CN111515503A - Non-collision path planning method for arc welding robot - Google Patents
Non-collision path planning method for arc welding robot Download PDFInfo
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- CN111515503A CN111515503A CN202010363612.2A CN202010363612A CN111515503A CN 111515503 A CN111515503 A CN 111515503A CN 202010363612 A CN202010363612 A CN 202010363612A CN 111515503 A CN111515503 A CN 111515503A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/24—Features related to electrodes
- B23K9/28—Supporting devices for electrodes
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K9/00—Arc welding or cutting
- B23K9/32—Accessories
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J11/00—Manipulators not otherwise provided for
- B25J11/005—Manipulators for mechanical processing tasks
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
- B25J9/1666—Avoiding collision or forbidden zones
Abstract
The invention relates to the technical field of welding robots, in particular to a collision-free path planning method, computer equipment and a computer readable storage medium for an arc welding robot, wherein the method comprises the following steps: establishing a welding environment model by adopting a mixed modeling mode of a grid method and a bounding box method, and determining an obstacle object and a guide space; searching a collision-free transfer track between welding seams by an RRT method to form a transfer matrix; discretizing the difference method through a discrete strategy, dynamically adjusting the variation step length by adopting a self-adaptive strategy, and screening the collision-free transfer tracks in the transfer matrix by taking the welding sequence and the welding direction as objects to determine the shortest collision-free welding path. The invention provides a set of complete solutions of model establishment, trajectory planning and path planning for complex welding tasks, gives consideration to calculation speed and precision, can provide path reference for engineers when facing new products, reduces experience acquisition cost and shortens development period.
Description
Technical Field
The invention relates to the technical field of welding robots, in particular to a collision-free path planning method for an arc welding robot, computer equipment and a computer readable storage medium.
Background
The path planning of the arc welding robot refers to planning a reasonable walking path which does not collide with an obstacle for a welding task of the robot, and comprises the following steps: designing a welding sequence of welding seams, designing a welding direction, and planning a track of a transfer path between the welding seams. The rationality of the path planning can improve the efficiency of the welding task and reduce costs.
At present, an arc welding path planning scheme is mainly completed by experienced high-grade engineers through manual teaching, the mode has high requirements on the engineers, the design scheme is long in making time, when a new product (a new welded workpiece) appears, the path planning is completed by directly adopting the manual teaching without guidance assistance, and the cost and the design period for obtaining experience are high.
In the prior art, besides manual teaching, paths can be planned by adopting an intelligent optimization method such as a Genetic Algorithm (GA), a Particle Swarm Optimization (PSO) and the like, reasonable welding paths can be found out more quickly and effectively by adopting the optimization method to plan the paths, guidance can be provided for welding operation, and engineers can adjust according to a scheme calculated by the optimization method so as to finish manual teaching quickly. However, most of the optimization methods adopted in the prior art are spot welding, the welding scheme is relatively simple, the welding direction is not considered, the combination of trajectory planning and path planning is not considered, and the optimization methods are difficult to apply to complex welding environments.
Disclosure of Invention
The invention aims to provide a collision-free path planning method for an arc welding robot, which can search the shortest collision-free welding path in a complex environment, aiming at least part of the defects or shortcomings.
In order to achieve the above object, the present invention provides a collision-free path planning method for an arc welding robot, comprising the steps of:
s1, establishing a welding environment model by adopting a grid method and bounding box method mixed modeling mode, and determining an obstacle object and a guide space; the method comprises the following steps of (1) modeling a welded workpiece and barriers in a coverage space area of the welded workpiece by adopting a grid method, and modeling other barriers by adopting a bounding box method;
s2, based on the barrier object and the guide space, searching a collision-free transfer track between welding seams by an RRT method to form a transfer matrix;
s3, discretizing the difference method through a discretization strategy, dynamically adjusting the variation step length by adopting a self-adaptive strategy, screening collision-free transfer tracks in the transfer matrix by taking the welding sequence and the welding direction as objects, and determining the shortest collision-free welding path.
Preferably, when the grid method is adopted for modeling in step S1, the welded workpiece and the obstacle in the coverage space area thereof are simplified into a combination of a plurality of triangles, the welding work space is divided into a plurality of cubes, the center point of each cube is projected onto each triangle, and if the projection point falls within one triangle and the distance between the center point of each cube and the projection point is not greater than the distance between the center point of each cube and the vertex, the cube belongs to the obstacle mesh and is drawn into the obstacle object.
Preferably, in step S2, when searching for a collision-free transfer trajectory between welding seams by using the RRT method, planning is performed based on a robot arm joint space of the robot, taking a robot arm starting point as a root node of the RRT tree, and obtaining a node of the RRT tree by sampling, where the sampling mode is a mixed sampling mode including random sampling and random sampling in a guide space.
Preferably, in the step S2, when finding the collision-free transfer trajectory between the welding seams by the RRT method, the method includes the following steps:
s2-1, initializing RRT parameters, and taking the starting point of the robot as a root node of the RRT tree;
s2-2, generating a random number between 0 and 1, if the random number is not smaller than a first threshold value, taking the mechanical arm end point of the robot as a target point, if the random number is smaller than the first threshold value and not smaller than a second threshold value, taking random sampling as the target point, and if the random number is smaller than the second threshold value, taking the random sampling as the target point in the guide space;
s2-3, selecting a point in the RRT tree closest to the target point as a departure point, and dividing a plurality of detection points between the departure point and the target point;
s2-4, detecting whether the mechanical arm of the robot collides with the obstacle object or not for each detection point, if the robot collides with the obstacle object at any detection point, giving up the growth, returning to the step S2-2, otherwise, continuing to execute the step S2-5;
and S2-5, judging whether the target point is the robot terminal point, if so, outputting the shortest track, otherwise, adding the target point into the RRT tree and returning to execute the step S2-2.
Preferably, in step S2, when it is detected whether or not the robot arm of the robot collides with the obstacle object, the robot arm is replaced with a non-width linear model, and the width of the robot arm is compensated by adding a margin to the obstacle object.
Preferably, in step S2, when detecting whether the robot arm collides with the obstacle object, sampling a non-width linear model representing the robot arm, and determining whether the robot arm collides with the obstacle object according to a relative position between each sampling point of the robot arm and the obstacle object;
for the obstacle object determined by the bounding box method, if projections of sampling points of the mechanical arm on three surfaces of the obstacle object are all in the bounding box with the added allowance, judging that the mechanical arm collides, and otherwise, judging that the mechanical arm does not collide with the obstacle object;
and for the barrier object determined by the grid method, if the distance from the sampling point of the mechanical arm to the peak of the grid after the allowance is increased is less than half of the length of the diagonal of the grid after the allowance is increased, judging that the mechanical arm collides, and otherwise, judging that the mechanical arm does not collide with the barrier object.
Preferably, in step S3, the discrete difference formula used for screening the collision-free transfer trajectory in the transfer matrix is as follows:
wherein, wparentRepresents the parent sequence, wnewRepresents the sub-sequence generated by the sub-sequence,
representing a mutation operation performed on the parent sequence, F ⊙ representing a probabilistic selection to perform a mutation,
representing sequences according to any two parents wr2、wr3Exchange the mutation rules to generate the calculation mutation rules.
Preferably, in step S3, the expression of the probability F is:
wherein, FinitRepresenting the initial probability, Iter representing the current iteration number, iteration representing the overall iteration number, and e representing a natural constant.
The invention also provides computer equipment which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the collision-free path planning method of the arc welding robot when executing the computer program.
The invention also provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the arc welding robot collision-free path planning method according to any one of the above.
The technical scheme of the invention has the following advantages: the invention provides a collision-free path planning method for an arc welding robot, computer equipment and a computer readable storage medium, wherein a complex task environment is modeled by a grid method and bounding box method mixed modeling mode, a collision-free transfer track between welding seams is searched by a rapid random search tree (RRT) method based on a guide space, a discretized adaptive differential (IDA-DE) method is adopted to optimally solve the searched collision-free path aiming at the problems of welding seam sequence and welding direction, and finally a high-efficiency welding path is searched to provide a scheme reference for an engineer.
Drawings
FIG. 1 is a schematic diagram illustrating steps of a collision-free path planning method for an arc welding robot according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a bounding box method for constructing an obstacle object according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a grid method for constructing an obstacle object according to an embodiment of the present invention;
FIG. 4 is a schematic illustration of determining a guiding space in an embodiment of the present invention;
FIG. 5 is a diagram illustrating a step of finding a collision-free transition trajectory between welds by the RRT method according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of collision detection in an embodiment of the present invention;
FIG. 7 is a schematic diagram of a sampling strategy in an embodiment of the invention;
FIG. 8 is a schematic flow chart of the method for finding the non-collision transfer trajectory between the welding seams by the RRT method in the embodiment of the present invention;
FIG. 9 is a schematic diagram of a process of screening found collision-free transfer trajectories by a discretization adaptive difference method according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of a transformation variation rule between two parents according to an embodiment of the present invention;
FIG. 11 is a DELMIA model diagram of a front subframe of an automobile in accordance with an embodiment of the present invention;
FIG. 12 is a routing diagram for a front subframe of a vehicle in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, a collision-free path planning method for an arc welding robot according to an embodiment of the present invention includes the following steps:
s1, establishing a welding environment model by adopting a grid method and bounding box method mixed modeling mode, and determining an obstacle object and a guide space in the welding environment; the method comprises the steps of performing grid modeling on a welded workpiece and barriers in a coverage space region of the welded workpiece, and performing bounding box modeling on other barriers.
When an arc welding robot welds a workpiece to be welded by a rotatable multi-segment robot arm, the workpiece to be welded is also divided into obstacle objects, because the obstacle objects include, in addition to the workpiece itself, a jig for fixing the workpiece to be welded, and the jig may collide with the robot arm of the robot. The grid method modeling can more accurately determine the obstacle object and the free space (i.e., the space not occupied by the obstacle), and has the disadvantage of large calculation amount when the welding environment is complicated. Therefore, in order to improve the calculation efficiency, the invention provides a method for modeling a welded workpiece and a clamp (a clamp in the range above and below the welded workpiece) in a coverage space area of the welded workpiece in a complex welding environment, namely a main body part and a clamp around the main body part by adopting a grid method; for the remaining obstacles, i.e. for example the clamps further away from the body, a bounding box method is used for modeling. The complex welding environment is modeled and simplified through a grid method and a bounding box method, and the obstacle object and the free space without the obstacle can be efficiently obtained.
The guiding space is a part of the free space, and can be regarded as a preferable space for planning the collision-free path of the arc welding robot in the obstacle-free space, for example, when the welding seam is concentrated on the upper side of the workpiece to be welded, the free space above the workpiece to be welded can be regarded as the guiding space. The user can divide the guiding space according to the actual situation, and the guiding space is not further limited.
And S2, based on the obstacle object and the guide space determined by modeling in the step S1, searching a collision-free transfer track between welding seams by an RRT method to form a transfer matrix. The transfer matrix consists of collision-free transfer trajectories between the individual welds (since the welds have two endpoints, the collision-free transfer trajectories are 2 times the number of welds).
After the obstacle object and the guiding space are determined by modeling in step S1, step S2 aims to determine the trajectory of collision-free transfer between the welding seams of the robot arm of the welding robot on the workpiece to be welded through trajectory planning, so as to ensure that the robot arm does not collide with the obstacle such as the workpiece to be welded and the clamp during the moving process.
S3, discretizing the difference method through a discretization strategy, dynamically adjusting the variation step length by adopting a self-adaptive strategy, screening collision-free transfer tracks in the transfer matrix by taking the welding sequence and the welding direction as objects, and determining the shortest collision-free welding path.
Step S3 is to perform path optimization on the collision-free trajectory of the robot obtained in step S2 by using a discretized adaptive differential (IDA-DE) method, and optimize the total length and time of the welding path by reasonably arranging the welding sequence and the welding direction of the welding line, thereby improving the welding efficiency. The discretization self-adaptive difference method improves the variation step length by using a self-adaptive strategy, and the discretization difference acts on cross variation, so that the local optimum can be escaped in the searching process, the convergence speed can be increased, and the precision is improved.
The invention provides a complete path planning scheme of an arc welding robot in a complex environment, which comprises the steps of establishing a welding environment model, planning a path and planning the path, and also takes the calculation efficiency and precision into consideration; and the universality is strong, and the method is suitable for different application scenes such as arc welding, spot welding, spraying and the like. When a new product is faced, the arc welding robot path is obtained through calculation by the arc welding robot collision-free path planning method provided by the invention, reference can be provided for engineers to design the path, the experience obtaining cost can be reduced, the development period is shortened, and the requirements on the engineers are reduced.
Preferably, as shown in fig. 2, fig. 2(a) shows the basic shape of a part, and fig. 2(b) shows the bounding box established for the part shown in fig. 2(a), the bounding box being a hexahedral bounding box with sides parallel to the coordinatesWhen modeling, the component data in stl format can be read by Matlab to obtain the bounding box reference position p and the length x of each side in three-dimensional spacelength,ylength,zlengthAnd then constructing a fully enclosed hexahedral bounding box. For how to implement bounding box modeling, reference may be made to the prior art, and details thereof are not repeated here. In fig. 2, the three axes are x, y and z axes, respectively, and the unit is mm.
Preferably, when the grid method modeling is adopted in step S1, the welded workpiece and the obstacles in the coverage space area thereof are first simplified into a combination of a plurality of triangles. For stl format files of three-dimensional objects, after Matlab is imported, the three-dimensional objects can be transformed into triangle combinations.
Then, a grid matrix is built based on the simplified triangular combination model, and a welding working space, namely the maximum range space where the workpiece is located, is divided into a plurality of grid cubes (the side length range of the cubes is 5-20 mm, and 10mm is preferred). Preferably, in order to improve the calculation efficiency, the welding work space needing rasterization can be defined by adopting a bounding box method, namely, the maximum range space where the welded workpiece and the obstacles in the coverage space area of the welded workpiece are located is defined by the bounding box method, then the welding work space in the range is rasterized, and all grids are calculated.
And projecting the center point of the cube obtained by rasterization to each triangle, wherein if the projection point falls in any one of the triangles and the distance between the center point of the cube and the projection point (namely the distance between the center of the cube and the vertical line of the triangle) is not more than the distance between the center point of the cube and the vertex, the cube belongs to an obstacle mesh and is drawn into an obstacle object, otherwise, the triangle is not an obstacle of the cube, and the cube belongs to a free mesh and is drawn into a free space.
For how to realize the modeling by the grid method, reference may be made to the prior art, and details thereof are not repeated herein. In a specific embodiment, for a front subframe (welded workpiece), a rasterized obstacle object obtained by modeling by a grid method is shown in fig. 3, and a guide space obtained by combining mixed modeling of a bounding box method and the grid method is shown in fig. 4. The complex welding environment is modeled by adopting a mixed modeling mode of a bounding box method and a grid method, and the precision and the efficiency of collision detection can be considered.
Preferably, in step S2, when searching for a collision-free transfer trajectory between welding seams in a three-dimensional space by using an RRT method, planning is performed based on a robot arm joint space of the robot, taking a robot arm starting point as a root node of an RRT tree, and obtaining a node of the RRT tree by sampling in a mixed sampling mode including random sampling and random sampling in a guidance space.
Step S2 is based on the robot joint space planning of the robot, i.e. constructing a coordinate system with the robot joints. Because planning is carried out in the joint space, sampling points can be distributed in the whole sampling space, and in a three-dimensional sampling space, especially in the environment with more obstacles and complexity, the searching blindness of the RRT method is high, the convergence efficiency is low, and the invention provides the method for adding the sampling in the guide space to accelerate the convergence in order to improve the path searching efficiency.
Further, as shown in fig. 5, when the collision-free transfer trajectory between the welding seams is found by the RRT method in step S2, the method includes the following steps:
s2-1, initializing RRT parameters, and taking the robot starting point as the root node of the RRT tree.
And S2-2, generating a random number between 0 and 1, if the random number is not less than a first threshold value, taking the mechanical arm end point of the robot as a target point, if the random number is less than the first threshold value and not less than a second threshold value, taking random sampling as the target point, and if the random number is less than the second threshold value, taking random sampling in the guide space as the target point.
Particularly, in order to improve the calculation efficiency and obtain the track more quickly, the first threshold value range is 0.6-0.8, preferably 0.7, and the second threshold value range is 0.3-0.5, preferably 0.4.
And S2-3, selecting the point closest to the target point in the RRT tree as a starting point, and dividing a plurality of detection points between the starting point and the target point.
Particularly, when a plurality of detection points are divided between the departure point and the target point, it is preferable to equally divide between the departure point and the target point to obtain 15 to 30 detection points. Of course, in a more complex welding environment, if the robot is prevented from generating a "crossing phenomenon", that is, the detection points cross the obstacle due to the fact that the equally dividing points are too sparse, the equally dividing density can be increased according to actual needs.
S2-4, detecting whether the mechanical arm of the robot collides with the obstacle object or not for each detection point, if the robot collides with the obstacle object at any detection point, giving up the growth, returning to the step S2-2, otherwise, continuing to execute the step S2-5.
And S2-5, judging whether the target point is the robot terminal point, if so, outputting the shortest track obtained by the RRT method, otherwise, adding the target point into the RRT tree and returning to execute the step S2-2.
Preferably, in step S2, when it is detected whether each segment of the robot arm collides with an obstacle object, the robot arm is replaced with a non-width linear model, and the width of the robot arm is compensated by adding a margin to the obstacle object.
Further, as shown in fig. 6, in step S2, when it is detected whether or not the arm of the robot collides with the obstacle object, a non-width straight line model representing the arm is sampled, and it is determined whether or not the arm collides with the obstacle object based on the relative position of each arm sampling point and the obstacle object. The specific position and the number of the sampling points of each mechanical arm can be set according to actual conditions. Preferably, when sampling the non-width linear model representing the mechanical arm, discretizing the equivalent non-width linear model for each of the multiple segments of the mechanical arm, equally dividing the equivalent non-width linear model into 10-20 parts (preferably equally dividing the equivalent non-width linear model into 10 parts), and obtaining the equally divided points as the sampling points of the mechanical arm.
Fig. 6(a) shows collision detection for an obstacle object determined by the bounding box method, and in fig. 6(a), d denotes the side length of a bounding box after increasing the margin according to the width of the robot arm. For the obstacle object determined by the bounding box method, if the projection of any mechanical arm sampling point on the three surfaces of the obstacle object is in the bounding box with the added margin (including the projection of the mechanical arm sampling point on the top point of the bounding box with the added margin), for example, the point p2 in fig. 6(a), it is determined that the mechanical arm collides, otherwise, it is determined that the mechanical arm does not collide with the obstacle object, for example, the point p1 in fig. 6 (a).
Fig. 6(b) shows collision detection for an obstacle object determined by the grid method, and in fig. 6(b), d represents a side length of a grid after an increase in margin according to the width of the robot arm. And for the obstacle object determined by the grid method, if the distance from the sampling point of any mechanical arm to the grid vertex added with the margin is smaller than a third threshold value, judging that the mechanical arm collides, and otherwise, judging that the mechanical arm does not collide with the obstacle object. As shown in fig. 6(b), during collision detection, a sphere is formed with the grid vertices (e.g., points o1 and o 2) with the margin added as the center and the third threshold as the radius, and if the mechanical arm sampling point appears in the sphere, a collision is considered to occur, as shown by point p4 in fig. 6(b), otherwise, no collision occurs, as shown by point p3 in fig. 6 (b). The specific value of the third threshold may be set according to actual needs, and is preferably one half of the diagonal length of the grid (cube) to which the margin is added.
FIG. 7 shows a sampling strategy of the present invention when finding a collision-free transition trajectory between welds by the RRT method, q in FIG. 7initRepresenting the starting point of the robot, i.e. the initial position of the robot arm, corresponding to the root node of the RRT tree, qgoalRepresenting the target point of the robot, i.e. the target pose of the robot arm, qrand1、qrand2、qrand3All represent nodes obtained by sampling by the RRT method, the sampling mode is a mixed sampling of random sampling and random sampling in a guide space, and when a new sampling point q is obtainednew，qnearestFor the new sampling point q on the RRT treenewNearest node, connection qnearestAnd q isnewEqually dividing the two points into 20 parts to obtain 20 detection points, approximately judging whether the robot collides with the obstacle under the path or not by detecting the collision condition of the robot on the 20 detection points, and if the 20 detection points detect that all the mechanical arms do not collide with the obstacle, determining the new sampling point qnewAdding RRT tree, otherwise abandoning this growth. The specific flow is shown in fig. 8.
Fig. 9 shows a flowchart of optimizing the total length of the jump path according to the present invention, further using the IDA-DE method to target the welding sequence and direction according to the trajectory planning in step S2. In fig. 9, the modeling corresponds to step S1, and the step S2 corresponds to the calculation of the transition matrix based on the guide space RRT trajectory planning, collision detection, and the calculation.
Preferably, the present invention discretizes the continuous difference method, and in step S3, the discretized adaptive difference method screens the collision-free transition trajectory in the transition matrix by using the discretized adaptive difference formula as follows:
wherein, wparentRepresents the parent sequence, wnewRepresenting the generated sub-sequence, the parent sequence and the sub-sequence are a welding seam sequence containing welding sequence information or a direction sequence containing welding direction information,
representing a mutation operation performed on the parent sequence, F ⊙ representing a probabilistic selection to perform a mutation,
representing sequences according to any two parents wr2、wr3Exchange the mutation rules to generate the calculation mutation rules.
FIG. 10 shows a sequence occurring between two parents wr2、wr3Rule of exchange variation between wr2＝w1＝[x1x2x3x4x5x7x8x6]，wr3＝w2＝[x2x5x1x6x4x7x8x3]，
I.e. w2By exchanging sequences to reach w1As shown in fig. 10, in this example,
when the next descendant is produced, i.e. according to w2To reach w1The crossover sequence of (a) is mutated in the parent. According to any two parent sequences
Resulting variation rule for parent sequence wparentPerforming variation to obtain a subsequence wnewIn the process of execution, the variation rule is dynamically changed, namely, a self-adaptive strategy is adopted to dynamically adjust the variation step length, so that local optimum is escaped in the search process, the convergence speed is increased, and the precision is improved.
Further, in step S3, the expression of the probability F is:
wherein, FinitRepresents the initial probability, preferably 0.9, Iter represents the current iteration number, iteration represents the overall iteration number, and e represents a natural constant.
The invention establishes two different populations for the welding sequence and the welding direction of the welding seam, and the two populations are crossed and varied in the same IDA-DE method in the same way, and then the two populations are combined to calculate the path length, thereby realizing the optimization searching of the shortest path for the welding sequence and the welding direction in the arc welding task.
In a specific embodiment, the method provided by the present invention is used to plan a path of the front subframe of the vehicle as shown in fig. 11, and the final path is shown in fig. 12, where three axes in fig. 12 are x, y, and z axes, respectively, and the unit is mm. L1-L15 each represent a weld, with the weld path having a transition point and points at either end of the weld (i.e., the end points of L1-L15, corresponding to the numbers 1-30). The time consumed by the path obtained by the method provided by the invention is only 28s, the planned path can be directly imported into DELMIA software and can be operated by adjustment, effective guidance help can be provided for engineers, and the development of products is accelerated.
In particular, in some preferred embodiments of the present invention, there is also provided a computer apparatus, including a memory and a processor, the memory storing a computer program, the processor implementing the steps of the arc welding robot collision-free path planning method in any of the above embodiments when executing the computer program.
In other preferred embodiments of the present invention, there is further provided a computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the collision-free path planning method for an arc welding robot in any of the above embodiments.
It will be understood by those skilled in the art that all or part of the processes of the above-described embodiments may be implemented by a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, the computer program may include the processes of the above-described embodiments of the collision-free path planning method for arc welding robot, and will not be described again here.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A collision-free path planning method for an arc welding robot is characterized by comprising the following steps:
s1, establishing a welding environment model by adopting a grid method and bounding box method mixed modeling mode, and determining an obstacle object and a guide space; the method comprises the following steps of (1) modeling a welded workpiece and barriers in a coverage space area of the welded workpiece by adopting a grid method, and modeling other barriers by adopting a bounding box method;
s2, based on the barrier object and the guide space, searching a collision-free transfer track between welding seams by an RRT method to form a transfer matrix;
s3, discretizing the difference method through a discretization strategy, dynamically adjusting the variation step length by adopting a self-adaptive strategy, screening collision-free transfer tracks in the transfer matrix by taking the welding sequence and the welding direction as objects, and determining the shortest collision-free welding path.
2. The arc welding robot collision-free path planning method according to claim 1, characterized in that:
when the grid method is adopted for modeling in the step S1, the welded workpiece and the obstacle in the coverage space area thereof are simplified into a combination of a plurality of triangles, the welding work space is divided into a plurality of cubes, the center point of each cube is projected to each triangle, and if the projection point falls within one triangle and the distance between the center point of each cube and the projection point is not greater than the distance between the center point of each cube and the vertex, the cube belongs to the obstacle mesh and is drawn into the obstacle object.
3. The arc welding robot collision-free path planning method according to claim 1, characterized in that:
in the step S2, when searching for a collision-free transfer trajectory between welds by using the RRT method, planning is performed based on a robot arm joint space, and a robot arm starting point is used as a root node of the RRT tree, and a node of the RRT tree is obtained by sampling in a mixed sampling manner including random sampling and random sampling in a guide space.
4. The arc welding robot collision-free path planning method according to claim 3, characterized in that:
in step S2, when finding a collision-free transfer trajectory between welds by the RRT method, the method includes the following steps:
s2-1, initializing RRT parameters, and taking the starting point of the robot as a root node of the RRT tree;
s2-2, generating a random number between 0 and 1, if the random number is not smaller than a first threshold value, taking the mechanical arm end point of the robot as a target point, if the random number is smaller than the first threshold value and not smaller than a second threshold value, taking random sampling as the target point, and if the random number is smaller than the second threshold value, taking the random sampling as the target point in the guide space;
s2-3, selecting a point in the RRT tree closest to the target point as a departure point, and dividing a plurality of detection points between the departure point and the target point;
s2-4, detecting whether the mechanical arm of the robot collides with the obstacle object or not for each detection point, if the robot collides with the obstacle object at any detection point, giving up the growth, returning to the step S2-2, otherwise, continuing to execute the step S2-5;
and S2-5, judging whether the target point is the robot terminal point, if so, outputting the shortest track, otherwise, adding the target point into the RRT tree and returning to execute the step S2-2.
5. The arc welding robot collision-free path planning method according to claim 4, characterized in that:
in step S2, when it is detected whether or not the robot arm collides with the obstacle object, the robot arm is replaced with a non-width linear model, and the arm width is compensated by adding a margin to the obstacle object.
6. The arc welding robot collision-free path planning method according to claim 5, characterized in that:
in step S2, when detecting whether a mechanical arm of the robot collides with an obstacle object, sampling a non-width linear model representing the mechanical arm, and determining whether the mechanical arm collides with the obstacle object based on the relative positions of the sampling points of the mechanical arm and the obstacle object;
for the obstacle object determined by the bounding box method, if projections of sampling points of the mechanical arm on three surfaces of the obstacle object are all in the bounding box with the added allowance, judging that the mechanical arm collides, and otherwise, judging that the mechanical arm does not collide with the obstacle object;
and for the barrier object determined by the grid method, if the distance from the sampling point of the mechanical arm to the peak of the grid after the allowance is increased is less than half of the length of the diagonal of the grid after the allowance is increased, judging that the mechanical arm collides, and otherwise, judging that the mechanical arm does not collide with the barrier object.
7. The arc welding robot collision-free path planning method according to claim 1, characterized in that:
in step S3, the discrete difference formula used for screening the collision-free transfer trajectories in the transfer matrix is as follows:
wherein, wparentRepresents the parent sequence, wnewRepresents the sub-sequence generated by the sub-sequence,
representing a mutation operation performed on the parent sequence, F ⊙ representing a probabilistic selection to perform a mutation,
representing sequences according to any two parents wr2、wr3Exchange the mutation rules to generate the calculation mutation rules.
8. The arc welding robot collision-free path planning method according to claim 7, characterized in that:
in step S3, the expression of the probability F is:
wherein, FinitRepresents the initial probability, Iter represents the current iteration number, iteration representsRepresenting the overall number of iterations and e representing a natural constant.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor when executing the computer program implements the steps of the collision-free path planning method for arc welding robots according to any of claims 1 to 8.
10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the collision-free path planning method for arc welding robots of any of claims 1 to 8.
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