A method for locating fire sources in a warehouse fire inspection robot
By combining a GNSS navigation module and a laser rangefinder, and through three-dimensional Cartesian coordinate system transformation and Kalman filter optimization, the problem of insufficient fire source positioning accuracy and stability of the warehouse fire inspection robot was solved, achieving high-precision and reliable fire source positioning.
Patent Information
- Application Number
- CN202610145621.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-02-02
- Publication Date
- 2026-05-26
AI Technical Summary
Existing fire source location technology for warehouse fire inspection robots suffers from insufficient positioning accuracy and poor reliability. It is particularly difficult to achieve high accuracy and stability in complex environments, and existing technologies lack effective data optimization mechanisms.
The robot's three-dimensional geographic coordinates are obtained using a GNSS navigation module, and distance is measured by a laser rangefinder and attitude sensors. The absolute coordinates of the fire source are optimized through three-dimensional Cartesian coordinate system transformation and Kalman filtering. By fusing information from multiple sensors, positioning accuracy and stability are ensured.
It improves the accuracy and reliability of fire source location, enabling rapid and accurate identification of fire source areas in complex environments, reducing manual inspection costs, and meeting the needs of warehouse fire inspection.
Smart Images
Figure CN122085284A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of fire-fighting robot positioning technology, specifically relating to a fire source positioning method for a warehouse fire inspection robot. Background Technology
[0002] Warehouses, as centralized storage locations for goods, are characterized by a large amount of flammable materials and complex structures, making them prone to rapid fire spread and difficult to rescue after a fire breaks out. Current warehouse fire safety inspections mainly rely on regular manual inspections, supplemented by equipment such as smoke detectors and temperature sensors to monitor fire conditions. However, this approach suffers from problems such as high inspection costs, difficulty in controlling the frequency of inspections, and delayed detection of fire sources.
[0003] With the development of smart warehousing technology, automated and intelligent inspections have become an industry trend. Fire inspection robots are gradually being applied to warehouse scenarios. However, the existing fire source location technology of robots still has many shortcomings: some robots rely on a single sensor for fire source identification, which has weak anti-interference capabilities and significantly reduces positioning accuracy in complex lighting and obstacle-blocking environments in warehouses; some technologies lack a unified coordinate system, GNSS navigation modules are easily attenuated by warehouse building structures, and although laser rangefinders can achieve high-precision distance measurement, they cannot directly obtain absolute geographic coordinates. The data from both are difficult to effectively integrate, resulting in deviations in positioning results; in addition, sensor data in warehouses are easily affected by environmental noise, resulting in large data fluctuations. Existing technologies lack effective data optimization mechanisms, making it difficult to guarantee the stability and reliability of positioning results.
[0004] Therefore, there is an urgent need for a fire source localization method that integrates information from multiple sensors, has coordinate system transformation capabilities, and is highly resistant to interference, in order to improve the fire source localization accuracy and reliability of warehouse fire inspection robots. Summary of the Invention
[0005] To achieve the above objectives, the present invention employs the following technical solution: This invention provides a method for locating fire sources using a warehouse fire inspection robot, comprising the following steps: S1. The warehouse fire inspection robot cruises along a preset path, collects temperature field data and infrared light source signals in the inspection area in real time, and locks the fire source area. S2. Activate the GNSS navigation module on the warehouse fire inspection robot to obtain the robot's three-dimensional geographic coordinates. ; S3. Control the laser rangefinder on the warehouse fire inspection robot to align with the center of the fire source and calculate the robot's horizontal turning angle. and pitch angle The straight-line distance between the warehouse fire inspection robot and the fire source was measured using a laser rangefinder. ; S4. Establish a three-dimensional Cartesian GNSS rectangular coordinate system and a three-dimensional Cartesian laser rectangular coordinate system. The coordinate axes of the two coordinate systems are parallel to each other and have the same direction. S5. The relative coordinates of the fire source in the three-dimensional Cartesian laser rectangular coordinate system. Convert to absolute coordinates in a three-dimensional Cartesian GNSS rectangular coordinate system ; S6. Kalman filtering is used to optimize the absolute coordinates of the fire source, resulting in the final absolute coordinates of the fire source. ; S7. Feed back the final absolute coordinates of the fire source to the cloud server, trigger the alarm device, and output the positioning result.
[0006] Furthermore, when an abnormal temperature rise occurs in the temperature field and the flame sensor's detection signal exceeds a set threshold, a fire source is identified, the robot stops its patrol, and locks onto the fire source area. Through dual monitoring of temperature field data and infrared light source signals, along with a threshold determination mechanism, fire hazards can be quickly identified, avoiding false alarms or missed alarms from a single sensor, ensuring the accuracy of fire source area locking. At the same time, the robot stops its patrol in time, allowing for precise target positioning and reducing unnecessary inspection actions.
[0007] Furthermore, the robot's three-dimensional geographic coordinates are as follows: ,in, This indicates the robot's eastward coordinates in the GNSS coordinate system; This indicates the robot's north coordinates in the GNSS coordinate system; This indicates the robot's elevation coordinates in the GNSS coordinate system. The GNSS navigation module of this invention provides the robot with accurate three-dimensional reference coordinates in the global coordinate system, covering the three dimensions of east, north, and elevation. This provides a reliable spatial reference origin for the subsequent derivation of the absolute coordinates of the fire source, solving the problem that laser ranging alone cannot obtain the absolute position.
[0008] Furthermore, in obtaining the horizontal turning angle During the process, the robot's forward direction is taken as 0°, and clockwise is positive; when obtaining the pitch angle... During the process, the horizontal direction is taken as 0° and the upward direction is taken as positive.
[0009] Furthermore, the three-dimensional Cartesian GNSS rectangular coordinate system is... , , , These represent the X, Y, and Z axes of a three-dimensional Cartesian GNSS rectangular coordinate system; the origin of the three-dimensional Cartesian GNSS rectangular coordinate system. 3D geographic coordinates for the robot The three-dimensional Cartesian laser rectangular coordinate system is , , , These represent the X, Y, and Z axes of a three-dimensional Cartesian laser rectangular coordinate system; the origin of the three-dimensional Cartesian laser rectangular coordinate system. The laser rangefinder's transmission center is located at the origin of the three-dimensional Cartesian GNSS rectangular coordinate system. There is a fixed translation relationship, and the translation parameters are: ,in, express Compared to exist Translation distance along the axis; express Compared to exist Translation distance along the axis; express Compared to exist Translation distance along the axis. By constructing a dual coordinate system with parallel and consistent coordinate axes, the coordinate system differences between GNSS and laser ranging sensors are eliminated. Fixed translation parameters provide a clear quantitative basis for subsequent coordinate transformation, avoiding data conflicts or transformation errors caused by inconsistencies in coordinate systems.
[0010] Furthermore, based on the horizontal angle Pitch angle and straight-line distance Calculate the relative coordinates of the fire source in a three-dimensional Cartesian laser rectangular coordinate system. The formula is expressed as follows: , in, This indicates that the fire source is in a three-dimensional Cartesian laser rectangular coordinate system. The coordinates of the axis; This indicates that the fire source is in a three-dimensional Cartesian laser rectangular coordinate system. The coordinates of the axis; This indicates that the fire source is in a three-dimensional Cartesian laser rectangular coordinate system. The coordinates of the axis.
[0011] Furthermore, the relative coordinates are converted to absolute coordinates in a three-dimensional Cartesian GNSS rectangular coordinate system. The formula is expressed as follows: , in, This indicates that the fire source is in a three-dimensional Cartesian GNSS rectangular coordinate system. The coordinates of the axis; This indicates that the fire source is in a three-dimensional Cartesian GNSS rectangular coordinate system. The coordinates of the axis; This indicates that the fire source is in a three-dimensional Cartesian GNSS rectangular coordinate system. The coordinates of the axis.
[0012] This invention addresses sensor data fluctuations caused by changes in lighting and obstructions within a warehouse. Kalman filtering, through an iterative prediction-update mechanism, effectively suppresses interference from process and observation noise, reduces random errors in coordinate data, and improves the stability and accuracy of fire source coordinates, ensuring high reliability of the positioning results even in complex environments. Furthermore, the specific process for optimizing the absolute coordinates of the fire source is as follows: S61. Define the state vector: ,express The three-dimensional coordinates of the center of the fire source at any given moment; S62. Establish the state equations: ,in, The state transition matrix is represented using the identity matrix. ; The process noise is represented by a Gaussian distribution. , The process noise covariance matrix; Indicates the sampling time, which is the same as the sampling frequency of the GNSS navigation module; S63. Establish the observation equation: ,in, The observation matrix is represented using the identity matrix. ; The observation noise represents a Gaussian distribution. , To observe the noise covariance matrix; S64. Kalman filter iterative calculation: Prediction Step: , ; Update steps: , , ; in, Represents the observation noise covariance matrix; This represents the state estimate at time k; This represents the predicted state value at time k; This represents the state estimate at time k-1; This represents the predicted covariance matrix at time k; This represents the estimation of the covariance matrix at time k-1; Indicates the Kalman gain at time k; Denotes the observation vector at time k, i.e. ; This indicates the estimation of the covariance matrix at time k; Represents the identity matrix; This represents the vector multiplication operation; S65. After iterative convergence, output the final absolute coordinates of the optimized fire source. .
[0013] The advantages of this invention are: This invention effectively avoids false alarms or missed alarms from a single sensor by employing a dual monitoring and threshold determination mechanism based on temperature field data and infrared light source signals, ensuring the accuracy of fire source area location. It integrates the advantages of a GNSS navigation module, a laser rangefinder, and an attitude sensor. Using GNSS to acquire precise three-dimensional geographic coordinates of the robot as an absolute positioning reference, and leveraging the high-precision distance measurement and horizontal / elevation angle data from the laser rangefinder, combined with trigonometric formulas, it accurately calculates the relative coordinates of the fire source. Furthermore, by constructing a three-dimensional Cartesian GNSS rectangular coordinate system and a laser rectangular coordinate system with parallel and consistent coordinate axes, and utilizing fixed translation parameters, it achieves the conversion from relative to absolute coordinates. The system employs a conversion method to improve positioning accuracy. Addressing the issue of sensor data fluctuations in complex warehouse environments, a Kalman filter is used with a prediction-update iterative mechanism to effectively suppress interference from process and observation noise, significantly improving the stability and accuracy of fire source coordinates. Finally, the optimized absolute coordinates of the fire source are fed back to the cloud server to trigger an alarm, enabling rapid and accurate positioning results. This meets the needs of precise fire suppression in warehouse scenarios, significantly reduces manual inspection costs, and solves the problems of inconsistent coordinate systems, data redundancy conflicts, and insufficient positioning accuracy and stability in existing technologies. This significantly enhances the reliability and practicality of fire source positioning for warehouse fire inspection robots. Attached Figure Description
[0014] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.
[0015] Figure 1 This is a flowchart of the steps of the method of the present invention; Figure 2 This is a comparison curve of the positioning coordinate stability of the present invention; Figure 3 This represents the average positioning error for different positioning methods. Detailed Implementation
[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0017] Example 1 In this embodiment, as Figure 1 As shown, this invention provides a method for locating fire sources using a warehouse fire inspection robot, the specific steps of which include: Step 1: The warehouse fire inspection robot cruises along a preset path, collecting temperature field data and infrared light source signals in the inspection area in real time; when the temperature field shows an abnormal rise and the flame sensor detection signal exceeds the set threshold, it is determined that there is a fire source, the robot stops cruising and locks the fire source area. Specifically, the temperature field data of the inspection area is collected in real time by an infrared thermal imager, and the flame sensor simultaneously detects infrared light source signals with wavelengths of 760 to 1100 nm.
[0018] Step 2: Activate the GNSS navigation module on the warehouse fire inspection robot to obtain the robot's three-dimensional geographic coordinates. ; Specifically, the GNSS navigation module has a positioning accuracy of less than 1 meter and a sampling frequency of 10 Hz; the robot's three-dimensional geographic coordinates are as follows: ,in, This indicates the robot's eastward coordinates in the GNSS coordinate system, in meters (m). This indicates the robot's north coordinates in the GNSS coordinate system, in meters (m). This indicates the robot's elevation coordinates in the GNSS coordinate system, in meters (m).
[0019] Step 3: Control the laser rangefinder on the warehouse fire inspection robot to aim at the center of the fire source and calculate the robot's horizontal turning angle. and pitch angle The straight-line distance between the warehouse fire inspection robot and the fire source was measured using a laser rangefinder. ; Specifically, in response to the fire source lock signal, the robot gimbal adjusts its attitude via two-stage servo motors: the lower servo motor achieves 360° horizontal rotation, and the upper servo motor achieves vertical rotation, until the laser rangefinder is aligned with the center of the fire source; the horizontal rotation angle of the gimbal is collected by the MPU6050 gyroscope. and pitch angle Using an LDS1000M laser rangefinder, infrared laser pulses are emitted towards the fire source to measure the straight-line distance L between the robot and the fire source (unit: m). The measurement accuracy is ±2mm, and the measurement range is 0.1~100m. High-precision measurements of horizontal rotation and elevation angles accurately describe the azimuth angle of the fire source relative to the robot, while the high-precision distance measurement of the laser rangefinder obtains the straight-line distance between the robot and the fire source. The combination of these two measurements provides core parameters for calculating the relative coordinates of the fire source, significantly reducing measurement errors in the azimuth and distance dimensions.
[0020] Obtaining horizontal angle During the process, the robot's forward direction is taken as 0°, clockwise is positive, the range is 0° to 360°, and the accuracy is ±0.1°; when obtaining the pitch angle... During the process, the horizontal direction is taken as 0°, and the upward direction is positive, with a range of -30° to 90° and an accuracy of ±0.1°.
[0021] Step 4: Establish a three-dimensional Cartesian GNSS rectangular coordinate system and a three-dimensional Cartesian laser rectangular coordinate system. The coordinate axes of the two coordinate systems are parallel to each other and have the same direction. Specifically, the three-dimensional Cartesian GNSS rectangular coordinate system is , , , These represent the X, Y, and Z axes of a three-dimensional Cartesian GNSS rectangular coordinate system; the origin of the three-dimensional Cartesian GNSS rectangular coordinate system. 3D geographic coordinates for the robot The three-dimensional Cartesian laser rectangular coordinate system is , , , These represent the X, Y, and Z axes of a three-dimensional Cartesian laser rectangular coordinate system; the origin of the three-dimensional Cartesian laser rectangular coordinate system. The laser rangefinder's transmission center is located at the origin of the three-dimensional Cartesian GNSS rectangular coordinate system. There is a fixed translation relationship, and the translation parameters are: ,in, express Compared to exist Translation distance along the axis, in meters (m). express Compared to exist Translation distance along the axis, in meters (m). express Compared to exist Translation distance along the axis, in meters; translation parameters remain constant. According to the horizontal angle Pitch angle and straight-line distance Calculate the relative coordinates of the fire source in a three-dimensional Cartesian laser rectangular coordinate system. The formula is expressed as follows: , in, This indicates that the fire source is in a three-dimensional Cartesian laser rectangular coordinate system. The coordinates of the axis, in meters (m). This indicates that the fire source is in a three-dimensional Cartesian laser rectangular coordinate system. The coordinates of the axis, in meters (m). This indicates that the fire source is in a three-dimensional Cartesian laser rectangular coordinate system. The coordinates of the axis, in meters (m).
[0022] Step 5: Position the fire source relative to the laser in a three-dimensional Cartesian laser rectangular coordinate system. Convert to absolute coordinates in a three-dimensional Cartesian GNSS rectangular coordinate system The formula is expressed as follows: , in, This indicates that the fire source is in a three-dimensional Cartesian GNSS rectangular coordinate system. The coordinates of the axis, in meters (m). This indicates that the fire source is in a three-dimensional Cartesian GNSS rectangular coordinate system. The coordinates of the axis, in meters (m). This indicates that the fire source is in a three-dimensional Cartesian GNSS rectangular coordinate system. The coordinates of the axis, in meters (m).
[0023] This invention uses trigonometric function formulas to accurately derive relative coordinates in the laser coordinate system. Then, by combining the robot's reference coordinates with fixed translation parameters, it achieves the conversion from relative coordinates to GNSS absolute coordinates. This retains the high-precision advantage of laser ranging while using GNSS to achieve absolute geographic positioning, thus improving positioning accuracy.
[0024] Step Six: To reduce environmental interference, such as positioning errors caused by laser reflection deviation and GNSS signal jitter, Kalman filtering is used to optimize the absolute coordinates of the fire source; the specific process is as follows: (1) Define the state vector ,express The three-dimensional coordinates of the center of the fire source at any given moment; (2) Establish the state equations: ,in, The state transition matrix is represented using the identity matrix. ; The process noise is represented by a Gaussian distribution. , The process noise covariance matrix was obtained through experimental calibration. Indicates the sampling time, which is the same as the sampling frequency of the GNSS navigation module; (3) Establish the observation equation: ,in, The observation matrix is represented using the identity matrix. This indicates that the observed value is directly related to the state vector; The observation noise represents a Gaussian distribution. , To observe the noise covariance matrix, the sensor accuracy is calibrated. (4) Kalman filter iterative calculation: Prediction Step: , ; Update steps: , , ; in, Represents the observation noise covariance matrix; This represents the state estimate at time k; This represents the predicted state value at time k, based on the state at the previous time. The prediction results do not incorporate the observation vector at time k. ; This represents the state estimate at time k-1; This represents the predicted covariance matrix at time k; This represents the estimation of the covariance matrix at time k-1; Indicates the Kalman gain at time k; Denotes the observation vector at time k, i.e. ; This represents the estimated covariance matrix at time k, used to describe... The uncertainty is updated iteratively and then stored as input for the next time step; Represents the identity matrix; This represents the vector multiplication operation; (5) After iterative convergence, output the final absolute coordinates of the optimized fire source. .
[0025] Step 7: Set the final absolute coordinates of the fire source. The system sends feedback to the cloud server, triggering an alarm and outputting the location result.
[0026] Example 2 In this embodiment, as Figure 2As shown, the horizontal axis represents time (seconds), and the vertical axis represents the X-axis coordinate deviation of the fire source (cm). The curves correspond to the fusion positioning method of this invention with and without Kalman filtering. The experimental scenario was set in a complex environment inside a warehouse simulating sudden changes in light and temporary obstruction by obstacles. Positioning data was continuously collected for 30 seconds, with the sampling frequency consistent with the GNSS module. The curve without Kalman filtering showed significant fluctuations, with the coordinate deviation surging to 35 cm at the moment of sudden light change, and remaining at 25-30 cm during the period of obstacle obstruction. The overall fluctuation range was large, and the data stability was poor. In contrast, the curve of the method of this invention remained stable throughout, with a maximum deviation of no more than 5 cm. Even during periods of interference, there were no significant sudden changes, and the deviation quickly converged to a stable range. The Kalman filtering mechanism introduced in this invention can effectively suppress sensor data fluctuations caused by environmental factors such as changes in light and obstacle obstruction inside the warehouse, reduce interference from process noise and observation noise, significantly improve the stability of fire source positioning coordinates, avoid the expansion of positioning deviation due to data fluctuations, and ensure the reliability of positioning results in complex environments.
[0027] Example 3 In this embodiment, the experimental scenario is a 1000㎡ standard warehouse, including shelving obstruction and a multi-light environment. A comparison is made between the laser ranging positioning method alone, the fusion positioning method without Kalman filtering, and the positioning method of this invention. Each method is measured 50 times and the average value is taken. Figure 3 As shown, the horizontal axis represents the three types of method objects, and the vertical axis represents the average positioning error on the X-axis, in centimeters. Experimental data shows that: laser ranging positioning alone cannot obtain absolute geographic coordinates and relies on relative position calculation, with an average error of 31.7 cm; although fusion positioning without Kalman filtering integrates data from two types of sensors, it is affected by environmental noise and coordinate system differences, resulting in an average error of 15.9 cm; the method of this invention, through the unification of three-dimensional Cartesian GNSS and laser rectangular coordinate systems, accurate transformation of relative coordinates using trigonometric functions, and noise optimization using Kalman filtering, achieves an average error of only 4.8 cm. This invention, through multi-sensor fusion, coordinate system unification, and data optimization, significantly reduces positioning errors, with accuracy far exceeding existing single-sensor or partial fusion technologies, meeting the core requirement of precise positioning accuracy for accurate fire suppression in warehouse scenarios.
[0028] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for locating fire sources in a warehouse fire inspection robot, characterized in that, Includes the following steps: S1. The warehouse fire inspection robot cruises along a preset path, collects temperature field data and infrared light source signals in the inspection area in real time, and locks the fire source area. S2. Activate the GNSS navigation module on the warehouse fire inspection robot to obtain the robot's three-dimensional geographic coordinates. ; S3. Control the laser rangefinder on the warehouse fire inspection robot to align with the center of the fire source and calculate the robot's horizontal turning angle. and pitch angle The straight-line distance between the warehouse fire inspection robot and the fire source was measured using a laser rangefinder. ; S4. Establish a three-dimensional Cartesian GNSS rectangular coordinate system and a three-dimensional Cartesian laser rectangular coordinate system. The coordinate axes of the two coordinate systems are parallel to each other and have the same direction. S5. The relative coordinates of the fire source in the three-dimensional Cartesian laser rectangular coordinate system. Convert to absolute coordinates in a three-dimensional Cartesian GNSS rectangular coordinate system ; S6. Kalman filtering is used to optimize the absolute coordinates of the fire source, resulting in the final absolute coordinates of the fire source. ; S7. Feed back the final absolute coordinates of the fire source to the cloud server, trigger the alarm device, and output the positioning result.
2. The fire source location method for a warehouse fire inspection robot according to claim 1, characterized in that, When the temperature field shows an abnormal rise and the flame sensor detects a signal exceeding the set threshold, it is determined that there is a fire source, the robot stops patrolling and locks onto the fire source area.
3. A method for locating fire sources for a warehouse fire inspection robot according to claim 2, characterized in that, The robot's three-dimensional geographic coordinates are: ,in, This indicates the robot's eastward coordinates in the GNSS coordinate system; This indicates the robot's north coordinates in the GNSS coordinate system; This indicates the robot's elevation coordinates in the GNSS coordinate system.
4. A method for locating a fire source for a warehouse fire inspection robot according to claim 3, characterized in that, Obtaining horizontal angle During the process, the robot's forward direction is taken as 0°, and clockwise is positive; when obtaining the pitch angle... During the process, the horizontal direction is taken as 0° and the upward direction is taken as positive.
5. A method for locating a fire source for a warehouse fire inspection robot according to claim 4, characterized in that, The three-dimensional Cartesian GNSS rectangular coordinate system is , , , These represent the X, Y, and Z axes of a three-dimensional Cartesian GNSS rectangular coordinate system; the origin of the three-dimensional Cartesian GNSS rectangular coordinate system. 3D geographic coordinates for the robot The three-dimensional Cartesian laser rectangular coordinate system is , , , These represent the X, Y, and Z axes of a three-dimensional Cartesian laser rectangular coordinate system; the origin of the three-dimensional Cartesian laser rectangular coordinate system. The laser rangefinder's transmission center is located at the origin of the three-dimensional Cartesian GNSS rectangular coordinate system. There is a fixed translation relationship, and the translation parameters are: ,in, express Compared to exist Translation distance along the axis; express Compared to exist Translation distance along the axis; express Compared to exist Translation distance along the axis.
6. A method for locating a fire source for a warehouse fire inspection robot according to claim 5, characterized in that, According to the horizontal angle Pitch angle and straight-line distance Calculate the relative coordinates of the fire source in a three-dimensional Cartesian laser rectangular coordinate system. The formula is expressed as follows: ， in, This indicates that the fire source is in a three-dimensional Cartesian laser rectangular coordinate system. The coordinates of the axis; This indicates that the fire source is in a three-dimensional Cartesian laser rectangular coordinate system. The coordinates of the axis; This indicates that the fire source is in a three-dimensional Cartesian laser rectangular coordinate system. The coordinates of the axis.
7. A fire source location method for a warehouse fire inspection robot according to claim 6, characterized in that, Convert relative coordinates to absolute coordinates in a three-dimensional Cartesian GNSS rectangular coordinate system. The formula is expressed as follows: , in, This indicates that the fire source is in a three-dimensional Cartesian GNSS rectangular coordinate system. The coordinates of the axis; This indicates that the fire source is in a three-dimensional Cartesian GNSS rectangular coordinate system. The coordinates of the axis; This indicates that the fire source is in a three-dimensional Cartesian GNSS rectangular coordinate system. The coordinates of the axis.
8. A method for locating a fire source for a warehouse fire inspection robot according to claim 7, characterized in that, The specific process for optimizing the absolute coordinates of the fire source is as follows: S61. Define the state vector: ,express The three-dimensional coordinates of the center of the fire source at any given moment; S62. Establish the state equations: ,in, The state transition matrix is represented using the identity matrix. ; The process noise is represented by a Gaussian distribution. , The process noise covariance matrix; Indicates the sampling time, which is the same as the sampling frequency of the GNSS navigation module; S63. Establish the observation equation: ,in, The observation matrix is represented using the identity matrix. ; The observation noise represents a Gaussian distribution. , To observe the noise covariance matrix; S64. Kalman filter iterative calculation: Prediction Step: , ; Update steps: , , ; in, Represents the observation noise covariance matrix; This represents the state estimate at time k; This represents the predicted state value at time k; This represents the state estimate at time k-1; This represents the predicted covariance matrix at time k; This represents the estimation of the covariance matrix at time k-1; Indicates the Kalman gain at time k; Denotes the observation vector at time k, i.e. ; This indicates the estimation of the covariance matrix at time k; Represents the identity matrix; This represents the vector multiplication operation; S65. After iterative convergence, output the final absolute coordinates of the optimized fire source. .