents the influence coefficient of comfort index on route selection, Representation node The set of adjacent nodes of ;

[0083] Execution process: First, for each pair of adjacent nodes and , calculate the probability of path selection , by pheromone concentration and heuristic information The decision reflects the quality of the path and the spraying efficiency. In order to further improve the accuracy of path selection, a weight coefficient is added. and comfort index , weight coefficient To measure the path To Path Contribution to overall path planning, comfort index It is used to measure the smoothness of the path or the frequency of changing the spraying angle. A higher comfort level indicates a smoother path and is suitable for spraying operations. Through the comprehensive influence of the parameters, the selection probability of each path is calculated, which further influences the ants to choose the most suitable path for spraying. The adjustment coefficient is optimized according to the actual spraying requirements and path planning goals to ensure that the pheromone concentration, heuristic information, path weight and comfort index are fully considered in path selection to achieve optimization of the spraying operation. Finally, the results of the path selection are normalized to ensure the rationality and optimization of the spraying path planning and achieve the goal of improving the spraying efficiency and quality.

[0084] The specific steps to generate the posture optimization matrix are:

[0085] Based on the spraying task scheduling diagram, the posture angle, path curvature, and motion speed of each path segment are extracted, and the angle change rate of each path segment is calculated. By calculating the angle deviation of each path segment, the angle change trend is evaluated and an angle change data set is generated.

[0086] Based on the angle change data set, the speed change in the path segment is analyzed. By adjusting the amplitude of the speed change in the path, the speed transition of the path is optimized, the angle and speed of the path are adjusted, and the posture optimization matrix is ​​generated.

[0087] Based on the spraying task scheduling diagram, the posture angle sequence, path curvature value sequence, and motion speed sequence of each path segment are extracted. The posture angle data corresponding to three consecutive nodes in each path segment are differentially calculated. The posture angle values ​​of adjacent nodes are extracted using a sequential index method. An angle change rate list is established according to the differential results. The curvature sequence is extracted according to the path segment index and aligned one by one with the corresponding node sequence. A corresponding table of posture angle change rate and curvature is constructed. Each set of data in the table is used to perform an angle deviation calculation operation. The degree of angle offset is calculated using the numerical difference between the angle change rate between nodes and the average curvature value of the path. The angle change direction and change amplitude of each path segment are recorded. A mapping relationship table between the path segment number and the angle offset record is established to generate an angle change data set.

[0088] Based on the angle change data set, the motion velocity value sequence of each path segment is extracted, the velocity change amplitude of consecutive nodes in the path is differentially counted, and a velocity change gradient list is constructed. The velocity data is processed using a sliding window smoothing method with a window size of 5 and a step size of 1. The average velocity of the velocity value sequence in each sliding window is calculated and the center value is replaced by the smoothed value. The index position of the segments with a difference change greater than 2.0 mm / s in the original velocity sequence is recorded, and the velocity values ​​of the preceding and following nodes of the corresponding segments are adjusted according to the angle offset direction. The velocity jump position in the path transition segment is corrected by pairing the velocity value with the angle change direction. A corresponding set of node index, corrected angle value, and velocity value in the path segment is constructed. The corresponding sets of all path segments are summarized and outputted in order of segment number to generate a posture optimization matrix.

[0089] The specific steps to generate a parameter injection control list are:

[0090] Based on the posture optimization matrix, the gun opening and closing time, gun distance, linear speed, and posture adjustment angle of the path node are extracted. By calculating the time synchronization of each path node, the control parameters are matched according to the node order to ensure that the gun opening and closing time and posture adjustment angle of each path node are accurately bound to the path control point, and a control parameter matching data set is generated;

[0091] Based on the control parameter matching data set, the matching spray gun opening and closing time, gun distance, linear speed, and posture adjustment angle are injected into the corresponding path control points in sequence. Parameter injection is performed simultaneously, and the spray gun operation data and speed adjustment parameters of each path point are recorded in the control system to generate a parameter injection control list.

[0092] Based on the posture optimization matrix, the spray gun opening and closing time value, spray gun gun distance value, linear speed value, and posture adjustment angle value corresponding to each path node are extracted, and the linear time synchronization calculation method is used to synchronize the time series of the path nodes. The synchronization base period is set to 0.05 seconds. For each node, the time difference between the current time and the previous node is calculated in sequence according to the path index position. The path segment node index and time mapping table is called to map the opening and closing time value to the start and end time interval of the time synchronization segment. The posture adjustment angle is sequentially bound to the index of the path control point, and a one-to-one mapping mechanism is used to complete the alignment and matching of the posture adjustment angle and the control point position. The spray gun gun distance value and the linear speed value are integrated into a control parameter column in the order of the nodes, and a control parameter index structure is constructed. The four parameter values ​​of the spray gun opening and closing time, gun distance, linear speed and posture angle are integrated for all nodes in the order of the path to generate a control parameter matching data set.

[0093] Based on the control parameter matching data set, the corresponding four matching parameter values ​​are read in turn for each path control point, and the sequential injection execution strategy is used to perform parameter injection operations. The control point number is called as the injection instruction index, and the spray gun opening and closing time value is written into each instruction and bound to the spray gun control channel. The gun distance value is written to the spray distance adjustment register of the control logic layer, the linear speed value is written to the speed cache unit of the motion control module, and the attitude angle value is written to the attitude angle compensation control table. The data writing process of all parameter fields in the control node is completed, and the control table item log is constructed for the numbers of all path control points and their corresponding parameters to generate a parameter injection control list.

[0094] See also Figure 2 A control system for a TC bearing spraying robot is provided. The control system for the TC bearing spraying robot is used to execute the control method for the TC bearing spraying robot. The system includes:

[0095] Path generation module: Based on the outer ring, inner ring, groove segment, and guide angle segment structural models, the node distribution range is set, the path length, direction angle, spatial distance, and node normal angle are calculated, and the coating uniformity is calculated using a non-dominated sorting genetic algorithm. Path segments are screened, node weights are adjusted, and a path planning structure is generated.

[0096] Path screening module: Based on the path planning structure, it extracts nodes, calculates the degree of spatial overlap with grooves, cut-angle surfaces, and curved areas, eliminates non-compliant path segments, and generates a path obstacle avoidance adjustment set;

[0097] Task scheduling module: Based on the path obstacle avoidance adjustment set, it extracts the start and end node numbers of the path segment, uses the ant colony algorithm to calculate the time interval and angle change, sorts the path, determines the parallel conditions, and generates the spraying task scheduling diagram;

[0098] Posture calculation module: Based on the spraying task scheduling diagram, it extracts the posture adjustment angle, curvature, and speed, calculates the angle change rate, adjusts the path angle and speed, and generates a posture optimization matrix;

[0099] Parameter binding module: Based on the posture optimization matrix, the spray gun opening and closing time, gun distance, linear speed, and posture angle are extracted and bound to the path control points to generate a parameter injection control list.

[0100] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A control method for a TC bearing spraying robot, characterized in that: The following steps are involved: Step 1: Based on the structural model of the TC bearing outer ring, inner ring, groove segment, and chamfer segment, the node distribution range is set, and the path length, direction angle, spatial distance between nodes, and node normal angle between nodes are calculated. The coating uniformity is calculated using a non-dominated sorting genetic algorithm, and the path segments are screened. The node selection weights are adjusted according to the target value to generate a path planning structure. Step 2: Based on the path planning structure, extract the nodes in the path, calculate the spatial overlap between the nodes and the inner and outer circle grooves, the angled surfaces, and the curved areas, screen out the compliant path segments, and generate a path obstacle avoidance adjustment set; Step 3: Based on the path obstacle avoidance adjustment set, the start and end node numbers of the path segment are extracted, the time intervals and angle changes between the nodes are calculated using the ant colony algorithm, the paths are sorted according to the dependencies between the paths, and the parallel execution conditions are detected. If they are met, the parallel path execution order is selected to generate a spraying task scheduling diagram; Step 4: Based on the spraying task scheduling diagram, the posture angle, path curvature, and motion speed in the path segment are extracted, the angle change rate of the path is calculated, the path angle and speed are adjusted, and a posture optimization matrix is ​​generated; Step 5: Based on the posture optimization matrix, the gun opening and closing time, gun distance, linear speed, and posture adjustment angle of the path node are extracted, matched and bound to the path control points in sequence, and parameter injection is performed to generate a parameter injection control list; The non-dominated sorting genetic algorithm is based on the formula: ； in: Indicates coating uniformity, Indicates the first The coating thickness of each node, represents the average coating thickness of all nodes in the path segment, represents the total number of nodes in the path segment, Representation node The weight coefficient of Represents the sum of weight coefficients of all nodes.

2. The control method of the TC bearing spraying robot according to claim 1, characterized in that: The path planning structure includes node position coordinates, path connection order, normal direction, path length and coating uniformity evaluation value; the path obstacle avoidance adjustment set includes path segment number, path node sequence, inter-node distance, obstacle area detection result and node connection relationship after path obstacle avoidance; the spraying task scheduling diagram includes path segment number, time sequence, parallel execution mark, node dependency; the posture optimization matrix includes path segment posture angle adjustment value, curvature correction value, speed optimization value; the parameter injection control list includes spray gun opening and closing time, gun distance, linear speed, posture adjustment angle, and path number.

3. The control method of the TC bearing spraying robot according to claim 1, characterized in that: The specific steps of generating the path planning structure are: Based on the structural model of the TC bearing outer ring, inner ring, groove section, and chamfer section, the spatial distribution range of the nodes is set. The spatial distance between each node is calculated according to the size, angle, and curvature of each structure. Based on the relative position relationship of the node positions in three-dimensional space, the spatial coordinates of each node are generated and a node distribution diagram is drawn. Based on the node distribution graph, for each pair of nodes, the path length between the nodes is calculated, the directional angle between the nodes is measured, and the angle change on the plane and curved surface of the path is calculated based on the geometric relationship between the nodes. The path length data and directional change data of each path segment are recorded, and a path geometry dataset is constructed; Based on the path geometry dataset, a non-dominated sorting genetic algorithm is used to calculate the coating uniformity of each node in the path segment. A coating thickness measurement tool is used to analyze the coating distribution of the path segment, evaluate the uniformity of the path, screen out path segments with coating uniformity that meets the standards, and generate a path planning structure.

4. The control method of the TC bearing spraying robot according to claim 1, characterized in that: The specific steps of generating the path obstacle avoidance adjustment set are: Based on the path planning structure, the coordinates and connection relationships of each node in the path are extracted, the spatial overlap between the node and the inner and outer ring grooves, the angled surface, and the curved area is calculated, and the overlap between each node position and the target area is compared to generate a node obstacle matching result; Based on the node obstacle matching results, eligible path segments are screened out, path segments that overlap with obstacle areas are eliminated, the connection relationships of the path segments are readjusted, the spatial layout of the path segments is optimized, and a path obstacle avoidance adjustment set is generated.

5. The control method of the TC bearing spraying robot according to claim 1, characterized in that: The specific steps of generating the spraying task scheduling diagram are: Based on the path obstacle avoidance adjustment set, the start and end node numbers in the path segment are extracted, and the ant colony algorithm is used to calculate the time interval for each pair of nodes in the path. At the same time, based on the spatial coordinates of the nodes, the angle change of each path segment is calculated. By comparing the angle change amplitude of each node in the path segment, the time interval data and angle change data of each path segment are recorded to generate the inter-node time and angle data set; Based on the inter-node time and angle dataset, all path segments are sorted. The priority of each path segment is calculated based on the time interval, angle change, and dependencies within the path segment. The execution order is adjusted based on the dependencies between the path segments to ensure that the path segments are executed in the appropriate order. A path execution order dataset is generated, and the parallelism conditions of the path are preliminarily verified. Based on the path execution sequence data set, the parallel execution conditions between path segments are further detected. By analyzing the time window and angle changes of each path segment, if the parallel execution conditions are met, the path segments are merged and executed according to the time window and parallel strategy to generate a spraying task scheduling diagram.

6. The control method of the TC bearing spraying robot according to claim 5, characterized in that: The ant colony algorithm is based on the formula: ； in: Heuristic information representing the path, Indicates that ants select slave nodes To Node The probability of Represents a slave node To Node The pheromone concentration, Representation node To Node The weight coefficient between Indicates the path To Path The comfort index, represents the influence coefficient of pheromone concentration on path selection, represents the influence coefficient of heuristic information on path selection, Represents the influence coefficient of weight coefficient on path selection, represents the influence coefficient of comfort index on route selection, Representation node The set of adjacent nodes.

7. The control method of the TC bearing spraying robot according to claim 1, characterized in that: The specific steps of generating the posture optimization matrix are: Based on the spraying task scheduling diagram, the posture angle, path curvature, and motion speed of each path segment are extracted, the angle change rate of each path segment is calculated, and the angle change trend is evaluated by calculating the angle deviation of each path segment to generate an angle change data set; Based on the angle change data set, the speed change in the path segment is analyzed, and the speed transition of the path is optimized by adjusting the amplitude of the speed change in the path, adjusting the angle and speed of the path, and generating a posture optimization matrix.

8. The control method of the TC bearing spraying robot according to claim 1, characterized in that: The specific steps for generating the parameter injection control list are: Based on the posture optimization matrix, the spray gun opening and closing time, gun distance, linear speed, and posture adjustment angle of the path node are extracted. By calculating the time synchronization of each path node, the control parameters are matched according to the node order to ensure that the spray gun opening and closing time and posture adjustment angle of each path node are accurately bound to the path control point, and a control parameter matching data set is generated; Based on the control parameter matching data set, the matching spray gun opening and closing time, gun distance, linear speed, and posture adjustment angle are injected into the corresponding path control points in sequence. Parameter injection is performed at the same time, and the spray gun operation data and speed adjustment parameters of each path point are recorded in the control system to generate a parameter injection control list.

9. A control system for a TC bearing spraying robot, characterized in that: According to the control method of the TC bearing spraying robot according to any one of claims 1 to 8, the system comprises: Path generation module: Based on the outer ring, inner ring, groove segment, and guide angle segment structural models, the node distribution range is set, the path length, direction angle, spatial distance, and node normal angle are calculated, and the coating uniformity is calculated using a non-dominated sorting genetic algorithm. Path segments are screened, node weights are adjusted, and a path planning structure is generated. Path screening module: Based on the path planning structure, it extracts nodes, calculates the degree of spatial overlap with grooves, cut-angle surfaces, and curved areas, eliminates non-compliant path segments, and generates a path obstacle avoidance adjustment set; Task scheduling module: Based on the path obstacle avoidance adjustment set, the start and end node numbers of the path segments are extracted, the ant colony algorithm is used to calculate the time interval and angle change, the paths are sorted, the parallel conditions are determined, and a spraying task scheduling diagram is generated; Posture calculation module: Based on the spraying task scheduling diagram, it extracts the posture adjustment angle, curvature, and speed, calculates the angle change rate, adjusts the path angle and speed, and generates a posture optimization matrix; Parameter binding module: Based on the posture optimization matrix, the spray gun opening and closing time, gun distance, linear speed, and posture angle are extracted and bound to the path control points to generate a parameter injection control list.

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