Consistency-constrained inspection robot task allocation method

Abstract

The present invention relates to the field of detection robot technology, and in particular to a detection robot task allocation method based on consistency constraints, comprising: determining the tasks to be detected and the number of detection robots; inputting the tasks to be detected and the number of detection robots into a task allocation model, and obtaining the task allocation results of the detection robots, wherein the task allocation model is constructed based on a consistency constraint algorithm; driving the detection robot to perform detection work based on the task allocation results, and updating the tasks to be detected and the number of detection robots in real time during the detection process, continuously inputting them into the task allocation model, and updating the task allocation results until the detection is completed. The detection robot task allocation method based on consistency bundles of the present invention is more practical after allocating tasks, and the allocation of detection tasks can be dynamically adjusted during the detection process, greatly reducing resource waste.

Description

Technical Field

[0001] The present invention relates to the technical field of detection robots, and in particular to a consistency-constrained detection robot task allocation method. Background Art

[0002] As the number of robots increases, how to reasonably and effectively allocate tasks to ensure that each robot can fully play its role has become an urgent problem to be solved. Especially in large-scale security inspection sites, the inspection tasks are heavy and complex, and a single inspection robot can no longer meet the requirements of efficient and comprehensive inspection. In the traditional inspection process, each inspection robot only performs one inspection task and then ends, which undoubtedly causes a huge waste of resources. Existing robot task allocation methods have the defects of independent task processing, lack of coordination and poor adaptability to state changes. Many task allocation methods assume that each robot can complete the task independently. Therefore, these methods usually assign tasks to each robot independently without considering the cooperation between robots. As a result, each robot only focuses on the completion of its own task, and it is difficult to coordinate and solve tasks that require collaboration. In a multi-robot system, some robots may fail during the execution of the task. The lack of adaptability cannot respond to these failures quickly, resulting in a decrease in system efficiency.

[0003] Therefore, how to achieve multi-robot collaborative work and optimize task allocation strategies has become a hot topic and difficulty in the current security inspection field research. Therefore, the present invention proposes a consistency-constrained inspection robot task allocation method. Summary of the Invention

[0004] The purpose of the present invention is to provide a consistency-constrained detection robot task allocation method for the problem of task allocation when the detection robot detects contraband, realize the collaborative work of multiple detection robots, and optimize the task allocation strategy.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] The consistency-constrained detection robot task allocation method includes:

[0007] Determine the tasks to be inspected and the number of inspection robots;

[0008] Inputting the task to be inspected and the number of inspection robots into a task allocation model to obtain a task allocation result for the inspection robots, wherein the task allocation model is constructed based on a consistency constraint algorithm;

[0009] Based on the task allocation result, the detection robot is driven to perform detection work, and the tasks to be detected and the number of detection robots are updated in real time during the detection process, and are continuously input into the task allocation model to update the task allocation result until the detection is completed.

[0010] Optionally, the task allocation model includes a first task allocation model and a second task allocation model, wherein the first task allocation model is used to make optimal allocation based on the task execution time of the detection robots when the number of the detection robots is less than the tasks to be detected; and the second task allocation model is used to make optimal allocation based on the task execution benefits of the detection robots when the number of the detection robots is greater than or equal to the tasks to be detected.

[0011] Optionally, the objective function of the task allocation model is:

[0012]

[0013] Among them, N is the number of detection robots, M is the number of tasks to be detected, and x i Is to detect the current state of robot i, p i is the list of tasks to be detected that the detection robot i is expected to perform, cx j (x i ,p i ) is the detection robot i execution list p i The profit after ij It is a binary decision variable, which indicates the task execution of the detection robot i. When x ij When x is equal to 1, it means that the detection robot i is assigned to perform the detection task j; when x ij When it is equal to 0, it means that the task j to be inspected has not been assigned to the inspection robot i for execution.

[0014] Optionally, the constraints of the task allocation model are:

[0015]

[0016] Among them, m is the upper limit of the execution task of detection robot i, I is the set of detection robots, J is the set of tasks to be detected, L min The minimum amount of tasks assigned to the detection robot.

[0017] Optionally, assigning tasks to the inspection robot based on the task assignment model constructed based on the consistency constraint algorithm includes:

[0018] Develop individual task sequences based on each inspection robot's own conditions;

[0019] All detection robots are required to exchange information, and during the interaction process, they bid for the same task based on the personal task sequence. If the bid is successful, the task is retained in the personal task sequence. If the bid fails, the task and subsequent tasks are removed from the personal task sequence, and the personal task sequence is re-established.

[0020] Optionally, formulating a personal task sequence for the detection robot includes:

[0021] Step 1: Input the task sequence, task execution path, winning bidder set, and winning bidder set of the detection robot;

[0022] Step 2: Determine whether the task sequence of the detection robot has reached the storage limit. If so, proceed to step 3; if not, proceed to step 4;

[0023] Step 3: Check whether the task to be detected exists in the task execution path. If so, check the next task to be detected; if not, proceed to step 4.

[0024] Step 4: Determine whether the value of the task to be tested exceeds the highest single bid in the winning bidder's bid set. If so, proceed to step 5; if not, proceed to step 6.

[0025] Step 5: Check whether the number of robots assigned to the tasks to be tested in the winning bidder set has reached the upper limit. If so, check the next task to be tested; if not, proceed to step 6.

[0026] Step 6: Based on the principle of maximizing the benefit of the task execution path, insert the task to be detected into the optimal position in the task execution path, and record the current position information, the benefit value obtained at the current position, and the task start time;

[0027] Step 7: Enter the next task to be detected and go to step 2 until the current position information, current position benefit value and task start time of all tasks to be detected are obtained;

[0028] Step 8: Based on the revenue values ​​of the current positions of all tasks to be detected, the task to be detected with the largest revenue value is added to the task sequence, and the task execution path, the winning bidder bid set, and the winning bidder set are updated based on the corresponding information;

[0029] Step 9: Enter the next detection robot and go to step 1 until all detection robots are traversed and the updated task sequence, task execution path, winning bidder set and winning bidder set of the detection robot are output.

[0030] Optionally, in step 6, inserting the task to be detected into the optimal position in the task execution path according to the principle of maximizing the benefit of the task execution path includes:

[0031] Randomly determining an insertion position of the task to be detected in the task execution path;

[0032] The benefits at the insertion position and the position before the insertion position are calculated. If the benefit at the insertion position is greater than the benefit at the position before the insertion position, the insertion position is the optimal insertion position in the execution path. If the benefit at the insertion position is less than the benefit at the position before the insertion position, the insertion position is changed, and the benefit calculation and comparison are performed again until the optimal insertion position is found.

[0033] The beneficial effects of the present invention are:

[0034] The present invention adopts a consistency constraint algorithm to allocate detection tasks, which better meets actual needs after allocation; during the execution of detection tasks, the allocation of detection tasks can be dynamically adjusted to greatly reduce resource waste; it has good versatility and can be applied to most task allocation problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 This is a flow chart of a method for assigning tasks to a detection robot with consistency constraints according to an embodiment of the present invention;

[0037] Figure 2 The detection robot task allocation result for scenario 1 of an embodiment of the present invention, wherein (a) is the detection robot path time graph for scenario 1, and (b) is the detection robot task timeline for scenario 1;

[0038] Figure 3 The detection task allocation results for scenario 2 of an embodiment of the present invention, where (a) is the detection robot path time diagram for scenario 2, and (b) is the detection robot task timeline for scenario 2;

[0039] Figure 4 This is the detection task allocation result of scenario three of the embodiment of the present invention, where (a) is the detection robot path time diagram of scenario two, and (b) is the detection robot task timeline of scenario two. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0042] This embodiment provides a method for assigning detection robot tasks with consistency constraints, including:

[0043] Determine the tasks to be inspected and the number of inspection robots, input the tasks to be inspected and the number of inspection robots into a task allocation model, and obtain the task allocation results of the inspection robots, wherein the task allocation model is constructed based on a consistency constraint algorithm;

[0044] Based on the task allocation results, the inspection robot is driven to perform inspection work, and the tasks to be inspected and the number of inspection robots are updated in real time during the inspection process. The task allocation model is continuously input to update the task allocation results until the inspection is completed.

[0045] Among them, the task allocation model includes a first task allocation model and a second task allocation model. The first task allocation model is used to make optimal allocation based on the task execution time of the detection robots when the number of detection robots is less than the tasks to be detected; the second task allocation model is used to make optimal allocation based on the task execution benefits of the detection robots when the number of detection robots is greater than or equal to the tasks to be detected.

[0046] Specifically, this embodiment uses a consistency algorithm to allocate detection tasks, which is more in line with actual needs after allocation; during the execution of detection tasks, the allocation of detection tasks can be dynamically adjusted to greatly reduce resource waste; it has good versatility and can be applied to most task allocation problems.

[0047] Furthermore, the objective function of the task allocation model is:

[0048]

[0049] Among them, N is the number of detection robots, M is the number of tasks to be detected, and x i Is to detect the current state of robot i, p i is the list vector of tasks to be detected that the detection robot i is expected to perform, c ij (x i ,p i ) is the detection robot i execution list p i The profit after ij It is a binary decision variable, which indicates the task execution of the detection robot i. When x ij When x is equal to 1, it means that the detection robot i is assigned to perform the detection task j; when x ij When it is equal to 0, it means that the task j to be inspected has not been assigned to the inspection robot i for execution.

[0050] Furthermore, the constraints of the task allocation model are:

[0051]

[0052] Among them, m is the upper limit of the execution task of detection robot i, I is the set of detection robots, J is the set of tasks to be detected, L min The minimum amount of tasks assigned to the detection robot.

[0053] In this embodiment, the ultimate goal is to obtain the maximum benefit from executing tasks. The consistency constraint algorithm iterates to obtain a conflict-free task allocation plan to maximize the overall benefit of the detection robot. The so-called conflict-free means ensuring that each detection robot executes one task at a time, and each task can only be assigned to one detection robot.

[0054] Furthermore, the task allocation model constructed based on the consistency constraint algorithm allocates tasks to the detection robots, including: formulating a personal task sequence based on the own conditions of each detection robot, exchanging information among all detection robots, bidding for the same task based on the personal task sequence during the interaction process, retaining the task in the personal task sequence if the bid is successful, and removing the task and subsequent tasks from the personal task sequence if the bid fails, and re-formulating the personal task sequence.

[0055] Specifically, developing a personal task sequence for the inspection robot includes:

[0056] Step 1: Input the task sequence, task execution path, winning bidder set, and winning bidder set of the detection robot;

[0057] Step 2: Determine whether the task sequence of the detection robot has reached the storage limit. If so, proceed to step 3; if not, proceed to step 4.

[0058] Step 3: Check whether the task to be detected exists in the task execution path. If so, check the next task to be detected; if not, proceed to step 4.

[0059] Step 4: Determine whether the value of the task to be tested exceeds the highest single bid in the winning bidder's bid set. If so, proceed to step 5; if not, proceed to step 6.

[0060] Step 5: Check whether the number of robots assigned to the tasks to be tested in the winning bidder set has reached the upper limit. If so, check the next task to be tested; if not, proceed to step 6.

[0061] Step 6: Based on the principle of maximizing the benefit of the task execution path, insert the task to be detected into the optimal position in the task execution path, and record the current position information, the benefit value obtained at the current position, and the task start time;

[0062] Step 7: Enter the next task to be detected and go to step 2 until the current position information, current position benefit value and task start time of all tasks to be detected are obtained;

[0063] Step 8: Based on the revenue values ​​of the current positions of all tasks to be detected, the task to be detected with the largest revenue value is added to the task sequence, and the task execution path, the winning bidder bid set, and the winning bidder set are updated based on the corresponding information;

[0064] Step 9: Enter the next detection robot and go to step 1 until all detection robots are traversed and the updated task sequence, task execution path, winning bidder set and winning bidder set of the detection robot are output.

[0065] Among them, in step 6, according to the principle of maximizing the benefit of the task execution path, the optimal position for inserting the task to be detected into the task execution path includes:

[0066] Randomly determine the insertion position of the task to be detected in the task execution path;

[0067] Calculate the benefits of the insertion position and the position before the insertion position. If the benefit of the insertion position is greater than the benefit of the position before the insertion position, the insertion position is the best insertion position in the execution path. If the benefit of the insertion position is less than the benefit of the position before the insertion position, change the insertion position and calculate and compare the benefits again until the best insertion position is found.

[0068] The following combination Figures 1-4 The consistency constraint detection robot task allocation method proposed in this embodiment is specifically described and applied, including:

[0069] Step 1: Confirm the number of prohibited items to be detected and the number of detection robots, and determine the relationship between the two quantities;

[0070] Step 2: If the number of robots is less than the number of contraband: Although the detection robots' mission capabilities meet existing operational requirements, the consistency bundle algorithm can allocate tasks based on the time it takes each robot to perform the inspection. This means that the detection robots can prioritize the targets with the highest time benefits to complete the inspection.

[0071] Step 3: If the number of robots is equal to the number of prohibited items: During the consistency bundle algorithm's contraband detection allocation process, the detection robot can be preferentially selected to perform the detection task based on the information about prohibited items during the task. This means the robot with the greatest benefit is selected to complete the detection. For example, if after completing detection task 1, detection robot 1 is closer to detection task 2 than detection robot 2, detection robot 1 can continue to complete detection task 2, while detection robot 2 can perform other detection tasks or not be assigned to any detection tasks.

[0072] Step 4: If the number of robots is greater than the number of prohibited items: Same as step 3;

[0073] Step 5: The detection robot performs the contraband detection task according to the allocation plan of steps 2 to 4 until the task is completed.

[0074] The traditional method, where each inspection robot performs only one inspection task, undoubtedly results in a huge waste of resources. In comparison, the inspection robot task allocation method based on the consistency constraint algorithm is more practical. The distribution of inspection tasks can be dynamically adjusted during the inspection process, greatly reducing resource waste.

[0075] Assuming that there are N detection robots and M prohibited items, the consistency constraint algorithm iterates to obtain a conflict-free task allocation plan to maximize the overall benefit of the detection robots. The so-called conflict-free means that each detection robot performs one task at a time, and each task can only be assigned to one detection robot. Under the premise of meeting its own conditions, assuming that each detection robot performs at most m tasks, the consistency constraint algorithm will assign at least L detection robots min =min{M, N×m} tasks.

[0076] The objective function of the algorithm is:

[0077]

[0078] The objective function is the overall benefit obtained by executing the current task allocation scheme, where p i is the task list vector that robot i is expected to perform, c ij (x i ,p i ) is the list of tasks executed by robot i p i The profit after ij It is a binary decision variable, which indicates the task execution status of robot i. When it is equal to 1, robot i is assigned to perform task j; when it is equal to 0, task j is not assigned to robot i.

[0079] The constraints are:

[0080]

[0081] Where I is the set of robots and J is the set of tasks to be performed: I = {1, 2, ..., N}, J = {1, 2, ..., M}.

[0082] The consistency constraint algorithm consists of two phases. In the first phase, task formulation, each robot formulates its own task sequence based on its own conditions. In the second phase, conflict resolution or consensus, each robot exchanges information with surrounding robots to determine who will be assigned the same bidding task. The following vectors must also be established in the first phase of the algorithm:

[0083] (1) Task sequence vector b i (beam vector): This vector is defined as in is the task sequence currently won by the robot, n t That is the number of tasks. The tasks in the task sequence are arranged according to priority. Indicates that robot i did not win the task.

[0084] (2) Task execution path vector p i :This vector is obtained by expanding the task list vector into a task path with a sequence, which contains the moving path between each task point and the related path optimization information, defined as p i ={p i1 ,p i2 ,……,p in},in Execute b for robot i running this path i The corresponding path vector.

[0085] (3) Time set τ i :The set is defined as τ i ={τ i1 ,τ i2 ,……,τ in},in To execute time.

[0086] (4) The set of successful bidders z i :The set is defined as z i ={z i1 ,z i2 ,……,z in},in For robot i, which robot performs task n.

[0087] (5) The winning bidder's bid set yi : The set is defined as y i ={y i1 ,y i2 ,……,y in},in For all robots to task n s The highest bid in the auction.

[0088] (6) Timestamps i : The set is defined as s i ={s i1 ,s i2 ,……,s in}, is the time when robot i receives the latest information of robot n from the communication network.

[0089] The following is a detailed introduction to the two phases of the consistency bundle algorithm, including:

[0090] (1) Task formulation phase: Each robot continuously adds and adjusts tasks in its own sequence to build the task sequence it is expected to perform.

[0091] Input: Task sequence b of robot i after the last iteration i , task execution path p i , winning bidder's bid set y i and the set of winning bidders z i ;

[0092] Step 1: Robot i determines task sequence b i Check whether the storage limit has been reached. If not, proceed to Step 2. If so, proceed to Step 9.

[0093] Step 2: Robot i checks whether task j exists in task path p i If exists, then j=j+1, if not, go to Step 3;

[0094] Step 3: Determine whether the value of task j exceeds the robot's highest single bid. If not, proceed to Step 5. If so, proceed to Step 4.

[0095] Step 4: Check z i Check whether the assigned robots for task j have reached the upper limit. If so, then j = j + 1. If not, go to Step 5.

[0096] Step 5: Insert task j into the best position in the sequence, and place it in the task execution path of robot i. i , for position k=1:|p i |+1, calculate the benefit of the entire path after inserting to position k;

[0097] Step 6: Determine the benefits of position k and k-1. If k is greater than k-1, record the current position information, the benefit value obtained at the current position, and the time when the task started. If k is less than k-1, then k=k+1 and proceed to Step 5.

[0098] Step 7: j = j + 1, go to Step 2;

[0099] Step 8: Compare each target and insert p i The maximum benefit in b is added to the maximum benefit target i and update b i 、p i 、y i and z i ;

[0100] Step 9: i=i+1, then determine whether all members of the robot have been traversed. If yes, go to the second stage, otherwise, continue to Step 1.

[0101] Output: Task sequence b of robot i after this iteration i , task execution path p i , winning bidder's bid set y i and the set of winning bidders z i .

[0102] (2) Consensus stage: Adjacent robots communicate with each other and obtain each other’s b i 、p i 、s i To determine whether the bid is successful, if the bid for task j is successful, all surrounding robots including those bidding for task j must be modified, j and tasks added later must be released, and the robot must be re-tasked.

[0103] Description of scene application and application effect:

[0104] Assuming that 10 prohibited items need to be detected, the number of detection robots is selected as 5, 10, and 18 as application scenarios.

[0105] Scenario 1: The number of detection robots is 5, which is less than the number of prohibited items. The result is as follows: Figure 2 As shown in Figure 1, (a) is the detection robot path time diagram for scenario 1, and (b) is the detection robot task timeline for scenario 1. Figure 2 It can be seen that all five detection robots followed the information preferential selection method, and there was no situation like the traditional method where a robot detected a prohibited item and then returned to the starting point.

[0106] Scenario 2: The number of detection robots is 10, which is equal to the number of prohibited items. The results are as follows: Figure 3 As shown in Figure 2, (a) is the detection robot path time diagram for scenario 2, and (b) is the detection robot task timeline for scenario 2. Figure 3 As can be seen, all ten inspection robots followed the principle of information-based selection, avoiding the situation where a robot would inspect one contraband item and then return to the starting point, as is often the case with traditional methods. Based on the principle of dynamic information selection, inspection robots 4, 5, 9, and 10 were not assigned inspection tasks.

[0107] Scenario 3: The number of detection robots is 18, which is greater than the number of prohibited items. The result is as follows: Figure 4 As shown in Figure 2, (a) is the detection robot path time diagram for scenario 2, and (b) is the detection robot task timeline for scenario 2. Figure 4 As can be seen, all 18 inspection robots followed the principle of information-based selection, avoiding the situation where a robot would inspect one contraband item and then return to the starting point, as is often the case with traditional methods. Based on the principle of dynamic information selection, inspection robots 4, 5, 6, 7, 8, 9, 13-18 were not assigned inspection tasks.

[0108] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for allocating tasks of a detection robot based on consistency constraints, characterized in that: include: Determine the tasks to be inspected and the number of inspection robots; Inputting the task to be inspected and the number of inspection robots into a task allocation model to obtain a task allocation result for the inspection robots, wherein the task allocation model is constructed based on a consistency constraint algorithm; Based on the task allocation results, the detection robot is driven to perform detection work, and the tasks to be detected and the number of detection robots are updated in real time during the detection process, and the task allocation results are continuously input into the task allocation model to update the task allocation results until the detection is completed; The task allocation model constructed based on the consistency constraint algorithm allocates tasks to the detection robot, including: Develop individual task sequences based on each inspection robot's own conditions; All inspection robots exchange information, and during the interaction process, bid for the same task based on the personal task sequence. If the bid is successful, the task is retained in the personal task sequence. If the bid is unsuccessful, the task and subsequent tasks are removed from the personal task sequence, and the personal task sequence is re-established; Formulating the individual task sequence of the inspection robot includes: Step 1: Input the task sequence, task execution path, winning bidder set, and winning bidder set of the detection robot; Step 2: Determine whether the task sequence of the detection robot has reached the storage limit. If so, proceed to step 3; if not, proceed to step 4. Step 3: Check whether the task to be detected exists in the task execution path. If so, check the next task to be detected; if not, proceed to step 4. Step 4: Determine whether the value of the task to be tested exceeds the highest single bid in the winning bidder's bid set. If so, proceed to step 5; if not, proceed to step 6. Step 5: Check whether the number of robots assigned to the tasks to be tested in the winning bidder set has reached the upper limit. If so, check the next task to be tested; if not, proceed to step 6. Step 6: Based on the principle of maximizing the benefit of the task execution path, insert the task to be detected into the optimal position in the task execution path, and record the current position information, the benefit value obtained at the current position, and the task start time; Step 7: Enter the next task to be detected and go to step 2 until the current position information, current position income value and task start time of all tasks to be detected are obtained; Step 8: Based on the revenue values ​​of the current positions of all tasks to be detected, the task to be detected with the largest revenue value is added to the task sequence, and the task execution path, the winning bidder bid set, and the winning bidder set are updated based on the corresponding information; Step 9: Enter the next detection robot and go to step 1 until all detection robots have completed the traversal, and output the updated task sequence, task execution path, winning bidder set, and winning bidder set of the detection robot; In step 6, inserting the task to be detected into the optimal position of the task execution path according to the principle of maximizing the benefit of the task execution path includes: Randomly determining an insertion position of the task to be detected in the task execution path; The benefits at the insertion position and the position before the insertion position are calculated. If the benefit at the insertion position is greater than the benefit at the position before the insertion position, the insertion position is the optimal insertion position in the execution path. If the benefit at the insertion position is less than the benefit at the position before the insertion position, the insertion position is changed, and the benefit calculation and comparison are performed again until the optimal insertion position is found.

2. The method for assigning tasks to detection robots based on consistency constraints according to claim 1, characterized in that: The task allocation model includes a first task allocation model and a second task allocation model, wherein the first task allocation model is used to optimally allocate tasks based on the task execution time of the detection robots when the number of the detection robots is less than the tasks to be detected; the second task allocation model is used to optimally allocate tasks based on the task execution benefits of the detection robots when the number of the detection robots is greater than or equal to the tasks to be detected.

3. The method for assigning tasks to detection robots based on consistency constraints according to claim 1, wherein: The objective function of the task allocation model is: Among them, N is the number of detection robots, M is the number of tasks to be detected, and x i Is to detect the current state of robot i, p i is the list vector of tasks to be detected that the detection robot i is expected to perform, c ij (x i ,p i ) is the detection robot i execution list p i The profit after ij It is a binary decision variable, which indicates the task execution of the detection robot i. When x ij When x is equal to 1, it means that the detection robot i is assigned to perform the detection task j; when x ij When it is equal to 0, it means that the task j to be inspected has not been assigned to the inspection robot i for execution.

4. The method for assigning tasks to detection robots based on consistency constraints according to claim 3, wherein: The constraints of the task allocation model are: Among them, m is the upper limit of the execution task of detection robot i, I is the set of detection robots, J is the set of tasks to be detected, L min The minimum amount of tasks assigned to the detection robot.

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