Control method for landing buffer of multi-legged robot and stress testing system during landing

Abstract

The application provides a control method for landing buffer of a multi-legged robot and a stress testing system during landing, and relates to the technical field of robot control. The control method for landing buffer of the multi-legged robot comprises the following steps: a leg folding trajectory model is constructed, and a corresponding leg folding trajectory library of the robot is obtained through the leg folding trajectory model; motion state data and sensor measurement data of the robot are obtained to determine a corresponding leg folding reference trajectory of the robot in the leg folding trajectory library; joint angular acceleration of the robot is calculated based on the leg folding reference trajectory; and joint control torque of the robot is calculated according to a preset robot dynamics equation and the joint angular acceleration, so as to perform feedback adjustment on the robot. According to the embodiment of the application, impact force buffer of the robot during landing can be realized, and the buffer capacity of the robot can be evaluated.

Description

Technical Field

[0001] This application relates to the field of robot control technology, and more specifically, to a control method for landing buffering of a multi-legged robot and a force testing system during landing. Background Technology

[0002] In recent years, with the continuous development of robotics technology, people have placed increasingly higher demands on the motion performance of robots. With continuous breakthroughs in software and hardware technology, the motion capabilities of robots are constantly improving, and the frequency of movement is increasing, greatly enhancing their mobility. However, this improvement in motion performance also brings high-frequency and enormous impact forces, which may further damage the robot.

[0003] Currently, some motion control algorithms have been developed to address the motion characteristics of robots and unlock their potential. However, research on landing cushioning control algorithms for robots (especially bipedal robots) remains limited. Furthermore, most bipedal robot landing cushioning control algorithms are designed for robots with low take-off heights, where impact forces have minimal impact on the robot's state; or where the robot's body is fixed to a vertical rail, eliminating the need to consider landing stability control.

[0004] In addition, for measuring the impact force on the foot, the most commonly used methods include robot-embedded sensors and force plates. Robot-embedded sensors mostly obtain the ground reaction force during robot movement by installing six-dimensional force sensors at the robot's ankle and attaching strain gauges to the sole. The structure is generally simple and highly integrated. However, this method relies on the robot's own sensor characteristics and is difficult to operate as a standalone testing system. Force plates are mostly used for analyzing the force state of the foot in humans or other animals, offering advantages such as high Z-axis accuracy and accurate force distribution. However, for robots, it is difficult to measure the horizontal foot force and torque state, and force plates are generally expensive and difficult to maintain. Summary of the Invention

[0005] According to one aspect of this application, a control method for landing buffering of a multi-legged robot is provided, comprising: constructing a leg retraction trajectory model and obtaining a corresponding leg retraction trajectory library for the robot through the leg retraction trajectory model; obtaining motion state data and sensor measurement data of the robot to determine a corresponding leg retraction reference trajectory for the robot in the leg retraction trajectory library; calculating the joint angular acceleration of the robot based on the leg retraction reference trajectory; and calculating the joint control torque of the robot according to a preset robot dynamics equation and joint angular acceleration to provide feedback adjustment for the robot.

[0006] According to some embodiments, a leg retraction trajectory model is constructed, and a corresponding leg retraction trajectory library for the robot is obtained through the leg retraction trajectory model. This includes: obtaining the parameters of the leg retraction trajectory model, which include the robot's center of mass, the stiffness and damping of the leg active compliance module, the robot's foot mass, the stiffness and damping of the foot cushioning module, the foot-to-ground contact surface mass, and the stiffness and damping of the foot-to-ground contact surface cushioning module; determining the dynamic equation and optimization objective of the leg retraction trajectory model based on the parameters of the leg retraction trajectory model; obtaining the robot's velocity at the moment of landing; and determining the amount of leg retraction at the moment of landing based on the dynamic equation, optimization objective, and velocity of the robot, in order to obtain the leg retraction trajectory library.

[0007] According to some embodiments, acquiring motion state data and sensor measurement data of a robot to determine the corresponding leg-retraction reference trajectory of the robot in a leg-retraction trajectory library includes: acquiring a prior state estimate of the robot based on the motion state data; acquiring a measurement state estimate of the robot based on the sensor measurement data; calculating a posterior state estimate of the robot based on the prior state estimate and the measurement state estimate; and determining the leg-retraction reference trajectory in the leg-retraction trajectory library based on the posterior state estimate.

[0008] According to some embodiments, the joint angular acceleration of the robot is calculated based on the leg retraction reference trajectory, including: determining the optimization target of the joint angular acceleration; obtaining the mapping relationship between the leg retraction reference trajectory and the joint angle of the robot to determine the constraints of the joint angle; determining the constraints of the joint angular acceleration; and calculating the joint angular acceleration based on the optimization target of the joint angular acceleration, the constraints of the joint angle, and the constraints of the joint angular acceleration.

[0009] According to some embodiments, a reference value for the joint angle is obtained; the actual value of the joint angle is obtained based on sensor measurement data; and the joint angle acceleration correction of the robot is calculated based on the reference value and the actual value of the joint angle.

[0010] According to some embodiments, within a preset collision time window, the landing impact force of the robot is inertially adjusted based on the parameters of the leg retraction trajectory model.

[0011] According to some embodiments, the reference position of the robot's center of mass is determined based on the parameters of the leg retraction trajectory model; the actual position of the robot's center of mass is determined based on sensor measurement data; and the virtual control force of the robot is calculated based on the reference position and the actual position of the robot's center of mass.

[0012] According to some embodiments, the joint control torque of the robot is calculated based on the preset robot dynamics equations and joint angular accelerations to perform feedback control on the robot, including: obtaining the robot's joint angular acceleration correction, the robot's landing impact force after inertial adjustment, and the robot's virtual control force; and calculating the joint control torque through the robot dynamics equations based on the joint angular acceleration, the joint angular acceleration correction, the landing impact force after inertial adjustment, and the virtual control force.

[0013] According to one aspect of this application, a force testing system for a multi-legged robot landing is provided, comprising: a bottom support frame, the lower end of which is fixed to the ground; a six-dimensional force sensor, the lower end of which is connected to the upper end of the bottom support frame; a middle support frame, the lower end of which is connected to the upper end of the six-dimensional force sensor; a pressure sensing module matrix, the bottom of which is connected to the upper end of the middle support frame, serving as a test platform for robot landing, including multiple strain gauges; a metal coating covering the upper surface of the multiple strain gauges; a data transmission line electrically connected to the six-dimensional force sensor and the pressure sensing module matrix respectively; and a data processing device electrically connected to the data transmission line to acquire data collected by the six-dimensional force sensor and the pressure sensing module matrix through the data transmission line; wherein, the six-dimensional force sensor is disposed at a corner of the test platform to collect the force and torque generated after the robot lands on the test platform; the multiple strain gauges in the pressure sensing module matrix are uniformly distributed in a polygonal pattern to collect the landing point position of the robot on the test platform.

[0014] According to some embodiments, the data processing device is used to: calculate a first impact torque on the robot's feet based on the force and torque generated when the robot, which has not been adjusted according to the above method, lands on the test platform, and the landing point of the robot on the test platform; calculate a second impact torque on the robot's feet based on the force and torque generated when the robot, which has been adjusted according to the above method, lands on the test platform, and the landing point of the robot on the test platform; and determine whether the robot's adjustment is effective based on the first impact torque and the second impact torque.

[0015] According to embodiments of this application, an optimized control algorithm is used to buffer the impact force of the robot upon landing, thereby ensuring the stability and safety of the robot and simultaneously evaluating the robot's buffering capacity.

[0016] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application.

[0018] Figure 1 A flowchart illustrating a control method for landing buffering of a multi-legged robot according to an example embodiment of this application is shown.

[0019] Figure 2 A schematic diagram showing a leg retraction trajectory model of a robot according to an example embodiment of this application is provided.

[0020] Figure 3 A graph showing the change in the leg retraction trajectory during the landing process of a robot according to an example embodiment of this application is displayed.

[0021] Figure 4 This diagram illustrates the variation of the ground impact force experienced by the robot during landing, according to an example embodiment of this application.

[0022] Figure 5 This diagram illustrates a force testing system for a multi-legged robot landing according to an example embodiment of this application.

[0023] Figure 6 A schematic diagram of an electronic device according to an example embodiment of this application is shown. Detailed Implementation

[0024] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0025] The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a full understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of these specific details, or other methods, components, materials, apparatus, or operations may be employed. In these cases, well-known structures, methods, apparatuses, implementations, materials, or operations will not be shown or described in detail.

[0026] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0027] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0028] This application provides a control method for landing cushioning of a multi-legged robot and a force testing system for landing. By considering the changes in the robot's center of mass, feet, and foot-ground contact surface, the method obtains the leg-retraction trajectory with the minimum impact force based on the changes in ground impact force, ensuring the effectiveness of the cushioning performance. The obtained leg-retraction trajectory is encapsulated into a leg-retraction trajectory library, and the closest reference trajectory is selected based on the robot's landing state. This allows the robot to cope with various cushioning needs without readjusting control parameters, and eliminates the need for real-time planning at landing, reducing the computational burden on the controller and improving control efficiency. Furthermore, by modulating the impact force of the robot's feet using inertial links, the disturbance of the control signal by strong impacts is reduced. Simultaneously, the introduction of virtual force and joint feedback control ensures the stability of the robot's landing cushioning.

[0029] The following will describe in detail, with reference to the accompanying drawings, a control method for landing buffering of a multi-legged robot and a force testing system during landing, according to embodiments of this application.

[0030] Figure 1 A flowchart illustrating a control method for landing buffering of a multi-legged robot according to an example embodiment of this application is shown.

[0031] like Figure 1 As shown, in step S100, a leg retraction trajectory model is constructed, and the corresponding leg retraction trajectory library of the robot is obtained through the leg retraction trajectory model.

[0032] The control device constructs a leg retraction trajectory model and obtains the parameters of the leg retraction trajectory model.

[0033] According to some embodiments, the leg retraction trajectory model can employ a multi-order spring-damped model. For example... Figure 2 As shown, the leg retraction trajectory model includes the robot's center of mass 1, the leg active compliance module 2, the robot's foot 3, the foot cushioning module 4, the foot-ground contact surface 5, the foot-ground contact surface cushioning module 6, and the ground 7.

[0034] According to some embodiments, the parameters of the leg retraction trajectory model include the robot's center of mass. m b Stiffness of the active compliance module for legsk 2 and damping c 2. Robot foot mass m f Stiffness of the foot cushioning module k 1 and damping c 1. Foot-ground contact surface quality m s and the stiffness of the foot-ground contact surface buffer module k 0 and damping c 0.

[0035] Based on the parameters of the leg retraction trajectory model, the control device determines the dynamic equation of the leg retraction trajectory model and the optimization objective of the leg retraction trajectory model.

[0036] According to some embodiments, the position of the robot's center of mass 1 is denoted as x b The position of the robot's foot 3 is denoted as x f The position of the foot-ground contact surface 5 is denoted as x s The dynamic equation of the leg retraction trajectory model can be expressed by the following formula.

[0037]

[0038]

[0039]

[0040] in, g It is the gravitational acceleration constant. F g It is the reaction force exerted by the ground on the feet, which can be calculated based on the nonlinear viscoelastic force function. F It is control.

[0041] F g The calculation formula is as follows, where... A c b, d, e These are contact parameters.

[0042]

[0043] F The calculation formula is as follows, where... u The acceleration for the robot to retract its legs.

[0044]

[0045] According to some embodiments, in order to minimize the impact force on the robot's feet, the reaction force exerted on the feet by the ground is considered.F g The calculation formula, when the contact parameters are constant, is the reaction force exerted by the ground on the foot. F g Depending on the position of the foot-ground contact surface 5 x s The velocity of the foot-ground contact surface 5 Therefore, in order to minimize the impact force on the robot during landing, the control device determines the optimization objective of the leg retraction trajectory model. It can be expressed by the following formula.

[0046]

[0047] The sign change needs to be handled by squaring the data to account for the vibrations caused by the robot's feet when it lands.

[0048] Furthermore, the control device obtains the robot's speed at the moment of landing.

[0049] According to some embodiments, the control device can obtain the robot's velocity at the moment of landing based on the robot's motion state. This refers to the initial conditions for optimizing the leg retraction trajectory model. Among them, h It is the highest height that the robot has reached in motion.

[0050] Based on the dynamic equations of the leg retraction trajectory model, the optimization objective of the leg retraction trajectory model, and the robot's velocity at the moment of landing, the control device determines the amount of leg retraction at the moment of landing to obtain the leg retraction trajectory library.

[0051] According to some embodiments, the position of the robot's centroid 1 within a certain period after landing can be obtained through nonlinear optimization using the Gaussian pseudospectral method. x b The position of the robot's feet 3 x f The position of foot-ground contact surface 5 x s And further obtain the amount of leg retraction at the moment the robot lands. .

[0052] According to some embodiments, based on different initial states of the robot, the control device generates different leg retraction trajectories using a formula for calculating the amount of leg retraction at the moment the robot lands, such as... Figure 3 As shown, the control device saves the data using the initial state as an identifier, thus obtaining a library of robot landing leg-retraction trajectories applicable to different initial states.

[0053] Furthermore, the control device will position the foot-to-ground contact surface 5. x s and speed Substitute the reaction force of the ground onto the feet F g The calculation formula can be used to obtain the ground impact force experienced by the robot upon landing, such as... Figure 4 As shown.

[0054] In step S200, the robot's motion state data and sensor measurement data are acquired to determine the corresponding leg retraction reference trajectory of the robot in the leg retraction trajectory library.

[0055] The control device acquires the robot's motion state data and obtains the robot's prior state estimate based on the motion state data.

[0056] According to some embodiments, given the robot's dynamic parameters and initial motion state, the control device can calculate the robot's theoretical motion state based on the dynamic equations. However, during the control process, there will be unmeasurable errors between the robot's theoretical motion state and its actual state, making it impossible to accurately obtain the robot's true state. Therefore, only the theoretical motion state (i.e., the prior state) can be used for calculation to obtain an estimate of the prior state. Prior state estimate Compared to the actual motion state of the robot, there is less process noise.

[0057] The control device acquires sensor measurement data from the robot and obtains an estimated value of the robot's measurement state based on the sensor measurement data.

[0058] According to some embodiments, the robot's measurement state can be obtained from sensor measurement data and robot parameters. For example, joint angles are obtained via encoders, and the height of the robot's center of mass in its current posture can be calculated based on the leg lengths. As another example, integrating the acceleration measured by the IMU yields the robot's velocity, and integrating the velocity yields the displacement. Let the sensor measurement data be... The robot's measured state estimate is ,definition .in H This is a pre-defined observation matrix used to map the relationship between the sensor and robot states. The measured state estimate is... Compared to the robot's actual motion state, there are additional sensor measurement errors.

[0059] Based on the prior state estimate and the measured state estimate, the control device calculates the posterior state estimate of the robot.

[0060] According to some embodiments, the prior state estimate is... and the estimated value of the measured state Through the preset fusion matrix G The posterior state estimate can be obtained using the following formula. .

[0061]

[0062] Furthermore, let the Kalman gain be... The control device can obtain the robot's posterior state estimate using the Kalman filtering method. Its formula is expressed as follows.

[0063]

[0064] in, It is the robot's sensor measurement data. It is an observation matrix calculated based on sensor measurement data and robot status.

[0065] Based on the posterior state estimate, the control device determines the leg retraction reference trajectory from the leg retraction trajectory library.

[0066] According to some embodiments, based on the posterior state estimate, the control device can obtain the robot's motion state upon landing during a high-burst motion using Kalman filtering. Using the robot's landing motion state as an initial state index, the control device can determine a suitable leg-retraction reference trajectory from a library of robot landing leg-retraction trajectories to achieve buffered control of the robot's landing.

[0067] In step S300, the joint angular acceleration of the robot is calculated based on the leg retraction reference trajectory.

[0068] The control device determines the optimization target for joint angular acceleration.

[0069] According to some embodiments, after determining the robot's leg retraction reference trajectory, it needs to be mapped onto the robot's individual joints. q This is used to obtain the joint motion trajectory. Based on the rigid body rotation formula... When the moment of inertia of the connecting rod At a certain time, the driving force required by the joint and joint angular acceleration There is a positive correlation. To reduce the driving force on robot joints... Select the robot's joint acceleration As optimization target It can be expressed by the following formula.

[0070]

[0071] in, It is the weight matrix corresponding to the preset correlation coefficient.

[0072] The control device acquires the mapping relationship between the leg retraction reference trajectory and the robot's joint angles to determine the constraints of the joint angles.

[0073] According to some embodiments, the mapping relationship between the robot's leg retraction reference trajectory and the robot's joint angles is as follows.

[0074]

[0075] in, It is the speed of the robot's feet. This is the Jacobian matrix for the robot's feet, which can be calculated based on the robot's dimensional parameters. By differentiating and rearranging this formula, the control device can then obtain the constraints for the joint angles.

[0076]

[0077] Furthermore, the constraints for controlling and determining the joint angular acceleration are expressed by the following formula.

[0078]

[0079] in, It is the identity matrix. and These are the upper and lower bounds of the robot's joint acceleration, respectively.

[0080] Based on the optimization objective of joint angular acceleration, the constraints of joint angle, and the constraints of joint angular acceleration, the control device calculates the joint angular acceleration to achieve tracking of the leg retraction reference trajectory.

[0081] According to some embodiments, due to discrepancies between the actual and planned movements during robot motion, the control device also needs to introduce feedback adjustments for each joint. This is achieved by introducing corrections to the joint angular accelerations. This allows for precise control of joint angles.

[0082] The control device obtains reference values ​​for the robot's joint angles. And obtain the actual value of the joint angle based on the sensor measurement data. .

[0083] Furthermore, the control device uses reference values ​​for the joint angles. and actual value The joint angular acceleration correction of the robot is calculated using the following formula. .

[0084]

[0085] in, , These are control parameters.

[0086] In some implementations, the robot experiences a significant impact force upon landing, which then decreases to a lower level over a period of time afterward. For floating base systems, this instantaneous change in control torque can easily cause the robot system to lose control. To mitigate the impact of this sudden change in impact force, inertial adjustment is required to regulate the ground impact force experienced by the robot.

[0087] Within a preset collision time window, the control device adjusts the robot's landing impact force based on the parameters of the leg retraction trajectory model.

[0088] According to some embodiments, an inertial element is introduced. It can be considered as a regulator, which adjusts the input force and outputs it, and its formula is as follows.

[0089]

[0090] in, It is a proportionality coefficient. It is the time constant. (Yes / No) The response of the inertial element can be obtained by performing the inverse Laplace transform and solving the differential equation.

[0091] According to some embodiments, in controlling the landing impact force of a robot, the collision time window is defined as... ,but The impact force upon landing is constantly adjusted by the inertial process. It can be expressed by the following formula.

[0092]

[0093]

[0094] in, The external forces acting on the robot serve as input to the inertial system. For the output of the inertial element, This is a proportional coefficient used to ensure that the impact force signal does not abruptly change when it is connected to the original signal after inertial adjustment within the collision time window.

[0095] According to some embodiments, during the actual movement of the robot, signal modulation of external forces can cause a deviation between the actual controller and the robot system's state. Therefore, it is necessary to introduce virtual forces into the robot to adjust its landing posture and correct movement deviations.

[0096] The control device determines the reference position of the robot's center of mass based on the parameters of the leg retraction trajectory model, and determines the actual position of the robot's center of mass based on the sensor measurement data.

[0097] According to some embodiments, the reference position of the robot's center of mass is... Figure 1 The position of the robot's center of mass 1. The actual position of the robot's center of mass can be calculated based on the current joint angles and leg lengths from the sensor measurement data.

[0098] Based on the reference position and actual position of the robot's center of mass, the control device calculates the robot's virtual control force.

[0099] According to some embodiments, virtual control force The calculation formula is as follows.

[0100]

[0101] in, It is the reference position of the robot's center of mass. It is the actual location of the robot's center of mass. and These are control parameters.

[0102] In step S400, the joint control torque of the robot is calculated based on the preset robot dynamics equations and joint angular accelerations, so as to make feedback adjustments to the robot.

[0103] The control device acquires the robot's joint angular acceleration correction, the robot's landing impact force after inertial adjustment, and the robot's virtual control force. Combined with the joint angular acceleration, it calculates the robot's joint control torque through preset robot dynamics equations.

[0104] According to some embodiments, the general form of the robot dynamics equations is shown below.

[0105]

[0106] in, For the robot's inertia matrix, Here is the coupling matrix of the Coriolis force and centripetal force of the robot. For the robot's gravity matrix, Since the introduction of a floating basis requires the selection matrix of the driving degrees of freedom, This is the transpose of the Jacobian matrix at the robot's contact points with the environment. The external forces acting on the robot.

[0107] The impact force of the robot landing after inertial adjustment Virtual control of robots And the joint angular acceleration correction of the robot Substituting these values ​​into the robot's dynamics equations, we can obtain the robot's joint control torques. , which is used as the control variable for robot motion, is expressed by the following formula.

[0108]

[0109] in, It is the transpose of the Jacobian matrix of the robot's center of mass.

[0110] Figure 5 This diagram illustrates a force testing system for a multi-legged robot landing according to an example embodiment of this application.

[0111] like Figure 5 As shown, the force testing system includes a bottom support frame 11, a six-dimensional force sensor 12, a middle support frame 13, a pressure sensing module matrix 14, a metal coating 15, a data transmission line 16, and a data processing device 17.

[0112] The bottom support frame 11 is fixed to the ground at its lower end.

[0113] The lower end of the six-dimensional force sensor 12 is connected to the upper end of the bottom support frame 11.

[0114] The lower end of the central support frame 13 is connected to the upper end of the six-dimensional force sensor 12.

[0115] The bottom of the pressure sensing module matrix 14 is connected to the upper end of the central support frame 13 to serve as a test platform for the robot to land.

[0116] According to some embodiments, such as Figure 5 As shown, the pressure sensing module matrix 14 (i.e., the test platform) is rectangular. There are four six-dimensional force sensors 12, which are respectively set at the four corners of the test platform to collect the force and torque generated after the robot lands on the test platform.

[0117] According to some embodiments, the pressure sensing module matrix 14 includes multiple strain gauges, which are uniformly distributed in a polygonal pattern to collect the landing point position of the robot on the test platform. The number of strain gauges, their distribution, and the size of each strain gauge can be adjusted according to the actual application scenario.

[0118] A metal coating 15 covers the upper surface of each strain gauge in the pressure sensing module matrix 14 to protect the pressure sensing module matrix 14 from damage when subjected to impact force and to ensure that each strain gauge is subjected to a uniform impact force.

[0119] The data processing device 17 is electrically connected to the six-dimensional force sensor 12 and the pressure sensing module matrix 14 via the data transmission line 16 to acquire the data collected by the six-dimensional force sensor 12 and the pressure sensing module matrix 14.

[0120] According to some embodiments, without pressing the robot... Figure 1 With adjustments made to each step, after the robot lands on the test platform, the data processing device 17 acquires the force collected by the six-dimensional force sensor 12. and torque The position vector of the robot's landing point is acquired by the pressure sensing module matrix 14. And calculate the torque of the first impact force on the robot's feet. First impact torque The calculation formula is as follows.

[0121]

[0122] Similarly, after the robot has been programmed... Figure 1 With adjustments made to each step, after the robot lands on the test platform, the data processing device 17 acquires the data collected by the six-dimensional force sensor 12 and the pressure sensing module matrix 14, and calculates the second impact torque on the robot's feet. .

[0123] Based on the first impact torque and the second impact torque, the control device can determine the action. Figure 1 The effectiveness of each step in the process of adjusting the robot is assessed to provide data feedback for the robot's landing cushioning algorithm, guiding the design of the robot's landing cushioning algorithm and improving the robot's mobility.

[0124] Figure 6 A schematic diagram of an electronic device according to an example embodiment of this application is shown.

[0125] like Figure 6 As shown, the electronic device 600 is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0126] like Figure 6 As shown, the electronic device 600 is manifested in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including the storage unit 620 and the processing unit 610), a display unit 640, etc. The storage unit stores program code, which can be executed by the processing unit 610, causing the processing unit 610 to perform the methods described in this specification according to the various exemplary embodiments of this application. For example, the processing unit 610 can perform, for example... Figure 1 The method shown.

[0127] Storage unit 620 may include readable media in the form of volatile storage units, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include read-only memory (ROM) 6203.

[0128] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0129] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0130] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0131] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. The technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this application.

[0132] Software products may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0133] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0134] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0135] The aforementioned computer-readable medium carries one or more programs, which, when executed by a device, cause the computer-readable medium to perform the aforementioned functions.

[0136] Those skilled in the art will understand that the above modules can be distributed in the device as described in the embodiments, or they can be modified accordingly and placed in one or more devices that are unique to this embodiment. The modules in the above embodiments can be combined into one module, or they can be further divided into multiple sub-modules.

[0137] The embodiments of this application have been described in detail above. These descriptions are solely for the purpose of helping to understand the method and core ideas of this application. Furthermore, any changes or modifications made by those skilled in the art based on the ideas of this application, its specific implementation methods, and its application scope, are all within the scope of protection of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A control method for landing buffering of a multi-legged robot, characterized in that, include: Construct a leg retraction trajectory model, and obtain the corresponding leg retraction trajectory library for the robot through the leg retraction trajectory model; The motion state data and sensor measurement data of the robot are acquired to determine the corresponding leg retraction reference trajectory of the robot in the leg retraction trajectory library; Based on the leg retraction reference trajectory, the joint angular acceleration of the robot is calculated; Based on the preset robot dynamics equations and the joint angular acceleration, the joint control torque of the robot is calculated to provide feedback adjustment for the robot.

2. The method according to claim 1, characterized in that, Constructing a leg-retraction trajectory model and obtaining a corresponding leg-retraction trajectory library for the robot through the leg-retraction trajectory model includes: Obtain the parameters of the leg retraction trajectory model, which include the robot's center of mass mass, the stiffness and damping of the leg active compliance module, the robot's foot mass, the stiffness and damping of the foot cushioning module, the foot-ground contact surface mass, and the stiffness and damping of the foot-ground contact surface cushioning module. Based on the parameters of the leg retraction trajectory model, the dynamic equation of the leg retraction trajectory model and the optimization objective of the leg retraction trajectory model are determined; Obtain the speed of the robot at the moment of landing; Based on the dynamic equation of the leg retraction trajectory model, the optimization objective of the leg retraction trajectory model, and the robot's velocity at the moment of landing, the amount of leg retraction at the moment of landing is determined to obtain the leg retraction trajectory library.

3. The method according to claim 2, characterized in that, Acquiring the robot's motion state data and sensor measurement data to determine the robot's corresponding leg retraction reference trajectory in the leg retraction trajectory library includes: Based on the motion state data, obtain the prior state estimate of the robot; Based on the sensor measurement data, obtain the estimated measurement state of the robot; Based on the prior state estimate and the measured state estimate, the posterior state estimate of the robot is calculated; Based on the posterior state estimate, the leg retraction reference trajectory is determined in the leg retraction trajectory library.

4. The method according to claim 3, characterized in that, Based on the leg retraction reference trajectory, the joint angular acceleration of the robot is calculated, including: Determine the optimization objective for the joint angular acceleration; Obtain the mapping relationship between the leg retraction reference trajectory and the joint angles of the robot to determine the constraint conditions of the joint angles; Determine the constraints for the joint angular acceleration; The joint angular acceleration is calculated based on the optimization objective of the joint angular acceleration, the constraints of the joint angle, and the constraints of the joint angular acceleration.

5. The method according to claim 4, characterized in that, The method further includes: Obtain the reference value of the joint angle; Based on the sensor measurement data, the actual value of the joint angle is obtained; Based on the reference and actual values ​​of the joint angles, the joint angle acceleration correction of the robot is calculated.

6. The method according to claim 2, characterized in that, The method further includes: Within a preset collision time window, the landing impact force of the robot is adjusted inertially based on the parameters of the leg retraction trajectory model.

7. The method according to claim 3, characterized in that, The method further includes: Based on the parameters of the leg retraction trajectory model, determine the reference position of the robot's center of mass; Based on the sensor measurement data, the actual position of the robot's center of mass is determined; The virtual control force of the robot is calculated based on the reference position and actual position of the robot's center of mass.

8. The method according to any one of claims 5 to 7, characterized in that, Based on the preset robot dynamics equations and the joint angular accelerations, the joint control torques of the robot are calculated to provide feedback control for the robot, including: The robot's joint angular acceleration correction, the robot's landing impact force after inertial adjustment, and the robot's virtual control force are obtained. The joint control torque is calculated using the robot dynamics equations based on the joint angular acceleration, the joint angular acceleration correction, the inertial-adjusted landing impact force, and the virtual control force.

9. A force testing system for a multi-legged robot upon landing, characterized in that, include: The bottom support frame is fixed to the ground at its lower end. The lower end of the six-dimensional force sensor is connected to the upper end of the bottom support frame; The lower end of the central support frame is connected to the upper end of the six-dimensional force sensor; The pressure sensing module matrix, with its bottom connected to the upper end of the central support frame, serves as a test platform for the robot's landing and includes multiple strain gauges. A metal coating is applied to the upper surface of the plurality of strain gauges; The data transmission line is electrically connected to the six-dimensional force sensor and the pressure sensing module matrix, respectively. A data processing device is electrically connected to the data transmission line to acquire data collected by the six-dimensional force sensor and the pressure sensing module matrix through the data transmission line. The six-dimensional force sensor is located in the corner of the test platform and is used to collect the force and torque generated after the robot lands on the test platform. The multiple strain gauges in the pressure sensing module matrix are evenly distributed in a polygonal pattern and are used to collect the landing point position of the robot when it lands on the test platform.

10. The force testing system according to claim 9, characterized in that, The data processing device is used for: Based on the force and torque generated after the robot, which has not been adjusted according to any one of claims 1 to 8, lands on the test platform, and the landing point of the robot on the test platform, calculate the first impact torque on the robot's feet. Based on the force and torque generated by the robot after it lands on the test platform, which has been adjusted according to any one of claims 1 to 8, and the landing point of the robot on the test platform, calculate the second impact torque on the robot's feet. The effectiveness of the robot's adjustment is determined based on the first impact torque and the second impact torque.

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