A robot standing balance control method and system

Abstract

The embodiments of the present application relate to the field of robotics, and in particular to a robot standing balance control method and system. The method comprises the following steps: first, dynamically modeling the robot, establishing a connecting rod model, a table car model, and a virtual spring damping model; then, based on the connecting rod model, the table car model, and the virtual spring damping model, obtaining an expression for the robot's center of mass adjustment; finally, based on the expression for the robot's center of mass adjustment and the robot composite control system, adjusting the robot's center of mass and ankle joint posture. The robot standing balance control method provided in the present application realizes real-time adjustment of the robot's center of mass by establishing a virtual spring damping model; and through the robot composite control system, alleviates the problem that the robot is susceptible to external disturbances during the standing stability stage, further improving the robot's standing stability.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of robotics technology, and in particular to a robot standing balance control method and system. Background Art

[0002] Exoskeletons are a new trend in medical rehabilitation. They offer hope to patients with existing limb health issues. Stroke, spinal cord injury, Parkinson's disease, and other conditions account for the majority of patients with movement disorders. Medical research indicates that movement disorders can lead to secondary health problems and lifelong disabilities. Lower limb exoskeletons can provide some relief for these patients and make their lives more convenient.

[0003] Currently, lower-limb exoskeleton robots can be broadly categorized into two categories: partially driven and fully driven. In recent years, partially driven exoskeletons have become relatively mature in the application of hemiplegic patients. These exoskeletons operate motors in some joints to assist patients in walking. Fully driven lower-limb exoskeletons are targeted at paralyzed patients who are unable to care for themselves, providing them with the possibility of walking.

[0004] However, exoskeleton walking requires a stable standing position. External interference while the exoskeleton is standing can cause it to lose its stability. Compared to bipedal robots, exoskeletons experience greater mass disturbances when carrying a person. Adjustment in the face of external impact is more difficult, and rapid and drastic adjustments can cause secondary injuries to the user. Summary of the Invention

[0005] The embodiments of the present application provide a robot standing balance control method and system to improve the stability of the robot during the standing phase.

[0006] To solve the above technical problems, in the first aspect, an embodiment of the present application provides a robot standing balance control method, comprising the following steps: first, performing dynamic modeling on the robot, establishing a connecting rod model, a table car model, and a virtual spring damping model; then, based on the connecting rod model, the table car model, and the virtual spring damping model, obtaining an expression for the robot's center of mass adjustment amount; finally, based on the expression for the robot's center of mass adjustment amount and the robot's composite control system, adjusting the robot's center of mass and ankle joint posture.

[0007] In some exemplary embodiments, an expression for the robot's center of mass adjustment is obtained based on a linkage model, a trolley model, and a virtual spring-damper model, including: obtaining the correspondence between the external force and the center of mass based on the measured torque and the torque required for the robot's current movement; equating the external force with the center of mass, and obtaining an expression for the robot's center of mass adjustment based on a discrete time variable.

[0008] In some exemplary embodiments, a sensor is used to measure the torque of the robot movement; the expression of the robot center of mass adjustment is shown as follows:

[0009]

[0010] Among them, T sensor represents the torque of the robot motion measured by the sensor; T d represents the torque required for the current motion of the robot; i represents a discrete time variable.

[0011] In some exemplary embodiments, the robot's center of mass is adjusted via a virtual spring-damper.

[0012] In some exemplary embodiments, the robot's ankle posture includes the robot's foot lift angle and the robot's foot posture.

[0013] In some exemplary embodiments, after adjusting the center of mass and ankle joint posture of the robot, the method further includes: building a joint simulation platform, and verifying the feasibility of the robot's standing balance control based on the joint simulation platform.

[0014] On the second aspect, an embodiment of the present application also provides a robot standing balance control system, which is sequentially connected to a gait generation module, an inverse kinematics solution module, an ankle joint local control module, a robot simulation model and a stabilizer; the gait generation module is used to generate initial data when the robot enters a walking preparation state from an initial state and maintains a standing state, and send the initial data to the inverse kinematics solution module; the inverse kinematics solution module is used to perform inverse kinematics solution on the initial data, and send the solution result to the ankle joint local control module; the ankle joint local control module obtains joint data and foot posture data based on the solution result, and sends the joint data and foot posture data to the robot simulation model; the robot simulation model is used to obtain external force and torque feedback values based on the joint data and foot posture data, and send the external force and torque feedback values to the stabilizer for center of mass adjustment to achieve closed-loop control.

[0015] In some exemplary embodiments, the ankle joint local control module includes a joint limit module and a local controller. The joint limit module is used to correct the angle at which the robot's feet leave the ground; the local controller is used to collect the posture of the robot's feet and correct it in real time.

[0016] The technical solution provided by the embodiments of the present application has at least the following advantages:

[0017] In order to solve the problem that an existing exoskeleton robot cannot walk stably when standing due to interference from external forces, the embodiments of the present application provide a robot standing balance control method and system, which includes the following steps: first, dynamically modeling the robot to establish a connecting rod model, a table car model and a virtual spring damping model; then, based on the connecting rod model, the table car model and the virtual spring damping model, an expression for the robot's center of mass adjustment amount is obtained; finally, based on the expression for the robot's center of mass adjustment amount and the robot's composite control system, the robot's center of mass and ankle joint posture are adjusted.

[0018] The robot standing balance control method provided by this application, based on the robot dynamics model, realizes the dynamic adjustment of the center of mass under the action of external forces during the standing phase of the exoskeleton robot by establishing a virtual spring damping model. At the same time, this application also proposes a robot composite control system to adjust the robot's center of mass and ankle joint posture, further improving the robot's stability when standing. In addition, the embodiment of this application also establishes a simulation model of the exoskeleton robot, and numerical simulation experiments verify the feasibility, effectiveness and anti-disturbance ability of the robot standing balance control method of this application under no-load and loaded conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] One or more embodiments are exemplarily described by pictures in the corresponding drawings. These exemplifications do not constitute limitations on the embodiments. Unless otherwise stated, the pictures in the drawings do not constitute proportional limitations.

[0020] Figure 1 A flowchart of a robot standing balance control method provided in one embodiment of the present application;

[0021] Figure 2 This is a schematic diagram of the structure of a self-balancing exoskeleton robot;

[0022] Figure 3 A schematic diagram of a simplified tabletop exoskeleton robot model provided in one embodiment of the present application;

[0023] Figure 4 A schematic structural diagram of an exoskeleton robot composite control system provided in one embodiment of the present application;

[0024] Figure 5 A schematic diagram of local control of the ankle joint of an exoskeleton robot provided in one embodiment of the present application;

[0025] Figure 6 A schematic structural diagram of a robot standing balance control system provided in one embodiment of the present application;

[0026] Figure 7A schematic diagram of an exoskeleton robot simulation experiment provided in one embodiment of the present application;

[0027] Figure 8 This is a schematic diagram showing how the distance between the center of mass and the equilibrium position of the robot changes with the control period in the X direction (sagittal plane) when the robot is unloaded, according to one embodiment of the present application;

[0028] Figure 9 A schematic diagram showing how the distance between the center of mass and the equilibrium position of the robot changes with the control period in the Y direction (coronal plane) when the robot is unloaded according to an embodiment of the present application;

[0029] Figure 10 This is a schematic diagram showing how the distance between the center of mass and the equilibrium position of the robot changes with the control period in the X direction (sagittal plane) when the robot provided by one embodiment of the present application is loaded;

[0030] Figure 11 This is a schematic diagram showing how the distance between the center of mass and the equilibrium position of the robot changes with the control period in the Y direction (coronal plane) when the robot provided in one embodiment of the present application is loaded. DETAILED DESCRIPTION

[0031] As can be seen from the background art, the existing exoskeleton robots may be unable to walk stably due to interference from external forces when standing.

[0032] Self-balancing exoskeletons are similar to traditional bipedal balancing robots in terms of modeling methods and balance control algorithms. Many exoskeletons also use more classical balance models. They also incorporate optimization algorithms similar to those used in bipedal robots. They also have numerous cross-applications, such as myoelectric control. Compared to traditional bipedal exoskeletons, self-balancing exoskeletons experience greater mass disturbances when carrying a person, making adjustment more difficult in the face of external impacts. Rapid and drastic adjustments can cause secondary injuries to the user. This requires the exoskeleton to be highly stable when standing.

[0033] In order to solve the above technical problems, an embodiment of the present application provides a robot standing balance control method, comprising the following steps: first, performing dynamic modeling on the robot, establishing a connecting rod model, a table car model and a virtual spring damping model; then, based on the connecting rod model, the table car model and the virtual spring damping model, obtaining an expression for the robot's center of mass adjustment amount; finally, based on the expression for the robot's center of mass adjustment amount and the robot's composite control system, adjusting the robot's center of mass and ankle joint posture. Currently in the field of exoskeleton robots, most emphasize the rehabilitation effect and motion coupling of exoskeleton robots, but this application provides a robot standing balance control method using a fully driven self-balancing exoskeleton robot, with the aim of enabling patients to walk independently. The purpose of this application is to improve the stability of the exoskeleton robot during the standing stage and lay the foundation for the exoskeleton robot to walk.

[0034] The following detailed description of the various embodiments of the present application is provided in conjunction with the accompanying drawings. However, those skilled in the art will appreciate that many technical details are provided in the various embodiments of the present application to facilitate a better understanding of the present application. However, even without these technical details and the various variations and modifications based on the following embodiments, the technical solutions claimed in the present application can still be implemented.

[0035] See Figure 1 , an embodiment of the present application provides a robot standing balance control method, comprising the following steps:

[0036] Step S1: Perform dynamic modeling on the robot, establish a connecting rod model, a table car model, and a virtual spring damping model.

[0037] Step S2: Based on the connecting rod model, the table car model and the virtual spring damping model, an expression for the robot center of mass adjustment amount is obtained.

[0038] Step S3: Based on the expression of the robot center of mass adjustment amount and the robot composite control system, adjust the robot center of mass and ankle joint posture.

[0039] In some embodiments, the robot's ankle pose includes the lift angle of the robot's foot and the pose of the robot's foot.

[0040] The robot in the embodiment of the present application adopts an exoskeleton robot. The robot standing balance control method of the present application is explained by taking the exoskeleton robot as an example. In the following description, the robot refers to the exoskeleton robot.

[0041] The exoskeleton robot used in the embodiment of the present application is based on the full-drive lower limb exoskeleton robot AUTO-LEE-II. Figure 2As shown, the AUTO-LEE-II is a fully driven lower limb exoskeleton robot powered by 12 motors, enabling paralyzed patients to walk rehabilitatively. The benefit of this exoskeleton design is that it more closely aligns with human movement patterns, enabling better interaction between patients and the robot for rehabilitation. This exoskeleton, through a series of control algorithm-driven actions, enables efficient human-robot interaction and data feedback.

[0042] Please continue to see Figure 2 , the exoskeleton robot used in the embodiment of the present application is a single leg with six degrees of freedom, three hip degrees of freedom, one knee degree of freedom and two ankle degrees of freedom from the waist to the foot. The sensors involved are two plantar pressure sensors and an inertial measurement unit (IMU) sensor, and the signals collected by these sensors are used to adjust the exoskeleton robot in real time. The motion logic of the exoskeleton robot is hierarchical control planning. Gait generation is performed by the host computer side to plan the required motion, which is then passed to the inverse kinematics solver side to generate each joint motion trajectory. When the motor trajectories are obtained, they are sent to the controller for transmission, ensuring that these instructions are passed to each motor and ultimately arrive at the center of mass mechanism, i.e., the exoskeleton robot. The motion of the robot is fed back to the main computer by the sensor for feedback control.

[0043] like Figure 3 As shown in the figure, the table-cart model is a common simplified dynamic model of bipedal robots. The lower limb exoskeleton model can adopt the bipedal robot model. This application uses the table-cart model to develop the center of mass adjustment component that conforms to the control algorithm. The mass of the workbench can be ignored, and the cart represents the mass dynamic center of the robot. The robot model in the standing state is as follows Figure 3 The parameters are shown in Table 1.

[0044] Table 1 Parameters of the robot model in standing position

[0045]

[0046]

[0047] In some embodiments, based on the linkage model, the trolley model and the virtual spring damping model, an expression for the robot's center of mass adjustment is obtained, including: obtaining the correspondence between the external force and the center of mass based on the measured torque and the torque required for the robot's current movement; equating the external force with the center of mass, and obtaining an expression for the robot's center of mass adjustment based on a discrete time variable.

[0048] In some embodiments, a sensor is used to measure the torque of the robot's motion; the expression of the robot's center of mass adjustment is as follows:

[0049]

[0050] Among them, T sensor represents the torque of the robot motion measured by the sensor; T d represents the torque required for the current motion of the robot; i represents a discrete time variable.

[0051] In some embodiments, the robot's center of mass is adjusted by a virtual spring damper. The following describes in detail the derivation process of the expression for the robot's center of mass adjustment.

[0052] To simulate the exoskeleton robot's ability to maintain sufficient stability when subjected to external forces, a virtual spring-damper is introduced to adjust the center of mass. This structure allows the robot to have a certain degree of flexibility. This is achieved by allowing the robot's center of mass to adjust dynamically, simulating the effect of a spring-damper. At this point, the actual impact of external forces on the center of mass can be measured by sensors:

[0053]

[0054] Among them, T sensor represents the torque measured by the sensor, T d Indicates the torque required for the robot's current motion. T d It can be expressed as:

[0055]

[0056] in The linear inverted pendulum model of the bipedal robot gait generation model can be expressed as:

[0057]

[0058] The exoskeleton is standing because the desired center of mass is consistent with the desired zmp plan. ref The value of is 0, so (2) can be written as:

[0059] T d =mgx ref (4)

[0060] By equating the external forces to the center of mass, the dynamic equations for the spring-damper can be expressed as:

[0061]

[0062] x d and x ref The relationship is as follows:

[0063]

[0064] Substituting (6) into (5) yields:

[0065]

[0066] It can be written in discrete form and introduce a discrete time variable i:

[0067]

[0068] After the system is discretized, (8) is substituted into (7) and sorted out to obtain the expression of the robot center of mass adjustment (center of mass adjustment value):

[0069]

[0070] The above derivation introduces a virtual spring damping model to increase the stability of the robot when standing. This is a conductive control that reduces the oscillation of the robot when subjected to external forces by balancing external forces. A recursive expression for the adjustment amount of the center of mass in the discrete system is derived. It should be noted that in the derivation of the above equation, only the X direction is considered. The movement of the exoskeleton robot in the two directions is independent, and a similar model can be established in the Y direction. After replacing the relevant parameters, the expression in the Y direction can also obtain a result similar to the expression (9) for the robot center of mass adjustment amount.

[0071] In the robot standing balance control method provided in the embodiments of this application, a simplified dynamic model of the exoskeleton robot is first constructed, and a simplified linkage model is established. A table-car model is used, and a virtual spring-damper model is introduced to achieve real-time adjustment of the exoskeleton robot's center of mass. After completing the dynamic modeling of the robot, a composite control system is proposed to reduce the error of the robot's dynamic model.

[0072] While exoskeleton robots are similar in principle to bipedal robots in maintaining balance, their actual control focuses differ. Traditional exoskeleton robots focus more on the effectiveness of rehabilitation exercises and the fit between the exoskeleton and the patient. Lower limb self-balancing exoskeleton robots need to consider the patient's balance and stability during rehabilitation. This application proposes a robotic composite control system that, combined with a robot dynamics model, improves the stability of a fully driven lower limb exoskeleton during walking and standing, making the exoskeleton safer and more suitable for patient rehabilitation.

[0073] In addition, if Figure 4As shown, an embodiment of the present application also provides a robot standing balance control system, which is sequentially connected to a gait generation module, an inverse kinematics solution module, an ankle joint local control module, a robot simulation model and a stabilizer; the gait generation module is used to generate initial data when the robot maintains a standing state after entering a walking preparation state from an initial state, and send the initial data to the inverse kinematics solution module; the inverse kinematics solution module is used to perform inverse kinematics solution on the initial data, and send the solution result to the ankle joint local control module; the ankle joint local control module obtains joint data and foot posture data based on the solution result, and sends the joint data and foot posture data to the robot simulation model; the robot simulation model is used to obtain external force and torque feedback values according to the joint data and foot posture data, and send the external force and torque feedback values to the stabilizer for center of mass adjustment to achieve closed-loop control.

[0074] In some embodiments, the ankle joint local control module includes a joint limit module and a local controller (PD controller). The joint limit module is used to correct the angle at which the robot's feet leave the ground; the local controller is used to collect the posture of the robot's feet and correct it in real time.

[0075] Specifically, such as Figure 4 As shown, the composite control system of the full-motion exoskeleton robot of the present application includes: gait generation, inverse kinematics solver, ankle joint local controller, robot simulation model and stabilizer. The present application ensures the accuracy of the model by adding local control of the ankle joint, and solves the model error caused by the large mass of the end of the exoskeleton robot. Generally speaking, the movement of the exoskeleton robot is first planned through the model, and the implementation of this section is relatively simple. The robot maintains a standing state after entering the walking preparation state from the initial state. Secondly, through inverse kinematics solution, after passing through the joint limit and local controller, the data is sent to the actuator of the exoskeleton robot to obtain the joint parameters. Then, the exoskeleton robot feeds back the required signal to the stabilizer for center of mass adjustment to achieve closed-loop control.

[0076] A normal bipedal robot can be balanced and controlled only by the above-mentioned admittance control method. However, exoskeleton robots and bipedal robots cannot be simply equated. The exoskeleton robot AUTO-LEE-II used in this application has a larger end mass compared to small and medium-sized bipedal robots. Normally, the feet of ordinary bipedal robots are not particularly large and have a certain stable space. However, the mass of the feet of the exoskeleton robot is relatively large so that the user can wear it stably. When the exoskeleton robot receives external force, the mass of the feet is large, and once it loses stability, it will lead to very serious consequences.

[0077] Based on the above analysis, the instability caused by model errors is significant. This state indicates that the prerequisites for the table-car model mentioned earlier have not been met. If the exoskeleton's feet leave the ground, this will cause the model to deviate. The result of this situation is usually that the exoskeleton will shake violently to regain balance, but will fall down when the external force reaches a certain level.

[0078] In order to solve this problem of the exoskeleton robot, a local control link of the ankle joint is introduced, such as Figure 5 When an external force acts on the exoskeleton robot and causes the robot's feet to leave the ground, it is necessary to control the ankle joint so that the feet are as close to the ground as possible. The PD controller is used to correct the lifting angle θ. Figure 5 As shown in Figure 1, θ is the angle between the robot's foot and the ground. The feedback value used in this process is the posture of the robot's foot. The posture of the foot is collected by the inertial measurement unit (IMU) and corrected in real time. This method can improve the stability of the robot and reduce vibration to a certain extent. By combining these two controllers, a control system is proposed as follows: Figure 4 The robot composite control system shown is used to improve the stability of the exoskeleton robot.

[0079] Based on the simple dynamic model, this application designs a composite control framework and introduces a robot composite control system in combination with actual conditions, which alleviates the robot from being disturbed by external factors during the standing stability stage, further improves the standing stability of the exoskeleton robot, and eliminates some of the errors of the simple dynamic model to a certain extent.

[0080] like Figure 6 As shown, in some embodiments, after adjusting the robot's center of mass and ankle joint posture in step S3, the method further includes step S4: building a co-simulation platform and verifying the feasibility of the robot's standing balance control based on the co-simulation platform. This application demonstrates the feasibility of the above-mentioned standing balance control method by building a co-simulation platform and designing experiments. The experimental results show that this method can improve the stability of the exoskeleton robot during the standing phase.

[0081] To verify the feasibility of the proposed composite control framework on an exoskeleton robot, the robot needs to be modeled and simulated. This application establishes a co-simulation model, builds a robot simulation model in CoppeliaSim, and plans simple motions to verify the feasibility of the algorithm. The Newton engine is used in this simulation software.

[0082] like Figure 7As shown in Figure 1, the simulation model consists of a robot and an external pendulum. Based on the above model, the robot adjusts the center of mass position and ankle motor parameters in real time. The initial center of mass height of the robot is set to 0.79 meters. After the movement starts, the robot will reach the center of mass reference height ( Figure 7 The display shows that the robot has reached the reference height (0.70 meters). During this process, the robot crouches and then remains stationary to maintain this height. After the robot remains stationary, a pendulum is drawn toward the robot at a constant speed. After the pendulum strikes the robot's surface and reaches a certain force, it returns. This process simulates the robot being subjected to external forces. During the simulation, the control loop operates at a frequency of 100 Hz.

[0083] In this context, two sets of experiments were designed to verify the reliability of the algorithm. One set of experiments examined the stability of the exoskeleton robot before and after the algorithm was implemented. Impact forces within the exoskeleton's tolerance range were applied to the robot, and the stability of the exoskeleton robot before and after the algorithm was implemented were compared without any load.

[0084] Figure 8 and Figure 9 These are the results of the first set of experiments. Under no-load conditions, the exoskeleton robot was subjected to collision tests in two directions. The impact force thresholds were 65N in the X direction and 150N in the Y direction. The force application time was approximately 0.5 seconds, and the robot's return to equilibrium took 8 seconds. The distance between the center of mass and the static equilibrium position was recorded. As can be seen from the figure, the addition of the composite control frame significantly reduced the exoskeleton's oscillations as it returned to equilibrium. The peak value of the secondary oscillation was very small, and the robot's posture remained stable, demonstrating a certain degree of compliance in the simulation.

[0085] The second set of experiments was conducted when the exoskeleton robot was carrying a patient with a mass of 65 kg and standing. The same impact strategy was used, and the experimental results were as follows: Figure 10 and Figure 11 As shown in the figure. Similar to the previous set of experiments, the control framework proposed in this application also played a role in reducing oscillations. It is worth noting that the response time of the system is slower than that of the no-load state. However, the stabilization effect of this algorithm is still reflected. The slowdown in system response is most obvious in the X direction. This may be due to model errors caused by the increase in waist mass and leg mass. At this time, the adjustment of the sagittal plane to the center of mass is very small, which is manifested as a slower return to the equilibrium position during the control cycle.

[0086] One thing to note from the above experiment is that the robot's final equilibrium point under open-loop control differs from that under closed-loop control (by approximately one to two centimeters). This is because the Newtonian engine in the simulation software models the contact between the robot's foot and the ground as a spring. This can cause the sensor values to not remain completely stable when the robot is stationary, resulting in a very small deviation.

[0087] In the experimental phase, a hybrid control method for the standing balance of a fully-actuated exoskeleton robot was proposed. By introducing a virtual spring-damper system and local control of the ankle joints, the fully-actuated exoskeleton robot was endowed with a certain degree of active flexibility. The feasibility of this control scheme was demonstrated in simulation experiments. The static stability of the exoskeleton robot was verified through two sets of simulation experiments, one conducted without a load and the other with a person. The stability of the exoskeleton robot during the standing phase before walking was improved. Although in the simulated manned experiments, the return to the stable position seemed slow and the oscillations could not be completely eliminated. Overall, the stability of the exoskeleton robot during the standing phase can be greatly improved compared to the state without the control algorithm. The control algorithm proposed in this paper improves the exoskeleton robot's anti-disturbance capability during the standing phase.

[0088] Based on the above technical solution, the embodiment of the present application addresses the problem that the existing exoskeleton robot cannot walk stably due to the interference of external forces when standing. The embodiment of the present application provides a robot standing balance control method and system, which includes the following steps: first, dynamically modeling the robot, establishing a connecting rod model, a table car model and a virtual spring damping model; then, based on the connecting rod model, the table car model and the virtual spring damping model, obtaining an expression for the robot's center of mass adjustment amount; finally, based on the expression for the robot's center of mass adjustment amount and the robot composite control system, adjusting the robot's center of mass and ankle joint posture.

[0089] The robot standing balance control method provided by this application, based on the robot dynamics model, realizes the dynamic adjustment of the center of mass under the action of external forces during the standing phase of the exoskeleton robot by establishing a virtual spring damping model. At the same time, this application also designs a robot composite control system to adjust the robot's center of mass and ankle joint posture, further improving the robot's stability when standing. In addition, the embodiment of this application also establishes a simulation model of the exoskeleton robot, and numerical simulation experiments verify the feasibility, effectiveness and anti-disturbance ability of the robot standing balance control method of this application under no-load and loaded conditions.

[0090] That is, those skilled in the art will understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing related hardware through a program.

[0091] Those skilled in the art will appreciate that the above-described embodiments are specific examples for implementing the present application, and that in actual applications, various changes in form and detail may be made thereto without departing from the spirit and scope of the present application. Any person skilled in the art may make changes and modifications without departing from the spirit and scope of the present application. Therefore, the scope of protection of the present application shall be subject to the scope defined in the claims.

Claims

1. A robot standing balance control method, characterized in that: include: Conduct dynamic modeling of the robot, establish a connecting rod model, a table car model, and a virtual spring damper model; Based on the connecting rod model, the table car model and the virtual spring damping model, an expression for the robot center of mass adjustment is obtained; Adjusting the robot's center of mass and ankle joint posture based on the expression of the robot's center of mass adjustment amount and the robot's composite control system; The expression for obtaining the robot center of mass adjustment amount based on the connecting rod model, the table car model, and the virtual spring damping model includes: Based on the measured torque and the torque required for the robot's current motion, the correspondence between the external force and the center of mass is obtained; Equating the external force to the center of mass, and obtaining an expression for the robot's center of mass adjustment based on a discrete time variable; Use sensors to measure the torque of the robot's motion; The expression of the robot center of mass adjustment is as follows: Among them, T sensor represents the torque of the robot motion measured by the sensor; T d represents the torque required for the current motion of the robot; i represents a discrete time variable.

2. The robot standing balance control method according to claim 1, characterized in that: The robot's center of mass is adjusted using a virtual spring-damper.

3. The robot standing balance control method according to claim 1, characterized in that: The ankle joint posture of the robot includes the lift angle of the robot foot and the posture of the robot foot.

4. The robot standing balance control method according to claim 1, characterized in that: After adjusting the robot's center of mass and ankle joint posture, it also includes: A joint simulation platform was built, and based on the joint simulation platform, the feasibility of the robot's standing balance control was verified.

5. The robot standing balance control method according to claim 1, characterized in that: The robot composite control system comprises: a gait generation module, an inverse kinematics solution module, an ankle joint local control module, a robot simulation model and a stabilizer connected in sequence; The gait generation module is used to generate initial data when the robot maintains a standing state after entering a walking preparation state from an initial state, and send the initial data to the inverse kinematics solution module; The inverse kinematics solution module is used to perform inverse kinematics solution on the initial data and send the solution result to the ankle joint local control module; The ankle joint local control module obtains joint data and foot posture data based on the solution result, and sends the joint data and foot posture data to the robot simulation model; The robot simulation model is used to obtain external force and torque feedback values based on the joint data and foot posture data, and send the external force and torque feedback values to the stabilizer for center of mass adjustment to achieve closed-loop control.

6. The robot standing balance control method according to claim 5, characterized in that: The ankle joint local control module includes a joint limit module and a local controller. The joint limit module is used to correct the angle at which the robot's feet leave the ground; the local controller is used to collect the posture of the robot's feet and correct it in real time.

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