A multi-legged robot based on air leakage monitoring in goaf

Abstract

The invention discloses a multi-legged robot based on goaf air leakage monitoring, and specifically relates to the field of robot technology. Through sensors and map building technology, the robot can autonomously plan paths, avoid obstacles, and accurately reach monitoring points. Monitoring equipment is used to complete the measurement of wind speed and environmental parameters. Through an API interface provided by a cloud platform, the air leakage monitoring data is transmitted to a control center client in real time. The preset Internet of Things inspection points underground are used as target points to reduce the risk of manual operation. Environmental data monitoring is achieved based on multi-physical parameter sensors. An algorithm for determining goaf air leakage is combined with a front-end detection robot to compositely determine whether there is air leakage in the goaf. A rechargeable battery is used as the power supply for the robot to provide endurance power for the safety inspection robot, prevent accidents, ensure the personal safety of miners, and improve mine mining efficiency.

Description

Technical Field

[0001] The invention relates to the field of robot technology, and more specifically to a multi-legged robot based on goaf area air leakage monitoring. Background Art

[0002] my country's coal mines are currently facing a major historical period of rapid transformation towards modernization, large-scale and technological intensification. Empowering coal mine construction with science and technology and reducing the use of human resources to improve efficiency and reduce costs through automation of production processes have always been the focus of scientific research and industry attention.

[0003] Due to the existence of goafs, underground mine working faces and supporting structures no longer play a sealing role, which may cause air leakage in goafs. Once it occurs, it will cause safety hazards such as gas explosions and coal dust explosions, so goaf leakage monitoring is very important. Traditional manual detection methods are limited by environmental constraints and need to enter untreated underground cavities. These cavities usually have complex terrain and unforeseen obstacles, making it difficult to implement manual detection methods for air leakage. Therefore, a multi-legged robot based on goaf leakage monitoring can adapt to complex terrain and goaf environments through the flexible movement of multiple feet, achieve high-mobility movement and operation, accurately determine whether there is air leakage in the goaf, and take corresponding preventive measures and ensure mine safety. It is crucial. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a multi-legged robot based on goaf air leakage monitoring, which realizes highly maneuverable movement and operation through the flexible movement of multiple feet, and accurately determines whether there is air leakage in the goaf, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution, a multi-legged robot based on goaf area air leakage monitoring, specifically comprising the following steps:

[0006] 101. Through sensors and map building technology, autonomously plan routes, avoid obstacles, and accurately reach monitoring points;

[0007] 102. Use underground environmental monitoring equipment to monitor the wind flow in the goaf area in real time and obtain wind speed and temperature data in the goaf area;

[0008] 103. Through the API interface provided by the cloud platform, the air leakage monitoring data is transmitted to the control center client in real time;

[0009] 104. Use professional modeling software to convert the collected data into a three-dimensional model to achieve comprehensive monitoring and analysis of the three-dimensional environmental information of the goaf;

[0010] 105. Through environmental monitoring means, combined with the algorithm for determining air leakage in the goaf and the front-end detection robot, it is determined whether there is air leakage in the goaf;

[0011] 106. Use rechargeable batteries as the power supply of the robot to provide endurance power for the entire safety inspection robot;

[0012] In a preferred embodiment, in step 101, the multi-legged robot has multiple independently controlled legs and can adapt to various terrains and environments. After being equipped with an autonomous navigation system, the robot obtains information about the surrounding environment through sensors to help the robot perceive obstacles, identify map features, and perform real-time updates, which specifically include the following:

[0013] S1. Perceiving obstacles: The multi-legged robot uses a solid-state blind spot laser radar to emit a laser beam and measure the return time to measure the distance of the surrounding environment and detect obstacles. The specific steps include:

[0014] Step 1: The laser radar sends a laser pulse to the target object and starts a timer. When the laser pulse reaches the target object, it is reflected and received by the laser radar receiver.

[0015] Step 2: The laser radar receives the reflected laser pulse, stops the timer, and obtains the time from the laser pulse being emitted to returning according to the time difference recorded by the timer. The distance between the target object and the laser radar is calculated using the following formula:

[0016]

[0017] Among them, r represents the distance from the laser radar to the obstacle, C represents the speed of light; t represents the total time taken for the laser to be sent to the receiver;

[0018] S2. Identify map features: The multi-legged robot uses a visible light camera to obtain image information in the environment and identifies obstacles through image processing algorithms, which specifically includes the following steps:

[0019] Step 1: Use the color moment method to represent the color features of the image, extract the color information in the image, convert the image into the RGB color space, divide the image into several small areas according to the selected color space, and for each small area, traverse each pixel in the area. For each pixel, extract the value of its color component, obtain the values ​​of the red, green, and blue components respectively, add the corresponding color component values ​​of each pixel, and get the sum of the color components in the area. Calculate the sum of the color components of the pixels in the area and the sum of the squares of the color components. The specific formula is as follows:

[0020] R=∑(r1+r2+...+rn )

[0021] R r =∑(r1+r2+...+r n ) 2

[0022] Among them, Σ represents the sum operation; R represents the sum of the red components; r represents the red component value; R r represents the sum of squares of red components;

[0023] S3. Dynamic obstacle avoidance: Install contact sensors on the robot's legs to detect whether it is in contact with obstacles. When the robot's body touches an obstacle, the contact sensor sends a signal and the robot avoids the obstacle.

[0024] In a preferred embodiment, in step 102, the multi-legged robot uses an ultrasonic laser sensor anemometer, a laser radar, a sensor, and an infrared imager to monitor the goaf environment in real time, collect working environment data, and determine the air leakage condition of the goaf, which specifically includes the following contents:

[0025] S1. Data collection and integration is completed by relying on the ultrasonic laser sensor anemometer and sound wave transmitter and receiver carried by the safety inspection robot. By measuring the reading of the anemometer, the specific calculation formula is as follows:

[0026]

[0027] Where V represents the wind speed, f represents the measured Doppler frequency shift, λ represents the wavelength of the emitted ultrasonic wave, and θ represents the angle between the measuring device and the wind direction;

[0028] S2. Use mining multi-parameter sensors to measure environmental parameters, understand the airflow conditions in the mining area in real time, divide the airflow data into different intervals through frequency distribution analysis, calculate the frequency of data values ​​in each interval, draw frequency polygons, and understand the distribution of airflow parameters in different ranges. The specific calculation formula is as follows:

[0029]

[0030] Among them, x represents the median of each interval, μ represents the mean of the data, f represents the frequency of each interval, and N represents the total number of samples;

[0031] S3, the multi-legged robot is equipped with an infrared imager to scan and capture hotspot images of the goaf in real time, and collect and record these data. The specific steps are as follows:

[0032] Step 1: Obtain infrared image: The infrared imager scans and photographs the target area to obtain an infrared image, and the temperature information of the location is recorded at each pixel;

[0033] Step 2, grayscale mapping: determine a temperature range, from the lowest temperature T-min to the highest temperature T-max, map the temperature range to the grayscale space, and obtain a grayscale image, recorded as G(x,y). The lower temperature area is represented by a darker grayscale, and the higher temperature area is represented by a brighter grayscale. According to different application requirements, different grayscale mapping tables are selected. For each pixel point (i,j), calculate the grayscale difference value Diff(i,j) between it and the neighboring pixels. The specific calculation formula is as follows:

[0034] Diff(i,j)=|G(i,j)-G(i',j')|

[0035] Among them, Diff(i,j) represents the grayscale difference value of the neighboring pixels, and (i',j') represents the neighboring pixels of (i,j);

[0036] Step 3, hotspot identification: According to the image after grayscale mapping, a threshold T is set to determine whether a pixel point can be considered a hotspot. The grayscale difference image Diff is traversed. For each pixel point (i, j), if Diff (i, j) exceeds the threshold T, the pixel point is marked as a hotspot.

[0037] Step 4: Hotspot image display: Pixels considered to be hotspots are marked on the original image and displayed in the form of bounding boxes to facilitate users to intuitively identify and analyze the hotspot distribution.

[0038] In a preferred embodiment, in step 103, the multi-legged robot transmits the air leakage monitoring data to the control center in real time through the API interface provided by the cloud platform to share the data, which specifically includes the following contents:

[0039] S1. Data transmission: The robot uploads the collected data to the cloud platform. Through the API interface provided by the cloud platform, the terminal can obtain the robot's data from the cloud platform. Specifically, the following steps are included:

[0040] Step 1: Authentication and authorization: The terminal first performs authentication and authorization to verify identity and permissions. Before using the API interface, the terminal needs to provide the identity credential API key, which is included in the API request;

[0041] Step 2: Send a request: The terminal uses the API interface to send a request to the cloud platform. The request needs to specify the relevant parameters for obtaining robot data, including data type, query conditions, and time range;

[0042] Step 3: Data processing: After receiving the request, the cloud platform performs a query operation based on the requested parameters and returns the robot data that meets the requested conditions;

[0043] Step 4: Return data: The cloud platform returns the processed robot data to the terminal as an API response. The terminal extracts the required robot data by parsing the API response to complete the data transmission.

[0044] In a preferred embodiment, in step 104, the preset IoT inspection point underground is used as the target point, and the path planning algorithm is used to generate the optimal inspection path for the robot. According to the positioning capability of the robot, the robot accurately reaches the inspection target point, improves the inspection efficiency, reduces the risk of manual operation, and ensures the safety of key areas and the reliability of data, which specifically includes the following contents:

[0045] S1, A-Star algorithm: Establish heuristic search rules during the search process, measure the distance relationship between the real-time search position and the target position, make the search direction preferentially toward the direction of the target point, calculate the f value of each child node from the starting point, select the child node with the smallest f value as the next point to search, and iterate back and forth until the next child node is the target point, so as to improve the search efficiency. The specific calculation formula is as follows:

[0046] f(x)=g(x)+h(x)

[0047] Among them, g(x) is the actual distance measure from the starting point to the current node x, and h(x) is the minimum distance estimate from node x to the end point.

[0048] S2. Precise positioning: Based on the information collected by the IMU attitude sensor and solid-state blind spot laser radar related hardware equipment, by measuring the changes in the robot's acceleration and angular velocity, the robot's travel distance and its own angle information are obtained to complete the function of accurate and reliable positioning in the underground tunnel space. The specific calculation formula is as follows:

[0049] x=x0+vt

[0050] Among them, x represents the position of the robot, x0 represents the initial position of the robot, v represents the speed of the robot, and t represents time.

[0051] In a preferred embodiment, in step 105, environmental data monitoring of multiple parameters is realized by environmental monitoring means, and the algorithm for determining air leakage in the goaf and the front-end detection robot are combined to determine whether there is air leakage in the goaf, which specifically includes the following contents:

[0052] S1. Through the mining multi-physical parameter sensor, which integrates temperature, humidity, carbon monoxide, methane, nitrogen oxides, and pressure sensors, the detection data is continuously and automatically transmitted to the main controller of the safety inspection robot in the form of standard electrical signals, and edge analysis and calculation are performed to process abnormal values. Through the outlier detection formula, the data is converted into a standard normal distribution, and the difference between each data point and the mean is calculated and divided by the standard deviation to obtain the outlier value. When the concentration, temperature, and humidity data information is abnormal and reported, it will be automatically corrected. The specific calculation formula is as follows:

[0053]

[0054] Where x' represents an outlier; x represents a data point; μ A represents the mean of the data points; σ A Indicates standard deviation; outliers within the range of -2 to 2 are considered normal values;

[0055] S2. Use the infrared radiation changes of the infrared thermal imager to determine whether there is an air leakage problem. Aim the infrared thermal imager at the surface of the goaf, observe the significant temperature difference, compare the 3D point cloud coordinate offset, issue a warning to the main control center, and take blocking measures in a timely manner. The specific steps include:

[0056] Step 1: Aim the infrared thermal imager at the surface of the goaf and observe the significant temperature difference. The collected images will show areas of different colors, indicating the infrared energy intensity and temperature radiated by the area. Abnormal temperature areas indicate air leakage.

[0057] Step 2: Use 3D point cloud technology to obtain the 3D coordinate offset data of the goaf. When air leakage occurs in the goaf, air flow will cause a slight deformation of the goaf, resulting in the offset of the 3D coordinates. By comparing the initial 3D coordinates and the current 3D coordinates of the goaf, the deformation of the goaf can be determined to further identify whether there is an air leakage problem. The specific calculation formula is as follows:

[0058]

[0059] Where d represents the distance, (xi,yi,zi) represents the initial three-dimensional coordinates of the goaf, and (xc,yc,zc) represents the current three-dimensional coordinates of the goaf;

[0060] Step 3: After detecting an abnormal temperature area and comparing it with the three-dimensional point cloud data, according to the set threshold M, the distance d exceeds the threshold M, deformation occurs, and it is determined that there is an air leakage problem in the goaf. The infrared thermal imager sends a warning message to the main control center, and blocking measures are taken in a timely manner.

[0061] In a preferred embodiment, in step 106, a rechargeable battery is used as the power supply of the robot to meet the power requirements of the drive motor, the gimbal servo, and the sensor input and output devices. When the robot runs to a low cruising range, a power saving scheme of the main control terminal is implemented to complete automatic charging, which specifically includes the following contents:

[0062] S1. Energy-saving strategy: Power control is performed according to usage scenarios and needs, and the device automatically enters a low-power standby state during idle periods, and automatically senses the switch to ensure that the main control terminal is in an energy-saving state in an idle state, thereby reducing power consumption;

[0063] S2. Automatic charging system: Using inductive charging technology, the robot can be paired with the charging base station and docked accurately, realizing automatic charging without human intervention;

[0064] S3. Charging management: Through the intelligent scheduling algorithm, the power and charging status of the main control terminal are monitored. According to the power forecast and task requirements, the charging time and location are reasonably arranged to ensure that the main control terminal has sufficient power during operation. The steps for power scheduling using the greedy algorithm are as follows:

[0065] Step 1: Determine the greedy selection strategy: When selecting the next task to be executed at each location, select the task that consumes the least power;

[0066] Step 2: Initialize power and task list: Set the initial power and sort the tasks in ascending order of power consumption;

[0067] Step 3, traverse the task list: traverse the task list in the sorted order;

[0068] Step 4: Determine whether the task can be executed: For each task, determine whether the current remaining power is sufficient to execute it. If not, return to the charging station for charging;

[0069] Step 5. Continue traversal: Repeat the above steps until all tasks are traversed.

[0070] Technical effects and advantages of the present invention:

[0071] The present invention uses sensors and map building technology to autonomously plan paths, avoid obstacles, and accurately reach monitoring points. It uses monitoring equipment to measure wind speed and environmental parameters, and transmits air leakage monitoring data to the control center client in real time through the API interface provided by the cloud platform. It takes the preset IoT inspection points underground as the target points to reduce the risk of manual operation. It uses environmental monitoring methods to realize environmental data monitoring of multiple parameters, combines the algorithm for determining air leakage in the goaf with the front-end detection robot to compositely determine whether there is air leakage in the goaf, uses a rechargeable battery as the power supply for the robot, and is responsible for the endurance power supply of the safety inspection robot, so as to prevent accidents, ensure the personal safety of miners, and improve mine mining efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a system flow chart of the present invention.

[0073] Figure 2 It is a system structure block diagram of the present invention.

[0074] Figure 3 It is a structural diagram of the present invention.

[0075] (in, Figure 1 It represents a solid-state blind spot laser radar, which is used to scan and collect deformation data of the roadway. Figure 2 Represents binocular vision, visible light infrared imager, Figure 3 Ultrasonic wind speed sensor, used to measure wind speed) DETAILED DESCRIPTION

[0076] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0077] Example 1

[0078] This embodiment provides Figure 1 A multi-legged robot based on goaf air leakage monitoring is shown, which specifically includes the following steps:

[0079] 101. Through sensors and map building technology, autonomously plan routes, avoid obstacles, and accurately reach monitoring points;

[0080] 102. Use underground environmental monitoring equipment to monitor the wind flow in the goaf area in real time and collect working environment data;

[0081] 103. Through the API interface provided by the cloud platform, the air leakage monitoring data is transmitted to the control center client in real time;

[0082] 104. According to the preset IoT inspection points in the mine, the robot takes them as the actual inspection target points, and realizes the inspection function between the target points;

[0083] 105. Through environmental monitoring means, realize multi-parameter environmental data monitoring, combine the algorithm for determining air leakage in goaf with the front-end detection robot to determine whether there is air leakage in goaf;

[0084] 106. Use rechargeable batteries as the power supply of the robot to provide power for the entire safety inspection robot.

[0085] like Figure 2 This embodiment provides a multi-legged robot based on goaf area air leakage monitoring, specifically comprising:

[0086] Autonomous navigation module: The multi-legged robot uses sensors and map building technology to autonomously plan paths, avoid obstacles, and accurately reach monitoring points.

[0087] Information collection module: The multi-legged robot uses ultrasonic laser sensor anemometer, laser radar, sensor and infrared imager to monitor the goaf environment in real time, collect working environment data and determine the air leakage condition of the goaf.

[0088] Communication interaction module: The multi-legged robot transmits the air leakage monitoring data to the control center client in real time through the API interface provided by the cloud platform for data sharing.

[0089] Inspection planning module: Take the preset IoT inspection points underground as the target points, use the path planning algorithm to generate the optimal inspection path for the robot, and accurately reach the inspection target points based on the robot's positioning capability, reducing the risk of manual operation.

[0090] Air leakage determination module: Through environmental monitoring means, it realizes environmental data monitoring of multiple parameters, and combines the algorithm for determining air leakage in the goaf with the front-end detection robot to compositely determine whether there is air leakage in the goaf.

[0091] Endurance management module: Use rechargeable batteries as the power supply of the robot to meet the power demand of the output device. When the robot runs to a low endurance state, the main control terminal power saving solution is implemented and automatic charging is completed.

[0092] 101. Through sensors and map building technology, autonomously plan routes, avoid obstacles, and accurately reach monitoring points;

[0093] Furthermore, the multi-legged robot has multiple independently controlled legs that can adapt to various terrains and environments. After being equipped with an autonomous navigation system, the robot obtains information about the surrounding environment through sensors, which helps the robot perceive obstacles, identify map features, and update them in real time, including the following:

[0094] S1. Perceiving obstacles: The multi-legged robot uses a solid-state blind spot laser radar to emit a laser beam and measure the return time to measure the distance of the surrounding environment and detect obstacles. The specific steps include:

[0095] Step 1: The laser radar sends a laser pulse to the target object and starts a timer. When the laser pulse reaches the target object, it is reflected and received by the laser radar receiver.

[0096] Step 2: The laser radar receives the reflected laser pulse, stops the timer, and obtains the time from the laser pulse being emitted to returning according to the time difference recorded by the timer. The distance between the target object and the laser radar is calculated using the following formula:

[0097]

[0098] Among them, r represents the distance from the laser radar to the obstacle, C represents the speed of light; t represents the total time taken for the laser to be sent to the receiver;

[0099] S2. Identify map features: The multi-legged robot uses a visible light camera to obtain image information in the environment and identifies obstacles through image processing algorithms, which specifically includes the following steps:

[0100] Step 1: Use the color moment method to represent the color features of the image, extract the color information in the image, convert the image into the RGB color space, divide the image into several small areas according to the selected color space, and for each small area, traverse each pixel in the area. For each pixel, extract the value of its color component, obtain the values ​​of the red, green, and blue components respectively, add the corresponding color component values ​​of each pixel, and get the sum of the color components in the area. Calculate the sum of the color components of the pixels in the area and the sum of the squares of the color components. The specific formula is as follows:

[0101] R=∑(r1+r2+...+r n )

[0102] R r =∑(r1+r2+...+r n ) 2

[0103] Among them, Σ represents the sum operation; R represents the sum of the red components; r represents the red component value; R rrepresents the sum of squares of red components;

[0104] S3. Dynamic obstacle avoidance: Install contact sensors on the robot's legs to detect whether it is in contact with obstacles. When the robot's body touches an obstacle, the contact sensor sends a signal and the robot avoids the obstacle.

[0105] 102. Use underground environmental monitoring equipment to monitor the wind flow in the goaf area in real time and collect working environment data;

[0106] Furthermore, the multi-legged robot uses ultrasonic laser sensor anemometer, laser radar, sensor and infrared imager to monitor the goaf environment in real time, collect working environment data, and determine the air leakage status of the goaf, including the following:

[0107] S1. Data collection and integration is completed by relying on the ultrasonic laser sensor anemometer and sound wave transmitter and receiver carried by the safety inspection robot. By measuring the reading of the anemometer, the specific calculation formula is as follows:

[0108]

[0109] Where V represents the wind speed, f represents the measured Doppler frequency shift, λ represents the wavelength of the emitted ultrasonic wave, and θ represents the angle between the measuring device and the wind direction;

[0110] S2. Use mining multi-parameter sensors to measure environmental parameters, understand the airflow conditions in the mining area in real time, divide the airflow data into different intervals through frequency distribution analysis, calculate the frequency of data values ​​in each interval, draw frequency polygons, and understand the distribution of airflow parameters in different ranges. The specific calculation formula is as follows:

[0111]

[0112] Among them, x represents the median of each interval, μ represents the mean of the data, f represents the frequency of each interval, and N represents the total number of samples;

[0113] S3, the multi-legged robot is equipped with an infrared imager to scan and capture hotspot images of the goaf in real time, and collect and record these data. The specific steps are as follows:

[0114] Step 1: Obtain infrared image: The infrared imager scans and photographs the target area to obtain an infrared image, and the temperature information of the location is recorded at each pixel;

[0115] Step 2, grayscale mapping: determine a temperature range, from the lowest temperature T-min to the highest temperature T-max, map the temperature range to the grayscale space, and obtain a grayscale image, recorded as G(x,y). The lower temperature area is represented by a darker grayscale, and the higher temperature area is represented by a brighter grayscale. According to different application requirements, different grayscale mapping tables are selected. For each pixel point (i,j), calculate the grayscale difference value Diff(i,j) between it and the neighboring pixels. The specific calculation formula is as follows:

[0116] Diff(i,j)=|G(i,j)-G(i',j')|

[0117] Among them, Diff(i,j) represents the grayscale difference value of the neighboring pixels, and (i',j') represents the neighboring pixels of (i,j);

[0118] Step 3, hotspot identification: According to the image after grayscale mapping, a threshold T is set to determine whether a pixel point can be considered a hotspot. The grayscale difference image Diff is traversed. For each pixel point (i, j), if Diff (i, j) exceeds the threshold T, the pixel point is marked as a hotspot.

[0119] Step 4: Hotspot image display: Pixels considered to be hotspots are marked on the original image and displayed in the form of bounding boxes to facilitate users to intuitively identify and analyze the hotspot distribution.

[0120] 103. Through the API interface provided by the cloud platform, the air leakage monitoring data is transmitted to the control center client in real time;

[0121] Furthermore, the multi-legged robot transmits the air leakage monitoring data to the control center in real time through the API interface provided by the cloud platform for data sharing, which specifically includes the following contents:

[0122] S1. Data transmission: The robot uploads the collected data to the cloud platform. Through the API interface provided by the cloud platform, the terminal can obtain the robot's data from the cloud platform. Specifically, the following steps are included:

[0123] Step 1: Authentication and authorization: The terminal first performs authentication and authorization to verify identity and permissions. Before using the API interface, the terminal needs to provide the identity credential API key, which is included in the API request;

[0124] Step 2: Send a request: The terminal uses the API interface to send a request to the cloud platform. The request needs to specify the relevant parameters for obtaining robot data, including data type, query conditions, and time range;

[0125] Step 3: Data processing: After receiving the request, the cloud platform performs a query operation based on the requested parameters and returns the robot data that meets the requested conditions;

[0126] Step 4: Return data: The cloud platform returns the processed robot data to the terminal as an API response. The terminal extracts the required robot data by parsing the API response to complete the data transmission.

[0127] 104. According to the preset IoT inspection points in the mine, the robot takes the actual inspection target points, and realizes the inspection function between the target points;

[0128] Furthermore, the preset IoT inspection points underground are used as target points, and the path planning algorithm is used to generate the optimal inspection path for the robot. According to the robot's positioning capability, the robot can accurately reach the inspection target point, improve inspection efficiency, reduce manual operation risks, and ensure the safety of key areas and data reliability. Specifically, it includes the following contents:

[0129] S1, A-Star algorithm: Establish heuristic search rules during the search process, measure the distance relationship between the real-time search position and the target position, make the search direction preferentially toward the direction of the target point, calculate the f value of each child node from the starting point, select the child node with the smallest f value as the next point to search, and iterate back and forth until the next child node is the target point, so as to improve the search efficiency. The specific calculation formula is as follows:

[0130] f(x)=g(x)+h(x)

[0131] Among them, g(x) is the actual distance measurement from the starting point to the current node x, and h(x) is the minimum distance estimate from node x to the end point;

[0132] S2. Precise positioning: Based on the information collected by the IMU attitude sensor and solid-state blind spot laser radar related hardware equipment, by measuring the changes in the robot's acceleration and angular velocity, the robot's travel distance and its own angle information are obtained to complete the function of accurate and reliable positioning in the underground tunnel space. The specific calculation formula is as follows:

[0133] x=x0+vt

[0134] Among them, x represents the position of the robot, x0 represents the initial position of the robot, v represents the speed of the robot, and t represents time.

[0135] 105. Through environmental monitoring means, combined with the algorithm for determining air leakage in the goaf and the front-end detection robot, it is determined whether there is air leakage in the goaf;

[0136] Furthermore, through environmental monitoring means, multi-parameter environmental data monitoring is realized, and the algorithm for determining air leakage in the goaf and the front-end detection robot are combined to determine whether there is air leakage in the goaf, which specifically includes the following contents:

[0137] S1. Through the mining multi-physical parameter sensor, which integrates temperature, humidity, carbon monoxide, methane, nitrogen oxides, and pressure sensors, the detection data is continuously and automatically transmitted to the main controller of the safety inspection robot in the form of standard electrical signals, and edge analysis and calculation are performed to process abnormal values. Through the outlier detection formula, the data is converted into a standard normal distribution, and the difference between each data point and the mean is calculated and divided by the standard deviation to obtain the outlier value. When the concentration, temperature, and humidity data information is abnormal and reported, it will be automatically corrected. The specific calculation formula is as follows:

[0138]

[0139] Where x' represents an outlier; x represents a data point; μ A represents the mean of the data points; σ A Indicates standard deviation; outliers within the range of -2 to 2 are considered normal values;

[0140] S2. Use the infrared radiation changes of the infrared thermal imager to determine whether there is an air leakage problem. Aim the infrared thermal imager at the surface of the goaf, observe the significant temperature difference, compare the 3D point cloud coordinate offset, issue a warning to the main control center, and take blocking measures in a timely manner. The specific steps include:

[0141] Step 1: Aim the infrared thermal imager at the surface of the goaf and observe the significant temperature difference. The collected images will show areas of different colors, indicating the infrared energy intensity and temperature radiated by the area. Abnormal temperature areas indicate air leakage.

[0142] Step 2: Use 3D point cloud technology to obtain the 3D coordinate offset data of the goaf. When air leakage occurs in the goaf, air flow will cause a slight deformation of the goaf, resulting in the offset of the 3D coordinates. By comparing the initial 3D coordinates and the current 3D coordinates of the goaf, the deformation of the goaf can be determined to further identify whether there is an air leakage problem. The specific calculation formula is as follows:

[0143]

[0144] Where d represents the distance, (xi,yi,zi) represents the initial three-dimensional coordinates of the goaf, and (xc,yc,zc) represents the current three-dimensional coordinates of the goaf;

[0145] Step 3: After detecting an abnormal temperature area and comparing it with the three-dimensional point cloud data, according to the set threshold M, the distance d exceeds the threshold M, deformation occurs, and it is determined that there is an air leakage problem in the goaf. The infrared thermal imager sends a warning message to the main control center, and blocking measures are taken in a timely manner.

[0146] 106. Use rechargeable batteries as the power supply of the robot to provide endurance power for the entire safety inspection robot;

[0147] Furthermore, rechargeable batteries are used as the power supply of the robot to meet the power requirements of the drive motor, gimbal servo, and sensor input and output devices. When the robot runs to a low mileage state, the main control terminal power saving solution is implemented to complete automatic charging, which specifically includes the following contents:

[0148] S1. Energy-saving strategy: Power control is performed according to usage scenarios and needs, and the device automatically enters a low-power standby state during idle periods, and automatically senses the switch to ensure that the main control terminal is in an energy-saving state in an idle state, thereby reducing power consumption;

[0149] S2. Automatic charging system: Using inductive charging technology, the robot can be paired with the charging base station and docked accurately, realizing automatic charging without human intervention;

[0150] S3. Charging management: Through the intelligent scheduling algorithm, the power and charging status of the main control terminal are monitored. According to the power forecast and task requirements, the charging time and location are reasonably arranged to ensure that the main control terminal has sufficient power during operation. The steps for power scheduling using the greedy algorithm are as follows:

[0151] Step 1: Determine the greedy selection strategy: When selecting the next task to be executed at each location, select the task that consumes the least power;

[0152] Step 2: Initialize power and task list: Set the initial power and sort the tasks in ascending order of power consumption;

[0153] Step 3, traverse the task list: traverse the task list in the sorted order;

[0154] Step 4: Determine whether the task can be executed: For each task, determine whether the current remaining power is sufficient to execute it. If not, return to the charging station for charging;

[0155] Step 5. Continue traversal: Repeat the above steps until all tasks are traversed.

[0156] The formula in the present invention is a formula that removes the dimension and takes its numerical calculation. A large amount of data is collected and software simulation is performed to obtain a formula that is closest to the actual situation. The preset proportional coefficient in the formula is set by technicians in this field according to actual conditions or obtained through large amount of data simulation.

[0157] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0158] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-legged robot based on goaf air leakage monitoring, characterized in that: The specific steps include:

101. Through sensors and map building technology, autonomously plan routes, avoid obstacles, and accurately reach monitoring points; 102. Use underground environmental monitoring equipment to monitor the wind flow in the goaf area in real time and collect working environment data; 103. Through the API interface provided by the cloud platform, the air leakage monitoring data is transmitted to the control center client in real time; 104. According to the preset IoT inspection points in the mine, the robot takes them as the actual inspection target points, and realizes the inspection function between the target points; 105. Through environmental monitoring means, combined with the algorithm for determining air leakage in the goaf and the front-end detection robot, it is determined whether there is air leakage in the goaf; 106. Use rechargeable batteries as the power supply of the robot to provide power for the entire safety inspection robot.

2. A multi-legged robot based on goaf air leakage monitoring according to claim 1, characterized in that: In step 101, the multi-legged robot autonomously plans a path, avoids obstacles, and accurately reaches the monitoring point through sensors and map building technology. The specific calculation formula is as follows: Where r represents the distance from the laser radar to the obstacle, C represents the speed of light, and t represents the total time taken for the laser to be sent and received.

3. The multi-legged robot based on goaf air leakage monitoring according to claim 1 is characterized in that: In step 102, the multi-legged robot uses an ultrasonic laser sensor anemometer, a laser radar, a sensor, and an infrared imager to monitor the environment of the goaf in real time, collect working environment data, and determine the air leakage condition of the goaf. The specific calculation formula is as follows: Among them, x represents the median of each interval, μ represents the mean of the data, f represents the frequency of each interval, and N represents the total number of samples.

4. The multi-legged robot based on goaf air leakage monitoring according to claim 1 is characterized in that: In step 103, the multi-legged robot transmits the air leakage monitoring data to the control center client in real time through the API interface provided by the cloud platform for data sharing.

5. The multi-legged robot based on goaf air leakage monitoring according to claim 1 is characterized in that: In step 104, the preset IoT inspection point in the mine is used as the target point, and the path planning algorithm is used to generate the optimal inspection path for the robot. According to the positioning capability of the robot, the robot can accurately reach the inspection target point to reduce the risk of manual operation. The specific calculation formula is as follows: f(x)=g(x)+h(x) Among them, g(x) is the actual distance measure from the starting point to the current node x, and h(x) is the minimum distance estimate from node x to the end point.

6. The multi-legged robot based on goaf air leakage monitoring according to claim 1, characterized in that: In step 105, environmental data monitoring of multiple parameters is achieved through environmental monitoring means, and the algorithm for determining air leakage in the goaf and the front-end detection robot are combined to determine whether there is air leakage in the goaf. The specific calculation formula is as follows: Among them, d represents the distance, (xi,yi,zi) represents the initial three-dimensional coordinates of the goaf, and (xc,yc,zc) represents the current three-dimensional coordinates of the goaf.

7. The multi-legged robot based on goaf air leakage monitoring according to claim 1 is characterized in that: In step 106, a rechargeable battery is used as the power supply of the robot to meet the power demand of the output device. When the robot runs to a low cruising range state, the main control terminal power saving solution is implemented to complete automatic charging.

8. A multi-legged robot based on goaf air leakage monitoring is applied as described in claims 1-7, characterized in that: It includes autonomous navigation module, information collection module, communication interaction module, inspection planning module, air leakage determination module, and endurance management module; Autonomous navigation module: The multi-legged robot uses sensors and map building technology to autonomously plan paths, avoid obstacles, and accurately reach monitoring points; Information collection module: The multi-legged robot uses ultrasonic laser sensor anemometer, laser radar, sensor and infrared imager to monitor the goaf environment in real time, collect working environment data and determine the air leakage status of the goaf; Communication interaction module: The multi-legged robot transmits the air leakage monitoring data to the control center client in real time through the API interface provided by the cloud platform for data sharing; Inspection planning module: Taking the preset IoT inspection points in the mine as the target points, the robot is generated the optimal inspection path using the path planning algorithm. The robot can accurately reach the inspection target point based on its positioning capability, thus reducing the risk of manual operation. Air leakage determination module: Through environmental monitoring means, it realizes environmental data monitoring of multiple parameters, uses the infrared radiation changes of the infrared thermal imager, combines the algorithm for determining air leakage in the goaf with the front-end detection robot to compositely determine whether there is air leakage in the goaf; Endurance management module: Use rechargeable batteries as the power supply of the robot to meet the power demand of the output device. When the robot runs to a low endurance state, the main control terminal power saving solution is implemented and automatic charging is completed.

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