Multi-robot collaborative additive manufacturing task allocation method and system

Abstract

The present invention relates to a method and system for task allocation in multi-robot collaborative additive manufacturing, including: S1. Taking the additive path to be completed as a task, and establishing a task library that each robot can complete according to the task type and the working range of each robot; S2. Calculating the distance from the center of gravity of each task in the task library that each robot can complete to the robot, and arranging the tasks in the task library that each robot can complete in ascending order of distance; S3. Comparing the total duration of the tasks already assigned to each current robot, selecting the robot with the shortest current working duration, adding the first 1 / k tasks in the task library that the robot can complete to the working sequence of the robot, and deleting these tasks from the task libraries of all robots. Repeat step S3 until all tasks are allocated. k is a coefficient related to the longest task and the shortest task lengths among all tasks, the number of robots, and the task length variance. It solves the problems of high time complexity and poor adjacency of the traditional allocation method.

Description

Technical Field

[0001] The present invention relates to the technical field of additive manufacturing, and in particular to a multi-robot collaborative additive manufacturing task allocation method and system. Background Art

[0002] The manufacturing of key components in fields such as aerospace vehicles, rail transit, ships, and energy is developing towards large-scale and integrated directions. The metal additive manufacturing technology for large metal components is the key. Currently, most metal additive manufacturing systems use a single robot as the actuator, with limited efficiency and long time consumption. Adopting a multi-robot collaboration method can improve the deposition efficiency, and realizing automatic task allocation for multiple robots has important research significance and application value.

[0003] Current task allocation methods include brute force method, sequential allocation, improved Hungarian algorithm, etc. During the task allocation process of multi-robot collaborative additive manufacturing for large-sized components, due to the extremely large number of deposition paths and their diverse spatial positions, directly applying the above methods takes a long time, and the tasks assigned to each robot are prone to "interleaving" situations. If the additive tasks of multiple robots are interleaved, the robotic arms are prone to interference during the additive process, and due to the deposition path stacking order problem, the interlayer lap quality is likely to be reduced. In the application of multi-robot collaborative additive manufacturing, there is currently no general algorithm that can effectively solve the problem of optimal allocation of stacking tasks.

[0004] Chinese Patent Application No. 202210995538.5 discloses a multi-robot collaborative arc welding task planning method based on multi-objective optimization, which uses a multi-objective genetic algorithm for solution. The genetic algorithm is adopted, with high programming difficulty and long algorithm running time, and it cannot solve the "interleaving" problem of deposition paths.

[0005] In summary, the existing task allocation algorithms have a high time complexity, long allocation time, poor task equality and adjacency in the obtained allocation results, increasing the difficulty of the multi-robot collaborative additive manufacturing process and reducing the forming quality of additive manufacturing. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides a multi-robot collaborative additive manufacturing task allocation method, which solves the problems of high time complexity and poor adjacency of traditional asymmetric allocation methods.

[0007] The technical solution adopted by the present invention is as follows:

[0008] The present application provides a multi-robot collaborative additive manufacturing task allocation method, including:

[0009] S1. Taking the additive path to be completed as a task, and establishing a task library that each robot can complete according to the task type and the working range of each robot;

[0010] S2. Calculate the distances from the centers of gravity of the tasks in the task libraries that each robot can complete to the robot, and sort the tasks in the task libraries that each robot can complete in ascending order according to the distances;

[0011] S3. Compare the total durations of the tasks already assigned to the current robots, select the robot with the shortest current working duration, add the first 1 / k tasks in the task library that this robot can complete to the working sequence of this robot, and delete these tasks from the task libraries of all robots. Repeat step S3 until all tasks are assigned;

[0012] where,

[0013]

[0014] In the formula, k is the assignment coefficient, L max and L min are respectively the lengths of the longest task and the shortest task among all tasks, n represents the number of robots, σ 2 is the variance of task lengths, and the expression is as follows:

[0015]

[0016] where, m is the number of all tasks, L j is the length of the jth task, is the average length of all tasks.

[0017] Further technical solution:

[0018] In step S3, the working time T i of the robot is calculated by the following formula:

[0019]

[0020] where, the subscript i represents the ith robot, m i is the number of tasks currently assigned to the ith robot, C ij represents the time required for robot i to complete task j, and X ij represents the task assignment mark:

[0021]

[0022] The multi-robot collaborative additive manufacturing task assignment method described above further includes: calculating the standard deviation V of the total working time of each robot:

[0023]

[0024] The multi-robot collaborative additive manufacturing task assignment method described above further includes: calculating the adjacency N of the task assignment result:

[0025]

[0026] In the formula,

[0027] This application also provides a multi-robot collaborative additive manufacturing task allocation system, including:

[0028] A task library modeling module that takes the additive manufacturing path to be completed as tasks and establishes a task library that each robot can complete according to the task type and the working range of each robot;

[0029] A sorting module that calculates the distance from the center of gravity of each task in the task library that each robot can complete to the robot, and sorts the tasks in the task library that each robot can complete in ascending order according to the distance;

[0030] A task allocation module that compares the total duration of the tasks already allocated to each current robot, selects the robot with the shortest current working duration, adds the first 1 / k tasks in the task library that the robot can complete to the working sequence of the robot, and deletes these tasks from the task libraries of all robots; repeatedly run the task allocation module until all tasks are allocated.

[0031] Among them,

[0032]

[0033] In the formula, k is the allocation coefficient, L max , L min are respectively the lengths of the longest task and the shortest task among all tasks, n represents the number of robots, and σ 2 is the task length variance, and the expression is as follows:

[0034]

[0035] Among them, m is the number of all tasks, and L j is the length of the jth task, is the average length of all tasks.

[0036] The beneficial effects of the present invention are as follows:

[0037] Based on the idea of "block allocation", compared with classical methods such as the improved Hungarian algorithm and the lane allocation method, the present invention not only has a low algorithm time complexity and high efficiency, but also ensures a high adjacency of the tasks to be completed by each robot, which is beneficial to ensuring the forming quality during the additive manufacturing process, and the working duration of each robot in the allocation result is well balanced.

[0038] Other features and advantages of the present invention will be described in the following specification, and will, in part, be obvious from the specification, or will be learned by practicing the present invention. Description of the Drawings

[0039] Figure 1 It is a schematic flow chart of the task allocation method in an embodiment of the present invention.

[0040] Figure 2 It is a schematic diagram of the automotive chassis part model in an embodiment of the present invention.

[0041] Figure 3 It is a schematic diagram of the positional relationship between the automotive chassis part in an embodiment of the present invention and three robots participating in additive manufacturing.

[0042] Figure 4 It is a schematic diagram of the task allocation result of Robot 1 in an embodiment of the present invention.

[0043] Figure 5 It is a schematic diagram of the task allocation result of Robot 2 in an embodiment of the present invention.

[0044] Figure 6 It is a schematic diagram of the task allocation result of Robot 3 in an embodiment of the present invention.

[0045] Figure 7 It is a schematic diagram of the task allocation result of Robot 1 in the comparative example of the present invention.

[0046] Figure 8 It is a schematic diagram of the task allocation result of Robot 2 in the comparative example of the present invention.

[0047] Figure 9 It is a schematic diagram of the task allocation result of Robot 3 in the comparative example of the present invention.

[0048] In the figure: 1. Center of Robot 1; 2. Working range of Robot 1; 3. Center of Robot 2; 4. Working range of Robot 2; 5. Cladding path to be allocated; 6. Workbench; 7. Center of Robot 3; 8. Working radius of Robot 3. Detailed Embodiments

[0049] The following describes the detailed embodiments of the present invention with reference to the drawings.

[0050] See Figure 1 , a multi-robot collaborative additive manufacturing task allocation method according to an embodiment of the present application includes:

[0051] S1. Taking the additive path to be completed as a task, and establishing a task library that each robot can complete according to the task type and the working range of each robot;

[0052] S2. Calculate the distances from the centroid of each task in the task library that each robot can complete to the robot, and sort the tasks in the task library that each robot can complete in ascending order according to the distances;

[0053] S3. Compare the total durations of the tasks already assigned to each robot currently, select the robot with the shortest current working duration, add the first 1 / k tasks in the task library that the robot can complete to the working sequence of the robot, and delete these tasks from the task libraries of all robots. Repeat step S3 until all tasks are assigned;

[0054] Among them,

[0055]

[0056] In the formula, k is the distribution coefficient, L max , L min are respectively the lengths of the longest task and the shortest task among all tasks, n represents the number of robots, σ 2 is the variance of task lengths, and the expression is as follows:

[0057]

[0058] Among them, m is the number of all tasks, L j is the length of the jth task, is the average length of all tasks.

[0059] In step S3, the working time T i of the robot is calculated by the following formula:

[0060]

[0061] Among them, the subscript i represents the ith robot, m i is the number of tasks currently assigned to the ith robot, C ij represents the time required for robot i to complete task j, and X ij represents the task assignment mark:

[0062]

[0063] Sorting the tasks in the task library that each robot can complete in ascending order according to the distances specifically includes:

[0064] Define the distance from the centroid of the additive manufacturing path of task j in the task library that robot i can complete to the (base center) of robot i as:

[0065] L ij ={L 11 , L 12 ,…, L 1m , L21 , …, L nm , arrange L ij in ascending order, and arrange the tasks in the task library that robot i can complete in this order.

[0066] A multi-robot collaborative additive manufacturing task allocation method according to an embodiment of the present application further includes: calculating the total working time of each robot

[0067] standard deviation V:

[0068]

[0069] Calculate the adjacency N of the task allocation result:

[0070]

[0071] In the formula,

[0072] The effectiveness of the task allocation result of the method of the present application can be verified through the evaluation of the adjacency.

[0073] The following further explains the calculation principle of the allocation coefficient k of the present application:

[0074] The range of the task length variance is:

[0075]

[0076] The variance is 0, that is, all task lengths are the same, which is an ideal task allocation situation.

[0077] Considering the most unfavorable situation of allocation, that is, the first robot is allocated for the first time and then no more tasks are allocated to this robot, then no more than tasks can be allocated to the first robot, so take The maximum variance is That is, half of the task lengths are L max , and the other half of the task lengths are L min . For this situation, considering the most unfavorable situation, that is, the k proportion of the tasks allocated to the first robot for the first time are all tasks with a length of L, then no more than tasks can be allocated to the first robot, so take The expression is as follows:

[0078]

[0079] Linear fitting gives:

[0080]

[0081] The task can be allocated according to the obtained distribution coefficient k to obtain an ideal allocation result.

[0082] The following takes a specific example 1 to further illustrate the effectiveness of a multi-robot collaborative additive manufacturing task allocation method and its allocation result of the present application.

[0083] Example 1:

[0084] A multi-robot collaborative additive manufacturing task allocation method in this example is applied to the additive manufacturing task allocation of a large-size component, namely an automotive chassis part, as shown in Figure 2 The additive manufacturing path of the automotive chassis part consists of curved and straight deposition tracks, with a total of 504 tracks. As shown in Figure 3 is a schematic diagram of the positional relationship between the automotive chassis part and three robots. In the figure, the center 1 of Robot 1, the working range 2 of Robot 1, the center 3 of Robot 2, the working range 4 of Robot 2, the deposition tracks to be allocated 5, the workbench 6, the center 7 of Robot 3, and the working radius 8 of Robot 3.

[0085] The specific steps of the additive manufacturing task allocation method are as follows:

[0086] 1) Read the additive manufacturing path information and calculate the distribution coefficient k;

[0087] 2) Establish the task libraries that can be completed by each robot according to the working spaces of the three robots. The overall size of the automotive chassis part is 1400mm * 700mm. Taking the geometric center of the part as the origin, the coordinates of the three robots are: Robot 1: (-1300, 0); Robot 2: (0, 950); Robot 3: (0, -950). The working radius of the robot is 1730mm;

[0088] And sort according to the distance from the task center of gravity to the robot;

[0089] 3) Sequentially allocate the first 1 / k tasks in the remaining task libraries that can be completed by the robot with the shortest total current task time until all tasks are allocated.

[0090] Through the task allocation using the algorithm of this example, the task allocation results of Robot 1, Robot 2, and Robot 3 are respectively as shown in Figure 4 , Figure 5 , Figure 6 .

[0091] The average running time is calculated to be 0.0047s;

[0092] The adjacency degree N of the calculated allocation result is calculated to be 0.9702;

[0093] Calculate the working hours of each robot task: T1 = 10296s, T2 = 12905s, T3 = 12964s.

[0094] To verify the effectiveness of the task allocation in this example, a comparative example is set: According to the algorithm of sequential allocation, the task allocation results of Robot One, Robot Two, and Robot Three for the parts in Example 1 above are respectively as Figure 7 、 Figure 8 、 Figure 9 shown.

[0095] The average algorithm running time is obtained as 0.0240s;

[0096] It is calculated that the adjacency degree N of the calculation allocation result is 0.9325;

[0097] Calculate the working hours of each robot task: T1 = 11576s, T2 = 13748s, T3 = 13873s.

[0098] It can be seen that compared with the comparative example, the task allocation result in Example 1 greatly shortens the algorithm running time, and at the same time, the adjacency degree of the obtained allocation result is good. And the working hours of each robot in the allocation result are evenly distributed.

[0099] Those of ordinary skill in the art can understand that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A task allocation method for multi-robot collaborative additive manufacturing, characterized in that, Including: S1. Take the additive manufacturing path to be completed as a task, and establish a task library that each robot can complete according to the task type and the working range of each robot; S2. Calculate the distance from the center of gravity of each task in the task library that each robot can complete to the robot, and sort the tasks in the task library that each robot can complete in ascending order according to the distance; S3. Compare the total duration of the tasks already assigned to each robot currently, select the robot with the shortest current working duration, add the first 1 / k tasks in the task library that this robot can complete to the working sequence of this robot, and delete these tasks from the task libraries of all robots. Repeat step S3 until all tasks are assigned; Wherein, where k is the distribution coefficient, L max , L min are the lengths of the longest task and the shortest task among all tasks respectively, n represents the number of robots, and σ 2 is the variance of task lengths, and the expression is as follows: where m is the number of all tasks, and L j is the length of the j-th task, and is the average length of all tasks.

2. The multi-robot collaborative additive manufacturing task allocation method according to claim 1, wherein In step S3, the working time T of the robot i is calculated by the following formula: Among them, the subscript i represents the i-th robot, and m i is the number of tasks currently assigned to the i-th robot, and C ij represents the time required for robot i to complete task j, and X ij represents the task assignment flag:

3. The multi-robot collaborative additive manufacturing task allocation method according to claim 2, wherein It also includes: Calculate the standard deviation V of the total working time of each robot:

4. The multi-robot collaborative additive manufacturing task allocation method according to claim 1, characterized in that It also includes: Calculate the adjacency N of the task assignment result: In the formula, the subscript i represents the i-th robot.

5. A multi-robot collaborative additive manufacturing task allocation system, characterized in that, Including: A task library modeling module, which takes the additive manufacturing path to be completed as a task, and establishes a task library that each robot can complete according to the task type and the working range of each robot; A sorting module, which calculates the distance from the center of gravity of each task in the task library that each robot can complete to the robot, and sorts the tasks in the task library that each robot can complete in ascending order according to the distance; A task assignment module, which compares the total duration of the tasks already assigned to each robot currently, selects the robot with the shortest current working duration, adds the first 1 / k tasks in the task library that this robot can complete to the working sequence of this robot, and deletes these tasks from the task libraries of all robots; Repeat running the task assignment module until all tasks are assigned, Wherein, where k is the distribution coefficient, L max and L min are the lengths of the longest task and the shortest task among all tasks respectively, n represents the number of robots, and σ 2 is the variance of task lengths, and the expression is as follows: where m is the number of all tasks, and L j is the length of the j-th task, and is the average length of all tasks.

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