Full-scale humanoid robot coordination control method and system

Abstract

The invention provides a full-scale humanoid robot coordination control method and system, and the method comprises the steps: building a nonlinear model prediction controller, carrying out the optimization solution through sequential quadratic optimization, and optimizing the system input and system state; a trunk pose task, a swing leg and support leg task and an arm task are extracted from the optimized trajectory, and a plurality of tracking tasks are mapped to a joint space according to an allocated priority weight by using a high-frequency hybrid whole body motion control HWBC algorithm, so that the robot can coordinately process the motions of the arms and the feet, the system response is improved, and the system reliability is improved. The method has the ability of multi-gait robust motion.

Description

Technical Field

[0001] The present invention belongs to the technical field related to humanoid robot control, and in particular relates to a coordinated control method and system for a full-scale humanoid robot. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] In recent years, the rapid development of humanoid robot technology has marked significant progress in the morphology, function and intelligence of robots. As a frontier field of robotics, full-size humanoid robots are widely used in many industries such as service, medical, manufacturing, and entertainment. However, with the evolution of technology, traditional humanoid robots often show poor stability and flexibility when handling their own movements.

[0004] Most of the current mainstream humanoid robot foot and arm planning methods use independent foot and arm position trajectory following modes. Although this method can achieve basic action execution in static or regular environments, in dynamic environments, the coordination between the foot and the arm is insufficient and cannot cope with complex terrain and dynamic disturbances. Humanoid robots need to coordinate multiple joints and degrees of freedom at the same time. When the feet move, the movement of the arms also needs to be adjusted according to factors such as balance and inertia. The independent planning method leads to rigid movements and difficulty in achieving flexible coordination in complex environments.

[0005] Secondly, common control methods for humanoid robots, such as simple feedback controllers or center of gravity planning control algorithms, have single functions and poor motion stability. Feedback control relies on sensors to measure the current state and then perform error correction. However, this method does not take into account the robot's movements in the future and is not robust when the robot loses balance or encounters external disturbances. Traditional center of gravity planning algorithms are usually based on static or quasi-static assumptions to plan and adjust the robot's center of gravity to maintain balance. However, this approach ignores the complexity of dynamics during movement. When the robot moves quickly or performs large movements, simple center of gravity planning algorithms are difficult to adapt to actual needs.

[0006] In summary, most of the current mainstream humanoid robot foot and arm planning methods use independent foot and arm position trajectory following modes, which are stiff and not very flexible. Secondly, the humanoid robot controllers have a single function, and most of them use simple feedback controllers or center of gravity planning control algorithms, which are slow to respond, lack strong robustness, and are difficult to meet actual needs. Therefore, developing a foot and arm trajectory planning and whole-body motion control framework for full-size humanoid robots that can coordinate the movement of the foot and arm and can stably perform multi-gait walking in the real world is a technical problem that needs to be solved. Summary of the invention

[0007] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a coordinated control method and system for a full-scale humanoid robot, so that the robot can coordinate the movement of the arms and feet, improve the system response, and have the ability of multiple gait robust movements.

[0008] In order to achieve the above object, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention provides a coordinated control method for a full-scale humanoid robot, comprising:

[0010] According to the system state and system input of the full-scale humanoid robot, a nonlinear model predictive controller is constructed, and sequential quadratic optimization is used to optimize and solve the system state and system input after optimization.

[0011] According to the optimized system state and system input, the arm trajectory, leg trajectory and trunk trajectory are extracted and classified into multiple tracking tasks; wherein the multiple tracking tasks include: supporting leg force following task, swing leg trajectory task, trunk position task, trunk posture task and arm trajectory task;

[0012] A high-frequency hybrid whole-body motion control algorithm is used to map multiple tracking tasks to the corresponding joint modules according to the assigned priority weights for a full-scale humanoid robot.

[0013] In a second aspect, the present invention provides a full-scale humanoid robot coordination control system, comprising:

[0014] An optimization module is configured to: construct a nonlinear model predictive controller according to the system state and system input of the full-scale humanoid robot, and perform optimization and solution using sequential quadratic optimization to obtain an optimized system state and system input;

[0015] A classification module, which is configured to: extract arm trajectories, leg trajectories and trunk trajectories according to the optimized system state and system input, and classify them into multiple tracking tasks; wherein the multiple tracking tasks include: a supporting leg force following task, a swing leg trajectory task, a trunk position task, a trunk posture task and an arm trajectory task;

[0016] A control module is configured to map multiple tracking tasks to corresponding joint modules according to assigned priority weights using a high-frequency hybrid whole-body motion control algorithm for a full-scale humanoid robot.

[0017] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in the first aspect is performed.

[0018] In a fourth aspect, the present invention provides a computer-readable storage medium for storing computer instructions, wherein when the computer instructions are executed by a processor, the method described in the first aspect is performed.

[0019] In a fifth aspect, the present invention provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0020] One or more of the above technical solutions have the following beneficial effects:

[0021] In the present invention, a nonlinear model predictive controller is constructed and sequential quadratic optimization is used for optimization and solution to optimize the system input and system state; the trunk posture task, the swing leg and supporting leg task, and the arm task are extracted from the optimized trajectory, and a high-frequency hybrid whole-body motion control HWBC algorithm is used to map multiple tracking tasks to the joint space according to the assigned priority weights, so that the robot can coordinate the movement of the arm and the foot, improve the system response, and have the ability of multiple gait robust movements.

[0022] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0024] Figure 1 It is a schematic diagram of the structure and joint module allocation of a full-size humanoid robot designed in the first embodiment of the present invention;

[0025] Figure 2 is a schematic diagram of a full-size humanoid robot joint communication network designed in Embodiment 1 of the present invention;

[0026] Figure 3 is a schematic diagram of the full-size humanoid robot arm motion designed in the first embodiment of the present invention;

[0027] Figure 4 It is a flow chart of the motion control framework of the full-size humanoid robot designed by the present invention. DETAILED DESCRIPTION

[0028] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0029] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0030] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0031] Embodiment 1

[0032] like Figure 1 As shown, in the joint module of the full-scale humanoid robot in this embodiment, each arm has 4 degrees of freedom, and each degree of freedom is connected to the arm rigid body of the corresponding index. The arm joint No. 1 is rigidly connected to the body and rotates around the roll direction. The arm joint No. 2 is rigidly connected to the transmission shaft of the arm joint No. 1 and rotates around the pitch direction. The arm joint No. 3 is rigidly connected to the arm rigid body No. 2 and rotates around the yaw direction. The arm joint No. 4 is rigidly connected to the arm rigid body No. 3 and rotates around the roll direction. The rotation of the other arm is similar to the connection method. Each leg has 5 degrees of freedom. The joint of leg 1 is rigidly connected to the body and rotates around the roll direction. The joint of leg 2 is rigidly connected to the transmission shaft of the joint of leg 1 and rotates around the yaw direction. The joint of leg 3 is rigidly connected to the joint of leg 2 and rotates around the yaw direction. The joint of leg 4 is rigidly connected to the joint of leg 3 and rotates around the roll direction. The joint of leg 5 is rigidly connected to the rigid body of leg 4 and rotates around the pitch direction. The rigid body of leg 5 and the joint of leg 5 are driven by a connecting rod, and the rotation of the other leg is similar to the connection method. The head has a redundant degree of freedom, a total of 19 degrees of freedom, and a single joint module is based on the Can communication method.

[0033] The onboard computer and power supply equipment are located on the upper side of the robot's trunk and protected by the robot's shell. The onboard computer is used to calculate planning and control algorithms. It also has a PCIE to Ethercat card inserted to communicate with the Ethercat network. The power supply equipment provides power to the onboard computer and communication card.

[0034] The inertial measurement unit is located in the middle of the robot's torso, aligned with the robot's torso coordinate system, and protected by the robot's shell. It can transmit the robot's torso posture, linear velocity, and angular acceleration to the onboard computer via serial communication at a frequency of 500Hz.

[0035] The communication system is located on the back of the robot. It bridges the whole body joint data with the onboard computer through the Ethercat communication board and the Ethercat to Can module to establish a whole body communication network. A single Ethercat to Can module can connect up to 6 joint modules, so there are 4 such modules in total.

[0036] The motion control framework based on NMPC and HWBC proposed in this embodiment uses nonlinear model predictive control NMPC to optimize the predefined arm and leg trajectories generated in the previous text within a period of time in the future, so that the optimized trajectory can respond to the next action of the robot in advance, and use the switching system to handle the cost and constraints in the optimization problem. Secondly, the trunk posture task, swing leg and support leg tasks, and arm tasks are extracted from the optimized trajectory, and the high-frequency hybrid whole-body motion control HWBC algorithm is used to map multiple tracking tasks to the joint space according to the assigned priority weights, so as to achieve foot-arm coordination and improve system response. Finally, the joint instructions are calculated and sent to the joint module using the communication system.

[0037] Step 1: Use NMPC to optimize the arm and leg trajectories and the trunk trajectory obtained by integration in the future period, which is solved by the following switching system-based optimization problem:

[0038]

[0039]

[0040] E(x,u,t)=0,I(x,u,t)≥0 (1)

[0041] Among them, x i with u i They represent the system state and system input respectively. The system state includes the robot's posture, position, angular velocity, linear velocity and the position of all joint modules in the world coordinate system, with a total of 31 dimensions. The system input includes the plantar force on the front and back sides of each sole of the robot and the speed of all joint modules, with a total of 31 dimensions. Φ(·) and L(·) represent the terminal cost and process cost of the system, which are used to ensure the tracking accuracy of the final system state and the optimal energy within the N·dt time. D(·), E(·) and I(·) represent the system's dynamic constraints, equality constraints and inequality constraints respectively. Equality constraints include trajectory following constraints, foot end state constraints, etc. Inequality constraints include friction cone constraints and foot end distance constraints, etc. These constraints ensure that the robot meets the physical feasibility and self-safety in the real world during movement.

[0042] This embodiment solves the above nonlinear optimization problem through sequential quadratic optimization, and uses the Lagrangian condition Taylor expansion to piecewise linearize the entire problem, which is equivalent to a quadratic convex optimization subproblem in each time period. The subproblems are iterated continuously until the optimal solution is obtained.

[0043] Step 2: After obtaining the system state and system input after NMPC optimization, extract the trajectories of the arms, legs, and trunk from them and classify them into supporting leg force following task, swing leg trajectory task, trunk position task, trunk posture task, and arm trajectory task.

[0044] WBC is usually used to handle multiple tracking tasks in a coordinated manner. Common fusion methods are divided into weight-based WBC and priority-based WBC. Weighted WBC allows multiple tasks to affect the control output at the same time, and balances the task priorities by adjusting the weights. This method is very flexible and can effectively handle conflicts between tasks. However, if the weights are not set properly, it may lead to degraded or unstable control performance. In contrast, the priority-based method executes tasks in a predetermined priority order, with high-priority tasks completely dominating the control output, and low-priority tasks are only executed if they do not interfere with high-priority tasks. This method ensures accurate tracking of critical tasks and avoids conflicts between tasks, but lacks flexibility and may hinder the effective execution of low-priority tasks.

[0045] Considering the advantages and disadvantages of the weight-based method and the strict priority-based method, this embodiment proposes a HWBC control strategy that combines the advantages of both. In this strategy, critical tasks such as the swing leg trajectory task are assigned the highest priority to ensure their accurate tracking. For other tasks such as trunk posture and support leg tasks, they are combined using a weight-based method, allowing maximum utilization of control resources while maintaining the priority of important tasks.

[0046] All task types are represented using a set of equality and inequality constraints:

[0047]

[0048] in, The robot's generalized spatial acceleration The feedforward torque τ of all joint modules ff ∈R 19 Composition, a total of 44 dimensions. w With δ v represents the slack variable to be optimized for this task, j represents the task index number, A j a j 、c j C j are the coefficients of different task formulas respectively.

[0049] By taking the first-order derivative of the mapping formula between the task in the operation space and the joint space, we can get:

[0050]

[0051] in, represents the control objective in the operation space, and J represents the Jacobian matrix of the task.

[0052] Therefore, a standardized formula can be established for all tasks, and the swing leg trajectory task is set as the highest priority, which can be obtained:

[0053]

[0054] Among them, J FS represents the swing leg trajectory task Jacobian, represents the optimized swing leg trajectory acceleration, represents the tracking error between the optimized swing leg trajectory and the actual position, K P With K D They are the stiffness and damping coefficients for reducing task errors.

[0055] Combine the trunk position task, trunk posture task and support leg task into a whole and give it the second priority:

[0056]

[0057] Among them, W represents the weight coefficient of different task fusion, J P , J φ With J FC Represent the Jacobian matrices of the trunk position task, trunk posture task, and supporting leg task respectively. represents the optimized torso position acceleration, represents the tracking error between the optimized torso trajectory and the actual position, and Represents the tracking error between the optimized angular velocity and attitude and the actual value.

[0058] The arm trajectory task is at the last priority:

[0059]

[0060] Among them, J A represents the Jacobian matrix of the arm trajectory task, represents the optimized arm trajectory acceleration, Represents the tracking of the optimized arm trajectory and the actual position.

[0061] Tasks of different priorities are integrated through the following quadratic programming problem:

[0062]

[0063] Among them, N represents the high-priority null space matrix, and n represents the solution space to be optimized.

[0064] The feedforward torque τ in the joint space is obtained by solving the HWBC ff After that, it can be transmitted to the joint module to allow the joint servo to operate at the specified torque.

[0065] The relationship between arm planning, foot gait and overall movement is Figure 4 It is shown in the figure that the state variables after arm planning and foot gait will be filled into the optimization variables of the overall motion.

[0066] The optimal path arm trajectory planning and foot end gait method proposed in this embodiment are specifically as follows:

[0067] Step 1: This embodiment designs three arm movements, namely bowing, waving and swinging hands. Bowing and waving hands can be performed in any gait, and swinging hands is designed for leg movements, and cooperates with the legs to correct the inertial deviation of the whole body.

[0068] Each action is functionally divided into two stages: the transition stage and the movement stage. In the transition stage, the motion trajectory is generated by the designed path energy optimization method. Secondly, in the movement stage, regular movements are performed according to the set cycle.

[0069] Define the starting and ending positions of the arm end points in the transition phase as follows: and The starting speed and the ending speed are and The following indicator function is established to optimize the path energy:

[0070]

[0071] Among them, t i With t f Represent the start time and end time of the trajectory, and Pa represents the position of the end point of the arm. It can be seen that the alternative objective function that minimizes the above cost is Applying the generalized Lagrangian function to solve the optimization problem and substituting the boundary adjustment, the analytical form of the trajectory can be obtained as:

[0072]

[0073] The trajectory formula (9) can ensure that the end point of the arm transitions in the Cartesian coordinate system in an energy-optimal form, and then the inverse kinematics formula is used to transform the trajectory to the joint space so that the joint servo is in the desired position.

[0074] During the motion phase, a specific joint is planned to generate a sinusoidal trajectory to simulate Figure 3 The green arrows show the periodic motion in the figure. The motion trajectory of the legs is based on the “three-stage” foot point selection and Bezier curve generation in the quadruped robot.

[0075] Step 2: For leg movements, three gaits are designed, namely standing, diagonal walking and galloping. The robot can perform tasks in the standing stage and can perform most of the movements in life through diagonal walking and galloping.

[0076] Since the switching system is needed to handle constraints and costs when optimizing the NMPC problem, the gait is defined directly in the switching system, which is composed of a set of switching times {t j} and switch mode {m j}, where t j Controls the switching mode m j-1 To m j transition.

[0077] The switching mode is composed of the support state of the robot's legs. The state of the robot's legs is defined as 1 when they are in support and 0 when they are swinging. The states of the two legs are filled into binary bits (the left leg is in high position and the right leg is in low position), and then the binary is converted to decimal to get the current mode. In this way, three gaits can be defined as standing {0.0, 0.3} {3}, diagonal walking {0.0, 0.3, 0.6} {2, 1} and galloping {0.0, 0.3, 0.4, 0.7, 0.8} {2, 0, 1, 0}. The first curly bracket represents the switching time in seconds, and the second curly bracket represents the switching mode. 3 means that both legs are in the support phase, then the corresponding binary bit is 11, and the value converted to decimal is 1*2+1=3. The calculation method of the remaining modes is the same. The planned gait pattern will be used for the state constraint in formula 1.

[0078] Embodiment 2

[0079] The purpose of this embodiment is to provide a full-scale humanoid robot coordinated control system, including:

[0080] An optimization module is configured to: construct a nonlinear model predictive controller according to the system state and system input of the full-scale humanoid robot, and perform optimization and solution using sequential quadratic optimization to obtain an optimized system state and system input;

[0081] A classification module, which is configured to: extract arm trajectories, leg trajectories and trunk trajectories according to the optimized system state and system input, and classify them into multiple tracking tasks; wherein the multiple tracking tasks include: a supporting leg force following task, a swing leg trajectory task, a trunk position task, a trunk posture task and an arm trajectory task;

[0082] A control module is configured to: map multiple tracking tasks to corresponding joint modules according to the assigned priority weights using a high-frequency hybrid whole-body motion control algorithm for a full-scale humanoid robot. In further embodiments, it is also provided:

[0083] An electronic device includes a memory and a processor, and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method described in Embodiment 1 is performed. For the sake of brevity, no further description is given here.

[0084] It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0085] The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0086] A computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the method described in embodiment 1 is completed.

[0087] The method in the first embodiment can be directly embodied as a hardware processor, or a combination of hardware and software modules in the processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0088] A computer program product includes a computer program, and when the computer program is executed by a processor, the method described in the first embodiment is implemented.

[0089] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer executable instructions, such as instructions included in a program module, which are executed in a device on a real or virtual processor of the target to perform the process / method as described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can be combined or divided between program modules as needed. Machine executable instructions for program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0090] The computer program code for implementing the method of the present invention can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the computer or other programmable data processing device, causes the function / operation specified in the flow chart and / or block diagram to be implemented. The program code can be executed completely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or completely on a remote computer or server.

[0091] In the context of the present invention, computer program codes or related data may be carried by any appropriate carrier to enable a device, apparatus or processor to perform the various processes and operations described above. Examples of carriers include signals, computer readable media, etc. Examples of signals may include electrical, optical, radio, acoustic or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0092] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0093] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A coordinated control method for a full-scale humanoid robot, characterized in that: include: According to the system state and system input of the full-scale humanoid robot, a nonlinear model predictive controller is constructed, and sequential quadratic optimization is used to optimize and solve the system state and system input after optimization. According to the optimized system state and system input, the arm trajectory, leg trajectory and trunk trajectory are extracted and classified into multiple tracking tasks; wherein the multiple tracking tasks include: supporting leg force following task, swing leg trajectory task, trunk position task, trunk posture task and arm trajectory task; A high-frequency hybrid whole-body motion control algorithm is used to map multiple tracking tasks to the corresponding joint modules according to the assigned priority weights for a full-scale humanoid robot.

2. A full-scale humanoid robot coordinated control method as claimed in claim 1, characterized in that: The system state includes the posture, position, angular velocity, linear velocity and the position of all joint modules of the full-scale humanoid robot in the world coordinate system; the system input includes the plantar force of the front and back sides of each sole of the full-scale humanoid robot and the speed of all joint modules.

3. A coordinated control method for a full-scale humanoid robot as claimed in claim 1, characterized in that: The arm trajectory task is divided into a transition stage and a motion stage. In the transition stage, the motion trajectory is generated by a designed path energy optimization method, and in the motion stage, regular motion is performed according to a set period. Among them, an index function is established to optimize the path energy through the starting position and the end position of the arm end point in the transition stage, as well as the starting speed and the end speed. The constructed index function is optimized and solved using the generalized Lagrangian function to obtain an analytical trajectory. According to the obtained analytical trajectory, the trajectory is transformed to the joint space through the inverse kinematics formula so that the joint servo is in the desired position.

4. A coordinated control method for a full-scale humanoid robot as claimed in claim 1, characterized in that: A high-frequency hybrid whole-body motion control algorithm is used to map multiple tracking tasks to the corresponding joint modules according to the assigned priority weights. For the full-scale humanoid robot, specifically: the swing leg trajectory task is assigned the highest priority; the torso position task, torso posture task and supporting leg task are combined in a weighted manner and assigned the second priority; the arm trajectory task is assigned the third priority.

5. A coordinated control method for a full-scale humanoid robot as claimed in claim 1, characterized in that: The leg movements are divided into standing, diagonal walking and galloping. The robot's legs are defined as being in a state of 1 when in support and 0 when in swing. The states of the two legs are filled into binary bits, and the binary bits are converted to decimal to obtain the current switching mode.

6. A coordinated control method for a full-scale humanoid robot as claimed in claim 1, characterized in that: The arm movements are divided into bowing, waving and swinging hands; each movement is divided into two stages: transition stage and movement stage. In the transition stage, the motion trajectory is generated by the path energy optimization method, and in the movement stage, regular movements are performed according to the set cycle.

7. A full-scale humanoid robot coordination control system, characterized in that: include: An optimization module is configured to: construct a nonlinear model predictive controller according to the system state and system input of the full-scale humanoid robot, and perform optimization and solution using sequential quadratic optimization to obtain an optimized system state and system input; A classification module, which is configured to: extract arm trajectories, leg trajectories and trunk trajectories according to the optimized system state and system input, and classify them into multiple tracking tasks; wherein the multiple tracking tasks include: a supporting leg force following task, a swing leg trajectory task, a trunk position task, a trunk posture task and an arm trajectory task; A control module is configured to map multiple tracking tasks to corresponding joint modules according to assigned priority weights using a high-frequency hybrid whole-body motion control algorithm for a full-scale humanoid robot.

8. An electronic device, characterized in that: The method comprises a memory and a processor and computer instructions stored in the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method according to any one of claims 1 to 6 is completed.

9. A computer-readable storage medium, characterized in that: Used to store computer instructions, which, when executed by a processor, complete the method described in any one of claims 1 to 6.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

Images