A Part Handling Path Planning Method and System Based on the Assembly Process

Abstract

The present invention discloses a part handling path planning method and system in the field of path planning, including: obtaining the position information of obstacles, assembly destinations, assembly personnel, and the coordinates (x i , y i ) of the assembly parts in the virtual assembly environment in the assembly scene coordinate system; selecting the assembly parts to be handled according to the priority order and subordination relationship between the assembly parts, and using the eight-angle A * algorithm to plan the obstacle avoidance path during the process of handling the assembly parts, and constructing an obstacle avoidance path set P; using the ant colony algorithm to solve the path L min with the shortest distance in the obstacle avoidance path set P; adding the picked-up assembly parts to the taboo list; continuously updating the shortest route according to the priority order and subordination relationship between the assembly parts, the handling amount of the assembly parts in the taboo list, and the handling amount threshold, so as to optimize the total path; the present invention is more intelligent and user-friendly in path planning, can effectively increase the accuracy of path planning, and thus improve the assembly efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of path planning, and particularly relates to a method and system for part handling path planning based on the assembly process. Background Art

[0002] The assembly simulation process of a product mainly includes assembly model construction, assembly process planning, assembly process simulation, assembly result display, and assembly animation generation. As the most important, time-consuming, and energy-consuming part in the product's entire life cycle, the planning and design of the assembly sequence play a decisive role in the product quality. The rise of virtual reality technology has made it possible for people to simulate the assembly process in a virtual environment. Compared with traditional assembly technology, virtual assembly avoids the waste of physical models, saves costs, and improves production efficiency.

[0003] In a virtual environment, how to enable a virtual human to select the shortest path according to the situation in the scene, reasonably avoid obstacles, and transport the parts in the scene to the assembly point according to its own situation is the focus of virtual assembly research. However, due to the different sizes and weights of the parts to be assembled, different handling methods need to be selected according to the physical characteristics of the parts. Therefore, in virtual assembly simulation, how to plan a flexible handling route for assembly parts has become an urgent problem to be solved. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for part handling path planning based on the assembly process, and plan the handling route according to the set assembly sequence and assembly scene, so as to improve the assembly efficiency.

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] The first aspect of the present invention provides a method for part handling path planning based on the assembly process, including:

[0007] Obtain the position information of obstacles, assembly destinations, assembly personnel, and the coordinates (x i , y i ) of the assembly parts in the virtual assembly environment of the assembly scene coordinate system;

[0008] Traverse the information of each assembly part in the assembly scene, determine the priority order and subordination relationship between the assembly parts, and construct an assembly part set E = [E1, E2, E3,..., E i ,..., E n , i = 1, 2, 3... n;

[0009] Select the assembly parts to be handled according to the priority order and subordination relationship between the assembly parts, and use the eight-angle A *The algorithm plans the obstacle avoidance path during the process of transporting and assembling parts, and constructs an obstacle avoidance path set P = [P1, P2, P3, …, P i , …, P n ;

[0010] The ant colony algorithm is used to solve the shortest path L in the obstacle avoidance path set P min , and it is used as the transportation path for the assembler to the assembled parts;

[0011] According to the path L min Pick up the assembled parts E i in sequence, and add the picked-up assembled parts to the taboo list; judge whether the number of remaining assembled parts in the assembly scene is greater than 0;

[0012] In response to the number of remaining assembled parts in the assembly scene being greater than 0; calculate the transportation volume in the taboo list if the assembled parts E i+1 are continued to be transported, and judge whether the transportation volume threshold is reached; when the transportation volume threshold is reached, use the eight-angle A * algorithm and the ant colony algorithm to plan the transportation path of the assembler to the assembly destination; when the transportation volume threshold is not reached, plan the transportation path of the assembler to the transported assembled parts E i+1 ;

[0013] Repeat the process of planning the transportation path until the number of assembled parts in the assembly scene is 0, and assist the assembler to transport the assembled parts according to the route indication of the transportation path.

[0014] Preferably, the method for establishing the assembly scene coordinate system includes: simulating a virtual assembly scene in computer simulation software according to the actual assembly environment, and drawing a grid-based scene coordinate system. The grid scale of the drawn scene coordinate system is greater than or equal to 0.1 m, and it is set that the grids occupied by the assembled parts and obstacles in the virtual assembly scene are all filled.

[0015] Preferably, the expression formula of the evaluation function in the eight-angle A * algorithm is

[0016] f(a) = g(a) + h(a);

[0017] h(a) = |x g - x a | + |y g - y a |

[0018] In the formula, a represents the current position of the assembler, f(a) is the evaluation function of the assembler at position a, g(a) is the actual cost value for the assembler to move from the starting position to the current position a, h(a) is the cost estimate value from the current position a to the target position, (xa , y a ), which is the current coordinate of the assembler, (x g , y g ) is the destination coordinate.

[0019] Preferably, the ant colony algorithm is used to solve the shortest path L in the obstacle avoidance path set P min The method includes:

[0020] Suppose there are n parts to be picked up and m ants in the ant colony; let b i (t) be the number of ants at the i-th assembly part at time t, and the expression formula is:

[0021]

[0022] The probability of the ant transferring from the i-th assembly part to the j-th assembly part at time t is:

[0023]

[0024] In the formula, τ ij (t) is the concentration heuristic factor, and the value of the concentration heuristic factor is the pheromone concentration between the i-th assembly part and the j-th assembly part at time t; η ij (t) is the path heuristic factor, and the value of the path heuristic factor is the reciprocal of the distance d ij between the i-th assembly part and the j-th assembly part; α is the weight coefficient of the residual pheromone on the path ij; β is the weight coefficient of the path heuristic factor; J k (i) is all the optional parts to be assembled for the ant k currently.

[0025] According to the probability of the ant transferring from i to j at time t, the shortest path L is selected from the obstacle avoidance path set P min .

[0026] Preferably, in the ant colony algorithm, the concentration heuristic factor τ ij (t) is updated iteratively, and the expression formula is:

[0027] τ ij (t + n) = (1 - ρ)τ ij (t) + Δτ ij (t)

[0028]

[0029]

[0030] In the formula, ρ represents the degree of attenuation of the pheromone over time, 0 < ρ < 1; Δτ ij(t) represents the pheromone increment on the path (i, j) during the iterative process at time t; represents the amount of pheromone left by the k-th ant on the path (i, j) during the iteration; NC is the number of iterations, and Q represents the pheromone constant, where, L k is the length of the path traveled by the ant k in this loop, and L best is the optimal solution found by the entire ant colony during the iterative process.

[0031] Preferably, the assembled parts after being picked up are deleted from the virtual assembly scene, and the un-picked assembled parts are regarded as obstacles during the process of planning the handling path.

[0032] Preferably, the priority order among the assembled parts is the assembly order of the assembled parts.

[0033] Preferably, a virtual human model is established according to the human skeleton and joint order, and a motion chain S = {S1, S2, S3,..., S n} is set according to the information of the assembled parts and stored in the motion database; the position (x n , y n ) of the virtual human model in the scene coordinate system is read in real time. When the human model reaches the set key point, the motion chain in the motion database is called, and the BML behavior markup language method is used to instruct the virtual human model to pick up the assembled parts.

[0034] Preferably, the key point is set at the second coordinate point before the virtual human model reaches the selected assembled part to be picked up.

[0035] The second aspect of the present invention provides a part handling path planning system based on the assembly process, including:

[0036] An acquisition module, which acquires the position information of obstacles, assembly destinations, assembly personnel, and the coordinates (x i , y i ) of the assembled parts in the virtual assembly environment of the assembly scene coordinate system; traverses the information of each assembled part in the assembly scene, determines the priority order and subordination relationship among the assembled parts, and constructs an assembled part set E = [E1, E2, E3,..., E i ,..., E n , i = 1, 2.3... n;

[0037] A path planning module, which is used to select the assembled parts to be handled according to the priority order and subordination relationship among the assembled parts, and uses the octagonal A * algorithm to plan the obstacle avoidance path during the process of handling the assembled parts, and constructs an obstacle avoidance path set P = [P1, P2, P3,..., P i ,..., P n; Use the ant colony algorithm to solve the shortest path L in the obstacle avoidance path set P min , and use it as the handling path for the assembler to the assembled parts; According to the path L min Pick up the assembled parts E i in sequence;

[0038] Judgment and analysis module, used to judge whether the number of remaining assembled parts in the assembly scene is greater than 0; In response to the number of remaining assembled parts in the assembly scene being greater than 0; Calculate the handling quantity in the taboo list if the assembled parts E are continued to be handled i+1 , and judge whether the handling quantity threshold is reached; When the handling quantity threshold is reached, use the eight-angle A * algorithm and the ant colony algorithm to plan the handling path of the assembler to the assembly destination; When the handling quantity threshold is not reached, plan the handling path of the assembler to handle the assembled parts E i+1 ; Repeat the process of planning the handling path until the number of assembled parts in the assembly scene is 0;

[0039] Indication module, according to the route indication of the handling path, assist the assembler to handle the assembled parts.

[0040] The third aspect of the present invention provides a computer-readable storage medium, which is characterized in that a computer program is stored thereon, and when the program is executed by a processor, the steps of the part handling path planning method are implemented.

[0041] Compared with the prior art, the beneficial effects of the present invention:

[0042] The present invention constructs a virtual assembly scene model with assembled parts and obstacles as key points; Through the eight-angle A * algorithm and the improved ant colony algorithm to plan the shortest path L min , as the initial path planning scheme for virtual assembly; According to the priority order and subordination relationship between the assembled parts, the handling quantity of the assembled parts in the taboo list, and the handling quantity threshold, continuously update the shortest line, so that the total path is optimized; The present invention is more intelligent and user-friendly in path planning, can effectively increase the accuracy of path planning, and thus improve the assembly efficiency. Description of the drawings

[0043] Figure 1 is a flowchart of a part handling path planning method based on the assembly process provided by an embodiment of the present invention;

[0044] Figure 2 is a schematic diagram of a virtual assembly scene of a part handling path planning method based on the assembly process provided by an embodiment of the present invention;

[0045] Figure 3It is a schematic diagram of the grid range occupied by the assembly parts and obstacles provided by the embodiments of the present invention;

[0046] Figure 4 It is the key point range around the assembly parts and obstacles provided by the embodiments of the present invention;

[0047] Figure 5 It is the initial path diagram of a part handling path planning method based on the assembly process provided by the embodiments of the present invention;

[0048] Figure 6 It is the path diagram after reaching the handling quantity threshold of a part handling path planning method based on the assembly process provided by the embodiments of the present invention;

[0049] Figure 7 It is the path diagram after reaching the assembly point of a part handling path planning method based on the assembly process provided by the embodiments of the present invention. Detailed implementation manners

[0050] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention.

[0051] Embodiment 1

[0052] As Figure 1 shown, a part handling path planning method based on the assembly process includes:

[0053] Simulate and obtain a virtual assembly scene in a computer three-dimensional simulation software according to the actual assembly environment. As Figure 2 shown, the virtual assembly scene includes a human body, assembly parts, and obstacles, etc. The assembly parts are a set of parts with a hierarchical relationship, such as the parts of a satellite, the parts of an armored vehicle, the parts of an aircraft, etc.; the obstacles are environmental objects that hinder the assembly path, such as workbenches, scaffolding, toolboxes, hoisting devices, etc.; draw a grid-based scene coordinate system in the virtual assembly scene, and obtain the position information of the obstacles and the position information of the assembly destination in the virtual assembly environment; As Figure 3 shown, the grid scale of the drawn scene coordinate system is greater than or equal to 0.1 m, and it is set that the grids occupied by the assembly parts and obstacles in the virtual assembly scene are all filled.

[0054] Traverse the information of each assembly part in the assembly scene to obtain the coordinates, priority order, and subordination relationship of the assembly parts, and construct an assembly part set E = [E1, E2, E3,..., E i ,..., E n , i = 1, 2, 3... n; the assembly part information includes the shape, weight, and volume of the assembly parts.

[0055] The position information of the assembler is determined by positioning the portable route indication device, and the assembled parts to be carried are selected according to the priority order and subordination relationship among the assembled parts. The priority order among the assembled parts is the assembly order of the assembled parts; the octagonal A * algorithm is used to plan the obstacle avoidance path during the process of carrying the assembled parts, and an obstacle avoidance path set P = [P1, P2, P3, …, P i , …, P n is constructed;

[0056] The evaluation function expression formula in the octagonal A * algorithm is

[0057] f(a) = g(a) + h(a);

[0058] h(a) = |x g - x a | + |y g - y a |

[0059] In the formula, a represents the current position of the assembler, f(a) is the evaluation function of the assembler at position a, g(a) is the actual cost value of the assembler moving from the starting position to the current position a, h(a) is the cost estimated value from the current position a to the target position, (x a , y a ) is the current coordinate of the assembler, and (x g , y g ) is the destination coordinate.

[0060] As Figure 5 shown, the ant colony algorithm is used to solve the shortest path L min in the obstacle avoidance path set P, and it is used as the handling path of the assembler to the assembled parts. The method includes:

[0061] There are n parts to be picked up, and there are m ants in the ant colony; let b i (t) be the number of ants at the i-th assembled part at time t, and the expression formula is:

[0062]

[0063] The probability of the ant transferring from the i-th assembled part to the j-th assembled part at time t is:

[0064]

[0065] In the formula, τ ij (t) is the concentration heuristic factor, and the value of the concentration heuristic factor is the pheromone concentration between the i-th assembled part and the j-th assembled part at time t; ηij (t) is the path heuristic factor, and the value of the path heuristic factor is the reciprocal of the distance d between the i-th assembled part and the j-th assembled part; ij α is the weight coefficient of the residual pheromone on path ij; β is the weight coefficient of the path heuristic factor; J k (i) is the set of all currently available parts to be assembled for ant k;

[0066] After all ants complete a tour, the paths they have walked form a solution. At this time, calculate the path length L that each ant has walked k and save the shortest path L min = min{L k | k = 1, 2, …, m}; The pheromone left before will gradually decrease over time. Therefore, the longer the path an ant walks, the more time it takes, resulting in a faster decrease in the pheromone left on the path. At this time, the amount of information on each path needs to be adjusted according to the following formula, and the expression formula is:

[0067] τ ij (t + n) = (1 - ρ)τ ij (t) + Δτ ij (t)

[0068] In the formula, ρ represents the degree of attenuation of the pheromone over time, 0 < ρ < 1; Through ρ, the pheromone concentration on the path can be adjusted in a timely manner; Δτ ij (t) represents the pheromone increment on path (i, j) during the iteration process. The pheromone increment Δτ ij (t) is expressed as follows:

[0069]

[0070] In the formula, represents the amount of pheromone left by the k-th ant on path (i, j) during the iteration; In order to speed up the distinction between better and other paths, through the dynamic update strategy of pheromone increment, the pheromone is dynamically updated using the number of iterations and the current optimal solution.

[0071] While ensuring that ants can find the optimal solution, accelerating the convergence speed and shortening the convergence time, the amount of pheromone is expressed as follows:

[0072]

[0073] In the formula, NC is the number of iterations, Q represents the pheromone constant, By setting the value f to avoid the pheromone increment being too small due to too large total number of iterations, L k is the length of the path that ant k walks in this loop, L bestIt is the optimal solution found by the entire ant colony during the current iteration process.

[0074] Judge whether the number of remaining assembly parts in the assembly scenario is greater than 0; in response to the number of remaining assembly parts in the assembly scenario being greater than 0; add the picked-up assembly part to the taboo list, and select the next assembly part E to be carried according to the priority order and subordination relationship between the assembly parts i+1 ; Calculate the handling quantity of the assembly parts in the taboo list if the next assembly part E is continued to be carried i+1 and judge whether the handling quantity threshold is reached; the handling quantity is selected according to the actual situation to calculate the handling quantity by volume or weight; as Figure 6 shown, when the handling quantity threshold is reached, that is, it reaches and cannot pick up the next assembly part E i+1 , use the eight-angle A * algorithm and the ant colony algorithm to plan the handling path of the assembly personnel to the assembly destination; clear the taboo list after the assembly part is carried to the assembly destination; as Figure 7 shown, when the handling quantity threshold is not reached, that is, it is possible to pick up the next assembly part E i+1 , plan the handling path of the assembly personnel to the next assembly part, and ensure that the assembly personnel can continue to carry when selecting the next assembly part to be carried; delete the picked-up assembly part from the virtual assembly scenario, and regard the un-picked assembly parts as obstacles during the process of planning the handling path.

[0075] Repeat planning the handling path, and set the termination condition to the number of assembly parts in the assembly scenario being 0; send the handling path to the portable route indication device in real time; assist the assembly personnel to carry the assembly parts according to the route indication of the handling path.

[0076] Establish a virtual human model according to the human skeleton and joint order, set the motion chain S = {S1, S2, S3,..., S n} and store it in the motion database, and the motion types include actions such as squatting and lifting; read the position (x n , y n ) of the virtual human model in the scene coordinate system in real time. When the human model reaches the set key point, call the motion chain in the motion database, and use the BML behavior markup language method to indicate the virtual human model to pick up the assembly part; as Figure 4 shown, the key point is set at the second coordinate point before the virtual human model reaches the selected pick-up assembly part; by instructing the virtual character to make corresponding actions, the assembly animation becomes more vivid.

[0077] Embodiment 2

[0078] A part handling path planning system based on the assembly process. The part handling path planning system provided in this embodiment can be applied to the part handling path planning method described in Embodiment 1. The part handling path planning system includes:

[0079] An acquisition module that acquires the position information of obstacles, assembly destinations, assembly personnel, and the coordinates (x i , y i ) of the assembly parts in the virtual assembly environment of the assembly scene coordinate system; traverses the information of each assembly part in the assembly scene to determine the priority order and subordination relationship between the assembly parts, and constructs an assembly part set E = [E1, E2, E3,..., E i ,..., E n , where i = 1, 2, 3... n;

[0080] A path planning module that selects the assembly parts to be handled according to the priority order and subordination relationship between the assembly parts, uses the eight-angle A * algorithm to plan the obstacle avoidance path during the process of handling the assembly parts, and constructs an obstacle avoidance path set P = [P1, P2, P3,..., P i ,..., P n ; uses the ant colony algorithm to solve the shortest path L min in the obstacle avoidance path set P and takes it as the handling path of the assembly personnel to the assembly parts; picks up the assembly parts E min in sequence according to the path L i ;

[0081] A judgment and analysis module that judges whether the number of remaining assembly parts in the assembly scene is greater than 0; in response to the number of remaining assembly parts in the assembly scene being greater than 0, calculates the handling quantity in the taboo list if the assembly parts E i+1 are continued to be handled, and judges whether the handling quantity threshold is reached; when the handling quantity threshold is reached, uses the eight-angle A * algorithm and the ant colony algorithm to plan the handling path of the assembly personnel to the assembly destination; when the handling quantity threshold is not reached, plans the handling path of the assembly personnel to the assembly parts E i+1 ; repeats the process of planning the handling path until the number of assembly parts in the assembly scene is 0;

[0082] An indication module that assists the assembly personnel in handling the assembly parts according to the route indication of the handling path.

[0083] Embodiment 3

[0084] A computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the steps of the part handling path planning method described in Embodiment 1.

[0085] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0086] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0087] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0089] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for planning the part handling path based on the assembly process, characterized in that, Including: Obtain the position information of obstacles, assembly destinations, and assembly personnel in the virtual assembly environment in the assembly scene coordinate system and the coordinates (x i , y i ) of the assembly parts; Traverse the information of each assembly part in the assembly scenario, determine the priority order and subordination relationship between the assembly parts, and construct an assembly part set E = [E1, E2, E3, …, E i , …, E n , where i = 1, 2, 3 … n; Select the assembled parts to be transported according to the priority order and subordination relationship between the assembled parts, and use the eight-angle A * algorithm to plan the obstacle avoidance path during the transportation of the assembled parts, and construct the obstacle avoidance path set P = [P1, P2, P3, …, P i , …, P n ; The ant colony algorithm is used to solve the shortest path L in the obstacle avoidance path set P min and use it as the handling path for the assembler to the assembled parts; According to path L min Pick up the assembled part E i in sequence, add the picked-up assembled part to the tabu list; determine whether the number of remaining assembled parts in the assembly scene is greater than 0; In response to the number of remaining assembly parts in the assembly scenario being greater than 0; calculate the handling quantity in the tabu list if the assembly part E is continued to be handled i+1 and determine whether the handling quantity threshold is reached; when the handling quantity threshold is reached, use the octagonal angle A * algorithm and the ant colony algorithm to plan the handling path of the assembly personnel to the assembly destination; when the handling quantity threshold is not reached, plan the handling path of the assembly personnel to handle the assembly part E i+1 of the handling path; Repeat the process of planning the handling path until the number of assembled parts in the assembly scene is 0, and assist the assembler in handling the assembled parts according to the route indication of the handling path.

2. The method for planning the part handling path based on the assembly process according to claim 1, characterized in that, The method for establishing the assembly scene coordinate system includes: simulating a virtual assembly scene in computer simulation software according to the actual assembly environment, and drawing a meshed assembly scene coordinate system, where the grid scale of the drawn assembly scene coordinate system is greater than or equal to 0.1 m, and setting that the grids occupied by the assembled parts and obstacles in the virtual assembly scene are all filled.

3. The method for planning the part handling path based on the assembly process according to claim 1, characterized in that, Using eight angles A * The expression formula of the evaluation function in the algorithm is f(a) = g(a) + h(a); h(a) = |x g - x a | + |y g - y a | Wherein, a represents the current position of the assembly worker, f(a) is the evaluation function when the assembly worker is at position a, g(a) is the actual cost value for the assembly worker to move from the starting position to the current position a, h(a) is the cost estimated value from the current position a to the target position, (x a , y a ) is the current coordinate of the assembly worker, and (x g , y g ) is the coordinate of the destination.

4. The method for planning the part handling path based on the assembly process according to claim 1, characterized in that, Use the ant colony algorithm to solve for the shortest path L in the obstacle avoidance path set P min The method includes: There are n parts to be picked up, and there are m ants in the ant colony; let b i (t) be the number of ants at the i-th assembly part at time t, and the expression formula is: The probability of the ant transferring from the i-th assembled part to the j-th assembled part at time t is: where τ ij (t) is the concentration heuristic factor, and the value of the concentration heuristic factor is the pheromone concentration between the i-th assembled part and the j-th assembled part at time t; η ij (t) is the path heuristic factor, and the value of the path heuristic factor is the reciprocal of the distance d ij between the i-th assembled part and the j-th assembled part; α is the weight coefficient of the residual pheromone on path ij; β is the weight coefficient of the path heuristic factor; J k (i) is all the currently available parts to be assembled for ant k; At time t, the path with the shortest distance is selected from the obstacle avoidance path set P according to the probability of the ant's transfer path from i to j. min .

5. The method for planning the part handling path based on the assembly process according to claim 4, characterized in that, Updating and iterating the pheromone heuristic factor τ in the ant colony algorithm ij (t), and the expression formula is as follows: τ ij (t + n) = (1 - ρ)τ ij (t) + Δτ ij (t) In the formula, ρ represents the degree of pheromone decay over time, where 0 < ρ < 1; Δτ ij (t) represents the pheromone increment on the path (i, j) during the iteration at time t; represents the amount of pheromone left by the k-th ant on the path (i, j) during the iteration; NC is the number of iterations, and Q represents the pheromone constant, where set value f; L k is the length of the path traveled by ant k in this cycle, L best is the optimal solution found by the entire ant colony during the iteration.

6. The method for planning the part handling path based on the assembly process according to any one of claims 1 to 5, characterized in that, Delete the assembled parts after picking up in the assembly scene coordinate system, and regard the un-picked assembled parts as obstacles during the process of planning the handling path.

7. The method for planning the part handling path based on the assembly process according to claim 1, characterized in that, Build a virtual human body model according to the order of the human bones and joints, and set the motion chain S = {S1, S2, S3, …, S n} according to the assembly part information and store it in the motion database; read the position (x n , y n ) of the virtual human body model in the scene coordinate system in real time. When the character model reaches the set key point, call the motion chain in the motion database and use the BML behavior markup language method to instruct the virtual human body model to pick up the assembly part.

8. A method for planning a part handling path based on the assembly process according to claim 7, wherein, The key point is set at the second coordinate point before the virtual human model reaches the selected assembled part to be picked up.

9. A system for planning a part handling path based on the assembly process, wherein, Including: An acquisition module acquires the position information of obstacles, assembly destinations, assembly personnel, and the coordinates (x i , y i ) of assembly parts in the virtual assembly environment in the assembly scene coordinate system; traverses the information of each assembly part in the assembly scene to determine the priority order and subordination relationship between the assembly parts, and constructs an assembly part set E = [E1, E2, E3, …, E i , …, E n , where i = 1, 2, 3…n; The path planning module is used to select the assembly parts to be carried according to the priority order and subordination relationship among the assembly parts, and uses the eight-angle A * algorithm to plan the obstacle avoidance path during the process of carrying the assembly parts, and construct the obstacle avoidance path set P = [P1, P2, P3, …, P i , …, P n ; The ant colony algorithm is used to solve the shortest path L min in the obstacle avoidance path set P, and use it as the carrying path from the assembler to the assembly parts; Pick up the assembly parts E in sequence according to the path L min i , and add the picked-up assembly parts to the taboo list; A judgment and analysis module is used to determine whether the number of remaining assembled parts in the assembly scenario is greater than 0; in response to the number of remaining assembled parts in the assembly scenario being greater than 0, calculate the handling quantity in the taboo list if the assembled part E is continuously handled i+1 and determine whether the handling quantity threshold is reached; when the handling quantity threshold is reached, use the octagonal A * algorithm and the ant colony algorithm to plan the handling path of the assembly personnel to the assembly destination; when the handling quantity threshold is not reached, plan the handling path of the assembly personnel to handle the assembled part E i+1 of the handling path; Repeat the process of planning the handling path until the number of assembled parts in the assembly scene is 0; An indication module that assists the assembler in handling the assembled parts according to the route indication of the handling path.

10. A computer-readable storage medium, wherein, A computer program is stored thereon, and when the program is executed by a processor, it implements the steps of the handling path planning method according to any one of claims 1 to 8.

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