Method and device for testing circuit board of multi-mechanical-arm automatic test system

Abstract

The invention discloses a circuit board testing method and device for a multi-mechanical-arm automatic testing system, and the method comprises the following steps and implementation devices: (1) obtaining testing data information, exporting the position information of a to-be-tested circuit board testing point, and generating a corresponding testing file; (2) importing the test file into an algorithm system in the test method; (3) preliminarily planning a test path of a tested point through an ant colony algorithm, and determining a test sequence of a test starting point, a test ending point and a middle test point; (4) reading the sequence of the test points planned by the ant colony algorithm, and further optimizing the test paths of the multiple mechanical arms by adopting a comprehensive optimization dual-conflict avoidance algorithm based on conflicts to prevent collision among the multiple mechanical arms; and (5) outputting a conflict-free optimal test path of the multiple mechanical arms, and controlling the multiple mechanical arms to move for testing according to the conflict-free optimal test path. According to the invention, not only can multi-region testing be completed, but also the testing efficiency can be improved under the condition of preventing collision of multiple mechanical arms.

Description

Technical Field

[0001] The present invention relates to the technical field of circuit board testing, and in particular to a circuit board testing method and device of a multi-mechanical arm automatic testing system for improving the efficiency and accuracy of circuit board testing and solving the conflict problem that may occur among the multi-mechanical arms during the testing process. Background Art

[0002] As the circuit board manufacturing process gradually develops towards precision and high integration, traditional manual detection methods can no longer meet the needs, and automatic testing systems have become an indispensable part of circuit board testing. Multiple independently controlled probes are moved to the solder joints of the test components to extract information, and then the electrical signals are analyzed through the system's built-in test program to achieve the purpose of circuit board testing. However, current research on circuit board testing is mostly focused on image processing and motion control, with less research on detection path planning for the components under test, and most of them are single robotic arm tests, with limited test efficiency.

[0003] The test path optimization of solder joints is similar to the traveling salesman problem (TSP): the probe starts from the initial position, passes through the solder joint position of the tested component, and moves along the planned test route until all components of the circuit board are tested and the test returns to the initial position. In this process, the quality of the test path planning can directly affect the test efficiency of the circuit board. A suitable test method will greatly improve the test efficiency of the circuit board.

[0004] Huang Tao et al. used the fusion clustering algorithm and simulated annealing algorithm for solder joint detection path planning, shortening the total test path, but the optimal fitness of their algorithm was poor, and the convergence speed of the simulated annealing algorithm was slow and inefficient. Wu Yashuai et al. applied the donkey and smuggler algorithm to the optimization of printed circuit board solder joint paths, effectively shortening the test time, and the average planning time could be as low as 2s, but their algorithm had no advantage in terms of optimal fitness. Xiao et al. applied an improved ant colony algorithm to circuit board surface defect detection, improving the disadvantage of the ant colony algorithm that it is easy to fall into the local optimum. The average length of the test path can be shortened by 13.7% without changing the total test time. However, the ant colony algorithm itself has a long running time, and the actual test operation effect is not good. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a circuit board testing method and device for a multi-mechanical arm automatic testing system with simple structure, low cost and easy operation.

[0006] In view of the problems of low testing efficiency and frequent conflicts during multi-robot testing in the prior art, the present invention proposes a circuit board testing method and device for a multi-robot automatic testing system, aiming to improve testing efficiency and solve the conflict problem in the multi-robot testing process by optimizing test path planning.

[0007] In order to solve the above technical problems, the present invention provides a circuit board testing method of a multi-manipulator automatic testing system, comprising the following steps:

[0008] (1) Obtain test data information, export the location information of the test points of the circuit board to be tested, and generate corresponding test files;

[0009] (2) Import the test file into the algorithm system in the test method;

[0010] (3) Preliminary planning of the test path of the test points through the ant colony algorithm, and determination of the test start point, end point, and test sequence of the intermediate test points;

[0011] (4) Read the test point sequence planned by the ant colony algorithm, and use the conflict-based comprehensive optimization dual conflict avoidance algorithm, namely the CBS algorithm, to further optimize the test path of the multi-manipulator to prevent collisions between the multi-manipulators;

[0012] (5) Output the optimal test path without conflict for multiple robotic arms, and control the multiple robotic arms to move to various test points on the circuit board for testing.

[0013] The present invention provides a circuit board testing method for a multi-robotic arm automatic testing system, which may also include a conflict detection step. The conflict detection step is: when the robotic arm moves to a test point, conflict detection is performed using a CBS algorithm. If a conflict is detected, the conflict attribute is determined to be an ARP conflict attribute or a URP conflict attribute, and the diagonal improved space-time A algorithm or the dynamic adaptive space-time A algorithm is used to adjust the path to avoid the conflict.

[0014] Preferably, during the execution of the ant colony algorithm, an appropriate number of iterations needs to be set to ensure that the optimal path is found without reducing the test efficiency. During the conflict detection process of the CBS algorithm, if it is determined to be an ARP conflict attribute, a constraint node is set at the high level, and the bottom level selects the diagonal improved space-time A algorithm for single path planning; if it is determined to be a URP conflict attribute, a constraint node is set at the high level, and the bottom level selects the dynamic adaptive space-time A algorithm for single path planning.

[0015] Preferably, the ant colony algorithm is used to plan paths for test points, find the optimal test sequence of test points, and determine the optimal test path for multiple robotic arms after multiple iterations; the CBS algorithm further optimizes the test path initially planned by the ant colony algorithm, so that multiple robotic arms can better cooperate during the test process and prevent collisions.

[0016] The present invention also provides a multi-mechanical arm automatic testing device for implementing the testing method, which comprises:

[0017] a. Data acquisition module, used to obtain the location information of the circuit board test points and generate test files;

[0018] b. Algorithm processing module, including ant colony algorithm unit and CBS algorithm unit. The ant colony algorithm unit is used to preliminarily plan the test path, and the CBS algorithm unit is used to further optimize the path and prevent multi-robot conflict;

[0019] c. Conflict detection and resolution module, used to detect conflicts in the paths of multiple robotic arms and take corresponding conflict avoidance measures according to the conflict type to avoid collisions between robotic arms;

[0020] d. A control execution module is used to control the multi-manipulator to move to the test point of the circuit board for testing according to the optimal test path output by the algorithm processing module.

[0021] Preferably, the conflict detection module can use different path planning algorithms to adjust the path according to different conflict attributes to ensure that there is no collision between the robotic arms. The ant colony algorithm in the algorithm processing module needs to set a reasonable number of iterations during execution to strike a balance between finding the optimal path and ensuring test efficiency.

[0022] Preferably, the device includes a plurality of independently controlled mechanical arms, and a control system and a data processing system connected thereto, for realizing the planning of the test path and the conflict detection of the multi-mechanical arms. And the device may also include a memory for storing the test data and the test results, and a display device for displaying the test process and the results.

[0023] Compared with the prior art, the circuit board testing method and device of the multi-manipulator automatic testing system of the present invention has at least the following beneficial effects:

[0024] 1. High efficiency: The present invention introduces multiple robotic arms into the circuit board automatic test system to improve the test efficiency. And through the combination of ant colony algorithm and CBS algorithm, the optimization of the multi-robotic arm test path is realized, which significantly improves the test efficiency.

[0025] 2. Conflict avoidance: It effectively solves the conflict problem of multiple robotic arms during the test process, ensuring the smooth progress of the test. The ant colony algorithm is used to perform preliminary path planning for the test points of the circuit board, and then the CBS algorithm is used to further optimize the test path, so that multiple robotic arms can better cooperate during the test process, prevent collisions, and improve test efficiency.

[0026] 3. Wide adaptability: It is suitable for testing circuit boards of various types and sizes, and has strong versatility and practicality.

[0027] The following is a further description of a circuit board testing method and device for a multi-manipulator automatic testing system of the present invention in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a circuit board testing method of a multi-manipulator automatic testing system of the present invention;

[0029] Figure 2 It is a calculation flow chart of the ant colony algorithm in a circuit board testing method of a multi-manipulator automatic testing system of the present invention;

[0030] Figure 3 The invention discloses a dual conflict avoidance algorithm model based on conflict-based comprehensive optimization for a circuit board testing method of a multi-manipulator automatic testing system. DETAILED DESCRIPTION

[0031] like Figure 1 As shown, the present invention provides a circuit board testing method and device for a multi-manipulator automatic testing system, and the basic steps are as follows:

[0032] 1. Data acquisition and preliminary processing:

[0033] (1) Obtain the location information of the test points on the test circuit board, export and generate corresponding test files;

[0034] (2) Import the test file into the algorithm of this test method;

[0035] 2. Preliminary route planning:

[0036] (3) Preliminary planning of the test path of the test point through the ant colony algorithm;

[0037] (4) Determine the optimal test start point, end point, and test sequence of intermediate test points through the preliminarily planned test path;

[0038] 3. Multi-robot path optimization:

[0039] (5) The initially planned test point location information is further processed through the CBS algorithm to plan the test path of the multiple robotic arms to prevent collisions between the multiple robotic arms during movement;

[0040] 4. Conflict detection and adjustment:

[0041] (6) When the robot moves to the test point, the CBS algorithm is used to detect conflicts and adjust the path planning according to the conflict attributes (such as ARP conflict or URP conflict) to ensure that the multi-robot arm successfully completes the test task;

[0042] (7) After resolving the multi-robot path conflict and completing the test path planning of the multi-robot, the test algorithm ends;

[0043] 5. Output and execution:

[0044] (8) Output the optimal test path of multiple robotic arms, and each robotic arm moves to the test point according to the planned path for testing.

[0045] In a specific embodiment of the present invention, the following steps may be included:

[0046] 1) Extract the location information of the test point from the information data of the circuit board, and put the extracted data information into the configuration file of the algorithm used in this test method.

[0047] 2) The test algorithm preliminarily plans the test path. First, the path of the test points is planned through the ant colony algorithm to find the optimal test order of the test points. After multiple iterations of the ant colony algorithm, the optimal test path of the multi-robot arm is found.

[0048] 3) The optimal path generated in step 2) is further optimized through the embedded CBS algorithm.

[0049] 4) The embedded CBS algorithm performs path planning for multiple robotic arms, allowing each robotic arm to move to the location of the test point and test the test point.

[0050] 5) Multi-arm conflict detection: When the robot moves to the test point, in order to prevent collision with other robots, the CBS algorithm is used to perform conflict detection on the multi-arm. (See Figure 3 Conflict-based comprehensive optimization dual conflict avoidance algorithm model)

[0051] 6) After detecting that there is no conflict among the multiple robotic arms, the test algorithm of this test method ends and the optimal test path for the multiple robotic arms is found. While improving the test efficiency, each robotic arm can complete the test of all test points together.

[0052] 7) Each robot executes the action according to the optimal path planned by the test algorithm, and the multiple robot arms move to each test point of the circuit board, and finally complete the test of the circuit board test points by the multiple robot arms.

[0053] in:

[0054] In step 2), the number of iterations of the ant colony algorithm must be set. The next step can only be entered after the algorithm completes the iteration. The number of iterations should not be too many or too few. Too many iterations will cause the algorithm to run for too long and reduce the test efficiency. Too few iterations will make the path found not the optimal path, affecting the subsequent execution of the robot arm.

[0055] In the conflict detection in step 5), it is necessary to determine whether the conflict attribute is an ARP conflict attribute or a URP conflict attribute. If the conflict attribute is determined to be an ARP conflict attribute, the high-level layer sets a constraint node, and when the bottom layer re-searches the path, the diagonal improved space-time A algorithm is selected for single path planning. Then re-execute step 5 to re-detect whether there is still a conflict. If the conflict attribute is determined to be a URP conflict attribute, the high-level layer sets a constraint node, and when the bottom layer re-searches the path, the dynamic adaptive space-time A algorithm is selected for single path planning. Then re-execute step 5 to re-detect whether there is still a conflict.

[0056] See also Figure 2 and Figure 3 , which is a flow chart of two algorithm models of the present invention.

[0057] Figure 2 The calculation process of the ant colony algorithm used for preliminary test path planning in the present invention is shown. The algorithm simulates the process of ants looking for food sources in nature and finds the shortest path through the accumulation and volatilization mechanism of pheromone;

[0058] The operation process is as follows:

[0059] 1. Initialization: Set algorithm parameters, including the number of ants, number of iterations, pheromone volatilization factor, etc. At the same time, use the test points on the circuit board as "food sources" to initialize the pheromone matrix, and set the pheromone concentration on all test points to the same value;

[0060] 2. Ant departure: Each ant starts from a set starting point (usually a fixed position or a randomly selected test point) and selects the next test point based on the pheromone concentration and heuristic information (such as the distance between test points) at the current position. The selection probability is usually proportional to the pheromone concentration and heuristic information;

[0061] 3. Movement and update: The ants move to the next test point along the selected path and release a certain amount of pheromone at the test point. At the same time, the pheromone concentration of each test point on the path is updated according to the pheromone volatilization factor to simulate the volatilization process of pheromones;

[0062] 4. Construct a solution: Each ant continues to move until it traverses all test points and returns to the starting point (or the specified end point), thus constructing a complete test path. In this process, the ant continuously makes decisions based on the pheromone and heuristic information on the current path to find a possible better path;

[0063] 5. Iterative optimization: Repeat steps 2 to 4 for multiple iterations. In each iteration, all ants will reselect paths based on the current pheromone distribution and influence the decisions of subsequent ants by releasing pheromones. As the iterations proceed, the pheromone concentration will gradually concentrate on shorter paths, thereby guiding the ants to gradually converge to the optimal or better test path;

[0064] 6. Output the optimal path: After the iteration, the path with the shortest length (or the highest pheromone concentration) among the paths constructed by all ants is output as the preliminary optimal test path. This path will serve as the basis for subsequent CBS algorithm optimization;

[0065] Through the operation process of the ant colony algorithm, the present invention can realize the preliminary path planning of the circuit board test points, and provide a reliable basis for subsequent multi-manipulator conflict avoidance and optimization of the test path.

[0066] See also Figure 3 As shown, Figure 3 This is a flow chart of the conflict-based comprehensive optimization dual conflict avoidance algorithm model, which describes the algorithm's workflow and conflict resolution mechanism in detail.

[0067] It mainly includes the following steps:

[0068] (a) Start: Start the conflict avoidance algorithm model:

[0069] (b) Read the preliminary path: Read the test point path that has been initially planned from the ant colony algorithm module:

[0070] (c) Conflict detection: Detect conflicts on the paths of multiple robotic arms to determine whether there is a risk of collision:

[0071] (d) Determine the conflict type: If there is a conflict, further determine the conflict type (ARP or URP):

[0072] (e) Set constraint nodes: According to the conflict type, set the corresponding constraint nodes at the high level:

[0073] (f) Path re-planning: At the bottom layer, a suitable path search algorithm (diagonal improved space-time A algorithm or dynamic adaptive space-time A algorithm) is selected according to the conflict type to perform a single path planning:

[0074] (g) Re-check: Re-check whether the newly planned path still has conflicts:

[0075] (h) End / Loop: If there is no conflict in the new path, the algorithm ends and outputs the optimized test path; if there is still a conflict, return to step (f) to continue path replanning until there is no conflict.

[0076] In summary, the present invention provides a circuit board testing method and device for a multi-robot automatic testing system. The traditional single-area single-arm circuit board test is added to the multi-robot automatic testing of the circuit board, and the CBS algorithm is added to the ant colony algorithm of the intelligent optimization algorithm. The purpose is to combine path planning with conflict-based comprehensive optimization to improve the test efficiency of the circuit board and solve the conflict problem of multiple robotic arms during the testing process. The ant colony algorithm in the intelligent optimization algorithm is used to plan the paths of the test points of the circuit board, that is, all the test points are preliminarily sorted for testing, and then the position information of the planned test points is transmitted to the robotic arm, and finally the CBS algorithm is used to process the transmitted position information to prevent collisions between multiple robotic arms during movement. Compared with the prior art, most of the circuit board testing processes are based on a single area. The present invention uses a multi-robot test method to not only complete the test of multiple areas, but also improve the test efficiency by preventing collisions between multiple robotic arms.

[0077] The embodiments described above are merely descriptions of preferred implementation modes of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A circuit board testing method for a multi-manipulator automatic testing system, characterized in that: The following steps are involved: (1) Obtain test data information, export the location information of the test points of the circuit board to be tested, and generate corresponding test files; (2) Import the test file into the algorithm system in the test method; (3) Preliminary planning of the test path of the test points through the ant colony algorithm, and determination of the test start point, end point, and test sequence of the intermediate test points; (4) Read the test point sequence planned by the ant colony algorithm, and use the conflict-based comprehensive optimization dual conflict avoidance algorithm, namely the CBS algorithm, to further optimize the test path of the multi-manipulator to prevent collisions between the multi-manipulators; (5) Output the optimal test path without conflict for multiple robotic arms, and control the multiple robotic arms to move to various test points on the circuit board for testing.

2. The circuit board testing method of the multi-manipulator automatic testing system according to claim 1, characterized in that: The method also includes a conflict detection step, which is as follows: when the robot arm moves to the test point, conflict detection is performed using the CBS algorithm. If a conflict is detected, the conflict attribute is determined to be an ARP conflict attribute or a URP conflict attribute, and the diagonal improved space-time A algorithm or the dynamic adaptive space-time A algorithm is used to adjust the path to avoid the conflict.

3. The circuit board testing method of the multi-manipulator automatic testing system according to claim 1 or 2, characterized in that: During the execution of the ant colony algorithm, an appropriate number of iterations needs to be set to ensure that the optimal path is found without reducing the test efficiency.

4. The circuit board testing method of the multi-manipulator automatic testing system according to claim 2, characterized in that: During the conflict detection process, if the CBS algorithm determines that it is an ARP conflict attribute, a constraint node is set at the high level, and the bottom level selects the diagonal improved space-time A algorithm for single path planning; if it is determined that it is a URP conflict attribute, a constraint node is set at the high level, and the bottom level selects the dynamic adaptive space-time A algorithm for single path planning.

5. The method according to claim 1, characterized in that The ant colony algorithm is used to plan the paths of the test points, find the optimal test sequence of the test points, and determine the optimal test path of the multiple robotic arms after multiple iterations; the CBS algorithm further optimizes the test path initially planned by the ant colony algorithm, so that the multiple robotic arms can better cooperate during the test process and prevent collisions.

6. A multi-manipulator automatic testing device for implementing the testing method according to any one of claims 1 to 5, characterized in that include: a. Data acquisition module, used to obtain the location information of the circuit board test points and generate test files; b. Algorithm processing module, including ant colony algorithm unit and CBS algorithm unit. The ant colony algorithm unit is used to preliminarily plan the test path, and the CBS algorithm unit is used to further optimize the path and prevent multi-robot conflict; c. Conflict detection and resolution module, used to detect conflicts in the paths of multiple robotic arms and take corresponding conflict avoidance measures according to the conflict type to avoid collisions between robotic arms; d. A control execution module is used to control the multi-robot arm to move to the test point of the circuit board for testing according to the optimal test path output by the algorithm processing module.

7. The multi-manipulator automatic testing device according to claim 6, characterized in that: The conflict detection module can use different path planning algorithms to adjust the path according to the different conflict attributes to ensure that there is no collision between the robotic arms.

8. The multi-manipulator automatic testing device according to claim 6 or 7, characterized in that: The ant colony algorithm in the algorithm processing module needs to set a reasonable number of iterations during execution to strike a balance between finding the optimal path and ensuring test efficiency.

9. The multi-manipulator automatic testing device according to claim 6 is characterized in that: The device comprises a plurality of independently controlled mechanical arms, and a control system and a data processing system connected thereto, and is used for realizing the planning of test paths and the conflict detection of the plurality of mechanical arms.

10. The multi-manipulator automatic testing device according to claim 6, characterized in that: The apparatus also includes a memory for storing test data and test results, and a display device for displaying the test process and results.

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