An overhead transmission line inspection robot tower-mounted intelligent control method and system

Abstract

The application relates to an overhead power transmission line inspection robot tower intelligent control method and system, which monitors the real-time residual power of an inspection robot, acquires an environment perception data set to calculate an environment compensation coefficient. A dynamic power threshold is calculated based on the environment compensation coefficient, and a charging request instruction is generated when the power is lower than the threshold. In response to the charging request, the path value of each charging dock is calculated according to the state of each charging dock and the environment data, and the target charging dock and the optimal moving path are determined accordingly. The robot is controlled to move along the path, and the walking parameters are adjusted in real time based on the environment perception data. After the robot enters the docking range, the pose deviation between the robot and the charging dock interface is fine-tuned until the tolerance is met, and a docking success signal is generated. Then, a charging connection is established, the charging current is dynamically adjusted according to the real-time battery temperature and the environment data, and the state data of the target charging dock is updated after charging, thereby improving the safety and reliability of the autonomous charging process of the inspection robot.

Description

Technical Field

[0001] This application relates to the field of power system operation and maintenance technology, and in particular to an intelligent control method and system for an overhead transmission line inspection robot on a tower. Background Technology

[0002] With the continuous expansion of my country's power grid and the constant upgrading of voltage levels, the safe and stable operation of overhead transmission lines, as the main artery of power transmission, is of paramount importance. Traditional manual inspection methods have inherent drawbacks such as low efficiency, high risk, and significant limitations imposed by terrain and climate. Therefore, the use of inspection robots to automatically travel along power lines and conduct inspections has become an important development direction for intelligent operation and maintenance in the power industry. These robots are typically equipped with high-definition cameras, infrared thermal imagers, and other equipment, enabling them to replace manual labor in tasks such as identifying line defects, checking for loose bolts, and assessing insulator contamination. To ensure that robots can perform long-distance, long-duration continuous inspection operations, deploying automatic charging docks along transmission line towers to achieve autonomous energy replenishment for the robots is a key supporting technology for maintaining their operational autonomy.

[0003] Currently, in practical applications, the working environment on power transmission line towers is extremely complex and variable. Factors such as strong winds, low temperatures, icing, and steep line gradients all affect the motion stability and energy consumption rate of inspection robots. Most existing systems do not consider the significant impact of environmental dynamics on energy demand, which can easily lead to inspection robots running out of power en route to charging points due to insufficient energy estimation in harsh environments, causing operational interruptions or even high-altitude stranding. Furthermore, when selecting charging docks for inspection robots, existing methods often rely solely on simple straight-line distances or preset sequences, potentially resulting in unavailable target charging docks, high failure rates, or excessive energy consumption along the route, reducing the overall efficiency and reliability of the charging process. Secondly, during robot movement and charging docking, the motion control parameters are usually preset or simply graded, making them susceptible to mechanical errors, sensor noise, and environmental disturbances, resulting in a need to improve docking success rates. These problems restrict the operational reliability and full automation level of inspection robot systems in real, complex environments. Summary of the Invention

[0004] To address the aforementioned technical issues, this application provides an intelligent control method for an overhead transmission line inspection robot on a tower.

[0005] In a first aspect, this application provides an intelligent control method for an overhead transmission line inspection robot on a tower, employing the following technical solution:

[0006] Monitor the real-time remaining power of the inspection robot installed on the ground wire of the overhead transmission line;

[0007] Obtain the environmental perception dataset of the inspection robot and calculate and generate the environmental compensation coefficient;

[0008] A dynamic power threshold is calculated based on the environmental compensation coefficient. When the real-time remaining power is lower than the dynamic power threshold, a charging request instruction is generated.

[0009] In response to the charging request command, the system obtains the status parameter set of each charging dock within a preset area and calculates the path cost of each charging dock by combining it with the environmental perception dataset.

[0010] Based on the comparison results of the path cost values, the target charging dock and the corresponding optimal movement path sequence are determined.

[0011] The inspection robot is controlled to move according to the optimal movement path sequence, and the walking parameters of the inspection robot are adjusted in real time based on the environmental perception dataset.

[0012] When the inspection robot enters the preset docking range of the target charging dock, the interface pose deviation between the inspection robot and the target charging dock is finely adjusted until the tolerance threshold is met and a docking success signal is generated.

[0013] In response to the successful docking signal, a charging connection is established, and the charging current parameters are dynamically adjusted based on the real-time battery temperature and environmental perception dataset of the inspection robot until charging is completed and the status dataset of the target charging dock is updated.

[0014] By adopting the above technical solutions, variable environmental factors are uniformly quantified through the core parameter of the environmental compensation coefficient, and this coefficient is integrated into the energy management, path decision-making, motion control, and charging adjustment processes of the inspection robot, achieving closed-loop compensation for environmental disturbances throughout the entire process. Furthermore, by designing a multi-factor fusion path cost model, intelligent and multi-objective optimization selection of the charging dock is achieved. Motion control integrating feedforward compensation and multi-closed-loop feedback ensures safe and stable movement in complex environments. Precision servo docking and temperature-adaptive charging control ensure a high success rate and safety in the energy replenishment process. Finally, a closed-loop performance data collection and health score update mechanism endows the entire system with self-learning and continuous optimization capabilities.

[0015] Secondly, this application provides an intelligent control system for an overhead transmission line inspection robot tower, which adopts the following technical solution:

[0016] The power monitoring module is used to monitor the real-time remaining power of the inspection robot installed on the ground wire of the overhead transmission line;

[0017] The environmental compensation module is used to acquire the environmental perception dataset of the inspection robot and calculate and generate environmental compensation coefficients.

[0018] The charging request module is used to calculate a dynamic power threshold based on the environmental compensation coefficient, and generate a charging request command when the real-time remaining power is lower than the dynamic power threshold.

[0019] The path cost calculation module is used to respond to the charging request instruction, obtain the state parameter set of each charging dock in the preset area, and calculate the path cost of each charging dock based on the state parameter set and the environmental perception dataset.

[0020] The target charging dock determination module is used to determine the target charging dock and the corresponding optimal movement path sequence based on the comparison results of the path cost values.

[0021] The movement control module is used to control the inspection robot to move according to the optimal movement path sequence and to adjust the walking parameters of the inspection robot in real time based on the environmental perception dataset.

[0022] The pose fine-tuning module is used to fine-tune the interface pose deviation between the inspection robot and the target charging dock when the inspection robot enters the preset docking range of the target charging dock, until the tolerance threshold is met and a docking success signal is generated.

[0023] The charging control module is used to establish a charging connection in response to the docking success signal, and dynamically adjust the charging current parameters based on the real-time battery temperature and environmental perception dataset of the inspection robot until charging is completed and the status dataset of the target charging dock is updated.

[0024] Thirdly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0025] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as in any of the methods in the first aspect.

[0026] In summary, this application includes at least one of the following beneficial technical effects:

[0027] 1. By introducing an environmental compensation coefficient, the power threshold is dynamically adjusted, enabling the timing of charging requests to proactively adapt to harsh conditions such as wind and temperature, effectively avoiding the risk of energy depletion that traditional fixed threshold strategies may cause in complex environments;

[0028] 2. Based on the path cost calculation model of multi-source information fusion, the model takes into account distance, slope, dock health and real-time occupancy status, and realizes global optimization of charging dock selection, thereby improving the overall efficiency and reliability of charging tasks.

[0029] 3. In the movement and docking control stage, the walking speed and torque are adjusted in real time by environmental parameters, and visual servo combined with inertial measurement is used to achieve dynamic pose compensation with sub-millimeter accuracy, which overcomes the alignment problem caused by low-frequency vibration of high-altitude lines and greatly improves the docking success rate.

[0030] 4. During the charging process, the current is dynamically adjusted based on the temperature difference between the battery and the environment, optimizing charging efficiency while ensuring safety; at the same time, the docking station health is updated by recording data such as docking time and protection count, forming a closed loop of continuous self-optimization of system performance. Attached Figure Description

[0031] Figure 1 This is a first flowchart illustrating the intelligent control method for an overhead transmission line inspection robot on a tower, according to one embodiment of this application.

[0032] Figure 2 This is a second flowchart illustrating the intelligent control method for an overhead transmission line inspection robot on a tower, according to one embodiment of this application.

[0033] Figure 3 This is a schematic diagram of the third process of an intelligent control method for an overhead transmission line inspection robot tower, according to one embodiment of this application.

[0034] Figure 4 This is a schematic diagram of the fourth process of the intelligent control method for an overhead transmission line inspection robot on a tower, according to one embodiment of this application.

[0035] Figure 5 This is a fifth flowchart illustrating the intelligent control method for an overhead transmission line inspection robot on a tower, according to one embodiment of this application.

[0036] Figure 6 This is a schematic diagram of the sixth process of the intelligent control method for an overhead transmission line inspection robot on a tower, according to one embodiment of this application.

[0037] Figure 7 This is a schematic diagram of the seventh process of the intelligent control method for an overhead transmission line inspection robot on a tower, according to one embodiment of this application.

[0038] Figure 8 This is the eighth flowchart of the intelligent control method for an overhead transmission line inspection robot on a tower, according to one embodiment of this application. Detailed Implementation

[0039] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1-8 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.

[0040] This application discloses an intelligent control method for an overhead transmission line inspection robot on a tower.

[0041] Reference Figure 1 A method for intelligent control of an overhead transmission line inspection robot on a tower, specifically including:

[0042] Step S101: Monitor the real-time remaining power of the inspection robot installed on the ground wire of the overhead transmission line;

[0043] For robots operating autonomously at high altitudes, their battery power is the most fundamental constraint determining their mobility and safety. In this embodiment, the real-time remaining battery power is not simply a reading of the battery terminal voltage, but rather an estimate of the State of Charge (SOC) obtained through real-time integration of voltage and current by the battery management system (using coulomb counting) or by combining it with a battery model. Continuous monitoring of SOC aims to accurately determine the robot's energy reserves, providing accurate and quantifiable internal state input for subsequent dynamic decision-making based on environmental variables.

[0044] Step S102: Obtain the environmental perception dataset of the inspection robot and calculate the generated environmental compensation coefficient;

[0045] Among these factors, the tower-top operating environment is the most significant external variable affecting the robot's motion dynamics, energy consumption, and component operating points. In this embodiment, the environmental perception dataset includes, but is not limited to, real-time collected GPS coordinates of the inspection robot, ambient temperature data, ambient wind speed data, and path slope data; GPS coordinates provide spatial positioning; ambient temperature affects battery chemical activity and material mechanical properties; ambient wind speed constitutes dynamic load and overturning moment; and path slope determines the constant load caused by the gravitational component.

[0046] Next, an environmental compensation coefficient is generated using ambient temperature and wind speed data. This coefficient quantifies the severity or deviation of the current comprehensive environmental conditions from the ideal conditions, providing a unified adjustment input for subsequent modules that require environmental adaptation, and realizing efficient information conversion from the perception layer to the control decision layer.

[0047] Step S103: Calculate the dynamic power threshold based on the environmental compensation coefficient, and generate a charging request command when the real-time remaining power is lower than the dynamic power threshold.

[0048] In complex environments, the timing of charging should not be fixed but dynamically adjusted according to environmental risks. Therefore, an offset adjusted by an environmental compensation coefficient Ke is superimposed on the preset basic power threshold Qb to obtain the dynamic power threshold Qt. The specific calculation formula is as follows:

[0049] In the above formula, α is the compensation coefficient.

[0050] In this embodiment, a smaller environmental compensation coefficient Ke indicates a harsh environment, and Qt increases. This means the robot will trigger a charging request command earlier when its remaining battery power is higher, reserving a more ample energy safety margin for the journey to the charging dock to cope with additional energy consumption caused by strong winds, climbing slopes, etc. Conversely, in a favorable environment, the robot is allowed to trigger the command when its battery power is lower, in order to maximize the working mileage after a single charge.

[0051] Step S104: In response to the charging request command, obtain the state parameter set of each charging dock in the preset area, and calculate the path cost of each charging dock in combination with the environmental perception dataset.

[0052] Specifically, the preset area is a geographical range centered on the real-time location of the inspection robot, defined based on the robot's current battery life and the density of charging docks. The status dataset includes, but is not limited to, the GPS coordinates of the charging docks, real-time occupancy status indicators, and health scores.

[0053] In this embodiment, the selection of the charging target is a comprehensive optimization problem that requires balancing distance, difficulty, availability, and reliability. The state parameter set (coordinates, occupancy status, and health) characterizes the static and dynamic attributes of the charging dock itself. Calculating the path cost essentially involves constructing a cost model. This model normalizes and integrates four major costs: 1) distance cost (path length); 2) terrain difficulty cost (absolute value of the average slope of the path, directly related to mobile energy consumption and time); 3) availability cost (current occupancy status); and 4) reliability cost (reciprocal of health; lower scores result in higher costs).

[0054] In addition, each type of cost is assigned a preset weight, allowing the priority to be adjusted according to the operation and maintenance strategy. This generates a comparable comprehensive evaluation value for each candidate charging dock through a quantifiable cost function, providing a clear mathematical basis for the optimal selection.

[0055] Step S105: Based on the comparison results of path cost values, determine the target charging dock and the corresponding optimal movement path sequence;

[0056] The system compares the path cost of all charging docks and selects the one with the lowest path cost as the target charging dock. Simultaneously, the specific path evaluated and planned during the path cost calculation is determined as the "optimal movement path sequence." This sequence is not only a series of spatial waypoints but also incorporates slope information along the path, serving as a crucial feedforward signal input to the motion controller for the next stage.

[0057] Step S106: Control the inspection robot to move according to the optimal movement path sequence, and adjust the walking parameters of the inspection robot in real time based on the environmental perception dataset.

[0058] Specifically, in the motion control phase, a base speed is determined based on real-time ambient wind speed using a preset mapping table, and then fine-tuned by introducing an environmental compensation coefficient. When the wind speed is high, the base speed is reduced to ensure stability, and further attenuation is achieved through the environmental compensation coefficient, enabling the speed to respond to the overall environmental severity. Based on path slope data, the motor torque is compensated by feedforward; torque output is increased when going uphill to overcome gravity, and braking torque may be applied when going downhill to prevent overspeeding. Machine vision recognition via camera is used to calculate the lateral offset of the robot from the ground line center in real time. This offset serves as a feedback signal to generate control commands (such as adjusting the differential speed of the drive wheels or the leveling mechanism), forming a fast-response closed-loop position control system that ensures the robot always moves safely along the ground line. This step deeply integrates environmental perception data (wind speed, slope) and real-time perception data (visual offset) to generate multi-level, adaptive motion control commands.

[0059] Step S107: When the inspection robot enters the preset docking range of the target charging dock, the interface pose deviation between the inspection robot and the target charging dock is finely adjusted until the tolerance threshold is met and a docking success signal is generated.

[0060] Specifically, within the preset docking range, the inspection robot switches to a high-precision sensing mode (such as laser rangefinder and visual marker recognition) to acquire the precise three-dimensional pose of the charging dock interface. Simultaneously, it acquires its own pose via an inertial measurement unit (IMU) and calculates the pose deviation between the two, which is a six-degree-of-freedom vector containing both three-dimensional position and angular attitude errors. The fine-tuning control aims to gradually eliminate this deviation with millimeter-level and degree-level accuracy until the tolerance threshold required for physical connection is met.

[0061] Step S108: In response to the docking success signal, establish a charging connection, dynamically adjust the charging current parameters based on the real-time battery temperature and environmental perception dataset of the inspection robot, until charging is completed and the status dataset of the target charging dock is updated.

[0062] The dynamic adjustment of the charging current setting is essentially a temperature-compensated constant current charging strategy. For example, the current is reduced at high temperatures to prevent overheating and triggering protection or damaging battery life, while at low temperatures, specific compensation may be allowed or implemented, thus achieving adaptive optimization of the charging process. After charging is completed, the time taken for this docking (reflecting docking smoothness) and the number of charging protection triggers (reflecting the stability of the charging process) can be recorded, and the health score of the charging dock can be quantitatively updated based on this. This allows the system's reliability assessment of the charging dock to continuously approach the real situation with the accumulation of historical data, thereby providing better decision-making basis for all inspection robots in the future and forming a system-level performance evolution.

[0063] In the above implementation, variable environmental factors are uniformly quantified using the environmental compensation coefficient as a core parameter, and this coefficient is integrated into the energy management, path decision-making, motion control, and charging adjustment processes of the inspection robot, achieving closed-loop compensation for environmental disturbances throughout the entire process. Furthermore, by designing a multi-factor fusion path cost model, intelligent and multi-objective optimization selection of the charging dock is achieved. Motion control integrating feedforward compensation and multi-closed-loop feedback ensures safe and stable movement in complex environments. Precision servo docking and temperature-adaptive charging control ensure a high success rate and safety in the energy replenishment process. Finally, a closed-loop performance data collection and health score update mechanism endows the entire system with self-learning and continuous optimization capabilities.

[0064] In practical applications, the technical solution of this application systematically solves the problems faced by overhead line inspection robots in the process of autonomous charging, such as extensive energy efficiency management, rigid decision-making, poor environmental adaptability, and low docking success rate. It improves the operational autonomy and safety of the inspection robot and the intelligence level and long-term operational efficiency of the entire operation and maintenance system.

[0065] Reference Figure 2 As one implementation of step S102, the step of acquiring the environmental perception dataset of the inspection robot and calculating the environmental compensation coefficient includes:

[0066] Step S201: Obtain the GPS coordinates, ambient temperature data, ambient wind speed data, and path slope data of the inspection robot to obtain the environmental perception dataset;

[0067] In this embodiment, GPS coordinates provide the robot's absolute spatial position and serve as the geometric reference origin for subsequent path planning and charging dock selection. Ambient temperature data directly affects the working characteristics of the inspection robot's core components: low temperatures significantly reduce the activity of lithium batteries, leading to a reduction in their usable capacity and an increase in internal resistance. Simultaneously, it may increase the viscosity of lubricating grease in mechanical structures, increasing movement resistance. High temperatures, on the other hand, may trigger battery thermal runaway and accelerate component aging.

[0068] Environmental wind speed data is the most significant source of uncertain dynamic disturbances in high-altitude operations. Strong winds not only directly increase the robot's drag, significantly increasing drive energy consumption, but can also generate lateral torques, threatening the robot's stability and balance on narrow ground lines, posing a primary safety threat. Path gradient data reflects the tilt angle of the robot as it moves or will move on the track (ground line). The gradient determines the component of gravity along the track direction. When going uphill, this component becomes a constant load that requires additional torque to overcome; when going downhill, it may be converted into accelerating potential energy that requires braking torque to balance, directly determining the steady-state load of the drive system. These four types of data collectively characterize the spatiotemporal and physical constraints of the inspection robot.

[0069] Step S202: Convert the ambient temperature data into a temperature influence factor and the ambient wind speed data into a wind speed influence factor.

[0070] This step is a crucial preprocessing step before data fusion, aiming to normalize the original physical quantities and map them to a unified scale of influence. For example, the temperature influence factor is typically converted based on a reference temperature (e.g., 25°C), using an exponential or piecewise function to describe the nonlinear attenuation effect on system performance when the temperature deviates from the reference. Both high and low temperatures cause this factor to decrease. The wind speed influence factor may use a piecewise linear or quadratic relationship function, mapping wind speed values ​​to a coefficient reflecting the increase in wind resistance energy consumption and risk level; the higher the wind speed, the stronger the negative impact represented by this factor. This conversion transforms the raw signals from temperature sensors and anemometers into comparable values ​​that characterize the degree of adverseness.

[0071] Step S203: Based on the preset weight allocation ratio, perform weighted fusion calculation on the temperature influence factor and the wind speed influence factor to generate the environmental compensation coefficient.

[0072] In some embodiments, the formula for calculating the environmental compensation coefficient Ke is:

[0073] ;

[0074] In the above formula, Vw is the ambient wind speed and Ta is the ambient temperature.

[0075] Specifically, the preset weighting ratios are not arbitrarily set (such as the implicit 0.6 and 0.4 in the above formula), but are based on a large amount of experimental data, simulation analysis, or domain knowledge, quantifying the relative importance of different environmental factors to the overall operational efficiency and safety of the robot in specific application scenarios. For example, in high-altitude strong wind scenarios, wind speed may be assigned a higher weight because it is the primary factor leading to instability and a sudden increase in energy; while in extreme temperature regions, the weight of temperature factors will be more prominent.

[0076] Subsequently, the aforementioned influencing factors are combined into a single environmental compensation coefficient through weighted fusion calculation. This coefficient, greater than 1, equal to 1, or less than 1, corresponds to environmental conditions that are better than, equal to, or worse than the baseline operating conditions, respectively. This coefficient serves as a global, standardized environmental adjustment factor, which is used by multiple subsequent modules to dynamically correct various thresholds and parameters. For example, when calculating the battery threshold, this coefficient is used to advance or postpone the charging trigger point; when setting the walking speed, this coefficient is used to scale the baseline speed.

[0077] In the above implementation, the system no longer rigidly responds to a single environmental variable, but is able to intelligently respond to the comprehensive environmental situation under the coupling effect of environmental factors, thereby making better overall decisions. This enables the inspection robot's decision system (when to charge) and execution system (how to move) to adaptively respond to the complex and ever-changing high-altitude environment, thereby achieving a dynamic balance between energy management, movement safety and work efficiency.

[0078] Reference Figure 3 As one implementation of step S104, in response to a charging request command, the step of obtaining a state parameter set for each charging dock within a preset area and calculating the path cost of each charging dock based on the state parameter set and the environmental perception dataset includes:

[0079] Step S301: In response to the charging request command, retrieve the status parameter set of all charging docks from the charging dock status database of the preset area, including the charging dock GPS coordinates, real-time occupancy status indicator and health score.

[0080] Among them, the GPS coordinates of the charging dock provide the absolute spatial location of the charging dock, which is the geometric basis for calculating spatial accessibility; the real-time occupancy status indicator (usually a Boolean value, such as 0 for idle, 1 for occupied or faulty) reflects the immediate availability of the charging dock at the current moment, which is key information to prevent robots from going to invalid targets, causing task failure and energy waste; the health score is a comprehensive evaluation index calculated based on historical operating data (such as past docking success rate, average docking time, number of failures, etc.), which quantifies the long-term reliability and performance status of the charging dock.

[0081] Step S302: Obtain the GPS coordinates and path slope data of the inspection robot from the environmental perception dataset;

[0082] The GPS coordinates of the inspection robot serve as the origin for calculating its spatial relationship with each charging dock. The path slope data is both part of the environmental perception dataset and a core terrain feature input for subsequent path assessment. On overhead power lines, the path between two points is not a flat straight line, but rather a series of line segments with different slopes (and possibly even pitch angles) between towers. This slope data is pre-stored or acquired in real-time by sensors, forming an indispensable geographic information layer for assessing mobile energy consumption and difficulty.

[0083] Step S303: Based on the GPS coordinates of the inspection robot and the GPS coordinates of the charging dock, calculate and generate the optimal movement path sequence for each charging dock;

[0084] In this embodiment, a path planning algorithm (such as the A* algorithm, Dijkstra's algorithm, or a search on a known topology) can be invoked to calculate the series of spatial locations (i.e., the optimal movement path sequence) that the robot must pass through to move from its current position to each charging dock, taking into account the actual route and connection relationships. This sequence is not merely a line connecting coordinate points; it includes the order of movement and the topological structure, serving as the basis for subsequent refined cost assessments (such as slope analysis).

[0085] Step S304: Extract the path slope data corresponding to the optimal mobile path sequence, and calculate and generate the path slope feature parameters corresponding to each charging dock.

[0086] The system extracts the slope values ​​of corresponding line segments from a global path slope database based on the locations traversed by the optimal movement path sequence, forming a slope sequence. Then, by statistically calculating this sequence, one or more path slope characteristic parameters are generated, such as the average absolute slope value, cumulative elevation gain, or maximum slope of the entire path. These parameters directly reflect the terrain difficulty of reaching the charging dock; the steeper and more undulating the slope, the more energy the robot requires to move, potentially leading to longer travel times and increased risks. In this embodiment, the average absolute slope value is used as the path slope characteristic parameter.

[0087] Step S305: Convert the real-time occupancy status identifier of each charging dock into an availability weight coefficient;

[0088] This step transforms the binary availability information into continuous or discrete weights that can be computed in a mathematical model. For example, "idle (0)" is mapped to a small fixed coefficient (such as 0), indicating availability with no additional penalty; while "occupied or faulty (1)" is mapped to a very large coefficient (such as a value much larger than other cost items), or the charging dock is directly removed from the candidate list. Through this transformation, it can be ensured in the subsequent fusion model that occupied charging docks are almost never selected due to their extremely high availability cost, thus achieving a hard constraint on the real-time state.

[0089] Step S306: Based on the optimal mobile path sequence, path slope characteristic parameters, availability weight coefficient, and health score, the path cost of each charging dock is generated through a multi-factor fusion model.

[0090] Specifically, this step involves constructing and solving a multi-attribute decision model, where the path cost Ci is calculated using the following formula:

[0091] ;

[0092] In the above formula, the optimal movement path sequence is quantified as a normalized distance cost Di / Dmax (Di is the optimal movement path length, and Dmax is the maximum possible distance), the path slope characteristic parameter (such as the absolute value of the average slope |Si|) is quantified as the terrain difficulty cost; the availability weight coefficient Oi is used as the availability cost term; and the health score (Hi) is converted into reliability cost (1-Hi, the higher the score, the higher the cost). The relative importance of the four decision dimensions—distance, difficulty, availability, and reliability—is clarified through preset weight coefficients w1, w2, w3, and w4. The system independently calculates a scalar path cost Ci for each charging dock, which comprehensively represents the total expected cost of selecting that charging dock and completing the mobile charging task. By comparing the Ci values ​​of all charging docks, the charging dock with the minimum value is the optimal choice under the current comprehensive consideration.

[0093] The above implementation systematically integrates spatial planning, terrain analysis, real-time status monitoring, and historical performance evaluation, upgrading the decision-making process of selecting charging docks from a simple judgment based on a single distance to a quantitative optimization process based on multi-source information fusion. This intelligent decision-making mechanism can effectively prevent robots from going to charging docks that are geographically close but have steep slopes and high energy consumption, or to charging docks that are currently occupied or in a faulty state. This improves the success rate of charging tasks and overall energy efficiency, enhances the robustness and intelligence level of the inspection system in complex environments, and is a key decision support for achieving fully autonomous energy replenishment for robots.

[0094] Reference Figure 4As one implementation of step S106, the steps of controlling the inspection robot to move according to the optimal movement path sequence and adjusting the walking parameters of the inspection robot in real time based on the environmental perception dataset include:

[0095] Step S401: Obtain the optimal movement path coordinate sequence corresponding to the pre-generated target charging dock, and extract the spatial distribution features of the path coordinate points;

[0096] The optimal movement path coordinate sequence defines a series of waypoints the robot must traverse from its current position to the target charging dock. Extracting the spatial distribution characteristics of the path coordinate points involves performing geometric analysis on the sequence to obtain prior knowledge that guides control, such as: the overall curvature trend of the path (whether it's a straight segment, a gentle curve, or a sharp curve), the spacing between adjacent coordinate points (reflecting the sparsity of the path points), and local direction vectors. These features provide crucial geometric contextual information for subsequent decisions on when to adjust speed for safe cornering and where to pre-compromise torque, enabling proactive control behavior.

[0097] Step S402: Real-time acquisition of environmental wind speed data and path slope data from the environmental perception dataset;

[0098] Among these factors, environmental wind speed data is the primary external disturbance affecting the robot's motion stability. Strong winds generate lateral forces, affecting the robot's lateral balance and increasing wind resistance, both of which directly impact motion safety and energy consumption. Path gradient data provides information on the track inclination angle at the robot's current or upcoming location. The gradient directly determines the component of gravity in the robot's direction of motion; uphill, it acts as a drag load, while downhill, it acts as a power load or requires braking. Real-time collection of these two types of data provides the necessary input for dynamic compensation against major environmental disturbances.

[0099] Step S403: Based on spatial distribution characteristics and environmental wind speed data, calculate and generate a real-time walking speed adjustment factor, and combine it with a preset benchmark speed to generate a walking speed setpoint.

[0100] In the embodiments of the present application, the real-time walking speed shall be determined by two parts: one is a preset reference speed based on path geometric features, for example, a higher cruise speed is adopted on straight line segments, and the reference speed is reduced in advance on turning segments (high curvature positions identified based on spatial distribution features) to ensure stability; the other is a real-time walking speed adjustment factor based on real-time ambient wind speed, which is usually generated according to a pre-calibrated wind speed-speed mapping relation table, and is essentially an attenuation coefficient. The higher the wind speed is, the smaller the value of this factor (which may be less than 1), and the stronger the attenuation effect on the reference speed, so that the speed is automatically reduced in a strong wind environment to counter wind resistance and improve stability. The finally generated walking speed set value is the product of the reference speed and the adjustment factor, which comprehensively combines the common influence of path geometric constraints and real-time wind disturbance.

[0101] For example, the wind speed-speed mapping table is: if the wind speed Vw ≤ 5 m / s, the preset reference speed is V b = 0.5 m / s; if 5 < Vw ≤ 10 m / s, the preset reference speed is Vb = 0.3 m / s; if Vw > 10 m / s, the preset reference speed is Vb = 0.1 m / s.

[0102] Step S404: calculating and generating a motor torque compensation coefficient based on the spatial distribution features and path gradient data, and generating a motor output torque set value in combination with a preset reference torque;

[0103] Wherein, the preset reference torque is a driving torque required to maintain the current speed, especially on a horizontal plane, and the motor torque compensation coefficient is an additional gain introduced to overcome the influence of gradient, which can be calculated by a gradient-torque linear or non-linear model.

[0104] In some embodiments, for example, when an uphill road section ahead is detected (the gradient change trend and real-time gradient data are predicted in combination with spatial distribution features), a compensation coefficient greater than 1 is generated, so that the motor output torque set value (the product of the reference torque and the compensation coefficient) increases, thereby providing sufficient additional traction in advance or synchronously to overcome the gravity component and prevent the robot from stalling or sliding down; conversely, a coefficient less than 1 or even a negative coefficient (representing braking torque) may be generated during downhill, which realizes dynamic compensation for known terrain loads.

[0105] Step S405: collecting a ground wire structure image through a machine vision module, and identifying and generating a real-time lateral offset between the inspection robot and the ground wire center line;

[0106] Wherein, despite the macro path guidance and environmental adaptation, the inspection robot will still produce lateral deviation relative to the ground wire center (its preset walking track) due to factors such as wind force, vibration, and mechanism clearance during actual walking.

[0107] In this embodiment, a machine vision module (typically a forward-facing or downward-facing camera) continuously captures images of the ground wire. Image processing algorithms (such as edge detection, Hough transform, or deep learning models) accurately identify the contour and centerline of the ground wire (typically a single or double-split wire). By comparing the robot's reference position in the image (such as the chassis center projection) with the identified ground wire centerline, the real-time lateral offset can be calculated. This offset is a direct and accurate measure of whether the robot has deviated from its course, serving as a feedback signal for high-precision correction control.

[0108] Step S406: Based on the optimal movement path coordinate sequence and real-time lateral offset, calculate and generate a heading angle correction command;

[0109] Specifically, based on the optimal movement path coordinate sequence, a desired target heading angle is generated by calculating the direction from the current robot position to the next path point. Based on the real-time lateral offset, the heading angle adjustment required to correct the current lateral deviation (i.e., differential steering command) is calculated by the controller (such as a proportional-derivative controller). The final heading angle correction command integrates these two aspects, aiming to ensure that the inspection robot not only moves towards the next target point but also eliminates lateral errors in real time, ensuring that its trajectory closely follows the ground centerline, forming a precise closed-loop control of the lateral position.

[0110] Step S407: Simultaneously execute the walking speed setting value, the motor output torque setting value, and the heading angle correction command to drive the inspection robot to move along the optimal movement path.

[0111] The walking speed setpoint and motor output torque setpoint primarily affect the robot's longitudinal drive system (such as the drive wheel motor), controlling the walking speed and output force respectively. They work together to cope with slope and wind resistance. The heading angle correction command affects the robot's steering system (such as the speed difference of the differential drive wheels or an independent steering servo), controlling its walking direction.

[0112] Subsequently, the motion controller (such as the vehicle controller, VMC) receives these three setpoints / instructions, converts them into specific control signals (such as PWM duty cycle and pulse count) for each actuator (drive motor, steering motor), and sends them out for execution synchronously. These three control quantities correspond to the longitudinal speed control loop, the longitudinal force control loop, and the lateral position control loop, respectively. Their coordinated action enables the robot to move stably and precisely along the planned path centerline with speed and torque adapted to the environment.

[0113] In the above implementation, feedforward control (pre-adjusting speed and torque based on path characteristics and environmental perception) is combined with feedback control (based on real-time visual correction), and macroscopic path tracking is combined with microscopic pose stabilization. This effectively overcomes the problems of unstable walking, high energy consumption, and easy derailment caused by environmental disturbances in traditional methods, improves the safety, smoothness and energy efficiency of the movement process, and lays the foundation for subsequent high-precision docking and charging.

[0114] Reference Figure 5 As one implementation of step S107, when the inspection robot enters the preset docking range of the target charging dock, the step of fine-tuning the interface pose deviation between the inspection robot and the target charging dock until the tolerance threshold is met and a docking success signal is generated includes:

[0115] Step S501: When the inspection robot enters the preset docking range of the target charging dock, it collects the spatial coordinate data of the feature points of the target charging dock interface through the machine vision module.

[0116] Specifically, the preset docking range is a short-range area (e.g., within 1 meter in front of the charging dock). Within this range, the robot needs to activate high-precision positioning sensors. The machine vision module (usually a binocular stereo vision system or a monocular camera with structured light) acts as the eyes, capturing pre-designed or inherent feature points on the charging dock interface (such as specific shaped markings, the contour corners of charging contacts, etc.). Through image processing and 3D reconstruction algorithms, the 3D coordinates of these feature points in the camera coordinate system are calculated, i.e., feature point spatial coordinate data. This provides a precise geometric description of the charging interface in space, which is the basis for subsequent pose calculation.

[0117] Step S502: Obtain the preset reference coordinate system parameters of the inspection robot docking end;

[0118] The docking end reference coordinate system is a pre-calibrated coordinate system, whose origin is usually defined at the center of the robot charging plug or a specific reference surface, and whose coordinate axis directions have a fixed transformation relationship with the robot body coordinate system. These parameters (i.e., the origin position of the coordinate system and the homogeneous transformation matrix of the attitude axes relative to the robot base or a certain reference sensor) are known constants. They represent the ideal position and orientation of the charging interface in space when the robot's pose is completely correct, thus constructing a known "ideal charging dock interface model" fixed in the robot docking end reference coordinate system.

[0119] Step S503: Based on the spatial coordinate data of feature points and the reference coordinate system parameters of the docking end of the inspection robot, calculate and generate the initial interface pose deviation.

[0120] In this embodiment, the coordinate transformation relationship between the known camera and the inspection robot body is utilized to uniformly transform the coordinates of the charging dock feature points measured by the vision system to the robot body coordinate system. Then, by solving a PnP problem or a similar algorithm, the optimal spatial transformation relationship between the currently observed charging dock interface feature point cloud and the ideal charging dock interface model is calculated. The translation (ΔX, ΔY, ΔZ) and rotation (usually expressed as Euler angles or quaternions around the X, Y, and Z axes, ΔRx, ΔRy, ΔRz) included in this optimal spatial transformation relationship constitute the initial interface pose deviation of six degrees of freedom, which completely and quantitatively describes the entire spatial difference between the robot's current pose and the pose required to achieve perfect docking.

[0121] Step S504: Input the initial interface pose deviation into the pre-trained Kalman filter and output the deviation prediction value.

[0122] In this embodiment, the Kalman filter is an optimal recursive state estimation algorithm. Based on the dynamic model of the robot's motion system (i.e., the relationship between the current pose deviation, control input, and pose deviation at the next moment) and an understanding of the statistical characteristics of sensor noise, it filters the measured values. Its working principle is to combine the state estimate from the previous moment with the noisy measurement at the current moment, and through algorithm iteration, generate an optimal estimate of the true pose deviation, while also predicting the deviation trend at the next moment. The final output deviation prediction is smoother and more accurate than the original measurement value, and includes information on future trends. This provides the controller with higher quality and more forward-looking input, helping to reduce overshoot and improve convergence speed.

[0123] Step S505: The deviation prediction value is converted into a pose compensation control command by the PID controller.

[0124] Specifically, the PID controller (proportional-integral-derivative controller) takes the predicted deviation value processed by a Kalman filter as input. Inside the controller, the proportional term generates a control action based on the magnitude of the current deviation, aiming to quickly reduce the deviation; the integral term accumulates historical deviations to eliminate steady-state errors (i.e., residual deviations that eventually cannot be perfectly aligned); and the derivative term generates a control action based on the rate of change of the deviation, having a damping effect to suppress system oscillations and overshoot.

[0125] In the embodiments of this application, the controller performs PID calculations independently or collaboratively on the deviations of the six degrees of freedom (three translations and three rotations) and outputs corresponding pose compensation control commands. These commands may be the distance, speed or torque settings that each joint motor needs to move, with the aim of driving the robot to move in the direction of reducing pose deviations.

[0126] Step S506: Execute the pose compensation control command to drive the joint actuators of the inspection robot to adjust the pose.

[0127] Specifically, joint actuators typically include linear modules (driven by stepper or servo motors) that enable lateral (X / Y) and longitudinal (Z) translation, and rotary joints (driven by servo motors) that enable pitch, yaw, and roll angle adjustments. The control system's underlying drivers receive pose compensation control commands from the PID controller, convert them into specific pulse sequences, speed, or position commands for each motor, and drive these mechanisms to coordinate their movements, thereby causing minute translations and rotations in the robot's pose, especially at its docking end, to approximate the target pose.

[0128] Step S507: Monitor the interface pose deviation after pose adjustment in real time until the interface pose deviation is less than the preset tolerance threshold, and generate a docking success signal.

[0129] Specifically, after a fine-tuning action is performed, the system immediately returns to the first step, re-collects feature point coordinate data through the machine vision module, and recalculates the new, adjusted real-time interface pose deviation. This new deviation is compared with a preset tolerance threshold (set according to the allowable fit tolerance of the charging interface physical structure, such as position deviation less than 1mm and angle deviation less than 0.5 degrees). If the deviation is still outside the threshold, the closed-loop process of "perception-filtering-control-execution" is repeated; if the deviation meets the requirement of being less than the threshold, it indicates that the robot docking end and the charging dock interface are fully aligned, meeting the accuracy conditions for physical insertion. At this time, the system generates a docking success signal. This signal will trigger subsequent linear insertion, locking, and power-on actions, marking the completion of the precision alignment task.

[0130] In the above implementation, the complex spatial positioning problem of robot charging and docking is transformed into a closed-loop feedback problem with six-degree-of-freedom pose deviation as the control objective. By introducing a Kalman filter, sensor noise is effectively filtered out and state prediction is provided, enhancing the system's anti-interference capability and response speed. A PID controller enables precise and stable elimination of deviations in each degree of freedom. This closed-loop process continues until the pose accuracy reaches the stringent standards required for mechanical connection. This technical solution solves the docking failure problem caused by factors such as high-altitude swaying and accumulated positioning errors, and is a core technical guarantee for ensuring a high success rate in the autonomous charging process of the inspection robot.

[0131] Reference Figure 6 As one implementation of step S108, the steps of establishing a charging connection in response to a successful docking signal, dynamically adjusting the charging current parameters based on the real-time battery temperature and environmental perception dataset of the inspection robot, until charging is complete and the status dataset of the target charging dock is updated, include:

[0132] Step S601: In response to the docking success signal, acquire the docking success time data and establish a physical charging connection between the inspection robot and the target charging dock.

[0133] The successful docking signal indicates that the charging interface of the inspection robot and the charging dock interface have reached the pose accuracy required for mechanical connection. The system then records the time (t) from issuing the fine-tuning command to generating this signal for successful docking. d This time parameter is a key indicator for evaluating the mechanical smoothness of the charging dock, the performance of the robot's servo control, and the efficiency of their coordination. The longer the time, the more potential problems or performance degradation there are. Subsequently, the control system triggers the locking mechanism (such as an electromagnetic lock or mechanical latch) to complete the final physical connection and closes the main contactor of the charging circuit, establishing a physical charging connection and preparing a physical path for energy transmission.

[0134] Step S602: Obtain the real-time battery temperature of the inspection robot and extract the ambient temperature data from the environmental perception dataset.

[0135] Among them, the real-time battery temperature (Tb) is directly measured by temperature sensors embedded inside or on the surface of the battery pack. It directly reflects the current thermal state of the battery cell and is the most critical parameter determining the acceptable charging current of the battery (i.e., the maximum charging current that it can withstand without compromising lifespan and safety). The ambient temperature data (Ta) is extracted from a continuously updated environmental perception dataset, reflecting the macroscopic thermal environment in which the robot operates.

[0136] By combining the two, a more comprehensive assessment of the battery's heat dissipation conditions and thermal behavior can be achieved: for example, even if the battery's current temperature is not high, if the ambient temperature is high, high-current charging may cause the battery temperature to rise rapidly due to poor heat dissipation; conversely, in low-temperature environments, the battery activity is low, and the charging current must be carefully controlled to prevent lithium deposition.

[0137] Step S603: Calculate and generate a temperature compensation coefficient based on real-time battery temperature and ambient temperature data;

[0138] Specifically, a function model is constructed with battery temperature and ambient temperature as inputs and a current adjustment coefficient as output. This model aims to limit the charging current to a range that the battery can safely and efficiently accept under the current thermal conditions. The resulting temperature compensation coefficient is a multiplier between 0 and a certain upper limit (such as 1.2), used to scale the reference current. For example, by calculating the temperature difference (Tb-Ta) between the battery temperature and the ambient temperature, a positive temperature difference indicates that the battery temperature is higher than the ambient temperature, possibly due to heat generated during charging or temperature rise caused by previous operation. In this case, the current should be reduced (coefficient < 1) to prevent overheating. When the temperature difference is negative or zero, the coefficient is 1 or slightly greater than 1.

[0139] In some embodiments, calculating the temperature compensation coefficient includes: establishing a two-dimensional mapping table of battery temperature and ambient temperature, and calculating the offset of the compensation coefficient through two-parameter interpolation, thereby more precisely characterizing the nonlinear effect of temperature on battery chemical properties.

[0140] Step S604: Adjust the preset reference charging current parameters according to the temperature compensation coefficient to generate a dynamic charging current setting value.

[0141] In some embodiments, the formula for calculating the dynamic charging current setpoint is as follows:

[0142] ;

[0143] In the above formula, I0 is the rated charging current, Tb is the battery temperature value, and Ta is the ambient temperature value;

[0144] Specifically, the preset reference charging current parameter (I0) is typically a rated current value set at a nominal ambient temperature of 25°C, when the battery is in its optimal temperature range, taking into account both charging speed and battery life. By multiplying the temperature compensation coefficient by this reference current, the dynamic charging current setting value (Ic) is obtained. This setting value is also limited to a preset absolute safety range (e.g., between 0 and the maximum allowable current), forming dual protection. This setting value is the real-time current control target of the charging power module (such as a DC-DC converter).

[0145] Step S605: Perform the charging operation according to the dynamic charging current setting value, and monitor the battery voltage change rate and the number of charging protection triggers in real time during the charging process.

[0146] The charging control system (usually a battery management system (BMS) working in conjunction with the charger) performs constant current charging based on a dynamic charging current setpoint. Simultaneously, the system performs two key monitoring tasks: First, it continuously calculates the battery voltage change rate (dV / dt). During the constant current charging phase, the battery voltage steadily increases. As the charge approaches saturation, the rate of voltage increase slows significantly, entering the so-called "trickle" or "full charge" plateau, where the voltage change rate becomes very small. This parameter is a sensitive indicator for judging the charging stage, especially identifying the final stage of charging (rather than relying solely on a fixed voltage point). Second, it records the number of charging protection triggers (Nf), including but not limited to the number of protection events triggered by the BMS such as over-temperature protection, over-current protection, and abnormal cell balancing. This parameter directly reflects the stability and safety of the charging process; frequent protection triggers indicate unsatisfactory charging conditions or potential problems with the battery / charging dock.

[0147] Step S606: When the battery voltage change rate is lower than the preset charging completion threshold, a charging completion signal is generated;

[0148] The preset charging completion threshold is a very small voltage rise rate. When the real-time monitored voltage change rate remains below this threshold for a period of time, it indicates that the battery is essentially fully charged. Continuing to charge with high current yields minimal benefits and may pose an overvoltage risk. At this point, the system generates a charging completion signal, rather than rigidly waiting for the voltage to reach a fixed threshold that may change with temperature and aging. This method can more accurately determine the saturation point, helping to improve the completeness of charging and avoid overcharging.

[0149] In step S607, in response to the charging completion signal, the health score in the target charging dock status dataset is updated based on the docking success time data and the number of charging protection triggers, and the real-time occupied status flag is updated to idle status.

[0150] Specifically, after charging is complete, the robot disconnects from the charging port, and the system resets the charging dock's occupancy status to "idle," making it available for use by other robots.

[0151] Simultaneously, the performance data generated from this task will be used to update the charging dock's health score (Hi), calculated using the following formula: ;

[0152] In the above formula, β is the preset attenuation coefficient. The updated health score;

[0153] Specifically, the time taken for a successful docking (t) d The longer the time, the less efficient the docking strategy of the charging dock's guidance mechanism or robot is at this point, and confidence in its reliability should decay exponentially with time cost (-β×td term); the more times the charging protection is triggered (Nf), the more problems occur in the electrical connection or power quality during this charging process, and the greater the penalty to its health (-0.1×Nf term).

[0154] Understandably, updating via an exponential function allows for significant impact from a single instance of poor performance, while long-term stable performance keeps the score high. The updated health score is written back into the state dataset. When any robot recalculates the path cost in the future, this more accurate and timely score will directly influence decisions, causing the system to favor charging docks with better historical performance, thereby driving the entire charging network to continuously evolve towards greater efficiency and reliability.

[0155] In the above implementation, a dual-parameter compensation model for battery temperature and ambient temperature is introduced to dynamically adjust the charging current, effectively avoiding the safety risks of the battery under overheating or low temperature conditions, and optimizing the balance between charging speed and battery life. By monitoring the voltage change rate, the charging endpoint is intelligently determined, improving the completeness and accuracy of charging.

[0156] In addition, the solution also designs a dynamic update mechanism for health scores based on the actual performance data of this task (docking time, number of protection triggers), which transforms each charging task into a system performance check and learning process. This makes the selection decision of the charging dock no longer based on a static, initial score, but on a dynamic score that continuously reflects its latest reliability. This endows the entire multi-robot inspection system with strong self-optimization and self-repair capabilities, continuously improving the operating efficiency and robustness of the entire energy supply network.

[0157] Reference Figure 7 As a further implementation of the intelligent control method, after step S104, which responds to a charging request command, obtains the state parameter set of each charging dock within a preset area, and calculates the path cost of each charging dock in conjunction with the environmental perception dataset, the method further includes:

[0158] Step S701: Obtain real-time weather warning data and historical fault record dataset within the preset area;

[0159] Specifically, real-time weather warning data typically originates from data interfaces with meteorological departments or local weather stations. It includes warnings of strong winds, lightning, hail, icing, and other conditions that pose a direct threat to high-altitude operations. Its characteristics include strong real-time performance and high predictability. The historical fault record dataset is a valuable experience base accumulated by the system itself or other robots in the same area through long-term operation and maintenance. It includes robot motion faults (slippage during grounding, obstacle crossing, motor overload), charging docking faults (excessive interface positioning deviation, charging connection failure), environmental faults (positioning deviation due to strong winds, path obstruction due to icing), and system-level faults (communication interruption, sensor failure, sudden drop in battery power), covering all dimensions of failure modes from mechanical and electrical to environmental.

[0160] Step S702: Extract the coordinate set of high-incidence fault areas based on the historical fault record dataset;

[0161] Specifically, by performing cluster analysis, frequency statistics, and spatial correlation analysis on a vast amount of historical fault records, the system can identify line segments or tower locations where the frequency of fault occurrence is significantly higher than the average in geographical space. These locations are marked as a set of coordinates for high-fault areas. For example, the coordinates of a long-span line segment that causes frequent robot slippage due to severe wind vibration, or a charging dock whose interface ages due to prolonged sun exposure, will be included in this set. This is equivalent to creating a dynamically updated risk map for the inspection robot, transforming abstract experience into specific spatial coordinate warnings.

[0162] Step S703: Based on the spatial relationship between the current GPS coordinates of the inspection robot and the coordinate set of high-fault areas, calculate and generate the risk weight of the current location.

[0163] The system calculates the Euclidean distance or path distance along the ground plane between the robot's current coordinates and the coordinates of all high-risk fault areas. Based on a preset distance-risk attenuation model (where the risk weight increases exponentially with closer distances), it calculates the risk weight for the current position. The closer the robot is to a high-risk fault point, the larger the weight value, indicating a higher probability that the robot is currently experiencing similar historical faults (such as jamming or communication interruption), requiring immediate preventative measures.

[0164] In some embodiments, an exponential decay model can be used to calculate the risk contribution value of each region to the current location. The formula is: contribution value = exp(-distance / decay constant). For example, if the inspection robot is 0.37 km away from high-fault area A and 4.8 km away from high-fault area B, assuming the decay constant is 2 km, the contribution value of high-fault area A = exp(-0.37 / 2) ≈ 0.83; the contribution value of high-fault area B = exp(-4.8 / 2) ≈ 0.09. The risk weight of the current location is the maximum value or sum of all contribution values. Here, the sum can be taken as: 0.83 + 0.09 = 0.92. The closer this value is to 1, the closer the robot's current location is to historically high-fault areas, and the higher the risk.

[0165] Step S704: Combine real-time meteorological early warning data with the current location risk weight to calculate and generate the current location environmental risk coefficient;

[0166] Real-time weather warning data (such as an upcoming level 10 gale warning) provides dynamic risk factors for the present and short term. The system integrates the warning level and type with the static background risk represented by the current location risk weight using a weighted or multiplicative model. For example, when a robot is located in a historically high-failure area (high static risk) and receives a strong wind warning (high dynamic risk), the calculated environmental risk coefficient for the current location will reach an extremely high value.

[0167] Step S705: Adjust the dynamic power threshold according to the current location environmental risk coefficient to generate an enhanced power threshold;

[0168] In this embodiment, the dynamic power threshold has been adjusted once based on the environmental compensation coefficient. In this enhancement scheme, the system uses the calculated environmental risk coefficient of the current location to make a second, more conservative upward adjustment to the threshold. When the comprehensive risk coefficient is high, it means that the uncertainty of the robot's subsequent movement, the potential for a sudden increase in energy consumption, or the risk of task interruption are extremely high. In order to ensure that there is still an absolutely sufficient power to reach the safe point (charging dock) in such extreme cases, the system generates a higher enhanced power threshold. This essentially injects additional energy safety redundancy into high-risk scenarios, causing the trigger point for charging requests to be further advanced.

[0169] Step S706: When the real-time remaining power is lower than the enhanced power threshold, the emergency charging mode is triggered and a charging path optimization instruction is generated.

[0170] If the robot's real-time remaining battery power drops below this enhanced, higher safety threshold, the system determines that conventional, efficiency-centric decision-making logic is insufficient to handle the current high-risk situation and must immediately activate the emergency charging mode. The triggering of this mode signifies a shift in the system's highest priority from "completing the inspection task" to "ensuring the robot's energy safety." Simultaneously, a charging path optimization instruction is generated, directing the downstream path decision-making module to immediately reassess all options with "risk avoidance" as the primary objective.

[0171] Step S707: Based on the spatial relationship between the GPS coordinates of each charging dock in the preset area and the coordinate set of high-fault areas, calculate and generate the path environment risk coefficient of each charging dock, and map it as a safety weight factor.

[0172] The safety weighting factor considers not only the location of the charging dock itself, but also the risk of the areas traversed by the entire path leading to the dock. For each charging dock, the system calculates the overlap and proximity between its corresponding planned path and the coordinate set of high-fault areas, thereby generating a path environmental risk coefficient. The higher this coefficient, the more or more dangerous historical fault points will be encountered when choosing this path to the charging dock.

[0173] In this embodiment, for a given charging dock j, its corresponding planned path Pj is discretized into a series of ordered path points {p1, p2, ..., pN}, where p1 is the current position of the inspection robot and pN is the position of the target charging dock. Assuming the coordinate set of high-risk fault areas is F = {f1, f2, ..., fM}, where each fk represents the core coordinates of a fault area or the coordinates of a fault point, the local risk value r(Pi) of each point Pi on the path is calculated as follows:

[0174] ;

[0175] In the above formula, This represents the Euclidean distance (or path distance along the ground line) from path point pi to the k-th fault region fk; L is the risk decay constant (e.g., set to 500 meters), which controls the rate at which risk decays with distance. The smaller L is, the faster the risk decays with increasing distance.

[0176] Next, the weighted average of the local risk values ​​of all sampling points along the entire path is calculated to generate the path environmental risk coefficient Rj.

[0177] ;

[0178] Here, wi is an optional weight, which can be simply set to 1 (arithmetic mean), or it can be set according to the characteristics of the path points, such as giving higher weights to points located near faulty structures like towers or suspension clamps. The higher the path environmental risk coefficient Rj value, the closer the path is to historical fault areas, and the higher the risk.

[0179] Subsequently, the coefficient is transformed into a penalty term, namely the safety weight factor, through a non-linear mapping function (for example, the safety weight factor increases sharply after the risk coefficient exceeds the threshold). This factor will significantly increase the cost of high-risk paths in subsequent calculations.

[0180] The nonlinear mapping function can be a modified Sigmoid function:

[0181] ;

[0182] In the above formula, T is the risk threshold (e.g., it can be set to 0.4), a key adjustment parameter that defines the boundary by which the system identifies high risk. g is the gain factor (e.g., it can be set to 10), which controls the steepness of the function near the threshold T; the larger g is, the more sensitive the system is to risk, and the penalty weight will rise sharply once the risk coefficient slightly exceeds the threshold. K is the maximum penalty coefficient (e.g., it can be set to 8), which defines the upper limit of the safety weight factor Sj. In emergency mode, the value of K will be set very large to ensure that the safety cost term dominates in the decision function.

[0183] Step S708: In response to the charging path optimization instruction, a safety weight factor is added to the calculation of the path cost value, and the path cost value of each charging dock is recalculated.

[0184] Specifically, in emergency mode, the decision function is reconstructed. The new cost value calculation might use the formula: Ci' = Ci + w5 * safety weight factor. Or, more aggressively, the risk-related weights (w2 and w4) are temporarily and significantly increased, and the w5 term is introduced. By adding this safety weight factor as a powerful penalty, the recalculated path cost value of charging docks that are close in distance but have high path risk will increase significantly, thus being relegated to a lower position in the decision ranking.

[0185] Step S709: Based on the comparison results of the recalculated path cost values, update the target charging dock and the optimal movement path sequence.

[0186] In emergency mode, the system recalculates the path cost based on safety as the primary objective, selects the option with the lowest overall cost (especially risk cost), and updates the target charging dock.

[0187] Meanwhile, the path planning algorithm will also try to avoid areas with a high incidence of historical failures, generating a potentially longer but safer updated optimal movement path sequence. Based on this new sequence, the robot will prioritize moving to the safest shelter (charging dock) while ensuring sufficient battery capacity.

[0188] The above implementation enhances the inspection robot's autonomous survival and decision-making capabilities in extreme weather and complex fault history environments. By integrating real-time meteorological and historical fault data, the system can proactively identify risks and quantify their levels. Then, through a series of chain reactions—such as raising the power safety threshold, triggering emergency mode, and strongly injecting risk penalty factors into path decision-making—it guides the robot to make the most conservative and safest decisions. This technical solution effectively avoids serious consequences such as power outages, entrapment, or damage to the robot due to decision-making delays or insufficient risk assessment in high-risk scenarios. It endows the entire system with advanced intelligence, similar to a "risk-avoidance instinct," which is crucial for ensuring unmanned and reliable operation and maintenance in areas with frequent severe weather and complex line conditions.

[0189] Reference Figure 8 As a further implementation of the intelligent control method, during the process of executing the pose compensation control command in step S506 to drive the joint actuator of the inspection robot to adjust its pose, the method further includes:

[0190] Step S801: Real-time acquisition of vibration frequency monitoring data of the target charging dock interface;

[0191] In the context of power transmission lines, charging docks, fixed to poles or ground wires, experience low-frequency vibrations along with the lines, primarily driven by wind. These vibrations typically range from 0.1 Hz to several hertz and can reach amplitudes of tens or even hundreds of millimeters. This vibration is the main cause of abrupt changes in visual positioning data and instability in pose closure loops.

[0192] In this embodiment of the application, by installing a high-precision inertial measurement unit (IMU) on the charging dock body or an adjacent stable structure, or by processing the temporal position data of visual feature points, the vibration frequency, amplitude and phase information of the charging dock interface in three-dimensional space can be calculated in real time to form vibration frequency monitoring data.

[0193] Step S802: When the vibration frequency monitoring data exceeds the preset frequency threshold, the vibration frequency monitoring data is matched with the preset mechanical resonance feature library to generate a vibration compensation vector.

[0194] The preset frequency threshold is used to filter out high-frequency noise or minor vibrations, and only responds to significant low-frequency vibrations that affect docking accuracy.

[0195] Specifically, through modal analysis or field testing, a database of typical vibration modes (including dominant frequencies, mode shapes, amplitude ratios in each direction, etc.) for specific tower types and specific lines (such as ±500kV~±1100kV) under different wind speeds has been established, namely, a pre-set mechanical resonance characteristic library.

[0196] Subsequently, during pattern matching, the system compares the real-time monitored vibration spectrum, dominant vibration direction, and other features with records in the feature library to identify which known resonance mode the current vibration most closely resembles. Based on the successfully matched mode, the system predicts the trajectory of the vibration's impact on the interface pose (i.e., the changes in displacement and angle) over a future control cycle and generates a vibration compensation vector of equal magnitude but opposite direction. This vector is a six-degree-of-freedom feedforward control variable designed to directly counteract the predicted vibration displacement.

[0197] Step S803: The vibration compensation vector is superimposed on the pose compensation control command to generate the disturbance-resistant pose compensation control command.

[0198] Among them, the basic pose compensation control command is generated by the PID controller based on the pose deviation prediction value output by the Kalman filter, while the vibration compensation vector is the feedforward control quantity based on the vibration model prediction.

[0199] In this embodiment, by vector superposition of the two, the generated anti-disturbance pose compensation control command contains both the intent of "correction" and "vibration resistance". This is equivalent to adding an inverse model feedforward channel for specific frequency disturbances to the control system, which can improve the system's ability to suppress periodic vibrations and enable the robot's joint actuator to generate a reverse motion synchronized with the vibration, thereby maintaining the relative pose stability between the end effector (joint) and the vibration target in a dynamic environment.

[0200] Step S804: Execute the anti-disturbance pose compensation control command, and at the same time monitor the convergence rate of the interface pose deviation.

[0201] The actuators (such as stepper motors and servo motors) provide a high-dynamic response based on the fused anti-vibration commands. Simultaneously, the system continuously monitors the changing trend of the core control objective—the interface pose deviation. The convergence rate (e.g., the slope of the deviation's decrease over time or its filtered derivative) is a key dynamic indicator for evaluating the effectiveness of vibration compensation and whether the entire closed-loop system stably approaches zero deviation. Ideally, after introducing vibration compensation, the deviation should decay rapidly during fluctuations.

[0202] In step S805, if the convergence rate is lower than the preset rate threshold, the spatial coordinate data of the feature points of the target charging dock interface are reacquired, and the pose compensation control command is regenerated.

[0203] In this embodiment, a preset rate threshold sets a lower limit for the deviation convergence speed. When the convergence rate is detected to be too low, or even when the deviation increases instead of decreasing, it indicates that the following may have occurred: the vibration mode has abruptly changed, exceeding the matching range of the preset feature library; the visual sensor has lost or misidentified feature points due to excessive vibration; or the feedforward compensation model is mismatched with the actual vibration, resulting in positive feedback.

[0204] At this point, the system can reset the alignment process: reacquire the spatial coordinate data of the feature points, and regenerate the pose compensation control command based on this new data. This is equivalent to restarting the closed-loop control starting from the current real instantaneous relative pose. This breaks the vicious cycle that may be caused by model mismatch or data contamination, and greatly enhances the system's robustness in the face of unknown or atypical disturbances.

[0205] In the above embodiments, mechanical resonance feature matching and feedforward-feedback composite control are applied to dynamic base alignment scenarios. By sensing and identifying the low-frequency vibration mode unique to the line in real time, and generating precise reverse motion commands for active cancellation, the inspection robot can anticipate and cancel the target's sway, thereby achieving high-precision pose locking even in a vibration environment.

[0206] Furthermore, by monitoring the convergence rate and introducing a reset mechanism, the system is endowed with the ability to self-diagnose and recover when faced with model uncertainties, thereby raising the docking success rate from a level severely constrained by vibration to a level of reliability and practicality, and promoting the practical application of unmanned inspection technology in harsh environments such as ultra-high voltage levels and high altitudes.

[0207] This application also discloses an intelligent control system for an overhead transmission line inspection robot on a tower.

[0208] An intelligent control system for an overhead transmission line inspection robot tower, specifically comprising:

[0209] The power monitoring module is used to monitor the real-time remaining power of the inspection robot installed on the ground wire of the overhead transmission line;

[0210] The environmental compensation module is used to acquire the environmental perception dataset of the inspection robot and calculate and generate environmental compensation coefficients.

[0211] The charging request module is used to calculate the dynamic power threshold based on the environmental compensation coefficient, and generate a charging request command when the real-time remaining power is lower than the dynamic power threshold.

[0212] The path cost calculation module is used to respond to charging request commands, obtain the status parameter set of each charging dock in the preset area, and calculate the path cost of each charging dock based on the status parameter set and the environmental perception dataset.

[0213] The target charging dock determination module is used to determine the target charging dock and the corresponding optimal movement path sequence based on the comparison results of the path cost values.

[0214] The motion control module is used to control the inspection robot to move according to the optimal movement path sequence and to adjust the walking parameters of the inspection robot in real time based on the environmental perception dataset.

[0215] The pose fine-tuning module is used to fine-tune the interface pose deviation between the inspection robot and the target charging dock when the inspection robot enters the preset docking range of the target charging dock, until the tolerance threshold is met and a docking success signal is generated.

[0216] The charging control module is used to establish a charging connection in response to a successful docking signal. It dynamically adjusts the charging current parameters based on the real-time battery temperature and environmental perception dataset of the inspection robot until charging is complete and the status dataset of the target charging dock is updated.

[0217] The intelligent control system for an overhead transmission line inspection robot on a tower according to an embodiment of this application can implement any of the above methods, and the specific working process of each module in the system can refer to the corresponding process in the above method embodiments.

[0218] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of a certain module is merely a logical functional division, and in actual implementation there may be other division methods, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0219] This application also discloses a computer-readable storage medium.

[0220] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described above in any of the methods of intelligent control of an overhead transmission line inspection robot on a tower.

[0221] The computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device; the program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0222] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. An overhead transmission line inspection robot tower intelligent control method, characterized in that, The method includes: Monitor the real-time remaining power of the inspection robot installed on the ground wire of the overhead transmission line; Obtain the environmental perception dataset of the inspection robot and calculate and generate the environmental compensation coefficient; A dynamic power threshold is calculated based on the environmental compensation coefficient. When the real-time remaining power is lower than the dynamic power threshold, a charging request instruction is generated. In response to the charging request command, the system obtains the status parameter set of each charging dock within a preset area and calculates the path cost of each charging dock by combining it with the environmental perception dataset. Based on the comparison results of the path cost values, the target charging dock and the corresponding optimal movement path sequence are determined. The inspection robot is controlled to move according to the optimal movement path sequence, and the walking parameters of the inspection robot are adjusted in real time based on the environmental perception dataset. When the inspection robot enters the preset docking range of the target charging dock, the interface pose deviation between the inspection robot and the target charging dock is finely adjusted until the tolerance threshold is met and a docking success signal is generated. In response to the successful docking signal, a charging connection is established, and the charging current parameters are dynamically adjusted based on the real-time battery temperature and environmental perception dataset of the inspection robot until charging is completed and the status dataset of the target charging dock is updated.

2. The intelligent control method for an overhead transmission line inspection robot on a tower according to claim 1, characterized in that, The steps for obtaining the environmental perception dataset of the inspection robot and calculating the environmental compensation coefficient include: The GPS coordinates, ambient temperature data, ambient wind speed data, and path slope data of the inspection robot are obtained to obtain an environmental perception dataset; The ambient temperature data is converted into a temperature influence factor, and the ambient wind speed data is converted into a wind speed influence factor. Based on a preset weighting ratio, the temperature influence factor and wind speed influence factor are weighted and fused to generate an environmental compensation coefficient.

3. The intelligent control method for an overhead transmission line inspection robot on a tower according to claim 2, characterized in that, In response to the charging request command, the steps of obtaining the state parameter set of each charging dock within a preset area and calculating the path cost of each charging dock based on the state parameter set and the environmental perception dataset include: In response to the charging request command, the status parameter set of all charging docks is retrieved from the charging dock status database of the preset area, including the charging dock GPS coordinates, real-time occupancy status indicator and health score; Obtain the GPS coordinates and path slope data of the inspection robot from the environmental perception dataset; Based on the GPS coordinates of the inspection robot and the GPS coordinates of the charging dock, the optimal movement path sequence corresponding to each charging dock is calculated and generated. Extract the path slope data corresponding to the optimal mobile path sequence, and calculate and generate the path slope feature parameters corresponding to each charging dock. The real-time occupancy status identifier of each of the charging docks is converted into an availability weighting coefficient; Based on the optimal mobile path sequence, path slope characteristic parameters, availability weight coefficient, and health score, the path cost of each charging dock is generated through a multi-factor fusion model.

4. The intelligent control method for an overhead transmission line inspection robot on a tower according to claim 3, characterized in that, The steps of controlling the inspection robot to move according to the optimal movement path sequence and adjusting the robot's walking parameters in real time based on the environmental perception dataset include: Obtain the optimal movement path coordinate sequence corresponding to the pre-generated target charging dock, and extract the spatial distribution features of the path coordinate points; Real-time acquisition of environmental wind speed data and path slope data from the environmental perception dataset; Based on the spatial distribution characteristics and environmental wind speed data, a real-time walking speed adjustment factor is calculated and generated, and a walking speed set value is generated by combining the preset benchmark speed. Based on the spatial distribution characteristics and path slope data, a motor torque compensation coefficient is calculated and generated, and a motor output torque setting value is generated by combining it with a preset reference torque. The machine vision module acquires images of the ground wire structure and identifies and generates the real-time lateral offset between the inspection robot and the center line of the ground wire. Based on the optimal movement path coordinate sequence and real-time lateral offset, a heading angle correction command is calculated and generated. The walking speed setting, motor output torque setting, and heading angle correction command are executed simultaneously to drive the inspection robot to move along the optimal movement path.

5. The intelligent control method for an overhead transmission line inspection robot on a tower according to claim 1, characterized in that, When the inspection robot enters the preset docking range of the target charging dock, the step of fine-tuning the interface pose deviation between the inspection robot and the target charging dock until the tolerance threshold is met and a docking success signal is generated includes: When the inspection robot enters the preset docking range of the target charging dock, it collects the spatial coordinate data of the feature points of the target charging dock interface through the machine vision module. Obtain the preset reference coordinate system parameters of the inspection robot docking end; Based on the spatial coordinate data of the feature points and the reference coordinate system parameters of the docking end of the inspection robot, the initial interface pose deviation is calculated and generated. The initial interface pose deviation is input into a pre-trained Kalman filter, which outputs a deviation prediction value. The deviation prediction value is converted into a pose compensation control command by a PID controller. The pose compensation control command is executed to drive the joint actuators of the inspection robot to adjust their pose. The interface pose deviation after pose adjustment is monitored in real time until the interface pose deviation is less than a preset tolerance threshold, at which point a docking success signal is generated.

6. The intelligent control method for an overhead transmission line inspection robot on a tower according to claim 5, characterized in that, In the process of executing the pose compensation control command to drive the joint actuators of the inspection robot to adjust their pose, the method further includes: Real-time acquisition of vibration frequency monitoring data of the target charging dock interface; When the vibration frequency monitoring data exceeds the preset frequency threshold, the vibration frequency monitoring data is matched with the preset mechanical resonance feature library to generate a vibration compensation vector. The vibration compensation vector is superimposed on the pose compensation control command to generate an anti-disturbance pose compensation control command. Execute the disturbance rejection pose compensation control command, and simultaneously monitor the convergence rate of the interface pose deviation. If the convergence rate is lower than the preset rate threshold, the spatial coordinate data of the feature points of the target charging dock interface will be reacquired, and the pose compensation control command will be regenerated.

7. The intelligent control method for an overhead transmission line inspection robot on a tower according to claim 3, characterized in that, The steps of establishing a charging connection in response to the docking success signal, dynamically adjusting the charging current parameters based on the real-time battery temperature and environmental perception dataset of the inspection robot, until charging is complete and updating the status dataset of the target charging dock, include: In response to the docking success signal, the docking success time data is obtained, and a physical charging connection is established between the inspection robot and the target charging dock. The real-time battery temperature of the inspection robot is obtained, and ambient temperature data is extracted from the environmental perception dataset. Based on the real-time battery temperature and ambient temperature data, a temperature compensation coefficient is calculated and generated. The preset reference charging current parameters are adjusted according to the temperature compensation coefficient to generate a dynamic charging current setting value. The charging operation is performed according to the dynamic charging current setting value, and the battery voltage change rate and the number of charging protection triggers are monitored in real time during the charging process. When the rate of change of the battery voltage is lower than the preset charging completion threshold, a charging completion signal is generated; In response to the charging completion signal, the health score in the target charging dock status dataset is updated based on the docking success time data and the number of charging protection triggers, and the real-time occupied status identifier is updated to idle status.

8. A method for intelligent control of an overhead transmission line inspection robot on a tower according to any one of claims 1 to 7, characterized in that, After the steps of responding to the charging request command, obtaining the state parameter set of each charging dock within a preset area, and calculating the path cost of each charging dock in conjunction with the environmental perception dataset, the method further includes: Acquire real-time weather warning data and historical fault record datasets within a preset area; Based on the historical fault record dataset, extract the coordinate set of high-incidence fault areas; Based on the spatial relationship between the current GPS coordinates of the inspection robot and the coordinate set of the high-fault area, the risk weight of the current location is calculated and generated. By combining the real-time meteorological warning data with the current location risk weight, the environmental risk coefficient for the current location is calculated and generated. The dynamic power threshold is adjusted based on the current location environmental risk coefficient to generate an enhanced power threshold. When the real-time remaining power is lower than the enhanced power threshold, the emergency charging mode is triggered and a charging path optimization instruction is generated. Based on the spatial relationship between the GPS coordinates of each charging dock in the preset area and the coordinate set of the high-incidence fault area, the path environment risk coefficient of each charging dock is calculated and generated, and mapped as a safety weight factor. In response to the charging path optimization instruction, the safety weight factor is added to the calculation of the path cost value, and the path cost value of each charging dock is recalculated. Based on the comparison results of the recalculated path cost values, the target charging dock and the optimal movement path sequence are updated.

9. A smart control system for an overhead transmission line inspection robot tower, characterized in that, The system is used to perform the intelligent control method for an overhead transmission line inspection robot on a tower as described in any one of claims 1 to 8, the system comprising: The power monitoring module is used to monitor the real-time remaining power of the inspection robot installed on the ground wire of the overhead transmission line; The environmental compensation module is used to acquire the environmental perception dataset of the inspection robot and calculate and generate environmental compensation coefficients. The charging request module is used to calculate a dynamic power threshold based on the environmental compensation coefficient, and generate a charging request command when the real-time remaining power is lower than the dynamic power threshold. The path cost calculation module is used to respond to the charging request instruction, obtain the state parameter set of each charging dock in the preset area, and calculate the path cost of each charging dock based on the state parameter set and the environmental perception dataset. The target charging dock determination module is used to determine the target charging dock and the corresponding optimal movement path sequence based on the comparison results of the path cost values. The movement control module is used to control the inspection robot to move according to the optimal movement path sequence and to adjust the walking parameters of the inspection robot in real time based on the environmental perception dataset. The pose fine-tuning module is used to fine-tune the interface pose deviation between the inspection robot and the target charging dock when the inspection robot enters the preset docking range of the target charging dock, until the tolerance threshold is met and a docking success signal is generated. The charging control module is used to establish a charging connection in response to the docking success signal, and dynamically adjust the charging current parameters based on the real-time battery temperature and environmental perception dataset of the inspection robot until charging is completed and the status dataset of the target charging dock is updated.

10. A computer-readable storage medium, characterized in that: The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1 to 8.

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