Unmanned aerial vehicle arm visual servo control system and method for power transmission line inspection

Abstract

The invention discloses an unmanned aerial vehicle arm visual servo control system and method for power transmission line inspection, and relates to the technical field of power transmission line inspection. Determining first energy consumption and second energy consumption of each power transmission line according to the inspection historical data of the power transmission lines; distributing an unmanned aerial vehicle to inspect the power transmission line according to the energy consumption information of the power transmission line; in the flying process, objects in the air and on the ground can be tracked and captured, and foreign matter and bird nests of the power transmission line are removed; the method can be widely applied to exploration tasks of unmanned areas; the robot can quickly reach changeable terrains where the pavement robot cannot reach to execute rescue tasks; a plurality of flying mechanical arms can actively and cooperatively carry and assemble a large load; the unmanned aerial vehicle can be used for high-risk occupations such as high-altitude rescue, construction and equipment maintenance instead of workers, can fly to inspect and can also complete various maintenance operations, so that the flexibility and efficiency of inspection are greatly improved, and the life safety of the workers is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of power transmission line inspection, and in particular to a visual servo control system and method for an unmanned aerial vehicle arm for power transmission line inspection. Background Art

[0002] With the rapid development of robot technology, power maintenance manipulators have been gradually applied to power transmission line maintenance operations. While improving the efficiency of power transmission line maintenance work, it has reduced the cost of professional personnel training and improved the safety of operations. However, the motion trajectory of the manipulator needs to be adjusted according to different operation tasks. Therefore, the rapid planning of the power maintenance manipulator action has become a new development demand. The drone with a manipulator cooperates with the operation and inspection personnel to grab objects and fly. It is currently in its infancy at home and abroad and can be used in a variety of power operation and inspection scenarios. The application field in the future will be very wide and has great transformation prospects. The multi-rotor drone is equipped with a six-degree-of-freedom tandem manipulator to carry out the aerial grabbing task of the target object. Compared with traditional drones, the flying manipulator has many advantages. It can track and capture objects in the air and on the ground, remove foreign objects and bird nests from power transmission lines, and multiple flying manipulators can actively cooperate to carry and assemble large loads. However, the carrying of the manipulator puts higher requirements on the endurance of the drone. In addition to the increase in the mass of the drone, the drone needs to consume additional energy to maintain balance when the manipulator is working. Therefore, how to assign inspection tasks to the drone according to its endurance has become an urgent problem to be solved. Summary of the invention

[0003] The purpose of the present invention is to provide a teaching resource management system and method based on multidimensional data analysis to solve the problems raised in the prior art.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a visual servo control system for a drone arm for power transmission line inspection, comprising a model building module, a drone module, a data storage module and a power transmission line allocation module; the output end of the model building module is connected to the input end of the drone module, so as to generate control signals for the drone and the mechanical arm; the output end of the drone module is connected to the input end of the data storage module, so as to obtain image data of the power transmission line and clean foreign objects on the power transmission line through the mechanical arm; the output end of the data storage module is connected to the input end of the power transmission line allocation module, so as to store the inspection data of the power transmission line by the drone; the power transmission line allocation module is used to allocate the power transmission line to the drone for inspection.

[0005] The transmission line allocation module also includes: an energy consumption calculation unit, a kernel density estimation unit and an allocation optimization unit; the energy consumption calculation unit is used to determine the power consumption of the drone on the transmission line when the robot arm has a foreign object cleaning behavior and does not have a foreign object cleaning behavior, and calculate the power consumption speed of the drone when it moves when there is no foreign object cleaning behavior; the kernel density estimation unit generates a probability density function of the drone's power consumption based on the drone's power consumption data on the transmission line; the allocation optimization unit, with the goal of minimizing the probability that the drone cannot complete the inspection of the transmission line, allocates the transmission line to the drone for inspection through a simulated annealing algorithm.

[0006] The model building module also includes: a visual servo unit, a robotic arm kinematic modeling unit and a UAV visual servo grasping unit; the visual servo unit is used for point feature pose information of foreign objects on the transmission line; the robotic arm kinematic modeling unit is used to analyze the differential kinematic model of the robotic arm during the flight of the UAV; the UAV visual servo grasping unit is used to verify the visual servo function on the ground. The UAV module also includes: a UAV, a control unit, a robotic arm and a camera; the UAV is used to inspect the transmission line; the control unit is used to control the movement of the UAV and the robotic arm; the robotic arm is used to clean foreign objects on the transmission line; the camera is used to obtain image data of the transmission line. The visual servo unit performs hybrid visual servoing based on the position-based visual servo PBVS and the image-based visual servo IBVS to achieve global optimization of the controller.

[0007] Position-based visual servo uses position and attitude information, and needs to obtain three-dimensional information through features in the image. It has a large amount of calculation, but the accuracy is not high. Image-based visual servo usually uses information on the pixel plane, such as points, lines, circles, image moments, etc. It has a small amount of calculation and high accuracy, but it may fall into local optimality. In view of the advantages and disadvantages of PBVS and IBVS, hybrid visual servo is proposed to ensure the global optimality of the controller.

[0008] To achieve the above object, the present invention provides the following technical solution: a visual servo control method for a drone arm for power transmission line inspection, comprising the following steps:

[0009] S11, obtaining inspection history data of the transmission line;

[0010] S12, determining a first energy consumption and a second energy consumption of each transmission line according to inspection history data of the transmission line;

[0011] S13, assigning drones to inspect the transmission lines according to the energy consumption information of the transmission lines.

[0012] In step S12, determining the first energy consumption and the second energy consumption of each transmission line according to the inspection history data of the transmission line further includes the following steps:

[0013] S21, in the inspection history data of the transmission line, according to the transmission line image data collected by the drone camera, when there is no foreign matter on the transmission line and the robot arm is working, according to the power C1u before the drone inspects the transmission line and the power C1d after the drone inspects the transmission line, the first energy consumption C1 of the transmission line is obtained, C1=C1d-C1u;

[0014] S22, when there is no foreign matter on the transmission line and the robot arm performs cleaning work, the second energy consumption C2 of the transmission line is obtained according to the power C2u before the drone inspects the transmission line and the power C2d after the drone inspects the transmission line, C2=C2d-C2u;

[0015] S23, based on the first energy consumption data and the second energy consumption data of the transmission line, obtain the power consumption of the UAV on the transmission line and the corresponding frequency, which are recorded as probability density function data samples, and obtain the probability density function of the UAV power consumption through kernel density estimation based on the probability density function data samples, and obtain the expected value of the UAV power consumption on the transmission line according to the probability density function.

[0016] After the UAV is equipped with a robotic arm, the dynamics of the two are coupled with each other, which means that the movement of the robotic arm will affect the flight state of the UAV, causing the UAV to consume additional energy to maintain stability and control; the movement of the robotic arm will generate additional load and energy consumption for the UAV. In the research on the rotor flight robotic arm, due to the movement and operation of the robotic arm, the UAV needs more energy to maintain flight and perform tasks, which directly affects the UAV's endurance. The situation of foreign matter on the transmission line will affect the movement of the robotic arm. Based on the historical inspection data of the transmission line, the impact of foreign matter on the UAV's endurance is obtained.

[0017] In step S13, the step of assigning a drone to inspect the power transmission line according to the energy consumption information of the power transmission line also includes the following steps:

[0018] S31, obtain the available power information of all current drones and the expected value of drone power consumption on each transmission line, assign transmission lines to all drones for inspection, determine the power cost required for drone inspection according to the transmission lines to which the drones are assigned, and set the maximum value of the power cost required for all drone inspections as the adjustment target; adjust the allocation method of the transmission lines so that the adjustment target is minimized, and use the allocation method of the transmission lines at this time as the current solution; set the initial value of the annealing temperature T, and obtain the estimated value of the current solution;

[0019] S32, repeating steps S33 and S34 until the number of inner loops reaches a first set value;

[0020] S33, based on the current solution, adjusting the allocation method of the transmission lines to generate a new solution, and calculating the estimated value of the new solution;

[0021] S34, calculate the increment of the estimated value between the new solution and the current solution. If the increment is less than 0, the new solution is accepted and regarded as the current solution. If the increment is not less than 0, the new solution is considered as the current solution according to the probability e -freq / T Accept the new solution and regard it as the current solution; where freq is the estimated value of the current solution and e is a natural number; increase the number of inner loops by one;

[0022] S35, lowering the annealing temperature T. If the annealing temperature T is not less than the second set value, returning to step S32, resetting the number of inner loops; if the annealing temperature T is less than the set value, stopping the algorithm, returning to the transmission line allocation method with the smallest estimated value in the algorithm process; controlling the drone to inspect the allocated transmission lines;

[0023] The sub-estimate is calculated using the following steps:

[0024] For a single UAV, obtain the power cost required for the UAV inspection and the existing power costm of the UAV, and calculate the surplus dcost, dcost = costm-cost; obtain the probability density function of the transmission line to which the UAV is assigned, and calculate the joint probability density of the power consumption on all the transmission lines to which the UAV is assigned; calculate the probability Pd that the UAV power is insufficient to complete the inspection task, and obtain the expected value Ecost of the power consumption on all the transmission lines to which the UAV is assigned based on the joint probability density, and calculate the probability that the power consumption on all the transmission lines to which the UAV is assigned is greater than Ecost + dcost to obtain Pd; add the probabilities that all UAVs have insufficient power to complete the inspection task to obtain the estimated value.

[0025] The existing power of the drone needs to be no less than the power required for inspection, otherwise there is a high probability that the drone's endurance is insufficient and it cannot complete the inspection task. Since the power required for inspection is the expected value, the actual power required may be higher. Therefore, the larger the margin between the existing power of the drone and the power required for inspection, the smaller the probability that the drone's power is insufficient to complete the inspection task, the stronger the resistance to foreign object addition events, and the higher the probability of completing the inspection task. The sub-estimate is set to the sum of the probabilities that all drones have insufficient power to complete the inspection task. Reducing the sub-estimate to the minimum is the optimization goal.

[0026] In step S31, the following steps are also included:

[0027] Let the transmission line to which the drone is assigned be the target line, obtain the expected value of the drone's power consumption on the kth target line and record it as E(k), and calculate the power cost required for the drone inspection, cost = ∑E(k) + TE + FE, where TE is the power consumed by the drone on the transition line connecting the target line, and FE is the power consumed by the drone when returning to the docking platform.

[0028] Compared with the prior art, the beneficial effects of the present invention are: it can track and capture aerial and ground objects during flight, and remove foreign objects and bird nests from power transmission lines; it can also be widely used in exploration tasks in uninhabited areas; it can quickly reach variable terrain that road robots cannot reach to perform rescue missions; multiple flying robotic arms can actively coordinate to carry and assemble large loads; it can replace workers in high-risk occupations such as high-altitude rescue, construction, and equipment maintenance, and can not only fly for inspections, but also complete various maintenance operations, greatly improving the flexibility and efficiency of inspections and protecting the lives of workers. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 The figure is a schematic diagram of the structure of the visual servo control system of the UAV arm for power transmission line inspection of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Example: Figure 1 As shown, the present invention provides a technical solution, a UAV arm visual servo control system for power transmission line inspection, comprising a model building module, a UAV module, a data storage module and a power transmission line allocation module; the output end of the model building module is connected to the input end of the UAV module, for generating control signals for the UAV and the mechanical arm; the output end of the UAV module is connected to the input end of the data storage module, for acquiring image data of the power transmission line, and cleaning foreign objects on the power transmission line by the mechanical arm; the output end of the data storage module is connected to the input end of the power transmission line allocation module, for storing the inspection data of the UAV on the power transmission line; the power transmission line allocation module is used to allocate the power transmission line to the UAV for inspection.

[0032] The model building module also includes: a visual servo unit, a robotic arm kinematic modeling unit and a drone visual servo grasping unit; the visual servo unit is used for point feature pose information of foreign objects on the transmission line; the visual servo unit performs hybrid visual servoing based on position visual servo PBVS and image-based visual servo IBVS to achieve global optimization of the controller.

[0033] There are two camera installation methods for the flying robot arm, the "eye-in-hand" type and the "eye-to-hand" type. In order to obtain a larger camera field of view and improve the flexibility of the camera, the "eye-in-hand" type is adopted, that is, the camera is installed on the end effector of the flying robot arm. The essence of visual servo control is that the error can converge to zero asymptotically, that is, reduce the error e between the image feedback amount s and the desired target s*. After determining the feedback amount s, the design of the visual servo controller is relatively direct, and the speed controller is designed.

[0034] The usual way to extract point features is AprilTag. AprilTag can quickly extract point and line features as well as the position and posture relationship between the camera and the QR code. Its disadvantage is manual labeling, which requires the marker to be attached to the target object. In actual scenes, AprilTag cannot be attached to every object. If the point features on the object can be directly extracted for visual servoing, it will get rid of manual labeling and enable it to be used more widely. Therefore, natural features ORB are directly extracted from the captured image to replace artificial features. With the point features and posture information of the target object, combined with the expected image point features, as the input of the front end of the image-based visual servo controller, the camera movement speed can be obtained, and then the speed control of the subsequent robotic arm and drone can be completed.

[0035] The robot arm kinematics modeling unit is used to analyze the differential kinematics model of the robot arm during the flight of the UAV;

[0036] Define the inertial coordinate system, the drone body coordinate system, the coordinate system of the robot arm base, the robot arm end effector coordinate system, the camera optical center coordinate system, the target object center coordinate system, and the expected grasping position coordinate system. Since the inverse solution is required for grasping after servo, it is necessary to define the expected grasping posture. Relative to the inertial coordinate system, the direction of the expected grasping position coordinate system is consistent with the end effector coordinate system, so the posture transformation between the target object center coordinate system and the expected grasping position coordinate system is fixed. The camera is installed at the end of the robot arm and has a fixed transformation relationship with the end of the robot arm; the speed of the robot arm end in the flying robot arm is equal to the sum of the drone movement speed and the robot arm movement speed; since the camera and the end of the robot arm are fixedly connected to the same rigid body, the robot arm end speed can be obtained through the camera movement speed, and then the angular velocity of each joint can be solved. Similarly, the robot arm end speed can also be transmitted to the drone body.

[0037] The UAV visual servo grasping unit is used to verify the visual servo function on the ground.

[0038] In order to complete the visual servoing of aerial drones, the visual servoing function was first verified on the ground. Initially, April Tag was used to provide point feature information. The network camera image size was 640*480, the maximum frame rate was 120 frames / second, and the physical size of the April Tag used was 34mm*34mm. Its detection was about 25 frames. The visual servoing solution process was also real-time, so the real-time performance of the entire visual servoing system could be guaranteed. When the camera is close to the April Tag target, the April Tag will not be detected, which will cause the visual servo controller to fail.

[0039] Based on the visual servoing method of natural features, relevant experiments are carried out. The method is applicable to any planar object and can get rid of the need to stick a QR code on the target object to obtain the corresponding point feature information and posture information. Therefore, the present invention uses a target with complex content to replace the target object with AprilTag, manually selects the target area for feature extraction, and uses a measuring tool to measure the actual size of the area. In addition, in the case of a depth camera, there is no need to use additional measuring tools. The actual size in the physical space can be calculated directly by using the pixel depth information and position combined with the camera's intrinsic reference information. The rectangular area selected by the target frame is 9.4 cm long and 11.1 cm wide.

[0040] The drone module also includes: a drone, a control unit, a robotic arm and a camera; the drone is used to inspect the transmission line; the control unit is used to control the movement of the drone and the robotic arm; the robotic arm is used to clean foreign objects on the transmission line; and the camera is used to obtain image data of the transmission line.

[0041] Embodiment: The present invention provides a technical solution, a visual servo control method for a drone arm for power transmission line inspection, comprising the following steps:

[0042] S11, obtaining inspection history data of the transmission line;

[0043] S12, determining the first energy consumption and the second energy consumption of each transmission line according to the inspection history data of the transmission line:

[0044] In the inspection history data of the transmission line, according to the transmission line image data collected by the drone camera, when there is no foreign matter on the transmission line and the robotic arm is working, the first energy consumption C1 of the transmission line is obtained according to the power C1u before the drone inspects the transmission line and the power C1d after the drone inspects the transmission line, C1=C1d-C1u;

[0045] When there is no foreign matter on the transmission line and the robot arm performs cleaning work, the second energy consumption C2 of the transmission line is obtained according to the power C2u before the drone inspects the transmission line and the power C2d after the drone inspects the transmission line, C2=C2d-C2u;

[0046] According to the first energy consumption data and the second energy consumption data of the transmission line, the power consumption of the UAV on the transmission line and the corresponding frequency are obtained, which are recorded as probability density function data samples. Based on the probability density function data samples, the probability density function of the power consumption of the UAV is obtained through the kernel density estimation unit, and the expected value of the power consumption of the UAV on the transmission line is obtained according to the probability density function.

[0047] S13, assigning drones to inspect the transmission lines according to the energy consumption information of the transmission lines, including the following steps:

[0048] S31, obtain the available power information of all current drones and the expected value of drone power consumption on each transmission line, assign transmission lines to all drones for inspection, determine the power cost required for drone inspection according to the transmission lines to which the drones are assigned, and set the maximum value of the power cost required for all drone inspections as the adjustment target; adjust the allocation method of the transmission lines so that the adjustment target is minimized, and use the allocation method of the transmission lines at this time as the current solution; set the initial value of the annealing temperature T, and obtain the estimated value of the current solution;

[0049] Let the transmission line to which the drone is assigned be the target line, obtain the expected value of the drone's power consumption on the kth target line and record it as E(k), and calculate the power cost required for the drone inspection, cost = ∑E(k) + TE + FE, where TE is the power consumed by the drone on the transition line connecting the target line, and FE is the power consumed by the drone when returning to the docking platform.

[0050] The first energy consumption data of the transmission line is taken as the data sample, and the probability density function of the power consumption on the transmission line when the robotic arm is not in motion is obtained through kernel density estimation based on the data sample; the expected value of the power consumption on the transmission line is obtained according to the probability density function, and the power consumption per unit length is obtained according to the length of the transmission line and the expected value of the power consumption on the transmission line; the average power consumption per unit length of all transmission lines is taken and recorded as the power consumption rate; the distance of the transition line and the distance for the drone to return to the docking platform are obtained, and multiplied by the power consumption rate to obtain TE and FE.

[0051] Kernel density estimation is a method for estimating probability density functions. The specific steps are as follows:

[0052] Select kernel function: The kernel function is a symmetric, non-negative function. Commonly used kernel functions include Gaussian kernel, uniform kernel, triangular kernel, etc.

[0053] Determine the bandwidth: Bandwidth is an important parameter in kernel density estimation, which controls the degree of smoothing. Too small bandwidth will lead to overfitting, while too large bandwidth will lead to underfitting.

[0054] Calculate the contribution of each data point in the data sample: for each data point, expand its kernel function along the center of the data point and scale it according to the bandwidth;

[0055] Sum: Add up the contributions of all the data in the data sample to get the kernel density estimate of the entire data set.

[0056] S32, repeating steps S33 and S34 until the number of inner loops reaches a first set value;

[0057] S33, based on the current solution, adjusting the allocation method of the transmission lines to generate a new solution, and calculating the estimated value of the new solution;

[0058] S34, calculate the increment of the estimated value between the new solution and the current solution. If the increment is less than 0, the new solution is accepted and regarded as the current solution. If the increment is not less than 0, the new solution is considered as the current solution according to the probability e -freq / T Accept the new solution and regard it as the current solution; where freq is the estimated value of the current solution and e is a natural number; increase the number of inner loops by one;

[0059] S35, lowering the annealing temperature T. If the annealing temperature T is not less than the second set value, returning to step S32, resetting the number of inner loops; if the annealing temperature T is less than the set value, stopping the algorithm, returning to the transmission line allocation method with the smallest estimated value in the algorithm process; controlling the drone to inspect the allocated transmission lines;

[0060] The initial temperature is the temperature value at the beginning of the algorithm, which is usually set to a relatively high value, such as 100. The second set value is the lowest temperature reached by the algorithm during the search process. When the temperature drops to this value, the algorithm stops iterating. It can be set to 20. The method of reducing the annealing temperature defines the speed at which the temperature drops during the search process of the algorithm. By setting a reasonable cooling rate, the breadth and depth of the search can be balanced. A linear cooling method can be used, and the coefficient can be set to 0.95, with the annealing temperature dropping by 5% each time. If the second set value is set too low, the algorithm may fall into a local optimal solution too early and fail to find a global optimal solution. If the number of inner loops is set too many, the computational complexity and time cost of the algorithm may increase, reducing the efficiency of the algorithm. The optimal second set value can be found through multiple trials and adjustments.

[0061] The sub-estimate is calculated using the following steps:

[0062] For a single UAV, obtain the power cost required for the UAV inspection and the existing power costm of the UAV, and calculate the surplus dcost, dcost = costm-cost; obtain the probability density function of the transmission line to which the UAV is assigned, and calculate the joint probability density of the power consumption on all the transmission lines to which the UAV is assigned; calculate the probability Pd that the UAV power is insufficient to complete the inspection task, and obtain the expected value Ecost of the power consumption on all the transmission lines to which the UAV is assigned based on the joint probability density, and calculate the probability that the power consumption on all the transmission lines to which the UAV is assigned is greater than Ecost + dcost to obtain Pd; add the probabilities that all UAVs have insufficient power to complete the inspection task to obtain the estimated value.

[0063] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A visual servo control method for drone arms for power transmission line inspection, characterized in that: The following steps are involved: S11, obtaining inspection history data of the transmission line; S12, determining a first energy consumption and a second energy consumption of each transmission line according to inspection history data of the transmission line; S13, assigning drones to inspect the transmission lines according to the energy consumption information of the transmission lines.

2. The visual servo control method for drone arms for power transmission line inspection according to claim 1 is characterized in that: In step S12, determining the first energy consumption and the second energy consumption of each transmission line according to the inspection history data of the transmission line further includes the following steps: S21, in the inspection history data of the transmission line, according to the transmission line image data collected by the drone camera, when there is no foreign matter on the transmission line and the robot arm is working, according to the power C1u before the drone inspects the transmission line and the power C1d after the drone inspects the transmission line, the first energy consumption C1 of the transmission line is obtained, C1=C1d-C1u; S22, when there is no foreign matter on the transmission line and the robot arm performs cleaning work, the second energy consumption C2 of the transmission line is obtained according to the power C2u before the drone inspects the transmission line and the power C2d after the drone inspects the transmission line, C2=C2d-C2u; S23, based on the first energy consumption data and the second energy consumption data of the transmission line, obtain the power consumption of the UAV on the transmission line and the corresponding frequency, which are recorded as probability density function data samples, and obtain the probability density function of the UAV power consumption through kernel density estimation based on the probability density function data samples, and obtain the expected value of the UAV power consumption on the transmission line according to the probability density function.

3. The visual servo control method for drone arms for power transmission line inspection according to claim 2 is characterized in that: In step S13, the step of assigning a drone to inspect the power transmission line according to the energy consumption information of the power transmission line also includes the following steps: S31, obtain the available power information of all current drones and the expected value of drone power consumption on each transmission line, assign transmission lines to all drones for inspection, determine the power cost required for drone inspection according to the transmission lines to which the drones are assigned, and set the maximum value of the power cost required for all drone inspections as the adjustment target; adjust the allocation method of the transmission lines so that the adjustment target is minimized, and use the allocation method of the transmission lines at this time as the current solution; set the initial value of the annealing temperature T, and obtain the estimated value of the current solution; S32, repeating steps S33 and S34 until the number of inner loops reaches a first set value; S33, based on the current solution, adjusting the allocation method of the transmission lines to generate a new solution, and calculating the estimated value of the new solution; S34, calculate the increment of the estimated value between the new solution and the current solution. If the increment is less than 0, the new solution is accepted and regarded as the current solution. If the increment is not less than 0, the new solution is considered as the current solution according to the probability e -freq / T Accept the new solution and regard it as the current solution; where freq is the estimated value of the current solution and e is a natural number; increase the number of times of the inner loop by one; S35, lowering the annealing temperature T. If the annealing temperature T is not less than the second set value, returning to step S32, resetting the number of inner loops; if the annealing temperature T is less than the set value, stopping the algorithm, returning to the transmission line allocation method with the smallest estimated value in the algorithm process; controlling the drone to inspect the allocated transmission lines; The sub-estimate is calculated using the following steps: For a single UAV, obtain the power cost required for the UAV inspection and the existing power costm of the UAV, and calculate the surplus dcost, dcost = costm-cost; obtain the probability density function of the transmission line to which the UAV is assigned, and calculate the joint probability density of the power consumption on all the transmission lines to which the UAV is assigned; calculate the probability Pd that the UAV power is insufficient to complete the inspection task, and obtain the expected value Ecost of the power consumption on all the transmission lines to which the UAV is assigned based on the joint probability density, and calculate the probability that the power consumption on all the transmission lines to which the UAV is assigned is greater than Ecost + dcost to obtain Pd; add the probabilities that all UAVs have insufficient power to complete the inspection task to obtain the estimated value.

4. The visual servo control method for drone arms for power transmission line inspection according to claim 3 is characterized in that: In step S31, the following steps are also included: Let the transmission line to which the drone is assigned be the target line, obtain the expected value of the drone's power consumption on the kth target line and record it as E(k), and calculate the power cost required for the drone inspection, cost = ∑E(k) + TE + FE, where TE is the power consumed by the drone on the transition line connecting the target line, and FE is the power consumed by the drone when returning to the docking platform.

5. The visual servo control method for drone arms for power transmission line inspection according to claim 4 is characterized in that: The amount of electricity TE and FE consumed by the drone on the transition line and returning to the docking platform is determined according to the distance and the first energy consumption of the transmission line, which specifically includes the following steps: S51, taking the first energy consumption data of the transmission line as a data sample, obtaining a probability density function of the power consumption on the transmission line when the manipulator is not in motion through kernel density estimation based on the data sample; obtaining an expected value of the power consumption on the transmission line according to the probability density function, and obtaining the power consumption per unit length according to the length of the transmission line and the expected value of the power consumption on the transmission line; taking the average value of the power consumption per unit length of all transmission lines and recording it as the power consumption rate; S52, obtain the distance of the transition line and the distance of the UAV returning to the docking platform, and multiply them by the power consumption rate to obtain TE and FE.

6. The visual servo control system of drone arms for power transmission line inspection is characterized by: It includes a model building module, a drone module, a data storage module and a power transmission line distribution module; the output end of the model building module is connected to the input end of the drone module to generate control signals for the drone and the mechanical arm; the output end of the drone module is connected to the input end of the data storage module to obtain image data of the power transmission line and clean foreign objects on the power transmission line through the mechanical arm; The output end of the data storage module is connected to the input end of the power transmission line allocation module, and is used to store the inspection data of the UAV on the power transmission line; The power transmission line allocation module is used to allocate power transmission lines to drones for inspection.

7. The visual servo control system for drone arms for power transmission line inspection according to claim 6 is characterized in that: The transmission line allocation module also includes: an energy consumption calculation unit, a kernel density estimation unit and an allocation optimization unit; the energy consumption calculation unit is used to determine the power consumption of the drone on the transmission line when the robot arm has a foreign object cleaning behavior and does not have a foreign object cleaning behavior, and calculate the power consumption speed of the drone when it moves when there is no foreign object cleaning behavior; the kernel density estimation unit generates a probability density function of the drone's power consumption based on the drone's power consumption data on the transmission line; the allocation optimization unit, with the goal of minimizing the probability that the drone cannot complete the inspection of the transmission line, allocates the transmission line to the drone for inspection through a simulated annealing algorithm.

8. The visual servo control system for drone arms for power transmission line inspection according to claim 6 is characterized in that: The model building module also includes: a visual servo unit, a robotic arm kinematic modeling unit and a UAV visual servo grasping unit; the visual servo unit is used to obtain the point feature posture information of foreign objects on the transmission line; the robotic arm kinematic modeling unit is used to analyze the differential kinematic model of the robotic arm during the flight of the UAV; the UAV visual servo grasping unit is used to verify the visual servo function on the ground.

9. The visual servo control system for drone arms for power transmission line inspection according to claim 6 is characterized in that: The drone module also includes: a drone, a control unit, a robotic arm and a camera; the drone is used to inspect the transmission line; the control unit is used to control the movement of the drone and the robotic arm; the robotic arm is used to clean foreign objects on the transmission line; and the camera is used to obtain image data of the transmission line.

10. The visual servo control system for drone arms for power transmission line inspection according to claim 8, characterized in that: The visual servo unit performs hybrid visual servoing based on position visual servo PBVS and image visual servo IBVS to achieve global optimization of the controller.

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