A multifunctional inspection robot with laser radar obstacle avoidance and a monitoring method

Abstract

The application discloses a kind of multifunctional inspection robot and monitoring method for barrier avoidance of laser radar, and relates to robot inspection technical field, comprising the following steps: obtaining global map, the position information of inspection robot is marked in global map, while marking to be inspected equipment and constructing basic road network diagram;Based on the position information of inspection robot and to be inspected equipment, the inspection path of inspection robot is allocated;Based on the inspection path of different inspection robots, congestion analysis is carried out, and the inspection path is adaptively changed;The application is used to solve the problem that the inspection path of robot is not reasonably planned in the existing robot inspection technology, which leads to congestion on the inspection path.

Description

Technical Field

[0001] This invention relates to the field of robot inspection technology, specifically to a multifunctional inspection robot and monitoring method that uses lidar obstacle avoidance. Background Technology

[0002] Robotic inspection technology refers to a comprehensive technology that utilizes a robotic platform with autonomous or semi-autonomous mobility, integrating multiple sensors and intelligent analysis systems, to replace or assist humans in performing systematic status checks, data collection, anomaly monitoring, and early warning tasks in specific environments.

[0003] Existing robot inspection technologies typically use fixed inspection routes to check equipment along those routes. However, not every inspection requires checking all equipment. In some cases, certain equipment has a higher inspection priority and should be inspected first. In such cases, it is necessary to select the optimal route for the inspection robot. However, there are usually multiple inspection robots in a factory area, and they will be assigned different inspection tasks. Existing robot inspection technologies do not comprehensively plan their inspection routes, which can easily lead to conflicts or congestion. For example, patent application CN119298396A discloses an "Inspection Control System Based on Power Distribution Room Inspection Robot". This scheme inspects the power distribution room sequentially based on the inspection value, but it does not specifically plan the inspection path. This may result in conflicts between the inspection paths of multiple inspection robots or congestion caused by multiple inspection robots clustering in a certain area. Existing robot inspection technologies do not rationally plan the robot's inspection path, which can easily lead to congestion on the inspection path. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. By acquiring a global map, marking the location information of the inspection robot and the equipment to be inspected on the global map, a basic road network map is constructed based on the inspection channels in the global map. Then, inspection paths are assigned to the inspection robots based on the location information of the inspection robots and the equipment to be inspected. At the same time, a congestion analysis group is obtained based on the inspection path analysis of different inspection robots. Finally, the congestion analysis group is analyzed, and the inspection paths of the inspection robots in the congestion analysis group are adaptively changed to solve the problem that existing robot inspection technology does not reasonably plan the inspection paths of the robots, resulting in congestion on the inspection paths.

[0005] To achieve the above objectives, this application provides a multifunctional inspection and monitoring method using lidar obstacle avoidance, comprising the following steps: Obtain the global map, mark the location information of the inspection robot in the global map, mark the equipment to be inspected, and build a basic road network map; The inspection robot is assigned an inspection path based on its location information and the equipment to be inspected. Congestion analysis is performed based on the inspection paths of different inspection robots, and the inspection paths are adaptively changed. Further, obtaining a global map, marking the location information of the inspection robot on the global map, marking the equipment to be inspected, and constructing a basic road network map includes the following sub-steps: Obtain the global map, mark the location information of the inspection robot on the global map, and mark the equipment to be inspected; A basic road network map is constructed based on the inspection channels in the global map.

[0006] Further, obtain a global map, mark the location information of the inspection robot on the global map, and mark the equipment to be inspected, including the following sub-steps: Obtain the global map, which is provided by the management and the equipment to be inspected has been marked on the global map. Name the location of the equipment to be inspected on the global map as the equipment coordinates. The global map can also automatically update the location information of the inspection robot, and the location of the inspection robot in the global map is named the machine coordinates.

[0007] Furthermore, constructing a basic road network map based on inspection lanes in the global map includes the following sub-steps: Number the inspection channels in the global map using the symbol ML. n This means that n is a non-zero natural number and n is the index of ML. An inspection channel is a channel between an intersection or two adjacent intersections. A square grid is used to illustrate an ML. n For any two adjacent inspection channels, name them as the first channel and the second channel respectively, and name the square grids corresponding to the first channel and the second channel as the first grid and the second grid respectively. The positional relationship between the first and second grids should be the same as the positional relationship between the first and second channels, and the edges where the second grid intersects with the first grid should completely overlap, thus merging all MLs. n The basic road network map is obtained by representing it with a square grid.

[0008] Furthermore, assigning inspection paths to the inspection robot based on its location information and the equipment to be inspected includes the following sub-steps: Name the square grid where the machine coordinates are located as the machine grid point, and name the square grid where the device coordinates are located as the device grid point; Construct a pathfinding model, set the path step size, represented by the symbol Q. Starting from the machine grid point, find the square grid adjacent to the machine grid point and name it the path neighbor. Set the grid step size of the path neighbor to Q and mark it in the path neighbor. Increment Q by one, and at the same time, use the path neighbor as the new machine grid point and find the new path neighbor. Repeat the search until the device grid point is found. At this point, the pathfinding model is completed. The inspection path of the inspection robot is analyzed using a pathfinding model.

[0009] Furthermore, the pathfinding model analysis of the inspection robot's inspection path includes the following sub-steps: Using the device grid points as new machine grid points, and resetting Q to 1, the pathfinding model is executed again. This process is repeated until all device grid points are found. The device grid points are then numbered according to the order in which they were found, using the symbol EG. m This indicates that m is a non-zero natural number and m is the index of EG; Starting with m=1, find EG using the pathfinding model. m When EG is found m Then, the record was retrieved to obtain EG. m The path is obtained, and a valid path is found. Meanwhile, the search continues in a loop. During the loop, EG... m Not used as a new machine grid point for analysis, and EG m It can be searched repeatedly, and the search stops after the first number of valid paths are found. The length of each valid path is calculated and named "path length". Valid paths are then numbered according to their path length, using the symbol YL. h The expression indicates that h is a non-zero natural number and h is the sequence number of YL. The effective path is the inspection path, and each inspection robot has an independent effective path.

[0010] Furthermore, based on the congestion analysis of different inspection robot paths, the adaptive modification of the inspection path includes the following sub-steps: Congestion analysis groups were obtained based on the inspection path analysis of different inspection robots; The congestion analysis group is analyzed, and the inspection paths of the inspection robots in the congestion analysis group are adaptively changed.

[0011] Furthermore, the congestion analysis group obtained based on the inspection path analysis of different inspection robots includes the following sub-steps: Control the inspection robot to move along the inspection path, prioritize assigning the inspection robot the effective path with the smallest h, name the equipment to be inspected currently being inspected as the target equipment, obtain the inspection robot's moving speed and mark it as RV, and simultaneously obtain the distance between the inspection robot and the inspection channel ML. n The distance between them is named channel distance and denoted as RL. n ; Calculate RL n / RV received the inspection robot arriving at ML n The required time is named the estimated channel start time, denoted by the symbol TT. n express; Get ML n The length, labeled MHL n Calculate MHL n / RV, obtain the inspection robot in ML n The duration of stay within the container is denoted as WT. n , range [TT n ,TT n +WT n Marked as FT n ; For any ML n Statistics on the FT of different inspection robots n If different inspection robots have different FT n If there is an overlap, the corresponding inspection robots will be grouped into a congestion analysis group.

[0012] Furthermore, the congestion analysis group is analyzed, and the inspection path of the inspection robot in the congestion analysis group is adaptively changed, including the following sub-steps: Set a congestion limit, count the number of inspection robots in the congestion analysis group, and name it the congestion number. If the congestion number is greater than the congestion limit, then name the congestion analysis group the congestion adjustment group. Analyze any congestion adjustment group and determine the effective path YL currently assigned to the inspection robot. h Marked as YIL h Get YIL h and YL h+1 The path lengths are denoted as LL. h and LL h+1 Calculate LL h+1 -LL hThe calculation result is marked as GL. The GL of each inspection robot in the congestion adjustment group is calculated. The inspection robot with the smallest GL is marked as the adjustment robot. The effective path of the adjustment robot is h+1. It is determined whether the congestion adjustment group has been changed to the congestion analysis group. If so, the remaining congestion adjustment groups are analyzed. If not, the adjustment robot is searched in a loop and the effective path is adjusted until the congestion adjustment group is changed to the congestion analysis group. Analyze each congestion adjustment group, and change all congestion adjustment groups into congestion analysis groups. The effective path finally assigned to each inspection robot is the optimal path for the inspection robot. The inspection robot uses lidar obstacle avoidance technology to avoid obstacles during its movement. After completing one obstacle avoidance, the robot re-analyzes the optimal path.

[0013] Secondly, this application provides a multi-functional inspection robot that uses lidar obstacle avoidance, including a road network generation module, a path allocation module, and a path adaptive planning module; the road network generation module and the path allocation module are respectively data connected to the path adaptive planning module. The road network generation module is used to obtain a global map, mark the location information of the inspection robot in the global map, mark the equipment to be inspected, and construct a basic road network map. The path allocation module is used to allocate inspection paths to the inspection robot based on the robot's location information and the equipment to be inspected. The path adaptive planning module is used to perform congestion analysis based on the inspection paths of different inspection robots and to adaptively change the inspection paths.

[0014] The beneficial effects of this invention are as follows: By acquiring a global map, the location information of the inspection robot is marked on the global map, and the equipment to be inspected is also marked. Then, a basic road network map is constructed based on the inspection channels in the global map. Finally, an inspection path is assigned to the inspection robot based on the location information of the inspection robot and the equipment to be inspected. The advantage is that a reasonable inspection path is planned for the inspection robot based on the current equipment to be inspected, which saves the action time of the inspection robot and improves the inspection efficiency and the rationality of the inspection path planning. This invention obtains a congestion analysis group based on the inspection paths of different inspection robots, and then analyzes the congestion analysis group to adaptively change the inspection paths of the inspection robots in the congestion analysis group. The advantage is that it analyzes whether each inspection channel in the inspection path is congested. If there is a possibility of congestion, it plans a new inspection path in advance for the inspection robots involved in the congestion, preventing too many inspection robots from piling up in the inspection channel, which would reduce inspection efficiency and improve the effectiveness and rationality of robot inspection. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the inspection robot of the present invention. Figure 2 This is a simplified schematic diagram of the global map of the present invention; Figure 3 This is a schematic diagram illustrating the numbering of inspection channels according to the present invention; Figure 4 This is a schematic diagram illustrating the positional relationship between the first and second grids of the present invention; Figure 5 This is a schematic diagram of the basic road network map of the present invention; Figure 6 This is a schematic diagram of the machine grid points and equipment grid points of the present invention; Figure 7 This is a schematic diagram of the path grid of the present invention; Figure 8 This is a schematic diagram of the predicted path of the present invention; Figure 9 This is a schematic diagram of the first effective path of the present invention; Figure 10 This is a schematic diagram of the second effective path of the present invention; Figure 11 This is a flowchart of the steps of the method of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1, please refer to Figure 1 As shown, this application provides a multi-functional inspection robot that uses LiDAR obstacle avoidance, including a road network generation module, a path allocation module, and a path adaptive planning module; the road network generation module and the path allocation module are respectively data connected to the path adaptive planning module; The road network generation module is used to acquire a global map, mark the location information of the inspection robot in the global map, mark the equipment to be inspected, and construct a basic road network map; the road network generation module includes a map information acquisition unit and a road network construction unit; The map information acquisition unit is used to acquire the global map, mark the location information of the inspection robot in the global map, and mark the equipment to be inspected. The map information acquisition unit is configured with a map information acquisition strategy, which includes: Please see Figure 2 As shown, obtain the global map. The global map is provided by the management and the equipment to be inspected has been marked on the global map. Name the location of the equipment to be inspected on the global map as the equipment coordinates. The global map can also automatically update the location information of the inspection robot, and name the location of the inspection robot in the global map as the machine coordinates; In practical applications, the global map and the device coordinate markers within it are provided by the manufacturer. The inspection robot is displayed as dots on the global map via a positioning device. The acquisition of the global map and the annotation of its information are existing technologies, therefore, they will not be specifically described in this embodiment. A simplified schematic diagram of the acquired global map is shown below. Figure 2 As shown, in Figure 2 In the diagram, hexagons represent equipment coordinates, and circles represent inspection robots. All inspection robots follow the rule of driving on the right.

[0018] The road network construction unit is used to construct a basic road network map based on the inspection channels in the global map; The road network construction unit is configured with a road network construction strategy, which includes: Please see Figure 3 As shown, the inspection channels in the global map are numbered using the symbol ML. n This means that n is a non-zero natural number and n is the index of ML. An inspection channel is a channel between an intersection or two adjacent intersections. A square grid is used to illustrate an ML. n For any two adjacent inspection channels, name them as the first channel and the second channel respectively, and name the square grids corresponding to the first channel and the second channel as the first grid and the second grid respectively. Please see Figures 4 to 5 As shown, the positional relationship between the first and second grids should be the same as the positional relationship between the first and second channels, and the edges where the second grid intersects with the first grid should completely overlap, thus merging all MLs. n The basic road network map is obtained by representing it using a square grid. In practical applications, inspection channels are numbered as follows: Figure 3 As shown, Figure 3 Each marked rectangle represents an inspection lane. There are 27 inspection lanes in total, numbered ML1 to ML2. 27 Meanwhile, for ease of observation, Figure 3Machine coordinates are omitted. Taking ML1 and ML2 as examples, ML1 is considered the first channel and ML2 the second channel. ML1 is above ML2, therefore the first grid should be above the second grid, and their intersecting edges should completely overlap. The positional relationship between the first and second grids is as follows: Figure 4 As shown, the upper square grid is the first grid, and the lower square grid is the second grid. Similarly, all ML grids are represented in this way. n The grid was changed to a square grid, resulting in the basic road network map as follows: Figure 5 As shown, Figure 5 Each gray grid is a square grid, representing an inspection channel.

[0019] The path allocation module is used to allocate inspection paths to the inspection robot based on the robot's location information and the equipment to be inspected; the path allocation module includes a pathfinding model construction unit and an inspection path analysis unit. The pathfinding model building unit is configured with a pathfinding model building strategy, which includes: Please see Figure 6 As shown, the square grid where the machine coordinates are located is named the machine grid point, and the square grid where the device coordinates are located is named the device grid point; In practical applications, in Figure 2 In this context, if the hexagon representing the equipment coordinates intersects with the inspection channel, it means that the equipment to be inspected corresponding to that coordinate is located at the intersection of the inspection channel. The inspection robot needs to inspect the equipment to be inspected at the corresponding inspection channel. Taking a certain equipment to be inspected as an example, the equipment is located at ML8, that is, the equipment grid point is the square grid corresponding to ML8, and at this time the inspection robot is at ML8. 26 Therefore, ML 26 The corresponding square grid points are the machine grid points. The machine grid points and equipment grid points are obtained as follows: Figure 6 As shown.

[0020] Please see Figures 7 to 8 As shown, a pathfinding model is constructed, and the path step size is set by the symbol Q. Starting from the machine grid point, the square grid adjacent to the machine grid point is found and named as the path neighbor. The grid step size of the path neighbor is set to Q and marked in the path neighbor. Q is incremented by one, and the path neighbor is used as the new machine grid point. The new path neighbor is searched. The search is repeated until the device grid point is found. At this point, the pathfinding model is completed. In practical applications, the initial path step size Q is 1. The path's neighboring cells are found and marked as follows: Figure 7As shown, Q is incremented by one and the search continues, and so on, until the device grid point is found. This process stops the search, resulting in a path, which in this embodiment is called the estimated path. The estimated path is as follows: Figure 8 As shown, Figure 8 The dark gray grid in the image represents the estimated path, with directions 1 to 7, but in reality... Figure 8 There are 3 predicted paths. Here, we only need to understand the principle of the pathfinding model, so we will not list all 3 predicted paths in detail.

[0021] The inspection path analysis unit is used to analyze the inspection path of the inspection robot through the pathfinding model; The inspection path analysis unit is configured with an inspection path analysis strategy, which includes: Using the device grid points as new machine grid points, and resetting Q to 1, the pathfinding model is executed again. This process is repeated until all device grid points are found. The device grid points are then numbered according to the order in which they were found, using the symbol EG. m This indicates that m is a non-zero natural number and m is the index of EG; In practical applications, the device grid points are used as the new machine grid points, and Q is reset to 1. The pathfinding model is executed again. At this time, the estimated path for the inspection robot to travel from one device to another can be obtained. The order in which the device grid points are found is the order in which the inspection robot travels to the devices. Performing inspections in this order ensures that the shortest route is taken. Assuming that EG1 is in ML in this embodiment... 19 EG2 is located at ML8, meaning the inspection robot needs to go to ML first. 19 Then proceed to ML8.

[0022] Please see Figures 9 to 10 As shown, starting with m=1, the pathfinding model is used to find EG. m When EG is found m Then, the record was retrieved to obtain EG. m The path is obtained, and a valid path is found. Meanwhile, the search continues in a loop. During the loop, EG... m Not used as a new machine grid point for analysis, and EG m It can be searched repeatedly, and the search stops after the first number of valid paths are found. The length of each valid path is calculated and named "path length". Valid paths are then numbered according to their path length, using the symbol YL. h This means that h is a non-zero natural number and h is the sequence number of YL. The valid path is the inspection path, and each inspection robot has an independent valid path. In practical applications, taking m=1 as an example, EG1 is found through the pathfinding model, which means finding ML through the pathfinding model. 19 The corresponding square grid, while the machine grid points are ML. 26 The first valid path is found within the corresponding square grid. Figure 9 As shown, Figure 9 The dark gray grid in the diagram represents the valid path. At this point, the search continues in a loop, and EG1 is not analyzed as a new machine grid point, indicating that ML... 19 The corresponding square grid cell will not search for neighboring cells in all directions, but the remaining neighboring cells will still be searched until a second valid path is found. Figure 10 As shown, the first quantity is usually set to 2 to ensure that the inspection robot has different inspection paths to choose from. At the same time, managers can also change the first quantity to provide more inspection paths for the robot. However, in most cases, a first quantity of 2 is sufficient to prevent congestion. Therefore, in this embodiment, a first quantity of 2 is sufficient. After finding two valid paths, the search stops, resulting in YL1 and YL2. The path length of YL1 is 49m, and the path length of YL2 is 63m. At this point, all valid paths are those where m=1. To find a valid path for EG2, the valid path for EG2 can be analyzed when the inspection robot inspects EG1. Each EG... m The effective paths are independent of each other.

[0023] The path adaptive planning module is used to perform congestion analysis based on different inspection paths of inspection robots and to adaptively change the inspection path; the path adaptive planning module includes a congestion analysis group extraction unit and a path adaptive change unit. The congestion analysis group extraction unit is used to obtain congestion analysis groups based on the inspection path analysis of different inspection robots; The congestion analysis group extraction unit is configured with a congestion analysis group extraction strategy, which includes: Control the inspection robot to move along the inspection path, prioritize assigning the inspection robot the effective path with the smallest h, name the equipment to be inspected currently being inspected as the target equipment, obtain the inspection robot's moving speed and mark it as RV, and simultaneously obtain the distance between the inspection robot and the inspection channel ML. n The distance between them is named channel distance and denoted as RL. n ; Calculate RL n / RV received the inspection robot arriving at ML n The required time is named the estimated channel start time, denoted by the symbol TT. n express; Get ML nThe length, labeled MHL n Calculate MHL n / RV, obtain the inspection robot in ML n The duration of stay within the container is denoted as WT. n , range [TT n ,TT n +WT n Marked as FT n ; For any ML n Statistics on the FT of different inspection robots n If different inspection robots have different FT n If there is an overlap, the corresponding inspection robots will be grouped into a congestion analysis group. In practical applications, the inspection robot is controlled to move according to YL1, and the target device at this time is EG1. The moving speed RV of the inspection robot is obtained as 1m / s. When obtaining the channel distance, it should be obtained according to the effective path, as in this embodiment. Figure 9 Taking the listed valid paths as an example, Figure 9 The valid paths in the sequence of travel are ML. 26 ML 25 ML 24 and ML 19 , with ML 24 For example, the arrival of the inspection robot in ML 24 The required distance is 40m, which is the channel distance RL. 24 The estimated channel start time TT is 40m. 24 ML was obtained in 40 seconds. 24 Length MHL 24 The value is 9m, and the calculated WT is... 24 For 9s, the range FT is obtained. 24 The range [40s, 49s] indicates that the inspection robot will be in ML mode between 40s and 49s. 24 Within this embodiment, for the inspection robot listed, its effective path is ML in the order of travel. 26 ML 25 ML 24 and ML 19 Therefore, the analysis of FT 26 FT 25 FT 24 and FT 19 The same logic applies to other inspection robots; the analysis yields the Fourier Transform (FT) for all inspection robots. n Subsequently, three inspection robots were detected, referred to in this embodiment as α, β, and γ, where α's FT... 24The FT of β is [35s, 44s]. 24 For [40s, 49s], the Fourier Transform of γ 24 If the range is [38s, 46s], and there is an intersection between them, then α, β, and γ will simultaneously be in the ML range between 40s and 44s. 24 In the middle, at this time ML 24 Three inspection robots will appear in the middle, and α, β and γ will be grouped into a congestion analysis group.

[0024] The path adaptive change unit is used to analyze the congestion analysis group and adaptively change the inspection path of the inspection robot in the congestion analysis group. The path adaptive change unit is configured with a path adaptive change strategy, which includes: Set a congestion limit, count the number of inspection robots in the congestion analysis group, and name it the congestion number. If the congestion number is greater than the congestion limit, then name the congestion analysis group the congestion adjustment group. In practical applications, the congestion limit is set by the management personnel themselves. The setting of the congestion limit needs to take into account the specific workplace and working environment. If the inspection channel is wider, it can accommodate more inspection robots, and vice versa. In this embodiment, the congestion limit is set to 2, that is, only two inspection robots can exist in each inspection channel at the same time. Taking the congestion analysis group listed in this embodiment as an example, the congestion number is 3, which is greater than the congestion limit. Therefore, it is named the congestion adjustment group.

[0025] Analyze any congestion adjustment group and determine the effective path YL currently assigned to the inspection robot. h Marked as YIL h Get YIL h and YL h+1 The path lengths are denoted as LL. h and LL h+1 Calculate LL h+1 -LL h The calculation result is marked as GL. The GL of each inspection robot in the congestion adjustment group is calculated. The inspection robot with the smallest GL is marked as the adjustment robot. The effective path of the adjustment robot is h+1. It is determined whether the congestion adjustment group has been changed to the congestion analysis group. If so, the remaining congestion adjustment groups are analyzed. If not, the adjustment robot is searched in a loop and the effective path is adjusted until the congestion adjustment group is changed to the congestion analysis group. In practical applications, α, β, and γ are the current YIL hThe paths are YIL1, YIL1, and YIL1 in sequence. It's important to note that YIL1 here refers to the individual valid paths for α, β, and γ, not the same path. Taking β as an example, YIL1 is YL1. LL1 and LL2 are obtained as 49m and 63m respectively, resulting in a calculated GL of 14m. This means that when the path is changed to the next valid path, β will move an additional 14m. Similarly, the calculated GLs for α and γ are 16m and 24m respectively. β has the smallest GL, therefore β is marked as the adjustment robot, and its valid path is changed from YL1 to YL2. β will then travel according to YL2, and after the path change, β will no longer enter ML. 24 Therefore, the congestion number in the current congestion adjustment group changes from 3 to 2, which is equal to the congestion limit. This means the congestion adjustment group reverts to the congestion analysis group, and the analysis stops. It should be noted that if β's travel path changes, it will still pass through ML. 24 Then the Fourier Transform (FT) of β needs to be recalculated. 24 Simultaneously determine the FT of β 24 Is it still in the current congestion adjustment group?

[0026] Analyze each congestion adjustment group, and change all congestion adjustment groups into congestion analysis groups. The effective path finally assigned to each inspection robot is the optimal path for the inspection robot. The inspection robot uses lidar obstacle avoidance technology to avoid obstacles during its movement. After completing one obstacle avoidance, the inspection robot re-analyzes the optimal path for the inspection robot. In practical applications, each congestion adjustment group is analyzed, transforming all congestion adjustment groups into congestion analysis groups. This prevents inspection robots from piling up in a particular inspection lane. Furthermore, after each obstacle avoidance maneuver, the optimal path for the inspection robot is re-analyzed, as its speed and path change during obstacle avoidance, affecting all corresponding FTs (Fault Tolerances). n Everything will change, so the optimal path needs to be reanalyzed. When reanalyzing the optimal path, the analysis needs to start from YL1 again, instead of starting from the previous optimal path of the inspection robot.

[0027] Example 2, please refer to Figure 11 As shown, this application provides a multifunctional inspection and monitoring method using lidar obstacle avoidance, comprising the following steps: Step S1: Obtain the global map, mark the location information of the inspection robot on the global map, mark the equipment to be inspected, and construct a basic road network map; Step S1 includes the following sub-steps: Step S101: Obtain the global map, mark the location information of the inspection robot in the global map, and mark the equipment to be inspected at the same time; Step S101 includes the following sub-steps: Step S1011: Obtain the global map. The global map is provided by the management and the equipment to be inspected has been marked on the global map. Name the location of the equipment to be inspected on the global map as the equipment coordinates. In step S1012, the location information of the inspection robot can also be automatically updated in the global map, and the location of the inspection robot in the global map is named the machine coordinates. Step S102: Construct a basic road network map based on the inspection channels in the global map; Step S102 includes the following sub-steps: Step S1021: Number the inspection channels in the global map using the symbol ML. n This means that n is a non-zero natural number and n is the index of ML. An inspection channel is a channel between an intersection or two adjacent intersections. Step S1022: Use a square grid to represent a line of ML. n For any two adjacent inspection channels, name them as the first channel and the second channel respectively, and name the square grids corresponding to the first channel and the second channel as the first grid and the second grid respectively. Step S1023: The positional relationship between the first grid and the second grid should be the same as the positional relationship between the first channel and the second channel, and the intersecting edges between the second grid and the first grid should completely overlap, thus completing all ML... n The basic road network map is obtained by representing it using a square grid. Step S2: Assign an inspection path to the inspection robot based on its location information and the equipment to be inspected; Step S2 includes the following sub-steps: Step S201: Name the square grid where the machine coordinates are located as the machine grid point, and name the square grid where the device coordinates are located as the device grid point; Step S202: Construct a pathfinding model and set the path step size, represented by the symbol Q. Starting from the machine grid point, find the square grid adjacent to the machine grid point and name it the path neighbor. Set the grid step size of the path neighbor to Q and mark it in the path neighbor. Increment Q by one. At the same time, use the path neighbor as the new machine grid point and find the new path neighbor. Repeat the search until the device grid point is found. The pathfinding model is now complete. Step S203: Analyze the inspection path of the inspection robot using a pathfinding model; Step S203 includes the following sub-steps: Step S2031: Use the device grid point as the new machine grid point, and reset Q to 1. Execute the pathfinding model again, and so on, until all device grid points are found. Number the device grid points according to the order in which they are found, using the symbol EG. m This indicates that m is a non-zero natural number and m is the index of EG; Step S2032: Starting with m=1, find EG using the pathfinding model. m When EG is found m Then, the record was retrieved to obtain EG. m The path is obtained, and a valid path is found. Meanwhile, the search continues in a loop. During the loop, EG... m Not used as a new machine grid point for analysis, and EG m It can be searched repeatedly, and the search stops after the first number of valid paths are found. Step S2033: Calculate the length of the valid paths, name them "path length", and number the valid paths according to their path lengths, using the symbol YL. h This means that h is a non-zero natural number and h is the sequence number of YL. The valid path is the inspection path, and each inspection robot has an independent valid path. Step S3 involves performing congestion analysis based on the inspection paths of different inspection robots and adaptively changing the inspection paths. Step S3 includes the following sub-steps: Step S301: Obtain congestion analysis groups based on the inspection path analysis of different inspection robots; Step S301 includes the following sub-steps: Step S3011: Control the inspection robot to move along the inspection path, prioritize assigning the inspection robot the effective path with the smallest h, name the equipment to be inspected that the inspection robot is currently heading to as the target equipment, obtain the moving speed of the inspection robot and mark it as RV, and at the same time obtain the distance between the inspection robot and the inspection channel ML. n The distance between them is named channel distance and denoted as RL. n ; Step S3012, calculate RL n / RV received the inspection robot arriving at ML n The required time is named the estimated channel start time, denoted by the symbol TT. n express; Step S3013, obtain ML n The length, labeled MHL n Calculate MHL n / RV, obtain the inspection robot in ML n The duration of stay within the container is denoted as WT. n , range [TTn ,TT n +WT n Marked as FT n ; Step S3014, for any ML n Statistics on the FT of different inspection robots n If different inspection robots have different FT n If there is an overlap, the corresponding inspection robots will be grouped into a congestion analysis group. Step S302: Analyze the congestion analysis group and adaptively change the inspection path of the inspection robot in the congestion analysis group. Step S302 includes the following sub-steps: Step S3021: Set a congestion limit, count the number of inspection robots in the congestion analysis group, and name it the congestion number. If the congestion number is greater than the congestion limit, then name the congestion analysis group the congestion adjustment group. Step S3022: Analyze any congestion adjustment group and determine the effective path YL currently assigned to the inspection robot. h Marked as YIL h Get YIL h and YL h+1 The path lengths are denoted as LL. h and LL h+1 Calculate LL h+1 -LL h The calculation result is marked as GL. The GL of each inspection robot in the congestion adjustment group is calculated. The inspection robot with the smallest GL is marked as the adjustment robot. The effective path of the adjustment robot is h+1. It is determined whether the congestion adjustment group has been changed to the congestion analysis group. If so, the remaining congestion adjustment groups are analyzed. If not, the adjustment robot is searched in a loop and the effective path is adjusted until the congestion adjustment group is changed to the congestion analysis group. Step S3023: Analyze each congestion adjustment group to change all congestion adjustment groups into congestion analysis groups. The effective path finally assigned to each inspection robot is the optimal path for the inspection robot. In step S3024, the inspection robot uses lidar obstacle avoidance technology to avoid obstacles during its movement. At the same time, after completing one obstacle avoidance, the inspection robot re-analyzes the optimal path for the inspection robot.

[0028] Example 3: This application provides an electronic device, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps of a multi-functional inspection and monitoring method using lidar obstacle avoidance are performed to achieve the following functions: acquiring a global map, marking the location information of the inspection robot on the global map, marking the equipment to be inspected, and constructing a basic road network map. The inspection robot is assigned an inspection path based on its location information and the equipment to be inspected. Congestion analysis is performed based on the inspection paths of different inspection robots, and the inspection paths are adaptively changed.

[0029] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0030] Example 4: This application also provides a computer-readable storage medium. This application provides a storage medium storing a computer program thereon. When the computer program is executed by a processor, it runs the steps in the above-mentioned multi-functional inspection and monitoring method using laser radar obstacle avoidance to achieve the following functions: acquiring a global map, marking the location information of the inspection robot in the global map, marking the equipment to be inspected, and constructing a basic road network map. The inspection robot is assigned an inspection path based on its location information and the equipment to be inspected. Congestion analysis is performed based on the inspection paths of different inspection robots, and the inspection paths are adaptively changed.

[0031] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0032] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multifunctional inspection and monitoring method using lidar obstacle avoidance, characterized in that, Includes the following steps: Obtain the global map, mark the location information of the inspection robot in the global map, mark the equipment to be inspected, and build a basic road network map; The inspection robot is assigned an inspection path based on its location information and the equipment to be inspected. Congestion analysis is performed based on the inspection paths of different inspection robots, and the inspection paths are adaptively changed.

2. The multifunctional inspection and monitoring method using lidar obstacle avoidance according to claim 1, characterized in that, Obtaining the global map, marking the location information of the inspection robot on the global map, marking the equipment to be inspected, and constructing a basic road network map includes the following sub-steps: Obtain the global map, mark the location information of the inspection robot on the global map, and mark the equipment to be inspected; A basic road network map is constructed based on the inspection channels in the global map.

3. A multifunctional inspection and monitoring method using lidar obstacle avoidance according to claim 2, characterized in that, Obtain the global map, mark the location information of the inspection robot on the global map, and mark the equipment to be inspected, including the following sub-steps: Obtain the global map, which is provided by the management and the equipment to be inspected has been marked on the global map. Name the location of the equipment to be inspected on the global map as the equipment coordinates. The global map can also automatically update the location information of the inspection robot, and the location of the inspection robot in the global map is named the machine coordinates.

4. A multifunctional inspection and monitoring method using lidar obstacle avoidance according to claim 3, characterized in that, Constructing a basic road network map based on inspection channels in the global map includes the following sub-steps: Number the inspection channels in the global map using the symbol ML. n This means that n is a non-zero natural number and n is the index of ML. An inspection channel is a channel between an intersection or two adjacent intersections. A square grid is used to illustrate an ML. n For any two adjacent inspection channels, name them as the first channel and the second channel respectively, and name the square grids corresponding to the first channel and the second channel as the first grid and the second grid respectively. The positional relationship between the first and second grids should be the same as the positional relationship between the first and second channels, and the edges where the second grid intersects with the first grid should completely overlap, thus merging all MLs. n The basic road network map is obtained by representing it with a square grid.

5. A multifunctional inspection and monitoring method using lidar obstacle avoidance according to claim 4, characterized in that, Assigning inspection paths to the inspection robot based on its location information and the equipment to be inspected includes the following sub-steps: Name the square grid where the machine coordinates are located as the machine grid point, and name the square grid where the device coordinates are located as the device grid point; Construct a pathfinding model, set the path step size, represented by the symbol Q. Starting from the machine grid point, find the square grid adjacent to the machine grid point and name it the path neighbor. Set the grid step size of the path neighbor to Q and mark it in the path neighbor. Increment Q by one, and at the same time, use the path neighbor as the new machine grid point and find the new path neighbor. Repeat the search until the device grid point is found. At this point, the pathfinding model is completed. The inspection path of the inspection robot is analyzed using a pathfinding model.

6. A multifunctional inspection and monitoring method using lidar obstacle avoidance according to claim 5, characterized in that, The pathfinding model analysis of the inspection robot's inspection path includes the following sub-steps: Using the device grid points as new machine grid points, and resetting Q to 1, the pathfinding model is executed again. This process is repeated until all device grid points are found. The device grid points are then numbered according to the order in which they were found, using the symbol EG. m This indicates that m is a non-zero natural number and m is the index of EG; Starting with m=1, find EG using the pathfinding model. m When EG is found m Then, the record was retrieved to obtain EG. m The path is obtained, and a valid path is found. Meanwhile, the search continues in a loop. During the loop, EG... m Not used as a new machine grid point for analysis, and EG m It can be searched repeatedly, and the search stops after the first number of valid paths are found. The length of each valid path is calculated and named "path length". Valid paths are then numbered according to their path length, using the symbol YL. h The expression indicates that h is a non-zero natural number and h is the sequence number of YL. The effective path is the inspection path, and each inspection robot has an independent effective path.

7. A multifunctional inspection and monitoring method using lidar obstacle avoidance according to claim 6, characterized in that, Congestion analysis is performed based on the inspection paths of different inspection robots, and the inspection paths are adaptively changed, including the following sub-steps: Congestion analysis groups were obtained based on the inspection path analysis of different inspection robots; The congestion analysis group is analyzed, and the inspection paths of the inspection robots in the congestion analysis group are adaptively changed.

8. A multifunctional inspection and monitoring method using lidar obstacle avoidance according to claim 7, characterized in that, The congestion analysis group, derived from the inspection path analysis of different inspection robots, includes the following sub-steps: Control the inspection robot to move along the inspection path, prioritize assigning the inspection robot the effective path with the smallest h, name the equipment to be inspected currently being inspected as the target equipment, obtain the inspection robot's moving speed and mark it as RV, and simultaneously obtain the distance between the inspection robot and the inspection channel ML. n The distance between them is named channel distance and denoted as RL. n ; Calculate RL n / RV received the inspection robot arriving at ML n The required time is named the estimated channel start time, denoted by the symbol TT. n express; Get ML n The length, labeled MHL n Calculate MHL n / RV, obtain the inspection robot in ML n The duration of stay within the container is denoted as WT. n , range [TT n ,TT n +WT n Marked as FT n ; For any ML n Statistics on the FT of different inspection robots n If different inspection robots have different FT n If there is an overlap, the corresponding inspection robots will be grouped into a congestion analysis group.

9. A multifunctional inspection and monitoring method using lidar obstacle avoidance according to claim 8, characterized in that, Analyzing the congestion analysis group and adaptively changing the inspection paths of the inspection robots within the group includes the following sub-steps: Set a congestion limit, count the number of inspection robots in the congestion analysis group, and name it the congestion number. If the congestion number is greater than the congestion limit, then name the congestion analysis group the congestion adjustment group. Analyze any congestion adjustment group and determine the effective path YL currently assigned to the inspection robot. h Marked as YIL h Get YIL h and YL h+1 The path lengths are denoted as LL. h and LL h+1 Calculate LL h+1 -LL h The calculation result is marked as GL. The GL of each inspection robot in the congestion adjustment group is calculated. The inspection robot with the smallest GL is marked as the adjustment robot. The effective path of the adjustment robot is h+1. It is determined whether the congestion adjustment group has been changed to the congestion analysis group. If so, the remaining congestion adjustment groups are analyzed. If not, the adjustment robot is searched in a loop and the effective path is adjusted until the congestion adjustment group is changed to the congestion analysis group. Analyze each congestion adjustment group, and change all congestion adjustment groups into congestion analysis groups. The effective path finally assigned to each inspection robot is the optimal path for the inspection robot. The inspection robot uses lidar obstacle avoidance technology to avoid obstacles during its movement. After completing one obstacle avoidance, the robot re-analyzes the optimal path.

10. A multi-functional inspection robot employing lidar obstacle avoidance, used to implement the multi-functional inspection and monitoring method employing lidar obstacle avoidance as described in any one of claims 1-9, characterized in that, It includes a road network generation module, a path allocation module, and a path adaptive planning module; the road network generation module and the path allocation module are respectively data-connected to the path adaptive planning module. The road network generation module is used to obtain a global map, mark the location information of the inspection robot in the global map, mark the equipment to be inspected, and construct a basic road network map. The path allocation module is used to allocate inspection paths to the inspection robot based on the robot's location information and the equipment to be inspected. The path adaptive planning module is used to perform congestion analysis based on the inspection paths of different inspection robots and to adaptively change the inspection paths.

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