A control method and system for multiple inspection robots integrating Internet of Things technology

Abstract

The present invention discloses a control method and system for multiple patrol robots integrating Internet of Things technology. The processing control layer of the present invention uses the transmission layer to issue a self-check instruction to each patrol robot. After receiving the self-check instruction, the patrol robot performs self-check and feeds back the self-check data to the processing control layer through the transmission layer. Based on the inspection target data, the route is assigned to each patrol robot that has passed the self-check and the inspection instruction is issued. The patrol robot performs inspection according to the instruction. The processing control layer controls the patrol robot to perform inspection in real time, determines the patrol route change node, and reassigns tasks and replans the route for the patrol robots that do not need endurance and have completed endurance in the working area according to the charging position in the unfinished route of the inspection; the processing control layer transmits the inspection data to the user layer in real time for display. The present invention enables the patrol robot to have high applicability and flexibility during the inspection process.

Description

Technical Field

[0001] The present invention relates to the field of control technology of inspection robots, and in particular to a control method and system for multiple inspection robots integrating Internet of Things technology. Background Art

[0002] With the advancement and development of science and technology, robots have gradually appeared in work, and inspection robots have also been born. They replace manual inspection work and are widely used in the fields of electricity, petrochemicals, rail transit, etc. They have the characteristics of high detection efficiency, wide monitoring range, real-time data and information, and high safety factor.

[0003] In complex scenarios, most of the time, multiple robots are used for joint inspections. The inspection robots have high requirements for the flatness of the tunnels. If the terrain is uneven and there are obstacles on the road, the inspection robots will change their routes to avoid obstacles. If the obstacles are too large, the inspection robots may not be able to move forward and staff will be required to clear the obstacles. At the same time, the inspection robots may stop moving due to battery life issues, resulting in delayed inspections in some areas, which may lead to untimely detection of safety hazards. This makes it difficult for multiple inspection robots to adapt to complex tunnel terrain in joint inspections in complex scenarios. This may lead to blind spots in the inspection range, untimely inspections, and other problems, resulting in low inspection efficiency and failure to fully meet the needs of safe production management in complex scenarios.

[0004] Therefore, a control method for multiple inspection robots is needed to reasonably allocate the inspection robots and plan routes, so as to meet the timeliness of the inspection and avoid inspection blind spots during the inspection. Summary of the invention

[0005] The purpose of the present invention is to provide a control method and system for multiple inspection robots integrating Internet of Things technology, which can timely plan the inspection route according to the power status of the inspection robots during the inspection process to solve the problem of inspection delays in some areas caused by stopping moving forward due to power endurance issues, thereby leading to low inspection efficiency as mentioned in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a control method for a multi-inspection robot integrating Internet of Things technology, the control method comprising:

[0007] The processing control layer uses the transmission layer to issue self-check instructions to each patrol robot. After receiving the self-check instructions, the patrol robot performs self-check and feeds back the self-check data to the processing control layer through the transmission layer.

[0008] Based on the inspection target data, each inspection robot that passes the self-inspection is assigned a route and issued an inspection instruction;

[0009] After receiving the inspection instruction, the inspection robot performs inspection according to the planned inspection route, and transmits the inspection data and the status data of the inspection robot to the processing control layer; the status data of the inspection robot includes the power status data of the inspection robot and the motion status data of the inspection robot;

[0010] The processing control layer compares the current power status data of each inspection robot with the power limit value, which refers to the minimum protection power of the inspection robot; if the power of the inspection robot is less than the power limit value, the inspection robots that do not need battery life and have completed battery life in the working area are reassigned tasks and replanned routes according to the charging positions in the unfinished inspection routes, and the charging position D of the inspection robots that need battery life is determined, which can reduce the loss of the inspection robot battery and extend the battery life;

[0011] The processing control layer transmits the inspection data to the user layer in real time for display and determines whether the inspection is completed, and then plans the warehousing route for each inspection robot. The inspection robot completes the warehousing according to the warehousing route and ends the inspection.

[0012] Furthermore, the inspection robot that passes the self-test has the following route allocation steps:

[0013] The number of inspection robots in each working area is determined according to the number of inspection robots that pass the self-inspection; the maximum interval between adjacent inspection sensors is the minimum value among the maximum communication distances of all sensors.

[0014] The working area is divided based on the number of inspection robots in the working area to obtain the initial inspection area, and the horizontal coordinates of the sensor positions in each initial inspection area are compared with the initial center horizontal coordinates at the starting position, and the sensor closest to the initial center horizontal coordinate is used as the initial point of the inspection route, and the sensors set in each working area are used as sub-targets to construct a sub-target set; the sub-target set includes the sensor at the charging position;

[0015] Based on the inspection route initial point and sub-goal set, all inspection robots in the working area use the improved ant colony algorithm to plan the path, and judge in real time whether sub-goal i is planned in at least two routes, so as to determine the attribution relationship of sub-goal i, and re-plan the route that does not contain sub-goal i; the improved ant colony algorithm refers to improving the traditional ant colony algorithm by introducing the cross node operation with the help of the roulette algorithm, so that the sub-goals are repeatedly planned on a planned route, which can reduce the time of route planning and make the planned route more concise;

[0016] When the route planning is completed, the shortest distance between the missed sub-target and all adjacent paths is calculated, the missed sub-target is divided into the paths with the shortest distance, and the planned path is readjusted to obtain the final planned route of the inspection robot, which can avoid the problem of missing sub-targets in the planned route to a certain extent.

[0017] Furthermore, the step of determining the belonging relationship of the sub-target i is as follows:

[0018] When sub-goal i appears in different order in different route plans, sub-goal i is assigned to the planned route with the earliest appearance order, and sub-goal i is removed from other routes. Then, the improved ant colony algorithm is used to re-plan the route from sub-goal j to the next sub-goal.

[0019] When target i appears in the same order in different route planning, the energy consumption from subtarget j to subtarget i is estimated, the energy consumption in different routes is compared, subtarget i is assigned to the planned route with the lowest energy consumption, and target i is removed from other routes. The route from subtarget j to the next subtarget is replanned using the improved ant colony algorithm.

[0020] Furthermore, the step of determining the charging position D includes:

[0021] Obtain all charging locations in the unfinished inspection route, and estimate the energy consumption of the inspection robot from the current location to each charging location in the unfinished route;

[0022] Compare the maximum energy limit that the current inspection robot can provide with the estimated energy consumption corresponding to each charging position of the current inspection robot; when the maximum energy limit is less than the estimated energy consumption corresponding to the nth charging position for the first time, determine whether the first n charging positions contain an inspection robot that has completed its endurance. If so, the first charging position that contains an inspection robot that has completed its endurance on the uninspected route is used as the charging position D of the current inspection robot; otherwise, the n-1th charging position is used as the charging position D of the current inspection robot, which can reduce the loss of the inspection robot battery;

[0023] The maximum energy limit refers to the maximum energy that can be provided by subtracting the power limit value from the current power of the inspection robot.

[0024] Among them, the energy consumption calculation is that when multiple robots are patrolling, the energy consumption is considered as walking joint energy consumption, friction energy consumption during driving, and working energy consumption.

[0025] Furthermore, the step of re-planning the route includes:

[0026] Estimate the sub-target m where the normal inspection robot is located when the inspection robot that needs to be extended inspects the charging position D;

[0027] The inspection robot that has completed the endurance test will use the current charging location as the starting point for re-planning the route, and other normal inspection robots will use the corresponding sub-goal m as the starting point for re-planning;

[0028] The charging position of the inspection robot that has completed the endurance test is used as the dividing point of the uninspected sub-target set to obtain the secondary sub-target set;

[0029] The inspection robot replans a route based on the corresponding secondary sub-goal set using the route allocation steps of the inspection robot that has passed the self-inspection.

[0030] This allows the inspection robots to avoid inspection delays in some areas due to battery life issues, and also avoids problems with unreasonable route planning or inspection blind spots due to changes in the number of inspection robots during the inspection process.

[0031] Furthermore, the warehousing route is planned based on the constraints of minimum energy consumption and shortest distance, so that the warehousing route can be concise and the energy consumption of the inspection robot can be reduced.

[0032] A control system for a multi-inspection robot integrating Internet of Things technology, comprising:

[0033] The acquisition control layer is used to collect inspection data and status data of inspection robots, including several inspection robots and sensors;

[0034] The transport layer is used for two-way communication between the acquisition control layer and the processing control layer;

[0035] The processing control layer belongs to the general control center and is used to process the data transmitted by the acquisition control layer and issue control instructions;

[0036] The user layer is used to receive and process the signals sent by the control layer and display the corresponding inspection data, including the monitoring system, analysis system, etc.

[0037] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention utilizes Internet technology to jointly control multiple inspection robots during the inspection process, determines the inspection route change node through the power status data of each inspection robot, and then re-assigns tasks and re-plans routes for inspection robots that do not need endurance and have completed endurance in the working area according to the charging positions in the unfinished inspection routes, thereby achieving the flexibility of multiple inspection robots during the inspection process, without having to worry about unreasonable route planning, inspection blind spots and inspection delays caused by power problems or a sudden increase or decrease in the number of inspection robots, thereby improving the efficiency and quality of the inspection and having higher applicability and flexibility during the inspection process. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0039] Figure 1 It is a schematic diagram of the steps of a control method of a multi-inspection robot integrating Internet of Things technology of the present invention;

[0040] Figure 2 It is a structural schematic diagram of a control system of a multi-inspection robot integrating Internet of Things technology according to the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0042] The present invention takes mine inspection as an example and explains a control method and system for multiple inspection robots integrating Internet of Things technology by controlling the inspection status of multiple inspection robots in the industrial Internet of Things.

[0043] The present invention provides a real-time auxiliary training method based on visual human posture estimation ( Figure 1 ):

[0044] S1. The processing control layer uses the transmission layer to issue a self-check command to each patrol robot. After receiving the self-check command, the patrol robot performs a self-check and feeds back the self-check data to the processing control layer through the transmission layer.

[0045] S2. Based on the inspection target data, a route is assigned to each inspection robot that has passed the self-inspection and an inspection instruction is issued. Specifically, after the processing control layer receives the self-inspection data of the inspection robot, the data of the inspection robot that has failed the self-inspection is transmitted to the user layer, so that the user can promptly repair the inspection robot that has failed the self-inspection;

[0046] Obtain the number of self-inspection robots and determine the number of inspection robots in each working area, divide the working area according to the number of inspection robots, obtain the initial inspection area, compare the horizontal coordinates of the sensor position in each initial inspection area with the initial center horizontal coordinate at the starting position, take the sensor closest to the initial center horizontal coordinate as the initial point of the inspection route, take the sensors set in each working area as sub-targets, and construct a sub-target set;

[0047] Based on the initial point of the inspection route and the sub-goal set, all inspection robots in the working area use the improved ant colony algorithm to plan the path, and judge in real time whether sub-goal i is planned in at least two routes, so as to determine the attribution of sub-goal i and re-plan the route that does not contain sub-goal i;

[0048] When the route planning is completed, the shortest distance between the missed sub-target and all adjacent paths is calculated, the missed sub-target is divided into the paths with the shortest distance, and the planned path is readjusted to obtain the final planned route of the inspection robot.

[0049] S3, after receiving the inspection instruction, the inspection robot performs inspection according to the planned inspection route, and transmits the inspection data and the status data of the inspection robot to the processing control layer; the status data of the inspection robot includes the power status data of the inspection robot and the motion status data of the inspection robot;

[0050] S4, the processing control layer compares the current power status data of each inspection robot with the power limit value. If the power of the inspection robot is less than the power limit value, the inspection robots that do not need endurance and have completed endurance in the working area are reassigned tasks and replanned routes according to the charging positions in the unfinished inspection routes, and the charging positions of the inspection robots that need endurance are determined; specifically:

[0051] Obtain all charging locations in the unfinished inspection route, and estimate the energy consumption of the inspection robot from the current location to each charging location in the unfinished route;

[0052] The maximum limited energy that the current inspection robot can provide is compared with the estimated energy consumption corresponding to the current inspection robot to each charging position; when the maximum limited energy is less than the estimated energy consumption corresponding to the nth charging position for the first time, determine whether the first n charging positions contain an inspection robot that has completed its endurance. If so, the first charging position that contains an inspection robot that has completed its endurance on the uninspected route is used as the charging position D of the current inspection robot; otherwise, the n-1th charging position is used as the charging position D of the current inspection robot.

[0053] Estimate the sub-target m that the normal inspection robot is located at when the inspection robot that needs endurance inspects the charging position D; the inspection robot that has completed endurance uses the current charging position as the initial point for re-planning the route, and other normal inspection robots use the corresponding sub-target m as the re-planning initial point; use the charging position of the inspection robot that has completed endurance as the division point of the uninspected sub-target set to obtain a secondary sub-target set; the inspection robot re-plans the route based on the corresponding secondary sub-target set using the route allocation step of the inspection robot that has passed the self-inspection.

[0054] S5. The processing control layer transmits the inspection data to the user layer in real time for display and determines whether the inspection is completed, and then plans the warehousing route for each inspection robot. The inspection robot completes the warehousing according to the warehousing route and ends the inspection.

[0055] Another embodiment, a control system of a multi-inspection robot integrating Internet of Things technology, includes:

[0056] The acquisition control layer is used to collect inspection data and status data of inspection robots, including several inspection robots and sensors;

[0057] The transport layer is used for two-way communication between the acquisition control layer and the processing control layer;

[0058] The processing control layer belongs to the general control center and is used to process the data transmitted by the acquisition control layer and issue control instructions;

[0059] The user layer is used to receive and process the signals sent by the control layer and display the corresponding inspection data, including the monitoring system, analysis system, etc.

[0060] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0061] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A control method for multiple inspection robots integrating Internet of Things technology, characterized in that: Control methods include: The processing control layer uses the transmission layer to issue self-check instructions to each patrol robot. After receiving the self-check instructions, the patrol robot performs self-check and feeds back the self-check data to the processing control layer through the transmission layer. Based on the inspection target data, each inspection robot that has passed the self-inspection is assigned a route and an inspection instruction is issued; the route assignment steps of the inspection robot that has passed the self-inspection are as follows: Determine the number of inspection robots in each work area based on the number of inspection robots that pass the self-inspection; The working area is divided based on the number of inspection robots in the working area to obtain the initial inspection area. The horizontal coordinates of the sensor positions in each initial inspection area are compared with the initial center horizontal coordinates at the starting position. The sensor closest to the initial center horizontal coordinate is used as the initial point of the inspection route. The sensors set in each working area are used as sub-targets to construct a sub-target set. Based on the initial point of the inspection route and the sub-goal set, all inspection robots in the working area use the improved ant colony algorithm to plan the path, and judge in real time whether sub-goal i is planned in at least two routes, so as to determine the attribution of sub-goal i and re-plan the route that does not contain sub-goal i; When the route planning is completed, the shortest distance between the missed sub-target and all adjacent paths is calculated, the missed sub-target is divided into the paths with the shortest distance, and the planned path is readjusted to obtain the final planned route of the inspection robot; After receiving the inspection instruction, the inspection robot performs inspection according to the planned inspection route, and transmits the inspection data and the status data of the inspection robot to the processing control layer; the status data of the inspection robot includes the power status data of the inspection robot and the motion status data of the inspection robot; The processing control layer compares the current power status data of each inspection robot with the power limit value. If the power of the inspection robot is less than the power limit value, the inspection robots that do not need battery life and have completed battery life in the working area are reassigned tasks and replanned routes according to the charging positions in the unfinished inspection routes, and the charging positions D of the inspection robots that need battery life are determined; The processing control layer transmits the inspection data to the user layer in real time for display and determines whether the inspection is completed, and then plans the warehousing route for each inspection robot. The inspection robot completes the warehousing according to the warehousing route and ends the inspection.

2. The control method of a multi-inspection robot integrating Internet of Things technology according to claim 1 is characterized in that: The steps for determining the belonging relationship of sub-target i are as follows: When sub-goal i appears in different order in different route plans, sub-goal i is assigned to the planned route with the earliest appearance order, and sub-goal i is removed from other routes. The route from sub-goal j to the next sub-goal is replanned using the improved ant colony algorithm. When target i appears in the same order in different route planning, the energy consumption from subtarget j to subtarget i is estimated, the energy consumption in different routes is compared, subtarget i is assigned to the planned route with the lowest energy consumption, and target i is removed from other routes. The route from subtarget j to the next subtarget is replanned using the improved ant colony algorithm.

3. The control method of a multi-inspection robot integrating Internet of Things technology according to claim 1 is characterized in that: The step of determining the charging position D comprises: Obtain all charging locations in the unfinished inspection route, and estimate the energy consumption of the inspection robot from the current location to each charging location in the unfinished route; The maximum limited energy that the current inspection robot can provide is compared with the estimated energy consumption corresponding to the current inspection robot to each charging position; when the maximum limited energy is less than the estimated energy consumption corresponding to the nth charging position for the first time, determine whether the first n charging positions contain an inspection robot that has completed its endurance. If so, the first charging position that contains an inspection robot that has completed its endurance on the uninspected route is used as the charging position D of the current inspection robot; otherwise, the n-1th charging position is used as the charging position D of the current inspection robot.

4. The control method of a multi-inspection robot integrating Internet of Things technology according to claim 1 is characterized in that: The steps of re-planning the route include: Estimate the sub-target m where the normal inspection robot is located when the inspection robot that needs to be extended inspects the charging position D; The inspection robot that has completed the endurance test will use the current charging location as the starting point for re-planning the route, and other normal inspection robots will use the corresponding sub-goal m as the starting point for re-planning; The charging position of the inspection robot that has completed the endurance test is used as the dividing point of the uninspected sub-target set to obtain the secondary sub-target set; The inspection robot replans a route based on the corresponding secondary sub-goal set using the route allocation steps of the inspection robot that has passed the self-inspection.

5. The control method of a multi-inspection robot integrating Internet of Things technology according to claim 4 is characterized in that: The number of the secondary sub-goal sets is at least one.

6. The control method of a multi-inspection robot integrating Internet of Things technology according to claim 1 is characterized in that: The warehousing route is planned based on the constraints of minimum energy consumption and shortest distance.

7. A control system for multiple inspection robots integrating Internet of Things technology, characterized in that: include: The acquisition control layer is used to collect inspection data and status data of inspection robots, including several inspection robots and sensors; The transport layer is used for two-way communication between the acquisition control layer and the processing control layer; The processing control layer belongs to the general control center and is used to process the data transmitted by the acquisition control layer and issue control instructions; The processing control layer performs the route allocation step of the inspection robot that passes the self-test, including: Determine the number of inspection robots in each work area based on the number of inspection robots that pass the self-inspection; The working area is divided based on the number of inspection robots in the working area to obtain the initial inspection area. The horizontal coordinates of the sensor positions in each initial inspection area are compared with the initial center horizontal coordinates at the starting position. The sensor closest to the initial center horizontal coordinate is used as the initial point of the inspection route. The sensors set in each working area are used as sub-targets to construct a sub-target set. Based on the initial point of the inspection route and the sub-goal set, all inspection robots in the working area use the improved ant colony algorithm to plan the path, and judge in real time whether sub-goal i is planned in at least two routes, so as to determine the attribution of sub-goal i and re-plan the route that does not contain sub-goal i; When the route planning is completed, the shortest distance between the missed sub-target and all adjacent paths is calculated, the missed sub-target is divided into the paths with the shortest distance, and the planned path is readjusted to obtain the final planned route of the inspection robot; The user layer is used to receive and process the signals sent by the control layer and display the corresponding inspection data.

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