Control method of wheeled robot, wheeled robot, and medium

Abstract

The application discloses a control method of a wheeled robot, the wheeled robot and a computer readable storage medium. The wheeled robot comprises a damping unit, and the method comprises the following steps: determining a target driving electric parameter of the damping unit according to first state data of the wheeled robot in a driving process and a current aging degree of the wheeled robot; and driving the damping unit to output a damping force according to the target driving electric parameter. In this way, the target driving electric parameter can be obtained according to the first state data of the wheeled robot in the driving process and the current aging degree of the wheeled robot, so that the damping unit can output the damping force corresponding to the working condition which can be dynamically adapted and the aging compensation, blind compensation without the working condition is avoided, the damping unit which is aging can still output the damping force close to an ideal value, and the wheeled robot can maintain stable damping performance to perform work in the whole life cycle and the whole working condition, so that the work precision and stability are ensured.

Description

Technical Field

[0001] This invention relates to the field of robotics, and in particular to a control method for a wheeled robot, the wheeled robot, and a computer-readable storage medium. Background Technology

[0002] In related technologies, wheeled robots can reduce vibrations through mechanical structures during task execution. However, the dampers in these mechanical structures are prone to aging after long-term use, and their damping characteristics are fixed, which affects the stability of the wheeled robot and makes it difficult to adapt to changing working environments. Summary of the Invention

[0003] This application provides a control method for a wheeled robot, a wheeled robot, and a computer-readable storage medium.

[0004] This application provides a control method for a wheeled robot, the method comprising: Based on the first state data of the wheeled robot during its driving process and the current aging degree of the wheeled robot, the target driving electrical parameters of the vibration damping unit are determined; The damping unit is driven to output damping force according to the target driving electrical parameters.

[0005] Thus, based on the first state data of the wheeled robot during its operation and its current aging level, target drive electrical parameters can be obtained. This allows the vibration damping unit to output corresponding damping forces that dynamically adapt to the working conditions and are compensated for by aging, thereby achieving vibration reduction and avoiding blind compensation without a basis for the working conditions. Compared to methods that do not consider the aging phenomenon of the vibration damping unit, the aging vibration damping unit in this embodiment can still output damping forces close to the ideal value, ensuring that the wheeled robot can maintain stable vibration reduction performance throughout its entire life cycle and under all working conditions, thus guaranteeing operational accuracy and stability.

[0006] In some embodiments, determining the target drive electrical parameters of the vibration damping unit based on first state data of the wheeled robot during its operation and the current aging degree of the wheeled robot includes: Based on the first state data, the required damping of the vibration reduction unit is determined; Determine the target damping based on the current aging level and the required damping for vibration reduction; The target driving electrical parameters are determined based on the target damping.

[0007] Thus, based on the first state data, the required damping of the vibration damping unit is determined; based on the current aging level and the required damping, the target damping is determined; and based on the target damping, the target driving electrical parameters are determined. In this way, by using the required damping as the benchmark damping target and combining it with the current aging level, aging compensation is applied to the actual output damping force to obtain the target damping. Based on the target driving parameters corresponding to the target damping, the output damping force of the vibration damping unit can be made close to the ideal damping force, i.e., the required damping, to offset the aging attenuation of the vibration damping unit and ensure that even if the vibration damping unit ages and the actual damping output capacity decreases, vibration suppression can still be accurately achieved.

[0008] In some implementations, determining the target damping based on the current degree of aging and the required damping for vibration reduction includes: Determine the damping compensation parameters based on the current degree of aging; The damping required for vibration reduction is compensated based on the damping compensation parameters to determine the target damping.

[0009] Thus, based on the current degree of aging, damping compensation parameters are determined; and based on these parameters, the required damping for vibration reduction is compensated to determine the target damping. In this way, compared to ignoring the aging phenomenon of the vibration damping unit, the implementation method of this application can compensate for the required damping for vibration reduction based on the damping compensation parameters determined by the current degree of aging. This allows the vibration damping unit to output a damping force close to the ideal value according to the compensated target damping, ensuring that the wheeled robot can maintain stable vibration reduction performance throughout its entire life cycle and under all working conditions, guaranteeing operational accuracy and stability.

[0010] In some embodiments, the method further includes: The current degree of aging is determined based on the second state data of the wheeled robot during its operation.

[0011] Thus, the current aging level is determined based on the second-state data of the wheeled robot during its movement. By calculating this second-state data, the current aging level can be determined, quantifying it and sensing the aging degradation of the vibration damping unit. This provides a data basis for compensating for the required damping based on damping compensation parameters, thereby increasing the target drive electrical parameters. The aged vibration damping unit can then output a corresponding damping force that dynamically adapts to the working conditions and achieves vibration reduction based on the target drive electrical parameters and the aging-compensated damping force.

[0012] In some implementations, determining the current aging level of the wheeled robot based on second state data during its operation includes: The second state data is input into a pre-trained aging prediction model so that the aging prediction model outputs the current aging level based on the second state data.

[0013] Thus, the second-state data is input into the pre-trained aging prediction model, enabling the model to output the current aging level based on the second-state data. In this way, the aging prediction model can capture changes in the performance degradation of the vibration damping unit during robot operation, dynamically identify the current aging level, and output it, providing a reliable basis for subsequent damping compensation. This ensures that vibration control can adapt to the performance degradation throughout the entire life cycle of the vibration damping unit, maintaining a stable vibration damping effect.

[0014] In some implementations, determining the current degree of aging based on second state data of the wheeled robot during its operation includes: When the wheeled robot is in the target driving condition, the current aging level is determined based on the second state data corresponding to the target driving condition.

[0015] Thus, when the wheeled robot is in the target driving condition, the current aging level is determined based on the second state data corresponding to the target driving condition. In this way, when the wheeled robot is in the target driving condition, the corresponding second state data can be selected as the input parameter for predicting the current aging level according to different target driving conditions, so as to accurately predict the current aging level and provide accurate data basis for subsequently determining the final output damping force.

[0016] In some embodiments, the driving conditions of the wheeled robot include a first acceleration condition, a second acceleration condition, a first deceleration condition, a second deceleration condition, a first steering condition, and a second steering condition. The step of determining the current aging level based on the second state data corresponding to the target driving condition when the wheeled robot is in the target driving condition includes: When the wheeled robot is in at least one of the first acceleration condition, the first deceleration condition, and the first turning condition, the current aging level is determined based on the second state data.

[0017] In some embodiments, determining the current aging level based on the second state data when the wheeled robot is in at least one of the first acceleration condition, the first deceleration condition, and the first turning condition includes: When the wheeled robot is in the first acceleration condition or the first deceleration condition, the first aging degree estimate is determined based on the current moving speed, current acceleration and maximum pitch angular velocity of the wheeled robot during the driving process; When the wheeled robot is in the first steering condition, a second aging degree estimate is determined based on the current moving speed, current steering angular velocity, and maximum roll acceleration of the wheeled robot during its driving process; The current aging level is determined based on the first aging level estimate and / or the second aging level estimate.

[0018] Thus, when the wheeled robot is in its first acceleration or deceleration condition, a first aging degree estimate is determined based on the robot's current speed, current acceleration, and maximum pitch rate. When the wheeled robot is in its first turning condition, a second aging degree estimate is determined based on the robot's current speed, current turning rate, and maximum roll rate. The current aging degree is then determined based on the first and / or second aging degree estimates. By differentiating operating scenarios, the first or second aging state of the vibration damping unit can be accurately identified under different conditions, adapting to the needs of various scenarios and avoiding omissions of aging effects in single-condition estimations. This ensures that regardless of the operating condition, the current aging degree, representing the actual attenuation state of the vibration damping unit, can be obtained, providing an accurate data foundation for subsequent control and adjustment of the vibration damping unit. This application provides a wheeled robot, including the vibration damping unit, memory, and processor, to implement the steps of the above method.

[0019] This application provides a computer-readable storage medium storing a computer program that, when executed by one or more processors, implements the steps of the above-described method.

[0020] The wheeled robot and computer-readable storage medium provided in this application determine the target driving electrical parameters of the vibration damping unit based on the first state data of the wheeled robot during its operation and the current aging degree of the wheeled robot; the vibration damping unit is then driven to output damping force according to the target driving electrical parameters. In this way, the target driving electrical parameters can be obtained based on the first state data of the wheeled robot during its operation and the current aging degree of the wheeled robot, enabling the vibration damping unit to output a corresponding damping force that dynamically adapts to the working conditions and is compensated for by aging, thus avoiding blind compensation without a basis in the working conditions. Compared to not considering the aging phenomenon of the vibration damping unit, the aging vibration damping unit in this application embodiment can still output a damping force close to the ideal value, ensuring that the wheeled robot maintains stable vibration damping performance throughout its entire life cycle and under all working conditions, guaranteeing operational accuracy and stability.

[0021] Additional aspects and advantages of embodiments of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of embodiments of this application. Attached Figure Description

[0022] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein: Figure 1 This is one of the flowcharts illustrating the control method of a wheeled robot according to certain embodiments of this application; Figure 2 This is a schematic diagram of the structure of a wheeled robot according to certain embodiments of this application; Figure 3 This is a second schematic flowchart of a control method for a wheeled robot according to certain embodiments of this application; Figure 4 This is a schematic diagram of the aging degree-compensation parameter mapping of a wheeled robot according to certain embodiments of this application; Figure 5 This is a schematic diagram of the aging compensation process of a wheeled robot according to certain embodiments of this application; Figure 6 This is the third flowchart illustrating the control method for a wheeled robot according to certain embodiments of this application; Figure 7 This is the fourth flowchart illustrating the control method for a wheeled robot according to certain embodiments of this application; Figure 8 This is one of the schematic diagrams illustrating the dataset creation process for the aging degree prediction model in certain embodiments of this application; Figure 9 This is the second schematic diagram of the dataset creation process for the aging degree prediction model in some embodiments of this application; Figure 10This is the fifth flowchart illustrating the control method for a wheeled robot according to certain embodiments of this application; Figure 11 This is a schematic diagram of the algorithm structure of each module of the wheeled robot according to certain embodiments of this application; Figure 12 This is a schematic diagram of the algorithm flow of a wheeled robot according to some embodiments of this application; Figure 13 This is a schematic diagram illustrating the effect of the aging compensation algorithm of a wheeled robot in certain embodiments of this application on its drive current. Detailed Implementation

[0023] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar components or components having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the embodiments of this application, and should not be construed as limiting the embodiments of this application.

[0024] In related technologies, wheeled robots often use a mechanical structure combining springs and dampers to reduce vibration when performing tasks such as inspection, material transportation, and site surveying. For example, the elastic deformation of mechanical parts buffers the impact of the road surface, and the energy dissipation of the damper weakens the transmission of vibration.

[0025] However, after long-term use, the internal damping oil will decrease in viscosity due to repeated cyclic friction, and the seals will also wear and age due to continuous mechanical stress. These problems will cause the actual output damping force of the damper to gradually decrease and the stability to deteriorate, which will weaken the vibration suppression effect of the wheeled robot when it is moving, affecting the operation accuracy and overall operation stability.

[0026] In addition, the damping characteristics of mechanical damping are relatively fixed. For varying working environments such as flat roads, gravel roads, and slopes, fixed damping cannot fully meet the vibration reduction requirements.

[0027] Based on the above issues, please refer to Figure 1 This application provides a control method for a wheeled robot, the method comprising: 01: Determine the target drive electrical parameters of the vibration damping unit based on the first state data of the wheeled robot during its operation and the current aging level of the wheeled robot; 02: Drive the vibration damping unit to output damping force according to the target driving electrical parameters.

[0028] This application provides a control device for a wheeled robot. The control method for the wheeled robot according to this application can be implemented by the control device. Specifically, the control device includes a control module. The control module is used to determine the target driving electrical parameters of the vibration damping unit based on first state data of the wheeled robot during its operation and the current aging level of the wheeled robot. The control module is also used to drive the vibration damping unit to output damping force according to the target driving electrical parameters.

[0029] Please see Figure 2 This application also provides a wheeled robot 100, which includes a vibration damping unit 110, a memory, and a processor. The control method of the wheeled robot 100 according to this application embodiment can be implemented by the wheeled robot 100. Specifically, the memory stores a computer program, and the processor is used to determine the target driving electrical parameters of the vibration damping unit 110 based on first state data of the wheeled robot 100 during its operation and the current aging degree of the wheeled robot 100. The processor is also used to drive the vibration damping unit 110 to output damping force according to the target driving electrical parameters.

[0030] Specifically, the first state data is the real-time operating condition characteristic data of the wheeled robot 100 during its driving process, including vertical acceleration data, longitudinal acceleration data, lateral acceleration data, pitch acceleration data, steering angular velocity data, and movement speed data, which can be used to calculate the ideal damping required for vibration reduction of the wheeled robot 100.

[0031] Please refer to it again. Figure 2 The chassis structure of the wheeled robot 100 shown includes the following data: vertical acceleration data can be acquired by unsprung sensor 120; longitudinal acceleration data can be acquired by acceleration sensor 150; lateral acceleration data and pitch acceleration data can be acquired by unsprung sensor 130, i.e., inertial measurement unit (IMU); steering angular velocity data can be acquired by steering angle sensor 160; and movement speed data can be acquired by velocity sensor 170.

[0032] The current aging level is a quantitative result of the performance degradation of the vibration damping unit 110, which characterizes the degree of performance degradation of the vibration damping unit 110 relative to its factory condition. It can be used to calculate the damping required for vibration reduction of the wheeled robot 100.

[0033] Figure 2The controller 140 shown can calculate the first state data and current aging degree of the wheeled robot 100 to obtain the actual damping required by the vibration damping unit 110, and obtain the corresponding drive signal, i.e. the target drive electrical parameters, and send them to the vibration damping unit 110. This allows the vibration damping unit 110 to adjust the opening of the solenoid valve through the target drive electrical parameters, control the flow rate of the damping oil flowing through the throttle port per unit time, thereby controlling and adjusting the damping of the semi-active vibration damping unit 110, compensating for the damping force attenuation caused by aging, reducing the vibration interference of the wheeled robot 100, and ensuring the accuracy and stability of operation.

[0034] Understandably, by calculating the first-state data, an ideal base damping value suitable for the current working conditions can be obtained. However, the vibration damping unit 110 may experience aging phenomena after long-term operation, such as nonlinear decay of the damping coefficient. If the target driving electrical parameters for driving the vibration damping unit 110 are determined solely based on the first-state data, the actual output damping force may be lower than the working condition requirements, which in turn may lead to increased vibration of the wheeled robot 100 or decreased component operation accuracy, affecting its operational stability.

[0035] By combining the first state data and the current aging level, the target drive electrical parameters for dynamically adapting to the working conditions and achieving aging compensation can be obtained, avoiding blind compensation without working condition basis. This ensures that the aging vibration damping unit 110 can still output actual damping close to the ideal value, ensuring that the wheeled robot 100 can maintain stable vibration damping performance throughout its entire life cycle and under all working conditions, thus guaranteeing operational accuracy and stability.

[0036] In summary, in this embodiment, based on the first state data of the wheeled robot 100 during its operation and its current aging level, target drive electrical parameters can be obtained. This allows the vibration damping unit 110 to output a damping force that dynamically adapts to the working conditions and is compensated for aging, thereby achieving vibration reduction and avoiding blind compensation without a basis for the working conditions. Compared to not considering the aging phenomenon of the vibration damping unit 110, the vibration damping unit 110 driven by the target drive parameters in this embodiment can still output a damping force close to the ideal value after aging, ensuring that the wheeled robot 100 can maintain stable vibration reduction performance throughout its entire life cycle and under all working conditions, thus guaranteeing operational accuracy and stability.

[0037] Please see Figure 3 In some embodiments, step 01 (determining the target drive electrical parameters of the vibration damping unit 110 based on the first state data of the wheeled robot 100 during its operation and the current aging level of the wheeled robot 100) includes: 011: Based on the first state data, determine the damping requirement of the vibration reduction unit 110; 012: Determine the target damping based on the current aging level and damping requirements; 013: Determine the target driving electrical parameters based on the target damping.

[0038] In some embodiments, the control module is further configured to determine the required damping of the vibration damping unit 110 based on the first state data. The control module is also configured to determine the target damping based on the current aging level and the required damping. The control module is further configured to determine the target drive electrical parameters based on the target damping.

[0039] In some embodiments, the processor is further configured to determine the required damping of the vibration damping unit 110 based on the first state data. The processor is also configured to determine a target damping based on the current aging level and the required damping. The processor is further configured to determine target drive electrical parameters based on the target damping.

[0040] Specifically, the damping requirement is the ideal damping force required for the wheeled robot 100 to reduce vibration under the current working conditions. It can be obtained by calculating the first state data, that is, by calculating at least one of the current moving speed, current angular velocity and current acceleration of the wheeled robot 100 during the driving process, for example, by using the ceiling algorithm.

[0041] Understandably, the damping requirement does not take into account the aging of the damping unit 110. If the damping unit 110 ages, under the same driving electrical parameters, the actual output damping force will decrease nonlinearly with the degree of aging.

[0042] By using the damping requirement as the benchmark damping target and combining it with the current aging level, the actual output damping force is compensated for by aging. The target damping can be obtained, and the output damping force of the damping unit 110 can be made close to the ideal damping force, i.e., the damping requirement, according to the target driving parameters corresponding to the target damping. This is to offset the aging attenuation of the damping unit 110 and ensure that even if the damping unit 110 ages and the actual damping output capacity decreases, vibration suppression can still be accurately achieved.

[0043] There is a one-to-one correspondence between the target damping and the target driving parameters. The target driving parameters can be obtained by looking up a table in the predetermined damping-electrical parameter mapping data based on the target damping, so as to convert the target damping into executable electrical parameters, so that the vibration reduction unit 110 can achieve both adaptability to real-time working conditions and vibration reduction effect to offset aging attenuation.

[0044] Thus, based on the first state data, the required damping of the vibration damping unit 110 is determined; based on the current aging level and the required damping, the target damping is determined; and based on the target damping, the target driving electrical parameters are determined. In this way, by using the required damping as the benchmark damping target and combining it with the current aging level, aging compensation is applied to the actual output damping force to obtain the target damping. Based on the target driving parameters corresponding to the target damping, the output damping force of the vibration damping unit 110 can be made close to the ideal damping force, i.e., the required damping, to offset the aging degradation of the vibration damping unit 110. This ensures that even if the vibration damping unit 110 ages and its actual damping output capacity decreases, vibration suppression can still be accurately achieved.

[0045] In some implementations, step 012 (determining the target damping based on the current degree of aging and the required damping for vibration reduction) includes: 0121: Determine the damping compensation parameters based on the current degree of aging; 0122: Compensate for the damping required for vibration reduction based on the damping compensation parameters, and determine the target damping.

[0046] In some implementations, the control module is also used to determine damping compensation parameters based on the current degree of aging. The control module is further used to compensate for the required damping based on the damping compensation parameters, thereby determining the target damping.

[0047] In some implementations, the processor is also configured to determine damping compensation parameters based on the current degree of aging. The processor is further configured to compensate for the required damping based on the damping compensation parameters, thereby determining the target damping.

[0048] Specifically, the damping compensation parameters are pre-calibrated parameters that have a one-to-one preset relationship with the current aging level. They can be used to ensure that the compensation coefficient corresponding to each aging level can just make up for the damping force loss in that state.

[0049] Based on the current aging level, the damping compensation parameter corresponding to the current aging level can be obtained by looking up a table in the pre-determined aging level-compensation parameter mapping data, so as to transform the current aging level in the abstract state into a damping compensation parameter that can be directly used for calculation.

[0050] Understandably, in Figure 4As shown in the pre-calibrated aging degree-compensation parameter mapping data, it can be seen that when the new damping unit 110 has no aging, the compensation coefficient is equal to 1, and no additional compensation is needed. The damping requirement can directly meet the damping needs of the wheeled robot. When there is slight aging, the compensation coefficient is slightly greater than 1, for example, 1.1, which corresponds to a slight decrease in damping characteristics. When the product of the damping requirement and the damping compensation coefficient is determined as the target damping, the damping requirement can be slightly increased so that the target driving electrical parameters corresponding to the damping unit 110 can drive the damping force output by the damping unit 110 to overcome the aging attenuation and match the ideal damping. Similarly, when there is moderate aging, the compensation coefficient is further increased, for example, 1.2. Similarly, when there is severe aging, the compensation coefficient is the largest, for example, 1.5, and the compensation force needs to be increased to offset the severe attenuation.

[0051] The damping requirement is compensated according to the damping compensation parameters. For example, the product of the damping requirement and the damping compensation coefficient is determined as the target damping. The target damping can be amplified to a level higher than the damping requirement, thereby increasing the subsequent target driving electrical parameters. This allows the aged damping unit 110 to output the corresponding damping force that can dynamically adapt to the working conditions and the damping force after aging compensation according to the target driving electrical parameters, thus achieving vibration reduction.

[0052] The following is Figure 5 Taking an example, the aging compensation process of the implementation method of this application will be explained: First, the current working condition can be identified based on the sensor signals, i.e., the first state data, which provides a basis for working condition adaptation for subsequent damping calculations. Then, the aging degree of the four vibration damping units 110 is estimated according to the aging estimation compensation control algorithm of the vibration damping unit 110; the compensation damping coefficient of the four vibration damping units 110 is calculated according to the damping compensation coefficient calculation module. The required damping force for the four damping units 110 is calculated based on the basic control algorithm of the semi-active damping unit 110; the damping force of the damping unit 110 is compensated according to the compensation coefficient. The compensated damping force is converted into driving current and applied to the vibration reduction unit 110.

[0053] Compared to ignoring the aging phenomenon of the vibration damping unit 110, the implementation method of this application can compensate for the damping requirement based on the damping compensation parameters determined by the current aging degree, so that the vibration damping unit 110 can output a damping force close to the ideal value according to the compensated target damping, ensuring that the wheeled robot 100 can maintain stable vibration damping performance throughout its entire life cycle and under all working conditions, and ensuring operational accuracy and stability.

[0054] Thus, based on the current degree of aging, damping compensation parameters are determined; based on the damping compensation parameters, the damping required for vibration reduction is compensated, and the target damping is determined. In this way, compared to ignoring the aging phenomenon of the vibration damping unit 110, the embodiment of this application can compensate for the damping required for vibration reduction based on the damping compensation parameters determined by the current degree of aging, so that the vibration damping unit 110 can output a damping force close to the ideal value according to the compensated target damping, ensuring that the wheeled robot 100 can maintain stable vibration reduction performance throughout its entire life cycle and under all working conditions, and ensuring operational accuracy and stability.

[0055] Please see Figure 6 In some implementations, the method further includes: 03: Determine the current aging level based on the second state data of the wheeled robot 100 during its operation.

[0056] In some implementations, the control module is also used to determine the current degree of aging based on second state data of the wheeled robot 100 during its movement.

[0057] In some implementations, the processor is also used to determine the current degree of aging based on second state data of the wheeled robot 100 during its movement.

[0058] Specifically, the second state data includes the tilt angular velocity data, pitch angular velocity data, movement speed data, steering angular velocity data, longitudinal acceleration data, and longitudinal deceleration data of the wheeled robot 100, which can be used to indicate the workload and aging driving factors of the vibration damping unit 110.

[0059] Understandably, the moving speed data, steering angular velocity data, longitudinal acceleration data, and longitudinal deceleration data can characterize the dynamic load intensity borne by the damping unit 110. For example, under large steering angular velocities, the vehicle body rolls violently, which exacerbates the lateral mechanical wear of the damping unit 110.

[0060] Roll rate and pitch rate data can characterize the vibration response intensity of the damping unit 110. For example, a large roll rate reflects severe body roll and increases the fatigue wear of the valve system and seals of the damping unit 110.

[0061] By calculating the second state data of the wheeled robot 100 during its driving process, the current aging degree can be determined and quantified. The aging attenuation of the vibration damping unit 110 can be sensed, providing a data basis for compensating the damping demand based on the damping compensation parameters. This increases the target drive electrical parameters, and the aged vibration damping unit 110 can output a corresponding damping force that can dynamically adapt to the working conditions and achieve vibration reduction based on the target drive electrical parameters and the damping force after aging compensation.

[0062] Thus, the current degree of aging is determined based on the second state data of the wheeled robot 100 during its operation. By calculating the second state data of the wheeled robot 100 during its operation, the current degree of aging can be determined, quantifying the current degree of aging and sensing the aging degradation of the vibration damping unit 110. This provides a data basis for compensating for the required damping based on damping compensation parameters, thereby increasing the target drive electrical parameters. The aged vibration damping unit 110 can then output a corresponding damping force that dynamically adapts to the working conditions and is compensated for by aging, achieving vibration reduction based on the target drive electrical parameters.

[0063] Please see Figure 7 In some embodiments, step 03 (determining the current aging level of the wheeled robot 100 based on second state data of the wheeled robot 100 during its operation) includes: 031: Input the second state data into the pre-trained aging prediction model so that the aging prediction model can output the current aging level based on the second state data.

[0064] In some implementations, the control module is also used to input the second state data into a pre-trained aging prediction model so that the aging prediction model outputs the current aging level based on the second state data.

[0065] In some implementations, the processor is also configured to input second state data into a pre-trained aging prediction model, so that the aging prediction model outputs the current aging level based on the second state data.

[0066] Specifically, the aging degree prediction model is a machine learning algorithm based on the idea of ​​ensemble learning, such as the gradient boosting tree model, which can be used to predict the aging degree of the vibration damping unit 110 of the wheeled robot 100 based on the second state data.

[0067] Understandably, the stress characteristics, aging causes, and data dimensions of the vibration damping unit 110 of the wheeled robot 100 differ under different driving conditions. The aging degree prediction model can be pre-trained according to different working conditions, and the corresponding second state data can be input into the corresponding pre-trained aging degree prediction model according to the working conditions to ensure that the model's estimation of the aging degree is accurate and targeted.

[0068] In one example, for vibration damping units 110 with different aging levels, under diverse driving conditions such as acceleration, deceleration, steering, and different speed combinations, second-state data can be continuously collected and labeled with the corresponding aging degree. A paired sample set of input features and output labels can be constructed. The gradient boosting tree algorithm is used to divide the sample set into a training set and a validation set. The training set is used to allow the model to learn the correlation between the second-state data features and the aging degree. The validation set is then used to evaluate the accuracy and fine-tune the parameters, ultimately obtaining an aging degree prediction model with strong generalization ability and accurate prediction.

[0069] The following is Figure 8 For example, let's explain the dataset creation process for the aging degree prediction model under acceleration / deceleration conditions: First, by presetting a vibration damping unit 110 with a certain degree of aging and fixing the moving speed of the wheeled robot 100, the maximum value data of the pitch angular velocity of the IMU under different acceleration / deceleration is repeatedly collected. By controlling the variables, a vibration damping unit 110 with a specific degree of aging is selected first, and the influence data of longitudinal acceleration and deceleration on the pitch motion of the vehicle body under a single degree of aging and a single moving speed are accumulated to provide basic samples for the model. Secondly, by replacing the vibration damping unit 110 with different aging levels and setting different robot movement speeds, the maximum value data of IMU pitch angular velocity under robot acceleration / deceleration can be repeatedly collected, which can expand the sample diversity and enable the model to learn more comprehensive correlation laws between the aging of the vibration damping unit 110 and motion parameters as well as pitch characteristics, thereby improving the model's generalization ability. Finally, a dataset was created based on the collected data. The aging degree of the vibration damping unit was used as the label, and the robot's moving speed, robot acceleration / deceleration, and maximum pitch angular velocity of the IMU were used as input features. This structured dataset provides training samples to support the subsequent accurate determination of the aging degree of the vibration damping unit under this type of working condition through machine learning. This enables the model to predict the current aging degree based on the second state data, ensuring the accuracy and reliability of the aging degree prediction model.

[0070] Please see Figure 9 The process of creating the dataset for the damping unit aging degree estimation model under steering conditions is similar to that of creating the dataset for the aging degree prediction model under acceleration / deceleration conditions, and will not be repeated here.

[0071] Based on the learned feature-aging mapping law, the pre-trained aging degree prediction model can perform feature analysis and pattern matching on the second state data. It can capture the changes in the performance degradation of the vibration damping unit 110 in a timely manner during the robot's movement, dynamically identify the current aging degree and output it, providing a reliable basis for subsequent damping compensation, ensuring that the vibration control can adapt to the performance degradation of the vibration damping unit 110 throughout its entire life cycle and maintain a stable vibration reduction effect.

[0072] Thus, the second-state data is input into the pre-trained aging prediction model, enabling the model to output the current aging level based on the second-state data. In this way, the aging prediction model can capture changes in the performance degradation of the vibration damping unit 110 during robot operation, dynamically identify the current aging level and output it, providing a reliable basis for subsequent damping compensation. This ensures that vibration control can adapt to the performance degradation of the vibration damping unit 110 throughout its entire lifecycle, maintaining a stable vibration damping effect.

[0073] Please see Figure 10 In some embodiments, step 03 (determining the current aging level of the wheeled robot 100 based on second state data of the wheeled robot 100 during its operation) includes: 032: When the wheeled robot 100 is in the target driving condition, determine the current aging level based on the second state data corresponding to the target driving condition.

[0074] In some implementations, the control module is also used to determine the current aging level based on second state data corresponding to the target driving condition when the wheeled robot 100 is in the target driving condition.

[0075] In some implementations, the processor is also configured to determine the current degree of aging based on second state data corresponding to the target driving condition when the wheeled robot 100 is in the target driving condition.

[0076] Specifically, the target driving condition is the condition in which the vibration damping unit 110 bears typical loads and the aging driving factors are clear and can be quantified by data during the driving of the wheeled robot 100. For example, the wheeled robot 100's longitudinal speed increases rapidly, the wheeled robot 100's longitudinal speed decreases sharply, and the wheeled robot 100 turns laterally. By determining the current aging degree based on the second state data under this condition, the uncertainty of aging degree prediction can be reduced.

[0077] Understandably, the target driving conditions include multiple conditions, and the multi-dimensional sensor data reflecting the aging driving factors of the damping unit 110 under different conditions will vary.

[0078] When the wheeled robot 100 is in the target driving condition, the corresponding second state data can be selected as the input parameter to predict the current aging degree according to different target driving conditions, so as to accurately predict the current aging degree and provide accurate data basis for determining the final output damping force.

[0079] The following is Figure 11 Taking an example, the algorithm structure of each module in the implementation method of this application will be explained: First, all sensor data must be filtered through a bandpass filter to remove noise and clutter in order to eliminate interference and provide a basis for subsequent damping calculations to adapt to the operating conditions. Subsequently, based on the processed data, the working condition identification module can classify the driving conditions of the wheeled robot 100 into two categories: emergency acceleration or deceleration conditions and turning conditions, which are the target driving conditions.

[0080] For emergency acceleration or deceleration conditions, according to Figure 8 The data acquisition process for the specific operating condition shown involves collecting relevant data and training a gradient boosting tree model, i.e., an aging prediction model, to predict the current aging level under that operating condition; the steering condition adopts a method according to... Figure 9 Similar to the logic shown, corresponding data is collected and the model is trained to predict the aging degree of the vibration damping unit 110.

[0081] If the robot is in multiple working conditions at the same time, the average aging degree is obtained by averaging the predicted aging degree under multiple working conditions. Then, based on the current aging level, the damping compensation parameters are calculated.

[0082] Meanwhile, the semi-active damping unit damping control module calculates the base damping, i.e. the damping requirement, by using the ceiling algorithm and combining the information from the unsprung sensor 130. Finally, the aging compensation response module combines the damping compensation parameters with the damping requirement to complete subsequent processing such as damping fusion, ultimately achieving precise control of the vibration reduction unit 110, ensuring that the vibration reduction performance is adapted to the working conditions and offsetting the effects of aging.

[0083] Thus, when the wheeled robot 100 is in the target driving condition, the current aging level is determined based on the second state data corresponding to the target driving condition. In this way, when the wheeled robot 100 is in the target driving condition, the corresponding second state data can be selected as the input parameter for predicting the current aging level according to different target driving conditions, so as to accurately predict the current aging level and provide accurate data basis for subsequently determining the final output damping force.

[0084] In some embodiments, the driving conditions of the wheeled robot 100 include a first acceleration condition, a second acceleration condition, a first deceleration condition, a second deceleration condition, a first steering condition, and a second steering condition. Step 032 (when the wheeled robot 100 is in the target driving condition, determining the current aging level based on the second state data corresponding to the target driving condition) includes: 0321: When the wheeled robot 100 is in at least one of the first acceleration mode, the first deceleration mode, and the first turning mode, the current aging level is determined based on the second state data.

[0085] In some implementations, the control module is also used to determine the current aging level based on the second state data when the wheeled robot 100 is in at least one of a first acceleration state, a first deceleration state, and a first turning state.

[0086] In some embodiments, the processor is further configured to determine the current aging level based on second state data when the wheeled robot 100 is in at least one of a first acceleration condition, a first deceleration condition, and a first turning condition.

[0087] Specifically, the driving conditions of the wheeled robot 100 include a first acceleration condition, a second acceleration condition, a first deceleration condition, a second deceleration condition, a first steering condition, and a second steering condition.

[0088] The target driving conditions include the first acceleration condition, the first deceleration condition, and the first steering condition.

[0089] The first acceleration condition refers to the condition in which the wheeled robot 100 accelerates or brakes urgently; the second acceleration condition refers to the condition in which it accelerates more slowly or moves at a constant speed; the first deceleration condition refers to the condition in which the wheeled robot 100 decelerates or brakes urgently; the second deceleration condition refers to the condition in which it decelerates more slowly or moves at a constant speed; and the first turning condition refers to the condition in which the wheeled robot 100 moves at a high speed and turns at a high angular velocity.

[0090] When the wheeled robot 100 is in at least one of the first acceleration mode, the first deceleration mode, and the first turning mode, it is necessary to select the appropriate second state data for different modes, and finally obtain the current aging degree that can comprehensively reflect the current performance degradation state of the vibration damping unit 110 based on the second state data.

[0091] In one example, under the first steering condition, the second state data focuses on the current moving speed, the current steering angular velocity, and the maximum roll acceleration. The current moving speed affects the magnitude of the centrifugal force during steering; the higher the speed, the greater the centrifugal force, the more pronounced the body roll, and the stronger the lateral force on the damping unit 110. The current steering angular velocity reflects the abruptness of the steering action; the greater the angular velocity, the higher the rate of change of the roll motion, the faster the response frequency of the damping unit 110 to the lateral load, and the more aggravated the mechanical wear. The maximum roll acceleration directly reflects the peak intensity of the roll motion; the greater the acceleration, the higher the peak lateral moment on the damping unit 110, and the more prominent the fatigue aging of the internal valve plates.

[0092] When the wheeled robot 100 is in at least one of the first acceleration condition, the first deceleration condition, and the first turning condition, based on the second state data corresponding to the condition matching, combined with model reasoning and multi-task condition fusion, the aging of the vibration damping unit 110 can be accurately perceived. This provides a reliable basis for subsequent calculation of damping compensation parameters and correction of target damping based on the degree of aging, ensuring that the vibration damping unit 110 can offset the aging effect through targeted compensation and maintain stable vibration damping performance in the daily main driving scenarios of the wheeled robot 100.

[0093] Thus, when the wheeled robot 100 is in at least one of the first acceleration, first deceleration, and first steering conditions, the current aging level is determined based on the second state data. In this way, when the wheeled robot 100 is in at least one of the first acceleration, first deceleration, and first steering conditions, based on the second state data corresponding to the operating condition, combined with model inference and multi-task fusion, accurate perception of the aging of the vibration damping unit 110 can be achieved. This provides a reliable basis for subsequent calculation of damping compensation parameters and correction of target damping based on the aging level, ensuring that the vibration damping unit 110 can offset the effects of aging through targeted compensation and maintain stable vibration damping performance in the main daily driving scenarios of the wheeled robot 100.

[0094] In some embodiments, step 0321 (determining the current aging level based on the second state data when the wheeled robot 100 is in at least one of a first acceleration condition, a first deceleration condition, and a first turning condition) includes: 03211: When the wheeled robot 100 is in the first acceleration or first deceleration condition, the first aging degree estimate is determined based on the current moving speed, current acceleration and maximum pitch angular velocity of the wheeled robot 100 during the driving process. 03212: When the wheeled robot 100 is in the first steering condition, the second aging degree estimate is determined based on the current moving speed, current steering angular velocity and maximum roll acceleration of the wheeled robot 100 during the driving process; 03213: Determine the current aging level based on the first aging level estimate and / or the second aging level estimate.

[0095] In some embodiments, the control module is further configured to determine a first aging degree estimate based on the current moving speed, current acceleration, and maximum pitch rate of the wheeled robot 100 during its travel when the wheeled robot 100 is in a first acceleration or first deceleration state. The control module is also configured to determine a second aging degree estimate based on the current moving speed, current steering rate, and maximum roll acceleration of the wheeled robot 100 during its travel when the wheeled robot 100 is in a first turning state. The control module is further configured to determine the current aging degree based on the first aging degree estimate and / or the second aging degree estimate.

[0096] In some embodiments, the processor is further configured to determine a first aging degree estimate based on the current moving speed, current acceleration, and maximum pitch rate of the wheeled robot 100 during its travel, when the wheeled robot 100 is in a first acceleration or first deceleration state. The processor is also configured to determine a second aging degree estimate based on the current moving speed, current steering rate, and maximum roll acceleration of the wheeled robot 100 during its travel, when the wheeled robot 100 is in a first turning state. The processor is further configured to determine the current aging degree based on the first aging degree estimate and / or the second aging degree estimate.

[0097] Specifically, when the wheeled robot 100 is in the first acceleration or first deceleration condition, that is, when the speed of the wheeled robot 100 increases or decreases rapidly, pitching motion may occur. The vibration damping unit 110 mainly bears axial tensile or compressive loads. Therefore, the degree of aging needs to be estimated based on the current moving speed data, current acceleration data and maximum pitch angular velocity data of the wheeled robot 100 during the driving process.

[0098] Among them, the maximum pitch angular velocity data represents the peak intensity of pitch motion. The greater the angular velocity, the higher the axial instantaneous load on the vibration damping unit 110, and the more obvious the performance degradation.

[0099] Understandably, when the current driving condition is the first acceleration condition or the first deceleration condition, and the wheeled robot 100 is in the second turning condition, the current moving speed data, current acceleration data and maximum pitch angular velocity data of the wheeled robot 100 during the driving process can be input into the pre-trained aging degree prediction model to output the first aging degree estimate, and then the first aging degree estimate can be determined as the current aging degree.

[0100] When the wheeled robot 100 is in the first turning condition, that is, when the wheeled robot 100 is in the turning condition, the robot may produce a tilting motion. The vibration damping unit 110 mainly bears the lateral torque. Therefore, the aging degree needs to be estimated based on the current moving speed data, current turning angular velocity data and maximum tilt angle acceleration data of the wheeled robot 100 during the driving process.

[0101] Among them, the maximum roll angle acceleration data represents the peak intensity of the roll motion. The greater the acceleration, the higher the lateral instantaneous torque borne by the damping unit 110, and the more obvious the fatigue aging of the internal structure.

[0102] Understandably, when the current driving condition is the second acceleration condition or the second deceleration condition, and the wheeled robot 100 is in the first steering condition, the current moving speed data, the current steering angular velocity data, and the maximum roll angle acceleration data can be input into a pre-trained aging degree prediction model to output a second aging degree estimate. This second aging degree estimate can then be determined as the current aging degree. Furthermore, when the current driving condition is the first acceleration condition or the first deceleration condition, and the wheeled robot 100 is in the first turning condition, that is, when it turns while accelerating or turning while decelerating, the first aging degree and the second aging degree can be obtained respectively, and the statistical value of the first aging degree and the second aging degree can be determined as the current aging degree, such as the arithmetic mean of the first aging degree and the second aging degree, to provide an accurate data basis for subsequent control and adjustment of the vibration damping unit 110.

[0103] By distinguishing between different working conditions, the first or second aging state of the vibration damping unit 110 can be accurately identified under different working conditions to adapt to the needs of different working conditions. This avoids missing some aging effects in the estimation of a single working condition and ensures that the current aging degree representing the actual attenuation state of the vibration damping unit 110 can be obtained regardless of the type of working condition, thus providing an accurate data basis for subsequent control and adjustment of the vibration damping unit 110.

[0104] Thus, when the wheeled robot 100 is in the first acceleration or deceleration condition, a first aging degree estimate is determined based on the current moving speed, current acceleration, and maximum pitch angular velocity of the wheeled robot 100 during its movement. When the wheeled robot 100 is in the first turning condition, a second aging degree estimate is determined based on the current moving speed, current turning angular velocity, and maximum roll angular acceleration of the wheeled robot 100 during its movement. The current aging degree is determined based on the first and / or second aging degree estimates. In this way, by distinguishing the working conditions, the first or second aging state of the vibration damping unit 110 can be accurately identified under different working conditions to adapt to the needs of different working conditions. This avoids missing some aging effects in a single working condition estimate and ensures that the current aging degree, which characterizes the actual attenuation state of the vibration damping unit 110, can be obtained regardless of the working condition type, providing an accurate data basis for subsequent control and adjustment of the vibration damping unit 110.

[0105] The following is Figure 12 Taking an example, the algorithm flow of the implementation method of this application will be explained: The sensor array can input the collected sensor signals to the control module of the solenoid valve semi-active vibration damping unit, i.e., the control module.

[0106] The control module includes a damper aging compensation module, a semi-active damper damping control module, a damping fusion module, and a damping conversion drive current calculation module. The damping unit aging compensation module includes a working condition identification module, an aging degree estimation module, and a damping compensation coefficient calculation module.

[0107] Among them, the working condition recognition module can be used to identify the working condition and determine whether the wheeled robot 100 is in a turning working condition or an emergency acceleration / braking working condition; Next, the aging degree estimation module assesses the aging degree of the vibration damping unit based on the identified operating conditions and sensor signals; Then, the damping compensation coefficient calculation module determines the corresponding damping compensation coefficient based on the degree of aging.

[0108] At the same time, the semi-active damping unit damping control module will calculate the basic damping required by the damping unit 110 based on the real-time operating conditions.

[0109] Then, the damping fusion module merges the aging-compensated damping with the damping required for vibration reduction to obtain the target damping; Then, the damping conversion drive current calculation module converts the target damping into drive current parameters, i.e., target drive electrical parameters; Finally, after receiving this parameter, the current driver processes the current and drives the vibration damping unit 110, so that the vibration damping unit 110 outputs a damping force that matches the operating conditions and can counteract aging degradation, such as... Figure 13The aging compensation algorithm shown has an effect on the driving current. It can be seen that through aging compensation, the driving current applied to the vibration damping unit 110, i.e. the target driving electrical parameter, is increased, thereby enhancing its actual output damping force. This compensates for the damping decrease caused by the performance degradation of the vibration damping unit 110, ensuring the stable vibration damping performance of the wheeled robot 100 under different working conditions.

[0110] This application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program processor executes, it implements the steps of the control method for the wheeled robot 100 as described above.

[0111] It is understood that a computer program includes computer program code. Computer program code can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), and software distribution media, etc.

[0112] In this specification, the terms "specifically," "furthermore," "particularly," "understandably," etc., refer to specific features, structures, materials, or characteristics described in connection with embodiments or examples that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0113] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of executable request code comprising one or more steps for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0114] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A control method for a wheeled robot, characterized in that, The wheeled robot includes a vibration damping unit, and the method includes: Based on the first state data of the wheeled robot during its driving process and the current aging degree of the wheeled robot, the target driving electrical parameters of the vibration damping unit are determined; The damping unit is driven to output damping force according to the target driving electrical parameters.

2. The method according to claim 1, characterized in that, The step of determining the target drive electrical parameters of the vibration damping unit based on the first state data of the wheeled robot during its operation and the current aging degree of the wheeled robot includes: Based on the first state data, the required damping of the vibration reduction unit is determined; Determine the target damping based on the current aging level and the required damping for vibration reduction; The target driving electrical parameters are determined based on the target damping.

3. The method according to claim 2, characterized in that, The step of determining the target damping based on the current aging level and the required damping for vibration reduction includes: Determine the damping compensation parameters based on the current degree of aging; The damping required for vibration reduction is compensated based on the damping compensation parameters to determine the target damping.

4. The method according to claim 1, characterized in that, The method further includes: The current degree of aging is determined based on the second state data of the wheeled robot during its operation.

5. The method according to claim 4, characterized in that, The step of determining the current aging level of the wheeled robot based on the second state data of the wheeled robot during its operation includes: The second state data is input into a pre-trained aging prediction model so that the aging prediction model outputs the current aging level based on the second state data.

6. The method according to claim 4, characterized in that, The step of determining the current degree of aging based on the second state data of the wheeled robot during its operation includes: When the wheeled robot is in the target driving condition, the current aging level is determined based on the second state data corresponding to the target driving condition.

7. The method according to claim 6, characterized in that, The driving conditions of the wheeled robot include a first acceleration condition, a second acceleration condition, a first deceleration condition, a second deceleration condition, a first steering condition, and a second steering condition. When the wheeled robot is in a target driving condition, determining the current aging level based on the second state data corresponding to the target driving condition includes: When the wheeled robot is in at least one of the first acceleration condition, the first deceleration condition, and the first turning condition, the current aging level is determined based on the second state data.

8. The method according to claim 7, characterized in that, When the wheeled robot is in at least one of the first acceleration condition, the first deceleration condition, and the first turning condition, determining the current aging level based on the second state data includes: When the wheeled robot is in the first acceleration condition or the first deceleration condition, the first aging degree estimate is determined based on the current moving speed, current acceleration and maximum pitch angular velocity of the wheeled robot during the driving process; When the wheeled robot is in the first steering condition, a second aging degree estimate is determined based on the current moving speed, current steering angular velocity, and maximum roll acceleration of the wheeled robot during its driving process; The current aging level is determined based on the first aging level estimate and / or the second aging level estimate.

9. A wheeled robot, characterized in that, It includes a vibration damping unit, a memory, and a processor. The memory stores a computer program, which, when executed by the processor, implements the method described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by one or more processors, implements the method of any one of claims 1-8.

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