A robot aerial pose real-time correction method based on visual prediction

Abstract

The application relates to the technical field of robot vision control, and discloses a robot air attitude real-time correction method based on vision prediction. The method collects a vision data sequence of a robot air attitude, calculates attitude prediction parameters at each time point through a vision processing algorithm, and generates an initial data set; subsequently, adjacent attitude points are analyzed through clustering based on a space-time proximity relationship, consistent sections and conflict sections of motion modes are identified, and a partitioned labeling result is formed. Then, the motion speed and time sequence data of the attitude points in the consistent sections are extracted, the fluctuation characteristics are analyzed in combination with the attitude angle change amount, the motion influence strength under the influence of external environment interference is evaluated, and motion influence superposition evaluation data is generated. Finally, the attitude deviation points in the evaluation data whose response values exceed a reference value and are located in the conflict sections are detected, a deviation point set is constructed, the points needing correction are marked according to the detailed attitude information of the deviation points, real-time attitude correction instructions are generated and output.

Description

Technical Field

[0001] This invention relates to the field of robot vision control technology, specifically to a method for real-time correction of robot aerial posture based on visual prediction. Background Technology

[0002] With the rapid development of technology, robots are increasingly being used in aerial operations. In logistics and delivery, drones can deliver goods quickly and efficiently, especially in remote or inaccessible areas, greatly improving delivery efficiency and saving labor costs. For example, in some mountainous or island regions, traditional logistics and delivery methods are insufficient, while drones can easily overcome geographical barriers and ensure timely delivery of goods. In the field of inspection, aerial robots play a vital role in the inspection of power lines, oil pipelines, and urban infrastructure. They can quickly reach designated locations, conduct comprehensive inspections of targets, and promptly identify potential problems, such as damaged power lines or leaking oil pipelines, providing crucial information for subsequent maintenance and repair work.

[0003] In these aerial operation scenarios, precise aerial attitude control of robots is crucial. Taking logistics delivery as an example, inaccurate robot attitude control can lead to deviations in cargo delivery locations or even damage to goods. During inspections, unstable attitude can affect the accuracy and comprehensiveness of detection, potentially missing critical fault points. Therefore, achieving precise aerial attitude control of robots is a core element in ensuring their efficient and reliable task execution.

[0004] Traditional methods for robot aerial attitude correction primarily rely on sensors or simple vision. Sensor-based methods, such as those using inertial sensors like gyroscopes and accelerometers, can acquire real-time motion information about the robot. However, these sensors are susceptible to noise interference and drift over time, leading to gradually increasing measurement errors and hindering accurate real-time attitude correction. For example, in environments with significant vibration, inertial sensor measurements can be severely affected, significantly reducing the accuracy of attitude correction.

[0005] Simple vision-based posture correction methods typically estimate a robot's posture by recognizing some basic visual features. However, this approach has significant limitations in complex environments. When there is occlusion, drastic lighting changes, or complex backgrounds, the extraction and recognition of visual features become extremely difficult, or even impossible, leading to posture correction failure. For example, at night or in inclement weather conditions, simple vision-based methods struggle to accurately acquire robot posture information, severely impacting the robot's operational performance. Summary of the Invention

[0006] The purpose of this invention is to provide a real-time aerial posture correction method for robots based on visual prediction, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides a real-time aerial posture correction method for robots based on visual prediction, the method comprising:

[0008] Visual data sequences of robot aerial postures are collected, including image frames and corresponding timestamp information. Posture prediction parameters at each time point are calculated using visual processing algorithms to generate an initial posture prediction dataset. Posture prediction parameters and spatial coordinates are extracted from the initial posture prediction dataset. Cluster analysis is performed on adjacent posture points based on spatiotemporal proximity to identify motion pattern consistent segments and motion pattern conflict segments, forming motion pattern partitioning and labeling results.

[0009] The attitude points located in the consistent motion pattern segment in the motion pattern partitioning labeling results are obtained. The motion velocity and time series data of the attitude points are extracted. The fluctuation characteristics are analyzed by combining the change in attitude angle. The intensity of motion influence under external environmental interference is evaluated, and motion influence superposition evaluation data is generated.

[0010] The system detects deviation points in the motion impact superposition evaluation data where the motion response value exceeds the dynamic average response benchmark and is located in the motion mode conflict zone, and constructs a set of attitude deviation points. Based on the detailed attitude information of all points in the set of attitude deviation points, including position coordinates and motion state, the system marks the points that need correction and generates and outputs real-time attitude correction commands.

[0011] Preferably, the initial attitude prediction dataset includes attitude prediction parameters, robot 3D spatial coordinates, and normalized visual feature vectors; the motion pattern partitioning labeling results include motion pattern consistent segment identifiers, motion pattern conflict segment identifiers, and the ratio of prediction parameter differences between adjacent attitude points; the motion influence superposition evaluation data includes the influence of motion acceleration, the influence of motion direction change rate, and a comparison of attitude responses under various interference conditions; the attitude deviation point set includes the spatial coordinates of deviation points, the motion velocity amplitude characteristics of deviation points, and the ratio of attitude angle to acceleration fluctuation of deviation points; and the real-time attitude correction command includes a list of correction points and a joint judgment identifier for multiple indicators of correction points.

[0012] Preferably, the operation of acquiring visual data sequences of robot aerial posture and generating an initial posture prediction dataset further includes: capturing continuous image frames of robot aerial motion through an airborne vision sensor, synchronously recording the timestamp and spatial location information of each image frame, and storing them as raw visual data; preprocessing the raw visual data, including image enhancement and feature point detection, extracting the motion trajectory and orientation data of key feature points, calculating posture prediction parameters using a prediction model, and integrating the timestamp and spatial location to generate an initial posture prediction dataset.

[0013] Preferably, the operation of extracting attitude prediction parameters and performing motion pattern recognition from the initial attitude prediction dataset further includes: reading the attitude prediction parameters and corresponding three-dimensional coordinates in the initial attitude prediction dataset; calculating the spatial distance between all attitude points based on the coordinates; sorting by proximity based on a preset distance threshold to generate a distance sorting list of adjacent attitude points; based on the distance sorting list of adjacent attitude points, calculating the difference ratio of their attitude prediction parameters for each pair of adjacent attitude points, which is obtained by the ratio of the parameter difference to the average parameter; summarizing the difference ratios of all point pairs to generate a prediction parameter difference ratio sequence; analyzing the prediction parameter difference ratio sequence, comparing the trend of motion direction change and the trend of velocity change of adjacent attitude points, labeling the segments according to the trend consistency, and recording the segments with consistent motion patterns and conflicting motion patterns to obtain motion pattern partitioning labeling results.

[0014] Preferably, the operation of obtaining the attitude points located in the motion pattern consistent segment in the motion pattern partitioning labeling results further includes: according to the motion pattern partitioning labeling results, filtering segments marked as having consistent motion patterns, extracting time series data of each attitude point, including motion velocity and direction angle, arranging them in chronological order to form a motion velocity sequence and a motion direction sequence, generating a motion attribute time series set; performing sliding window analysis on the motion attribute time series set, calculating the rate of change of motion acceleration and the rate of change of motion direction angle within each window, weighting and fusing the two under the same attitude background, evaluating the comprehensive influence value, and generating motion influence superposition evaluation data.

[0015] Preferably, the operation of detecting deviation points in the motion impact superposition evaluation data further includes: screening attitude points whose motion response values ​​are higher than the dynamic average response benchmark from the motion impact superposition evaluation data, and simultaneously screening attitude points located in the motion mode conflict zone, extracting continuous motion records of attitude points in chronological order, including velocity values ​​and direction values, to form a continuous motion record set;

[0016] Process the continuous motion record set, calculate the velocity change ratio and direction change ratio between consecutive time points, integrate them into velocity change ratio sequence and direction change ratio sequence, and construct a motion fluctuation change dataset;

[0017] By combining the motion fluctuation change dataset and real-time environmental data, it is checked whether the velocity change rate and direction change rate exceed preset thresholds. Points that exceed the preset thresholds are marked as deviation points, and a set of attitude deviation points is generated.

[0018] Preferably, the operation of generating correction instructions based on the set of attitude deviation points further includes: obtaining the motion response parameters and environmental suitability parameters of all points in the set of attitude deviation points; calculating the joint risk judgment value of each point, which is normalized based on the degree of deviation between the parameters and the threshold to generate a joint risk judgment value sequence; selecting points from the joint risk judgment value sequence whose motion response parameters exceed the risk threshold, whose environmental suitability parameters are lower than the benchmark value, and which are located in the motion mode conflict zone; extracting their numbers and location descriptions; marking them as high-priority correction points; and outputting real-time attitude correction instructions in a structured format.

[0019] Preferably, the specific steps for calculating the joint risk assessment value for each location include: constructing a multidimensional feature vector containing motion response parameters, environmental suitability parameters, and motion pattern labels; performing feature dimensionality reduction on the multidimensional feature vector using principal component analysis to extract risk feature components; calculating the comprehensive risk value using a weighted summation method based on the risk feature components; and normalizing the comprehensive risk value to generate a standardized sequence of joint risk assessment values.

[0020] Preferably, when capturing continuous image frames of the robot's aerial movement using an airborne vision sensor, a multi-resolution acquisition strategy is adopted: when the robot's movement speed exceeds a set threshold, a high-resolution acquisition mode is enabled; when the movement speed is below the set threshold, the acquisition mode is switched to a standard resolution acquisition mode; and the acquisition frame rate is dynamically adjusted in a positive correlation with the motion acceleration value.

[0021] Preferably, the specific implementation steps of the sliding window analysis include: setting a variable-length analysis window, the window size of which is adaptively adjusted according to the motion speed; calculating the spectral characteristics of motion acceleration and the gradient of motion direction angle change in each window; fusing the spectral characteristics and gradient changes in the time and frequency domains through multi-scale analysis; establishing a motion impact assessment matrix, combining the feature values ​​at different scales to generate the motion impact superposition assessment data.

[0022] Compared with the prior art, the beneficial effects of the present invention are:

[0023] During the data acquisition phase, this method collects visual data sequences of the robot's aerial posture, encompassing image frames and corresponding timestamp information, enabling comprehensive and detailed acquisition of the robot's posture information in the air. This rich data provides a solid foundation for subsequent posture prediction and analysis. Compared to traditional methods that rely solely on simple sensor data, it provides more dimensional information, resulting in a deeper and more accurate understanding of the robot's posture.

[0024] After generating the initial attitude prediction dataset, this method extracts attitude prediction parameters and spatial coordinates from the dataset and performs cluster analysis on adjacent attitude points based on spatiotemporal proximity. This method can clearly distinguish between motion pattern consistent segments and motion pattern conflict segments, resulting in accurate motion pattern partitioning and labeling. This precise division of motion patterns helps to quickly locate potential attitude deviation points. For example, during the flight of a logistics delivery drone, the attitude is relatively stable when the drone is in a motion pattern consistent segment of uniform straight flight; however, attitude deviations are more likely to occur in motion pattern conflict segments such as turning and obstacle avoidance. Through cluster analysis using this method, these potentially problematic segments can be identified in a timely manner, providing clear targets for subsequent attitude correction.

[0025] When faced with complex and ever-changing external environments, this method acquires attitude points within segments of consistent motion patterns, extracts the motion velocity and time-series data of these attitude points, and combines this with analysis of fluctuation characteristics based on attitude angle changes. This allows for accurate assessment of the intensity of motion impact under external environmental disturbances, generating precise motion impact superposition assessment data. Taking an inspection drone flying in strong winds as an example, strong winds interfere with the drone's attitude, causing changes in its attitude angles and affecting its flight speed. This method can accurately determine the degree of impact of strong winds on the drone's motion by analyzing the fluctuations in attitude angles and motion velocity, enabling proactive countermeasures such as adjusting flight speed and changing flight attitude. This ensures stable operation of the robot in complex environments and avoids attitude loss of control and mission failure due to environmental disturbances.

[0026] When a deviation point is detected in the motion impact overlay evaluation data, where the motion response value exceeds the dynamic average response benchmark and is located in the motion mode conflict zone, and a set of attitude deviation points is constructed, this method, based on the detailed attitude information of all points in the set, including position coordinates and motion state, can quickly and accurately mark points requiring correction and generate and output real-time attitude correction commands. This real-time correction mechanism enables the robot to react and correct quickly once an attitude deviation occurs during aerial operations. For example, in drone operations for high-altitude mapping, the drone needs to maintain a precise attitude to obtain accurate mapping data. If the drone experiences an attitude deviation, this method can detect and correct it in a very short time, significantly improving the accuracy of the robot's aerial attitude, meeting real-time operation requirements, and ensuring the accuracy and reliability of the mapping data. Attached Figure Description

[0027] Figure 1 This is a schematic diagram illustrating the working principle of the real-time aerial posture correction method for robots based on vision prediction as described in this invention.

[0028] Figure 2 A flowchart illustrating the initial dataset and annotation results;

[0029] Figure 3 A detailed flowchart for motion pattern recognition. Detailed Implementation

[0030] 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.

[0031] Please see Figure 1This invention provides a real-time robot aerial posture correction method based on visual prediction. The method includes predicting robot posture through visual data sequences, identifying motion patterns, assessing the impact of environmental interference, and generating real-time correction instructions for detected posture deviations. The method begins with the acquisition of a visual data sequence of robot aerial posture, which includes image frames and corresponding timestamps. Posture prediction parameters are calculated at each time point using a visual processing algorithm to form an initial posture prediction dataset. Posture prediction parameters and spatial coordinates are extracted from the initial posture prediction dataset. Adjacent posture points are clustered based on spatiotemporal proximity to identify consistent and conflicting motion pattern segments, forming motion pattern partitioning labels. Posture points located in consistent motion pattern segments from the motion pattern partitioning labels are acquired. The motion velocity and time series data of these posture points are extracted, and their fluctuation characteristics are analyzed in conjunction with posture angle changes. The intensity of motion impact under external environmental interference is assessed, generating motion impact superposition assessment data. Deviation points in the motion impact superposition assessment data whose motion response values ​​exceed the dynamic average response benchmark and are located in conflicting motion pattern segments are detected, constructing a set of posture deviation points. Based on the detailed attitude information of all points in the attitude deviation point set, including position coordinates and motion state, points with correction requirements are marked, and real-time attitude correction commands are generated and output.

[0032] Example 1: See Figure 2 The initial attitude prediction dataset includes attitude prediction parameters, robot 3D spatial coordinates, and normalized visual feature vectors. Motion pattern partitioning and labeling results include identifiers for consistent motion patterns, conflicting motion patterns, and the ratio of predicted parameter differences between adjacent attitude points. Motion impact overlay evaluation data includes the impact of motion acceleration, the impact of the rate of change of motion direction, and a comparison of attitude responses under various disturbance conditions. The attitude deviation point set includes the spatial coordinates of deviation points, the velocity amplitude characteristics of deviation points, and the ratio of attitude angle to acceleration fluctuations at deviation points. Real-time attitude correction instructions include a list of correction points and a joint judgment identifier for multiple indicators at correction points. The operation to generate the initial attitude prediction dataset involves capturing continuous image frames of the robot's aerial motion using an airborne vision sensor. The airborne vision sensor employs a global shutter CMOS image sensor to reduce motion blur, with a sensor resolution of 1920×1080 pixels, an optical lens focal length of 4 mm, and a field of view covering 120 degrees. The system synchronously records the timestamp and spatial location information of each image frame. The timestamp information is obtained from the onboard high-precision clock circuit, with a timest accuracy down to the microsecond level. The spatial location information comes from the fusion data of the GPS module and the inertial measurement unit and is stored as raw visual data. The raw visual data is cached in the onboard solid-state memory in H.264 encoding format. Each image frame is appended with a metadata header containing latitude and longitude coordinates, altitude, and attitude angle.

[0033] The raw visual data undergoes preprocessing, including image enhancement and feature point detection. Image enhancement employs a contrast-limited adaptive histogram equalization method to enhance local image contrast, and a bilateral filter is applied to preserve edge information while reducing noise. Feature point detection uses the ORB algorithm to extract FAST corner points from the image and calculates BRIEF descriptors, with the number of feature points controlled between 200 and 300 to ensure real-time performance. Motion trajectory and orientation data of key feature points are extracted. Motion trajectory tracking is performed using optical flow to track the displacement of feature points in consecutive frames, and orientation data is obtained by calculating the 3D position of feature points in the world coordinate system using the PnP algorithm. A prediction model is used to calculate attitude prediction parameters. This model employs a temporal prediction model based on a Long Short-Term Memory (LSTM) network. The LSM network structure contains two hidden layers, each with 128 neurons. The input sequence length is 10 frames, and the output is the attitude prediction parameters for the next time point. The attitude prediction parameters include the three-axis Euler angles (pitch, yaw, roll) and linear velocity components in the X, Y, and Z directions. The initial attitude prediction dataset is generated by integrating timestamps and spatial locations. The initial attitude prediction dataset is organized in the form of a time series array. Each data point contains a timestamp, three-dimensional spatial coordinates, attitude prediction parameter triplet, linear velocity vector, and normalized visual feature vector.

[0034] Normalized visual feature vectors are obtained by performing principal component analysis (PCA) on the feature point descriptors, followed by dimensionality reduction and normalization. PCA retains 95% of the variance contribution rate, reducing the 256-dimensional BRIEF descriptors to 32-dimensional feature vectors. Z-score normalization is used for normalization. Three-dimensional spatial coordinates are calculated using visual odometry combined with an extended Kalman filter, fusing GPS and inertial measurement unit data, achieving centimeter-level accuracy. Timestamp information is strictly synchronized with image frames, and hardware trigger signals ensure time alignment between image acquisition and pose recording. When the airborne vision sensor captures continuous image frames of the robot's aerial motion, a multi-resolution acquisition strategy is employed. This strategy dynamically adjusts acquisition parameters based on the robot's motion state. When the robot's speed exceeds a set threshold, a high-resolution acquisition mode is activated, using the sensor's full resolution of 1920×1080 for output, increasing the frame rate to 60 frames per second. When the speed falls below the set threshold, a standard resolution acquisition mode is switched to, using 2×2 pixel binning technology to reduce the effective resolution to 960×540, maintaining a frame rate of 30 frames per second. The frame rate is dynamically adjusted in a positive correlation with the motion acceleration value. For every 1 m / s² increase in acceleration, the frame rate increases by 5 frames / second, up to a maximum of 100 frames / second.

[0035] Image enhancement processing employs a hybrid algorithm combining adaptive histogram equalization and guided filtering. First, gamma correction is applied to the original image to adjust overall brightness. Then, the CLAHE algorithm is applied to enhance local contrast, with the tile grid size set to 8×8. Noise filtering uses a nonlocal mean denoising algorithm with a search window size of 21×21 pixels and a similarity window size of 7×7 pixels. Feature point detection utilizes the ORB algorithm with a multi-scale pyramid structure, constructing a three-layer image pyramid with a scale factor of 1.2 for each layer. Feature points are detected at different scales to improve robustness. Predictive model training employs temporal cross-validation, dividing continuous motion data into training and test sets. The training set contains 80% of the motion sequences, and the test set contains 20%. The Long Short-Term Memory (LSTM) network is trained using the Adam optimizer with an initial learning rate of 0.001, a batch size of 32, and a training cycle of 100 epochs. The calculation of pose prediction parameters includes six-DOF pose estimation. Rotational components are represented using quaternions to avoid gimbal lock issues, and translational components are measured in meters.

[0036] The initial attitude prediction dataset is stored using a circular buffer with a capacity of 1000 data points. When the buffer is full, the oldest data is automatically overwritten. Each data point is stored as a structure containing a 64-bit timestamp, spatial coordinates represented by three double-precision floating-point numbers, attitude prediction parameters represented by six single-precision floating-point numbers, and a normalized visual feature vector represented by 32 single-precision floating-point numbers. The data access interface provides two retrieval methods: query by timestamp range and query by spatial proximity, supporting multi-threaded concurrent read and write operations. The motion pattern partitioning and annotation results include motion pattern consistent segment identifiers, motion pattern conflict segment identifiers, and the difference ratio of predicted parameters between adjacent attitude points. Motion pattern consistent segment identifiers use a Boolean array to mark consecutive consistent motion segments, while motion pattern conflict segment identifiers use bitmap encoding to record intervals of abrupt motion pattern changes. The difference ratio of predicted parameters between adjacent attitude points calculates the relative magnitude of attitude parameter changes between two adjacent points; a difference ratio exceeding 5% is marked as a potential conflict point. The generation of motion impact superposition assessment data is based on sliding window statistical analysis. The window size adapts to the motion speed, shrinking to 10 data points during high-speed motion and expanding to 50 data points during low-speed motion.

[0037] The construction of the attitude deviation point set employs a multi-condition filtering mechanism. First, points whose motion response values ​​exceed the dynamic average response benchmark are selected. The dynamic average response benchmark is calculated using the exponential moving average of historical data, with a smoothing factor set to 0.2. Next, it checks whether the point is located in a motion pattern conflict zone. The criteria for determining a motion pattern conflict zone is that the difference ratio of predicted parameters for three or more consecutive data points exceeds a threshold. Finally, the persistence of the deviation is verified using real-time environmental data, including wind speed, air pressure, and temperature sensor readings. Only points with persistent deviations exceeding three sampling periods are included in the attitude deviation point set. Real-time attitude correction commands are encoded using a lightweight binary protocol. The message header includes the command version number, timestamp, and robot identifier. The correction point list is presented as an array listing all point indices requiring correction, with each point accompanied by its three-dimensional coordinates and expected correction amount. The multi-index joint judgment identifier for correction points uses bit-field encoding, with each bit representing an out-of-limit state of a risk indicator, including attitude angle deviation, velocity anomaly, acceleration mutation, and environmental interference dimension. Command transmission is conducted via a wireless communication module using broadcast, with the transmission frequency synchronized with the image acquisition frame rate to ensure the real-time nature of the correction commands.

[0038] Example 2: See Figure 3The process involves extracting attitude prediction parameters from the initial attitude prediction dataset and performing motion pattern recognition. The dataset reads the attitude prediction parameters and corresponding 3D coordinates. These parameters include predicted values ​​for pitch, yaw, and roll angles, as well as linear velocity components along the X, Y, and Z axes. The 3D coordinates are obtained through fusion calculation using a Global Positioning System (GPS) and an Inertial Measurement Unit (INS), with the Earth's fixed coordinate system as the reference frame. The spatial distance between all attitude points is calculated using the Euclidean distance formula. Each attitude point's coordinates are represented as a (x, y, z) triplet, with the upper triangular portion of the distance matrix used for subsequent analysis. Neighborhood sorting is performed based on a preset distance threshold, which is dynamically adjusted according to the robot's motion characteristics. The threshold increases to 5 meters during high-speed motion and decreases to 1 meter during low-speed, fine-grained operations, generating a sorted list of adjacent attitude point distances. Based on this sorted list, for each pair of adjacent attitude points, the difference ratio of their attitude prediction parameters is calculated. This difference ratio is obtained by comparing the parameter difference to the average parameter. The pitch angle difference ratio for each pair of adjacent attitude points is calculated by dividing the absolute value of the pitch angle difference between the two points by the arithmetic mean of the pitch angles. The yaw angle difference ratio is calculated using the same method, while the roll angle difference ratio is calculated separately. The difference ratios of the linear velocity components are calculated separately for the rate of change of velocity in the X, Y, and Z directions. The difference ratios of all point pairs are summarized to generate a sequence of predicted parameter difference ratios. The sequence of predicted parameter difference ratios is arranged in chronological order, with each time point corresponding to a vector containing six difference ratio values. The vector elements correspond to the pitch angle, yaw angle, roll angle, and the difference ratios of the three linear velocity components, respectively.

[0039] The sequence of predicted parameter difference ratios is analyzed, and the trends of motion direction and velocity changes of adjacent attitude points are compared. The motion direction change trend is judged based on the moving average of the yaw angle difference ratio, and the velocity change trend is evaluated based on the weighted sum of the linear velocity component difference ratios. Segments are labeled according to trend consistency. A dual-threshold mechanism is used to judge trend consistency: when the difference ratio of five or more consecutive time points is below the low threshold and the trend correlation coefficient is above 0.8, it is marked as a motion pattern consistent segment. When the difference ratio exceeds the high threshold or the trend correlation coefficient is below 0.3, it is marked as a motion pattern conflict segment. Segments with consistent and conflicting motion patterns are recorded separately to obtain the motion pattern partitioning labeling results. Preset distance thresholds are implemented through a lookup table based on the robot's dynamic adjustment mechanism according to its motion speed. The motion speed is divided into three levels: low speed (0-2 m / s), medium speed (2-5 m / s), and high speed (above 5 m / s), each corresponding to a different distance threshold parameter. Nearest neighbor sorting uses the KD-tree algorithm to accelerate spatial lookup. The KD-tree is constructed using the dimension with the largest variance as the splitting dimension, and each leaf node contains a maximum of 10 data points. The difference ratio calculation covers all six attitude prediction parameters. After calculating the difference ratio for each parameter, outlier removal is performed using a three-standard-deviation criterion. Trend consistency is judged using the Pearson correlation coefficient within a sliding window, with the window size set to 10 consecutive time points. The Pearson correlation coefficient calculates the linear correlation between the difference ratio sequences within two consecutive windows. Motion direction change trend analysis is based on the change in yaw angle per unit time, and velocity change trend analysis is based on the rate of change of the magnitude of the acceleration vector. Motion pattern consistency segments are identified as intervals where the difference ratio of multiple consecutive attitude points is below a threshold and the trend correlation coefficient is above a set value. Motion pattern conflict segments are identified as intervals where the difference ratio suddenly increases or the trend correlation coefficient becomes negative.

[0040] The difference ratio sequence of adjacent attitude point predicted parameters is used for subsequent abnormal fluctuation detection. This difference ratio sequence is stored as a circular buffer data structure with a buffer length of 1000 time points. Motion mode partitioning and labeling results are stored as a list of time intervals, each interval recording the start and end timestamps and motion mode type label. The motion mode type label uses an enumeration type definition, including two states: MODE_CONSISTENT (consistent) and MODE_CONFLICT (conflicting). Spatial distance calculations use single-precision floating-point arithmetic to ensure real-time performance, and the distance matrix is ​​stored in a sparse matrix format to save memory. The nearest neighbor sorting results are output as an index list, with list elements arranged in ascending order of distance. Each index corresponds to the position of an attitude point in the initial attitude prediction dataset. Smoothing is introduced into the calculation of the difference ratio of attitude prediction parameters, using an exponential moving average method to smooth the original difference ratio with a smoothing factor of 0.3. The analysis of the predicted parameter difference ratio sequence includes time-domain and frequency-domain feature extraction. Time-domain features include mean, variance, and extreme values, while frequency-domain features are extracted using a fast Fourier transform to extract the dominant frequency components. The determination of motion direction change trends incorporates curvature analysis of historical motion trajectories, while the determination of velocity change trends references the directional consistency of acceleration vectors. The generation of motion pattern partitioning and labeling results employs a finite state machine model, with state transition conditions based on a combination of difference ratio thresholds and trend correlation coefficients. The reference coordinate system for 3D coordinates is converted to a geocentric fixed coordinate system, with coordinate transformation parameters acquired in real-time from the navigation system. Time alignment of attitude prediction parameters utilizes interpolation methods to ensure accurate synchronization of timestamps from different sensors. The update frequency of the adjacent attitude point distance sorting list is consistent with the visual data acquisition frame rate, typically 30-60Hz. An outlier removal mechanism is introduced during the generation of the difference ratio sequence, using the median absolute deviation method to detect and remove abnormal data points.

[0041] The identification of motion pattern consistent segments needs to meet the dual requirements of spatial and temporal continuity. Spatial continuity requires that the distance between consecutive attitude points is less than a threshold, while temporal continuity requires that the time interval is within a reasonable range. The detection of motion pattern conflict segments pays particular attention to abrupt changes in the difference ratio, defined as the location where the first-order difference of the difference ratio exceeds three times the historical average. The motion pattern partitioning labeling results are output as an array of structures, each containing an interval start index, an interval end index, a motion pattern label, and an average difference ratio field. The difference ratio calculation for attitude prediction parameters uses normalization processing, mapping the original difference values ​​to the range of 0-1, facilitating comparison and analysis between different parameters. The analysis of motion direction change trends introduces a Kalman filter to smooth the original direction data, reducing the impact of measurement noise. The evaluation of velocity change trends incorporates the robot dynamics model, considering acceleration limits and jerk constraints. The verification of motion pattern partitioning labeling results is achieved through cross-comparison of visual odometry data and inertial measurement unit data, ensuring the reliability of pattern recognition. The nearest neighbor ranking algorithm is implemented using a spatial partitioning tree structure, supporting not only KD-trees but also quadtrees and octrees. The difference ratio sequence is stored in a time-series database format, supporting fast querying and aggregation analysis by time range. The motion pattern partitioning and labeling results are persistently stored in a binary file format, with the file header containing version identifier, data length, and time range information. The computational complexity of the entire motion pattern recognition process has been optimized to meet the real-time requirements of the robot system, with an average processing latency controlled within 10 milliseconds.

[0042] Example 3: Operation of obtaining attitude points located in the motion pattern consistent segment in the motion pattern partitioning labeling results. Based on the motion pattern partitioning labeling results, segments marked as having consistent motion patterns are selected. The criteria for determining a consistent motion pattern segment are that the predicted parameter difference ratio of more than ten consecutive attitude points is less than 5% and the motion trend correlation coefficient is greater than 0.8. Time series data for each attitude point is extracted. The time series data includes motion velocity and orientation angle. Motion velocity is obtained through differential GPS carrier phase measurement, and orientation angle is calculated by fusing data from the magnetometer and inertial measurement unit. These are arranged in chronological order to form a motion velocity sequence and a motion orientation sequence. The motion velocity sequence is stored as a floating-point array in meters per second, and the motion orientation sequence is stored as a floating-point array in radians, generating a motion attribute time series set.

[0043] A sliding window analysis was performed on the time series dataset of motion attributes. The sliding window analysis used a variable-length analysis window, whose size adaptively adjusted based on the motion speed. For every meter-per-second increase in speed, the window size decreased by five sampling points, with a minimum window size of ten sampling points. The rate of change of motion acceleration and the rate of change of motion direction angle were calculated within each window. The rate of change of motion acceleration was calculated using the second difference of the velocity sequence, and the rate of change of motion direction angle was calculated using the first difference. The rates of change of motion acceleration and motion direction angle were then weighted and fused under the same attitude background. The weighting coefficients were dynamically adjusted based on the stability of the motion state. During stable flight, the acceleration weight was set to 0.7 and the direction angle weight to 0.3; during maneuvering flight, the acceleration weight was adjusted to 0.4 and the direction angle weight to 0.6. The comprehensive impact value was evaluated using the following normalized formula:

[0044]

[0045] Where: characters Represents the overall impact value. The weighting coefficient representing the rate of change of acceleration. The weighting coefficient representing the rate of change of the orientation angle. Represents the normalized rate of change of acceleration. This represents the normalized rate of change of the direction angle. It generates motion impact superposition assessment data, which is stored in time series format. Each time point includes the original velocity value, direction angle value, acceleration change, direction angle change, and overall impact value.

[0046] The impact of motion acceleration in the motion impact superposition assessment data is represented as the integral value of the spectral energy, calculated by performing a Fast Fourier Transform on the rate of acceleration change and then calculating the energy in the 0-10 Hz frequency band. The impact of the rate of change of motion direction is represented as the sliding variance of the gradient of the direction angle change, with the sliding window size consistent with the main analysis window. The comparison of attitude responses under various disturbance conditions is achieved by comparing the deviation between the actual motion trajectory and the ideal motion trajectory. The ideal motion trajectory is generated based on simulation data of the robot's dynamics model under disturbance-free conditions. The operation to detect deviation points in the motion impact superposition assessment data involves screening attitude points whose motion response values ​​are higher than the dynamic average response benchmark. The dynamic average response benchmark is calculated using the exponential moving average of historical comprehensive impact values, with a smoothing factor set to 0.2. Simultaneously, attitude points located in motion mode conflict zones are screened; the criteria for judging motion mode conflict zones is that the difference ratio of predicted parameters among three consecutive sampling points exceeds 10%. Continuous motion records of attitude points are extracted in chronological order. These continuous motion records include velocity and direction values, with velocity values ​​accurate to centimeters per second and direction values ​​accurate to 0.1 degrees. A continuous motion record set is formed, which is stored using a circular buffer with a capacity of two hundred record points.

[0047] This process processes a continuous set of motion records, calculating the velocity change ratio and direction change ratio between consecutive time points. The velocity change ratio is defined as the ratio of the velocity difference between two adjacent points to the average velocity, and the direction change ratio is defined as the ratio of the angular difference between two adjacent points to the sampling time interval. These are integrated into velocity change ratio sequences and direction change ratio sequences. The velocity change ratio sequence is stored using a double-precision floating-point array, and the direction change ratio sequence is stored using a single-precision floating-point array. A motion fluctuation change dataset is then constructed, containing a fluctuation intensity index indexed by timestamps. The fluctuation intensity index is derived from the geometric mean of the velocity change ratio and the direction change ratio.

[0048] Combining motion fluctuation datasets and real-time environmental data, including wind speed, air pressure, and temperature sensor readings (wind speed acquired via ultrasonic anemometer, air pressure via digital barometer, and temperature via thermocouple), the algorithm checks whether the velocity change rate and direction change rate exceed preset thresholds. The preset thresholds for the velocity change rate and direction change rate are 0.15 and 0.2 radians per second, respectively. Points exceeding these thresholds are marked as deviation points, requiring both the velocity change rate and direction change rate to exceed their limits simultaneously. This generates a set of attitude deviation points.

[0049] The specific implementation steps of sliding window analysis include setting a variable-length analysis window. The window size is adaptively adjusted according to the motion speed using a lookup table. The motion speed is divided into three intervals: low, medium, and high, corresponding to window lengths of 20, 15, and 10 sampling points, respectively. Within each window, the spectral characteristics of the motion acceleration and the gradient of the motion direction angle are calculated. The spectral characteristics of the motion acceleration are calculated using a short-time Fourier transform, with a Hanning window as the window function and a spectral resolution of 0.5 Hz. The gradient of the motion direction angle is obtained by smoothing using a Savitzky-Gore filter and then differentiating it. The filter window size is seven points, and the polynomial order is two.

[0050] Multi-scale analysis is used to fuse spectral features and gradient changes in the time and frequency domains. The multi-scale analysis employs discrete wavelet transform to decompose the signal into three scales, using the db4 wavelet as the wavelet basis function. Time-frequency fusion uses a signal-to-noise ratio (SNR) weighted fusion rule, where the SNR is calculated as the ratio of signal power to noise power. A motion impact assessment matrix is ​​established, with rows corresponding to time points and columns corresponding to fused feature values ​​at different scales. Matrix elements are normalized feature intensity values. Feature values ​​at different scales are combined using principal component analysis (PCA) to reduce dimensionality and extract the first two principal components, generating motion impact superposition assessment data. The dynamic average response benchmark is updated using incremental learning, updating the benchmark value according to the exponential moving average formula each time new data arrives. A hysteresis mechanism is introduced to detect motion mode conflict zones, preventing frequent switching of zone markers near thresholds. The processing of continuous motion record sets employs a parallel pipeline architecture, distributing the calculation of velocity change ratio and direction change ratio to different processing cores simultaneously. The preset threshold settings consider the robot's dynamic characteristics, switching threshold configuration files according to different flight modes.

[0051] The data structure of the attitude deviation point set includes the spatial coordinates of the deviation points, the velocity amplitude characteristics of the deviation points, and the ratio of attitude angle to acceleration fluctuation. The spatial coordinates of the deviation points are improved with an interpolation algorithm to enhance positioning accuracy, and the velocity amplitude characteristics record the root mean square value of the velocity changes. The ratio of attitude angle to acceleration fluctuation calculates the correlation coefficient between the rate of change of attitude angle and the rate of change of acceleration. A Kalman filter is used to fuse real-time environmental data, reducing the interference of sensor noise on deviation judgment. A quality assessment mechanism is introduced in the construction of the motion fluctuation change dataset; only data with a signal-to-noise ratio higher than 10^-10 BJ is included in the analysis. Median filtering is used to remove impulse noise during the generation of the velocity change ratio sequence, and the calculation of the direction change ratio sequence considers the periodicity of the angle. A confidence assessment is introduced in the labeling strategy for deviation points; only points with a confidence level higher than 95% are ultimately included in the attitude deviation point set. The temporal consistency of the entire detection process is ensured by hardware timestamps, and all data processing steps strictly follow the chronological order. A symmetrical expansion method is used for the boundary processing of the variable-length analysis window to avoid data loss at the window edges. The motion impact assessment matrix is ​​stored in a sparse matrix format, storing only non-zero elements to save memory. Principal component analysis employs a power-law iteration method to quickly extract key feature components. The sampling frequency of real-time environmental data is synchronized with the motion data acquisition frequency to ensure data time alignment. Real-time deviation detection is ensured through multi-threading technology, with data processing latency controlled within 20 milliseconds. The output format of the attitude deviation point set includes both binary and JSON formats to support the needs of different downstream systems. All calculation processes are optimized using fixed-point arithmetic to improve computational efficiency while maintaining accuracy.

[0052] Example 4: Based on a set of attitude deviation points, the operation generates correction commands. This involves acquiring the motion response parameters and environmental suitability parameters of all points in the attitude deviation point set. The motion response parameters include the ratio of attitude angle to acceleration fluctuation and the velocity amplitude characteristic value. The environmental suitability parameters are derived from wind speed, temperature, and air pressure readings collected by the airborne atmospheric data system. A joint risk assessment value is calculated for each point. This value is normalized based on the degree of deviation between the parameters and a threshold, generating a joint risk assessment value sequence. Points whose motion response parameters exceed the risk threshold, whose environmental suitability parameters are below the baseline value, and who are located in the motion mode conflict zone are selected from the joint risk assessment value sequence. The risk threshold is dynamically configured according to the robot model, and the baseline value is derived from historical flight environment data statistics. The selected points are identified by their numbers and location descriptions. The location descriptions use a latitude, longitude, and elevation coordinate system and are marked as high-priority correction points. Real-time attitude correction commands are output in a structured format. The specific steps for calculating the joint risk assessment value for each point include constructing a multi-dimensional feature vector containing motion response parameters, environmental suitability parameters, and motion mode labels. Motion response parameters include three dimensions: attitude angle fluctuation ratio, acceleration fluctuation ratio, and velocity amplitude coefficient; environmental suitability parameters include four dimensions: wind speed, temperature, air pressure, and humidity; motion mode labels use one-hot encoding to represent consistent or conflicting motion mode segments. Principal component analysis (PCA) is used to reduce the dimensionality of the multidimensional feature vectors, retaining 95% of the cumulative contribution rate, reducing the original seven-dimensional features to a three-dimensional feature space. Based on the risk feature components, a weighted summation method is used to calculate the comprehensive risk value, with weight coefficients determined using the analytic hierarchy process (AHP), resulting in a consistency ratio of less than 0.1 for the judgment matrix. The comprehensive risk value is normalized using a min-max scaling method to map the values ​​to the range of 0 to 100, generating a standardized sequence of joint risk judgment values.

[0053] The structure of a real-time attitude correction command consists of three parts: a message header, a list of correction points, and a set of correction parameters. The message header includes the command version number, timestamp, robot identifier, and data checksum. The list of correction points is an array listing the indices of the points that need correction, with each point containing its three-dimensional coordinates and correction priority. The set of correction parameters includes the attitude angle correction amount, velocity adjustment value, and expected trajectory coordinates. The command encoding uses a lightweight binary protocol, with a fixed message length of 256 bytes, and the transmission frequency is consistent with the control cycle. Refer to Table 1, which illustrates the feature vector composition and weight allocation during the joint risk assessment value calculation process.

[0054] Table 1: Multidimensional Eigenvector Composition and Principal Component Analysis Weight Allocation Table

[0055]

[0056] The construction of multidimensional feature vectors employs a dynamic dimensionality adjustment mechanism, automatically adapting the feature dimensions based on sensor availability. Principal component analysis (PCA) is used for feature dimensionality reduction to calculate the eigenvalues ​​and eigenvectors of the covariance matrix; after sorting the eigenvalues, the top three principal components are retained. The weighting coefficients in the weighted summation calculation are determined using an expert scoring method, with five UAV control experts independently scoring and the average value taken. The minimum-maximum scaling formula for normalization is: the standardized risk value equals the original risk value minus the minimum value divided by the maximum value minus the minimum value.

[0057] The high-priority correction point marking strategy introduces a time decay factor, automatically increasing the priority of points with an unprocessed time exceeding five seconds by one level. The correction point list is generated using a spatial clustering algorithm, merging neighboring deviation points into groups for batch correction. The calculation of the correction parameter set is based on the robot's inverse dynamics model, solving for the required control quantities according to the desired trajectory and current state. The acquisition frequency of motion response parameters is 100 Hz, and the sampling frequency of environmental suitability parameters is 10 Hz; synchronization of data at different frequencies uses an interpolation alignment method. Risk threshold settings consider the robot's dynamic performance boundaries, loading different threshold configuration files based on different flight modes. Baseline values ​​are calculated using a time sliding window with a window size of 30 seconds, statistically analyzing the mean and variance of environmental parameters. Principal component analysis feature dimensionality reduction is implemented using a power iteration method to solve for eigenvectors, with one hundred iterations and a convergence tolerance of 0.001. Weighted summation calculations are performed on the embedded system using fixed-point arithmetic, retaining four decimal places of precision. The parameter range for normalization is dynamically updated based on historical data, with the minimum and maximum values ​​recalculated every ten minutes.

[0058] The transmission of real-time attitude correction commands employs a reliable transmission protocol with retransmission mechanisms and acknowledgment functions. The list of correction points is sorted by risk value from highest to lowest, and for points with the same risk value, they are sorted by time sequence. Smoothing filters are incorporated into the generation of the correction parameter set to avoid drastic jumps in correction commands. Real-time updates of multidimensional feature vectors utilize incremental learning, updating only the corresponding feature components when new data arrives. The feature dimensionality reduction results from principal component analysis are retrained every five minutes to adapt to environmental changes. The weight coefficients for weighted summation calculations can be adjusted online, with weight update commands sent via ground stations. The joint risk assessment value sequence is stored using a circular buffer, saving the most recent 300 calculation results. High-priority correction points are marked in red, while ordinary priority points are marked in yellow. The structured format of correction commands supports both JSON and binary encoding, automatically selecting the appropriate encoding based on channel quality. Feature vector normalization uses rolling window statistics, with a window size of 100 sampling points. The feature projection matrix from principal component analysis is persistently stored and loaded from flash memory upon system startup. The coefficient matrix for weighted summation calculations is symmetrically processed to ensure the monotonicity of risk values. Real-time attitude correction commands are broadcast, allowing multiple robots within the same area to receive the same command. The compressed transmission of the correction point list uses differential encoding, transmitting only the changed point information. The calibration parameter set is verified in advance using a simulator to ensure the security of the correction commands.

[0059] The integrity check of multidimensional feature vectors uses CRC32 checksums, and data from the previous period is used when feature vectors are corrupted. When singular values ​​appear in the feature dimensionality reduction of principal component analysis, the system automatically switches to a backup feature set. Overflow protection for weighted summation calculations uses saturation operations, limiting risk values ​​to the range of zero to one hundred. Anomaly detection for the joint risk judgment value sequence uses the Isolation Forest algorithm to identify anomalous risk values. The confirmation mechanism for high-priority correction points requires three consecutive periods of risk values ​​exceeding the limit. The digital signature of correction instructions uses the ECDSA algorithm to prevent instruction tampering. Time alignment of feature vectors uses hardware timestamps with microsecond-level accuracy. The feature dimensionality reduction calculation of principal component analysis is distributed across multiple control periods to avoid concentrated computational load. Parallel processing of weighted summation calculations is accelerated using SIMD instructions. Parameter estimation for normalization processing uses robust statistical methods to avoid the influence of outliers.

[0060] Real-time attitude correction commands are managed using a multi-level queue scheduling system, with urgent commands sent first. The list of correction points is updated incrementally to reduce data transmission. The correction parameter set is optimized using model predictive control, calculating the correction sequence for the next three steps. Kalman filtering is used for noise reduction of multidimensional feature vectors to improve feature quality. A warning threshold is set for monitoring the feature contribution rate of principal component analysis; a warning is triggered when the feature contribution rate falls below 90%. The weighting coefficients for weighted summation calculations support remote calibration and are adjusted in real-time via ground control stations. The parameter range initialization for normalization processing uses data statistics from the previous 30 seconds. Trend analysis of the joint risk assessment value sequence uses linear regression to predict risk change trends. A timeout mechanism is set for high-priority correction points; unprocessed points automatically degrade. The correction command log fully records the decision-making process, supporting post-event analysis.

[0061] Example 5: Multi-resolution Acquisition Strategy and Sliding Window Analysis of Airborne Visual Sensors. This example illustrates a specific quadcopter UAV aerial inspection mission. The quadcopter UAV performs building exterior inspection in an urban environment, with its flight path including low-speed hovering observation and high-speed transit flight. The multi-resolution acquisition strategy is implemented based on real-time dynamic adjustment of acquisition parameters according to the UAV's speed. When the UAV's speed exceeds a set threshold of five meters per second, a high-resolution acquisition mode is automatically activated. In this mode, the sensor outputs a full resolution of 1920×1080 pixels with a 12-bit RAW image depth, and the frame rate is increased from the baseline of 30 frames per second to 60 frames per second to capture richer motion details. When the speed is below five meters per second, the system switches to a standard resolution acquisition mode. This mode uses pixel binning technology to reduce the effective resolution to 960×540 pixels, maintaining a frame rate of 30 frames per second to balance the processing load. The frame rate is dynamically adjusted in a positive correlation with the motion acceleration value; for every 1 meter per second increase in acceleration², the frame rate increases by 5 frames per second, up to a hardware limit of 100 frames per second.

[0062] The specific implementation steps of sliding window analysis include setting a variable-length analysis window, with the window size adaptively adjusted according to the motion speed. During the high-speed flight phase of the UAV, the window size is reduced to 10 consecutive sampling points to quickly respond to motion changes, while during the low-speed hovering phase, the window is expanded to 50 sampling points to smooth noise. Within each window, the spectral characteristics of motion acceleration and the gradient of motion direction angle are calculated. The spectral characteristics of motion acceleration are extracted using a short-time Fourier transform, with a Hanning window as the window function and a spectral resolution of 0.5 Hz, focusing on the energy distribution in the 0-10 Hz frequency band. The gradient of motion direction angle is smoothed using a Savitzky-Gore filter with a 7-point window and a polynomial order of 2. The first derivative is then calculated to obtain the rate of change. Multi-scale analysis is used to fuse the spectral characteristics and gradient in the time and frequency domains. The multi-scale analysis uses discrete wavelet transform to decompose the signal into three scales, with the Db4 wavelet as the wavelet basis function. The fusion rule is based on signal-to-noise ratio weighting, with the signal-to-noise ratio determined by calculating the ratio of signal power to noise power. A motion impact assessment matrix is ​​established, where rows correspond to time points and columns correspond to fused feature values ​​at different scales. Feature values ​​include spectral energy, gradient magnitude, and temporal variance. Feature values ​​at different scales are combined using principal component analysis (PCA) to reduce dimensionality and extract the first two principal components, generating a motion impact overlay assessment dataset.

[0063] In a specific example, the drone accelerates from a hovering position at a height of 50 meters towards a tall building, increasing its speed from 0 to 10 meters per second within 3 seconds, with an acceleration of 3 meters per second². The multi-resolution acquisition strategy triggers two mode switches during this process: when the speed exceeds 5 meters per second, it enters high-resolution mode, with the frame rate linearly increasing from 30 frames per second to 45 frames per second; when the speed stabilizes at 10 meters per second, the frame rate is fixed at 60 frames per second. The visual sensor synchronously adjusts the exposure time and gain, shortening the exposure time to 1 / 1000 of a second under high-speed motion to reduce motion blur, and increasing the gain by 6dB to compensate for insufficient light intake. The sliding window analysis adaptively changes with the motion state, rapidly adjusting the window size from an initial 50 points to 10 points, and compressing the frame length of the short-time Fourier transform from 256 points to 64 points to ensure real-time performance. The spectral characteristics of motion acceleration show an energy peak in the 5-8 Hz frequency band, corresponding to the vibration of the building structure caused by wind disturbance. The gradient of the motion direction angle reaches 0.8 radians / second at the turning point, and the Savitzky-Gore filter effectively suppresses noise caused by GPS jump points. The decision logic of the multi-resolution acquisition strategy is based on the motion velocity vector magnitude calculated in real time by the embedded processor. The velocity data comes from the fusion output of the GPS receiver and the inertial measurement unit, with an update frequency of 100 Hz. The threshold comparator adopts a hysteresis comparison design, with a high-speed threshold set at 5 m / s and a low-speed threshold set at 4.5 m / s to prevent frequent mode switching. During resolution switching, the image processor performs pixel merging operations, merging four adjacent pixels in high-resolution mode into one superpixel, reducing the data volume by 75%. Frame rate adjustment is achieved by changing the sensor readout timing. The clock generator dynamically adjusts the master clock frequency according to the acceleration value, and the acceleration data is filtered by a low-pass filter to remove high-frequency noise.

[0064] The sliding window analysis employs a dual-buffer mechanism: one buffer stores the current window data, while the other performs background analysis. Analysis results are transferred to the main processor via a DMA channel. The variable window size adjustment algorithm is based on the exponential moving average prediction of motion speed, with a prediction window of 5 sampling points and a smoothing factor of 0.3. The optimized implementation of the short-time Fourier transform uses the FFTW library, compiled for embedded platforms using the NEON instruction set for acceleration. The convolution operation of the Savitzky-Gore filter is optimized using a lookup table, pre-compiling the polynomial coefficients in a fixed-point format. The wavelet transform for multi-scale analysis uses a lifting scheme, reducing computational complexity by more than 50%. When the drone approaches a building facade and encounters a sudden crosswind, its motion speed fluctuates more. The frame rate of the multi-resolution acquisition strategy is dynamically adjusted between 45-60 frames per second, and the visual sensor simultaneously activates electronic image stabilization, compensating for drone shake through pixel shifting. The sliding window analysis detects a new peak in the motion acceleration spectrum between 2-4 Hz, with a gradient of motion direction angle exceeding 1.0 radians per second. Multi-scale analysis identifies this as a combined effect of wind disturbance and building wake. The motion impact assessment matrix updates its eigenvalues ​​in a timely manner. Principal component analysis shows that the first principal component contributes 65% and the second principal component contributes 23%, effectively capturing motion anomalies. Power consumption management for the multi-resolution acquisition strategy is dynamically adjusted according to the acquisition mode. In high-resolution mode, sensor power consumption increases to 3.5W, and the processor load reaches 80%; in standard resolution mode, power consumption decreases to 2.1W, and the load decreases to 50%. The system is equipped with a temperature monitoring circuit, automatically reducing the frequency when the chip temperature exceeds 85℃. Memory management for sliding window analysis employs a dynamic allocation strategy, reallocating the buffer when the window size changes, with a maximum memory usage limit of 512KB. Data analysis tasks are prioritized in real-time to ensure processing is completed within 10 milliseconds. Throughout the implementation process, the data stream is strictly synchronized; the timestamps of the visual sensors are aligned with the GPS clock via the PTP protocol, achieving microsecond-level accuracy. The output format of the motion impact overlay assessment data includes timestamps, window indexes, and feature vector fields, and is transmitted to the ground station via a gigabit Ethernet interface.

[0065] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for real-time aerial posture correction of a robot based on visual prediction, characterized in that, The method includes: Visual data sequences of robot aerial postures are collected, including image frames and corresponding timestamp information. Posture prediction parameters at each time point are calculated using visual processing algorithms to generate an initial posture prediction dataset. Posture prediction parameters and spatial coordinates are extracted from the initial posture prediction dataset. Cluster analysis is performed on adjacent posture points based on spatiotemporal proximity to identify motion pattern consistent segments and motion pattern conflict segments, forming motion pattern partitioning and labeling results. The attitude points located in the consistent motion pattern segment in the motion pattern partitioning labeling results are obtained. The motion velocity and time series data of the attitude points are extracted. The fluctuation characteristics are analyzed by combining the change in attitude angle. The intensity of motion influence under external environmental interference is evaluated, and motion influence superposition evaluation data is generated. From the motion impact superposition evaluation data, attitude points with motion response values ​​higher than the dynamic average response benchmark are selected, and attitude points located in motion mode conflict zones are also selected. Continuous motion records of attitude points, including velocity and direction values, are extracted in chronological order to form a continuous motion record set. Process the continuous motion record set, calculate the velocity change ratio and direction change ratio between consecutive time points, integrate them into velocity change ratio sequence and direction change ratio sequence, and construct a motion fluctuation change dataset; By combining the motion fluctuation change dataset and real-time environmental data, check whether the velocity change rate and direction change rate exceed preset thresholds. Points that exceed the preset thresholds are marked as deviation points, and a set of attitude deviation points is generated. The motion impact superposition evaluation data includes the impact of motion acceleration, the impact of the rate of change of motion direction, and a comparison of attitude response under various disturbance conditions; The motion response value is a specific value of the influence of the motion acceleration, the influence of the rate of change of motion direction, and the attitude response comparison under various disturbance conditions; Based on the detailed attitude information of all points in the set of attitude deviation points, including position coordinates and motion state, points with correction requirements are marked, and real-time attitude correction commands are generated and output.

2. The method for real-time aerial posture correction of a robot based on vision prediction according to claim 1, characterized in that, The initial attitude prediction dataset includes attitude prediction parameters, robot 3D spatial coordinates, and normalized visual feature vectors. The motion pattern partitioning and labeling results include motion pattern consistent segment identifiers, motion pattern conflict segment identifiers, and the ratio of prediction parameter differences between adjacent attitude points. The attitude deviation point set includes deviation point spatial coordinates, deviation point motion velocity amplitude characteristics, and deviation point attitude angle and acceleration fluctuation ratio. The real-time attitude correction command includes a correction point list and a multi-index joint judgment identifier for correction points.

3. The method for real-time correction of robot aerial posture based on vision prediction according to claim 1, characterized in that, The operation of acquiring visual data sequences of robot aerial posture and generating an initial posture prediction dataset further includes: capturing continuous image frames of robot aerial motion through an airborne vision sensor, synchronously recording the timestamp and spatial location information of each image frame, and storing them as raw visual data; preprocessing the raw visual data, including image enhancement and feature point detection, extracting the motion trajectory and orientation data of key feature points, calculating posture prediction parameters using a prediction model, and integrating the timestamp and spatial location to generate an initial posture prediction dataset.

4. The method for real-time correction of robot aerial posture based on vision prediction according to claim 1, characterized in that, The operation of extracting attitude prediction parameters and performing motion pattern recognition from the initial attitude prediction dataset further includes: reading the attitude prediction parameters and corresponding three-dimensional coordinates from the initial attitude prediction dataset; calculating the spatial distance between all attitude points based on the coordinates; sorting by proximity based on a preset distance threshold to generate a sorted list of adjacent attitude points by distance; based on the sorted list of adjacent attitude points by distance, calculating the difference ratio of their attitude prediction parameters for each pair of adjacent attitude points, which is obtained by the ratio of the parameter difference to the average parameter; summarizing the difference ratios of all point pairs to generate a sequence of prediction parameter difference ratios; analyzing the sequence of prediction parameter difference ratios, comparing the trends of motion direction changes and velocity changes of adjacent attitude points, labeling segments according to trend consistency, and recording segments with consistent motion patterns and segments with conflicting motion patterns to obtain motion pattern partitioning labeling results.

5. The method for real-time correction of robot aerial posture based on vision prediction according to claim 1, characterized in that, The operation of obtaining the attitude points located in the motion pattern consistent segment in the motion pattern partitioning labeling results further includes: according to the motion pattern partitioning labeling results, filtering segments marked as having consistent motion patterns, extracting time series data of each attitude point, including motion velocity and direction angle, arranging them in chronological order to form a motion velocity sequence and a motion direction sequence, generating a motion attribute time series set; performing sliding window analysis on the motion attribute time series set, calculating the rate of change of motion acceleration and the rate of change of motion direction angle within each window, weighting and fusing the two under the same attitude background, evaluating the comprehensive impact value, and generating motion impact superposition evaluation data.

6. The method for real-time correction of robot aerial posture based on vision prediction according to claim 1, characterized in that, The operation of generating correction instructions based on the set of attitude deviation points further includes: obtaining the motion response parameters and environmental suitability parameters of all points in the set of attitude deviation points; calculating the joint risk judgment value of each point; normalizing the value based on the degree of deviation between the parameters and the threshold to generate a joint risk judgment value sequence; selecting points from the joint risk judgment value sequence whose motion response parameters exceed the risk threshold, whose environmental suitability parameters are lower than the benchmark value, and whose points are located in the motion mode conflict zone; extracting their numbers and location descriptions; marking them as high-priority correction points; and outputting real-time attitude correction instructions in a structured format.

7. The method for real-time correction of robot aerial posture based on vision prediction according to claim 6, characterized in that, The specific steps for calculating the joint risk assessment value for each location include: constructing a multidimensional feature vector containing motion response parameters, environmental suitability parameters, and motion pattern labels; performing feature dimensionality reduction on the multidimensional feature vector using principal component analysis to extract risk feature components; calculating the comprehensive risk value using a weighted summation method based on the risk feature components; and normalizing the comprehensive risk value to generate a standardized sequence of joint risk assessment values.

8. The method for real-time correction of robot aerial posture based on vision prediction according to claim 3, characterized in that, When capturing continuous image frames of the robot's aerial movement using an airborne vision sensor, a multi-resolution acquisition strategy is adopted: when the robot's movement speed exceeds a set threshold, a high-resolution acquisition mode is activated; when the movement speed is below the set threshold, the acquisition mode is switched to a standard resolution acquisition mode; and the acquisition frame rate is dynamically adjusted in a positive correlation with the motion acceleration value.

9. The method for real-time correction of robot aerial posture based on vision prediction according to claim 5, characterized in that, The specific implementation steps of the sliding window analysis include: setting a variable-length analysis window, the window size of which is adaptively adjusted according to the motion speed; calculating the spectral characteristics of motion acceleration and the gradient of motion direction angle within each window; fusing the spectral characteristics and gradient in the time and frequency domains through multi-scale analysis; establishing a motion impact assessment matrix, combining the feature values ​​at different scales to generate the motion impact superposition assessment data.

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