Inspection system and method for collaborative operation of aerial drone and ground inspection robot

Abstract

The present invention discloses an inspection system and method for collaborative operation of an aerial drone and a ground inspection robot. The system includes a ground inspection robot, an aerial drone, an inspection host and a server. The ground inspection robot and the aerial drone are respectively connected to the inspection host for communication, and the ground inspection robot and the aerial drone are respectively connected to the server for communication. The inspection host processes the data collected by the ground inspection robot and the aerial drone, and the aerial drone cooperates with the ground inspection robot to realize three-dimensional inspection without blind spots. At the same time, the aerial drone can assist the ground inspection robot to adapt to various complex terrains, cross obstacles and automatically find the shortest path from the air. There is no need for manual large-scale inspection, and there is no need for manual control of the ground inspection robot for inspection, which greatly improves the inspection efficiency and ensures the stable operation of the power grid.

Description

Technical Field

[0001] The present invention belongs to the technical field of operation and maintenance of power system substations, and specifically relates to an inspection system and method for collaborative operation of an aerial unmanned aerial vehicle and a ground inspection robot. Background Art

[0002] At present, my country has built the world's largest power grid. With the rapid increase in the scale of the power grid, the grid structure has become more complex, the voltage level has become higher, and the operating risks have become greater. The traditional manual single inspection mode can no longer meet the development of the power grid in terms of inspection efficiency, informatization, intelligence, safety and reliability.

[0003] Substations are the "heart" of the power grid, and the workload of power equipment inspection is huge. Substation ground inspection robots can inspect outdoor high-voltage equipment in unmanned or undermanned substations in an autonomous or remotely controlled manner, and timely discover thermal defects, abnormal appearance, abnormal status and other problems of equipment and automatically alarm. It can replace a large amount of manual inspection work, reduce human interference factors, and effectively reduce the operation and maintenance costs of substations. Relying solely on ground inspection robots, it is impossible to find hidden defects in high places and on the top of equipment and facilities in substations, and there are blind spots for inspection. Although aerial drone inspections have the advantages of low cost, high efficiency, and no blind spots for inspections, due to the complex environment, compact space, and strong electromagnetic interference of substations, it is easy to cause abnormal flight control systems and unstable data transmission. Therefore, aerial drone inspections of substations have not been widely used and promoted. Summary of the invention

[0004] The purpose of the present invention is to provide an inspection system and method for collaborative operation of an aerial drone and a ground inspection robot, so as to improve the inspection efficiency of substation equipment and ensure the stable operation of the power grid.

[0005] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: an inspection system in which an aerial drone and a ground inspection robot work together, including a ground inspection robot for collecting ground environmental data information of a substation, an aerial drone for collecting aerial data information of the substation, an inspection host for processing data information and sending instructions, and a server for remote communication between the ground inspection robot and the aerial drone, the ground inspection robot and the aerial drone are respectively connected to the inspection host for communication, and the ground inspection robot and the aerial drone are respectively connected to the server for communication.

[0006] Furthermore, the ground inspection robot includes a detection module, a first motion unit, a first control module, a first information acquisition module, a first three-dimensional inspection module and a first wireless communication module. The detection module, the first motion unit, the first information acquisition module and the first wireless communication module are respectively connected to the first control module, and the detection module is connected to the first wireless communication module; the first information acquisition module is connected to the first wireless communication module, and the first three-dimensional inspection module is respectively connected to the first control module and the server.

[0007] Furthermore, the aerial drone includes a second motion unit, a second control module, a second information acquisition module, a second three-dimensional inspection module and a second wireless communication module. The second motion unit, the second information acquisition module and the second wireless communication module are respectively connected to the second control module, and the second three-dimensional inspection module is respectively connected to the second control module and the server.

[0008] Furthermore, the inspection host includes a data processing module, a navigation and positioning module and a path planning module. The data processing module is respectively communicatively connected to the first wireless communication module and the second wireless communication module, and the navigation and positioning module is connected to the data processing module.

[0009] Furthermore, the detection module includes a noise detection unit, a SF6 gas detection unit, a visible light and infrared vision detection unit, an ambient temperature and humidity detection unit, a partial discharge detection unit and a dial data detection unit.

[0010] Furthermore, the first information acquisition module includes a lidar unit, an odometer and an IMU sensor, and the second information acquisition module includes a depth camera sensor.

[0011] Furthermore, it also includes a charging nest. The ground inspection robot and the aerial drone are respectively provided with power modules. The power modules have built-in Bluetooth modules and are connected and paired with the charging nest via the Bluetooth module.

[0012] An inspection method for collaborative operation of an aerial drone and a ground inspection robot is characterized by the following steps:

[0013] Step 1: Prepare the ground inspection robot and the aerial drone and wait for the inspection task to be issued;

[0014] Step 2: The inspection host issues an inspection command, and the ground inspection robot and the aerial drone collect data information about the substation environment, including the ground lidar point cloud and aerial drone images of the substation, and transmit the information back to the inspection host;

[0015] Step 3: Based on the sparse point cloud generated by the aerial drone image, the oblique photography technology is used to fuse the point cloud with the ground lidar to generate a 360° high-precision inspection map; then the posture state of the ground inspection robot on the high-precision inspection map is calculated;

[0016] Step 4: Based on the high-precision inspection map and the position and posture of the ground inspection robot on the high-precision inspection map, combined with the real-time perception and information collection of the ground environment of the substation by the aerial drone, the optimal local path without obstacle collision is planned in real time;

[0017] Step 5: According to the optimal local path, the aerial drone and the ground inspection robot conduct autonomous collaborative inspections of the required inspection areas of the power grid equipment in the air and on the ground, and use the ground inspection robot to collect the operating status data of the substation equipment;

[0018] Step 6: The collected operating status data of the substation equipment is transmitted to the inspection host, the status information data of the substation equipment is identified through artificial intelligence, the status information data is compared with the steady-state operation data, and an alarm is issued for the status information data of the substation equipment with abnormal data.

[0019] Furthermore, in step 2, during the data information collection process, both the aerial drone and the ground inspection robot use the MQTT protocol to communicate with the background program on the server. The aerial drone uses a power-specific SIM card and TF encryption card. The data displayed locally on the aerial drone uses the key field hiding method to desensitize sensitive data. It is securely connected to the substation intranet through two-way identity authentication and two-way data encryption, and the data information is transmitted back to the inspection host through the substation intranet.

[0020] Furthermore, the fusion modeling process described in step 3 is: obtaining drone images of the substation through a drone, and generating a sparse point cloud based on the drone images, obtaining a ground lidar map of the substation through a ground inspection robot, and generating a ground lidar point cloud based on the ground lidar map, and generating a 360° stereo high-precision inspection map model by fusing the sparse point cloud with the ground lidar point cloud.

[0021] Furthermore, the data information also includes data information on the angular velocity and acceleration of the ground inspection robot, and the mileage of the ground inspection robot. The angular velocity and acceleration information of the ground inspection robot are obtained through the IMU sensor, and the local and precise displacement information of the ground inspection robot is obtained through the odometer; the surrounding environment information of the ground inspection robot during its movement is perceived through the lidar unit; and a composite processing strategy is adopted that integrates the lidar unit, odometer, and IMU sensor information to improve the mapping effect, positioning accuracy, and robustness.

[0022] Furthermore, the position and posture state of the ground inspection robot on the high-precision inspection map is calculated: the information of the IMU sensor, odometer and lidar unit is integrated through the extended Kalman filtering technology to predict the local position and posture of the ground inspection robot. The scanning data of the lidar unit of the ground inspection robot is directly matched with the ground lidar map to obtain the local positioning result of the ground inspection robot, and the position and posture state of the ground inspection robot on the high-precision inspection map is output.

[0023] Furthermore, in the process of acquiring the ground lidar map, the local map is updated according to the local positioning results of the ground inspection robot and the surrounding environment information data perceived by the lidar unit of the ground inspection robot; loop detection is performed based on the updated local map by matching the lidar unit scanning data with the historical local map, and the ground lidar map is generated according to the optimization results by globally optimizing all historical local maps and the trajectory information of the ground inspection robot.

[0024] Furthermore, the ground lidar map is used as a feature map for positioning, and the local map is updated according to the local positioning results of the ground inspection robot and the surrounding environment information data perceived by the lidar unit of the ground inspection robot. According to the updated local map, the lidar unit scanning data is matched with the constructed feature map and globally optimized to eliminate the accumulated errors, and then the posture state of the ground inspection robot on the high-precision inspection map is output.

[0025] Furthermore, in the process of planning the optimal local path in step 4, combined with the multi-objective graph optimization idea, time information parameters are introduced between the posture state of the ground inspection robot and the obstacles and global path points on the high-precision inspection map model. At the same time, the distance relationship between the posture state of the ground inspection robot and the obstacles and global path points on the high-precision inspection map model, as well as the speed and acceleration constraints of the ground inspection robot are considered. Based on the TEB trajectory planning algorithm and the parallel search method based on the topological map, the optimal local path without obstacle collision is planned in real time.

[0026] Furthermore, in the process of TEB path planning, a new posture state needs to be inserted or the processed old posture state needs to be deleted in each iteration to ensure the speed of solving the optimization problem; then, by establishing the relationship between the TEB state and the path points and obstacles, the vertex table and edge table are defined in the general graph optimization g2o framework, and the posture state of the ground inspection robot, the time interval between the outputs of adjacent posture states, and the obstacles are added to the vertex table, and the objective function is added to the edge table, so as to transform the problem and construct a hypergraph, and finally complete the solution and optimization with the help of the g2o framework; according to the real-time perception of the external environment by the laser radar unit, the new obstacles encountered are added to the vertex table, and the relationship between the TEB state and the path points and obstacles is established again, thereby forming a TEB path planning cycle. After obtaining the optimal solution by establishing and optimizing the hypergraph, the path adjustment and local path generation are performed according to the calculation results.

[0027] Furthermore, the autonomous collaborative inspection process in step 5 includes two inspection methods. The first inspection method is: when the aerial drone finds suspected defects or blind spots during high-altitude inspection, it sends a message to the ground inspection robot, and the ground inspection robot conducts a coordinated inspection; the second inspection method is: when the ground inspection robot finds suspected defects or blind spots during ground inspection, it notifies the aerial drone to conduct a high-altitude coordinated inspection according to the preset route.

[0028] Furthermore, the operating status data of the substation equipment obtained by the detection module in step 5 includes position status monitoring data, ambient temperature and humidity monitoring data, partial discharge monitoring data, equipment operation noise data, SF6 gas detection data, visible light image recognition data and dial monitoring data.

[0029] Furthermore, in step 6, the AI ​​recognition scope includes the position status, meter readings, and temperature of switchgear, circuit breakers, disconnectors, lightning arresters, transformers, and oil level gauges.

[0030] Compared with the prior art, the present invention has the following beneficial effects: the present invention uses aerial drones to assist ground inspection robots in adapting to various complex terrains, crossing obstacles and automatically finding the shortest path. Ground inspection robots conduct inspections through infrared, ultraviolet, noise, and partial discharge detection methods, and cooperate with each other to achieve full coverage of the inspection of the power grid equipment area. Artificial intelligence identifies whether the data is abnormal, and the back end issues an alarm for unreasonable data, and manual intervention is required for maintenance. The technical solution proposed by the present invention has the effect of improving the inspection efficiency of substation equipment. During the inspection of equipment in the substation area, there is no need for manual large-scale inspections, nor is there any need for manual control of ground inspection robots for inspection, which ensures the stable operation of the power grid and is more conducive to practical application. It can provide a reference for the three-dimensional inspection of substations in which aerial drones and ground inspection robots work together. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a connection block diagram of the system of the present invention;

[0032] Figure 2 This is a connection block diagram of the ground inspection robot of the present invention;

[0033] Figure 3 It is a connection block diagram of the aerial drone of the present invention;

[0034] Figure 4 It is a connection block diagram of the inspection host of the present invention;

[0035] Figure 5 It is a detection function diagram of the detection module of the present invention;

[0036] Figure 6 The figure is a flow chart of the inspection method of the present invention. DETAILED DESCRIPTION

[0037] Reference Figure 1 , an inspection system in which an aerial drone and a ground inspection robot work together, including a ground inspection robot for collecting ground environmental data information of a substation, an aerial drone for collecting aerial data information of the substation, an inspection host for processing data information and sending instructions, and a server for remote communication between the ground inspection robot and the aerial drone, the ground inspection robot and the aerial drone are respectively connected to the inspection host for communication, and the ground inspection robot and the aerial drone are respectively connected to the server for communication.

[0038] Reference Figure 2Furthermore, the ground inspection robot includes a detection module, a first motion unit, a first control module, a first information acquisition module, a first three-dimensional inspection module and a first wireless communication module. The detection module, the first motion unit, the first information acquisition module and the first wireless communication module are respectively connected to the first control module, and the detection module is connected to the first wireless communication module; the first information acquisition module is connected to the first wireless communication module, and the first three-dimensional inspection module is respectively connected to the first control module and the server; wherein the detection module is used to detect the operating status data of the substation equipment, and store it in the first control module, and then transmit it to the inspection host through the first wireless communication module; the first information acquisition module is used to collect environmental data information on the ground of the substation, and store it in the first control module, and then transmit it to the inspection host through the first wireless communication module; the first motion unit is used to execute the operation of the first control module Instructions are provided to control the inspection movement of the ground inspection robot; the first control module is used to store data information and execute the operation instructions of the inspection host and the aerial drone; during the inspection process of the ground inspection robot, the ground surrounding environment information data during the inspection process is obtained through the first information acquisition module, and the obtained ground surrounding environment information data is sent to the inspection host, and matched with the high-precision inspection map, so as to find suspected defects or inspection blind spots, and then notify the aerial drone through the first three-dimensional inspection module to perform high-altitude linkage inspection according to the preset route, and enable the aerial drone and the inspection robot to perform autonomous inspection according to the path planning. The inspection content includes current transformers, voltage transformers, switch cabinets, oil-immersed transformers (reactors), disconnectors, lightning arresters, busbars and insulators, wall bushings, arc suppression coils, station transformers, grounding devices, terminal boxes and maintenance power boxes.

[0039] Reference Figure 3Furthermore, the aerial drone includes a second motion unit, a second control module, a second information acquisition module, a second three-dimensional inspection module and a second wireless communication module. The second motion unit, the second information acquisition module and the second wireless communication module are respectively connected to the second control module, and the second three-dimensional inspection module is respectively connected to the second control module and the server. Among them, the second information acquisition module is used to collect environmental data information of the substation in the air, and store it in the second control module, and then transmit it to the inspection host through the second wireless communication module; the second motion unit is used to execute the operation instructions of the second control module to control the inspection movement of the aerial drone; the second control module is used to store data information and execute the operation instructions of the inspection host and the ground robot; during the aerial drone inspection process, the aerial surrounding environment information data during the inspection process is obtained through the second information acquisition module, and the obtained aerial surrounding environment information data is sent to the inspection host and matched with the high-precision inspection map. If suspected defects or inspection blind spots are found, a message is sent to the inspection robot, and the inspection robot conducts joint inspections. The first three-dimensional inspection module and the second three-dimensional inspection module exchange information through the server to assist the inspection robot in real-time communication with the aerial drone, find inspection blind spots, and cover the power grid equipment area.

[0040] Reference Figure 4Furthermore, the inspection host includes a data processing module, a navigation and positioning module and a path planning module. The data processing module is respectively connected to the first wireless communication module and the second wireless communication module for communication. The data processing module stores and analyzes the data collected by the first information acquisition module, the second information acquisition module and the detection module, and compares the substation equipment operation status data collected by the detection module with the substation equipment steady-state operation data, and issues an alarm instruction for data exceeding the error threshold; the navigation and positioning module is connected to the data processing module, extracts and integrates the data information collected by the first information acquisition module and the second information acquisition module stored in the data processing module, constructs a 360° three-dimensional high-precision inspection map, and collects the ground inspection robot angular velocity and acceleration data information, the ground inspection robot mileage data information and the ground inspection robot during its movement. The environmental information data is used to locate the ground inspection robot in real time, and the high-precision inspection map and the posture status information of the ground inspection robot are stored in the data processing module; the path planning module is connected to the data processing module, and according to the high-precision inspection map and the posture status information of the ground inspection robot stored in the data processing module, time information parameters are introduced between the posture status of the ground inspection robot and the obstacles and global path points on the high-precision inspection map model, and the distance relationship between the posture status of the ground inspection robot and the obstacles and global path points on the high-precision inspection map model is considered at the same time, and an optimal local path without collision with obstacles is planned in real time, and the optimal local path data is stored in the data processing module and sent to the first control module, and the first control module sends a control instruction to the first motion unit according to the optimal local path data, so that the ground inspection robot performs the inspection operation of the optimal local path.

[0041] Reference Figure 5 Furthermore, the detection module includes a noise detection unit, an SF6 gas detection unit, a visible light and infrared visual detection unit, an ambient temperature and humidity detection unit, a partial discharge detection unit, and a dial data detection unit. The noise detection unit detects the noise during the operation of the equipment. The ambient temperature and humidity detection unit is equipped with an infrared thermal imager and a temperature and humidity sensor to detect the temperature and humidity in the switch room of the substation. The partial discharge detection unit evaluates the insulation degradation degree in the equipment by analyzing the amplitude and spectrum of the partial discharge signal, and provides a scientific decision-making basis for the maintenance of the equipment. The SF6 gas detection unit detects the gas concentration in the environment around the equipment. The visible light and infrared visual detection unit detects whether the air switch, disconnector, indicator light, etc. are in normal condition. The dial data detection unit reads the data of the dial with readings or detects, automatically records and judges the switch position.

[0042] Furthermore, the first information collection module includes a lidar unit, an odometer and an IMU sensor, and the second information collection module includes a depth camera sensor. The lidar unit is used to collect surrounding environment information during the movement of the ground inspection robot, the odometer is used to calculate the mileage of the inspection robot, the IMU sensor is used to obtain the angular velocity and acceleration of the ground inspection robot during its movement, and the depth camera sensor is used to obtain aerial drone images of the substation.

[0043] Furthermore, it also includes a charging nest. The ground inspection robot and the aerial UAV are respectively provided with power modules. The power modules have built-in Bluetooth modules and are connected and paired with the charging nest through the Bluetooth module. When the aerial UAV and the inspection robot have no tasks or low battery warnings, the aerial UAV and the inspection robot return to the charging nest and record the charging data.

[0044] Reference Figure 6 , an inspection method for collaborative operation of an aerial drone and a ground inspection robot, characterized in that the steps are as follows:

[0045] Step 1: Prepare the ground inspection robot and the aerial drone and wait for the inspection task to be issued;

[0046] Step 2: The inspection host issues an inspection command, and the ground inspection robot and the aerial drone collect data information about the substation environment, including the ground lidar point cloud and aerial drone images of the substation, and transmit the information back to the inspection host;

[0047] Furthermore, in step 2, during the data information collection process, both the aerial drone and the ground inspection robot use the MQTT protocol to communicate with the background program on the server. The aerial drone uses a power-specific SIM card and TF encryption card. The data displayed locally on the aerial drone uses the key field hiding method to desensitize sensitive data. It is securely connected to the substation intranet through two-way identity authentication and two-way data encryption, and the data information is transmitted back to the inspection host through the substation's in-station wifi.

[0048] Step 3: Based on the sparse point cloud generated by the aerial drone image, the oblique photography technology is used to fuse the point cloud with the ground lidar to generate a 360° high-precision inspection map; then the posture state of the ground inspection robot on the high-precision inspection map is calculated;

[0049] Furthermore, the fusion modeling process described in step 3 is: obtaining drone images of the substation through a drone, and generating a sparse point cloud based on the drone images, obtaining a ground lidar map of the substation through a ground inspection robot, and generating a ground lidar point cloud based on the ground lidar map, and generating a 360° stereo high-precision inspection map model by fusing the sparse point cloud with the ground lidar point cloud.

[0050] Furthermore, the data information also includes data information on the angular velocity and acceleration of the ground inspection robot, and the mileage of the ground inspection robot. The angular velocity and acceleration information of the ground inspection robot are obtained through the IMU sensor, and the local and precise displacement information of the ground inspection robot is obtained through the odometer. The surrounding environment information of the ground inspection robot during its movement is perceived by the lidar unit, and position sensors such as ultrasonic or depth cameras can also be used to obtain the surrounding environment information of the ground inspection robot during its movement. A composite processing strategy is adopted that integrates the lidar unit, odometer, and IMU sensor information to improve the mapping effect, positioning accuracy, and robustness.

[0051] Furthermore, the position and posture state of the ground inspection robot on the high-precision inspection map is calculated: the information of the IMU sensor, odometer and lidar unit is integrated through the extended Kalman filtering technology to predict the local position and posture of the ground inspection robot. The scanning data of the lidar unit of the ground inspection robot is directly matched with the ground lidar map to obtain the local positioning result of the ground inspection robot, and the position and posture state of the ground inspection robot on the high-precision inspection map is output.

[0052] Furthermore, in the process of acquiring the ground lidar map, the local map is updated according to the local positioning results of the ground inspection robot and the surrounding environment information data perceived by the lidar unit of the ground inspection robot; loop detection is performed based on the updated local map by matching the lidar unit scanning data with the historical local map, and the ground lidar map is generated according to the optimization results by globally optimizing all historical local maps and the trajectory information of the ground inspection robot.

[0053] Furthermore, the ground lidar map is used as a feature map for positioning, and the local map is updated according to the local positioning results of the ground inspection robot and the surrounding environment information data perceived by the lidar unit of the ground inspection robot. According to the updated local map, the lidar unit scanning data is matched with the constructed feature map and globally optimized to eliminate the accumulated errors, and then the posture state of the ground inspection robot on the high-precision inspection map is output.

[0054] Step 4: Based on the high-precision inspection map and the position and posture of the ground inspection robot on the high-precision inspection map, combined with the real-time perception and information collection of the ground environment of the substation by the aerial drone, the optimal local path without obstacle collision is planned in real time;

[0055] Furthermore, in the process of planning the optimal local path in step 4, combined with the multi-objective graph optimization idea, time information parameters are introduced between the posture state of the ground inspection robot and the obstacles and global path points on the high-precision inspection map model. At the same time, the distance relationship between the posture state of the ground inspection robot and the obstacles and global path points on the high-precision inspection map model, as well as the speed and acceleration constraints of the ground inspection robot are considered. Based on the TEB trajectory planning algorithm and the parallel search method based on the topological map, the optimal local path without obstacle collision is planned in real time.

[0056] Furthermore, in the process of TEB path planning, a new posture state needs to be inserted or the processed old posture state needs to be deleted in each iteration to ensure the speed of solving the optimization problem; then, by establishing the relationship between the TEB state and the path points and obstacles, the vertex table and edge table are defined in the general graph optimization g2o framework, and the posture state of the ground inspection robot, the time interval between the outputs of adjacent posture states, and the obstacles are added to the vertex table, and the objective function is added to the edge table, so as to transform the problem and construct a hypergraph, and finally complete the solution and optimization with the help of the g2o framework; according to the real-time perception of the external environment by the laser radar unit, the new obstacles encountered are added to the vertex table, and the relationship between the TEB state and the path points and obstacles is established again, thereby forming a TEB path planning cycle. After obtaining the optimal solution by establishing and optimizing the hypergraph, the path adjustment and local path generation are performed according to the calculation results.

[0057] Step 5: According to the optimal local path, the aerial drone and the ground inspection robot conduct autonomous collaborative inspections of the required inspection areas of the power grid equipment in the air and on the ground, and use the ground inspection robot to collect the operating status data of the substation equipment;

[0058] Furthermore, the autonomous collaborative inspection process in step 5 includes two inspection methods. The first inspection method is: when the aerial drone finds suspected defects or blind spots during high-altitude inspection, it sends a message to the ground inspection robot, and the ground inspection robot conducts a coordinated inspection; the second inspection method is: when the ground inspection robot finds suspected defects or blind spots during ground inspection, it notifies the aerial drone to conduct a high-altitude coordinated inspection according to the preset route.

[0059] Furthermore, the operating status data of the substation equipment obtained by the detection module in step 5 includes position status monitoring data, ambient temperature and humidity monitoring data, partial discharge monitoring data, equipment operation noise data, SF6 gas detection data, visible light image recognition data and dial monitoring data.

[0060] Step 6: The collected operating status data of the substation equipment is transmitted to the inspection host, the status information data of the substation equipment is identified through artificial intelligence, the status information data is compared with the steady-state operation data, and an alarm is issued for the status information data of the substation equipment with abnormal data.

[0061] Furthermore, in step 6, the AI ​​recognition scope includes the position status, meter readings, and temperature of switchgear, circuit breakers, disconnectors, lightning arresters, transformers, and oil level gauges.

[0062] Finally, it should be noted that the above 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. An inspection method for collaborative operation of an aerial drone and a ground inspection robot, characterized in that the steps are: as follows: Step 1: Prepare the ground inspection robot and the aerial drone and wait for the inspection task to be issued; Step 2: The inspection host issues an inspection command, and the ground inspection robot and the aerial drone collect data information about the substation environment, including the ground lidar point cloud and aerial drone images of the substation, and transmit the information back to the inspection host; Step 3: Based on the sparse point cloud generated by the aerial drone image, the oblique photography technology is used to fuse the point cloud with the ground lidar to generate a 360° high-precision inspection map; then the posture state of the ground inspection robot on the high-precision inspection map is calculated; Step 4: Based on the high-precision inspection map and the position and posture of the ground inspection robot on the high-precision inspection map, combined with the real-time perception and information collection of the ground environment of the substation by the aerial drone, the optimal local path without obstacle collision is planned in real time; Step 5: According to the optimal local path, the aerial drone and the ground inspection robot conduct autonomous collaborative inspections of the required inspection areas of the power grid equipment in the air and on the ground, and use the ground inspection robot to collect the operating status data of the substation equipment; Step 6: The collected operating status data of the substation equipment is transmitted to the inspection host, the status information data of the substation equipment is identified by artificial intelligence, the status information data is compared with the steady-state operation data, and an alarm is issued for the status information data of the substation equipment with abnormal data; The fusion modeling process described in step 3 is as follows: obtaining a drone image map of the substation through a drone, and generating a sparse point cloud based on the drone image map; obtaining a ground lidar map of the substation through a ground inspection robot, and generating a ground lidar point cloud based on the ground lidar map; and generating a 360° stereo high-precision inspection map model by fusing the sparse point cloud with the ground lidar point cloud; The data information also includes the angular velocity and acceleration of the ground inspection robot, and the mileage of the ground inspection robot. The angular velocity and acceleration information of the ground inspection robot are obtained through the IMU sensor, and the local precise displacement information of the ground inspection robot is obtained through the odometer. The surrounding environment information of the ground inspection robot during its movement is sensed through the laser radar unit. A composite processing strategy that integrates the laser radar unit, odometer, and IMU sensor information is adopted to improve the mapping effect and positioning accuracy and robustness. Calculate the posture state of the ground inspection robot on the high-precision inspection map: Use the extended Kalman filter technology to fuse the information of the IMU sensor, odometer and lidar unit to predict the local posture of the ground inspection robot. The scanning data of the lidar unit of the ground inspection robot is directly matched with the ground lidar map to obtain the local positioning result of the ground inspection robot, and the posture state of the ground inspection robot on the high-precision inspection map is output; Step 4: In the process of planning the optimal local path, the multi-objective graph optimization idea is combined to introduce time information parameters between the posture state of the ground inspection robot and the obstacles and global path points on the high-precision inspection map model. At the same time, the distance relationship between the posture state of the ground inspection robot and the obstacles and global path points on the high-precision inspection map model, as well as the speed and acceleration constraints of the ground inspection robot are considered. Based on the TEB trajectory planning algorithm and the parallel search method based on the topological map, the optimal local path without obstacle collision is planned in real time; In the process of TEB path planning, each iteration needs to insert a new posture state or delete the processed old posture state to ensure the speed of solving the optimization problem; then, by establishing the relationship between the TEB state and the path points and obstacles, the vertex table and edge table are defined in the general graph optimization g2o framework, and the posture state of the ground inspection robot, the time interval between the output of adjacent posture states, and the obstacles are added to the vertex table, and the objective function is added to the edge table, so as to transform the problem and construct a hypergraph, and finally complete the solution and optimization with the help of the g2o framework; according to the real-time perception of the external environment by the laser radar unit, the new obstacles encountered are added to the vertex table, and the relationship between the TEB state and the path points and obstacles is established again, thus forming a TEB path planning cycle. After obtaining the optimal solution by establishing and optimizing the hypergraph, the path adjustment and local path generation are performed according to the calculation results.

2. The inspection method of claim 1 in which an aerial drone and a ground inspection robot work in collaboration, characterized in that: In the process of obtaining the ground lidar map, the local map is updated according to the local positioning results of the ground inspection robot and the surrounding environment information data perceived by the lidar unit of the ground inspection robot. According to the updated local map, loop detection is performed by matching the lidar unit scanning data with the historical local map, and all historical local maps and ground inspection robot trajectory information are globally optimized to generate a ground lidar map according to the optimization results.

3. The inspection method of claim 1 in which an aerial drone and a ground inspection robot work in collaboration, characterized in that: The ground lidar map is used as the feature map for positioning. The local map is updated according to the local positioning results of the ground inspection robot and the surrounding environment information data perceived by the lidar unit of the ground inspection robot. According to the updated local map, the lidar unit scanning data is matched with the constructed feature map and globally optimized to eliminate the accumulated errors, and then the posture state of the ground inspection robot on the high-precision inspection map is output.

4. The inspection method of claim 1 in which an aerial drone and a ground inspection robot work in collaboration, characterized in that: Step 5 The autonomous collaborative inspection process includes two inspection methods. The first inspection method is: when the aerial drone finds suspected defects or blind spots during high-altitude inspection, it sends a message to the ground inspection robot, and the ground inspection robot conducts a coordinated inspection. The second inspection method is: when the ground inspection robot finds suspected defects or blind spots during ground inspection, it notifies the aerial drone to conduct a high-altitude coordinated inspection according to the preset route.

5. An inspection system for implementing the inspection method of collaborative operation of an aerial drone and a ground inspection robot as described in any one of claims 1 to 4, characterized in that: It includes a ground inspection robot for collecting ground environmental data information of a substation, an aerial drone for collecting aerial data information of a substation, an inspection host for processing data information and sending instructions, and a server for remote communication between the ground inspection robot and the aerial drone. The ground inspection robot and the aerial drone are respectively connected to the inspection host for communication, and the ground inspection robot and the aerial drone are respectively connected to the server for communication. The ground inspection robot includes a detection module, a first motion unit, a first control module, a first information collection module, a first three-dimensional inspection module and a first wireless communication module. The detection module, the first motion unit, the first information collection module and the first wireless communication module are respectively connected to the first control module, and the detection module is connected to the first wireless communication module; the first information collection module is connected to the first wireless communication module, the first three-dimensional inspection module is respectively connected to the first control module and the server, and the aerial drone includes a second motion unit, a first control module, a first information collection module, a first three-dimensional inspection module and a first wireless communication module. The second control module, the second information acquisition module, the second three-dimensional inspection module and the second wireless communication module, the second motion unit, the second information acquisition module and the second wireless communication module are respectively connected to the second control module, the second three-dimensional inspection module is respectively connected to the second control module and the server, the inspection host includes a data processing module, a navigation and positioning module and a path planning module, the data processing module is respectively communicated with the first wireless communication module and the second wireless communication module; the navigation and positioning module is connected to the data processing module; the path planning module is connected to the data processing module, the detection module includes a noise detection unit, an SF6 gas detection unit, a visible light and infrared visual detection unit, an ambient temperature and humidity detection unit, a partial discharge detection unit and a dial data detection unit, and also includes a charger nest, the ground inspection robot and the aerial drone are respectively provided with power modules, the power modules have built-in Bluetooth modules, and are connected and paired with the charger nest through the Bluetooth module.

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