Prism reference point-based in-tunnel unmanned aerial vehicle and robot dog combined positioning method and system

Abstract

The invention discloses an in-tunnel unmanned aerial vehicle and robot dog combined positioning method and system based on prism reference points, and belongs to the technical field of information processing. The method comprises the following steps: constructing a tunnel prism reference point array, acquiring an initial reflection signal, and preprocessing the initial reflection signal to obtain a processed signal set; determining a signal dynamic threshold, and obtaining a reference point set based on the signal dynamic threshold; acquiring reflected signal data of the unmanned aerial vehicle and the robot dog through the reference point set, and extracting reflected signal features; determining initial position distribution of the unmanned aerial vehicle and the robot dog based on the reflected signal features; obtaining dynamic reflection data of the unmanned aerial vehicle and the robot dog on the basis of the initial position distribution, and processing the dynamic reflection data to obtain a smooth position sequence; calculating a relative motion trend based on the smooth position sequence and motion difference characteristics of the unmanned aerial vehicle and the robot dog; the position consistency of the unmanned aerial vehicle and the robot dog is determined based on the relative movement trend; and determining real-time positioning coordinates of the unmanned aerial vehicle and the robot dog based on the position consistency.

Description

Technical Field

[0001] The present invention belongs to the technical field of information processing, and particularly relates to a method and system for joint positioning of an unmanned aerial vehicle (UAV) and a legged robot in a tunnel based on prism reference points. Background Art

[0002] In complex underground environments such as tunnels, the joint positioning technology of UAVs and legged robots is of crucial significance for ensuring efficient collaborative operations. Research in this field not only promotes the application of intelligent robot systems in unmanned environments but also provides key technical support for scenarios such as disaster rescue and underground exploration. However, existing positioning methods face significant limitations in tunnel environments. Traditional GPS-based positioning technology almost completely fails when signals are blocked in tunnels, while solutions based on inertial navigation or visual SLAM are prone to error accumulation during long-term operation or in complex dynamic environments, making it difficult to meet the requirements of high precision and stability. In addition, single-device positioning is difficult to achieve multi-device collaboration, restricting task efficiency and coverage.

[0003] In the joint positioning method based on prism reference points, the core challenges focus on how to efficiently and stably track prism reflection signals and convert them into accurate positioning data. First, the deployment of prisms and signal capture need to remain stable in the complex tunnel environment to avoid signal loss caused by light interference or device movement. Second, the significant differences in the motion characteristics of UAVs and legged robots make it a technical difficulty to achieve collaborative positioning of the two through prism reference points and ensure real-time performance and consistency. Finally, the system needs to quickly process the relative position changes of reflected signals with limited computing resources to meet the real-time positioning requirements. These unresolved technical factors lead to fluctuations in positioning accuracy and insufficient multi-device collaboration efficiency, thereby affecting the execution effect of complex tasks.

[0004] Therefore, how to achieve efficient signal tracking and collaborative positioning of UAVs and legged robots through prism reference points in a tunnel environment has become a key problem that urgently needs to be solved. Summary of the Invention

[0005] The present invention aims to solve the deficiencies of the prior art and proposes a method for joint positioning of an unmanned aerial vehicle and a legged robot in a tunnel based on prism reference points, including the following steps:

[0006] S1. Construct a tunnel prism reference point array, obtain initial reflection signals based on the prism reference point array, preprocess the initial reflection signals to obtain a set of processed signals; combine the distribution characteristics of tunnel light interference to determine a signal dynamic threshold, and obtain a reference point set based on the set of processed signals and the signal dynamic threshold;

[0007] S2. Obtain the reflected signal data of the drone and the robotic dog through the reference point set, and extract the reflected signal features; based on the reflected signal features, determine the initial position distribution of the drone and the robotic dog;

[0008] S3. Obtain the dynamic reflected data of the drone and the robotic dog based on the initial position distribution, process the dynamic reflected data, and obtain a smooth position sequence;

[0009] S4. Calculate the relative motion trend based on the smooth position sequence and the motion difference characteristics of the drone and the robotic dog; and determine the position consistency of the drone and the robotic dog based on the relative motion trend;

[0010] S5. Determine the real-time positioning coordinates of the drone and the robotic dog based on the position consistency.

[0011] Further preferably, S2 includes the following steps:

[0012] S21. Obtain the reflected signal data of the drone and the robotic dog, and perform feature extraction on the reflected signal data to obtain the reflected signal features; determine the distribution characteristics of signal intensity and signal distance based on the reflected signal features;

[0013] S22. Based on the distribution characteristics of the signal intensity and the signal distance, use the triangulation method to calculate the preliminary position estimation values of the drone and the robotic dog;

[0014] S23. Correct the preliminary position estimation values, calculate the matching degree between the corrected preliminary position estimation values and the tunnel environment, and obtain the initial position distribution.

[0015] Further preferably, S3 includes the following steps:

[0016] S31. Obtain the dynamic reflected data of the drone and the robotic dog, and use the Kalman filter algorithm to fuse time series to determine the position estimation values;

[0017] S32. Based on the position estimation values, determine the dynamic change characteristics caused by movement; judge the continuity of the movement trajectory based on the dynamic change characteristics;

[0018] When the continuity of the movement trajectory meets the preset threshold, optimize the position estimation values to obtain an updated position sequence; when the continuity of the movement trajectory does not meet the preset threshold, return to S31;

[0019] S34. Calculate the trajectory smoothness based on the updated position sequence, and adjust the parameters of the Kalman filter algorithm based on the trajectory smoothness, and then obtain the smooth position sequence.

[0020] Further preferably, the method for calculating the relative motion trend based on the smooth position sequence and the motion difference characteristics of the drone and the robot dog includes the following steps:

[0021] S41. Extract the motion trajectory points of the drone and the robot dog according to the smooth position sequence, and determine the motion characteristics of the drone and the robot dog based on the motion trajectory points;

[0022] S42. Calculate the displacement difference between the drone and the robot dog at each time point based on the motion characteristics, and then obtain the velocity vector;

[0023] S43. Calculate the direction vector based on the angular change of the velocity vector to obtain the motion directions of the drone and the robot dog; and obtain the relative motion trend based on the motion directions.

[0024] Further preferably, the method for determining the position consistency of the drone and the robot dog based on the relative motion trend includes:

[0025] S44. Obtain a preliminary position result set based on the acquired real-time reflection signal data and the relative motion trend;

[0026] S45. Extract the reference coordinate data from the preliminary position result set to determine the relative position distribution of the drone and the robot dog;

[0027] S46. According to the relative position distribution, perform signal fusion processing on the real-time reflection signal to obtain an adjusted position solution result; judge the deviation between the adjusted position solution result and the relative motion trend. When the deviation exceeds the set deviation threshold, perform signal fusion processing again; when it is lower than the set deviation threshold, obtain the updated position data of the drone and the robot dog, and then obtain the real-time position relationship between the drone and the robot dog;

[0028] S47. Optimize the motion trend based on the real-time position relationship to obtain an optimized position solution result; and determine the position consistency of the drone and the robot dog based on the optimized position solution result.

[0029] Further preferably, the method for determining the position consistency of the drone and the robot dog includes:

[0030] S471. Extract the signal tracking deviation values from the optimized position solution result to obtain a deviation value sequence;

[0031] S472. Compare the values in the deviation value sequence with a preset deviation threshold. When it exceeds the deviation threshold, determine the signal segment that needs to be resampled to obtain resampled data; otherwise, judge that the positions of the drone and the robot dog are consistent;

[0032] S473. Extract features from the resampled data to obtain a signal feature set; and perform smoothing processing on the signal features to obtain smoothed feature data;

[0033] S474. Calculate a position correction amount based on the smoothed feature data to obtain position correction data; input the position correction data into S471 to extract the signal tracking deviation value.

[0034] The present invention also proposes a combined positioning system for an unmanned aerial vehicle and a robotic dog in a tunnel based on a prism reference point, which is used to implement the above method, including: a reference point acquisition module, a feature extraction module, a processing module, a position calculation module, and a positioning module;

[0035] The reference point acquisition module is used to construct a tunnel prism reference point array, obtain an initial reflection signal based on the prism reference point array, preprocess the initial reflection signal to obtain a set of processed signals; combine the tunnel light interference distribution characteristics to determine a signal dynamic threshold, and based on the set of processed signals and the signal dynamic threshold, obtain a reference point set;

[0036] The feature extraction module obtains the reflection signal data of the unmanned aerial vehicle and the robotic dog through the reference point set, and extracts the reflection signal features; based on the reflection signal features, determine the initial position distribution of the unmanned aerial vehicle and the robotic dog;

[0037] The processing module is used to obtain the dynamic reflection data of the unmanned aerial vehicle and the robotic dog based on the initial position distribution, process the dynamic reflection data to obtain a smoothed position sequence;

[0038] The position calculation module is used to calculate the relative motion trend based on the smoothed position sequence and the motion difference characteristics of the unmanned aerial vehicle and the robotic dog; and determine the position consistency of the unmanned aerial vehicle and the robotic dog based on the relative motion trend;

[0039] The positioning module is used to determine the real-time positioning coordinates of the unmanned aerial vehicle and the robotic dog based on the position consistency.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] The present invention obtains initial reflected signal data by presetting a prism reference point array, determines the coordinates of stable reference points according to the characteristics of light interference, processes the signal data using the triangulation method to initially determine the distribution of device positions, uses the Kalman filtering algorithm to fuse time series data to obtain smooth position updates, calculates the velocity direction vector based on the differences in device motion characteristics to judge the relative motion trend, determines the consistent position by fusing real-time signal changes, and when the deviation exceeds the threshold, uses particle filter resampling to optimize the positioning. Finally, the processing frequency is adjusted according to the calculation resource limitations to output real-time positioning coordinates. The present invention solves the problem of collaborative positioning of multiple devices in the complex environment of tunnels, and improves the positioning accuracy and real-time performance. Brief Description of the Drawings

[0042] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0043] Figure 1 It is a schematic flow chart of a method for joint positioning of an unmanned aerial vehicle and a robot dog in a tunnel based on a prism reference point proposed in an embodiment of the present invention. Detailed Embodiments

[0044] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] In order to make the above objects, features, and advantages of the present invention more obvious and understandable, the present invention will be further described in detail below with reference to the drawings and specific embodiments.

[0046] Embodiment 1:

[0047] As Figure 1 shown, this embodiment proposes a method for joint positioning of an unmanned aerial vehicle and a robot dog in a tunnel based on a prism reference point, including the following steps:

[0048] S1. Construct a tunnel prism reference point array, obtain an initial reflected signal based on the prism reference point array, preprocess the initial reflected signal to obtain a set of processed signals, determine a signal dynamic threshold in combination with the tunnel light interference distribution characteristics, and obtain a reference point set based on the set of processed signals and the signal dynamic threshold.

[0049] Specifically, a preset prism reference point array is used to capture optical signals in the environment by utilizing the high reflectivity of the prism. In one implementation, in a tunnel scenario, the prism array is deployed on the tunnel wall, with a prism set every 5 meters to collect the reflected light emitted by the laser rangefinder. In the initially acquired reflected signals, there are both valid reflected signals and background light noise. Therefore, a time-domain analysis method is used to process the initial reflected signals through a time window to separate the valid signals and noise, obtaining a set of processed signals. If there is high-frequency interference or the like in the set of processed signals, filtering is performed using a filter. For example, inside a tunnel, electrical equipment may introduce interference above 50KHz, and the cut-off frequency of a low-pass filter can be set to retain the low-frequency part. The filtered signal is smoother, and the noise spikes are suppressed.

[0050] Combined with the distribution characteristics of light interference in the tunnel environment, the mean value and variance of the signal intensity are calculated to determine the dynamic threshold. For example, if the mean value of the filtered signal intensity is 100 units and the variance is 15 units, the dynamic threshold can be set to the mean value plus twice the variance, that is, 130 units. This method adapts to light changes and avoids misjudgment by a fixed threshold. The application of the dynamic threshold improves the robustness of signal screening.

[0051] For the dynamic threshold range, the reflection points with signal intensity exceeding the dynamic threshold are screened one by one to obtain a set of reference points. When screening, the time stamp can be combined to exclude abnormally dense interference points, which can further improve the reliability of the reference points. Cluster analysis is used to group adjacent reflection points in the set of reference points to obtain a stable set of reference points. For example, using the DBSCAN clustering algorithm, with the neighborhood radius set to 0.5 meters and the minimum number of points set to 3, 200 reference points are divided into 10 clusters, and each cluster represents a stable reflection area. Cluster analysis effectively eliminates isolated points and enhances the stability of the set of reference points.

[0052] The spatial coordinates of the reference points can be calculated using a geometric positioning algorithm. For example, based on the triangulation method, using the relative positions of the laser rangefinder and the prism, the coordinates of a certain reference point are calculated as (10, 2, 1.5) meters. The final set of position coordinates contains the three-dimensional coordinates of all reference points, forming a high-precision positioning framework inside the tunnel. This positioning accuracy can reach the centimeter level, providing reliable data support for tunnel monitoring.

[0053] Through signal acquisition, filtering, threshold screening, clustering, and positioning in sequence, the data quality and positioning accuracy are ensured. Especially in the complex light tunnel environment, the combination of the dynamic threshold and cluster analysis significantly improves the anti-interference ability. The final set of coordinates can be used for tunnel deformation monitoring or automatic navigation, greatly enhancing safety and efficiency.

[0054] S2. Obtain the reflection signal data of the drone and the robotic dog through the reference point set, and extract the reflection signal features; based on the reflection signal features, determine the initial position distribution of the drone and the robotic dog.

[0055] Further implementation lies in that S2 includes the following steps:

[0056] S21. Obtain the reflection signal data of the drone and the robotic dog, perform feature extraction on the reflection signal data to obtain the reflection signal features; based on the reflection signal features, determine the distribution characteristics of the signal strength and the signal distance.

[0057] Obtain the reflection signal data of the drone and the robotic dog through the reference point coordinate set, where the reflection signal data usually contains information such as time and intensity. Use feature extraction technology to process the reflection signal data and determine the distribution characteristics of the signal strength and the signal distance.

[0058] S22. Based on the distribution characteristics of the signal strength and the signal distance, use the triangulation method to calculate the preliminary position estimation values of the drone and the robotic dog.

[0059] S23. Correct the preliminary position estimation values, calculate the matching degree between the corrected preliminary position estimation values and the tunnel environment, and obtain the initial position distribution.

[0060] Exemplarily, if a reference point is 50 meters away from the drone, the round-trip time of the reflected signal is approximately 0.33 microseconds, and the signal strength is weakened due to the reflection of the tunnel wall. This signal characteristic provides basic data for subsequent analysis. When using feature extraction technology to process the reflected signal and determine the distribution characteristics of signal strength and signal distance. If the reflected signals come from multiple directions, the feature extraction identifies the main angular range, such as 30 degrees to 45 degrees, and calculates the distance by combining the signal propagation time. Assuming the signal speed is the speed of light, a time difference of 0.5 microseconds corresponds to approximately 75 meters. This distribution characteristic helps to clarify the signal source direction and distance range, thereby improving the positioning accuracy. If the angles of the drone signals measured at reference points A and B are 40 degrees and 50 degrees respectively, and the distance between the two points is 100 meters, the position of the drone can be initially estimated to be approximately 60 meters in front of the line connecting the two points through geometric relationships. Similarly, the position of the robot dog can also be deduced from another set of signal data. This method is simple and intuitive and is suitable for regular environments in the tunnel. When adjusting the initial estimate value through the constraint conditions of the tunnel environment to obtain the corrected position distribution data. The constraint conditions include the tunnel width, tunnel height, etc. For example, if the tunnel width is only 10 meters and the initial estimate shows that the drone is at 15 meters, which is obviously out of range, the position can be corrected to within the boundary at this time, such as adjusted to 9 meters. The corrected data is more in line with the actual environment, avoiding obvious errors and helping to improve the positioning reliability. If the deviation between the corrected position distribution data and the reference point coordinates exceeds the preset threshold, such as the threshold is 2 meters and the deviation between the corrected position and the reference point reaches 3 meters, then recalculate the reflected signal characteristics. By re-analyzing the intensity distribution of the reflected signal, it is found that the data deviation is caused by interference at the tunnel corner, and the deviation can be reduced to within 1.5 meters after adjustment. This iterative optimization ensures data consistency. Comparing the reflected signal characteristics with the tunnel environment, such as a certain section of the tunnel has a metal wall surface and the reflected signal strength is extremely high, which is consistent with the corresponding environmental results. Then it is judged that this is the final position. Extract the key point coordinates from the final position distribution to determine the relative position relationship between the two devices in the tunnel environment. For example, if the coordinates of the drone are (50, 5, 3) and the robot dog is (45, 6, 1), it can be inferred that the drone is 5 meters in front of the robot dog and the height difference is 2 meters. Such a relative relationship can be used to coordinate the actions of the two devices, such as the drone guiding the robot dog to avoid obstacles. Such analysis results provide a clear spatial basis for collaborative tasks in the tunnel.

[0061] S3. Obtain the dynamic reflection data of the drone and the robot dog based on the initial position distribution, process the dynamic reflection data, and obtain a smooth position sequence.

[0062] A further implementation is that S3 includes the following steps:

[0063] S31. Obtain the dynamic reflection data of the drone and the robot dog, and use the Kalman filter algorithm to fuse the time series to determine the position estimate value.

[0064] The drone or robotic dog emits ultrasonic signals in the tunnel. After the receiving device receives the signals, a time series is formed, and the duration from signal emission to return is recorded. For example, if the measurement interval is 0.1 second each time and data is continuously collected for 10 seconds, 100 data points are obtained. These data points are used to reflect the distance relationship between the drone or robotic dog and the surrounding environment, forming a rough position distribution.

[0065] For example, if the initial predicted position is (11, 21, 5.5) and the measured value is (12, 22, 6), the smoothed position estimate of (11.8, 21.8, 5.9) is obtained after fusion. This can effectively reduce noise interference and improve position stability. The position estimate can be used to analyze dynamic change characteristics.

[0066] S32. Based on the position estimate, determine the dynamic change characteristics caused by movement; based on the dynamic change characteristics, judge the continuity of the movement trajectory.

[0067] For example, if 10 consecutive position estimates show that the drone is moving in a straight line with an average displacement of about 0.5 meters per second, then it is judged that its movement is continuous. Specifically, continuity can be evaluated by calculating the distance difference between adjacent points. If the displacement difference each time is less than 1 meter, it is considered to meet the condition. Through the judgment of continuity, the authenticity of the device's movement can be determined to avoid misjudgment.

[0068] S33. When the continuity of the movement trajectory meets the preset threshold, optimize the position estimate to obtain an updated position sequence; when the continuity of the movement trajectory does not meet the preset threshold, return to S31.

[0069] When the continuity of the movement trajectory meets the standard, perform low-pass filtering on the dynamic reflection data to remove high-frequency noise, retain the main trend, and generate an updated position sequence. This can more clearly show the dynamic change trend of the position distribution, such as the trajectory of the drone moving smoothly along the tunnel axis.

[0070] S34. Calculate the trajectory smoothness based on the updated position sequence, and adjust the parameters of the Kalman filter algorithm based on the trajectory smoothness, thereby obtaining a smoothed position sequence.

[0071] Extract the trend from the updated position sequence and calculate the smoothness. The smoothness can be measured by the change in curvature. If the curvature remains below 0.1, it indicates that the trajectory is smooth. Adjust the Kalman filter parameters according to the smoothing result, such as increasing the process noise covariance, etc., to generate a smoothed position sequence.

[0072] S4. Calculate the relative movement trend based on the smoothed position sequence and the motion difference characteristics of the drone and the robotic dog; and determine the position consistency of the drone and the robotic dog based on the relative movement trend.

[0073] A further implementation lies in that the method for calculating the relative motion trend based on the smooth position sequence and the motion difference characteristics of the drone and the robotic dog includes the following steps:

[0074] S41. Extract the motion trajectory points of the drone and the robotic dog from the smooth position sequence, and determine the motion characteristics of the drone and the robotic dog respectively based on the motion trajectory points.

[0075] S42. Calculate the displacement differences of the drone and the robotic dog at each time point based on the motion characteristics, and then obtain the velocity vectors.

[0076] S43. Calculate the direction vectors based on the angular changes of the velocity vectors to obtain the motion directions of the drone and the robotic dog; and obtain the relative motion trend based on the motion directions.

[0077] Extract the motion trajectory points of the drone and the robotic dog from the smooth position sequence, and record the coordinates of the drone and the robotic dog at time t1 respectively. Then calculate the displacement differences between the drone and the robotic dog from time t1 to time t2. For example, at t1, the coordinates of the drone and the robotic dog are (10, 20, 5) and (8, 15, 0) respectively; at t2, the drone and the robotic dog move to (12, 22, 3) and (10, 17, 0) respectively, and the displacement differences are (2, 2, -2) and (2, 2, 0) respectively. Through the displacement differences, it is found that the drone dives downward, while the robotic dog moves horizontally.

[0078] Calculate the velocity components according to the displacement differences. The magnitude of the velocity components reflects the displacement speed of the drone and the robotic dog, and the angle indicates the directionality. If the velocity vector of the drone is (2, 2, -2) and that of the robotic dog is (1, 1, 0), by calculating the direction vector through the angular change, it is found that the drone dives downward, while the robotic dog moves horizontally. This difference helps to judge the relative motion relationship. If the modulus difference between the velocity vector and the direction vector exceeds the preset threshold, the characteristic difference is calculated through the vector inner product. Assuming that the inner product result shows that the included angle between the motion directions of the drone and the robotic dog is 45 degrees, it indicates that the two are not completely synchronized. This degree of deviation quantifies the motion independence of the two devices and provides a basis for subsequent analysis.

[0079] Linear regression is used to fit the relative trend. Suppose the data of 5 time points show that the displacement difference gradually decreases, from 5 to 2, then it is judged that the two are approaching. The stability of this trend helps to predict the cooperation or conflict between devices. According to the change rate of the relative trend, when determining the future motion relationship, if the change rate approaches 0, it indicates that the two devices may enter a stable following state. For example, the robotic dog adjusts its pace to follow the descending point of the drone, or the drone hovers waiting for the robotic dog to approach. This prediction can optimize path planning and improve collaborative efficiency. If the change rate is positive and increasing, such as from 0.1 to 0.5, it indicates that the two are moving away, and it may be necessary to adjust the control strategy to maintain the task goal. This analysis method ensures a comprehensive understanding of the dynamic relationship through the comprehensive consideration of trajectory, speed and direction, and effectively supports the real-time monitoring and adjustment of the movement between devices. Through the collaborative analysis of the direction vector and the speed vector, it can also provide a reference for navigation in complex environments and improve the robustness of the motion trajectory.

[0080] A further implementation lies in that the method for determining the position consistency of the drone and the robotic dog based on the relative motion trend includes:

[0081] S44. Obtain a preliminary position result set based on the acquired real-time reflection signal data and the relative motion trend.

[0082] For example, when the drone hovers at a certain height, the change in the intensity of the ground reflection signal is detected, and when the robotic dog moves on the ground, its signal is affected by the terrain undulation. After fusing the relative motion trend, the initial position calculation result is initially deduced through the time difference and motion direction of the signal. For example, the drone hovers at a height of 10 meters, and the robotic dog moves eastward on the ground at 2 meters per second. The initial calculation may show that the horizontal distance between the two is 15 meters.

[0083] S45. Extract the reference coordinate data from the preliminary position result set to determine the relative position distribution of the drone and the robotic dog.

[0084] When extracting the reference coordinate data from the initial position calculation result, the position of the drone can be set as the coordinate origin (0, 0, 10), and the position of the robotic dog is (15, 0, 0). The establishment of this reference coordinate helps to intuitively reflect their distribution in the three-dimensional space. For the relative position distribution, the signal fusion method can adjust the coordinates by combining the intensity and angle information of the reflection signal. For example, the attenuation of the signal intensity indicates an increase in distance, and the angle offset indicates a change in direction, thereby correcting the initial calculation deviation.

[0085] S46. Perform signal fusion processing on the real-time reflection signals according to the relative position distribution to obtain the adjusted position calculation result; judge the deviation between the adjusted position calculation result and the relative motion trend. When the deviation exceeds the set deviation threshold, re-perform signal fusion processing; when it is lower than the set deviation threshold, obtain the updated position data of the drone and the robot dog, and then obtain the real-time position relationship between the drone and the robot dog.

[0086] During the actual operation process, there may be multi-sensor data, such as the gyroscope of the robot dog and the GPS of the drone. Performing signal fusion processing on them can effectively reduce the error of a single signal source and ensure that the calculation result is closer to the actual situation.

[0087] S47. Based on the real-time position relationship, use the Kalman filter algorithm to optimize the motion trend to obtain the optimized position calculation result, and based on the optimized position calculation result, determine the position consistency between the drone and the robot dog.

[0088] Furthermore, the implementation lies in that the method for determining the position consistency between the drone and the robot dog includes:

[0089] S471. Extract the signal tracking deviation value from the optimized position calculation result, and use the statistical analysis method to obtain the deviation value sequence.

[0090] S472. Compare the values in the deviation value sequence with the preset deviation threshold. When it exceeds the deviation threshold, determine the signal segment that needs to be resampled to obtain the resampled data; otherwise, judge that the positions of the drone and the robot dog are consistent.

[0091] S473. Extract the features of the resampled data to obtain the signal feature set; and perform smoothing processing on the signal features to obtain the smoothed feature data.

[0092] S474. Calculate the position correction amount based on the smoothed feature data to obtain the position correction data; input the position correction data into S471 to extract the signal tracking deviation value.

[0093] In the collaborative positioning scenario of the drone and the robot dog, assuming that the signal tracking deviation value comes from the timestamp difference of the reflection signal, the deviation value at a certain moment may be 5 milliseconds, and at another moment it is 8 milliseconds. Calculate the deviation value sequence through the statistical analysis method. Specifically, the sliding window average method can be used to obtain a sequence containing 10 consecutive deviation values, such as 5, 6, 7, 8, 6, 5, 4, 3, 2, 1 millisecond. If the preset threshold is 7 milliseconds, then 8 in the sequence exceeds the threshold, indicating that the signal may be interfered and needs further processing. This method intuitively reflects the stability of the signal and helps to quickly locate the problem segment. Judge the resampled signal segment for the deviation value that exceeds the threshold. The specific interval can be determined by timestamp comparison. For example, the signal segment corresponding to the deviation value 8 is the data segment from the 4th to the 6th second.

[0094] This judgment not only depends on the magnitude of the deviation value, but also needs to consider the continuity of the signal to avoid misjudging a single abnormal point. After resampling, the signal quality is improved, which can effectively reduce the error in subsequent analysis. Using the particle filter algorithm to resample the reflected signal, the signal can be divided into multiple particle swarms, and each particle represents a possible signal state.

[0095] For example, for the signal segment from the 4th to the 6th second, 100 particles are generated to simulate the signal forms under different noise levels, and finally the optimal solution is selected. This method can retain the diversity characteristics of the signal and ensure that the resampled data is closer to the real situation. When extracting feature points from the resampled signal data, the peaks and valleys of the signal can be focused on. For example, the time-delay feature may be manifested as a 2-millisecond delay of the signal, and the amplitude feature is manifested as a 10-decibel change in intensity. The formation of the signal feature set provides a reliable basis for subsequent smoothing processing. Using the Kalman filter algorithm to smooth the feature data, this smoothing process reduces the noise influence and improves the data consistency. The position correction amount can be calculated based on the smoothed feature data according to the change trends of the time delay and amplitude. For example, a 0.2-millisecond reduction in the time delay may correspond to a 0.5-meter eastward offset of the UAV position. This correction amount directly optimizes the positioning result.

[0096] S5. Determine the real-time positioning coordinates of the UAV and the robot dog based on position consistency.

[0097] Considering the limited capabilities of the processing device, optimize and adjust the signal processing frequency, and calculate the position correction data to obtain the real-time positioning coordinates.

[0098] Through the above joint positioning method, the accurate position information of the UAV and the robot dog can be obtained. During the tunnel operation process, the robot dog can serve as the landing pad for the UAV, and both can play their respective advantages. The UAV can obtain accurate image data or laser data of the tunnel face or other engineering operation surfaces with height requirements at high altitude; the combination of the robot dog and the UAV has great application value in the field of engineering detection.

[0099] Embodiment 2:

[0100] This embodiment provides a joint positioning system for a UAV and a robot dog in a tunnel based on a prism reference point, which is used to implement the method provided in the above embodiment, and includes: a reference point acquisition module, a feature extraction module, a processing module, a position calculation module, and a positioning module.

[0101] The reference point acquisition module is used to construct a tunnel prism reference point array, obtain an initial reflection signal based on the prism reference point array, preprocess the initial reflection signal to obtain a set of processed signals, determine a signal dynamic threshold in combination with the characteristics of tunnel light interference distribution, and obtain a reference point set based on the set of processed signals and the signal dynamic threshold.

[0102] The feature extraction module obtains the reflection signal data of the drone and the robot dog through the reference point set and extracts the reflection signal features, and determines the initial position distribution of the drone and the robot dog based on the reflection signal features.

[0103] The processing module is used to obtain the dynamic reflection data of the drone and the robot dog based on the initial position distribution, process the dynamic reflection data to obtain a smooth position sequence.

[0104] The position calculation module is used to calculate the relative motion trend based on the smooth position sequence and the motion difference characteristics of the drone and the robot dog, and determine the position consistency of the drone and the robot dog based on the relative motion trend.

[0105] The positioning module is used to determine the real-time positioning coordinates of the drone and the robot dog based on the position consistency.

[0106] The embodiments described above are only descriptions of the preferred embodiments of the present invention and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall fall within the protection scope determined by the claims of the present invention.

Claims

1. A combined positioning method for an unmanned aerial vehicle and a robotic dog in a tunnel based on a prism reference point, characterized in that It includes the following steps: S1. Construct a tunnel prism reference point array, obtain an initial reflection signal based on the prism reference point array, and preprocess the initial reflection signal to obtain a set of processed signals; Combine the characteristics of tunnel light interference distribution to determine a signal dynamic threshold, and obtain a reference point set based on the set of processed signals and the signal dynamic threshold; S2. Obtain the reflection signal data of the drone and the robotic dog through the reference point set, and extract the reflection signal features; based on the reflection signal features, determine the initial position distribution of the drone and the robotic dog; S3. Obtain the dynamic reflection data of the drone and the robotic dog based on the initial position distribution, and process the dynamic reflection data to obtain a smooth position sequence; S4. Calculate the relative motion trend based on the smooth position sequence and the motion difference characteristics of the drone and the robotic dog; and determine the position consistency of the drone and the robotic dog based on the relative motion trend; S5. Determine the real-time positioning coordinates of the drone and the robotic dog based on the position consistency.

2. The method for joint positioning of an unmanned aerial vehicle and a robotic dog in a tunnel based on the prism reference point according to claim 1, wherein S2 includes the following steps: S21. Obtain the reflection signal data of the drone and the robotic dog, and extract the reflection signal features from the reflection signal data to obtain the reflection signal features; determine the distribution characteristics of signal intensity and signal distance based on the reflection signal features; S22. Based on the distribution characteristics of the signal intensity and the signal distance, use the triangulation method to calculate the preliminary position estimation values of the drone and the robotic dog; S23. Correct the preliminary position estimation values, calculate the matching degree between the corrected preliminary position estimation values and the tunnel environment, and obtain the initial position distribution.

3. The method for joint positioning of an unmanned aerial vehicle and a legged robot in a tunnel based on the prism reference point according to claim 1, wherein, S3 includes the following steps: S31. Obtain the dynamic reflection data of the drone and the robotic dog, and use the Kalman filter algorithm to fuse the time series to determine the position estimation values; S32. Based on the position estimation values, determine the dynamic change characteristics caused by movement; Judge the continuity of the movement trajectory based on the dynamic change characteristics; When the continuity of the movement trajectory meets the preset threshold, optimize the position estimation values to obtain an updated position sequence; when the continuity of the movement trajectory does not meet the preset threshold, return to S31; S34. Calculate the trajectory smoothness based on the updated position sequence, and adjust the parameters of the Kalman filter algorithm based on the trajectory smoothness, thereby obtaining the smooth position sequence.

4. The method for joint positioning of an unmanned aerial vehicle and a robotic dog in a tunnel based on a prism reference point according to claim 1, wherein The method for calculating the relative motion trend based on the smooth position sequence and the motion difference characteristics of the drone and the robotic dog includes the following steps: S41. Extract the motion trajectory points of the drone and the robotic dog according to the smooth position sequence, and determine the motion characteristics of the drone and the robotic dog based on the motion trajectory points; S42. Calculate the displacement difference between the drone and the robotic dog at each time point based on the motion characteristics, and thus obtain the velocity vector; S43. Calculate the direction vector based on the angular change of the velocity vector to obtain the motion directions of the drone and the robotic dog; and obtain the relative motion trend based on the motion directions.

5. The method for joint positioning of an unmanned aerial vehicle and a legged robot in a tunnel based on the prism reference point according to claim 1, wherein The method for determining the position consistency of the drone and the robotic dog based on the relative motion trend includes: S44. Obtain a preliminary position result set based on the acquired real-time reflection signal data and the relative motion trend; S45. Extract reference coordinate data from the preliminary position result set to determine the relative position distribution of the UAV and the legged robot; S46. Perform signal fusion processing on the real-time reflection signal according to the relative position distribution to obtain an adjusted position solution result; judge the deviation between the adjusted position solution result and the relative motion trend. When the deviation exceeds the set deviation threshold, re-perform signal fusion processing; when it is lower than the set deviation threshold, obtain the updated position data of the UAV and the legged robot, and further obtain the real-time position relationship between the UAV and the legged robot; S47. Optimize the motion trend based on the real-time position relationship to obtain an optimized position solution result; and determine the position consistency between the UAV and the legged robot based on the optimized position solution result.

6. The method for joint positioning of an unmanned aerial vehicle and a robotic dog in a tunnel based on the prism reference point according to claim 5, wherein, The method for determining the position consistency between the UAV and the legged robot includes: S471. Extract the signal tracking deviation values from the optimized position solution result to obtain a deviation value sequence; S472. Compare the values in the deviation value sequence with a preset deviation threshold. When it exceeds the deviation threshold, determine the signal segment that needs to be re-sampled to obtain re-sampled data; otherwise, judge that the positions of the UAV and the legged robot are consistent; S473. Extract features from the re-sampled data to obtain a signal feature set; and perform smoothing processing on the signal features to obtain smoothed feature data; S474. Calculate a position correction amount based on the smoothed feature data to obtain position correction data; input the position correction data into S471 to extract the signal tracking deviation value.

7. Tunnel UAV and robot dog joint positioning system based on prism reference points, the system is used to implement the method described in any one of claims 1-6, characterized in that, It includes: A reference point acquisition module, a feature extraction module, a processing module, a position calculation module, and a positioning module; The reference point acquisition module is used to construct a tunnel prism reference point array, acquire an initial reflection signal based on the prism reference point array, and preprocess the initial reflection signal to obtain a set of processed signals; Combine the tunnel light interference distribution characteristics to determine a signal dynamic threshold, and obtain a reference point set based on the set of processed signals and the signal dynamic threshold; The feature extraction module acquires the reflection signal data of the UAV and the legged robot through the reference point set and extracts the reflection signal features; based on the reflection signal features, determine the initial position distribution of the UAV and the legged robot; The processing module is used to acquire the dynamic reflection data of the UAV and the legged robot on the basis of the initial position distribution, process the dynamic reflection data to obtain a smoothed position sequence; The position calculation module is used to calculate the relative motion trend based on the smoothed position sequence and the motion difference characteristics of the UAV and the legged robot; and determine the position consistency between the UAV and the legged robot based on the relative motion trend; The positioning module is used to determine the real-time positioning coordinates of the UAV and the legged robot based on the position consistency.

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