Quality detection robot multi-machine cooperative detection task scheduling and path planning method

Abstract

The invention discloses a quality detection robot multi-machine cooperative detection task scheduling and path planning method. Comprising the steps of collecting production information of a production line and detecting state information of a robot; and then, the detection task of each detection robot is reasonably distributed according to the generation information and the state information. And the working load of each detection robot is kept balanced, so that the detection efficiency of the whole production system is improved. And then, the detection robot goes to the position of the corresponding to-be-detected element to execute the detection task according to the allocated detection task. And the production information of the production line and the state information of each detection robot are monitored in real time. And then, the priority of each detection task, the availability of each detection robot and the production efficiency of the production line are evaluated according to the detection information and the state information. And finally, dynamically adjusting the detection task of each detection robot based on the priority, the availability and the production efficiency. The production system can flexibly cope with various production change conditions, and the detection efficiency of the whole production system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of adaptive control systems using models or simulators, and in particular to a method for scheduling and path planning multi-machine collaborative inspection tasks of quality inspection robots. Background Art

[0002] As we all know, in the battery manufacturing industry, quality inspection of finished batteries is an important part of ensuring product quality. In recent years, with the rapid development of robotics technology, more and more companies have begun to use robots to inspect the quality of finished batteries. Traditional inspection methods usually rely on manual operations, and there are problems such as low inspection efficiency and highly subjective inspection results.

[0003] The patent application with publication number CN117260725A discloses a food delivery robot method, equipment, storage system and device, which includes a data receiving unit, a task scheduling unit, a dispatching unit, a target confirmation unit, a monitoring unit, a server, a detection unit and a judgment unit. The food delivery robot device realizes an automated food delivery process through the collaborative work of task scheduling, dispatching, target confirmation and monitoring units. However, this technical solution is a single robot operation, and it is impossible to achieve efficient collaboration between multiple robots, resulting in the detection efficiency still needs to be improved. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a method for scheduling and planning the detection tasks of multiple quality inspection robots in a coordinated manner, which can improve the detection efficiency. The specific technical solution is as follows:

[0005] In a first aspect, a method for scheduling multi-machine collaborative inspection tasks of quality inspection robots is provided. In a first implementable manner of the first aspect, the method includes:

[0006] Obtain the current production information of each production line and the status information of each inspection robot;

[0007] Allocating inspection tasks to the inspection robots based on the production information and corresponding status information;

[0008] Obtain real-time production information of each production line, status information of each inspection robot, and inspection information corresponding to each inspection task;

[0009] Evaluate the priority of each inspection task according to the inspection information, evaluate the availability of each inspection robot according to the status information, and evaluate the production efficiency of each production line according to the production information and status information;

[0010] Based on the priority corresponding to each inspection task, the availability of each inspection robot and the production efficiency of each production line, the inspection tasks of each inspection robot are dynamically adjusted.

[0011] In combination with the first implementable manner of the first aspect, in a second implementable manner of the first aspect, allocating detection tasks of the detection robot based on production information and status information includes:

[0012] Evaluate the inspection time required for each inspection task based on the device type and inspection requirements in the production information;

[0013] The detection tasks of the detection robots are allocated using a load balancing algorithm based on the detection time and status information required for each detection task.

[0014] In combination with the first implementable manner of the first aspect, in a third implementable manner of the first aspect, dynamically adjusting the detection tasks of each of the detection robots includes:

[0015] Based on the priority of each inspection task, the availability of each inspection robot and the production efficiency of each production line, quantitatively evaluate the suitability of each inspection robot to perform different inspection tasks on different production lines;

[0016] According to the suitability of each inspection robot for performing different inspection tasks on different production lines, the inspection tasks of each inspection robot are dynamically adjusted.

[0017] In combination with the first implementable manner of the first aspect, in a fourth implementable manner of the first aspect, evaluating the priority corresponding to the detection task according to the detection information includes:

[0018] The priority of the detection task is evaluated according to the urgency, task type and detection requirements of the detection task included in the detection information.

[0019] In combination with the first implementable manner of the first aspect, in a fifth implementable manner of the first aspect, evaluating the availability of the detection robot according to the state information includes:

[0020] The availability of the detection robot is evaluated based on the power information and health status information in the status information.

[0021] In combination with the first implementable manner of the first aspect, in a sixth implementable manner of the first aspect, evaluating the production efficiency of the production line according to the production information and the status information includes:

[0022] The production efficiency of the production line is evaluated according to the production speed and work efficiency factor in the production information.

[0023] In a second aspect, a method for multi-machine collaborative inspection path planning of quality inspection robots is provided. In a first implementable manner of the second aspect, the method includes:

[0024] Adopting the method for scheduling multi-machine collaborative inspection tasks of quality inspection robots as described in any one of the first to sixth possible implementations of the first aspect to adjust the inspection tasks of each inspection robot in real time;

[0025] The target position currently corresponding to each detection robot is determined according to the corresponding detection task, and the moving path of each detection robot is planned according to the current position and the target position corresponding to the detection robot.

[0026] In combination with the first implementable manner of the second aspect, in a second implementable manner of the second aspect, planning a moving path of the detection robot includes:

[0027] Marking the current position, target position and obstacle position of the inspection robot in the two-dimensional map corresponding to the production area;

[0028] A path search algorithm is used to search for an optimal path from the current position to the target position in the two-dimensional map.

[0029] In a third aspect, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements the steps of the method for scheduling multi-machine collaborative inspection tasks of quality inspection robots as described in any of the first to sixth implementable methods of the first aspect.

[0030] In a fourth aspect, a computer program product is provided, comprising a computer program / instruction, which, when executed by a processor, implements the steps of the method for multi-machine collaborative inspection path planning of quality inspection robots as described in the first implementable manner of the second aspect.

[0031] Beneficial effect: By adopting the multi-machine collaborative inspection task scheduling and path planning method of the quality inspection robot of the present invention, the inspection task of each inspection robot can be reasonably allocated according to the current production information of each production line and the status information of each inspection robot, ensuring that the workload of each inspection robot remains balanced, thereby improving the inspection efficiency of the entire production system, and by real-time monitoring the production situation of each production line and the operating status of each inspection robot, the inspection task of each inspection robot can be adaptively and dynamically adjusted, so that the production system can flexibly respond to various production changes, ensure that the inspection can be carried out stably, and further improve the inspection efficiency of the entire production system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the specific implementation of the present invention, the following will briefly introduce the drawings required for use in the specific implementation. In all the drawings, each element or part is not necessarily drawn according to the actual scale.

[0033] Figure 1A flowchart of a method for scheduling multi-machine collaborative inspection tasks of quality inspection robots provided by an embodiment of the present invention;

[0034] Figure 2 A flowchart of a method for collaborative inspection path planning of multiple quality inspection robots provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following embodiments of the technical solution of the present invention are described in detail in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples, and cannot be used to limit the protection scope of the present invention.

[0036] like Figure 1 The flowchart of the method for scheduling multi-machine collaborative inspection tasks of quality inspection robots shown in FIG. 1 includes:

[0037] Step 1: Obtain the current production information of each production line and the status information of each detection robot;

[0038] Step 2: assigning detection tasks to each of the detection robots based on the production information and corresponding status information;

[0039] Step 3: Obtain the production information of each production line, the status information of each inspection robot, and the inspection information corresponding to each inspection task in real time;

[0040] Step 4: evaluating the priority of each inspection task according to the inspection information, evaluating the availability of each inspection robot according to the status information, and evaluating the production efficiency of each production line according to the production information and status information;

[0041] Step 5: dynamically adjust the inspection tasks of each inspection robot based on the priority corresponding to each inspection task, the availability of each inspection robot and the production efficiency of each production line.

[0042] Specifically, first, the production information of each current production line and the status information of each inspection robot can be collected. The production information includes the number, type and location of the components to be inspected on the production line, that is, the battery. The status information includes the power, location, standby status, etc. of the inspection robot. Then, the inspection tasks of each inspection robot can be reasonably allocated based on all the collected production information and status information. Ensure that the workload of each inspection robot remains balanced, thereby improving the inspection efficiency of the entire production system.

[0043] After that, each detection robot can go to the location of the corresponding component to be inspected according to the assigned detection task and detect the corresponding component to be inspected. During the detection process, the production information of each production line, the status information of each detection robot, and the detection information of each detection task can be monitored in real time. Then, the priority corresponding to each detection task can be evaluated according to the detection information, and the availability of each detection robot can be evaluated according to the status information, and the production efficiency of each production line can be evaluated according to the production information and status information. Finally, based on the priority corresponding to each detection task, the availability of each detection robot, and the production efficiency of each production line, the detection tasks of each detection robot can be adaptively and dynamically adjusted. Thereby, the production system can flexibly respond to various production changes, ensure that the detection can be carried out stably, and further improve the detection efficiency of the entire production system.

[0044] In this embodiment, optionally, in step 2, allocating detection tasks of the detection robot based on the production information and the status information includes:

[0045] Evaluate the inspection time required for each inspection task based on the device type and inspection requirements in the production information;

[0046] The detection tasks of the detection robots are allocated using a load balancing algorithm based on the detection time and status information required for each detection task.

[0047] Specifically, first, the inspection time required for each component to be inspected can be evaluated based on the device type and inspection requirements of the components to be inspected on the production line. Then, the load balancing algorithm can be used to reasonably allocate the inspection tasks of each inspection robot based on the inspection time required for each component to be inspected. The load balancing algorithm used can be the existing minimum workload priority algorithm, load balancing polling algorithm, random load balancing algorithm, etc. This ensures that the workload of each inspection robot is relatively balanced, thereby improving the inspection efficiency of the entire system.

[0048] Taking the minimum workload priority algorithm as an example, the inspection task of inspecting component j can be assigned to the inspection robot that satisfies min_{i\inR}(w_i+t_{i,j}), where t_{i,j} is the inspection time required for the inspection robot i to complete the inspection task of component j, w_i is the total inspection time required for the inspection robot i to have assigned tasks, and R is the set of available robots. In this way, the inspection task can be assigned to the robot with the smallest sum of the current workload plus the time required to complete the inspection task corresponding to component j. This ensures that each inspection task can be assigned to the inspection robot that can complete it the fastest, thereby helping to balance the workload of the entire system.

[0049] In this embodiment, optionally, in step 4, evaluating the priority corresponding to the detection task according to the detection information includes:

[0050] The priority of the detection task is evaluated according to the urgency, task type and detection requirements of the detection task included in the detection information.

[0051] Specifically, the detection information monitored in real time includes the urgency, type and detection requirements of the components to be detected, etc. The priority of the detection task can be evaluated according to the urgency, type and detection requirements of the components to be detected corresponding to the detection task.

[0052] Specifically, the priority of the components to be inspected can be quantitatively evaluated based on factors such as the urgency, type, and inspection requirements of the battery. The specific calculation formula is as follows:

[0053] P j =α·Emergency j +β·Type j +γ·Inspection j ;

[0054] Among them, P j is the priority of the jth component to be inspected, Emergency is the urgency of the component to be inspected, Type is the type of the component to be inspected, and Inspection is the inspection requirement of the component to be inspected. α, β, and γ are the weights corresponding to the urgency, type, and inspection requirement, respectively, and α+β+γ=1.

[0055] The degree of urgency is a quantitative value, and different levels can be defined according to actual needs, for example, using integers from 1 (lowest urgency) to 10 (highest urgency) to represent it.

[0056] Type is also a quantitative value, indicating the impact of the type of component to be tested on the priority. Different types of batteries may have different priorities. For example, some special types of batteries may always have a higher priority, which can be represented by a value between 0 and 1, where 0 represents the lowest priority type and 1 represents the highest priority type.

[0057] The quantitative value of the inspection requirement reflects the complexity and / or importance of the inspection required for the component to be inspected, similar to the urgency level, and can be expressed using an integer from 1 to 10 or other appropriate quantitative methods.

[0058] In this embodiment, optionally, in step 4, evaluating the availability of the detection robot according to the state information includes:

[0059] The availability of the detection robot is evaluated based on the power information and health status information in the status information.

[0060] Specifically, the real-time monitored status information includes the power of the detection robot, health status, current workload, work efficiency, etc. The availability of the detection robot can be evaluated based on the power, health status, and current workload of the detection robot.

[0061] Specifically, the availability of the robot can be quantitatively evaluated based on factors such as power, health status, and current workload. The specific calculation formula is as follows:

[0062] U i =δ·Battery i +ε·Health i +ζ·(1-Workload i );

[0063] Among them, U i is the availability of the ith robot, Battery is the battery power of the robot, Health is the health status of the robot, and Workload is the current workload of the robot. δ, ε, ζ are the weights corresponding to the battery power, health status, and current workload, respectively.

[0064] Battery is a quantitative value that indicates the current battery level of the robot. It can be expressed as a percentage, for example, from 0% (completely out of power) to 100% (fully charged). In calculations, this percentage value can be used directly, or adjusted as needed, for example, through a nonlinear function to emphasize the urgency of low battery.

[0065] Health status is also a quantitative value that indicates the health status or maintenance level of the robot. It can be represented by a value between 0 and 1, where 0 indicates complete damage or inability to work and 1 indicates optimal condition. The health status can be evaluated based on a variety of factors, such as wear of mechanical parts, accuracy of sensors, stability of software, etc.

[0066] The quantitative value of the current workload reflects the current task burden of the robot and can be represented by a value between 0 and 1, where 0 represents idle state and 1 represents full load. The workload can be evaluated based on factors such as the number of current tasks, the complexity of tasks, and the urgency of tasks.

[0067] δ, ε, and ζ are used to adjust the relative importance of power, health status, and workload in availability assessment. Their sum should be 1, and the selection of weight coefficients should be based on the specific requirements and business logic of the problem.

[0068] In this embodiment, optionally, in step 4, evaluating the production efficiency of the production line according to the production information and the status information includes:

[0069] The production efficiency of the production line is evaluated according to the production speed and work efficiency factor in the production information.

[0070] Specifically, the real-time monitored production information includes the production speed of the production line. The production efficiency of the production line can be evaluated based on the production speed and work efficiency factor of the production line.

[0071] Specifically, the production efficiency of the production line can be quantitatively evaluated based on factors such as the current production speed and work efficiency factor. The specific calculation formula is as follows:

[0072] E k =Speed k ·EfficiencyFactor k ;

[0073] Among them, E k is the production efficiency of the kth production line, Speed ​​is the production speed of the production line, and EfficiencyFactor is the work efficiency factor of the production line.

[0074] Production speed Speed ​​is a quantitative value that indicates the number of products that a production line can produce or the amount of work that can be completed per unit time (such as per hour, per minute). It can be expressed in specific units, such as "pieces / hour", "units / minute", etc.

[0075] The efficiency factor EfficiencyFactor is a value between 0 and 1, which is used to represent the efficiency level of the production line. It can be calculated based on multiple factors, such as resource utilization, error rate, downtime, product quality, etc.

[0076] In this embodiment, optionally, in step 5, dynamically adjusting the detection tasks of each of the detection robots includes:

[0077] Based on the priority of each inspection task, the availability of each inspection robot and the production efficiency of each production line, quantitatively evaluate the suitability of each inspection robot to perform different inspection tasks on different production lines;

[0078] According to the suitability of each inspection robot for performing different inspection tasks on different production lines, the inspection tasks of each inspection robot are dynamically adjusted.

[0079] Specifically, when adjusting the inspection tasks of each of the inspection robots, the priority corresponding to each inspection task, the availability of each inspection robot and the production efficiency of each production line can be first determined to quantitatively evaluate the suitability of each inspection robot in performing different inspection tasks on different production lines.

[0080] The following calculation formula can be used to calculate the suitability of the inspection robot to perform inspection tasks on the production line:

[0081] F ijk =ω P ·P j +ω E ·E k +ω U ·U i ;

[0082] Among them, F ijk is the suitability of the i-th robot to detect the j-th component to be inspected on the k-th production line, ω P ,ω E ,ω U are the weights corresponding to priority, production efficiency, and availability, respectively, ω P +ω E +ω U =1.

[0083] The following calculation formula can also be used to calculate the suitability of the inspection robot to perform inspection tasks on the production line:

[0084] F ijk =P j ·E k ·U i ;

[0085] In this way, if any one of the priority, productivity, and availability factors is very low, the overall evaluation value will also be low, which helps to quickly eliminate those inappropriate combinations.

[0086] After the corresponding fitness is calculated, the combination with the highest fitness value can be selected to assign the task, that is, for production line k, the robot i with the highest fitness is selected to inspect component j.

[0087] like Figure 2 The flowchart of the method for planning a path for multi-machine collaborative inspection of quality inspection robots shown in FIG. 1 includes:

[0088] Step S1, using the above-mentioned multi-machine collaborative inspection task scheduling method for quality inspection robots to adjust the inspection tasks of each inspection robot in real time;

[0089] Step S2: determine the current target position corresponding to each detection robot according to the corresponding detection task, and plan the moving path of each detection robot according to the current position and target position corresponding to the detection robot.

[0090] Specifically, first, the above scheduling method can be used to reasonably allocate the inspection tasks of each inspection robot to ensure that the workload of each inspection robot remains balanced, thereby improving the inspection efficiency of the entire production system. During the inspection process, the inspection tasks of each inspection robot are adaptively and dynamically adjusted according to various production changes to ensure that the inspection can be carried out stably, further improving the inspection efficiency of the entire production system.

[0091] During the inspection process, the current position of the component to be inspected in the inspection task after the inspection robot is adjusted can be used as the target position, and the existing path planning algorithm can be used to reasonably plan the moving path from the current position of the inspection robot to the target position, so that the inspection robot can quickly move to the target position to perform the inspection task, further improving the inspection efficiency of the system.

[0092] In this embodiment, optionally, in step S2, planning the moving path of the detection robot includes:

[0093] Marking the current position, target position and obstacle position of the inspection robot in the two-dimensional map corresponding to the production area;

[0094] A path search algorithm is used to search for an optimal path from the current position to the target position in the two-dimensional map.

[0095] Specifically, the layout of the production area can be abstracted and converted into a two-dimensional map, and the current position, target position, and obstacle position of each robot can be marked in the two-dimensional map. Then, the existing path search algorithm, such as the A* path search algorithm, the Dijkstra path search algorithm, the D*Lite path search algorithm, etc., can be used to plan the best path for the detection robot to move from the current position to the target position. When planning the path, the position and movement direction of other robots are considered to avoid collisions.

[0096] Taking the A* path search algorithm as an example, the core of the A* path search algorithm is the estimation function:

[0097] f(n)=g(n)+h(n);

[0098]

[0099] Among them, g(n) is the actual cost from the current position to node n, and h(n) is the estimated cost from node n to the target position. The next moving direction is selected by comparing the estimated function values ​​of different nodes until the target position is reached.

[0100] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned method for scheduling multi-machine collaborative inspection tasks of quality inspection robots.

[0101] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned quality inspection robot multi-machine collaborative inspection path planning method.

[0102] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for scheduling multi-machine collaborative inspection tasks of quality inspection robots, characterized in that: include: Obtain the current production information of each production line and the status information of each inspection robot; Allocating inspection tasks to the inspection robots based on the production information and corresponding status information; Obtain real-time production information of each production line, status information of each inspection robot, and inspection information corresponding to each inspection task; Evaluate the priority of each inspection task according to the inspection information, evaluate the availability of each inspection robot according to the status information, and evaluate the production efficiency of each production line according to the production information and status information; Based on the priority corresponding to each inspection task, the availability of each inspection robot and the production efficiency of each production line, the inspection tasks of each inspection robot are dynamically adjusted.

2. The method for scheduling multi-machine collaborative inspection tasks of quality inspection robots according to claim 1 is characterized in that: Assign inspection tasks to the inspection robot based on production information and status information, including: Evaluate the inspection time required for each inspection task based on the device type and inspection requirements in the production information; The detection tasks of the detection robots are allocated using a load balancing algorithm based on the detection time and status information required for each detection task.

3. The method for scheduling multi-machine collaborative inspection tasks of quality inspection robots according to claim 1 is characterized in that: Dynamically adjusting the detection tasks of each of the detection robots includes: Based on the priority of each inspection task, the availability of each inspection robot and the production efficiency of each production line, quantitatively evaluate the suitability of each inspection robot to perform different inspection tasks on different production lines; According to the suitability of each inspection robot for performing different inspection tasks on different production lines, the inspection tasks of each inspection robot are dynamically adjusted.

4. The method for scheduling multi-machine collaborative inspection tasks of quality inspection robots according to claim 1 is characterized in that: Evaluating the priority of the detection task according to the detection information, including: The priority of the detection task is evaluated according to the urgency, task type and detection requirements of the detection task included in the detection information.

5. The method for scheduling multi-machine collaborative inspection tasks of quality inspection robots according to claim 1 is characterized in that: Evaluating the availability of the detection robot according to the state information includes: The availability of the detection robot is evaluated based on the power information and health status information in the status information.

6. The method for scheduling multi-machine collaborative inspection tasks of quality inspection robots according to claim 1 is characterized in that: The production efficiency of the production line is evaluated based on the production information and the status information, including: The production efficiency of the production line is evaluated according to the production speed and work efficiency factor in the production information.

7. A method for multi-machine collaborative inspection path planning of quality inspection robots, characterized in that: include: Adopting the multi-machine collaborative inspection task scheduling method of quality inspection robots as described in any one of claims 1 to 6 to adjust the inspection tasks of each inspection robot in real time; The target position currently corresponding to each detection robot is determined according to the corresponding detection task, and the moving path of each detection robot is planned according to the current position and the target position corresponding to the detection robot.

8. The method for multi-machine collaborative inspection path planning of quality inspection robots according to claim 7 is characterized in that: Planning the moving path of the detection robot includes: Marking the current position, target position and obstacle position of the inspection robot in the two-dimensional map corresponding to the production area; A path search algorithm is used to search for an optimal path from the current position to the target position in the two-dimensional map.

9. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method for scheduling multi-machine collaborative inspection tasks of quality inspection robots as described in any one of claims 1-6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by the processor, the steps of the method for multi-machine collaborative inspection path planning of quality inspection robots as described in claim 7 are implemented.

Images