A method for controlling the grasping action of a robotic arm on a drone for power transmission line inspection

Abstract

This invention relates to the field of program-controlled robotic arms, specifically to a method for grasping and controlling a drone robotic arm for power transmission line inspection. The invention first collects visual perception data and operational condition impact data under various operational condition influencing indicators during the drone inspection process, and determines the target operating area. During drone operation control, based on the temporal correlation and fluctuation deviation characteristics between operational condition influencing indicators, the interference sensitivity coefficient of each indicator is obtained. Furthermore, combined with the positional deviation of the drone robotic arm relative to the target operating area, the operational instability risk coefficient of the drone is obtained, thereby controlling the grasping posture of the drone robotic arm. This invention analyzes the coupled influence of multiple operational condition influencing indicators on the drone's cruise operation, and combines this with the positioning deviation of the drone robotic arm to comprehensively analyze the operational instability risk coefficient, thus rationally controlling the drone under complex cruise conditions and improving the grasping control accuracy of the drone robotic arm.

Description

Technical Field

[0001] This invention relates to the field of program-controlled robotic arms, specifically to a method for gripping and controlling a drone robotic arm for power transmission line inspection. Background Technology

[0002] Power transmission line inspection is a crucial step in ensuring the safe, stable, and efficient operation of power transmission systems. Regular inspections allow for the timely detection and handling of potential faults, thereby reducing accident risks and ensuring the continuity of power supply. However, traditional inspection methods suffer from low efficiency, involve working at heights, and have numerous blind spots.

[0003] Currently, drones equipped with robotic arms are widely used for power transmission line inspections. These drones, equipped with visual recognition modules, can locate the target work area and then control the robotic arm to perform grasping and maintenance, improving the intelligence and automation level of power transmission line inspections, reducing the risks of high-altitude operations, and increasing work efficiency. However, during power transmission line inspections using drones equipped with robotic arms, complex working conditions in the environment, such as strong winds and airflow interference in mountainous or open areas, may cause the drone's body to vibrate during the inspection process. This can lead to positioning deviations at the robotic arm's end effector, resulting in significant deviations in the drone's grasping and control, making it difficult to meet the requirements of precision operations. Summary of the Invention

[0004] To address the technical problem of significant control deviations in the grasping and gripping control of unmanned aerial vehicles (UAVs) for power transmission line inspection, the present invention aims to provide a grasping and gripping control method for UAVs used in power transmission line inspection. The specific technical solution adopted is as follows:

[0005] A method for controlling the grasping action of a drone robotic arm for power transmission line inspection, the method comprising:

[0006] The system acquires visual perception data and operational impact data for each operational condition under each influencing index at each collection moment during the UAV's inspection of power transmission lines, and determines the target work area based on the visual perception data.

[0007] For the drone operation process in each target operation area, within the preset historical time period of the time to be controlled, based on the temporal change correlation between the operating condition impact data under different operating condition impact indicators, and the fluctuation deviation characteristics of the operating condition impact data under each operating condition impact indicator at each collection time, the interference sensitivity coefficient of each operating condition impact indicator is obtained.

[0008] Based on the positional deviation of the UAV robotic arm relative to the target work area, the temporal variation correlation between the work condition impact data under each work condition impact index and the interference sensitivity coefficient of each work condition impact index, the operational instability risk coefficient of the UAV at the time to be controlled is obtained, and the grasping posture of the UAV robotic arm is adjusted based on the operational instability risk coefficient.

[0009] Furthermore, the operating condition impact indicators include at least the inertial attitude indicators of the UAV and the operating environment indicators. The inertial attitude indicators include at least the roll angle, pitch angle and yaw angle of the UAV, and the operating environment indicators include at least wind speed, temperature and humidity.

[0010] Furthermore, the method for obtaining the interference sensitivity coefficient includes:

[0011] For each operating condition impact index, based on the temporal changes of the operating condition impact data, a preset historical time period is divided for the time to be controlled, and all change time periods are obtained;

[0012] Within each of the aforementioned change periods, the operating condition response weights of each operating condition impact index are obtained based on the temporal change correlation between each operating condition impact index and the corresponding operating condition impact data of the other operating condition impact indices.

[0013] For each operating condition impact index, an interference sensitivity coefficient is obtained based on the operating condition response weight of the operating condition impact index within each change period and the deviation of the operating condition impact data relative to a preset standard at each collection time.

[0014] Furthermore, the method for obtaining the time period of change includes:

[0015] For each operating condition impact index, a time series curve of the operating condition impact data within a preset historical period of the time to be controlled is fitted, and the peak and trough times in the time series curve are used as segmentation points to divide all change periods.

[0016] Furthermore, the method for obtaining the operating condition response weights includes:

[0017] The weights of the influencing indicators for each operating condition are determined based on the CRITIC algorithm, and these weights are the operating condition response weights.

[0018] Furthermore, based on the operating condition response weights of the operating condition impact indicators within each of the said changing time periods, and the deviation of the operating condition impact data relative to a preset standard at each collection time, an interference sensitivity coefficient is obtained, including:

[0019] Within each of the aforementioned change periods, based on the difference between each operating condition impact index and the corresponding operating condition response weights of the other operating condition impact indices, an interference sensitivity significant parameter for each operating condition impact index is obtained; the mean deviation of the operating condition impact data of each operating condition impact index relative to a preset standard at each collection time is used as the interference response deviation parameter.

[0020] By combining the significant disturbance sensitivity parameter and the disturbance response deviation parameter of each operating condition influence index across all the said change periods, the disturbance sensitivity coefficient of each operating condition influence index is obtained.

[0021] Furthermore, the method for obtaining the operational instability risk coefficient includes:

[0022] The spatial distance between the grasping coordinates of the target grasping position in the target work area and the pose coordinates of the end effector of the UAV robotic arm at each acquisition time is taken as the positioning deviation of the UAV robotic arm; the temporal sequence of the positioning deviation within a preset historical period of the time to be controlled is fitted and detrended to obtain the positioning deviation fluctuation sequence.

[0023] For each operating condition impact index, the positioning impact parameter of each operating condition impact index is obtained based on the sequence correlation coefficient between the positioning deviation fluctuation sequence and the time series sequence of the operating condition impact data; by combining the interference sensitivity coefficient of each operating condition impact index with the positioning impact parameter, the operational instability risk coefficient of the UAV at the time to be controlled is obtained.

[0024] Furthermore, detrending processing includes linear detrending processing.

[0025] Furthermore, adjusting the grasping posture of the drone's robotic arm based on the aforementioned operational instability risk coefficient includes:

[0026] At the moment of adjustment, based on the deviation of the operational instability risk coefficient from the preset risk coefficient, the objective function of the MPC model predictive control algorithm is constructed, and the grasping posture of the UAV robotic arm is adjusted based on the MPC model predictive control algorithm.

[0027] Furthermore, the method for obtaining the objective function includes:

[0028] The ratio of the operational instability risk coefficient to the preset risk coefficient is used as the positioning error weight; the positioning error is weighted using the positioning error weight, and the attitude error is weighted using the attitude error weight, and the weighted sum is used as the objective function; wherein, the sum of the positioning error weight and the attitude error weight is 1, and both the positioning error and the attitude error are functions containing the control variables to be solved.

[0029] The present invention has the following beneficial effects:

[0030] This invention first acquires visual perception data and operational condition impact data for each operating condition indicator at each acquisition time during the UAV's inspection of power transmission lines. Based on the visual perception data, the target operation area is determined, providing an analytical basis for subsequent cruise control. For the UAV operation process in each target operation area, within a preset historical time period at the time to be controlled, the interference sensitivity coefficient of each operational condition indicator is obtained based on the temporal correlation between operational condition impact data under different operational condition indicators and the fluctuation deviation characteristics of operational condition impact data under each operational condition indicator at each acquisition time. The interference sensitivity coefficient reflects how each operational condition indicator causes the UAV to be in a complex cruise operation condition. The possibility of each working condition influence index is reflected in the degree of influence on the grasping control accuracy of the UAV robotic arm, which prepares for subsequent assessment of the UAV's operational instability risk coefficient and adjustment of the UAV robotic arm based on the UAV's positioning information. Then, based on the positional deviation of the UAV robotic arm relative to the target working area and the temporal change correlation between the working condition influence data under each working condition influence index, and combined with the interference sensitivity coefficient of each working condition influence index, the operational instability risk coefficient of the UAV at the time to be adjusted is obtained, which provides a basis for subsequent adjustment of the UAV to improve the grasping attitude control accuracy of the UAV robotic arm. Finally, the grasping attitude of the UAV robotic arm is adjusted based on the operational instability risk coefficient. This invention analyzes the coupled influence of multiple working condition influence indices on the UAV's cruise working condition, and combines the positioning deviation of the UAV robotic arm to comprehensively analyze the operational instability risk coefficient of the UAV robotic arm, thereby reasonably adjusting the UAV under complex cruise working conditions and improving the grasping control accuracy of the UAV robotic arm. Attached Figure Description

[0031] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart of a drone robotic arm grasping and control method for power transmission line inspection provided in an embodiment of the present invention;

[0033] Figure 2 This is a flowchart illustrating a method for obtaining interference sensitivity coefficients according to an embodiment of the present invention. Detailed Implementation

[0034] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a UAV robotic arm grasping control method for power transmission line inspection proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0036] The following describes in detail, with reference to the accompanying drawings, a specific scheme for a drone robotic arm grasping and control method for power transmission line inspection provided by the present invention.

[0037] Please see Figure 1 The diagram illustrates a flowchart of a UAV robotic arm grasping control method for power transmission line inspection, provided by an embodiment of the present invention, specifically including:

[0038] Step S1: Acquire visual perception data and working condition impact data for each working condition impact index at each collection moment during the UAV's inspection of power transmission lines, and determine the target operation area based on the visual perception data.

[0039] In one embodiment of the present invention, a drone equipped with a robotic arm is used to inspect power transmission lines and collect visual perception data and working condition information in real time during the inspection process. The working condition information specifically refers to the working condition impact data under each working condition impact index, which provides a basis for subsequent positioning of the target work area and adjustment of the robotic arm's grasping posture.

[0040] The visual perception data is used for target recognition, contour extraction, and component heating status monitoring. It includes at least RGB images, depth images, and infrared images, which are collected by the visual perception module mounted on the UAV. The visual perception module includes an RGB-D camera and an infrared thermal imaging sensor. The RGB-D camera is used to collect RGB and depth images, which can help the UAV locate and identify the target work area. The infrared thermal imaging sensor is used to collect infrared images, which can help determine the abnormal heating area of ​​the power transmission line, thereby determining the target work area.

[0041] Because the actual inspection environment of drones is quite complex, environmental factors (such as strong winds) and changes in the drone's body attitude (such as drone body roll) can both cause the drone's body to shake. At the same time, environmental interference and drone body attitude adjustments may occur simultaneously, creating a coupled effect on the drone's body shaking, which may lead to deviations in the grasping control of the drone's robotic arm. Both environmental interference and changes in the drone's body attitude may put the drone in complex cruise conditions, thus affecting the grasping control accuracy of the drone's robotic arm.

[0042] Based on this, in a preferred embodiment of the present invention, the operating condition impact indicators include at least the inertial attitude indicators of the UAV and the operating environment indicators. The inertial attitude indicators include at least the roll angle, pitch angle, and yaw angle of the UAV, and the operating environment indicators include at least wind speed, temperature, and humidity. In other embodiments, operating condition impact indicators such as meteorological indicators can also be added.

[0043] Specifically, the roll angle, pitch angle, and yaw angle data for each inertial attitude index are collected by the IMU (Inertial Measurement Unit) on the drone, which helps to assess changes in the drone's attitude. Environmental data for each operational environment index are collected by the environmental perception module on the drone. The environmental perception module includes an anemometer, temperature sensor, and humidity sensor, and the environmental data includes wind speed data, temperature data, and humidity data, which helps to assess environmental interference during the drone inspection process.

[0044] In this embodiment, the acquisition frequency of the aforementioned visual perception data and operating condition impact data is set to 20 Hz, and they are acquired synchronously to ensure temporal alignment for subsequent coupling and correlation analysis. In other embodiments, the implementer may also adjust the acquisition frequency of various acquisition units or sensors and then resample different data for alignment analysis.

[0045] Furthermore, the aforementioned visual perception data and operational condition impact data are transmitted in real time via wireless transmission technology. Specifically, the collected data (visual perception data and operational condition impact data) are first cached by the edge computing module mounted on the UAV, and then the cached data is transmitted to the ground control center via 4G / 5G wireless transmission technology, with a transmission rate of greater than or equal to 100Mbps and an end-to-end latency of less than 50ms.

[0046] After receiving the collected data, the ground control center processes the data in real time using the deployed data preprocessing module. Specifically, Gaussian filters and histogram equalization are used to denoise the collected visual perception data. Kalman filters are used to preprocess the operational condition impact data under inertial attitude indicators. The 3σ principle and linear interpolation method are used to remove outliers and fill in missing values ​​in the operational environment impact data. The inertial attitude data and environmental perception data are then standardized and dimensionless. All of the above preprocessing methods are well-known and will not be described in detail here.

[0047] Then, based on the (preprocessed) visual perception data, the target work area during the transmission line inspection process is determined; the target work area refers to the area where foreign objects are attached and the area where faulty components are distributed on the transmission line, such as foreign objects such as plastic film, kite strings and tree branches attached to the core equipment area of ​​the transmission line conductors, insulator strings and towers, or local overheating.

[0048] Specifically, the target work area is determined based on a pre-trained target work area recognition model: the pre-processed RGB image and infrared image data are used as model input, and the trained YOLOv8 model is used to identify the transmission line inspection area, thereby automatically outputting the target work area.

[0049] It should be noted that the training and application of the target operation area identification model are well-known technologies and will not be described in detail here; in other embodiments, implementers may also adopt other model architectures.

[0050] Step S2: For the UAV operation process in each target operation area, within the preset historical time period of the time to be controlled, based on the temporal change correlation between the operating condition impact data under different operating condition impact indicators, and the fluctuation deviation characteristics of the operating condition impact data under each operating condition impact indicator at each collection time, the interference sensitivity coefficient of each operating condition impact indicator is obtained.

[0051] It should be noted that during the inspection of power transmission lines by drones, there may be multiple target work areas. In this embodiment of the invention, the drone's cruise process (the drone's operation process in the target work area) is taken as an example to analyze and control the drone's cruise.

[0052] During the operation of the drone in the target work area, the built-in control module of the drone will automatically issue operation instructions to control the drone to gradually move closer to the target work area and control the drone's robotic arm to perform grasping and maintenance. In one embodiment of the present invention, the control cycle is set to, for example, 1 minute, to periodically judge and control the drone, and the time interval between the control moments is 1 minute. In other embodiments, the implementer can also adjust the control cycle himself.

[0053] Furthermore, at each time point to be regulated, a preset historical period is used as a periodic regulation period. The preset historical period is 1 minute in history, and the time point to be regulated is the end time of the preset historical period. That is, the time period between adjacent times to be regulated is a periodic regulation period, which prepares for subsequent periodic analysis and regulation.

[0054] It should be noted that the periodic analysis and control method for each time period to be controlled is consistent within the preset historical period. Here, we will only take the preset historical period of any time to be controlled as an example for analysis and description, and will not go into detail again.

[0055] Since the fluctuation and deviation of the operating condition impact data under each operating condition impact index can reflect the changes in the environment or the attitude of the UAV, and there may be coupling effects between different operating condition impact indices, the temporal change correlation between the operating condition impact data under different operating condition impact indices can be combined to help comprehensively assess the possibility that each operating condition impact index will cause the UAV to be in a complex cruise condition, that is, the interference sensitivity coefficient of each operating condition impact index.

[0056] The interference sensitivity coefficient reflects the likelihood that each operating condition influence index will cause the UAV to be in a complex cruise condition. It also reflects the degree of influence of each operating condition influence index on the grasping control accuracy of the UAV robotic arm. This prepares for subsequent assessment of the UAV's operational instability risk coefficient and adjustment of the UAV robotic arm based on the UAV's positioning information.

[0057] It should be noted that the analysis method for the interference sensitivity coefficient of each working condition influence index is consistent. Here, any working condition influence index is taken as the target influence index, and the other working condition influence indices are taken as reference influence indices. The analysis and description are based on the target influence index as an example, and will not be repeated one by one.

[0058] Preferably, in one embodiment of the present invention, the method for obtaining the interference sensitivity coefficient includes:

[0059] Please see Figure 2 The flowchart illustrates a method for obtaining the interference sensitivity coefficient according to an embodiment of the present invention, specifically including:

[0060] Step S201: For each type of operating condition impact index, based on the temporal changes of the operating condition impact data, divide the time period to be controlled into preset historical periods and obtain all change periods.

[0061] Considering that the operating condition impact data under the target impact index is usually dynamic and time-varying, directly analyzing the temporal change correlation between the operating condition impact data within the preset historical period of the entire time to be controlled may smooth out some fluctuation characteristics, thus affecting the effect of time-series change analysis.

[0062] Therefore, this embodiment first divides the preset historical period of the time to be controlled into several change periods based on the temporal changes of the operating condition impact data under the target impact index, and then analyzes the temporal change characteristics of the operating condition impact data in each independent time window.

[0063] In a preferred embodiment of the present invention, considering that the operating condition impact data may fluctuate to some extent, and that peaks or troughs can usually represent a turning point of change, which can help determine a monotonically changing time window, i.e., a period of change, for subsequent time-series analysis of the operating condition impact data; therefore, the method for obtaining the period of change includes:

[0064] For each operating condition impact index, a time series curve of the operating condition impact data within a preset historical period of the time to be controlled is fitted, and the peak and trough times in the time series curve are used as segmentation points to divide all change periods.

[0065] Specifically, the least squares method is used to fit the time series curve of the target influence index, and the determination of the corresponding times of peaks and troughs are well-known techniques and will not be elaborated further. In another embodiment of the present invention, after fitting the time series curve, the implementer can also use the autocorrelation function to analyze the period of the time series curve, divide the preset historical time period by the period, and thus determine all the changing time periods. This is a well-known technique and will not be elaborated further.

[0066] Step S202: Within each change period, based on the temporal change correlation between each working condition impact index and the corresponding working condition impact data of other working condition impact indices, obtain the working condition response weight of each working condition impact index.

[0067] Since there may be coupling relationships between the operating condition impact indicators, analyzing the temporal change relationship between the target impact indicator and other reference impact indicators can help assess the relative importance of the target impact indicator in the coupling relationship. This allows us to determine the operating condition response weight of the target impact indicator, which characterizes the degree of relative decision-making influence of the target impact indicator on putting the UAV in a complex cruise condition, thus preparing for subsequent measurement of its interference sensitivity coefficient.

[0068] In a preferred embodiment of the present invention, considering that the CRITIC algorithm (Criteria Importance Through Intercriteria Correlation) is an analytical method for objectively assigning weights based on the correlation and conflict of indicators, and can be used for multi-indicator decision analysis to determine the importance weight of each indicator; the method for obtaining the working condition response weight includes: determining the weight of each working condition influencing indicator based on the CRITIC algorithm, and the weight is the working condition response weight.

[0069] It should be noted that the CRITIC algorithm is a well-known technique, and is briefly described here: Within each change period, the time series of the operating condition impact data for each operating condition impact indicator is used as a row vector to construct a data matrix. This data matrix is ​​then used as input to the CRITIC algorithm to calculate the correlation and conflict among the operating condition impact indicators within the change period, and outputs the weight of each operating condition impact indicator (including the target impact indicator and all reference impact indicators), i.e., the operating condition response weight. When the length of the divided change period is less than the preset number of sampling points, such as 10, it is merged with adjacent periods to avoid the CRITIC algorithm failing due to extremely short periods.

[0070] In another embodiment of the present invention, the entropy weight method can also be used to determine the operating condition response weight of each operating condition influence index. This is a well-known technical means and will not be described in detail here.

[0071] Step S203: For each type of operating condition impact index, obtain the interference sensitivity coefficient based on the operating condition response weight of the operating condition impact index in each change period and the deviation of the operating condition impact data from the preset standard at each collection time.

[0072] Since the operating condition response weight can characterize the relative importance of the target impact index in the coupling and correlation of multiple operating condition impact indices, the larger the operating condition response weight, the greater the possibility of the UAV experiencing complex cruise conditions. The deviation of the operating condition impact data under the target impact index from the preset standard directly reflects its change. Therefore, the operating condition response weight can be combined to comprehensively evaluate the impact of changes in the target impact index on decision-making in complex cruise conditions, and thus determine the interference sensitivity coefficient of the target impact index.

[0073] Based on this, in a preferred embodiment of the present invention, obtaining the interference sensitivity coefficient includes:

[0074] Within each period of change, based on the difference between each working condition influence index and the corresponding working condition response weight of each other working condition influence index, the interference sensitivity significant parameter of each working condition influence index is obtained; the mean of the deviation of the working condition influence data of each working condition influence index relative to the preset standard at each collection time is used as the interference response deviation parameter.

[0075] By combining the significant parameters of interference sensitivity and the interference response deviation parameters of each operating condition's impact index across all time periods, the interference sensitivity coefficient of each operating condition's impact index is obtained.

[0076] As an example, within each period of change, the difference between the target impact indicator and the corresponding operating condition response weights of each reference impact indicator is measured. Specifically, the difference is obtained by subtracting the operating condition response weights of each reference impact indicator from the operating condition response weight of the target impact indicator. This difference is then normalized, for example, by using maximum and minimum value normalization, and the average of all normalized differences is calculated to obtain the interference-sensitive significant parameter of the target impact indicator. The larger the operating condition response weight of the target impact indicator relative to the operating condition response weights of the other reference impact indicators, the larger the interference-sensitive significant parameter of the target impact indicator. The interference-sensitive significant parameter reflects the interference significance characteristics of the target impact indicator.

[0077] Furthermore, at each acquisition time within each change period, the absolute value of the difference between the operating condition impact data of the target impact index and the preset standard is calculated, and the mean of the absolute values ​​of the differences at all acquisition times is used as the interference response deviation parameter. The larger the mean of the absolute values ​​of the differences, the greater the fluctuation and deviation of the operating condition impact data under the target impact index, the larger the interference response deviation parameter, and the greater the possibility of triggering complex cruise conditions. The interference response deviation parameter reflects the fluctuation and interference characteristics of the target impact index.

[0078] For each inertial attitude index, the preset standard refers to the inertial attitude data preset by the UAV control command during the operation. For example, when the target influence index is pitch angle, the preset standard refers to the pitch angle value preset by the UAV command at each data collection moment. For each operational environment index, the preset standard refers to the meteorological monitoring data value corresponding to the UAV's flight area during the operation, or the average value of the operational environment data can be used instead. For example, when the target influence index is temperature, the preset standard refers to the meteorological temperature of the UAV at each data collection moment.

[0079] Then, the interference sensitivity significant parameters and interference response deviation parameters of the target impact index in each change period are fused. Specifically, this can be done by multiplication or by weighted summation to obtain the interference sensitivity sub-parameters of the target impact index in each change period. Then, the interference sensitivity sub-parameters of the target impact index in all change periods are averaged to obtain the interference sensitivity coefficient of the target impact index.

[0080] By changing the target impact index, the time period of change under each working condition impact index can be redefined, thereby determining the interference sensitivity coefficient of each working condition impact index.

[0081] Step S3: Based on the positional deviation of the UAV robotic arm relative to the target work area and the temporal change correlation between the work condition impact data under each work condition impact index, and combined with the interference sensitivity coefficient of each work condition impact index, obtain the operational instability risk coefficient of the UAV at the time to be controlled, and adjust the grasping posture of the UAV robotic arm based on the operational instability risk coefficient.

[0082] Complex cruise conditions can cause drone body attitude jitter, resulting in positioning errors. Analyzing the temporal correlation between the relative positioning error of the drone (robotic arm) and target influence indicators can help assess the degree of impact of target influence indicators on positioning errors. The interference sensitivity coefficient can reflect the degree of impact of target influence indicators on the grasping control accuracy of the drone robotic arm. In addition, all operating condition influence indicators can be comprehensively evaluated to assess the operational instability risk coefficient of the drone, providing a basis for subsequent drone control to improve the grasping attitude control accuracy of the drone robotic arm.

[0083] Preferably, in one embodiment of the present invention, considering that during the operation of the UAV, the UAV may not only be in a hovering grasping state, but also in a flying state, and its position relative to the target operation area may change due to movement, directly using the spatial distance between the target grasping position in the target operation area and the end effector of the UAV's robotic arm as the positioning error may affect the accuracy of subsequent time-series correlation analysis between positioning error and target impact indicators; however, detrending the change in spatial distance can help assess the relative fluctuation of spatial distance, which can be regarded as a positioning error, thereby providing a data basis for subsequent analysis of the positioning impact parameters of target impact indicators; thus comprehensively assessing the operational instability risk coefficient of the UAV; the method for obtaining the operational instability risk coefficient includes:

[0084] The spatial distance between the grasping coordinates of the target grasping position in the target operation area and the pose coordinates of the end effector of the UAV robotic arm at each acquisition time is taken as the positioning deviation of the UAV robotic arm; the time series sequence of the positioning deviation within the preset historical period of the time to be controlled is fitted and detrended to obtain the positioning deviation fluctuation sequence.

[0085] For each operating condition impact index, the positioning impact parameter of each operating condition impact index is obtained based on the serial correlation coefficient between the positioning deviation fluctuation sequence and the time series sequence of the operating condition impact data; by combining the interference sensitivity coefficient of each operating condition impact index with the positioning impact parameter, the operational instability risk coefficient of the UAV at the time to be controlled is obtained.

[0086] As an example, firstly, at each acquisition time within the preset historical period of the time to be controlled, based on the depth information provided by the visual perception module and the recognition results of the target operation area, the three-dimensional coordinates of the target grasping position in the target operation area, i.e., the grasping coordinates, are calculated; based on the angle sensor data of each joint of the robotic arm and through the forward kinematics, the pose coordinates of the end effector of the UAV robotic arm are calculated; these are well-known technologies and will not be elaborated further.

[0087] Then, at each acquisition time, the spatial distance between the grasping coordinates of the target grasping position in the target operation area and the pose coordinates of the end effector of the UAV robotic arm is taken as the positioning deviation of the UAV robotic arm. The positioning deviation is a relative position difference, which may include positioning errors caused by UAV pose jitter. Then, the positioning deviation is sorted in time series to fit the time series sequence, and further detrending processing is performed on the time series sequence to obtain the positioning deviation fluctuation sequence.

[0088] In a preferred embodiment of the present invention, the detrending process includes linear detrending; linear detrending is a well-known technical means, which is briefly described here: first, the trend curve of the positioning deviation is fitted based on the least squares method, and each positioning deviation in the time series of the positioning deviation is subtracted from the fitted positioning deviation in the trend curve, and the difference is used as a sequence element to fit the positioning deviation fluctuation sequence.

[0089] In other embodiments, implementers may also use moving average detrending processing, which will not be elaborated further.

[0090] After determining the positioning deviation fluctuation sequence, we can further analyze its correlation with the time series of operating condition impact data under each operating condition impact index, thereby obtaining the positioning impact parameters of each operating condition impact index. Specifically, taking any operating condition impact index as an example, we first sort the operating condition impact data within a preset historical period of the time to be controlled, and construct a time series of operating condition impact data. We then use the Pearson correlation coefficient to characterize the correlation between the positioning deviation fluctuation sequence and the operating condition impact data time series, and use the absolute value of the Pearson correlation coefficient as the positioning impact parameter. The positioning impact parameter directly reflects the impact of the operating condition impact index on the positioning of the UAV from the perspective of the positioning deviation fluctuation caused by the operating condition impact index, preparing for the subsequent assessment of the operational instability risk coefficient.

[0091] Further integrate the interference sensitivity coefficient and positioning influence parameter of each working condition influence index. Specifically, this can be done by multiplication or by weighted summation to obtain the sub-parameter of the UAV's operational instability risk under each working condition influence index. Then, sum the sub-parameters of the operational instability risk under all working condition influence indices to comprehensively obtain the operational instability risk coefficient of the UAV at the time to be controlled.

[0092] After obtaining the operational instability risk coefficient of the drone at the time to be controlled, the grasping posture of the drone's robotic arm can be adjusted based on the operational instability risk coefficient.

[0093] Preferably, in one embodiment of the present invention, considering that Model Predictive Control (MPC) can perform multi-objective optimization in complex inspection tasks of UAV robotic arms, such as balancing multiple objectives like the positioning accuracy and attitude accuracy of the UAV, thereby providing better control performance; and that the operational instability risk coefficient can provide positioning fluctuation information of the UAV, thereby helping to balance positioning accuracy and attitude accuracy, constructing an MPC objective function, so that when positioning is accurate, the focus can be on precise attitude adjustment, or when positioning deviation is large, priority can be given to precise positioning, thereby improving control performance; therefore, adjusting the grasping attitude of the UAV robotic arm based on the operational instability risk coefficient includes:

[0094] At the moment of waiting for adjustment, the objective function of the MPC model predictive control algorithm is constructed based on the deviation of the operational instability risk coefficient from the preset risk coefficient, and the grasping posture of the UAV robotic arm is adjusted based on the MPC model predictive control algorithm.

[0095] In a preferred embodiment of the present invention, the method for obtaining the objective function includes:

[0096] The ratio of the operational instability risk coefficient to the preset risk coefficient is used as the positioning error weight; the positioning error is weighted using the positioning error weight, and the model's predicted attitude error is weighted using the attitude error weight. The weighted sum is used as the objective function; where the sum of the positioning error weight and the attitude error weight is 1, and both the positioning error and the attitude error are functions containing the control variables to be solved.

[0097] As an example, the objective function of the MPC model predictive control algorithm is: ; Represent the objective function; Indicates positioning error. Indicates the positioning error weight; Indicates attitude error. Indicates the attitude error weights. ; This represents a function that takes the minimum value. This represents the control variable to be solved;

[0098] Among them, the positioning error weight is calculated at each time point to be adjusted. = ; This is the initial value of the positioning error weight or the positioning error weight determined at the previous time point to be adjusted; The coefficient for operational instability risk; To preset the risk coefficient; in this embodiment, a positioning error weight is set. The value range is 0.3-0.7. When it exceeds the range, the upper or lower limit of the range will be automatically used.

[0099] It should be noted that the preset risk coefficient is a non-zero constant benchmark value obtained through historical experiments based on the maximum allowable positioning error tolerance and attitude safety threshold of the UAV under ideal windless conditions.

[0100] Since the MPC objective function is an optimization objective, the drone's built-in control module automatically calculates and issues operation commands. The operation commands contain control quantities u for each step of the drone's movement, such as the drone's flight speed, robotic arm joint rotation angle, angular velocity, etc. Through MPC optimization, different control quantities u can be tried continuously to find the "golden control quantity u" that minimizes the MPC objective function, thereby determining the optimal operation commands to control the drone's pose and the robotic arm's grasping pose.

[0101] Thus, during the waiting period, the grasping posture of the UAV robotic arm is adjusted based on the MPC model predictive control algorithm, thereby improving the control accuracy of the UAV robotic arm under complex inspection conditions and enabling refined inspection of power transmission lines.

[0102] Furthermore, after the drone robotic arm finishes its work within the target work area, it analyzes the visual perception data of the target work area to assess whether the inspection work of the current target work area should be terminated. For example, based on the YOLOv8 model, it analyzes and identifies whether the removal of foreign objects within the target work area has been completed. By comparing the identification results of the target area before and after the operation, it calculates the proportion of the residual area of ​​foreign objects. If the residual area is ≤0.01m², it indicates that the robotic arm has completed the grabbing and removal of the attached foreign objects, and the inspection work of the next target work area can be carried out.

[0103] In summary, this invention first acquires visual perception data and operational condition impact data for each operating condition influencing indicator at each acquisition time during the UAV's power transmission line inspection process, and determines the target operation area based on the visual perception data. For the UAV operation process in each target operation area, within a preset historical time period at the time to be controlled, the interference sensitivity coefficient of each operating condition influencing indicator is obtained based on the temporal variation correlation between operational condition impact data under different operating condition influencing indicators and the fluctuation deviation characteristics of operational condition impact data under each operating condition influencing indicator at each acquisition time. Based on the positional deviation of the UAV's robotic arm relative to the target operation area and the temporal variation correlation between operational condition impact data under each operating condition influencing indicator, and combined with the interference sensitivity coefficient of each operating condition influencing indicator, the operational instability risk coefficient of the UAV at the time to be controlled is obtained, and the grasping posture of the UAV's robotic arm is adjusted based on the operational instability risk coefficient. This invention analyzes the coupled influence of multiple operating condition influencing indicators on the UAV's cruise operation, and combines it with the positioning deviation of the UAV's robotic arm to comprehensively analyze the operational instability risk coefficient of the UAV's robotic arm, thereby reasonably adjusting the UAV under complex cruise operation conditions and improving the grasping control accuracy of the UAV's robotic arm.

[0104] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A UAV manipulator grasping control method for power line inspection, characterized in that, The method comprises: acquiring visual perception data of the unmanned aerial vehicle at each collection time and work condition influence data under each work condition influence index during power line inspection, and determining a target operation area based on the visual perception data; for the unmanned aerial vehicle operation process of each target operation area, within a preset historical period at a to-be-regulated time, acquiring an interference sensitivity coefficient of each work condition influence index according to the time sequence change correlation between the work condition influence data under different work condition influence indexes and the fluctuation deviation characteristics of the work condition influence data under each work condition influence index at each collection time; acquiring an operation instability risk coefficient of the unmanned aerial vehicle at the to-be-regulated time according to the position deviation of the unmanned aerial vehicle mechanical arm relative to the target operation area, the time sequence change correlation between the work condition influence data under each work condition influence index, and the interference sensitivity coefficient of each work condition influence index, and regulating the grabbing posture of the unmanned aerial vehicle mechanical arm based on the operation instability risk coefficient; the interference sensitivity coefficient acquisition method comprises: for each work condition influence index, dividing a preset historical period at a to-be-regulated time according to the time sequence change of the work condition influence data, and acquiring all change periods; within each change period, acquiring a work condition response weight of each work condition influence index according to the time sequence change correlation between the work condition influence data corresponding to each work condition influence index and the remaining work condition influence indexes; for each work condition influence index, acquiring an interference sensitivity coefficient according to the work condition response weight of the work condition influence index within each change period and the deviation of the work condition influence data at each collection time relative to a preset standard; the operation instability risk coefficient acquisition method comprises: taking the spatial distance between the grabbing coordinates of the target grabbing position in the target operation area at each collection time and the pose coordinates of the end effector of the unmanned aerial vehicle mechanical arm as the positioning deviation of the unmanned aerial vehicle mechanical arm, fitting the time sequence of the positioning deviation within a preset historical period at a to-be-regulated time and performing detrending processing to acquire a positioning deviation fluctuation sequence; for each work condition influence index, acquiring a positioning influence parameter of each work condition influence index according to the sequence correlation coefficient between the positioning deviation fluctuation sequence and the time sequence of the work condition influence data, and acquiring an operation instability risk coefficient of the unmanned aerial vehicle at the to-be-regulated time by comprehensively considering the interference sensitivity coefficient and the positioning influence parameter of each work condition influence index.

2. The unmanned aerial vehicle mechanical arm grabbing control method for power transmission line inspection according to claim 1, characterized in that, The work condition influence index at least includes an inertial attitude index of the unmanned aerial vehicle and an operation environment index, the inertial attitude index at least includes a roll angle, a pitch angle and a yaw angle of the unmanned aerial vehicle, and the operation environment index at least includes a wind speed, a temperature and a humidity.

3. The unmanned aerial vehicle mechanical arm grabbing control method for power transmission line inspection according to claim 1, characterized in that, The change period acquisition method comprises: for each work condition influence index, fitting a time sequence curve of the work condition influence data within a preset historical period at a to-be-regulated time, and taking both the wave peak time and the wave trough time in the time sequence curve as segmentation points to divide all change periods.

4. The unmanned aerial vehicle mechanical arm grabbing control method for power transmission line inspection according to claim 1, characterized in that, The work condition response weight acquisition method comprises: determining a weight of each work condition influence index based on a CRITIC algorithm, and the weight is a work condition response weight.

5. The unmanned aerial vehicle mechanical arm grabbing control method for power transmission line inspection according to claim 1, characterized in that, According to the working condition response weight of each working condition influence index in each change period and the deviation of the working condition influence data at each collection time from the preset standard, an interference sensitive coefficient is obtained, comprising: In each change period, according to the difference between each working condition influence index and the working condition response weight corresponding to each working condition influence index, an interference sensitive significant parameter of each working condition influence index is obtained; the mean of the deviation of the working condition influence data of each working condition influence index at each collection time from the preset standard is taken as an interference response deviation parameter; The interference sensitive significant parameter and the interference response deviation parameter of each working condition influence index in all change periods are integrated to obtain an interference sensitive coefficient of each working condition influence index.

6. The unmanned aerial vehicle mechanical arm grabbing control method for power transmission line inspection according to claim 1, characterized in that, The detrending processing includes linear detrending processing.

7. The unmanned aerial vehicle mechanical arm grabbing control method for power transmission line inspection according to claim 1, characterized in that, Based on the operation instability risk coefficient, the grasping posture of the unmanned aerial vehicle mechanical arm is regulated, comprising: At the time to be regulated, according to the deviation of the operation instability risk coefficient from the preset risk coefficient, a target function of the MPC model predictive control algorithm is constructed, and the grasping posture of the unmanned aerial vehicle mechanical arm is regulated based on the MPC model predictive control algorithm.

8. The unmanned aerial vehicle mechanical arm grabbing control method for power transmission line inspection according to claim 7, characterized in that, The target function comprises: The ratio of the operation instability risk coefficient to the preset risk coefficient is taken as a positioning error weight; the positioning error is weighted by using the positioning error weight, the attitude error is weighted by using an attitude error weight, and the weighted sum result is taken as the target function; wherein the sum of the positioning error weight and the attitude error weight is 1, and the positioning error and the attitude error are both functions containing to-be-solved control variables.

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