Intelligent motion control method and system applied to insulator detection device

Abstract

The invention relates to the technical field of insulator detection, and discloses an intelligent motion control method and system applied to an insulator detection device, and the method comprises the steps: controlling a joint of a mechanical arm to execute a preset excitation instruction when the mechanical arm of the insulator detection device is in an idle state, and synchronously collecting the real-time operation data of the joint; analyzing the real-time operation data to determine a motion response characteristic parameter of the current joint, and comparing the motion response characteristic parameter with a reference parameter of the joint in an initial state to identify a motion response characteristic deviation of the joint; and according to the motion response characteristic deviation, motion controller parameters of the joints are adjusted, so that the mechanical arm executes follow-up operation tasks based on the adjusted controller parameters. According to the invention, a self-adaptive control mechanism based on online parameter identification and thermal state sensing is introduced for the insulator detection device, and the precision, reliability and self-adaptive capability of the robot in motion in a complex high-altitude environment are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of insulator testing technology, and more specifically, to an intelligent motion control method and system for insulator testing devices. Background Technology

[0002] In the routine maintenance of power infrastructure, drone-mounted insulator inspection devices play a crucial role in high-altitude inspections. The core of these devices lies in their robotic arm system, which needs to precisely contact or perform close-range operations with the inspection module against the insulator strings on high-voltage lines. However, in practical applications, due to the long-term operation of mechanical components and the influence of environmental factors, the motion control accuracy and stability of the robotic arm face severe challenges, directly affecting the efficiency and reliability of the inspection task. Especially in scenarios requiring high-precision alignment, traditional motion control methods often struggle to cope, easily leading to omissions or repetitions during the inspection process.

[0003] Traditional controllers, when attempting to precisely track a target position, find they cannot completely eliminate the deviations caused by nonlinear friction and changes in dynamic characteristics. This can lead to the joint not stabilizing immediately after reaching the target position, but instead exhibiting a persistent, minute vibration or overshoot that is barely perceptible to the naked eye. Although this vibration is small in amplitude, it significantly reduces the joint's stable stopping performance, making it impossible to maintain absolute stillness for extended periods.

[0004] Because the visual positioning system cannot provide stable and accurate position feedback due to image motion blur, the control system receives constantly fluctuating or invalid position data. The system repeatedly performs position fine-tuning, but these adjustments are based on inaccurate visual input, which may exacerbate the instability of the end contact head. Ultimately, the docking process may fail due to timeouts caused by the inability to meet stable conditions for an extended period, or the inspection may be hastily completed under unstable contact conditions. This directly results in missed or repeated inspections, severely impacting the reliability and efficiency of power equipment inspection. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention discloses an intelligent motion control method and system for insulator testing devices. It aims to solve the technical problem that the motion control accuracy and stability of existing insulator testing device robotic arms decrease under long-term operation and environmental factors, resulting in reduced efficiency and reliability of testing tasks. This is especially true in high-precision docking scenarios, where traditional control methods are difficult to handle, easily leading to missed detections or repeated detections.

[0006] The technical solution of the present invention is as follows: In a first aspect, the present invention discloses an intelligent motion control method for an insulator detection device, comprising: When the robotic arm of the insulator detection device is idle, the joints of the robotic arm are controlled to execute preset excitation commands, and real-time running data of the joints are collected simultaneously. Analyze real-time operating data to determine the current joint motion response characteristic parameters, and compare the motion response characteristic parameters with the reference parameters of the joint in the initial state to identify deviations in the joint's motion response characteristics. Based on the deviation in motion response characteristics, the motion controller parameters of the joints are adjusted so that the robotic arm can perform subsequent tasks based on the adjusted controller parameters.

[0007] This technical solution enables the robotic arm to actively detect deviations in the motion response characteristics of its joints when it is idle, and adjusts the controller parameters accordingly. This effectively compensates for performance drift caused by long-term operation and environmental factors, improves the motion control accuracy and stability of the robotic arm, and solves the shortcomings of traditional control methods in high-precision docking scenarios.

[0008] Furthermore, the preset excitation commands include sinusoidal torque commands and square wave angle commands.

[0009] Specifically, the real-time operating data includes the joint's motor current, the actual angle fed back by the encoder, and the joint's instantaneous acceleration.

[0010] More specifically, motion response characteristics include response delay, overshoot, and the time required to eliminate backlash; The response delay is obtained by calculating the difference between the response timing of the motor current and the response timing of the actual angle fed back by the encoder. The overshoot is calculated by measuring the extent by which the actual angle fed back by the encoder exceeds the preset steady-state value. The time required to eliminate backlash is obtained by calculating the time interval between the moment when the instantaneous acceleration of the joint changes accordingly and the moment when the actual angle fed back by the encoder begins to produce a stable reverse change.

[0011] Furthermore, the steps for adjusting the motion controller parameters of the joint based on the deviation in motion response characteristics include: Based on preset adjustment rules or adaptive algorithms and deviations in motion response characteristics, the motor feedforward control quantity and PID gain of the joint are adjusted in real time.

[0012] The present invention also proposes that the method further includes: During the intervals when the robotic arm is performing low-load tasks, temperature data is collected from one or more key locations inside the joints of the robotic arm. Temperature data is analyzed to obtain the thermal characteristic parameters of the joint; The state parameters inside the joint are evaluated based on thermal characteristic parameters. The state parameters are compared with the benchmark reference characteristics inside the joint in the initial state to determine whether there is joint performance drift. In response to the detection of joint performance drift, the motion controller parameters of the joint are adjusted to compensate.

[0013] Furthermore, the thermal characteristic parameters include temperature distribution patterns and heat flow characteristics.

[0014] Based on the above, the present invention further proposes a method for evaluating the state parameters inside a joint based on thermal characteristic parameters, and comparing the state parameters with the benchmark reference characteristics inside the joint in its initial state to determine whether there is joint performance drift. This method includes the following steps: Analyze temperature distribution patterns and heat flow characteristics to assess the current frictional state inside the joint and the viscosity change of the lubricating grease. Friction state and viscosity change indicators are compared with baseline reference characteristics to determine whether there is joint performance drift.

[0015] Furthermore, the steps of comparing frictional states and viscosity changes with baseline reference characteristics to determine whether joint performance drift exists include: If the difference between the friction state or viscosity change index and the benchmark reference characteristic exceeds a preset threshold, it is determined that there is joint performance drift. In response to the detection of joint performance drift, the steps of adjusting the joint's motion controller parameters to compensate include: When joint performance drift is detected, the motor feedforward control quantity and PID gain of the joint are adjusted in real time according to the preset adjustment rules or adaptive algorithm and the difference between the friction state or viscosity change index and the benchmark reference characteristics.

[0016] Secondly, the present invention also discloses an intelligent motion control system applied to an insulator detection device, comprising: The data acquisition module is used to control the joints of the insulator detection device to execute preset excitation commands when the insulator detection device is idle, and to collect the real-time running data of the joints simultaneously. The deviation identification module is used to analyze real-time operating data to determine the motion response characteristic parameters of the current joint, and compare the motion response characteristic parameters with the reference parameters of the joint in the initial state to identify the deviation of the joint's motion response characteristics. The controller parameter adjustment module is used to adjust the motion controller parameters of the joints according to the deviation of the motion response characteristics, so that the robotic arm can perform subsequent tasks based on the adjusted controller parameters.

[0017] This technical solution provides an intelligent motion control system that integrates data acquisition, deviation identification, and controller parameter adjustment. It can automatically monitor and optimize the motion performance of the robotic arm in real time, effectively solving the problem of performance degradation during long-term operation of the robotic arm and improving the efficiency and reliability of detection tasks.

[0018] In summary, this invention provides an intelligent motion control method and system for insulator detection devices. The method, when the robotic arm is idle, controls its joints to execute preset excitation commands and simultaneously collects real-time joint operation data, enabling proactive and non-invasive acquisition of joint dynamic response information. Subsequently, the collected real-time operation data is analyzed in depth to determine the current joint motion response characteristic parameters, such as response delay, overshoot, and the time required to eliminate backlash. These parameters are precisely compared with reference parameters of the joint in its initial state to identify deviations in the joint's motion response characteristics. Finally, based on the identified deviations, the joint's motion controller parameters, such as motor feedforward control and PID gain, are intelligently adjusted so that the robotic arm can perform subsequent tasks based on the adjusted controller parameters. This invention, through proactive detection and adaptive adjustment, can compensate for joint performance drift in real time, enabling the robotic arm to maintain near-initial state motion control accuracy and stability under any operating conditions. This not only avoids visual positioning failure and docking failure caused by motion blur, significantly improving the efficiency and reliability of insulator detection tasks, but also extends the service life of the robotic arm, reduces maintenance costs, overcomes the shortcomings of traditional control methods in high-precision docking scenarios, and achieves unexpected technical results. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an intelligent motion control method for an insulator detection device provided in an embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the structure of an intelligent motion control system applied to an insulator detection device, provided in an embodiment of the present invention.

[0021] Labeling Explanation: 210, Operation Data Acquisition Module; 220, Deviation Identification Module; 230, Controller Parameter Adjustment Module. Detailed Implementation

[0022] The technical solutions of this invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some, not all, of the embodiments of this invention. The components of this invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without inventive effort are within the scope of protection of this invention.

[0023] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0024] Traditional unmanned aerial vehicle (UAV) insulator inspection devices face severe challenges in terms of the motion control accuracy and stability of their robotic arms under long-term operation and complex environments. Especially in scenarios requiring high-precision docking, traditional motion control methods struggle to cope with performance drift caused by long-term operation of mechanical components, easily leading to omissions or repetitions during the inspection process, seriously affecting the efficiency and reliability of the inspection task.

[0025] Firstly, please see Figure 1 This invention proposes an intelligent motion control method for insulator detection devices, comprising: S1. When the robotic arm of the insulator detection device is idle, control the joints of the robotic arm to execute preset excitation commands and simultaneously collect real-time running data of the joints. S2. Analyze the real-time running data to determine the current joint motion response characteristic parameters, and compare the motion response characteristic parameters with the reference parameters of the joint in the initial state to identify the deviation of the joint's motion response characteristics. S3. Adjust the motion controller parameters of the joints according to the deviation of the motion response characteristics so that the robotic arm can perform subsequent tasks based on the adjusted controller parameters.

[0026] The intelligent motion control method of this invention first controls the joints of the robotic arm to execute preset excitation commands when the robotic arm of the insulator detection device is in an idle state. The purpose of this step is to actively detect the current motion characteristics of the joints. For example, a step signal can be sent to the joint's motor and its response can be observed; or a periodic torque command can be applied and the joint's vibration can be recorded. The type of excitation command can be selected as needed, for example, it can be a simple pulse signal or a more complex frequency sweep signal. Simultaneously, real-time operating data of the joint is collected. This data is the basis for evaluating joint performance; for example, information such as the joint's actual position, velocity, acceleration, and the current of the drive motor can be obtained through sensors.

[0027] Next, the collected real-time operational data is analyzed to determine the current motion response characteristics of the joint. For example, by analyzing the joint's response curve to excitation commands, dynamic performance indicators such as response time, overshoot, and steady-state error can be calculated. These parameters quantify the joint's current motion performance. Subsequently, these motion response characteristics are compared with reference parameters of the joint in its initial state. The initial reference parameters are obtained when the robotic arm leaves the factory or after rigorous calibration, representing the joint's optimal performance under ideal working conditions. By comparison, deviations in the joint's motion response characteristics can be identified, i.e., the gap between the current performance and the ideal performance. For example, if the response time becomes longer or the overshoot increases, it indicates that the joint's motion characteristics have drifted.

[0028] Finally, the motion controller parameters of the joint are adjusted based on the identified deviations in the motion response characteristics. This step is crucial for achieving adaptive control. For example, if a sluggish joint response is detected, the proportional gain or feedforward control input of the controller can be appropriately increased; if oscillations are detected, the integral or derivative gain may need to be adjusted. The adjusted controller parameters will be used for subsequent tasks of the robotic arm, enabling it to perform tasks based on updated control strategies, compensating for the effects of performance drift, and ensuring the stability and accuracy of the robotic arm in high-precision operations.

[0029] The intelligent motion control method proposed in this invention actively acquires dynamic performance information of the joints by controlling the joints of the robotic arm to execute preset excitation commands when the robotic arm of the insulator detection device is in an idle state, and simultaneously collecting real-time operating data of the joints. For example, when the robotic arm completes an insulator detection task, the system can automatically trigger a short excitation test while waiting for the next task command. During this process, the joints will perform minute movements according to a preset trajectory or torque, and data such as motor current and the actual angle fed back by the encoder will be accurately recorded.

[0030] Subsequently, the collected real-time operational data is analyzed to determine the current motion response characteristics of the joint. For example, by performing time-domain or frequency-domain analysis on motor current and actual angle data, key indicators such as joint response delay and overshoot can be calculated. These parameters intuitively reflect the current dynamic performance of the joint. Next, these motion response characteristics are compared with reference parameters of the joint in its initial state. The initial reference parameters are obtained after rigorous testing and calibration when the robotic arm leaves the factory, representing the optimal performance of the joint under ideal conditions. Through this comparison, deviations in the joint's motion response characteristics can be identified. For example, if the response delay increases, it indicates that the joint's response speed has slowed down; if the overshoot increases, it may mean that the joint's stability has decreased.

[0031] Finally, based on the identified deviations in motion response characteristics, the motion controller parameters of the joint are adjusted. For example, if excessive response delay is detected, the system can increase the feedforward control of the motor in real time according to preset adjustment rules or adaptive algorithms to compensate for the joint's inertia or friction in advance; if excessive overshoot is detected, the proportional gain in the PID controller can be appropriately reduced or the derivative gain increased to improve the system's damping effect. In this way, the robotic arm can perform subsequent tasks based on the adjusted controller parameters. For example, when performing high-precision insulator docking, the adjusted controller parameters ensure that the robotic arm's end effector reaches the target position more smoothly and accurately, effectively avoiding positioning inaccuracies or jitter caused by joint performance drift, thereby significantly improving the efficiency and reliability of insulator inspection tasks.

[0032] Compared to traditional motion control methods, the core innovation of this invention lies in its proactive and adaptive performance monitoring and compensation mechanism. Traditional methods typically rely on fixed controller parameters, making it difficult to cope with performance drift that occurs during long-term operation of the robotic arm, resulting in poor performance in complex or high-precision tasks. This invention, by proactively executing excitation commands and collecting real-time operational data when the robotic arm is idle, can dynamically identify deviations in the motion response characteristics of the joints. This "health check-up" self-diagnostic mechanism enables the system to monitor the health status of the robotic arm in real time.

[0033] In some of the embodiments of the present invention described above, in order to more comprehensively stimulate the dynamic characteristics of the robotic arm joints and accurately collect its operating data, the preset excitation commands may include sinusoidal torque commands and square wave angle commands.

[0034] Specifically, preset excitation commands refer to one or more pre-defined control signals applied to the joints of the insulator detection device when the insulator arm is idle, aiming to induce a measurable motion response in the joints. The purpose of these commands is to stimulate the dynamic characteristics of the joints without affecting normal operation, so as to collect operational data and analyze their motion response characteristics. Among them, the sinusoidal torque command is a periodically varying torque signal whose amplitude and frequency can be set as needed. By applying the sinusoidal torque command, the force conditions of the joint at different frequencies can be simulated, thereby more comprehensively evaluating the joint's dynamic response, such as its stiffness, damping, and inertial characteristics. The square wave angle command is a command that periodically switches between two preset angle values. By applying the square wave angle command, the target position of the joint can be quickly changed, thereby stimulating the joint's response characteristics during rapid start-up, stopping, and reverse movement, such as its response speed, overshoot, and backlash.

[0035] The present invention employs sinusoidal torque commands and square wave angle commands as preset excitation commands, enabling a more comprehensive and accurate stimulation of the dynamic characteristics of robotic arm joints. The sinusoidal torque command effectively detects the joint's frequency response at different frequencies, revealing its internal parameters such as elasticity, damping, and inertia, which is crucial for identifying potential problems in continuous joint motion. Simultaneously, the square wave angle command simulates the joint's operating conditions during rapid positioning and reverse movement, helping to expose deviations in transient response, backlash, and frictional characteristics. The combined use of these two excitation commands allows the acquired real-time operational data to more fully reflect the joint's true motion response characteristics, thus providing a high-quality data foundation for subsequent characteristic parameter analysis and deviation identification.

[0036] Specifically, in some implementations of the above-mentioned intelligent motion control method, in order to obtain more comprehensive and accurate motion state information of the robotic arm joints to support subsequent motion response characteristic analysis, the specific composition of the real-time operation data has been further defined.

[0037] Real-time operating data includes the joint's motor current, the actual angle fed back by the encoder, and the joint's instantaneous acceleration.

[0038] Among these metrics, motor current refers to the current consumed by the motor driving the joint movement. It reflects the magnitude and variation of the motor's output torque and is a key indicator for assessing the joint's stress state and load conditions. The actual angle fed back by the encoder refers to the actual rotation angle of the joint detected in real time by an encoder mounted on the joint, directly reflecting the joint's motion trajectory and positional accuracy. The instantaneous acceleration of the joint refers to the rate of change of velocity of the joint at a specific moment during movement. It reveals the dynamic characteristics of joint movement, such as impacts, vibrations, and the presence and elimination of backlash. By collecting this multi-dimensional data, rich and detailed raw information can be provided for subsequent analysis of motion response characteristics.

[0039] The present invention collects real-time operating data, including the joint's motor current, the actual angle fed back by the encoder, and the instantaneous acceleration of the joint, enabling a more comprehensive and in-depth analysis of the joint's motion response characteristics. Motor current data reflects the joint's driving torque and load changes, helping to assess the joint's stiffness and frictional characteristics; the actual angle data fed back by the encoder directly provides the joint's motion trajectory and position information, forming the basis for calculating response delay and overshoot; and the instantaneous acceleration data is crucial for identifying and quantifying dynamic characteristics such as backlash. The comprehensive collection and analysis of this data allows the system to more accurately capture the joint's true dynamic behavior under excitation commands, thus providing reliable data support for the subsequent determination of motion response characteristic parameters.

[0040] In some embodiments of the present invention described above, in order to accurately identify deviations in the motion response characteristics of the robotic arm joints of the insulator detection device, it is necessary to analyze the real-time operating data of the joints to determine the current motion response characteristic parameters of the joints. Specifically, the aforementioned motion response characteristic parameters may include response delay, overshoot, and the time required to eliminate backlash.

[0041] Among them, the motion response characteristic parameters include response delay, overshoot, and the time required to eliminate backlash; The response delay is obtained by calculating the difference between the response timing of the motor current and the response timing of the actual angle fed back by the encoder; The overshoot is calculated by measuring the extent by which the actual angle fed back by the encoder exceeds the preset steady-state value. The time required to eliminate backlash is obtained by calculating the time interval between the moment when the instantaneous acceleration of the joint changes accordingly and the moment when the actual angle fed back by the encoder begins to produce a stable reverse change.

[0042] Specifically, response delay refers to the time required from the issuance of a control command to the actual joint motion response reaching a certain level, reflecting the system's dynamic response speed to the command. This parameter is precisely calculated by comparing the difference between the response timing of the motor current and the response timing of the actual angle fed back by the encoder. The response timing of the motor current can be considered a direct manifestation of the control command, while the response timing of the actual angle fed back by the encoder represents the actual motion state of the joint. Overshoot refers to the portion of the joint's motion trajectory that temporarily exceeds the target value when reaching the target position or steady-state value. This parameter reflects the system's stability and damping characteristics. Overshoot is obtained by calculating the magnitude by which the actual angle fed back by the encoder exceeds the preset steady-state value. A large overshoot usually indicates that the system has oscillations or underdamping. The time required to eliminate backlash refers to the time required for the backlash (i.e., backlash) in the mechanical transmission chain to be completely eliminated and for effective reverse motion to begin. This parameter is calculated by monitoring the time interval between the moment when the instantaneous acceleration of the joint changes accordingly and the moment when the actual angle fed back by the encoder begins to produce a stable reverse change. It can directly quantify the impact of nonlinear backlash in the mechanical transmission on motion accuracy.

[0043] This invention, by introducing and precisely calculating three key motion response characteristic parameters—response delay, overshoot, and the time required to eliminate backlash—enables a comprehensive and detailed evaluation of the dynamic performance, stability, and mechanical health of the robotic arm joints in an insulator detection device. Calculating the response delay helps identify time lag issues in the control system or mechanical transmission chain; calculating the overshoot reveals the oscillation characteristics of the system when reaching the target position, thus assessing its stability; and calculating the time required to eliminate backlash directly quantifies the impact of nonlinear backlash in the mechanical transmission on motion accuracy. Therefore, the comprehensive analysis of these parameters makes the identification of joint motion response characteristic deviations more refined and accurate, providing a more precise basis for subsequent adjustment of motion controller parameters.

[0044] In some embodiments of the present invention described above, a scheme for adjusting the motion controller parameters of a joint based on deviations in motion response characteristics was proposed. However, in practical applications, simply adjusting the parameters in a general manner may not be sufficient to cope with the complex changes in the joint's motion response characteristics, resulting in poor adjustment effects or insufficient response speed. To address this, the present invention further proposes a scheme for adjusting the motor feedforward control quantity and PID gain of the joint in real time based on preset adjustment rules or adaptive algorithms and deviations in motion response characteristics, thereby achieving more accurate and real-time optimization of controller parameters.

[0045] The steps described above for adjusting the motion controller parameters of the joint based on the deviation in motion response characteristics include: Based on preset adjustment rules or adaptive algorithms and deviations in motion response characteristics, the motor feedforward control quantity and PID gain of the joint are adjusted in real time.

[0046] Specifically, preset adjustment rules can refer to a series of logical judgments and parameter mapping relationships based on experience or models. For example, when the response delay exceeds a certain threshold, the feedforward control quantity is increased or the integral term in the PID parameters is adjusted. Adaptive algorithms can be understood as algorithms that can automatically adjust their own parameters according to the system's operating state and performance indicators. For example, based on reinforcement learning, fuzzy logic control, or neural networks, they can learn and optimize controller parameters online. Motion response characteristic deviation refers to the difference between the joint's motion response characteristic parameters and the initial reference parameters; this deviation is the basis for adjusting controller parameters. Real-time adjustment means that parameter updates and applications are continuous or periodic to ensure that the controller can always adapt to the current state of the joint. Motor feedforward control quantity refers to the control signal applied to the motor in advance according to the desired output or known disturbance in the control system. Its purpose is to compensate for the known dynamic characteristics of the system and reduce tracking errors. PID gain includes the gain coefficients of proportional (P), integral (I), and derivative (D) components, which together determine the controller's response to errors. By adjusting these gains, the system's stability, response speed, and steady-state accuracy can be optimized.

[0047] The solution of this invention, by introducing preset adjustment rules or adaptive algorithms and focusing on real-time adjustment of the motor feedforward control quantity and PID gain, can more precisely address deviations in the motion response characteristics of the joint. Specifically, when a deviation in the motion response characteristics of the joint is identified, such as excessive response delay or excessive overshoot, the preset adjustment rules can directly provide corresponding adjustment suggestions for the motor feedforward control quantity and PID gain based on these deviations. For example, if the response delay is large, the system inertia can be compensated in advance by increasing the feedforward control quantity, thereby shortening the response time; if the overshoot is large, the overshoot can be suppressed by adjusting the proportional or derivative term in the PID gain. The adaptive algorithm can dynamically learn and optimize these control parameters based on real-time operating data and performance feedback, so that it can maintain optimal control performance even under different operating conditions or joint wear levels. This targeted, real-time parameter adjustment mechanism enables the motion controller of the robotic arm to more accurately compensate for the nonlinear characteristics and dynamic changes of the joint, thereby improving the accuracy and stability of its motion control.

[0048] In some embodiments of the present invention described above, by executing excitation commands and analyzing real-time joint operation data when the robotic arm is idle, deviations in the joint's motion response characteristics can be identified and compensated. However, performance drift in robotic arm joints may not only stem from dynamic changes in the mechanical structure or control parameters, but may also be affected by the internal thermal state of the joint. For example, during prolonged operation or under different loads, thermal characteristics such as friction and lubricant viscosity within the joint may change, thereby affecting the joint's motion accuracy and stability. If these performance drifts caused by thermal factors are not monitored and compensated for in a timely manner, the long-term operational accuracy and reliability of the robotic arm will be difficult to guarantee.

[0049] In response, this invention further proposes an intelligent motion control method for insulator detection devices, the method further comprising: During the intervals when the robotic arm is performing low-load tasks, temperature data is collected from one or more key locations inside the joints of the robotic arm. Temperature data is analyzed to obtain the thermal characteristic parameters of the joint; The state parameters inside the joint are evaluated based on thermal characteristic parameters. The state parameters are compared with the benchmark reference characteristics inside the joint in the initial state to determine whether there is joint performance drift. In response to the detection of joint performance drift, the motion controller parameters of the joint are adjusted to compensate.

[0050] Specifically, the aforementioned "intervals during which the robotic arm performs low-load tasks" refers to the idle or semi-idle time periods that occur after the robotic arm completes a major task and before the next task begins, or when performing auxiliary tasks with low precision requirements and slow movement speeds. Selecting this period for data acquisition aims to avoid interfering with the robotic arm's normal high-precision operation and to ensure that the acquired temperature data stably reflects the internal thermal state of the joint. "Acquiring temperature data from one or more key components inside the robotic arm's joint" refers to pre-installing temperature sensors (such as thermocouples and thermistors) inside the joint—components that are susceptible to heat and crucial to performance, such as bearings, gearboxes, and motor windings—to obtain real-time temperature values ​​for these components.

[0051] The phrase "analyzing temperature data to obtain the thermal characteristic parameters of the joint" can be understood as processing and extracting from the collected raw temperature data to obtain quantitative indicators that characterize the thermal state of the joint. These thermal characteristic parameters may include, but are not limited to, temperature distribution patterns, heat flow characteristics, temperature change rate, highest temperature point, and average temperature. In practical applications, these parameters can be extracted from the raw temperature data through statistical analysis, thermal model calculations, or machine learning algorithms.

[0052] Furthermore, "evaluating the internal state parameters of a joint based on thermal characteristic parameters" refers to using the obtained thermal characteristic parameters, combined with a preset physical model, empirical formula, or data-driven model, to infer the actual physical state inside the joint. These state parameters may include the joint's friction state (e.g., coefficient of friction), viscosity changes in lubricating grease, and degree of wear. For example, an abnormal increase in joint temperature or a change in temperature distribution may indicate increased friction or poor lubrication. "Comparing the state parameters with the baseline reference characteristics of the joint's internal state in its initial state to determine if there is joint performance drift" refers to comparing the currently evaluated internal state parameters of the joint with the baseline reference characteristics recorded by the robotic arm at the time of manufacture or in its initial state after calibration. The baseline reference characteristics represent the ideal state of the joint under healthy and normal working conditions. If the difference between the current state parameters and the baseline reference characteristics exceeds a preset threshold, it can be determined that the joint has performance drift, meaning its motion performance has deviated from the normal level.

[0053] Therefore, "adjusting the joint's motion controller parameters to compensate for joint performance drift" means that once joint performance drift is identified, the system will automatically or semi-automatically modify the joint's motion controller parameters according to the degree and nature of the drift. These parameters may include the gain (such as Kp, Ki, Kd) of the proportional-integral-derivative (PID) controller, feedforward control variables, etc. The purpose of the adjustment is to counteract the effects of thermal changes (such as increased friction) on the joint's motion accuracy and response speed, enabling the robotic arm to continue performing subsequent tasks with the desired performance.

[0054] This invention utilizes the relatively idle time window during the intervals of a robotic arm performing low-load tasks to collect internal joint temperature data, thereby avoiding interference with normal operations. In-depth analysis of this temperature data allows for the extraction of thermal characteristic parameters of the joint, which indirectly reflect key information such as the internal friction state and lubricant viscosity changes. Furthermore, by comparing these evaluated internal joint state parameters with the baseline reference characteristics in the initial state, it is possible to promptly and accurately determine whether there is joint performance drift caused by thermal factors. Once performance drift is identified, the system can responsively adjust the joint's motion controller parameters, such as modifying the PID gain or feedforward control input, to compensate for motion characteristic deviations caused by thermal changes. This real-time compensation mechanism based on thermal state enables the robotic arm to maintain its motion accuracy and stability during long-term operation, effectively extending the equipment's service life and improving operational reliability.

[0055] In some embodiments of the present invention described above, in order to obtain the thermal characteristic parameters of the joint, it is necessary to collect temperature data from one or more key locations inside the joint of the robotic arm and analyze the temperature data. However, collecting only temperature data may not fully reflect the complex thermodynamic state inside the joint, such as the path and efficiency of heat transfer and the precise location of local heat sources, which may lead to an insufficiently precise evaluation of the internal state parameters of the joint.

[0056] In response, the present invention further proposes that the aforementioned thermal characteristic parameters include temperature distribution patterns and heat flow characteristics.

[0057] Specifically, thermal characteristic parameters refer to quantitative indicators used to describe the internal thermal state and behavior of a joint. Among them, temperature distribution patterns refer to the temperature values ​​and their spatial distribution at different locations within the joint, reflecting issues such as localized overheating and uneven heat dissipation. Heat flow characteristics refer to the direction, rate, and intensity of heat transfer within the joint, revealing information such as the generation of heat sources, heat conduction and convection, and heat dissipation efficiency. By acquiring and analyzing these parameters, a more comprehensive and in-depth understanding of the thermodynamic state within the joint can be achieved, providing crucial data for subsequent performance evaluation.

[0058] The present invention, by defining temperature distribution patterns and heat flow characteristics as thermal characteristic parameters, enables a more specific and in-depth analysis of the internal thermal state of the joint. Temperature distribution patterns visually reveal the heating status of various components within the joint, such as temperature changes in critical parts like bearings and gears, thus helping to identify potential areas of increased friction or poor lubrication. Heat flow characteristics further quantify the heat transfer path and efficiency within the joint; for example, by analyzing the direction and intensity of heat flow, the cooling effect of the lubricating oil or the presence of abnormal heat sources can be determined. This detailed thermal information provides a precise data foundation for subsequent assessments of internal joint friction states, lubricating grease viscosity changes, and other state parameters, thereby improving the accuracy of joint performance drift assessment.

[0059] In some embodiments of the present invention described above, it is proposed that during the intervals when the robotic arm is performing low-load tasks, temperature data of one or more key components inside the joint of the robotic arm be collected, and the temperature data be analyzed to obtain thermal characteristic parameters of the joint. Then, based on the thermal characteristic parameters, the state parameters inside the joint are evaluated, and the state parameters are compared with the reference characteristics of the joint in its initial state to determine whether there is joint performance drift. Specifically, the step of evaluating the state parameters inside the joint based on the thermal characteristic parameters and comparing the state parameters with the reference characteristics of the joint in its initial state to determine whether there is joint performance drift can be further refined as follows.

[0060] The above-mentioned steps for evaluating the internal state parameters of a joint based on thermal characteristic parameters, and comparing these state parameters with the baseline reference characteristics of the joint in its initial state to determine whether there is joint performance drift, include: Analyze temperature distribution patterns and heat flow characteristics to assess the current frictional state inside the joint and the viscosity change of the lubricating grease. Friction state and viscosity change indicators are compared with baseline reference characteristics to determine whether there is joint performance drift.

[0061] Temperature distribution patterns refer to the two-dimensional or three-dimensional spatial distribution patterns formed by temperature measurements at different locations within the joint, reflecting the transfer and accumulation of heat within the joint. Heat flow characteristics refer to the direction, rate, and intensity of heat flow within the joint, revealing the distribution of heat sources and heat dissipation efficiency. In-depth analysis of these thermal characteristic parameters can indirectly infer the mechanical state within the joint. Specifically, friction state refers to the magnitude and stability of resistance when moving parts of the joint come into contact, which is usually closely related to the degree of joint wear, lubrication condition, and assembly precision. The viscosity change index of lubricating grease refers to the change in viscosity of the lubricating grease at operating temperature relative to its initial state. Viscosity is a key parameter affecting lubrication effectiveness, and its changes may indicate aging, contamination, or failure of the lubricating grease.

[0062] In practical applications, various techniques can be employed to analyze temperature distribution patterns and heat flow characteristics. For example, thermal imaging technology can be used to acquire the temperature distribution on the joint surface, and a built-in temperature sensor array can be used to obtain temperature data at key internal points. This data is then input into a pre-trained model (e.g., a physics-based model or a machine learning model), which can calculate and evaluate the current friction state and lubricant viscosity changes based on the trends and patterns of changes in thermal characteristic parameters. For instance, abnormally high localized areas of heat or concentrated heat flow may indicate an increase in friction, while an overall increase in temperature and a decrease in heat dissipation efficiency may be related to a decrease in lubricant viscosity or failure.

[0063] This invention, by analyzing temperature distribution patterns and heat flow characteristics, enables a more precise assessment of the friction state and lubricant viscosity changes within a joint. The friction state and lubricant viscosity within a joint are key factors directly affecting joint performance and lifespan. Increased friction or significant changes in lubricant viscosity can lead to increased joint resistance, abnormal heat generation, and decreased motion accuracy, resulting in joint performance drift. By comparing these assessed friction state and viscosity change indicators with the joint's baseline reference characteristics in its initial state, abnormal changes in these key parameters can be accurately identified. This indirect assessment method based on thermal characteristic parameters avoids the difficulties of directly measuring internal joint friction and lubricant viscosity, providing a non-invasive and efficient diagnostic tool.

[0064] In some embodiments of the present invention described above, the internal state parameters of the joint are evaluated based on thermal characteristic parameters, and these state parameters are compared with the reference characteristics of the joint in its initial state to determine whether joint performance drift exists. In response to the determination of joint performance drift, the motion controller parameters of the joint are adjusted for compensation. However, in practical applications, the above solutions do not explicitly specify how to quantitatively determine joint performance drift, nor do they detail how to precisely adjust the motion controller parameters based on the degree of drift. This may lead to ambiguity in the judgment or coarseness in the compensation measures, thereby affecting the long-term stability and operational accuracy of the robotic arm.

[0065] To address this, the present invention further proposes a step that compares the aforementioned friction state and viscosity change indicators with a benchmark reference characteristic to determine whether joint performance drift exists, including: If the difference between the friction state or viscosity change index and the benchmark reference characteristic exceeds a preset threshold, it is determined that there is joint performance drift. The steps described above for adjusting the motion controller parameters of the joint to compensate for joint performance drift include: When joint performance drift is detected, the motor feedforward control quantity and PID gain of the joint are adjusted in real time according to the preset adjustment rules or adaptive algorithm and the difference between the friction state or viscosity change index and the benchmark reference characteristics.

[0066] Specifically, a preset threshold refers to a pre-defined numerical limit used to quantify whether the difference between the friction state or viscosity change index and the benchmark reference characteristic reaches a level requiring performance drift assessment. This threshold can be set based on the robot arm's design requirements, material properties, expected lifespan, and actual operating experience, for example, through experimental testing or simulation analysis. Pre-defined adjustment rules can be understood as a series of predefined logical judgments or mathematical functions used to determine specific motor feedforward control quantities and PID gain adjustment schemes based on the difference between the friction state or viscosity change index and the benchmark reference characteristic after identifying joint performance drift. For example, it could be a lookup table method, with different adjustment amounts corresponding to different difference ranges; or it could be a linear or nonlinear mapping function. Adaptive algorithms are a more intelligent adjustment mechanism that can dynamically learn and optimize adjustment rules based on the robot arm's real-time operating feedback and historical data to achieve more accurate and robust controller parameter adjustments. For example, reinforcement learning, fuzzy control, or neural network algorithms can be used. Motor feedforward control refers to the control signal applied to the motor in advance, outside the feedback control loop, based on a known system model or anticipated disturbances. Its purpose is to improve the system's response speed and disturbance rejection capability. PID gain refers to the gain coefficients of the proportional (P), integral (I), and derivative (D) controllers. They collectively determine the controller's response characteristics to errors. By adjusting these gains, the system's stability, response speed, and steady-state accuracy can be optimized.

[0067] The present invention provides a clear quantitative standard for judging joint performance drift by introducing a preset threshold. When the difference between the friction state or viscosity change index and the reference characteristic exceeds this threshold, the system can accurately identify joint performance drift, thereby avoiding misjudgment or omission caused by subjective judgment or vague standards. Furthermore, by adjusting the motor feedforward control quantity and PID gain of the joint in real time according to preset adjustment rules or adaptive algorithms, combined with the difference between the friction state or viscosity change index and the reference characteristic, the compensation measures can be precisely matched with the degree of performance drift. For example, when the difference is small, fine-tuning is performed; when the difference is large, more significant adjustment is performed. This fine-tuning based on the quantified difference makes the compensation process more accurate and efficient, thereby effectively solving the problem of coarseness that may exist in the compensation measures of traditional solutions.

[0068] Secondly, see Figure 2 The present invention also discloses an intelligent motion control system for an insulator detection device, comprising: The data acquisition module 210 is used to control the joints of the robotic arm to execute preset excitation commands and simultaneously collect real-time running data of the joints when the robotic arm of the insulator detection device is in an idle state. The deviation identification module 220 is used to analyze real-time running data to determine the motion response characteristic parameters of the current joint, and compare the motion response characteristic parameters with the reference parameters of the joint in the initial state to identify the deviation of the joint's motion response characteristics. The controller parameter adjustment module 230 is used to adjust the motion controller parameters of the joint according to the deviation of the motion response characteristics, so that the robotic arm can perform subsequent tasks based on the adjusted controller parameters.

[0069] This intelligent motion control system, through its modular design, enables real-time monitoring, evaluation, and adaptive adjustment of the motion performance of the robotic arm in an insulator inspection device. The operational data acquisition module 210 actively acquires dynamic response information from the joints, the deviation identification module 220 precisely quantifies the degree of performance degradation, and the controller parameter adjustment module 230 dynamically optimizes the control strategy based on the identified deviations. This systematic solution effectively overcomes the limitations of traditional control methods in dealing with performance drift caused by long-term robotic arm operation and environmental changes, significantly improving the accuracy, stability, and reliability of insulator inspection tasks.

[0070] The specific details of the intelligent motion control method applied to the insulator detection device have been described in the above embodiments and will not be repeated here. It should be emphasized that the present invention further provides an intelligent motion control system, which realizes the various steps of the above method through the coordinated operation of functional modules.

[0071] The intelligent motion control system of this invention forms a closed-loop adaptive control mechanism by introducing a running data acquisition module 210, a deviation identification module 220, and a controller parameter adjustment module 230. This system can proactively "check up" the joints of the robotic arm, acquire its dynamic performance data in real time, and intelligently identify performance deviations. For example, when the friction of the joint increases due to the aging of the lubricating grease, causing a response delay, the system can accurately quantify this delay through the deviation identification module 220, and the controller parameter adjustment module 230 can automatically adjust the feedforward control quantity, thereby effectively compensating for the influence of friction and restoring the joint to near-ideal response characteristics. This proactive monitoring and adaptive adjustment capability enables the robotic arm of the insulator detection device to maintain high precision and high stability throughout its entire lifespan, greatly improving its reliability and efficiency in complex and high-precision tasks, and effectively solving the problems mentioned in the background art, such as visual positioning failure and docking failure caused by end-effector tremors.

[0072] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An intelligent motion control method applied to an insulator detection device, characterized in that, include: When the robotic arm of the insulator detection device is idle, the joints of the robotic arm are controlled to execute preset excitation commands, and the real-time operating data of the joints are collected simultaneously. The real-time operating data is analyzed to determine the current motion response characteristic parameters of the joint, and the motion response characteristic parameters are compared with the reference parameters of the joint in the initial state to identify the deviation of the joint's motion response characteristics. Based on the deviation in motion response characteristics, the motion controller parameters of the joint are adjusted so that the robotic arm can perform subsequent tasks based on the adjusted controller parameters.

2. The intelligent motion control method for an insulator detection device according to claim 1, characterized in that, The preset excitation commands include sinusoidal torque commands and square wave angle commands.

3. The intelligent motion control method for an insulator detection device according to claim 1, characterized in that, The real-time operating data includes the motor current of the joint, the actual angle fed back by the encoder, and the instantaneous acceleration of the joint.

4. The intelligent motion control method for an insulator detection device according to claim 3, characterized in that, The motion response characteristic parameters include response delay, overshoot, and the time required to eliminate backlash. The response delay is obtained by calculating the difference between the response timing of the motor current and the response timing of the actual angle fed back by the encoder. The overshoot is obtained by calculating the extent by which the actual angle fed back by the encoder exceeds a preset steady-state value. The time required to eliminate the backlash is obtained by calculating the time interval between the moment when the instantaneous acceleration of the joint changes accordingly and the moment when the actual angle fed back by the encoder begins to produce a stable reverse change.

5. The intelligent motion control method for an insulator detection device according to claim 1, characterized in that, The step of adjusting the motion controller parameters of the joint based on the deviation in motion response characteristics includes: Based on preset adjustment rules or adaptive algorithms and the deviation of the motion response characteristics, the motor feedforward control quantity and PID gain of the joint are adjusted in real time.

6. The intelligent motion control method for an insulator detection device according to claim 1, characterized in that, The method further includes: During the intervals when the robotic arm is performing low-load tasks, temperature data is collected at one or more key locations inside the joints of the robotic arm. The temperature data is analyzed to obtain the thermal characteristic parameters of the joint; The state parameters inside the joint are evaluated based on the thermal characteristic parameters, and the state parameters are compared with the benchmark reference characteristics inside the joint in the initial state to determine whether there is joint performance drift. In response to the determination that there is a drift in the joint performance, the motion controller parameters of the joint are adjusted to compensate for it.

7. The intelligent motion control method for an insulator detection device according to claim 6, characterized in that, The thermal characteristic parameters include temperature distribution patterns and heat flow characteristics.

8. The intelligent motion control method for an insulator detection device according to claim 7, characterized in that, The step of evaluating the state parameters inside the joint based on the thermal characteristic parameters, and comparing the state parameters with the benchmark reference characteristics inside the joint in the initial state to determine whether there is joint performance drift includes: Analyze the temperature distribution pattern and the heat flow characteristics to assess the current friction state inside the joint and the viscosity change index of the lubricating grease; The friction state and viscosity change index are compared with the benchmark reference characteristics to determine whether there is joint performance drift.

9. The intelligent motion control method for an insulator detection device according to claim 8, characterized in that, The step of comparing the friction state and the viscosity change index with the benchmark reference characteristics to determine whether there is joint performance drift includes: If the difference between the friction state or the viscosity change index and the benchmark reference characteristic exceeds a preset threshold, it is determined that there is a drift in the joint performance. The step of adjusting the motion controller parameters of the joint to compensate for joint performance drift includes: When a joint performance drift is detected, the motor feedforward control quantity and PID gain of the joint are adjusted in real time according to the preset adjustment rules or adaptive algorithm and the difference between the friction state or viscosity change index and the reference characteristic.

10. An intelligent motion control system for an insulator detection device, characterized in that, include: The data acquisition module is used to control the joints of the insulator detection device to execute preset excitation commands when the insulator detection device is idle, and to simultaneously collect the real-time running data of the joints. The deviation identification module is used to analyze the real-time running data to determine the current motion response characteristic parameters of the joint, and compare the motion response characteristic parameters with the reference parameters of the joint in the initial state to identify the deviation of the joint's motion response characteristics. The controller parameter adjustment module is used to adjust the motion controller parameters of the joint according to the deviation of the motion response characteristics, so that the robotic arm can perform subsequent tasks based on the adjusted controller parameters.

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