A Dynamic Robust Motion Control Method and System for a Wheeled Biped Robot

Abstract

The present invention provides a dynamic robust motion control method and system for a wheeled biped robot, which relates to the technical field of robot motion control. The method includes receiving a user input instruction; obtaining a desired wheel position according to the user input instruction; obtaining an optimal control input according to the integrated dynamics model of the robot, the robot state, the user input instruction, and the desired wheel position; obtaining robot control parameters according to the integrated dynamics model of the robot and the optimal control input; and performing motion control on the robot based on the robot control parameters. Wherein, in the integrated dynamics model, the relative position between the robot torso and the robot wheels in the forward direction is used as a control variable, and the relative position between the robot torso and the robot wheels in the forward direction is obtained according to the dynamic analysis results of the robot wheels and the robot torso. The present invention can achieve dynamic coordinated control of the wheeled biped robot.

Description

Technical Field

[0001] The present invention relates to the technical field of robot motion control, and particularly to a dynamic robust motion control method and system for a wheeled biped robot. Background Art

[0002] The combination of wheeled and legged mechanisms is considered an effective solution for mobile robots to cope with complex terrains. In the field of mobile robots, traditional wheeled robots have been widely used due to their high moving speed and energy efficiency on flat ground. However, their adaptability is significantly limited when facing complex terrains. Relatively speaking, although legged robots can have better stability and adaptability in complex terrains, their moving speed and efficiency are relatively low. Wheeled biped robots combine the advantages of wheeled and legged motions, integrating wheeled and legged motion modes, making up for the deficiencies of single motion modes, and significantly improving the mobility and adaptability of the robots, enabling them to smoothly cope with stairs, slopes, and various unstructured terrains.

[0003] However, as a typical underactuated system, wheeled biped robots face multiple challenges in control. In existing work, wheeled biped robots are usually simplified as wheeled inverted pendulum models during modeling to maintain balance and achieve speed tracking. However, this overly simplified assumption only applies to specific types of tasks and cannot fully capture the complex motion behaviors of wheeled biped robots. In addition, the response lag caused by non-minimum phase characteristics increases the control complexity. At the same time, the strong coupling and nonlinearity of the system make the mutual influence of each motion component complex, thus posing higher requirements for control strategies. Summary of the Invention

[0004] To address at least one deficiency of the prior art, an object of the present invention is to provide a dynamic robust motion control method and system for a wheeled biped robot, which realizes dynamic coordinated control of the wheeled biped robot by constructing a robot integrated dynamics model applicable to the wheeled biped robot.

[0005] To achieve the above object, according to some embodiments, in a first aspect of the present invention, there is provided a dynamic robust motion control method for a wheeled biped robot, including:

[0006] Receiving a user input instruction;

[0007] Obtaining the desired position of the wheels according to the user input instruction;

[0008] Obtaining the optimal control input according to the robot integrated dynamics model, the robot state, the user input instruction, and the desired position of the wheels;

[0009] Obtaining the robot control parameters according to the robot integrated dynamics model and the optimal control input;

[0010] Perform motion control on the robot based on the robot control parameters;

[0011] Among them, in the integrated dynamics model, the relative position between the robot torso and the robot wheels in the forward direction is used as the control variable, and the relative position between the robot torso and the robot wheels in the forward direction is obtained according to the dynamic analysis results of the robot wheels and the robot torso.

[0012] In a second aspect of the present invention, there is provided a dynamic robust motion control system for a wheeled biped robot, including:

[0013] An input module configured to receive user input instructions;

[0014] A dynamic programming module configured to obtain the desired wheel position according to the user input instructions;

[0015] A predictive control module configured to obtain the optimal control input according to the robot integrated dynamics model, the robot state, the user input instructions, and the desired wheel position;

[0016] A whole-body control module configured to obtain the robot control parameters according to the robot integrated dynamics model and the optimal control input;

[0017] A motion control module configured to perform motion control on the robot based on the robot control parameters;

[0018] Among them, in the integrated dynamics model, the relative position between the robot torso and the robot wheels in the forward direction is used as the control variable, and the relative position between the robot torso and the robot wheels in the forward direction is obtained according to the dynamic analysis results of the robot wheels and the robot torso.

[0019] Compared with the prior art, the beneficial effects of the present invention are:

[0020] The present invention provides a dynamic robust motion control method and system for a wheeled biped robot. Aiming at the problem that the existing wheeled inverted pendulum model is difficult to fully capture the complex motion behavior of the wheeled biped robot, based on the rolling constraint and the interaction force transfer, the wheel dynamics and the biped torso centroid dynamics are integrated to construct a robot integrated dynamics model applicable to the wheeled biped robot to achieve dynamic coordinated control between the wheels and the torso (base), so as to be able to achieve precise trajectory tracking, adapt to various terrains, and have strong robustness to interference.

[0021] The advantages of the additional aspects of the present invention will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present invention. Description of the Drawings

[0022] The accompanying drawings forming a part of this invention are used to provide a further understanding of the invention. The schematic embodiments and descriptions thereof of the invention are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 It is a schematic structural diagram of the robot in the embodiment of the present invention;

[0024] Figure 2 It is a schematic diagram of the wheeled inverted pendulum model of the robot in the embodiment of the present invention;

[0025] Figure 3 It is a schematic diagram of the overall control flow of the robot in the embodiment of the present invention. Detailed implementation manners

[0026] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0027] Embodiment 1

[0028] In Embodiment 1 of the present invention, as Figures 1 - 3 shown, a dynamic robust motion control method for a wheeled biped robot is provided, including:

[0029] S1. Receive a user input instruction;

[0030] S2. Obtain the desired wheel position according to the user input instruction;

[0031] S3. Obtain the optimal control input according to the robot integrated dynamics model, the robot state, the user input instruction, and the desired wheel position;

[0032] S4. Obtain the robot control parameters according to the robot integrated dynamics model and the optimal control input;

[0033] S5. Perform motion control on the robot based on the robot control parameters;

[0034] Among them, in the integrated dynamics model, the relative position between the robot torso and the robot wheels in the forward direction is used as the control variable, and the relative position between the robot torso and the robot wheels in the forward direction is obtained according to the dynamic analysis results of the robot wheels and the robot torso.

[0035] First, the coordinate system is defined. The x-axis of the inertial coordinate system { } is horizontally forward, the z-axis is vertically upward, the y-axis is perpendicular to the xoz plane, and the origin of the base coordinate system (base coordinate system) { } is located at the centroid of the torso, and the initial direction is the same as that of the inertial coordinate system and rotates with the rotation of the torso. Use to represent the interaction force screw between the wheel and the leg, which is composed of the interaction force and the interaction moment is composed of, and the subscripts and are used to distinguish the left and right legs, as shown in Figure 1 . The generalized coordinate matrix , the velocity matrix and the driving torque matrix are expressed as:

[0036] ;

[0037] wherein, , , , represents the number of joints of the robot, and respectively represent the translation and rotation of the torso, and respectively represent the linear velocity and angular velocity of the torso.

[0038] In addition, a control coordinate system is defined, and the origin of this coordinate system is located at the midpoint of the line connecting the two wheels of the robot, and the x-axis points in the forward direction of the robot.

[0039] The rotation relationship between the control coordinate system and the inertial coordinate system is represented by the rotation matrix , where , represents the yaw angle of the robot's torso, represents the rotation matrix corresponding to the rotation angle around the z-axis. The rotation matrix represents the mapping of the vector in the control coordinate system to the inertial coordinate system . Similarly, the rotation matrix represents the mapping of the vector in the inertial coordinate system to the control coordinate system . The definition of the control coordinate system implies the nonholonomic constraint characteristics of the wheeled biped robot, which facilitates the generalization of motion analysis from the sagittal plane to three-dimensional space. In this embodiment, to facilitate the distinction between different coordinate systems, the coordinate system to which each symbol belongs is marked in the upper left corner of each symbol, and symbols without a marked coordinate system in the upper left corner are default to be represented in the world coordinate system.

[0040] To address the problems existing in the control of existing wheeled biped robots and solve the challenges brought by underactuated characteristics and complex dynamic couplings, a novel control framework for wheeled biped robots is developed in this embodiment. Based on rolling constraints and interactive force transmission, the wheel dynamics and the centroid dynamics of the biped body are integrated to construct an integrated dynamic model applicable to wheeled biped robots to achieve dynamic coordination control between the wheels and the base. In addition, considering the non-minimum phase behavior of wheeled biped robots, online dynamic programming is designed to generate wheel position constraints to meet the dynamic balance requirements at the current centroid height. Further, considering the whole-body dynamics, non-holonomic constraints, optimal interactive forces, and multi-task coordination of the robot, a hierarchical optimization control framework combining model predictive control and whole-body control is proposed to compensate for the potential errors brought by the simplified model and enhance the real-time control stability of the robot. This algorithm can achieve precise trajectory tracking, adapt to various terrains, and has strong robustness to disturbances.

[0041] Based on the dynamic analysis of the wheels and the base of the robot, an integrated dynamic model is constructed.

[0042] Visually, a wheeled biped robot can be regarded as a biped robot with a pair of roller skates with active driving ability integrated at the ends of its legs. The wheels receive contact forces from the ground and simultaneously transmit forces and torques to the upper body at the wheel-leg connection points. For simplicity, the left and right wheels are not distinguished in the following motion analysis. The wheel dynamics equation in the control coordinate system is:

[0043] ;

[0044] ;

[0045] where and are the weights and inertia tensors of the wheels, represents the acceleration of the wheels in the control coordinate system, represents the supporting force provided by the ground in the control coordinate system, and respectively represent the interactive force and torque between the wheels and the legs in the world coordinate system, represents the vector pointing from the wheel center to the wheel-ground contact point, represents the wheel radius, represents the wheel angular velocity, represents the wheel angular velocity about the y-axis in the control coordinate system, and g represents the acceleration due to gravity. Assuming no relative sliding between the wheels and the ground, the following constraint is satisfied:

[0046] .

[0047] Combining the above formulas to eliminate the ground contact force and obtain the inertial coordinate system The wheel dynamics equation in the x-axis direction in the inertial coordinate system is as follows:

[0048] ;

[0049] In the formula, the numbers in the upper right corner of the matrix represent the specific rows of the matrix. For example, represents the first row of the rotation matrix. For a wheeled biped robot, the wheels and the torso need to always maintain coordination. If the positions of the two are controlled separately, over time, the cumulative error will continuously increase. This error accumulation will cause system instability, resulting in the robot being unable to maintain dynamic balance and even causing it to fall. Therefore, we use the relative position relationship between the wheels and the torso as the control variable to achieve the coordinated control of the overall system. Determined by the mechanical structure of the robot, the distance in the y-axis direction between the two wheels in the control coordinate system remains constant. Through forward kinematics derivation, the relationship between the vectors , and at the acceleration level can be represented by the following equation:

[0050] ;

[0051] Among them, The first two terms of represent the roll angular velocity and pitch angular velocity of the torso, but do not include the yaw angular velocity of the torso. Since the pitch and roll angular velocities of the robot are relatively small during the movement and can be ignored, the above formula can be approximated as:

[0052] ;

[0053] Among them, represents the distance between the robot torso and the robot wheels in the x-axis direction (i.e., the forward direction of the robot) in the control coordinate system .

[0054] Building an optimal control problem (OCP) based on a model to achieve the motion control of a robot is a widely used and effective method. Considering the high dimension and nonlinearity of the full-body dynamics model, it will lead to a long calculation time and difficult solution of the optimization problem in predictive control, thus posing strict requirements on the real-time performance and computing resources of the on-board controller. To ensure real-time performance, it is necessary to adopt a simplified model to reduce the scale of the optimization problem. Since the mass of the robot is mainly concentrated in the torso and wheels, and the limbs are relatively light, when simplifying the full-body dynamics model, in this embodiment, the constraints in the joint space are removed, and the influence of joint velocity on the linear momentum of the center of mass and the influence of the movement of the limb links on the system inertia tensor are ignored. Assuming that the center of mass of all links except the wheels is concentrated at the center of mass of the torso, the robot torso is modeled as a floating base. In the world coordinate system, the single-rigid-body dynamics model of the torso is as follows:

[0055] ;

[0056] ;

[0057] where, represents the mass of the torso, represents the inertia tensor of the torso, and the subscript represents the left and right wheel legs. For example, and respectively represent the vectors from the center of mass of the torso to the interaction force application points of the left and right wheel legs, and respectively represent the interaction forces between the left and right wheels and the legs in the world coordinate system, and respectively represent the interaction force moments between the left and right wheels and the legs in the world coordinate system.

[0058] Assuming that the roll and pitch velocities are small and the off-diagonal terms of the inertia tensor can be ignored, the above equation can be approximated as:

[0059] ;

[0060] Due to the small pitch and roll angles, the inertia tensor in the inertial system can be obtained by the following formula:

[0061] ;

[0062] where, represents the fixed inertia tensor in the base coordinate system B.

[0063] After the dynamics of the wheel and the torso are fully characterized, it can be found that the motion states of the wheel and the torso are both determined by the screw of the interaction force, and this interaction force shows equal magnitude and opposite signs. According to the previous analysis, replacing the wheel position with the relative position of the torso and the wheel in the forward direction as the control variable not only realizes the dimensionality reduction of the state vector to shorten the calculation time of MPC (Model Predictive Control), but also effectively realizes the coordinated control of the wheel and leg. Combining the above formulas, we can get:

[0064] ;

[0065] where, represents the acceleration of the distance between the wheel and the torso along the x-axis direction in the control coordinate system, and is defined as , and respectively represent the distances between the left and right wheels and the torso along the x-axis direction in the control coordinate system. The state variables are selected as , and with as the control input, the integrated dynamic equation that satisfies the non-holonomic constraints and the internal force and torque transmission is as follows:

[0066] ;

[0067] where:

[0068] ;

[0069] ;

[0070] ;

[0071] where, and involve a row permutation operation, that is: , and are the same by analogy.

[0072] In step S2, the desired position of the wheel is obtained through an online dynamic planner.

[0073] A system with non-minimum phase characteristics may initially exhibit a reverse output change after receiving a control signal and then finally converge to the desired result. This behavior is reflected in the wheeled biped robot. During forward or backward acceleration, the wheel will move a certain distance in the opposite direction to satisfy the dynamic constraints of the wheeled inverted pendulum model. The intuitive condition only applies to the static equilibrium state, where and respectively represent the distances of the desired left and right wheels and the torso along the x-axis in the control coordinate system. At this time, the system is in a static state or moving at a constant speed, and is not applicable to the acceleration situation. Therefore, when selecting the reference state, the position, speed, and angular velocity commands can be specified according to user input or predefined trajectories, and the desired position of the wheels should be dynamically planned in real time according to the basic dynamics of the wheeled biped robot.

[0074] As a variant of the standard linear inverted pendulum model, the wheeled inverted pendulum model adopts a dynamic equation in a similar form:

[0075] ;

[0076] As Figure 2 shown, we take the geometric midpoint of the line connecting the centers of rotation of the two wheels as the equivalent wheel axis, where represents the vector from the center of mass of the torso to the equivalent wheel axis in the control coordinate system, represents the height of the torso relative to the equivalent wheel axis in the z direction, 、 and respectively represent the displacement, acceleration of the center of mass of the torso, and the displacement of the equivalent wheels in the control coordinate system. The above dynamic equation reveals the linear relationship between the acceleration of the center of mass of the torso and the relative distance between the torso and the equivalent wheel axis, which can be expressed in state space form as:

[0077] ;

[0078] where, 、 、 、 and respectively represent the state vector, the derivative of the state vector with respect to time, the input vector, the state transition matrix, and the input matrix, depends on the value of , and can be calculated in advance as a known quantity and input to obtain a linearized state space equation. Given that the system is linear, a linear quadratic regulator (LQR) can be used to stabilize the system, and the optimal state feedback control of the continuous-time system is calculated by minimizing a specific quadratic cost function:

[0079] ;

[0080] where, and are the weight matrices of the state quantity and the input quantity respectively. Since the system is highly sensitive to control inputs, To suppress its influence. Considering the changing trunk height, we sample equidistantly in the range [0.1, 0.3] and implement LQR solution based on the algebraic Riccati equation to obtain the optimal gain matrix. The feedback gain matrix is obtained through polynomial fitting , this strategy is beneficial to shorten the time-consuming of the planning process and improve the control bandwidth of the system. The desired position of the equivalent wheel axle that satisfies the dynamics of the wheeled inverted pendulum model at the current center of gravity height can be obtained by the following formula:

[0081] ;

[0082] where, represents the expected value of the state vector.

[0083] Therefore, the online planning conditions for the left and right wheel positions are: .

[0084] In step S3, the optimal control input is obtained according to the model predictive controller. In this embodiment, the desired force screw of the interaction between the wheel and the trunk is generated by a discrete-time finite-horizon model predictive controller. When facing a multi-input multi-output (MIMO) system, MPC (Model Predictive Control) can coordinate multiple control variables to ensure the overall coordination and optimal performance of the system. Based on the constructed reduced-order system dynamics model (i.e., the integrated dynamics model) and the current system state (i.e., the state variables in the integrated dynamics model), the future system behavior is predicted using a rolling optimization window, and control measures are taken in advance, thereby improving the response speed and accuracy of the system. By directly incorporating the constraints into the optimization problem, MPC can effectively handle input and state constraints, thereby achieving the desired trajectory tracking. This optimization process loops continuously, applying only the optimal control input at the current moment in each iteration and recalculating at the next moment.

[0085] Fusing the gravity term into the state variables, taking the state variables as , the discrete state-space form of the integrated dynamics equation can be expressed as:

[0086] ;

[0087] where, , are coefficient matrices, and represent the system state and control input at time k (i.e., the interaction force and moment between the wheel and the leg), represents the system state at time k+1. Assuming the prediction step size is , the MPC optimization problem is constructed as follows:

[0088] ;

[0089] Among them, represents the system state referenced at time k + 1, represents the control input of the system at time k, , and represent diagonal positive semi - definite weight matrices. The term imposes a penalty on the sudden change of the control input between consecutive time steps to prevent the instability of the robot. To prevent the hub motor current from overloading and the wheels from slipping, a safety constraint is used to limit the wheel reaction force within a conical boundary:

[0090] ;

[0091] Among them, represents the components of the wheel - leg interaction force along the x, y, and z axes, represents the drive coefficient, and its magnitude is related to the performance of the wheel motor. The above - mentioned constraint equation is approximately linearized using the friction cone as follows:

[0092] ;

[0093] In addition, due to non - holonomic constraints, the wheels cannot actively provide lateral driving force and driving torque about the x - axis and z - axis in the control coordinate system. Therefore, this constraint condition needs to be incorporated into the framework of the optimization problem, expressed as:

[0094] ;

[0095] The qpOASES solver is used to solve the optimization problem in real - time, where is selected as the optimal input vector for the current control period.

[0096] In step S4, the control tasks are processed through a weighted multi - task whole - body controller to obtain control parameters for realizing the motion control of the robot. The robot system usually shows significant advantages in multi - task processing capabilities. Especially when the legs have redundant degrees of freedom, these degrees of freedom can be used to complete specific control tasks. Ignoring the dynamic characteristics of the legs may lead to a decrease in the reliability and accuracy of task execution, thus affecting the effective synchronization of multiple tasks. Considering these factors, integrating the leg dynamics into the whole - body control framework can improve the stability and accuracy of task execution and optimize the overall dynamic performance and response speed of the system.

[0097] Since there may be conflicts between tasks, executing multiple tasks simultaneously is very complex in control. Currently, there are various methods to solve this problem. For example, in the null space projection method, by assigning priorities to multiple tasks, the low-priority tasks are solved within the null space of the high-priority tasks. However, this method involves high-dimensional matrix calculations, which are time-consuming and may not have enough computational space to solve the low-priority tasks.

[0098] This embodiment proposes a weighted multi-task whole-body controller, formulating the control problem as a weighted optimization, where each task is assigned a specific weight. This method allows the controller to handle all tasks simultaneously and simplifies the optimization process by eliminating sequential optimization and null space projection. In the controller, the acceleration in the operational space can be converted into the acceleration in the joint space through differential forward kinematics as follows:

[0099] ;

[0100] where and represent the velocity and acceleration of the control quantity corresponding to the task, and represent the Jacobian matrix corresponding to the task and its derivative with respect to time.

[0101] To achieve the generalized acceleration that meets the requirements of multiple tasks while balancing the optimization performance and constraint satisfaction, a quadratic optimization problem is formulated:

[0102] ;

[0103] where the first constraint term is the dynamic equation constraint, generated by the dynamics library according to the whole-body dynamics model of the robot, , , , and are the inertia matrix, the centrifugal and Coriolis force matrix, the gravity matrix, the driving joint selection matrix, and the contact Jacobian matrix respectively, represents the wheel-leg interaction force and moment optimized by the model predictive controller, represents the slack variable, represents the final desired wheel-leg interaction force and moment, represents the task corresponding weight matrix, represents the non-holonomic constraint matrix, represents the force and joint torque constraint matrix. Each task is described as this form of equality constraint. To satisfy the dynamic constraints and coordinate the balance between model predictive control and whole-body control, the slack variable Relaxing the constraints increases the solvability of the problem. The optimization variables are defined as: , Next, specific descriptions of each task are given.

[0104] (1) Task space motion tracking. This task is used to track the motion trajectory required by the human body, including translation and rotation, as well as the target position of the wheels.

[0105] ① Trunk translation, the trunk translation task in the acceleration layer is defined as:

[0106] ;

[0107] ;

[0108] Among them, represents the Jacobian matrix corresponding to the trunk translation task, represents the desired acceleration of the trunk, , and respectively represent the reference displacement, velocity, and acceleration of the trunk, and represent the feedback gain matrix. The desired trunk acceleration is generated by the reference trajectory including displacement, velocity, and acceleration through the PD control law, and the current motion state of the corresponding trunk is estimated by fusing feedback information such as IMU and joint encoders by the Kalman filter.

[0109] ② Trunk rotation, the trunk rotation task in the acceleration layer is defined as:

[0110] ;

[0111] ;

[0112] Among them, represents the Jacobian matrix corresponding to the trunk rotation task, represents the desired angular acceleration of the trunk, and represent the quaternion corresponding to the reference rotation direction and the quaternion corresponding to the current rotation direction, represents the reference angular velocity of rotation. The error of the rotation direction is defined using the minus box operator which calculates the difference between two quaternions and converts it into a three-dimensional rotation.

[0113] ③ Leg swing, for a wheeled biped robot, the leg swing has the ability to adjust the position of the wheel relative to the base trunk along the forward direction. The leg swing task in the acceleration layer is defined as:

[0114] ;

[0115] ;

[0116] wherein, represents the acceleration of the desired relative distance between the wheel and the torso, represents the reference value of the relative distance between the wheel and the torso.

[0117] The Jacobian matrix of this task can be obtained through the following equation:

[0118] ;

[0119] wherein, and respectively represent the Jacobian matrices corresponding to the positions of the wheel and the torso in the world coordinate system. By taking the difference between the two and transforming it into the control coordinate system, the Jacobian matrix of the leg swing task can be obtained.

[0120] (2) Energy efficiency and slack optimization: To optimize the joint torque output, an energy efficiency function is introduced. Compared with the leg joints, a larger weight coefficient is assigned to the wheel joints, thereby reducing significant oscillations in the system. In addition, minimizing the slack variable can maintain the prediction performance of the system while ensuring strict compliance with the constraints during the optimization process. This task can be described as:

[0121] .

[0122] Embodiment 2

[0123] This embodiment provides a dynamic robust motion control system for a wheeled bipedal robot, including:

[0124] An input module, configured to receive user input instructions;

[0125] A dynamic programming module, configured to obtain the desired wheel position according to the user input instructions;

[0126] A predictive control module, configured to obtain the optimal control input according to the robot integrated dynamics model, the robot state, the user input instructions, and the desired wheel position;

[0127] A whole body control module, configured to obtain the robot control parameters according to the robot integrated dynamics model and the optimal control input;

[0128] A motion control module, configured to perform motion control on the robot based on the robot control parameters;

[0129] Among them, in the integrated kinetic model, the relative position between the robot torso and the robot wheels in the forward direction is used as the control variable, and the relative position between the robot torso and the robot wheels in the forward direction is obtained according to the results of the kinetic analysis of the robot wheels and the robot torso.

[0130] It should be noted here that each module in this embodiment corresponds to the steps of the method in Embodiment 1 one by one, and the specific implementation process is the same, so it will not be repeated here.

[0131] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dynamic robust motion control method for a wheeled bipedal robot, characterized in that: include: Receive user input instructions; Obtaining the desired wheel position according to the user input command; Obtain the optimal control input based on the robot's integrated dynamics model, robot state, user input commands, and desired wheel positions; According to the robot integrated dynamics model and the optimal control input, the robot control parameters are obtained; Controlling the robot's motion based on the robot's control parameters; Wherein, in the integrated dynamics model, the relative position of the robot trunk and the robot wheels in the forward direction is used as the control variable, and the relative position of the robot trunk and the robot wheels in the forward direction is obtained according to the dynamics analysis results of the robot wheels and the robot trunk; The relative position of the robot trunk and the robot wheels in the forward direction is constrained by the following formula: ； in, In the control coordinate system , the distance between the robot trunk and the robot wheels in the forward direction; represents the first row of the rotation matrix, V represents the linear velocity of the torso, Represents the first row of the acceleration matrix of the wheel in the control coordinate system; The integrated dynamics equation of the robot is: ； in, and denote the translation and rotation of the torso, respectively. and denote the linear velocity and angular velocity of the trunk, respectively. , and They represent the distances between the left and right wheels and the trunk along the x-axis in the control coordinate system. Indicates left and right wheels, and There are row permutation operations involved. and There are row permutation operations involved. represents the mass of the torso, represents the inertia tensor of the torso, and They represent the vectors of the trunk mass center pointing to the points of action of the left and right wheel-leg interaction forces, and They represent the interaction forces between the left and right wheels and the legs in the world coordinate system, and They represent the interaction torque between the left and right wheels and the legs in the world coordinate system, and g represents the acceleration due to gravity; , ， ， Indicates the rotation angle around the z axis The corresponding rotation matrix is, represents the wheel radius, and is the weight and inertia tensor of the wheel, with the superscript Represents the controlling coordinate system.

2. A wheeled biped robot dynamic robust motion control method as claimed in claim 1, characterized in that: The expected position of the wheel is obtained based on the wheeled inverted pendulum model at the current center of gravity height: ， in, and They represent the desired distances between the left and right wheels and the trunk along the x-axis in the control coordinate system, , is the feedback gain matrix, represents the state vector, represents the expected value of the state vector.

3. A wheeled biped robot dynamic robust motion control method as claimed in claim 1, characterized in that: The optimal control input is the desired force rotation of the interaction between the wheel and the trunk, which is obtained according to the model predictive controller.

4. A method for dynamic robust motion control of a wheeled bipedal robot as claimed in claim 3, characterized in that: The prediction step length is , the optimization problem of the model predictive controller for the optimal control input is constructed as follows: ； in, represents the system state at time k+1, represents the system state referenced at time k+1, represents the control input of the system at time k, , and represents a diagonal positive semidefinite weight matrix, The term imposes a penalty on abrupt changes in the control input between consecutive time steps to prevent the robot from becoming unstable. , is the coefficient matrix, and represents the system state and control input at time k.

5. A wheeled biped robot dynamic robust motion control method as claimed in claim 1, characterized in that: According to the robot's integrated dynamics model and the optimal control input, the control tasks are processed by a weighted multi-task whole-body controller to obtain the robot's control parameters.

6. A method for dynamic robust motion control of a wheeled bipedal robot as claimed in claim 5, characterized in that: The control tasks are processed by a weighted multi-task whole-body controller to obtain the robot control parameters, including assigning specific weights to each control task, formulating a quadratic optimization problem, describing each control task in the form of an equality constraint, and solving the quadratic optimization problem to obtain the robot control parameters.

7. A wheeled biped robot dynamic robust motion control method as claimed in claim 6, characterized in that: The control task includes task-space motion tracking including trunk translation, trunk rotation, and leg swinging, as well as energy efficiency and relaxation optimization.

8. A wheeled bipedal robot dynamic robust motion control system using a wheeled bipedal robot dynamic robust motion control method as claimed in claim 1, characterized in that: include: An input module, configured to receive user input instructions; A dynamic programming module is configured to obtain a desired wheel position according to a user input instruction; A predictive control module is configured to obtain an optimal control input based on an integrated dynamics model of the robot, a state of the robot, a user input command, and a desired position of the wheel; A whole body control module is configured to obtain robot control parameters according to the robot integrated dynamics model and the optimal control input; A motion control module is configured to perform motion control on the robot based on the robot control parameters; Among them, in the integrated dynamics model, the relative position of the robot trunk and the robot wheels in the forward direction is used as the control variable, and the relative position of the robot trunk and the robot wheels in the forward direction is obtained according to the dynamics analysis results of the robot wheels and the robot trunk.

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