CN115202365A - Robot welding obstacle avoidance and optimization path planning method based on three-dimensional discrete point construction - Google Patents
Robot welding obstacle avoidance and optimization path planning method based on three-dimensional discrete point construction Download PDFInfo
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Abstract
The invention discloses a robot welding obstacle avoidance optimizing path planning method based on three-dimensional discrete points, which comprises the steps of adopting a grid method to model a three-dimensional path planning space and dividing the three-dimensional path planning space into obstacle discrete points and free discrete points according to whether an obstacle is included or not; secondly, carrying out uneven distribution on the information concentration of each discrete point during initialization; adding feasibility factors to the heuristic function and adding welding gun rotation angle influence factors to the path transition probability to obtain an improved state transition probability; step four, adopting an improved pheromone updating rule to obtain an improved welding path; and fifthly, screening the improved welding path turning points by adopting a dynamic welding path turning point evaluation function, and obtaining the highest-evaluation welding path turning point to obtain the global optimal welding obstacle avoidance path of two adjacent welding points. The invention solves the problems that the global path planning in the robot welding process cannot effectively avoid dynamic obstacles and the path of the obstacle can not be too long.
Description
Technical Field
The invention relates to the technical field of robot welding path planning, in particular to a robot welding obstacle avoidance and optimization path planning method based on three-dimensional discrete point construction.
Background
When a robot welds a vehicle body, obstacle avoidance processing needs to be carried out on a path between two adjacent welding spots, collision between a welding gun and a clamp or a part is prevented, and many existing algorithms are used for robot welding path planning, such as an A-algorithm, an artificial potential field algorithm and the like, but most of the algorithms are based on a two-dimensional environment, and the robot has many limitations on a three-dimensional space with a complex environment. With the development of the algorithm, an ant colony algorithm, a genetic algorithm and the like appear, wherein the ant colony algorithm has strong adaptability and is easy to combine with other methods, so that the ant colony algorithm is widely applied to robot welding path planning, but has many disadvantages, for example, although the planned path can avoid obstacles, the planned path can increase many paths that the robot walks in the air, so that the total time of robot welding is increased, and the time of an engineer for debugging equipment is increased. In addition, global path planning is carried out in the robot welding process, and the problems that dynamic obstacles cannot be effectively avoided and the path of the obstacles is too long exist.
Disclosure of Invention
Aiming at the defects, the invention provides a robot welding obstacle avoidance and optimization path planning method based on three-dimensional discrete point construction, and aims to solve the problems that a dynamic obstacle cannot be effectively avoided and the path of the obstacle cannot be too long when global path planning is carried out in the robot welding process.
In order to achieve the purpose, the invention adopts the following technical scheme:
the robot welding obstacle avoidance and optimization path planning method based on the construction of the three-dimensional discrete points comprises the following steps:
step S1: modeling a three-dimensional path planning space by adopting a grid method, and dividing the three-dimensional path planning space into barrier discrete points and free discrete points according to whether barriers are included or not;
step S2: carrying out uneven distribution on the information concentration of each discrete point during initialization;
and step S3: based on the improved information concentration, adding feasibility factors to the heuristic function and adding welding gun rotation angle influence factors to the path transition probability to obtain an improved state transition probability;
and step S4: an improved pheromone updating rule is adopted, and an improved welding path is obtained by combining the improved state transition probability;
step S5: and screening the improved welding path turning points by adopting a dynamic welding path turning point evaluation function, selecting the welding path turning point with the highest evaluation to obtain a global optimal welding obstacle avoidance path of two adjacent welding points, and covering all the welding points.
Preferably, step S1 specifically includes the following steps:
step S11: establishing a three-dimensional coordinate system by taking any vertex of a three-dimensional space as a coordinate origin;
step S12: uniformly dividing a three-dimensional space by using a plane perpendicular to an x axis and a plane perpendicular to a y axis in the three-dimensional coordinate system to obtain a plurality of cubic grids with equal volumes, wherein each vertex of each cubic grid is used as a discrete point in the welding path planning;
step S13: selecting discrete points required by welding path planning, and judging the types of the selected discrete points; if the workpiece model covers the discrete points of the cubic grid, the discrete points are shown to have obstacles on the workpiece model, and the discrete points are obstacle discrete points; if the workpiece model does not cover the discrete points of the cubic grid, the discrete points are indicated to have no obstacles on the workpiece model, and the discrete points are free discrete points.
Preferably, the step S2 specifically includes the following steps:
step S21: connecting two adjacent welding spots to obtain a line segment L, and equally dividing the three-dimensional space by taking a plane vertical to the x axis;
step S22: constructing a circle center L which is the intersection point of L and each plane m Taking a point in a circle as a better discrete point, and dividing different initial pheromone concentrations according to the distance from the better discrete point to a line segment L, wherein the mathematical expression is as follows:
wherein, tau 0 The concentration of the initial pheromone is,
for improved initialization pheromone concentration, L ymax And L zmax The maximum lateral and longitudinal travel distances, dis (H) of the line segment L, respectively o ,L m ) As discrete points H o The distance to the line segment L, α, is a weighted value and is determined according to the specific parameters of the three-dimensional space.
Preferably, in step S3, a new heuristic function is constructed by adding feasibility factors, and the specific mathematical expression is as follows:
wherein eta ij Representing the expected degree of moving from the current discrete point i to the next discrete point j as a heuristic function; d ij The distance from the current discrete point i to the next discrete point j is obtained;
is a magnification factor;
for the feasibility influencing factor, different magnification factors are taken according to the peripheral discrete point of the next discrete point j.
Preferably, in step S3, adding a welding gun rotation angle influence factor to the path transition probability includes the following steps:
step S31: using intervals
Represents a power interval around the minimum value of the average power consumption, wherein
Is the minimum value of the average power consumption of the robot;
is the difference between the maximum and minimum values of the average power consumption;
step S32: taking a corresponding welding gun starting point and a corresponding welding gun end point in a power interval near the minimum value, wherein the welding gun rotation angles of the welding gun starting point and the welding gun end point are respectively gamma a And gamma b (ii) a The specific mathematical formula of the influence factor of the self-rotation angle of the welding gun is as follows:
wherein, V ij Representing influence factors of the self-rotation angle of the welding gun; gamma ray i Representing the self-rotation angle of the welding gun of the current discrete point i; gamma ray j Indicating the gun rotation angle of the next discrete point j.
Preferably, in step S3, according to the new heuristic function for adding the feasibility factor and the added influence factor of the self-rotation angle of the welding gun, an improved state transition probability formula is obtained as follows:
wherein the content of the first and second substances,
representing the probability of moving from a discrete point i to a discrete point j at the moment t; tau is ij (t) represents the information density moving from discrete point i to discrete point j at time t; d ij Representing the distance between the discrete point i and the discrete point j; eta ij (t) denotes the degree of elicitation, whichThe value being the inverse of the distance between the discrete point i and the discrete point j, i.e. eta ij (t)＝1/d ij ；V ij (t) represents a welding gun rotation angle influence factor at time t; alpha is used to control the information concentration; beta is used to control path visibility; delta is the important degree of the influence factor of the self-rotation angle of the welding gun, and is properly selected according to the actual welding condition of the robot; d k Is the set of discrete points that all the next step of the kth discrete point can reach directly.
Preferably, step S4 specifically includes the following steps:
arranging all path lengths according to an ascending order, selecting the path arranged at the front part for pheromone updating, wherein the updating rule is as follows:
τ ijg ＝(1-ρ)τ ijg +ρΔτ ijg (7)
wherein, tau ijg The pheromone concentration left by the g-th path after the complete welding path is ranked; delta tau ijg Pheromone content assigned to the g-th path; len (g) is the path length of the g-th path; rank (g) is the ranking of path g among all paths; num r Is the number of paths to be updated; rho is an pheromone volatilization factor, and the initial value of rho is set to be 0.9; q is a pheromone constant.
Preferably, in step S5, a specific mathematical formula of the evaluation function of the turning point of the dynamic welding path is as follows:
H(ψ，ω)＝ξ*(θ*Gunangle(ω)+λ*Wpointangle(ψ)) (9)
wherein, H (psi, omega) represents the evaluation function of the turning point of the dynamic welding path; gunangle (v, omega) is an evaluation function of the opening and closing angle of the welding gun, and represents the difference of the opening and closing angle of the welding gun between turning points of the two paths; wpointangle (v, omega) is a path angle evaluation function and represents a dynamic angle between a direction vector formed by adjacent path turning points and two final welding point vectors; xi is a smoothing coefficient; theta and lambda are weighted values of the evaluation functions and are weighted according to actual conditions; ω represents the absolute value of the difference between the welding gun opening and closing angles; psi is the absolute value of the included angle of the two vectors; phi is an angle parameter greater than 0; psi is an angle parameter greater than 0;
the turning points of two adjacent paths form a vector,
is a fixed vector of two welding points.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
according to the scheme, firstly, a proper path turning point is designed and selected based on improved welding path planning, then an evaluation scheme is selected based on the path turning point to further optimize the taken path turning point, so that the optimization of the welding path design under the condition of existence of an obstacle is realized, and a path with the shortest welding path, the minimum average power consumption of a robot and the minimum difference of the opening and closing angles of a welding gun is selected.
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FIG. 1 is a diagram of steps of a robot welding obstacle avoidance and optimization path planning method based on construction of three-dimensional discrete points;
FIG. 2 is a schematic diagram of an embodiment.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
The robot welding obstacle avoidance optimizing path planning method based on the construction of the three-dimensional discrete points comprises the following steps:
step S1: modeling a three-dimensional path planning space by adopting a grid method, and dividing the three-dimensional path planning space into barrier discrete points and free discrete points according to whether the barriers are included;
step S2: carrying out uneven distribution on the information concentration of each discrete point during initialization;
and step S3: based on the improved information concentration, adding feasibility factors to the heuristic function and adding welding gun rotation angle influence factors to the path transition probability to obtain an improved state transition probability;
and step S4: an improved pheromone updating rule is adopted, and an improved state transition probability is combined to obtain an improved welding path;
step S5: and screening the improved welding path turning points by adopting a dynamic welding path turning point evaluation function, selecting the welding path turning point with the highest evaluation to obtain a global optimal welding obstacle avoidance path of two adjacent welding points, and covering all the welding points.
The robot welding obstacle avoidance optimizing path planning method based on the three-dimensional discrete point construction comprises the first step of modeling a three-dimensional path planning space by adopting a grid method, providing a basis for selecting an optimal welding obstacle avoidance path according to whether an obstacle is divided into obstacle discrete points and free discrete points or not, and improving the search efficiency of an obstacle avoidance path algorithm. The second step is to carry out uneven distribution on the information concentration of each discrete point during initialization, so that the algorithm complexity can be simplified, the early searching efficiency can be improved, and the approximate direction of the welding path can be found quickly. The third step is based on the improved information concentration, and adds feasibility factors to the heuristic function and adds welding gun self-rotation angle influence factors in the path transition probability to obtain the improved state transition probability, wherein the feasibility factors are added to the heuristic function, different amplification parameters are given according to the barrier discrete point distribution situation around the current discrete point, the heuristic information difference of adjacent discrete points is improved, and the algorithm is favorably prevented from falling into local optimization; and adding a welding rotation angle influence factor to ensure that the robot consumes the least energy and the least average power in the obtained welding obstacle avoidance path. And the fourth step is to adopt an improved pheromone updating rule and combine the improved state transition probability to obtain an improved welding path, wherein the improved welding path is the path with the shortest length and the fewest turning times, so that the path with higher smoothness and the least power consumption of the robot is obtained. And a fifth step of screening the improved welding path turning points by adopting a dynamic welding path turning point evaluation function, selecting the welding path turning point with the highest evaluation, obtaining the globally optimal welding obstacle avoidance path of two adjacent welding points, and covering all the welding points, thereby ensuring the reasonability and optimality of the obtained welding obstacle avoidance path.
According to the scheme, firstly, based on the improved welding path planning design and the selection of a proper path turning point, and then based on the path turning point, an evaluation scheme is selected to further optimize the path turning point, so that the optimization of the welding path design under the condition of the existence of obstacles is realized, and a path with the shortest welding path, the minimum average robot power consumption and the minimum welding gun opening and closing angle difference is selected.
Preferably, the step S1 specifically includes the following steps:
step S11: establishing a three-dimensional coordinate system by taking any vertex of a three-dimensional space as a coordinate origin;
step S12: uniformly dividing a three-dimensional space by using a plane perpendicular to an x axis and a plane perpendicular to a y axis in the three-dimensional coordinate system to obtain a plurality of cubic grids with equal volume, wherein each vertex of each cubic grid is used as a discrete point in the welding path planning;
step S13: selecting discrete points required by welding path planning, and judging the types of the selected discrete points; if the workpiece model covers the discrete points of the cubic grid, the discrete points are shown to have obstacles on the workpiece model, and the discrete points are obstacle discrete points; if the workpiece model does not cover the discrete points of the cubic grid, the discrete points are indicated to have no obstacles on the workpiece model, and the discrete points are free discrete points.
In one embodiment, as shown in fig. 2, a lower left corner vertex of a three-dimensional map is used as a coordinate origin O to establish a three-dimensional coordinate system xyz, a plane perpendicular to an x axis and a plane perpendicular to a y axis in the three-dimensional coordinate system are used to divide a three-dimensional space into a plurality of cube grids with equal volumes, each vertex A, B, C of the cube grid is a discrete point in a welding path planning, a node in a traditional welding path planning algorithm is replaced, and path planning is facilitated. Selecting discrete points A, B and C required by welding path planning, and if the workpiece model covers discrete points A, B and C of the cubic grid, indicating that the discrete points A, B and C have obstacles on the workpiece model, wherein the discrete points A, B and C are obstacle discrete points; if the workpiece model does not cover discrete points A, B and C of the cubic grid, it indicates that the discrete points A, B and C do not have obstacles on the workpiece model, and the discrete points A, B and C are free discrete points.
Preferably, the step S2 specifically includes the following steps:
step S21: connecting two adjacent welding spots to obtain a line segment L, and equally dividing the three-dimensional space by taking a plane vertical to the x axis;
step S22: constructing a circle center L which is the intersection point of L and each plane m Taking a point in a circle as a better discrete point, and dividing different initial pheromone concentrations according to the distance from the better discrete point to a line segment L, wherein the mathematical expression is as follows:
wherein, tau 0 The concentration of the initial pheromone is,
for improved initialization pheromone concentration, L ymax And L zmax The maximum lateral and longitudinal travel distances, dis (H) of the line segment L, respectively o ,L m As discrete points H o The distance to the line segment L, α, is a weighted value and is determined according to specific parameters of the three-dimensional space.
In the traditional welding path planning algorithm, the initial pheromone concentration is the same, so that the walking randomness in the searching process is too strong, the convergence speed is too slow, and the pheromone carrier is a line segment between discrete points, thereby greatly increasing the space complexity of the algorithm. The improved algorithm of the scheme stores the pheromone in discrete points and performs uneven distribution on the pheromone of each point during initialization, thereby simplifying the complexity of the algorithm, improving the early-stage searching efficiency and accelerating the finding of the approximate direction of the welding path.
Preferably, in step S3, a new heuristic function is constructed by adding feasibility factors, and the specific mathematical expression is as follows:
wherein eta is ij Representing the expected degree of movement from the current discrete point i to the next discrete point j as a heuristic function; d ij The distance from the current discrete point i to the next discrete point j is obtained;
is a magnification factor;
for the feasibility influencing factor, different magnification factors are taken according to the peripheral discrete points of the next discrete point j.
Heuristic functions are an important group in the present weld path planning algorithmThe function of the component is to utilize the distance information to guide and select the shortest path, and directly influences the convergence, stability and optimality of the algorithm. Heuristic function eta of traditional welding path planning algorithm ij The distance between the discrete point i and the discrete point j is inversely proportional to the inverse proportion, but when the distance difference between the adjacent discrete points is small, the guiding effect on the path is not large, and meanwhile, when the discrete points are searched, the factors of surrounding obstacles are often ignored, and the discrete point closest to the current discrete point is preferentially selected, so that the local optimum is involved. Aiming at the problem, the scheme adds feasibility factors to construct a new heuristic function, increases the heuristic information difference of adjacent discrete points, and enables the search of the optimal path to have directionality.
Preferably, in step S3, a welding gun rotation angle influence factor is added to the path transition probability, and the method specifically includes the following steps:
step S31: using intervals
Represents a power interval around the minimum value of the average power consumption, wherein
Is the minimum value of the average power consumption of the robot;
is the difference between the maximum and minimum values of the average power consumption;
step S32: taking a corresponding welding gun starting point and a corresponding welding gun end point in a power interval near the minimum value, wherein the welding gun rotation angles of the welding gun starting point and the welding gun end point are respectively gamma a And gamma b (ii) a The specific mathematical formula of the influence factor of the self-rotation angle of the welding gun is as follows:
wherein, V ij Representing influence factors of the self-rotation angle of the welding gun; gamma ray i Representing the self-rotation angle of the welding gun of the current discrete point i; gamma ray j Representing the next discrete pointThe gun self-rotation angle of j.
Specifically, the welding wire rotates around the axis during arc welding, and the rotating angle is called as a welding gun rotation angle. The angle does not affect the quality of the welding process, but has difference in energy consumption in the welding process of the robot, mainly because the position, the rotation angle and the speed of each joint shaft of the corresponding robot are different when the rotation angle of the welding gun is changed. Randomly selecting a set of gun yaw angles for the start and end of the gun will cause the robot to vary the average power during welding, while in actual production, the combination of the start and end gun yaw angles for the minimum value of average power consumption is desired. According to the scheme, the welding rotation angle influence factor is added, and the value of the welding gun rotation angle influence factor is closer to 1, so that the power consumed by the robot when the robot passes through the two discrete points during welding is smaller, and the energy consumed by the robot in the obtained welding obstacle avoidance path is minimum and the average power is minimum.
Preferably, in step S3, the improved state transition probability formula is obtained according to the new heuristic function for adding the feasibility factor and the added influence factor of the welding gun rotation angle as follows:
wherein, the first and the second end of the pipe are connected with each other,
representing the probability of moving from a discrete point i to a discrete point j at the moment t; tau is ij (t) represents the information density moving from discrete point i to discrete point j at time t; d is a radical of ij Representing the distance between the discrete point i and the discrete point j; eta ij (t) represents the degree of inspiration, whose value is the reciprocal of the distance between the discrete points i and j, i.e. η ij (t)＝1/d ij ；V ij (t) represents a welding gun rotation angle influence factor at time t; alpha is used to control the information concentration; beta is used to control path visibility; delta is the important degree of the influence factor of the self-rotation angle of the welding gun, and is properly selected according to the actual welding condition of the robot; d k Is all next to the kth discrete pointA collection of discrete points that a step can reach directly.
In the scheme, feasibility factors are introduced into the heuristic function, welding self-rotation angle influence factors are added, the state transition probability function is improved, and the possibility of finding out the optimal path is improved.
Preferably, the step S4 specifically includes the following steps:
arranging all path lengths in ascending order, selecting the path arranged at the front part to update pheromones, wherein the updating rule is as follows:
τ ijg ＝(1-ρ)τ ijg +ρΔτ ijg (7)
wherein, tau ijg The pheromone concentration left by the g-th path after the complete welding path is ranked; delta tau ijg Pheromone content assigned to the g-th path; len (g) is the path length of the g-th path; rank (g) is the ranking of path g among all paths; num of r Is the number of paths to be updated; rho is an pheromone volatilization factor, and the initial value of rho is set to be 0.9; q is a pheromone constant.
When a path search is completed once, the lengths are used as evaluation values, all the path lengths are arranged in ascending order, and only the path arranged at the front part is selected for pheromone updating. Specifically, the total number G of times that the iteration is needed is set (0 < G < G), the value of G is increased by one every time the iteration is performed, all paths are sorted after the iteration is finished, and if the lengths of the welding paths of the two paths are similar, the sorting is performed according to the number of times of turning. The path with the large number of turns increases the power consumption of the robot, the path with the small number of turns is selected to be placed in front, and finally the path with the shortest length and the minimum number of turns is selected as the optimal path, so that the path with the higher smoothness and the minimum power consumption of the robot is obtained.
Preferably, in step S5, a specific mathematical formula of the dynamic welding path turning point evaluation function is as follows:
H(ψ，ω)＝ξ*(θ*Gunangle(ω)+λ*Wpointangle(ψ)) (9)
wherein H (psi, omega) represents a dynamic welding path turning point evaluation function; gunangle (v, omega) is an evaluation function of the opening and closing angle of the welding gun, and represents the difference of the opening and closing angle of the welding gun between turning points of two paths; wpointangle (v, omega) is a path angle evaluation function and represents a dynamic angle between a direction vector formed by adjacent path turning points and two final welding point vectors; xi is a smoothing coefficient; theta and lambda are weighted values of the evaluation functions and are weighted according to actual conditions; ω represents the absolute value of the difference between the welding gun opening and closing angles; psi is the absolute value of the included angle of the two vectors; phi is an angle parameter greater than 0; psi is an angle parameter greater than 0;
the turning points of two adjacent paths form a vector,
is a fixed vector of two welding points.
The design criterion of the evaluation function is that a welding gun at the tail end of the robot can avoid obstacles such as clamps, workpieces and the like as much as possible and quickly advance towards the next welding point. In the welding process, the difference between the opening and closing angles of the welding gun is not easy to be overlarge, and the turning angle of each path turning point is not easy to be overlarge, so that the values of theta and lambda are 1 in the embodiment, and if the deviation of the opening and closing angles of the welding gun is overlarge, the theta can be properly reduced; the distance between the path planning earlier stage and the next welding point is far, and lambda is required to be a small value; and in the later stage of path planning, the distance between the two welding points is closer to the next welding point, and the lambda is required to take a larger value. Further, when the evaluation function of a turning point of a certain path is too low, the turning point can be discarded, the turning point of the path is searched again by taking the turning point of the previous path as the starting point, and the turning point of the path is searched reversely by taking the turning point of the next path as the end point, and the two are combined to calculate the proper intermediate turning point.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (8)
1. The robot welding obstacle avoidance optimizing path planning method based on the construction of the three-dimensional discrete points is characterized by comprising the following steps of:
step S1: modeling a three-dimensional path planning space by adopting a grid method, and dividing the three-dimensional path planning space into barrier discrete points and free discrete points according to whether the barriers are included;
step S2: carrying out uneven distribution on the information concentration of each discrete point during initialization;
and step S3: based on the improved information concentration, adding feasibility factors to the heuristic function and adding welding gun rotation angle influence factors to the path transition probability to obtain an improved state transition probability;
and step S4: an improved pheromone updating rule is adopted, and an improved state transition probability is combined to obtain an improved welding path;
step S5: and screening the improved welding path turning points by adopting a dynamic welding path turning point evaluation function, selecting the welding path turning point with the highest evaluation to obtain a global optimal welding obstacle avoidance path of two adjacent welding points, and covering all the welding points.
2. The robot welding obstacle avoidance and optimization path planning method based on the construction of the three-dimensional discrete points according to claim 1, characterized in that: in step S1, the method specifically includes the following steps:
step S11: establishing a three-dimensional coordinate system by taking any vertex of a three-dimensional space as a coordinate origin;
step S12: uniformly dividing a three-dimensional space by using a plane perpendicular to an x axis and a plane perpendicular to a y axis in the three-dimensional coordinate system to obtain a plurality of cubic grids with equal volumes, wherein each vertex of each cubic grid is used as a discrete point in the welding path planning;
step S13: selecting discrete points required by welding path planning, and judging the types of the selected discrete points; if the workpiece model covers the discrete points of the cubic grid, the discrete points are shown to have obstacles on the workpiece model, and the discrete points are obstacle discrete points; if the workpiece model does not cover the discrete points of the cubic grid, the discrete points are indicated to have no obstacles on the workpiece model, and the discrete points are free discrete points.
3. The robot welding obstacle avoidance and optimization path planning method based on the construction of the three-dimensional discrete points according to claim 1, characterized in that: in step S2, the method specifically includes the following steps:
step S21: connecting two adjacent welding spots to obtain a line segment L, and equally dividing the three-dimensional space by taking a plane vertical to the x axis;
step S22: constructing a circle center L which is the intersection point of L and each plane m Taking a point in a circle as a better discrete point, and dividing different initial pheromone concentrations according to the distance from the better discrete point to a line segment L, wherein the mathematical expression is as follows:
wherein, tau 0 The concentration of the initial pheromone is,
for improved initialization of pheromone concentration, L ymax And L zmax The maximum lateral and longitudinal travel distances, dis (H) of the line segment L, respectively o ,L m ) As discrete points H o The distance to the line segment L, α, is a weighted value and is determined according to the specific parameters of the three-dimensional space.
4. The robot welding obstacle avoidance and optimization path planning method based on the construction of the three-dimensional discrete points according to claim 1, characterized in that: in step S3, a new heuristic function is constructed by adding feasibility factors, and the specific mathematical expression is as follows:
wherein eta is ij Representing the expected degree of moving from the current discrete point i to the next discrete point j as a heuristic function; d ij The distance from the current discrete point i to the next discrete point j is calculated;
is a magnification factor;
for the feasibility influencing factor, different magnification factors are taken according to the peripheral discrete points of the next discrete point j.
5. The robot welding obstacle avoidance and optimization path planning method based on the construction of the three-dimensional discrete points according to claim 1, characterized in that: in the step S3, a welding gun rotation angle influence factor is added to the path transition probability, specifically including the following steps:
step S31: using intervals
Represents a power interval around the minimum value of the average power consumption, wherein
Is the minimum value of the average power consumption of the robot;
is the difference between the maximum and minimum values of the average power consumption;
step S32: taking the corresponding welding gun starting point and end point in the power interval near the minimum value, wherein the welding gun rotation angles of the welding gun starting point and the welding gun end point are gamma respectively a And gamma b (ii) a The specific mathematical formula of the influence factor of the self-rotation angle of the welding gun is as follows:
wherein, V ij Representing influence factors of the self-rotation angle of the welding gun; gamma ray i Representing the self-rotation angle of the welding gun of the current discrete point i; gamma ray j Indicating the gun rotation angle of the next discrete point j.
6. The robot welding obstacle avoidance and optimization path planning method based on the construction of the three-dimensional discrete points according to claim 1, characterized in that: in step S3, according to the new heuristic function for adding feasibility factors and the added influence factor of the self-rotation angle of the welding gun, an improved state transition probability formula is obtained as follows:
wherein the content of the first and second substances,
representing the probability of moving from a discrete point i to a discrete point j at the moment t; tau is ij (t) represents the information density moving from discrete point i to discrete point j at time t; d ij Representing the distance between the discrete point i and the discrete point j; eta ij (t) represents the degree of inspiration, whose value is the reciprocal of the distance between the discrete points i and j, i.e. η ij (t)＝1/d ij ；V ij (t) represents a welding gun rotation angle influence factor at time t; alpha is used to control the information concentration; beta is used to control path visibility; delta is the important degree of the influence factor of the self-rotation angle of the welding gun, and is properly selected according to the actual welding condition of the robot; d k Is the set of discrete points that all the next steps of the kth discrete point can reach directly.
7. The robot welding obstacle avoidance and optimization path planning method based on the construction of the three-dimensional discrete points according to claim 1, characterized in that: in step S4, the method specifically includes the following steps:
arranging all path lengths according to an ascending order, selecting the path arranged at the front part for pheromone updating, wherein the updating rule is as follows:
τ ijg ＝(1-ρ)τ ijg +ρΔτ ijg (7)
wherein, tau ijg The pheromone concentration left by the g-th path after the complete welding path is ranked; delta tau ijg Pheromone content assigned to the g-th path; len (g) is the path length of the g-th path; rank (g) is the ranking of path g among all paths; num r Is the number of paths to be updated; rho is an pheromone volatilization factor, and the initial value of rho is set to be 0.9; q is a pheromone constant.
8. The robot welding obstacle avoidance and optimization path planning method based on construction of the three-dimensional discrete points according to claim 1, characterized in that: in step S5, a specific mathematical formula of the evaluation function of the turning point of the dynamic welding path is as follows:
wherein, H (psi, omega) represents the evaluation function of the turning point of the dynamic welding path; gunangle (v, omega) is an evaluation function of the opening and closing angle of the welding gun, and represents the difference of the opening and closing angle of the welding gun between turning points of two paths; wpointangle (v, omega) is a path angle evaluation function and represents a dynamic angle between a direction vector formed by adjacent path turning points and two final welding point vectors; xi is a smoothing coefficient;
and lambda is the weighted value of each evaluation function, and the weighting is carried out according to the actual condition; ω represents the absolute value of the difference between the opening and closing angles of the welding gun; psi is the absolute value of the included angle of the two vectors;phi is an angle parameter greater than 0; psi is an angle parameter greater than 0;
the turning points of two adjacent paths form a vector,
is a fixed vector of two welding points.
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