A robot driving device

Abstract

The present invention relates to the field of robot driving, and specifically to a robot driving device, comprising: a driving control module, a sensing module, a power management module, an adaptive damping control module and an actuator; the driving control module uses the real-time load data provided by the sensing module to realize dynamic load analysis and power adjustment through load prediction and power distribution algorithm, so that the device can maintain efficient and stable power output under load fluctuation conditions. The power management module provides stable power output under load changes, avoids the influence of power fluctuations on the accuracy and stability of the actuator, and significantly improves the working reliability of the robot equipment. The adaptive damping control module uses real-time vibration data to dynamically adjust the damping coefficient to ensure the stable operation of the robot under the influence of external vibration. The actuator accurately performs operations according to the real-time load and vibration adjustment signals.

Description

Technical Field

[0001] The invention relates to the field of robot driving, in particular to a robot driving device. Background Art

[0002] With the rapid development of industrial automation and intelligent manufacturing, the demand for robotic equipment to perform tasks under complex working conditions is increasing. Especially in high-load or variable vibration environments, industrial robots are widely used in scenarios such as assembly, transportation, and precision machining. However, traditional robot drive systems often lack flexible power adjustment capabilities and vibration resistance when facing complex loads and dynamic environments, resulting in reduced efficiency of the drive system, increased power waste, and even operational errors. In addition, the various control modules of the robot equipment usually operate independently, and the signal linkage between the modules is insufficient, resulting in response delays and regulation lags, which cannot meet the requirements of complex tasks for real-time response and high-precision control. To solve these problems, the industry usually uses preset power to provide constant drive for the robot, or uses mechanical vibration reduction equipment to achieve basic anti-vibration effects, but these methods all have obvious limitations.

[0003] Most existing robot drive devices use a fixed power output mode, and its power output is difficult to dynamically adjust according to the real-time load conditions. When the robot encounters load fluctuations during operation, the fixed power output cannot meet the different load requirements, resulting in energy waste when the power is too high or slow operation when the power is insufficient. In addition, this single power mode will also increase equipment wear and shorten the service life of the robot.

[0004] Some existing technologies attempt to add load sensors to the drive system to detect the load and assist in regulating power. However, existing power regulation mechanisms often rely on preset parameters and do not take into account the real-time dynamic changes of the load, resulting in untimely response to power distribution and inability to achieve precise control. This approach is not ideal in scenarios where the load changes frequently, especially in situations where high load and low load switch quickly, the delay problem of power regulation is more prominent, making it difficult to achieve efficient energy management.

[0005] In the industrial environment where robot equipment is running, vibration is a common interference factor. Especially under high-speed operation and heavy load conditions, external vibration will aggravate the motion error and position offset of the actuator, directly affecting the accuracy and stability of the equipment. In the existing technology, fixed damping coefficients or mechanical vibration reduction structures are usually used to deal with the impact of vibration, but such methods have poor adaptability and cannot be effectively adjusted according to the real-time vibration frequency and amplitude, resulting in unstable operation of the equipment in high-frequency or strong vibration environments, and prone to vibration superposition effects.

[0006] Although some existing anti-vibration measures use adjustable damping mechanisms, the method of adjusting the damping coefficient usually relies on manual adjustment or requires the equipment to be shut down for adjustment, which is difficult to meet real-time requirements. The lag in this damping setting will not only reduce the anti-vibration ability of the robot equipment, but may also cause unnecessary downtime for maintenance during operation, seriously affecting work efficiency. In addition, mechanical vibration reduction measures are usually accompanied by problems such as increased equipment weight and increased energy consumption, which poses a challenge to the miniaturization and efficiency of robot equipment.

[0007] Traditional robot drive systems are mainly operated by individual modules. There is a lack of effective linkage signal transmission between modules such as power control, damping adjustment, and load detection, which results in the system being unable to make real-time adjustments and synchronous responses under conditions of load changes or vibration interference. Since real-time data sharing and comprehensive regulation cannot be achieved between modules, the driving effect and response speed of the equipment are limited, especially in complex working environments, where the control lag problem is more obvious.

[0008] When current robotic equipment encounters frequent switching of loads and vibration environments, the overall response speed of the system is often limited because the regulation of the power module and the damping module cannot be effectively linked, resulting in a large regulation lag, affecting the accuracy and efficiency of task execution. The independent working mode of modules in the existing technology leads to regulation lag and even execution delays in some fast-response tasks, which cannot meet the needs of high-speed and high-precision tasks. Summary of the invention

[0009] The purpose of the present invention is to provide a robot driving device to solve the technical problems raised in the above background technology.

[0010] Based on the above ideas, the present invention provides the following technical solutions: a robot driving device, comprising: a driving control module, a sensing module, a power management module, an adaptive damping control module and an actuator;

[0011] The drive control module receives the real-time load data from the perception module, analyzes the current load situation based on the load prediction and power allocation algorithm, and generates a corresponding power allocation signal. The drive control module includes a signal receiver, a data processor and a controller, and compares the load data with historical data to obtain an optimized power output signal, which is transmitted to the power management module;

[0012] The perception module is installed at the node of the actuator and is used to collect load data in real time. The perception module transmits the collected load data to the drive control module and the adaptive damping control module through the data interface to provide a basis for dynamic adjustment;

[0013] The power management module receives the power output signal from the drive control module, and controls the output of electric energy to the actuator based on the power output signal to achieve dynamic adjustment of the drive. The power management module includes a power conversion unit and a voltage stabilization unit, which are used to achieve stable power output under load fluctuations and respond to instantaneous power demand;

[0014] The adaptive damping control module is electrically connected to the perception module, the adaptive damping control module receives the load data collected by the perception module, calculates the damping coefficient based on the damping adjustment algorithm, so as to adjust the damping setting of the actuator to ensure the operation stability of the robot in a vibration environment; the output of the adaptive damping control module is transmitted to the actuator through an electrical connection to dynamically adjust the mechanical damping;

[0015] The actuator receives electrical energy from the power management module and operates according to the damping adjustment signal of the adaptive damping control module. The actuator maintains a predetermined motion accuracy under different load conditions and external environments. The sensing unit installed inside the actuator can continuously monitor the actual load state and feed back to the sensing module to further optimize the control.

[0016] The drive control module uses the real-time load data provided by the perception module to achieve dynamic load analysis and power adjustment through load prediction and power allocation algorithms, so that the equipment can maintain efficient and stable power output under load fluctuation conditions. The power management module provides stable power output under load changes, avoiding the impact of power fluctuations on the accuracy and stability of the actuator, and significantly improving the working reliability of the robot equipment. The adaptive damping control module uses real-time vibration data to dynamically adjust the damping coefficient to ensure the stable operation of the robot under the influence of external vibrations, and improves the anti-interference ability of vibration in complex environments. The actuator accurately performs operations according to real-time load and vibration adjustment signals, achieving high-precision control under different loads and external environments, so that the robot can maintain the predetermined motion accuracy in a dynamic environment.

[0017] Preferably, the drive control module includes a load prediction and power distribution algorithm for realizing accurate power distribution according to a real-time load prediction model. The specific steps of the load prediction and power distribution algorithm include:

[0018] S1. Receive load data provided by the perception module, where the load data is the force condition and quality information of the robot actuator during operation, including the weight, acceleration, and resistance caused by operation or physical position change of the current load;

[0019] S2. Calculate the load forecast value based on the current load data and historical load data;

[0020] S3. Adjust the power factor based on the predicted value to ensure accurate power output to cope with load fluctuations.

[0021] The drive control module analyzes the current load data through load prediction and power distribution algorithms, enabling the device to predict and adapt to dynamic changes in the load, improving the system's response speed and stability. The power output is adjusted based on the load prediction data, enabling the device to automatically optimize power distribution under different load conditions, effectively reducing energy consumption, extending the service life of the device, and ensuring smooth operation. The algorithm takes into account multiple data sources such as real-time load, historical load, and environmental impact, improving the accuracy of load prediction and thus improving the adaptability of the device under different operating conditions.

[0022] Preferably, the load prediction value is calculated by the following formula:

[0023] L pred =γ×L current +(1−γ)×L prev

[0024] Among them, L pred is the load forecast value, which indicates the load forecast result for the next time period at the current moment;

[0025] L current The currently collected load data includes real-time weight, acceleration, resistance and other information;

[0026] L prev Load data at previous time points, serving as a historical reference for current predictions;

[0027] γ is a weighting factor, which is a constant between 0 and 1 and is used to adjust the relative weight of current and historical load data;

[0028] The formula for adjusting the power factor based on the predicted value is as follows:

[0029] P opt =α×L pred +β

[0030] Among them, P opt The power value output by the drive control module represents the electric energy output allocated to the load;

[0031] α is the power-to-load proportionality factor, which controls the relationship between power and predicted load and is preset according to the specifications of the actuator and the load capacity;

[0032] β is the power regulation constant, which is used to fine-tune the system output to ensure the stability of power under slight load fluctuations.

[0033] The dynamic calculation of load prediction value is realized through the formula, so that the equipment can more accurately predict the future load state, effectively reducing the impact of load fluctuations on driving efficiency. The power output formula provides a power adjustment mechanism for load fluctuations, so that the drive system can automatically adjust the output as the load changes, ensuring that the power is always in the best working state. Through the combined use of prediction and adjustment algorithms, a smooth power output is provided when the load changes greatly, which significantly improves the continuity of the actuator operation and the stability of the equipment.

[0034] Preferably, the adaptive damping control module includes a damping adjustment algorithm, which is used to automatically adjust the damping coefficient based on real-time vibration data to improve the stability of the actuator in a vibration environment, including the following steps:

[0035] S1. Obtain the vibration frequency and vibration amplitude through the sensing module. The vibration frequency indicates the number of vibrations per second of the actuator, which is used to identify the frequency of environmental vibrations; the vibration amplitude indicates the intensity or amplitude of the vibration, which is used to judge the severity of the vibration impact;

[0036] S2. Calculate the damping adjustment coefficient according to the vibration data to adjust the damping property of the actuator;

[0037] S3. The adaptive damping control module outputs the damping adjustment coefficient to the actuator, so that it can make real-time damping adjustments according to the environmental vibration to ensure the smooth operation of the robot under the influence of vibration.

[0038] The damping adjustment algorithm automatically adjusts the damping coefficient through real-time vibration data, allowing the device to maintain stability in a vibration environment, significantly improving the vibration resistance and adaptability of the system. Through the vibration frequency and vibration amplitude data collected by the sensing module, the adaptive damping control module can adjust the damping coefficient according to actual environmental conditions to ensure smooth operation in a high-frequency vibration environment. Because the adaptive damping control module can dynamically adjust the damping, the device can avoid the loss of accuracy caused by excessive vibration in a complex vibration environment, improving the reliability of the device.

[0039] Preferably, the damping adjustment coefficient calculation formula includes:

[0040] C damp = K×fvib+Cinit

[0041] Among them, C damp is the adjusted damping coefficient, which indicates the damping adjustment value of the actuator under the current vibration conditions;

[0042] K is the damping proportional constant, which is used to amplify the effect of vibration frequency on damping and is obtained by equipment debugging;

[0043] C initis the initial damping coefficient, which is the basic damping coefficient set at the factory and is suitable for static or low vibration environments.

[0044] The damping adjustment formula provides an accurate response to the current environmental vibration. The device can automatically adjust the damping coefficient based on the vibration frequency, thereby more effectively controlling the stability of the actuator. Dynamic adjustment of the damping coefficient ensures that the device can achieve adaptive adjustment in both static and dynamic environments, so that it can maintain optimal working conditions under different vibration conditions. By accurately controlling the damping coefficient, excessive energy consumption is avoided, and the power usage of the device in a vibrating environment is more efficient, thereby extending the service life of the device.

[0045] Preferably, the power management module includes a linkage optimization algorithm for coordinating the output signals of the drive control module and the adaptive damping control module to achieve linkage optimization control of load and vibration, and the linkage optimization algorithm includes the following steps:

[0046] S1, receiving a power allocation signal from a load prediction and power allocation algorithm and a damping adjustment signal from a damping adjustment algorithm, so as to achieve load and vibration linkage optimization output;

[0047] S2, calculates the linkage optimization power output based on the two input signals, which is used for comprehensive control of power and damping;

[0048] S3. The linkage optimized power output is output to the actuator to achieve an organic combination of power output and damping adjustment.

[0049] The linkage optimization algorithm ensures the coordination of power and damping control by linking load prediction with power distribution signals and damping adjustment signals, so that the system can cope with changes in load and vibration at the same time. By optimizing power output through linkage, the device can maintain the best balance between drive and stability under different load and vibration conditions, improving the device's adaptability in changing environments. The linkage algorithm outputs power through the integrated output of the two signals, allowing the device to achieve precise drive under load and vibration conditions, effectively reducing operational delays and improving the overall response efficiency of the system.

[0050] Preferably, the linkage optimization power output calculation formula includes:

[0051] P sync =λ×P opt1 +μ C damp

[0052] Among them, P sync It is used to optimize the power output after linkage and to comprehensively control the power;

[0053] λ power regulation coefficient, determines P opt1 The weight of the impact on the overall power;

[0054] μ is the damping adjustment coefficient, which determines C damp weight in linkage power;

[0055] It further includes a response time adjustment formula to control the response speed of the robot under variable load and vibration environment. The calculation formula is as follows:

[0056] T response =η÷P sync

[0057] Among them, T response The response time of the robot device is the delay time from when the system receives the instruction to when it executes the action;

[0058] η is the response adjustment parameter, which is set according to the robot's response characteristics to different task requirements.

[0059] The linkage optimization power output formula combined with the response time adjustment formula realizes the optimization of the system's dynamic response under variable load and vibration environments. By adjusting the response time, the device can maintain the shortest command response delay under different load conditions, ensuring the efficient operation of the robot equipment and adapting to complex task requirements. Response time control enables the device to respond to external commands at the optimal speed, thereby maintaining continuous and stable operation in a changing environment, which helps to improve the overall task completion and operating efficiency of the robot.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] Through load prediction and power allocation algorithms, the equipment can dynamically adjust power output to match real-time load demand, thereby achieving accurate power allocation under load fluctuation conditions. This intelligent power management significantly improves the energy efficiency of the equipment, reduces unnecessary energy consumption, extends the service life of the equipment, and ensures the continuity and reliability of the equipment.

[0062] The adaptive damping control module collects vibration data in real time and dynamically adjusts the damping coefficient to keep the equipment running smoothly in a high-frequency vibration environment. This function greatly improves the vibration resistance of the equipment in complex environments, avoids the loss of accuracy caused by environmental changes, and ensures the reliability of the equipment in different working scenarios.

[0063] By comprehensively managing load prediction and damping adjustment signals through a linkage optimization algorithm, the device achieves linkage control of power and damping, enabling the system to quickly respond to external commands under variable load and vibration conditions. This function optimizes the device's response speed and ensures the robot's operating efficiency and execution accuracy in complex tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1This is a schematic diagram of the module relationship of a robot driving device of the present invention. DETAILED DESCRIPTION

[0065] A robot driving device, comprising: a driving control module, a sensing module, a power management module, an adaptive damping control module and an actuator;

[0066] The drive control module receives the real-time load data from the perception module, analyzes the current load situation based on the load prediction and power allocation algorithm, and generates a corresponding power allocation signal. The drive control module includes a signal receiver, a data processor and a controller, and compares the load data with historical data to obtain an optimized power output signal, which is transmitted to the power management module;

[0067] The perception module is installed at the node of the actuator and is used to collect load data in real time. The perception module transmits the collected load data to the drive control module and the adaptive damping control module through the data interface to provide a basis for dynamic adjustment;

[0068] The power management module receives the power output signal from the drive control module, and controls the output of electric energy to the actuator based on the power output signal to achieve dynamic adjustment of the drive. The power management module includes a power conversion unit and a voltage stabilization unit, which are used to achieve stable power output under load fluctuations and respond to instantaneous power demand;

[0069] The adaptive damping control module is electrically connected to the perception module, the adaptive damping control module receives the load data collected by the perception module, calculates the damping coefficient based on the damping adjustment algorithm, so as to adjust the damping setting of the actuator to ensure the operation stability of the robot in a vibration environment; the output of the adaptive damping control module is transmitted to the actuator through an electrical connection to dynamically adjust the mechanical damping;

[0070] The actuator receives electrical energy from the power management module and operates according to the damping adjustment signal of the adaptive damping control module. The actuator maintains a predetermined motion accuracy under different load conditions and external environments. The sensing unit installed inside the actuator can continuously monitor the actual load state and feed back to the sensing module to further optimize the control.

[0071] The drive control module uses the real-time load data provided by the perception module to implement dynamic load analysis and power adjustment through load prediction and power distribution algorithms, so that the equipment can maintain efficient and stable power output under load fluctuation conditions. The power management module provides stable power output under load changes, avoiding the impact of power fluctuations on the accuracy and stability of the actuator, and significantly improving the working reliability of the robot equipment. The adaptive damping control module uses real-time vibration data to dynamically adjust the damping coefficient to ensure the stable operation of the robot under the influence of external vibrations, and improves the ability to resist vibration in complex environments. The actuator accurately performs operations based on real-time load and vibration adjustment signals, achieving high-precision control under different loads and external environments, allowing the robot to maintain predetermined motion accuracy in a dynamic environment.

[0072] Specifically, the drive control module includes a load prediction and power distribution algorithm for realizing accurate power distribution according to a real-time load prediction model. The specific steps of the load prediction and power distribution algorithm include:

[0073] S1. Receive load data provided by the perception module, where the load data is the force condition and quality information of the robot actuator during operation, including the weight, acceleration, and resistance caused by operation or physical position change of the current load;

[0074] S2. Calculate the load forecast value based on the current load data and historical load data;

[0075] S3. Adjust the power factor based on the predicted value to ensure accurate power output to cope with load fluctuations.

[0076] The drive control module analyzes the current load data through load prediction and power distribution algorithms, enabling the device to predict and adapt to dynamic changes in the load, improving the system's response speed and stability. The power output is adjusted based on the load prediction data, enabling the device to automatically optimize power distribution under different load conditions, effectively reducing energy consumption, extending the service life of the device, and ensuring smooth operation. The algorithm takes into account multiple data sources such as real-time load, historical load, and environmental impact, improving the accuracy of load prediction and thus improving the adaptability of the device under different operating conditions.

[0077] When in use, the load sensor installed on the actuator is used to collect real-time load data. The sensor can be a pressure sensor or a strain gauge sensor to detect the weight, acceleration and resistance of the actuator during operation. The data is amplified and filtered by the signal processor in the sensing module and then transmitted to the drive control module.

[0078] The data processor of the drive control module receives the current load and previous load data from the perception module and calculates the predicted load through a weighted formula. This formula is based on the weighted moving average principle, and γ is a weighting factor (a constant between 0 and 1) used to balance the weights of real-time data and historical data. By setting the γγ value reasonably, the system can strike a balance between response speed and prediction accuracy.

[0079] Using the calculated L pred Predict load data and calculate required output power.

[0080] Weighted average and linear fitting principle: By balancing historical data and real-time data by weight, the load change can be predicted so as to provide appropriate power under load fluctuation conditions.

[0081] P calculated by the formula opt As a power signal, it is transmitted to the power management module to control the power supply of the actuator, so that the equipment can provide higher power at high load and reduce power output at low load, thereby optimizing energy consumption.

[0082] Specifically, the load prediction value is calculated by the following formula:

[0083] L pred =γ×L current +(1−γ)×L prev

[0084] Among them, L pred is the load forecast value, which indicates the load forecast result for the next time period at the current moment;

[0085] L current The currently collected load data includes real-time weight, acceleration, resistance and other information;

[0086] L prev Load data at previous time points, serving as a historical reference for current predictions;

[0087] γ is a weighting factor, which is a constant between 0 and 1 and is used to adjust the relative weight of current and historical load data;

[0088] The formula for adjusting the power factor based on the predicted value is as follows:

[0089] P opt =α×L pred +β

[0090] Among them, P opt The power value output by the drive control module represents the electric energy output allocated to the load;

[0091] α is the power-to-load proportionality factor, which controls the relationship between power and predicted load and is preset according to the specifications of the actuator and the load capacity;

[0092] β is the power regulation constant, which is used to fine-tune the system output to ensure the stability of power under slight load fluctuations.

[0093] The dynamic calculation of load prediction value is realized through the formula, so that the equipment can more accurately predict the future load state, effectively reducing the impact of load fluctuations on driving efficiency. The power output formula provides a power adjustment mechanism for load fluctuations, so that the drive system can automatically adjust the output as the load changes, ensuring that the power is always in the best working state. Through the combined use of prediction and adjustment algorithms, a smooth power output is provided when the load changes greatly, which significantly improves the continuity of the actuator operation and the stability of the equipment.

[0094] Specifically, the adaptive damping control module includes a damping adjustment algorithm, which is used to automatically adjust the damping coefficient based on real-time vibration data to improve the stability of the actuator in a vibration environment, including the following steps:

[0095] S1. Obtain the vibration frequency and vibration amplitude through the sensing module. The vibration frequency indicates the number of vibrations per second of the actuator, which is used to identify the frequency of environmental vibrations; the vibration amplitude indicates the intensity or amplitude of the vibration, which is used to judge the severity of the vibration impact;

[0096] S2. Calculate the damping adjustment coefficient according to the vibration data to adjust the damping property of the actuator;

[0097] S3. The adaptive damping control module outputs the damping adjustment coefficient to the actuator, so that it can make real-time damping adjustments according to the environmental vibration to ensure the smooth operation of the robot under the influence of vibration.

[0098] The damping adjustment algorithm automatically adjusts the damping coefficient through real-time vibration data, allowing the device to maintain stability in a vibration environment, significantly improving the vibration resistance and adaptability of the system. Through the vibration frequency and vibration amplitude data collected by the sensing module, the adaptive damping control module can adjust the damping coefficient according to actual environmental conditions to ensure smooth operation in a high-frequency vibration environment. Because the adaptive damping control module can dynamically adjust the damping, the device can avoid the loss of accuracy caused by excessive vibration in a complex vibration environment, improving the reliability of the device.

[0099] When in use, the accelerometer and vibration sensor are used to collect the vibration frequency and amplitude of the actuator in real time. The accelerometer detects the vibration amplitude and the vibration sensor provides a frequency signal. The data is transmitted to the adaptive damping control module through the sensor module for processing.

[0100] The adaptive damping control module uses the vibration data to calculate the adjusted damping coefficient, which is based on linear control principles to ensure basic damping levels at low frequencies.

[0101] The damping coefficient generated by the adaptive damping control module is sent to the actuator through the drive signal generator to adjust the working state of its internal damper to ensure that the actuator has better stability under high-frequency vibration conditions.

[0102] Linear Response Control Through linear amplification of vibration frequency, the system can adaptively adjust damping to ensure increased damping and improved stability under high vibration conditions.

[0103] The calculated C damp is sent to the damping adjustment unit of the actuator, which adjusts the damp The size of the mechanical damping level can be adjusted to effectively reduce the impact of vibration on the equipment and improve the stability of the system in complex environments.

[0104] Specifically, the damping adjustment coefficient calculation formula includes:

[0105] C damp = K×fvib+Cinit

[0106] Among them, C damp is the adjusted damping coefficient, which indicates the damping adjustment value of the actuator under the current vibration conditions;

[0107] K is the damping proportional constant, which is used to amplify the effect of vibration frequency on damping and is obtained by equipment debugging;

[0108] C init is the initial damping coefficient, which is the basic damping coefficient set at the factory and is suitable for static or low vibration environments.

[0109] The damping adjustment formula provides an accurate response to the current environmental vibration. The device can automatically adjust the damping coefficient based on the vibration frequency, thereby more effectively controlling the stability of the actuator. Dynamic adjustment of the damping coefficient ensures that the device can achieve adaptive adjustment in both static and dynamic environments, so that it can maintain optimal working conditions under different vibration conditions. By accurately controlling the damping coefficient, excessive energy consumption is avoided, and the power usage of the device in a vibrating environment is more efficient, thereby extending the service life of the device.

[0110] Specifically, the power management module includes a linkage optimization algorithm for coordinating the output signals of the drive control module and the adaptive damping control module to achieve linkage optimization control of load and vibration. The linkage optimization algorithm includes the following steps:

[0111] S1, receiving a power allocation signal from a load prediction and power allocation algorithm and a damping adjustment signal from a damping adjustment algorithm, so as to achieve load and vibration linkage optimization output;

[0112] S2, calculates the linkage optimization power output based on the two input signals, which is used for comprehensive control of power and damping;

[0113] S3. The linkage optimized power output is output to the actuator to achieve an organic combination of power output and damping adjustment.

[0114] The linkage optimization algorithm ensures the coordination of power and damping control by linking load prediction with power distribution signals and damping adjustment signals, so that the system can cope with changes in load and vibration at the same time. By optimizing power output through linkage, the device can maintain the best balance between drive and stability under different load and vibration conditions, improving the device's adaptability in changing environments. The linkage algorithm outputs power through the integrated output of the two signals, allowing the device to achieve precise drive under load and vibration conditions, effectively reducing operational delays and improving the overall response efficiency of the system.

[0115] Specifically, the linkage optimization power output calculation formula includes:

[0116] P sync =λ×P opt1 +μ C damp

[0117] Among them, P sync It is used to optimize the power output after linkage and to comprehensively control the power;

[0118] λ power regulation coefficient, determines P opt1 The weight of the impact on the overall power;

[0119] μ is the damping adjustment coefficient, which determines C damp weight in linkage power;

[0120] It further includes a response time adjustment formula to control the response speed of the robot under variable load and vibration environment. The calculation formula is as follows:

[0121] T response =η÷P sync

[0122] Among them, T response The response time of the robot device is the delay time from when the system receives the instruction to when it executes the action;

[0123] η is the response adjustment parameter, which is set according to the robot's response characteristics to different task requirements.

[0124] The linkage optimization power output formula combined with the response time adjustment formula realizes the optimization of the system's dynamic response under variable load and vibration environments. By adjusting the response time, the device can maintain the shortest command response delay under different load conditions, ensuring the efficient operation of the robot equipment and adapting to complex task requirements. Response time control enables the device to respond to external commands at the optimal speed, thereby maintaining continuous and stable operation in a changing environment, which helps to improve the overall task completion and operating efficiency of the robot.

[0125] The power output signal and the damping adjustment signal of the adaptive damping control module are converted into a unified voltage signal through the interface circuit for subsequent calculation.

[0126] The microcontroller unit (MCU) calculates the linkage optimization power output P according to the formula sync The formula is based on the weighted summation principle and obtains a more adaptable power output signal by combining the power output and the damping adjustment signal.

[0127] P sync The power management circuit transmits the power to the actuator's power control unit, which directly controls the power supply to the drive motor. This allows the actuator to automatically adjust power output when load and vibration conditions change, ensuring stable and efficient operation.

[0128] After the linkage optimization output calculation is completed, the system also adjusts the response time through a formula.

[0129] Weighted summation and inverse response principle. The power output and damping adjustment signal are integrated through a weighted formula to ensure that the drive power adapts to different loads and vibration conditions, enhancing the adaptability of the system.

[0130] Calculated P sync Control the power supply to the actuator to increase power output when the load increases and to enhance damping when vibration is severe. response Adjust the response time so that the device can provide fast and smooth operational response in changing environments.

Claims

1. A robot driving device, characterized in that: include: Drive control module, perception module, power management module, adaptive damping control module and actuator; The drive control module receives the real-time load data from the perception module, analyzes the current load situation based on the load prediction and power allocation algorithm, and generates a corresponding power allocation signal. The drive control module includes a signal receiver, a data processor and a controller, and compares the load data with historical data to obtain an optimized power output signal, which is transmitted to the power management module; The perception module is installed at the node of the actuator and is used to collect load data in real time. The perception module transmits the collected load data to the drive control module and the adaptive damping control module through the data interface to provide a basis for dynamic adjustment; The power management module receives the power output signal from the drive control module, and controls the output of electric energy to the actuator based on the power output signal to achieve dynamic adjustment of the drive. The power management module includes a power conversion unit and a voltage stabilization unit, which are used to achieve stable power output under load fluctuations and respond to instantaneous power demand; The adaptive damping control module is electrically connected to the perception module, the adaptive damping control module receives the load data collected by the perception module, calculates the damping coefficient based on the damping adjustment algorithm, so as to adjust the damping setting of the actuator to ensure the operation stability of the robot in a vibration environment; the output of the adaptive damping control module is transmitted to the actuator through an electrical connection to dynamically adjust the mechanical damping; The actuator receives electrical energy from the power management module and operates according to the damping adjustment signal of the adaptive damping control module. The actuator maintains a predetermined motion accuracy under different load conditions and external environments. The sensing unit installed inside the actuator can continuously monitor the actual load state and feed back to the sensing module to further optimize the control.

2. A robot driving device according to claim 1, characterized in that: The drive control module includes a load prediction and power distribution algorithm for realizing accurate power distribution according to a real-time load prediction model. The specific steps of the load prediction and power distribution algorithm include: S1. Receive load data provided by the perception module, where the load data is the force condition and quality information of the robot actuator during operation, including the weight, acceleration, and resistance caused by operation or physical position change of the current load; S2. Calculate the load forecast value based on the current load data and historical load data; S3. Adjust the power factor based on the predicted value to ensure accurate power output to cope with load fluctuations.

3. A robot driving device according to claim 2, characterized in that: The load prediction value is calculated by the following formula: L pred =γ×L current +(1−γ)×L prev Among them, L pred is the load forecast value, which indicates the load forecast result for the next time period at the current moment; L current The currently collected load data includes real-time weight, acceleration, resistance and other information; L prev Load data at previous time points, serving as a historical reference for current predictions; γ is a weighting factor, which is a constant between 0 and 1 and is used to adjust the relative weight of current and historical load data; The formula for adjusting the power factor based on the predicted value is as follows: P opt =α×L pred +b Among them, P opt The power value output by the drive control module represents the electric energy output allocated to the load; α is the power-to-load proportionality factor, which controls the relationship between power and predicted load and is preset according to the specifications of the actuator and the load capacity; β is the power regulation constant, which is used to fine-tune the system output to ensure power stability under slight load fluctuations.

4. A robot driving device according to claim 3, characterized in that: The adaptive damping control module includes a damping adjustment algorithm, which is used to automatically adjust the damping coefficient based on real-time vibration data to improve the stability of the actuator in a vibration environment, including the following steps: S1. Obtain the vibration frequency and vibration amplitude through the sensing module. The vibration frequency indicates the number of vibrations per second of the actuator, which is used to identify the frequency of environmental vibrations; the vibration amplitude indicates the intensity or amplitude of the vibration, which is used to judge the severity of the vibration impact; S2. Calculate the damping adjustment coefficient according to the vibration data to adjust the damping property of the actuator; S3. The adaptive damping control module outputs the damping adjustment coefficient to the actuator, so that it can make real-time damping adjustments according to the environmental vibration to ensure the smooth operation of the robot under the influence of vibration.

5. A robot driving device according to claim 4, characterized in that: The damping adjustment coefficient calculation formula includes: C damp =K×f vib +C init in: C damp is the adjusted damping coefficient, which indicates the damping adjustment value of the actuator under the current vibration condition; K is the damping proportional constant, which is used to amplify the effect of vibration frequency on damping and is obtained by equipment debugging; C init is the initial damping coefficient, which is the basic damping coefficient set at the factory and is suitable for static or low vibration environments.

6. A robot driving device according to claim 5, characterized in that: The power management module includes a linkage optimization algorithm for coordinating the output signals of the drive control module and the adaptive damping control module to achieve linkage optimization control of load and vibration. The linkage optimization algorithm includes the following steps: S1, receiving a power allocation signal from a load prediction and power allocation algorithm and a damping adjustment signal from a damping adjustment algorithm, so as to achieve load and vibration linkage optimization output; S2, calculates the linkage optimization power output based on the two input signals, which is used for comprehensive control of power and damping; S3. The linkage optimized power output is output to the actuator to achieve an organic combination of power output and damping adjustment.

7. A robot driving device according to claim 6, characterized in that: Linkage optimization power output calculation formula include: P sync =λ×P opt1 +μ×C damp Among them, P sync It is used to optimize the power output after linkage and to comprehensively control the power; λ power regulation coefficient, determines P opt1 The weight of the impact on the overall power; μ is the damping adjustment coefficient, which determines C damp weight in linkage power; It further includes a response time adjustment formula to control the response speed of the robot under variable load and vibration environment. The calculation formula is as follows: T response =η÷P sync Among them, T response The response time of the robot device is the delay time from when the system receives the instruction to when it executes the action; η is the response adjustment parameter, which is set according to the robot's response characteristics to different task requirements.

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