Mobile robot pose control method, apparatus, device, and medium

Abstract

The application relates to the technical field of mobile robot posture control, and provides a mobile robot posture control method, device, equipment and medium, which can detect whether a load change of a mobile robot occurs in real time, and reconfirms based on a robot dynamics equation, real-time motion and posture data, so that the detection accuracy is improved; an extended Kalman filtering algorithm and real-time motion and posture data are used for new load modeling, a center of mass is reconstructed according to a new load mass estimation value and a new load position estimation value, the whole processing process does not need to stop and does not need to rearrange sensors, and the compatibility is improved; a model predictive control model is called, the mobile robot is controlled to adjust the posture according to the reconstruction result and the real-time motion and posture data, dynamic compensation for the center of gravity change can be realized, instability, skidding or falling caused by the center of mass drift can be effectively prevented, and the task execution stability of the mobile robot in a changeable and unpredictable environment is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mobile robot posture control, and in particular to a mobile robot posture control method, device, equipment and medium. BACKGROUND

[0002] At present, mobile robots such as bionic robot dogs usually complete the setting of the center of gravity through a static parameter configuration method when leaving the factory. Such setting is debugged based on the dynamic model of the robot under no load or standard load conditions, and is suitable for factory test environment.

[0003] However, in industrial actual application, users often add different types of load devices to the mobile robot, such as a pan-tilt camera, a fire extinguisher, a sensor module, a communication device, etc. These additional loads have characteristics such as large weight, high center of gravity, and complex shape, which seriously affect the stability and motion coordination of the robot.

[0004] In view of the above problems, the existing technology mainly adopts the following coping strategies:

[0005] (1) Remote manual intervention by the manufacturer to reconfigure the control parameters;

[0006] (2) Return to the factory for calibration after user feedback of abnormalities;

[0007] (3) Simple fault tolerance mechanism, but cannot achieve active stability control.

[0008] The above coping strategies still have problems of slow reaction, strong dependence on manual work, and inability to adapt in real time, which limits the application potential of mobile robots such as robot dogs in variable task environments. SUMMARY

[0009] In view of the above, it is necessary to provide a mobile robot posture control method, device, equipment and medium, aiming to solve the problem that the mobile robot cannot dynamically adapt to load changes in a variable task environment.

[0010] A mobile robot posture control method, the mobile robot posture control method comprising:

[0011] In response to a posture control instruction of a target mobile robot, detecting whether the target mobile robot has a load change in real time;

[0012] When detecting that the target mobile robot has a load change, collecting real-time motion and posture data of the target mobile robot, and confirming whether the target mobile robot has a load change based on a robot dynamics equation and the real-time motion and posture data;

[0013] When it is confirmed that the target mobile robot has a load change, a new load modeling is performed by using an extended Kalman filtering algorithm and the real-time motion and attitude data, to obtain a new load mass estimation value and a new load position estimation value;

[0014] A centroid reconstruction is performed according to the new load mass estimation value and the new load position estimation value, to obtain a reconstruction result;

[0015] A model predictive control model is called, and the target mobile robot is controlled to adjust the attitude according to the reconstruction result and the real-time motion and attitude data.

[0016] According to the preferred embodiment of the present application, the real-time detection of whether the target mobile robot has a load change comprises:

[0017] The force of the supporting leg of the target mobile robot is detected in real time by using the foot force sensor of the target mobile robot itself to detect whether the force of the supporting leg of the target mobile robot has an asymmetric fluctuation;

[0018] The trunk attitude of the target mobile robot is detected in real time by using the inertial measurement unit of the target mobile robot itself to detect whether the trunk attitude of the target mobile robot has a continuous deviation within a first preset time length;

[0019] The actual current, actual angular velocity and actual torque of each joint of the target mobile robot are acquired in real time, and whether the target mobile robot has a disturbance beyond the existing motion model is identified according to the actual current, actual angular velocity and actual torque of each joint of the target mobile robot; wherein the existing motion model is used to reflect the mapping relationship between the expected current, expected angular velocity and expected torque of each joint;

[0020] Whether a load change flag is received is detected in real time;

[0021] When it is detected that the force of the supporting leg of the target mobile robot has an asymmetric fluctuation, and / or the trunk attitude of the target mobile robot has a continuous deviation within the first preset time length, and / or the target mobile robot has a disturbance beyond the existing motion model, and / or the load change flag is received, it is determined that the target mobile robot has a load change; or

[0022] When it is detected that the force of the supporting leg of the target mobile robot does not have an asymmetric fluctuation, the trunk attitude of the target mobile robot does not have a continuous deviation within the first preset time length, the target mobile robot does not have a disturbance beyond the existing motion model, and the load change flag is not received, it is determined that the target mobile robot does not have a load change.

[0023] According to a preferred embodiment of the present application, the step of identifying whether the target mobile robot has a disturbance beyond the existing motion model based on the actual current, the actual angular velocity and the actual torque of each joint of the target mobile robot comprises:

[0024] when the actual current of the first joint is detected to be greater than or equal to the configured current value and the actual angular velocity of the first joint is normal according to the existing motion model, determining that the target mobile robot has a first risk of load increase;

[0025] when the actual torque of the second joint is detected to be continuously greater than the product of the expected torque in the existing motion model and the preset proportion within a second preset time period, determining that the target mobile robot has a second risk of carrying an extra object or encountering external resistance;

[0026] when over-torque and / or saturated current of multiple joints are detected according to the existing motion model, determining that the target mobile robot has a third risk of overall load imbalance or posture imbalance;

[0027] when the first risk, the second risk and / or the third risk of the target mobile robot is determined, it is identified that the target mobile robot has a disturbance beyond the existing motion model; or

[0028] when the first risk, the second risk and the third risk of the target mobile robot are determined to be absent, it is identified that the target mobile robot does not have a disturbance beyond the existing motion model.

[0029] According to a preferred embodiment of the present application, the step of confirming whether the target mobile robot has a load change based on the robot dynamics equation and the real-time motion and posture data comprises:

[0030] inputting the real-time motion and posture data into the robot dynamics equation to obtain a theoretical torque;

[0031] collecting an actual torque of the target mobile robot;

[0032] comparing the actual torque with the theoretical torque to obtain an absolute value of the difference between the actual torque and the theoretical torque;

[0033] when the absolute value of the difference is greater than or equal to a preset threshold, confirming that the target mobile robot has a load change; or

[0034] when the absolute value of the difference is less than the preset threshold, confirming that the target mobile robot does not have a load change.

[0035] According to the preferred embodiment of the present application, the centroid reconstruction according to the estimated value of the added load mass and the estimated value of the added load position comprises:

[0036] Obtaining the original total mass and the original centroid position of the target mobile robot;

[0037] Calculating the product of the original total mass and the original centroid position to obtain a first value;

[0038] Calculating the product of the estimated value of the added load mass and the estimated value of the added load position to obtain a second value;

[0039] Calculating the sum of the first value and the second value to obtain a third value;

[0040] Calculating the sum of the original total mass and the estimated value of the added load mass to obtain a fourth value;

[0041] Calculating the quotient of the third value and the fourth value to obtain a reconstructed centroid;

[0042] Updating the original mass matrix of the target mobile robot according to the estimated value of the added load mass and the estimated value of the added load position to obtain a reconstructed mass matrix;

[0043] Updating the position, acceleration and mass of each mass point of the target mobile robot according to the estimated value of the added load mass and the estimated value of the added load position;

[0044] Updating the original zero moment point of the target mobile robot according to the updated position, acceleration and mass of each mass point to obtain a reconstructed zero moment point;

[0045] Integrating the reconstructed centroid, the reconstructed mass matrix and the reconstructed zero moment point to obtain the reconstruction result.

[0046] According to the preferred embodiment of the present application, the calling of the model predictive control model controls the target mobile robot to adjust the posture according to the reconstruction result and the real-time motion and posture data comprises:

[0047] Correcting the gait characteristic parameters, posture controller gain and support surface strategy of the target mobile robot according to the reconstruction result and the real-time motion and posture data;

[0048] Obtaining an optimization target configured according to the zero moment point and the centroid position;

[0049] Calling the model predictive control model to predict through a limited time domain based on the optimization target to obtain a gait sequence and a joint control signal;

[0050] The target mobile robot is controlled to perform posture adjustment according to the gait characteristic parameters, the posture controller gain, the support surface strategy, the gait sequence, and the joint control signal.

[0051] According to the preferred embodiment of the present application, after the target mobile robot is controlled to perform posture adjustment according to the reconstruction result and the real-time motion and posture data, the method further comprises:

[0052] When it is detected that the posture adjustment is successful, the gait characteristic parameters, the posture controller gain, the support surface strategy, the gait sequence, the joint control signal, the estimated value of the new load mass, and the estimated value of the new load position are stored as a load sample in a configuration database;

[0053] Every preset time interval, new load samples are incrementally obtained from the configuration database to perform reward training on the model predictive control model;

[0054] When a new posture control instruction is received, a new estimated value of the new load mass and a new estimated value of the new load position are obtained, the similarity between the obtained new estimated value of the new load mass and the new estimated value of the new load position and the estimated value of the new load mass and the estimated value of the new load position stored in the configuration database is calculated, a corresponding load sample is obtained from the configuration database according to the similarity, and the target mobile robot is controlled to perform posture adjustment according to the obtained load sample.

[0055] A mobile robot posture control device, comprising:

[0056] A detection unit configured to, in response to a posture control instruction for a target mobile robot, detect whether the target mobile robot has undergone a load change in real time;

[0057] A confirmation unit configured to, when it is detected that the target mobile robot has undergone a load change, collect real-time motion and posture data of the target mobile robot, and confirm whether the target mobile robot has undergone a load change based on a robot dynamics equation and the real-time motion and posture data;

[0058] A modeling unit configured to, when it is confirmed that the target mobile robot has undergone a load change, perform new load modeling by using an extended Kalman filtering algorithm and the real-time motion and posture data to obtain an estimated value of a new load mass and an estimated value of a new load position;

[0059] A reconstruction unit configured to perform centroid reconstruction according to the estimated value of the new load mass and the estimated value of the new load position to obtain a reconstruction result;

[0060] A control unit is configured to call a model predictive control model and control the target mobile robot to adjust the posture according to the reconstruction result and real-time motion and posture data.

[0061] A computer device comprises:

[0062] A memory is configured to store at least one instruction; and

[0063] A processor is configured to execute the instruction stored in the memory to implement the mobile robot posture control method.

[0064] A computer readable storage medium stores at least one instruction, which is executed by a processor in a computer device to implement the mobile robot posture control method.

[0065] As can be seen from the above technical solutions, the present application can detect whether the target mobile robot has a load change in real time, and confirm again whether the target mobile robot has a load change based on a robot dynamics equation and real-time motion and posture data, so as to improve the detection accuracy; the extended Kalman filtering algorithm and real-time motion and posture data are used to perform new load modeling, the center of mass is reconstructed according to a new load mass estimation value and a new load position estimation value, the whole processing process does not need to stop and does not need to re-arrange sensors, and the compatibility is improved; the model predictive control model is called, and the target mobile robot is controlled to adjust the posture according to the reconstruction result and real-time motion and posture data, so that the dynamic compensation for the center of gravity change can be realized, the instability, skidding or falling caused by the center of mass drift can be effectively prevented through the posture adjustment of the mobile robot, and the task execution stability of the mobile robot in a changeable and unpredictable environment is improved. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 is a flowchart of a preferred embodiment of the mobile robot posture control method of the present application;

[0067] Figure 2 is a functional module diagram of a preferred embodiment of the mobile robot posture control device of the present application;

[0068] Figure 3 is a structural schematic diagram of a computer device of a preferred embodiment of the present application for implementing the mobile robot posture control method. DETAILED DESCRIPTION

[0069] In order to make the objects, technical solutions and advantages of the present application clearer, the present application will be described in detail below with reference to the drawings and specific embodiments.

[0070] As Figure 1The flow chart shown is a flow chart of a preferred embodiment of the mobile robot posture control method. The order of the steps in the flow chart can be changed according to different needs, and some steps can be omitted.

[0071] The mobile robot posture control method is applied to one or more computer devices, which is a device capable of automatically performing numerical calculation and / or information processing according to pre-set or stored instructions. The hardware thereof includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0072] The computer device can be any electronic product that can interact with the user, such as a personal computer, a tablet computer, a smartphone, a personal digital assistant (PDA), a game console, an interactive Internet Protocol Television (IPTV), a smart wearable device, etc.

[0073] The computer device can also include network devices and / or user devices. The network devices include, but are not limited to, a single network server, a server group composed of multiple network servers, or a cloud composed of a large number of hosts or network servers based on cloud computing.

[0074] The server can be a standalone server, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms, etc. basic cloud computing services.

[0075] Among them, artificial intelligence (AI) is to use digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. Theory, method, technology and application system.

[0076] The artificial intelligence basic technology generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction system, mechatronics, etc. The artificial intelligence software technology mainly includes computer vision technology, robot technology, biometric identification technology, speech processing technology, natural language processing technology, and machine learning / deep learning, etc.

[0077] The network in which the computer device is located includes, but is not limited to, the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc.

[0078] S10, in response to the attitude control instruction of the target mobile robot, detecting whether the target mobile robot has a load change in real time.

[0079] In the embodiment, the target mobile robot can include, but is not limited to, a robot dog capable of carrying a load device, etc.

[0080] The load device can include, but is not limited to, a gimbal camera, a fire extinguisher, a sensor module, a communication device, etc.

[0081] In the embodiment, the attitude control instruction can be automatically triggered when it is detected that the target mobile robot is started, so as to realize comprehensive control of the target robot.

[0082] In the embodiment, the real-time detection of whether the target mobile robot has a load change includes:

[0083] Using the foot end force sensor of the target mobile robot itself to detect whether the force of the supporting foot of the target mobile robot has an asymmetric fluctuation in real time;

[0084] Using the inertial measurement unit (IMU) of the target mobile robot itself to detect whether the torso posture of the target mobile robot continuously deviates within a first preset time length in real time;

[0085] Real-time acquisition of actual currents, actual angular velocities and actual torques of each joint of the target mobile robot, and identification of whether the target mobile robot has a disturbance exceeding an existing motion model according to the actual currents, actual angular velocities and actual torques of each joint of the target mobile robot; wherein the existing motion model is used to reflect the mapping relationship between the expected current, the expected angular velocity and the expected torque of each joint;

[0086] Real-time detection of whether a load change flag is received;

[0087] When it is detected that the force of the support leg of the target mobile robot is asymmetrically fluctuated, and / or the torso posture of the target mobile robot is continuously deviated within the first preset time length, and / or the target mobile robot has a disturbance beyond the existing motion model, and / or the load change flag is received, it is determined that the load change of the target mobile robot is detected.

[0088] When it is detected that the force of the support leg of the target mobile robot is not asymmetrically fluctuated, the torso posture of the target mobile robot is not continuously deviated within the first preset time length, the target mobile robot does not have a disturbance beyond the existing motion model, and the load change flag is not received, it is determined that the load change of the target mobile robot is not detected.

[0089] The first preset time length can be configured according to the actual motion demand of the target mobile robot.

[0090] The inertial measurement unit can include a three-axis accelerometer, a three-axis gyroscope, a three-axis magnetometer, etc.

[0091] For example, the three-axis accelerometer can be used to measure the linear acceleration of the body of the target mobile robot in the X-axis direction, the Y-axis direction and the Z-axis direction, to determine whether the body is tilted, inertially displaced, etc.; the three-axis gyroscope can be used to measure the angular velocity of the target mobile robot, to determine the angular change in a short time; and the three-axis magnetometer can be used to detect the geomagnetic direction, to determine the heading angle stability over a long period of time.

[0092] When the mobile robot such as a robot dog performs actions such as walking, turning, climbing, etc. without external load, the current, angular velocity and torque of each joint have a standard model, i.e. the existing motion model. The existing motion model can be constructed by experiment in advance. For example, the existing motion model can record the expected curve reflecting the mapping relationship between the expected current, expected angular velocity and expected torque of each joint (such as an expected current-angular velocity-torque mapping curve of each joint of the robot when walking in a load-free state, which can be stored as a standard model parameter table).

[0093] The actual current of each joint can be obtained by a Hall current loop, and the actual torque of each joint can be obtained by multiplying the actual current of each joint by a torque constant.

[0094] The load change flag can be obtained through a pre-configured modular interface. The load change flag is used to reflect the change of the load and can be directly obtained through communication with the target mobile robot.

[0095] Through the above embodiment, the load change can be preliminarily detected.

[0096] In the embodiment, the identifying whether the target mobile robot has the disturbance beyond the existing motion model comprises:

[0097] When it is detected according to the existing motion model that the actual current of the first joint has a rising value greater than or equal to the configured current value, and the actual angular velocity of the first joint has no abnormal change, it is determined that the target mobile robot has a first risk of load increase;

[0098] When it is detected that the actual torque of the second joint is continuously higher than the product of the corresponding expected torque in the existing motion model and a preset proportion within a second preset time length, it is determined that the target mobile robot has a second risk of carrying an extra object or encountering external resistance;

[0099] When it is detected according to the existing motion model that multiple joints simultaneously have over-torque and / or saturated current, it is determined that the target mobile robot has a third risk of overall load imbalance or posture imbalance;

[0100] When it is determined that the target mobile robot has the first risk, and / or the second risk, and / or the third risk, it is identified that the target mobile robot has the disturbance beyond the existing motion model; or

[0101] When it is determined that the target mobile robot does not have the first risk, the second risk and the third risk, it is identified that the target mobile robot does not have the disturbance beyond the existing motion model.

[0102] The configured current value, the second preset time length and the preset proportion can be optimal values selected according to a large number of experiments. For example, after a large number of experiments, the configured current value can be configured to be within 120%-150% of the rated current, the second preset time length can be configured to be 100-300 milliseconds, and the preset proportion can be configured to be 30% or 20%-25%, etc.

[0103] Through the above embodiment, the target mobile robot can be effectively identified whether the disturbance beyond the existing motion model exists, and then the target mobile robot can be assisted to determine whether the load change occurs.

[0104] S11, when it is detected that the target mobile robot has a load change, real-time motion and posture data of the target mobile robot are collected, and whether the target mobile robot has a load change is confirmed based on a robot dynamics equation and the real-time motion and posture data.

[0105] In the embodiment, the real-time motion and posture data of the target mobile robot can be collected by various sensors, such as the inertial measurement unit and the Hall current loop of the target mobile robot itself.

[0106] In the embodiment, the determination of whether the target mobile robot has a load change based on the robot dynamics equation and the real-time motion and posture data comprises:

[0107] inputting the real-time motion and posture data into the robot dynamics equation to obtain a theoretical torque;

[0108] collecting an actual torque of the target mobile robot;

[0109] comparing the actual torque with the theoretical torque to obtain an absolute value of a difference between the actual torque and the theoretical torque;

[0110] when the absolute value of the difference is greater than or equal to a preset threshold, it is determined that the target mobile robot has a load change; or

[0111] when the absolute value of the difference is less than the preset threshold, it is determined that the target mobile robot does not have a load change.

[0112] The robot dynamics equation can be constructed based on a Lagrange algorithm or a Newton-Euler algorithm.

[0113] For example, the robot dynamics equation can be expressed as follows:

[0114] ;

[0115] wherein, represents a joint driving torque vector of the target mobile robot; represents a mass matrix or an inertia matrix of the target mobile robot; represents a joint position of the target mobile robot; represents an acceleration of the target mobile robot; represents a Coriolis or centrifugal force matrix; represents a velocity of the target mobile robot; represents a gravity torque vector of the target mobile robot.

[0116] Based on the inverse kinematics-dynamics solution, the theoretical torque generated by the body mass distribution and the motion state of the target mobile robot under the current action can be derived by the robot dynamics equation.

[0117] The actual torque can be collected by a corresponding sensor.

[0118] The preset threshold can be configured according to experiments. For example, the preset threshold can be configured as 5%-10% of the theoretical torque.

[0119] In the above embodiment, whether the load changes can be further confirmed by comparing the actual torque with the theoretical torque, thereby improving the accuracy of the load change event detection.

[0120] The embodiment has strong dynamic load sensing capability and fast adaptation to environmental changes. By fusing IMU, joint current and torque sensors, pose encoders and other multi-source data, the change of external load (including the increase, transfer and removal of the load) can be identified in real time during the operation of the mobile robot, and the rapid detection and response to sudden load disturbance can be realized without manual input or recalibration.

[0121] S12, when confirming that the target mobile robot has a load change, using an extended Kalman filter algorithm (EKF) and the real-time motion and attitude data to perform new load modeling, to obtain a new load mass estimation value and a new load position estimation value.

[0122] In the embodiment, the load can be modeled as an additional mass body (the state variable can include the mass of the load), and the dynamic response caused by the additional mass body can be recovered by state estimation technology.

[0123] For example, the attitude, acceleration and torque value of the target robot can be taken as input, the system state of the target mobile robot can be predicted by the extended Kalman filter algorithm, the measurement error can be corrected, and iterative convergence can be continuously performed, so as to finally obtain the new load mass estimation value and the new load position estimation value.

[0124] S13, according to the new load mass estimation value and the new load position estimation value, the center of mass is reconstructed to obtain a reconstruction result.

[0125] In the embodiment, the center of mass (CoM) is reconstructed according to the new load mass estimation value and the new load position estimation value to obtain a reconstruction result, which includes:

[0126] The original total mass and the original center of mass position of the target mobile robot are obtained.

[0127] The product of the original total mass and the original center of mass position is calculated to obtain a first value.

[0128] calculating a product of the new load mass estimation value and the new load position estimation value to obtain a second value;

[0129] calculating a sum of the first value and the second value to obtain a third value;

[0130] calculating a sum of the original total mass and the new load mass estimation value to obtain a fourth value;

[0131] calculating a quotient of the third value and the fourth value to obtain a reconstructed centroid;

[0132] updating an original inertia matrix (IM) of the target mobile robot according to the new load mass estimation value and the new load position estimation value to obtain a reconstructed inertia matrix;

[0133] updating a position, an acceleration and a mass of each mass point of the target mobile robot according to the new load mass estimation value and the new load position estimation value;

[0134] updating an original zero moment point (ZMP) of the target mobile robot according to the updated position, the acceleration and the mass of each mass point to obtain a reconstructed zero moment point;

[0135] integrating the reconstructed centroid, the reconstructed inertia matrix and the reconstructed zero moment point to obtain the reconstruction result.

[0136] wherein the centroid reconstruction is realized by weighted calculation of centroids of each module of the target mobile robot.

[0137] wherein the inertia tensor of the target mobile robot needs to be corrected through the reconstructed inertia matrix if a large load influence is detected.

[0138] wherein after the reconstructed inertia matrix is obtained, the robot dynamics equation can also be updated synchronously to facilitate subsequent control.

[0139] wherein the zero moment point is an important reference point for the target mobile robot to keep balance and is a resultant force point reflecting the ground reaction force. If the zero moment point exceeds a support polygon, the target mobile robot can be unstable, and therefore one of the control targets of the target mobile robot is to keep the zero moment point in the support area.

[0140] Through the above embodiments, the new dynamics state of the target mobile robot can be recorded in real time through the centroid reconstruction.

[0141] The embodiment realizes online load modeling and adaptive center of mass reconstruction by combining various algorithms. By identifying the mass, eccentric position and other parameters of the newly added load online, the mass distribution model, center of mass position and zero moment point area of the entire mobile robot system are dynamically updated. The modeling process does not require shutdown or sensor rearrangement, and can be compatible with robots of different models and structures.

[0142] S14, calling a model predictive control model (MPC), controlling the target mobile robot to adjust the posture according to the reconstruction result and the real-time motion and posture data.

[0143] In the embodiment, the calling of the model predictive control model to control the target mobile robot to adjust the posture according to the reconstruction result and the real-time motion and posture data comprises:

[0144] According to the reconstruction result and the real-time motion and posture data, correcting the gait characteristic parameters, posture controller gain and support surface strategy of the target mobile robot;

[0145] Obtaining an optimization target configured according to the zero moment point and the center of mass position;

[0146] Calling the model predictive control model to predict through a limited time domain based on the optimization target, to obtain a gait sequence and a joint control signal;

[0147] Controlling the target mobile robot to adjust the posture according to the gait characteristic parameters, the posture controller gain, the support surface strategy, the gait sequence and the joint control signal.

[0148] The gait characteristic parameters can include, but are not limited to, one or a combination of the following parameters: gait cycle, support phase ratio, step length, step frequency, trunk height, etc.

[0149] The posture controller gain can include, but is not limited to, one or a combination of the following parameters: waist LQR (Linear Quadratic Regulator) control coefficient, self-balancing PID (Proportional Integral Derivative) parameter, etc.

[0150] The support surface strategy can include, but is not limited to, whether to switch the number of foot supports (such as whether to switch to double foot support, six foot support, etc.), whether to extend the rear foot support phase, and other mobile robot form change strategies.

[0151] The optimization target can include, but is not limited to, minimizing the deviation of the center of mass position from an ideal trajectory, minimizing the offset of the center of mass position from a zero moment point region, controlling torque redundancy, etc.

[0152] The model predictive control model can further include constraints, such as keeping the zero moment point inside the support polygon, keeping the attitude angle within a set threshold, limiting energy consumption, etc.

[0153] The gait feature parameters, the attitude controller gain, the support surface strategy, the gait sequence, and the joint control signal can be sent to the underlying actuator to achieve attitude adjustment of the target mobile robot.

[0154] Through experiments, it takes about 50 ms to complete an adjustment, ensuring that the target mobile robot can stably transition to a new load state.

[0155] For example, in various scenarios such as industry, firefighting, logistics, etc., mobile robots (such as bionic robot dogs) need to frequently carry different task modules or equipment (such as fire extinguishers, cameras, gimbals, etc.). The embodiment can automatically adjust the attitude control strategy of the mobile robot according to the task changes without human intervention, improving the task execution stability and system robustness of the mobile robot in complex task scenarios with changing and unpredictable environments.

[0156] Through the above embodiments, adaptive optimization of mobile robot control parameters can be achieved, improving the stability of the mobile robot attitude. When the center of mass offset caused by the load is detected, the core parameters such as gait control, joint impedance adjustment, and torso attitude control are automatically updated. By minimizing the offset of the center of mass position from the zero moment point region or the control torque redundancy, dynamic compensation for the center of gravity change can be achieved, effectively preventing instability, slipping, or falling caused by center of mass drift.

[0157] In the embodiment, after the target mobile robot is controlled to perform attitude adjustment according to the reconstruction result and the real-time motion and attitude data, the method further includes:

[0158] When it is detected that the attitude adjustment is successful, the gait feature parameters, the attitude controller gain, the support surface strategy, the gait sequence, the joint control signal, the estimated value of the added load mass, and the estimated value of the added load position are stored as a load sample in a configuration database;

[0159] Every preset time interval, the incremental load samples are obtained from the configuration database to perform reward training on the model predictive control model;

[0160] When a new attitude control instruction is received, a new added load mass estimation value and an added load position estimation value are obtained, the similarity of the obtained new added load mass estimation value and added load position estimation value and the added load mass estimation value and added load position estimation value stored in the configuration database is calculated, the corresponding load sample is obtained from the configuration database according to the similarity, and the target mobile robot is adjusted in attitude according to the obtained load sample.

[0161] The model output can be continuously optimized by reward training on the model.

[0162] The preset time interval can be configured according to actual needs.

[0163] When a new load is detected as a similar item of a historical load, the corresponding parameters and control instructions can be quickly switched, avoiding retraining.

[0164] The embodiment has a learning and memory function and supports repeated load rapid identification. By introducing a learning mechanism and a task memory mode, past load types and their corresponding control parameters can be recorded. Once a similar load is identified, the historical optimal control strategy can be quickly switched to, reducing the consumption of computing resources and improving the response speed.

[0165] The embodiment can realize a complete closed loop, including load change perception, load modeling, mobile robot attitude control adjustment, optimization feedback and experience accumulation, and can continuously enhance the load adaptability and control robustness of the mobile robot in operation, significantly improving the task execution stability of the mobile robot in industrial applications, emergency rescue, complex environment inspection and other tasks.

[0166] As can be seen from the above technical solutions, the present application can detect whether the target mobile robot has a load change in real time, and further confirm whether the target mobile robot has a load change based on the robot dynamics equation and real-time motion and attitude data to improve the accuracy of detection. The extended Kalman filter algorithm and real-time motion and attitude data are used for added load modeling, the center of mass is reconstructed according to the added load mass estimation value and added load position estimation value, the entire processing process does not need to stop and does not need to re-arrange sensors, and the compatibility is improved. The model predictive control model is called, the target mobile robot is controlled for attitude adjustment according to the reconstruction result and real-time motion and attitude data, dynamic compensation of the center of gravity change can be realized, instability, skidding or falling caused by center of mass drift can be effectively prevented by adjusting the attitude of the mobile robot, and the task execution stability of the mobile robot in a variable and unpredictable environment is improved.

[0167] As Figure 2As shown is a functional module diagram of a preferred embodiment of the mobile robot posture control device. The mobile robot posture control device 11 includes a detection unit 110, a confirmation unit 111, a modeling unit 112, a reconstruction unit 113, and a control unit 114. The module / unit referred to in the present application refers to a series of computer program segments capable of being executed by a processor and capable of completing a fixed function, which are stored in a memory. In the present embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0168] The detection unit 110 is configured to detect whether a target mobile robot has a load change in real time in response to a posture control instruction of the target mobile robot.

[0169] The confirmation unit 111 is configured to collect real-time motion and posture data of the target mobile robot when it is detected that the target mobile robot has a load change, and confirm whether the target mobile robot has a load change based on a robot dynamics equation and the real-time motion and posture data.

[0170] The modeling unit 112 is configured to perform new load modeling using an extended Kalman filtering algorithm and the real-time motion and posture data when it is confirmed that the target mobile robot has a load change, to obtain a new load mass estimation value and a new load position estimation value.

[0171] The reconstruction unit 113 is configured to perform centroid reconstruction according to the new load mass estimation value and the new load position estimation value, to obtain a reconstruction result.

[0172] The control unit 114 is configured to call a model predictive control model, and control the target mobile robot to perform posture adjustment according to the reconstruction result and the real-time motion and posture data.

[0173] As can be seen from the above technical solutions, the present application can detect whether a target mobile robot has a load change in real time, and confirm whether the target mobile robot has a load change again based on a robot dynamics equation and real-time motion and posture data, to improve the accuracy of detection. The present application performs new load modeling using an extended Kalman filtering algorithm and real-time motion and posture data, and performs centroid reconstruction according to a new load mass estimation value and a new load position estimation value, so that the entire processing process does not need to stop and does not need to re-arrange sensors, thereby improving compatibility. The present application calls a model predictive control model, and controls a target mobile robot to perform posture adjustment according to a reconstruction result and real-time motion and posture data, so that dynamic compensation of a center of gravity can be achieved, and instability, skidding, or falling caused by centroid drift can be effectively prevented through posture adjustment of the mobile robot, thereby improving the stability of task execution of the mobile robot in a variable and unpredictable environment.

[0174] As shown in FIG. 2, the mobile robot posture control device 11 includes a detection unit 110, a confirmation unit 111, a modeling unit 112, a reconstruction unit 113, and a control unit 114.Figure 3 Fig. 1 is a structural schematic diagram of a computer device for implementing the preferred embodiment of the mobile robot posture control method.

[0175] The computer device 1 can include a memory 12, a processor 13 and a bus (the arrow in the figure is the bus), and can further include a computer program, such as a mobile robot posture control program, stored in the memory 12 and executable on the processor 13.

[0176] Those skilled in the art can understand that the schematic diagram is only an example of the computer device 1 and does not constitute a limitation on the computer device 1, which can be a bus type structure or a star type structure, and can further include more or less other hardware or software or different component arrangement, such as an input / output device, a network access device, etc.

[0177] It should be noted that the computer device 1 is only an example, and other existing or future electronic products, such as those adaptable to the present application, should also be included in the protection scope of the present application and are hereby included by reference.

[0178] The memory 12 includes at least one type of readable storage medium, such as a flash memory, a mobile hard disk, a multimedia card, a card type memory (such as an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 12 can be an internal storage unit of the computer device 1 in some embodiments, such as a mobile hard disk of the computer device 1. The memory 12 can also be an external storage device of the computer device 1 in other embodiments, such as a plug-in mobile hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 12 can include both an internal storage unit and an external storage device of the computer device 1. The memory 12 can be used not only to store application software and various data installed in the computer device 1, such as the code of the mobile robot posture control program, but also to temporarily store data that has been output or will be output.

[0179] The processor 13 can be composed of integrated circuits in some embodiments, for example, can be composed of a single packaged integrated circuit, or can be composed of multiple packaged integrated circuits with the same function or different functions, including one or more central processing units (CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, etc. The processor 13 is the control core (Control Unit) of the computer device 1, which connects various components of the entire computer device 1 through various interfaces and lines, executes programs or modules stored in the memory 12 (such as executing mobile robot posture control programs, etc.), and calls data stored in the memory 12, to execute various functions and process data of the computer device 1.

[0180] The processor 13 executes the operating system of the computer device 1 and various installed application programs. The processor 13 executes the application programs to implement the steps in each of the above mobile robot posture control method embodiments, for example Figure 1 The steps shown.

[0181] For example, the computer program can be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to complete the present application. The one or more modules / units can be a series of computer-readable instruction segments that can complete a specific function, which are used to describe the execution process of the computer program in the computer device 1. For example, the computer program can be divided into a detection unit 110, a confirmation unit 111, a modeling unit 112, a reconstruction unit 113, and a control unit 114.

[0182] The integrated units implemented in the form of software function modules described above can be stored in a computer-readable storage medium. The software function modules described above are stored in a storage medium, including a number of instructions for causing a computer device (which can be a personal computer, a computer device, or a network device, etc.) or a processor to execute part of the mobile robot posture control method described in each embodiment of the present application.

[0183] The modules / units integrated in the computer device 1, if implemented in the form of software function units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiments can also be implemented by a computer program to instruct related hardware devices to complete, and the computer program can be stored in a computer-readable storage medium. The computer program is executed by the processor to implement the steps of each of the above method embodiments.

[0184] The computer program includes computer program code in the form of source code, object code, executable code, or some intermediate form. The computer readable medium can include any entity or apparatus capable of carrying the computer program code, a recording medium, a USB flash disk, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory, and the like.

[0185] Further, the computer readable storage medium can mainly include a storage program area and a storage data area, wherein the storage program area can store an operating system, an application program required by at least one function, and the like; and the storage data area can store data created according to the use of the blockchain node, and the like.

[0186] The blockchain referred to in the present application is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm. The blockchain is essentially a decentralized database, and is a chain of data blocks associated using cryptographic methods. Each data block contains information of a batch of network transactions, and is used to verify the validity (anti-fake) of the information and generate the next block. The blockchain can include a blockchain underlying platform, a platform product service layer, and an application service layer, and the like.

[0187] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one straight line is used in the drawings, but this does not mean that there is only one bus or only one type of bus. The bus is configured to enable connection and communication between the memory 12 and the at least one processor 13, etc. Figure 3

[0188] Although not shown, the computer device 1 can also include a power supply (such as a battery) for powering the various components. Preferably, the power supply can be logically connected to the at least one processor 13 through a power management device, so as to realize functions such as charge management, discharge management, and power consumption management through the power management device. The power supply can also include one or more direct current or alternating current power supplies, a recharging device, a power supply fault detection circuit, a power supply converter or inverter, a power supply status indicator, and the like. The computer device 1 can also include various sensors, a Bluetooth module, a Wi-Fi module, and the like, which will not be described here.

[0189] ​Further, the computer device 1 can further comprise a network interface, which can optionally comprise a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), and is generally used to establish a communication connection between the computer device 1 and other computer devices.

[0190] Optionally, the computer device 1 can further comprise a user interface, which can be a display, an input unit (such as a keyboard), and optionally can be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display can be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, etc. The display can also be appropriately referred to as a display screen or a display unit, and is used to display information processed in the computer device 1 and to display a visualized user interface.

[0191] It should be understood that the embodiments are only for illustration and do not limit the scope of the patent application.

[0192] Those skilled in the art can understand that, Figure 3 The structure shown does not constitute a limitation on the computer device 1, and can include fewer or more components than shown, or combine certain components, or different component arrangements.

[0193] In combination Figure 1 The memory 12 in the computer device 1 stores a plurality of instructions to implement a mobile robot posture control method, and the processor 13 can execute the plurality of instructions to implement:

[0194] In response to a posture control instruction of a target mobile robot, real-time detection is performed on whether the target mobile robot has a load change;

[0195] When it is detected that the target mobile robot has a load change, real-time motion and posture data of the target mobile robot are collected, and whether the target mobile robot has a load change is confirmed based on a robot dynamics equation and the real-time motion and posture data;

[0196] When it is confirmed that the target mobile robot has a load change, an extended Kalman filtering algorithm and the real-time motion and posture data are used to perform new load modeling, to obtain a new load mass estimation value and a new load position estimation value;

[0197] Centroid reconstruction is performed according to the new load mass estimation value and the new load position estimation value, to obtain a reconstruction result;

[0198] The model predictive control model is called to control the target mobile robot to adjust the posture according to the reconstruction result and the real-time motion and posture data.

[0199] Specifically, the specific implementation method of the processor 13 to the above instructions can refer to Figure 1 The description of related steps in the corresponding embodiments will not be repeated here.

[0200] It should be noted that the data involved in the present case are all legally obtained. The non-company software tools or components appearing in the embodiments of the present application are only examples for introduction and do not represent actual use.

[0201] In several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented by other manners. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner.

[0202] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0203] The modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical units, i.e. they can be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment.

[0204] In addition, the functional modules in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically independently, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional modules.

[0205] It will be obvious to a person skilled in the art that the application is not limited to the details of the above-described exemplary embodiments but can be implemented in other embodiments without departing from the scope of the application.

[0206] The embodiments should therefore be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the description given above, so that all changes coming within the meaning and equivalency range of the claims are intended to be embraced therein. Any reference signs in the claims should not be construed as limiting the scope of the claims.

[0207] Furthermore, the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural and vice versa. Use of the expression "one" or "the" in relation to an element or step of the application does not exclude the presence of more than one element or step. The data, steps and / or functions can be carried out in any other order than the one described above without departing from the scope of the application.

[0208] Finally, it should be noted that the above-described embodiments are merely intended to illustrate the technical solutions of the present application, rather than limit the scope of the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalent replaced without departing from the spirit and scope of the present application.

Claims

1. A method for posture control of a mobile robot, characterized in that, The mobile robot posture control method includes: In response to attitude control commands to a target mobile robot, the system detects in real-time whether the target mobile robot experiences load changes, including: using the target mobile robot's own foot force sensors to detect in real-time whether the force on the supporting foot of the target mobile robot undergoes asymmetrical fluctuations; using the target mobile robot's own inertial measurement unit to detect in real-time whether the torso posture of the target mobile robot continuously shifts within a first preset time period; acquiring in real-time the actual current, actual angular velocity, and actual torque of each joint of the target mobile robot, and identifying whether the target mobile robot has disturbances exceeding the existing motion model based on the actual current, actual angular velocity, and actual torque of each joint; wherein, the existing motion model is used to reflect the expected current, expected angular velocity, and expected torque of each joint. The mapping relationship between torque and speed is observed; real-time detection is performed to check whether a load change flag is received; when it is detected that the supporting leg of the target mobile robot experiences asymmetrical fluctuations in force, and / or the torso posture of the target mobile robot continuously shifts within the first preset time period, and / or the target mobile robot exhibits disturbances exceeding the existing motion model, and / or the load change flag is received, it is determined that a load change has been detected in the target mobile robot; or when it is detected that the supporting leg of the target mobile robot does not experience asymmetrical fluctuations in force, the torso posture of the target mobile robot does not continuously shift within the first preset time period, the target mobile robot does not exhibit disturbances exceeding the existing motion model, and the load change flag is not received, it is determined that no load change has been detected in the target mobile robot. When a load change is detected in the target mobile robot, real-time motion and attitude data of the target mobile robot are collected, and the load change of the target mobile robot is confirmed based on the robot dynamics equations and the real-time motion and attitude data. When it is confirmed that the target mobile robot has experienced a load change, the extended Kalman filter algorithm and the real-time motion and attitude data are used to model the new load, and the estimated value of the new load mass and the estimated value of the new load position are obtained. Based on the estimated mass and location of the new load, the centroid is reconstructed to obtain the reconstruction result; The model predicts and controls the target mobile robot to adjust its posture based on the reconstruction results and the real-time motion and posture data.

2. The mobile robot posture control method as described in claim 1, characterized in that, The step of identifying whether the target mobile robot has disturbances exceeding the existing motion model based on the actual current, actual angular velocity, and actual torque of each joint of the target mobile robot includes: When the existing motion model detects that the actual current rise of the first joint is greater than or equal to the configured current value, and the actual angular velocity change of the first joint is normal, it is determined that the target mobile robot has a first risk of increased load. When it is detected that the actual torque of the second joint is continuously higher than the product of the corresponding expected torque and the preset ratio in the existing motion model for a second preset time period, it is determined that the target mobile robot has a second risk of carrying an extra object or encountering external resistance. When multiple joints are simultaneously subjected to overtorque and / or saturation current according to the existing motion model, it is determined that the target mobile robot has a third risk of overall load imbalance or posture imbalance. When it is determined that the target mobile robot has the first risk, and / or the second risk, and / or the third risk, a disturbance exceeding the existing motion model of the target mobile robot is identified; or When it is determined that the target mobile robot does not have the first risk, the second risk, and the third risk, it is identified that the target mobile robot does not have any disturbances beyond the existing motion model.

3. The mobile robot posture control method as described in claim 1, characterized in that, The process of confirming whether the target mobile robot experiences a load change based on the robot dynamics equations and the real-time motion and attitude data includes: The real-time motion and attitude data are input into the robot dynamics equations to obtain the theoretical torque; The actual torque of the target mobile robot was collected; By comparing the actual torque with the theoretical torque, the absolute value of the difference between the actual torque and the theoretical torque is obtained; When the absolute value of the difference is greater than or equal to a preset threshold, it is confirmed that the target mobile robot has experienced a load change; or When the absolute value of the difference is less than the preset threshold, it is confirmed that the target mobile robot has not experienced a load change.

4. The mobile robot posture control method as described in claim 1, characterized in that, The centroid reconstruction based on the estimated mass and location of the new load, yielding the reconstruction result, includes: Obtain the original total mass and original centroid position of the target mobile robot; Calculate the product of the original total mass and the original centroid position to obtain the first value; Calculate the product of the estimated quality of the new load and the estimated location of the new load to obtain the second value; Calculate the sum of the first value and the second value to obtain the third value; The fourth value is obtained by summing the original total mass with the estimated mass of the new load. The quotient of the third value and the fourth value is calculated to obtain the reconstructed centroid; The original mass matrix of the target mobile robot is updated based on the estimated mass value of the new load and the estimated location value of the new load to obtain the reconstructed mass matrix; The position, acceleration, and mass of each mass point of the target mobile robot are updated based on the estimated mass and position of the new load. The original zero-torque point of the target mobile robot is updated based on the updated position, acceleration, and mass of each mass point to obtain the reconstructed zero-torque point; The reconstruction result is obtained by integrating the reconstructed centroid, the reconstructed mass matrix, and the reconstructed zero moment point.

5. The mobile robot posture control method as described in claim 1, characterized in that, The invoked model predictive control model, based on the reconstruction result and the real-time motion and attitude data, controls the target mobile robot to adjust its attitude, including: Based on the reconstruction results and the real-time motion and attitude data, the gait characteristic parameters, attitude controller gain, and support surface strategy of the target mobile robot are corrected. Obtain the optimization objective configured based on the zero torque point and the position of the center of mass; The model prediction control model is invoked to predict gait sequences and joint control signals based on the optimization objective and through a finite time domain. The target mobile robot is controlled to adjust its posture based on the gait characteristic parameters, the posture controller gain, the support surface strategy, the gait sequence, and the joint control signals.

6. The mobile robot posture control method as described in claim 5, characterized in that, After controlling the target mobile robot to adjust its posture based on the reconstruction result and the real-time motion and posture data, the method further includes: When the attitude adjustment is successfully detected, the gait feature parameters, the attitude controller gain, the support surface strategy, the gait sequence, the joint control signal, the new load mass estimate, and the new load position estimate are stored as load samples in the configuration database. At preset time intervals, new load samples are incrementally retrieved from the configuration database to perform reward training on the model prediction control model. When a new attitude control command is received, the new estimated value of the new load mass and the new estimated value of the new load position are obtained. The similarity between the obtained new estimated value of the new load mass and the new estimated value of the new load position and the new estimated value of the new load mass and the new estimated value of the new load position stored in the configuration database is calculated. Based on the similarity, the corresponding load sample is obtained from the configuration database, and the attitude of the target mobile robot is adjusted according to the obtained load sample.

7. A mobile robot posture control device, characterized in that, The mobile robot posture control device includes: The detection unit, in response to attitude control commands to the target mobile robot, detects in real time whether the target mobile robot experiences load changes. This includes: using the target mobile robot's own foot force sensors to detect in real time whether the force on the supporting foot of the target mobile robot experiences asymmetrical fluctuations; using the target mobile robot's own inertial measurement unit to detect in real time whether the torso posture of the target mobile robot continuously shifts within a first preset time period; acquiring in real time the actual current, actual angular velocity, and actual torque of each joint of the target mobile robot, and identifying whether the target mobile robot experiences disturbances exceeding the existing motion model based on the actual current, actual angular velocity, and actual torque of each joint; wherein the existing motion model reflects the desired current and desired angular velocity of each joint. The mapping relationship between speed and desired torque; real-time detection of whether a load change flag is received; when it is detected that the supporting leg of the target mobile robot experiences asymmetrical fluctuations in force, and / or the torso posture of the target mobile robot continuously shifts within the first preset time period, and / or the target mobile robot has a disturbance exceeding the existing motion model, and / or the load change flag is received, it is determined that a load change has been detected in the target mobile robot; or when it is detected that the supporting leg of the target mobile robot does not experience asymmetrical fluctuations in force, the torso posture of the target mobile robot does not continuously shift within the first preset time period, the target mobile robot does not have a disturbance exceeding the existing motion model, and the load change flag is not received, it is determined that no load change has been detected in the target mobile robot. The confirmation unit is used to collect real-time motion and attitude data of the target mobile robot when a load change is detected, and to confirm whether the target mobile robot has experienced a load change based on the robot dynamics equations and the real-time motion and attitude data. The modeling unit is used to model the new load using the extended Kalman filter algorithm and the real-time motion and attitude data when it is confirmed that the target mobile robot has experienced a load change, so as to obtain the estimated value of the new load mass and the estimated value of the new load position. The reconstruction unit is used to reconstruct the centroid based on the estimated value of the new load quality and the estimated value of the new load location, and obtain the reconstruction result. The control unit is used to invoke the model predictive control model and control the target mobile robot to adjust its posture based on the reconstruction results and the real-time motion and posture data.

8. A computer device, characterized in that, The computer device includes: Memory, storing at least one instruction; and The processor executes instructions stored in the memory to implement the mobile robot posture control method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, which is executed by a processor in a computer device to implement the mobile robot posture control method as described in any one of claims 1 to 6.

Images