Biped robot active balance control system
Through the coordinated work of sensor module, attitude estimation, filter module, balance control module and gait adjustment module, the stability and gait adjustment problems of bipedal robots in complex environments are solved, and efficient balance control and adaptability are achieved.
Patent Information
- Application Number
- CN202510154665.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-12
- Publication Date
- 2025-08-19
AI Technical Summary
When faced with complex environments and external disturbances, existing bipedal robots have poor stability, cannot efficiently maintain balance, lack comprehensive perception of ground state and external environment, and insufficient gait adjustment ability, resulting in high risk of dumping.
The sensor module is used to collect data, and attitude estimation and balance control are performed through the Kalman filtering algorithm and the PID controller. The gait adjustment module is used to adjust the robot's leg movement in real time. Multi-sensor data fusion and dynamic balance control are used to enhance the perception of ground state and external environment.
It achieves high stability and adaptability in complex environments, improves the real-time and accuracy of the robot, enhances the perception of ground state and external environment, and ensures that the robot maintains balance in various environments.
Smart Images
Figure CN120508125A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of robot control technology, and in particular to an active balance control system for a bipedal robot. Background Art
[0002] Bipedal robots are widely used in services, healthcare, and other fields due to their high flexibility and ability to mimic human movements. However, due to factors such as uneven surfaces and external disturbances, bipedal robots are prone to tipping over while walking, running, or performing tasks. This instability severely limits the performance and efficiency of bipedal robots in practical applications.
[0003] Existing balance control methods often rely on passive stabilization or simple feedback mechanisms, which often perform poorly in complex environments and unexpected situations. For example, traditional passive stabilization techniques rely primarily on mechanical structural design to maintain balance, but their adaptability to varying terrains or external impacts is poor. While simple feedback mechanisms can respond to posture changes, their speed and accuracy are often insufficient to cope with rapidly changing environments.
[0004] Existing technologies also lack real-time performance and accuracy. Many systems are unable to quickly and accurately estimate the robot's posture, resulting in delayed or inaccurate control commands. This is particularly noticeable in scenarios involving high-speed motion or delicate manipulation, potentially causing the robot to lose balance or fail to complete its intended task.
[0005] Another key issue is that existing systems lack comprehensive awareness of the ground surface and external environment. Most control systems focus solely on the robot's posture, ignoring its interaction with the ground and changes in the surrounding environment. This limitation makes it difficult for robots to adapt to complex and changing real-world environments, such as uneven surfaces, slopes, and dynamic obstacles.
[0006] Finally, existing balance control systems often lack flexible gait adjustment capabilities. They often employ fixed gait patterns and are unable to dynamically adjust step length, frequency, and direction based on real-time balance requirements. This rigid control strategy limits the robot's ability to respond to unexpected situations and increases the risk of tipping over.
[0007] In response to the above problems, the existing technology for active balance control of bipedal robots still needs to be improved. Summary of the Invention
[0008] The present invention provides an active balance control system for a bipedal robot, which solves the technical problem in the prior art of relying on passive stabilization and simple feedback mechanisms, resulting in poor stability in complex environments and inability to maintain high efficiency.
[0009] The technical solution of the present invention is achieved as follows:
[0010] A biped robot active balance control system, comprising:
[0011] A sensor module is used to collect motion state data of the robot during motion, and the sensor module includes an accelerometer, a gyroscope and a force sensor;
[0012] The posture estimation and filtering module is connected to the sensor module and is used to fuse the collected motion state data through the Kalman filter algorithm and estimate the posture information of the robot;
[0013] The balance control module and the posture estimation and filtering module are used to generate a balance compensation control signal based on the posture information of the robot estimated by the posture estimation and filtering module, and dynamically adjust the control output through a PID controller;
[0014] The gait adjustment and execution module is connected to the balance control module and is used to control the stepping motor according to the balance compensation control signal of the balance control module to adjust the robot's leg movement to adapt to the ground state and balance requirements.
[0015] On the basis of this technical solution, further, the force sensor is installed on the sole of the robot foot to detect the contact force between the foot and the ground and the distribution of the ground reaction force in real time.
[0016] On the basis of this technical solution, the sensor module further includes an infrared distance sensor, which is arranged at the end of the robot leg and is used to detect the real-time distance between the foot and the ground.
[0017] On the basis of this technical solution, further, the attitude estimation and filtering module uses a complementary filtering algorithm to perform a secondary correction on the Kalman filtering result to reduce high-frequency noise interference.
[0018] On the basis of this technical solution, the posture estimation and filtering module further has a built-in machine learning model, which optimizes the posture information estimation accuracy through historical motion data training, and the model output weights correct the Kalman filter parameters in real time.
[0019] On the basis of this technical solution, further, the balance control module adopts a PID controller to dynamically adjust the PID parameters according to the logic of the posture information deviation.
[0020] On the basis of this technical solution, further, the proportional, integral and differential coefficients of the PID controller are dynamically adjusted through an online learning algorithm, and the learning algorithm is optimized based on the feedback signal of the robot's motion stability.
[0021] On the basis of this technical solution, further, the stepper motor drive adopts micro-step drive technology, and the drive current is adaptively adjusted according to the leg load pressure.
[0022] On the basis of this technical solution, the gait adjustment and execution module further integrates a plantar pressure sensor array to correct the stepper motor torque output in real time through pressure distribution data.
[0023] On the basis of this technical solution, it further includes a wireless communication module connected to the balance control module, which is used to transmit posture information and control signals to an external monitoring terminal via WiFi / Bluetooth.
[0024] The active balance control system for a bipedal robot provided by the present invention has the following advantages over the prior art:
[0025] The present invention discloses an active balance control system for a bipedal robot, comprising a sensor module, a posture estimation and filtering module, a balance control module, and a gait adjustment and execution module. The system uses a Kalman filter algorithm to fuse data from different sensors through multi-sensor data fusion, precise posture estimation, dynamic balance control, and flexible gait adjustment, thereby achieving precise estimation of the robot's posture and improving the accuracy of balance control. The system dynamically adjusts PID control parameters according to the real-time state of the robot, so that the robot can adapt to different gaits and environmental conditions. On the basis of the control system, a feedback adjustment mechanism based on a foot force sensor is added to improve the adaptability and stability of the robot. The system further achieves adaptability and rapid response capabilities to complex environments, and has the advantages of improving the stability and balance of the bipedal robot in complex environments, enhancing real-time performance and accuracy, improving the perception of ground conditions and external environment, and achieving flexible gait adjustment. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0027] Figure 1 A framework diagram of the bipedal robot balance control system according to Example 1 of the present invention is shown;
[0028] Figure 2 The flowchart of the operation of the active balance control system of the bipedal robot in the second embodiment of the present invention is shown. DETAILED DESCRIPTION
[0029] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.
[0030] During movement, bipedal robots often face problems such as uneven ground and external disturbances, which affect their balance and stability. In order to address these problems, the present invention proposes an active balance control system that monitors the robot's posture, ground conditions, and external disturbance information in real time, and uses a Kalman filter algorithm and a PID controller to adjust the balance, thereby improving the robot's adaptability and stability in complex environments. The data collected by the sensor module is processed by the posture estimation and filtering module to obtain accurate posture information, which is then passed to the balance control module. The balance control module calculates the control signal based on the posture information and dynamically adjusts it through the PID controller. The gait adjustment and execution module adjusts the movement of the stepper motor according to the control signal to ensure that the robot maintains balance in various environments.
[0031] In a preferred embodiment, the sensor module further includes: an accelerometer for monitoring the linear acceleration of the robot; a gyroscope for measuring the angular velocity of the robot; and a force sensor for monitoring the contact force between the robot and the ground.
[0032] In a preferred embodiment, the posture estimation and filtering module performs state prediction and update using the following formula:
[0033] State prediction: x k =A*x k-1 +B*u k +w k , status update: z k =H*x k +v k .
[0034] In a preferred embodiment, the balance control module adopts the PID control formula:
[0035]
[0036] In a preferred embodiment, the gait adjustment and execution module controls the stepper motor in the following manner: sending a pulse signal to control the rotation angle of the stepper motor; sending a direction signal to control the rotation direction of the stepper motor; and controlling the frequency of the pulse signal to adjust the speed of the stepper motor.
[0037] In a preferred embodiment, the gait adjustment and execution module further includes a gait optimization function, which combines force sensor data to optimize the robot's gait pattern.
[0038] In a preferred embodiment, it is used to improve the stability and balance of the robot during movement.
[0039] In a preferred embodiment, the control strategy of the gait adjustment and execution module includes:
[0040] An input parameter determination step for determining a target step length, step frequency, and direction of the stepper motor according to an adjustment instruction from the balance control module;
[0041] A pulse generation step generates a corresponding pulse signal through a controller and sends it to a driver of a stepper motor;
[0042] Gait adjustment steps, including modifying the step length of the stepper motor to adjust the leg swing angle and adjusting the walking speed according to the cadence;
[0043] Feedback detection step, detecting the effect of gait adjustment in real time through sensors and feeding back to the control system;
[0044] Repeat the steps and repeat the above steps according to the real-time environment and robot balance state.
[0045] In a preferred embodiment, the step length control and speed control formulas are:
[0046] Step size control formula: θ step =N pulse *α,
[0047] Among them, θ step is the total rotation angle of the stepper motor, N pulse is the number of input pulses, α is the step angle;
[0048] Speed control formula:
[0049] Among them, v motor is the rotation speed of the stepper motor (r / s), f pulse is the pulse signal frequency (pulses / second), and α is the step angle.
[0050] In a preferred embodiment, the gait adjustment of the optimized stepper motor control includes: micro-step drive technology, which improves the angular resolution of the stepper motor through micro-step control, makes the robot gait smoother, and reduces the vibration caused by stepping motion; real-time feedback adjustment, combined with the force feedback and posture information provided by the sensor module, dynamically adjusts the pulse parameters of the stepper motor to ensure the accuracy of gait adjustment.
[0051] Example 1
[0052] An active balance control system for a bipedal robot, such as Figure 1 As shown, it includes: a sensor module, a posture estimation and filtering module, a balance control module, and a gait adjustment and execution module.
[0053] Specifically, the sensor module collects various data during the robot's motion, including accelerometers, gyroscopes, and force sensors. The posture estimation and filtering module fuses the output data from the accelerometers and gyroscopes using a Kalman filter algorithm to accurately estimate the robot's posture. The balance control module calculates control signals based on the data provided by the posture estimation and filtering module and dynamically adjusts the control output using a PID controller. The gait adjustment and execution module adjusts the robot's leg motion by controlling the stepper motors to adapt to the ground conditions and balance requirements.
[0054] The sensor module includes an accelerometer, a gyroscope, and a force sensor. The accelerometer is used to monitor the robot's linear acceleration, the gyroscope is used to measure the robot's angular velocity, and the force sensor is used to monitor the contact force between the robot and the ground.
[0055] The attitude estimation and filtering module uses the Kalman filter algorithm to fuse the output data from the accelerometer and gyroscope to accurately estimate the robot's attitude. The Kalman filter algorithm is a linear quadratic estimation method that predicts and updates sensor data to obtain the optimal attitude estimate.
[0056] The balance control module calculates control signals based on data provided by the posture estimation and filtering module and dynamically adjusts the control output using a PID controller. The PID controller is a classic control algorithm that adjusts the control signal through proportional, integral, and differential steps to achieve precise control of the robot. The gait adjustment and execution module controls the stepper motor to adjust the robot's leg movements to suit the ground conditions and balance requirements. The stepper motor receives pulse signals and direction signals to control its rotation angle and direction. The frequency of the pulse signal determines the stepper motor's speed.
[0057] Compared to existing technologies, the active balance control system of the present invention maintains the robot's stability and balance in complex environments through real-time monitoring and dynamic adjustment. Existing technologies often rely on passive stabilization or simple feedback mechanisms, which are unable to effectively maintain stability in complex environments. The present invention achieves precise estimation and dynamic adjustment of the robot's posture through the coordinated operation of a sensor module, a posture estimation and filtering module, a balance control module, and a gait adjustment and execution module, significantly improving the robot's balance and stability.
[0058] The active balance control system of the present invention collects the robot's motion data through a sensor module. The attitude estimation and filtering module uses a Kalman filter algorithm to fuse data from the accelerometer and gyroscope to obtain an accurate attitude estimation result. The balance control module calculates the control signal based on the attitude estimation result and dynamically adjusts it through a PID controller. The gait adjustment and execution module adjusts the robot's leg movement to adapt to the ground conditions and balance requirements by controlling the stepper motor. The accelerometer, gyroscope, and force sensor in the sensor module respectively monitor the robot's linear acceleration, angular velocity, and contact force, providing comprehensive motion data. The attitude estimation and filtering module predicts and updates the sensor data through a Kalman filter algorithm to obtain the optimal attitude estimation result. The balance control module uses a PID controller to adjust the control signal to achieve precise control of the robot. The gait adjustment and execution module ensures that the robot maintains balance in various environments by controlling the rotation angle, direction, and speed of the stepper motor.
[0059] Example 2
[0060] Further, if Figure 2 As shown, this embodiment also proposes that the sensor module further includes an accelerometer, a gyroscope, and a force sensor. The accelerometer is used to monitor the linear acceleration of the robot, the gyroscope is used to measure the angular velocity of the robot, and the force sensor is used to monitor the contact force between the robot and the ground.
[0061] In the technical solution of this embodiment, the sensor module is characterized by including an accelerometer, a gyroscope, and a force sensor. The accelerometer can monitor the robot's linear acceleration in real time, providing key data for the posture estimation and filtering modules. The gyroscope is responsible for measuring the robot's angular velocity to ensure the accuracy of posture estimation. The force sensor is used to monitor the contact force between the robot and the ground, which is crucial for the control strategy of the gait adjustment and execution modules.
[0062] Specifically, the accelerometer measures changes in the robot's acceleration in different directions, helping the system understand the robot's motion state. The gyroscope measures angular velocity, providing information about the robot's rotation. Combined with accelerometer data, this data can more accurately estimate the robot's posture. The force sensor monitors the contact force between the robot's feet and the ground, helping the system determine the ground surface and the force applied to the robot, thereby providing feedback to the gait adjustment and execution modules.
[0063] Specifically, the output data formula of the accelerometer is:
[0064] a(t)=[a x (t), a y (t), a z (t)]
[0065] Where a(t) is the acceleration vector, acquired by the accelerometer and representing the robot's instantaneous acceleration in these three spatial directions. This is the three-dimensional acceleration vector output by the accelerometer, representing the robot's total linear acceleration at a given moment t. Each of its components corresponds to the robot's acceleration in three spatial directions.
[0066] a x (t): Acceleration along the X-axis (the robot's forward-backward direction), representing the robot's instantaneous acceleration in the forward-backward direction (assuming the robot is moving forward). This acceleration typically affects the robot's forward and backward motion and can be affected by external factors (such as the slope of the ground) or the robot's powertrain (such as the movement of its legs).
[0067] a y (t): Acceleration along the Y-axis (the robot's left-right direction), representing the robot's instantaneous left-right acceleration. This acceleration component affects the robot's left-right stability, especially when the robot is turning or making sideways steps. It is used to determine whether the robot is tilting or drifting sideways.
[0068] a z (t): The acceleration along the Z axis (the left and right direction of the robot), which reflects the change of the robot's center of gravity relative to the ground. z (t) is about 9.81m / s 2 (acceleration due to gravity). By monitoring a z (t), the system can detect whether the robot is tilted, disturbed or needs balance adjustment, which is one of the important data for achieving active balance control.
[0069] The output data formula of the gyroscope is:
[0070] ω(t)=[ω x (t),ω y (t),ω z (t)]
[0071] Where ω(t) is the three-dimensional angular velocity vector output by the gyroscope, representing the robot's angular velocity at a given moment t. Angular velocity describes the rate of change of the robot's posture and determines the robot's rotation rate.
[0072] ω x (t): angular velocity around the X axis
[0073] Represents the robot's angular velocity around the front-to-back direction. This angular velocity component usually corresponds to whether the robot is tilting forward or backward, or the robot's pitching motion during walking (such as shaking its head up and down).
[0074] ω y(t): angular velocity around the Y axis
[0075] Indicates the robot's angular velocity around the left and right directions. This angular velocity component affects whether the robot tilts sideways or whether the robot makes a turning motion while walking. When the robot tilts or adjusts its gait, ω y (t) helps predict and adjust the posture to prevent the robot from rolling over.
[0076] ω z (t): Angular velocity around the Z axis
[0077] This represents the angular velocity of the robot about its vertical axis. Typically, this angular velocity component is related to the robot's rotation, turning, or pivoting around its center point.
[0078] The output data formula of the force sensor is:
[0079] F foot =[F x (t), F y (t), F z (t)]
[0080] Among them, F x (t), F y (t), F z (t) are the contact forces in three directions respectively.
[0081] By integrating an accelerometer, gyroscope, and force sensor into the sensor module, this embodiment can acquire various data about the robot in real time during its motion. This data, processed by the posture estimation and filtering module, provides accurate posture information to the balance control module. This information is then dynamically adjusted through the gait adjustment and execution module, ensuring the robot's stability and adaptability in complex environments. Compared to existing technologies, this embodiment can more efficiently monitor and adjust the robot's posture, significantly improving its balance and stability during motion.
[0082] Example 3
[0083] Furthermore, this embodiment also proposes that the posture estimation and filtering module performs state prediction and update through the following formula:
[0084] State prediction formula: x k =A*x k-1 +B*u k +w k ,
[0085] State update formula: z k =H*x k +v k
[0086] The attitude estimation and filtering module uses the Kalman filter algorithm to fuse the output data from the accelerometer and gyroscope to accurately estimate the robot's attitude. The Kalman filter is a recursive algorithm capable of estimating state in noisy environments. Its state prediction formula and state update formula are used to predict the state at the next moment and update the state at the current moment, respectively.
[0087] The state prediction formula is used to predict the state at the next moment based on the current state and control input. The state update formula corrects the predicted state based on the sensor measurements to obtain a more accurate state estimate.
[0088] Specifically, the state prediction formula can be expressed as:
[0089] x k =A*x k-1 +B*u k +w k
[0090] Among them, x k is the estimated state vector (including robot posture angle and angular velocity, etc.), A is the state transfer matrix, B is the control matrix, u k is the control input, w k is the process noise, z k is the sensor measurement value, H is the observation matrix, v k In this way, accurate pose estimation can be achieved in environments with high noise and uncertainty.
[0091] The state update formula can be expressed as:
[0092] z k =H*x k +v k
[0093] Among them, z k is the sensor measurement value, H is the observation matrix, x k is the estimated state vector (including robot posture angle and angular velocity, etc.), v k To measure noise.
[0094] Through the above state prediction and state update formulas, the posture estimation and filtering module can accurately estimate the robot's posture in real time based on the fusion of accelerometer and gyroscope data.
[0095] This implementation, by introducing the Kalman filter algorithm, accurately estimates the robot's posture, resolving the existing issue of maintaining stability in complex environments. Compared to traditional passive stabilization or simple feedback mechanisms, this solution better adapts to ground conditions and external disturbances, improving the robot's stability and balance during movement.
[0096] Example 4
[0097] Furthermore, this embodiment also proposes that the balance control module adopts a PID control formula.
[0098] The balance control module uses the PID control formula to correct the robot's posture using three control parameters: proportional, integral, and differential. Specifically, proportional control adjusts the control output based on the current error value, integral control corrects system deviations based on the accumulated error value, and differential control predicts future errors based on the error rate of change and performs pre-adjustments. This allows the control signal to be adjusted in real time during the robot's motion to maintain balance.
[0099] Specifically, the PID control formula is:
[0100]
[0101] Among them, e(t) is the posture error (i.e. the difference between the current posture and the target posture), K p , K i , K d Represent the proportional, integral, and differential coefficients of the PID controller, respectively. This control module ensures the robot's center of gravity remains within its support area to prevent tipping. It adaptively adjusts the control strategy based on real-time data to optimize the robot's motion path and gait.
[0102] There are many ways to implement the PID control formula. For example, the PID control algorithm can be implemented through software programming, using data collected by a sensor module as input and calculating the control output in real time. Alternatively, PID control can be implemented in hardware, using a dedicated PID controller chip to calculate and output the control signal. As a preferred embodiment, the PID control parameters can be adaptively adjusted to adapt to different environments and motion states, thereby improving the accuracy and stability of the robot's balance control.
[0103] By employing the PID control formula, the balance control module of this embodiment can more precisely adjust the robot's posture, resolving the issue of inaccurate balance control in existing technologies. Compared to existing technologies, the balance control module of this embodiment can more effectively maintain the robot's balance in complex environments, improving the robot's stability and adaptability during movement.
[0104] Example 5
[0105] Furthermore, this embodiment also proposes that the gait adjustment and execution module controls the stepper motor in the following ways: sending a pulse signal to control the rotation angle of the stepper motor; sending a direction signal to control the rotation direction of the stepper motor; controlling the frequency of the pulse signal to adjust the speed of the stepper motor.
[0106] The gait adjustment and execution module in this embodiment controls the stepper motor's rotation angle by sending pulse signals, controls the stepper motor's rotation direction by sending direction signals, and adjusts the stepper motor's speed by controlling the frequency of the pulse signals. Specifically, the number of pulse signals determines the stepper motor's rotation angle, the direction signal determines the stepper motor's rotation direction, and the frequency of the pulse signals directly affects the stepper motor's speed. In this way, the gait adjustment and execution module can precisely control the robot's leg movements, thereby achieving balance adjustment.
[0107] The control method for the gait adjustment and execution module can be implemented in a variety of ways. For example, a microcontroller can generate pulse signals and direction signals and send these signals to a stepper motor driver to control the stepper motor's rotation angle and direction. The frequency of the pulse signal can be adjusted programmatically to change the stepper motor's speed. Furthermore, the parameters of the pulse signal can be adjusted in real time based on data from a force sensor to optimize the gait adjustment effect.
[0108] This embodiment, through the precise control of the stepper motors by the gait adjustment and execution module, effectively adjusts the robot's leg movements to suit the ground conditions and balance requirements. Compared to existing technologies, this embodiment's technical solution provides greater stability and adaptability in complex environments, resolving the existing problem of robots having difficulty maintaining stability in complex environments. As such, this embodiment's technical solution represents a significant technological advancement, significantly improving the motion stability of bipedal robots in a variety of environments.
[0109] Example 6
[0110] Furthermore, this embodiment also proposes that the gait adjustment and execution module further includes a gait optimization function, which optimizes the robot's gait pattern in combination with force sensor data.
[0111] The gait adjustment and execution module optimizes the robot's gait by integrating data from force sensors. This optimization aims to adjust the robot's gait pattern in real time to adapt to varying ground conditions and maintain balance. Specifically, when the force sensors detect changes in the contact force between the robot and the ground, the system adjusts the gait based on this data to ensure the robot remains stable under various ground conditions.
[0112] Gait optimization can be achieved in a variety of ways. For example, the control parameters of the stepper motor, including pulse frequency, step length, and cadence, can be dynamically adjusted based on force sensor data to achieve gait optimization. Furthermore, advanced control algorithms, such as fuzzy control and neural networks, can be combined with force sensor data for gait optimization. These algorithms can automatically adjust gait patterns based on real-time feedback, improving the robot's adaptability in complex terrain conditions.
[0113] Through gait optimization, this embodiment significantly improves the robot's stability and adaptability, especially in complex and changing terrain environments. Compared to existing technologies, this gait optimization function more effectively prevents the robot from tipping over due to uneven surfaces or external disturbances, thereby improving the robot's safety and reliability.
[0114] Example 7
[0115] Furthermore, this embodiment also proposes a method for improving the stability and balance of the robot during movement.
[0116] The active balance control system of this embodiment includes a sensor module, a posture estimation and filtering module, a balance control module, and a gait adjustment and execution module. The sensor module is used to collect various data of the robot during movement, including accelerometers, gyroscopes, and force sensors. The posture estimation and filtering module fuses the output data of the accelerometer and gyroscope through the Kalman filter algorithm to accurately estimate the posture of the robot. The balance control module calculates the control signal based on the data provided by the posture estimation and filtering module, and dynamically adjusts the control output through the PID controller. The gait adjustment and execution module adjusts the robot's leg movement by controlling the stepper motor to adapt to the ground conditions and balance requirements.
[0117] The technical solution of this embodiment aims to address the problem of a robot tipping over during movement due to factors such as uneven ground and external disturbances. By real-time monitoring of the robot's posture, ground conditions, and external disturbance information, and utilizing advanced control algorithms for balance adjustments, the robot's adaptability and stability in complex environments are improved. Specifically, the data collected by the sensor module is processed by the posture estimation and filtering module and then provided to the balance control module for calculation and control signal generation. This control signal is then transmitted to the gait adjustment and execution module, which dynamically adjusts the robot's posture by adjusting the movement of the stepper motor, ensuring its stability during movement.
[0118] The key to this active balance control system lies in the collaborative operation of multiple modules. Through real-time sensor data acquisition and processing, the application of the Kalman filter algorithm, dynamic adjustment of the PID controller, and precise control of the stepper motor, it achieves balance control of the robot during motion. The diversity and real-time nature of the sensor modules ensures comprehensiveness and accuracy of the data; the Kalman filter algorithm improves the accuracy of posture estimation; the application of the PID controller enables dynamic adjustment of control signal generation; and the gait adjustment and execution module, through precise control of the stepper motor, ensures that the robot's gait adjustment can adapt to different ground conditions and external disturbances.
[0119] Example 8
[0120] Furthermore, this embodiment also proposes a control strategy for the gait adjustment and execution module, including an input parameter determination step, which is used to determine the target step length, step frequency and direction of the stepper motor according to the adjustment instructions of the balance control module; a pulse generation step, which generates a corresponding pulse signal through a controller and sends it to the driver of the stepper motor; a gait adjustment step, which includes modifying the step length of the stepper motor to adjust the swing angle of the leg and adjusting the walking speed according to the step frequency; a feedback detection step, which detects the effect of the gait adjustment in real time through a sensor and feeds back to the control system; and a repeated execution step, which repeats the above steps according to the real-time environment and the balance state of the robot.
[0121] Example 9
[0122] Furthermore, this embodiment also proposes step length control and speed control formulas, which are:
[0123] Step size control formula: θ step =N pulse *α,
[0124] Among them, θ step is the total rotation angle of the stepper motor, N pulse is the number of input pulses, α is the step angle;
[0125] Speed control formula:
[0126] Among them, v motor is the rotation speed of the stepper motor (r / s), f pulse is the pulse signal frequency (pulses / second), and α is the step angle.
[0127] This embodiment primarily achieves precise control of the stepper motor through step-length and speed control formulas. The step-length control formula determines the total rotation angle of the stepper motor, thereby controlling the robot's step length. The speed control formula controls the stepper motor's rotational speed by adjusting the frequency of the pulse signal. These formulas ensure the robot maintains a stable gait under varying surface conditions, thereby improving its balance and stability during movement.
[0128] The step length control formula can be implemented through pulse control of a stepper motor. Specifically, by inputting a certain number of pulse signals, the stepper motor can be controlled to rotate to a predetermined angle, thereby achieving step length control of the robot. The speed control formula can be implemented by adjusting the frequency of the pulse signal. In practical applications, the control system can monitor the robot's motion state in real time and adjust the pulse signal frequency as needed to ensure that the stepper motor rotates at the desired speed.
[0129] Through the above-mentioned technical means, this embodiment effectively solves the existing problem of bipedal robots being prone to tipping over in complex environments. Compared to existing technologies, the active balance control system of this embodiment achieves precise control of the stepper motor through step length control and speed control formulas, thereby improving the robot's stability and adaptability during movement. As a result, this embodiment has significant advantages in improving the stability of bipedal robots.
[0130] Example 10
[0131] Furthermore, this embodiment also proposes to optimize the gait adjustment controlled by the stepper motor, including micro-step drive technology, which improves the angular resolution of the stepper motor through micro-step control, makes the robot gait smoother, and reduces the vibration caused by stepping motion; the stepper motor drive adopts micro-step drive technology, and the drive current is adaptively adjusted according to the leg load pressure; and real-time feedback adjustment, combined with the force feedback and posture information provided by the sensor module, dynamically adjusts the pulse parameters of the stepper motor to ensure the accuracy of gait adjustment.
[0132] The technical solution of this embodiment aims to enhance the robot's balance and stability in complex environments by optimizing stepper motor control. Microstepping technology increases the angular resolution of the stepper motor, resulting in a smoother gait and reduced vibration. Real-time feedback control combines force feedback and posture information provided by the sensor module to dynamically adjust the stepper motor's pulse parameters, ensuring precise gait adjustments.
[0133] Microstepping technology can be achieved by subdividing the step angle of a stepper motor. For example, a microstepping driver can be used to subdivide the step angle of a stepper motor from 1.8 degrees to 0.9 degrees or less, thereby improving angular resolution. This method can significantly reduce vibration during the robot's stepping process, resulting in a smoother gait. Real-time feedback regulation dynamically adjusts the stepper motor's pulse frequency and direction based on real-time data provided by the sensor module. For example, if the sensor detects that the robot is tilting while walking, the stepper motor's control parameters can be immediately adjusted to restore balance.
[0134] This implementation significantly improves the robot's balance and stability during movement through micro-stepping technology and real-time feedback regulation. Compared to existing technologies, this implementation more effectively maintains the robot's stability in complex environments, reducing the risk of tipping over, thereby improving the robot's adaptability and reliability.
[0135] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.
Claims
1. A bipedal robot active balance control system, characterized in that: A sensor module is used to collect motion state data of the robot during motion, and the sensor module includes an accelerometer, a gyroscope and a force sensor; The posture estimation and filtering module is connected to the sensor module and is used to fuse the collected motion state data through the Kalman filter algorithm and estimate the posture information of the robot; The balance control module and the posture estimation and filtering module are used to generate a balance compensation control signal based on the posture information of the robot estimated by the posture estimation and filtering module, and dynamically adjust the control output through a PID controller; The gait adjustment and execution module is connected to the balance control module and is used to control the stepping motor according to the balance compensation control signal of the balance control module to adjust the robot's leg movement to adapt to the ground state and balance requirements.
2. The biped robot active balance control system according to claim 1, characterized in that: The force sensor is installed on the bottom of the robot's foot and is used to detect the contact force between the foot and the ground and the distribution of the ground reaction force in real time.
3. The biped robot active balance control system according to claim 2, characterized in that: The sensor module also includes an infrared distance sensor, which is arranged at the end of the robot's legs and is used to detect the real-time distance between the feet and the ground.
4. The biped robot active balance control system according to claim 1, characterized in that: The attitude estimation and filtering module uses a complementary filtering algorithm to perform secondary correction on the Kalman filtering result to reduce high-frequency noise interference.
5. The biped robot active balance control system according to claim 4, characterized in that: The posture estimation and filtering module has a built-in machine learning model, which optimizes the accuracy of posture information estimation through historical motion data training, and the model output weights correct the Kalman filter parameters in real time.
6. The biped robot active balance control system according to claim 1, characterized in that: The balance control module adopts a PID controller to dynamically adjust the PID parameters according to the logic of posture information deviation.
7. The biped robot active balance control system according to claim 6, characterized in that: The proportional, integral and differential coefficients of the PID controller are dynamically adjusted through an online learning algorithm, and the learning algorithm is optimized based on the feedback signal of the robot's motion stability.
8. The biped robot active balance control system according to claim 1, characterized in that: The stepper motor drive adopts micro-step drive technology, and the drive current is adaptively adjusted according to the leg load pressure.
9. The biped robot active balance control system according to claim 8, characterized in that: The gait adjustment and execution module integrates a plantar pressure sensor array and uses pressure distribution data to correct the stepper motor torque output in real time.
10. The biped robot active balance control system according to claim 1, characterized in that: It also includes a wireless communication module connected to the balance control module, which is used to transmit posture information and control signals to an external monitoring terminal via WiFi / Bluetooth.