Mobile robot attitude control method and device, equipment and medium

Abstract

The invention relates to the technical field of mobile robot attitude control, and provides a mobile robot attitude control method, device and equipment and a medium, which can detect whether the load of a mobile robot changes in real time and perform reconfirmation based on a robot kinetic equation and real-time motion and attitude data, thereby improving the detection accuracy. Newly-added load modeling is carried out by utilizing an extended Kalman filtering algorithm and real-time motion and attitude data, and centroid reconstruction is carried out according to a newly-added load mass estimation value and a newly-added load position estimation value, so that the whole processing process does not need to stop and does not need to rearrange a sensor, and the compatibility is improved; and the model prediction control model is called, and the mobile robot is controlled to carry out attitude adjustment according to the reconstruction result and the real-time motion and attitude data, so that dynamic compensation for gravity center change can be realized, instability, slipping or falling caused by mass center drift is effectively prevented, and the task execution stability of the mobile robot in a changeable and unpredictable environment is improved.

Description

Technical Field

[0001] This invention relates to the field of mobile robot posture control technology, and in particular to a mobile robot posture control method, device, equipment and medium. Background Technology

[0002] Currently, mobile robots, such as bionic robot dogs, typically have their center of gravity set at the factory through static parameter configuration. This type of setting is based on a dynamic model under no-load or standard load conditions and is suitable for factory testing environments.

[0003] However, in practical industrial applications, users often add different types of load devices to mobile robots, such as gimbal cameras, fire extinguishers, sensor modules, and communication equipment. These additional loads are characterized by their large weight, high center of gravity, and complex shape, which seriously affect the stability and motion coordination of the robot.

[0004] To address the above problems, the existing technologies mainly employ the following strategies: (1) The manufacturer remotely intervenes and reconfigures the control parameters; (2) After receiving abnormal feedback from users, the device is returned to the factory for calibration; (3) Simple fault-tolerant mechanism, but cannot achieve active stability control.

[0005] The aforementioned strategies still suffer from slow response, heavy reliance on human intervention, and inability to adapt in real time, which limits the application potential of mobile robots such as robot dogs in variable task environments. Summary of the Invention

[0006] In view of the above, it is necessary to provide a mobile robot posture control method, device, equipment and medium, which aims to solve the problem that mobile robots cannot dynamically adapt to load changes in variable task environments.

[0007] A mobile robot posture control method, the mobile robot posture control method comprising: In response to attitude control commands to the target mobile robot, the system detects in real time whether the target mobile robot experiences load changes. 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.

[0008] According to a preferred embodiment of the present invention, the real-time detection of whether the target mobile robot experiences a load change includes: The target mobile robot uses its own foot force sensors to detect in real time whether there is asymmetrical fluctuation in the force on the supporting foot of the target mobile robot; The inertial measurement unit of the target mobile robot itself is used to detect in real time whether the torso posture of the target mobile robot continues to deviate within a first preset time period; The actual current, actual angular velocity, and actual torque of each joint of the target mobile robot are acquired in real time, and the target mobile robot is identified as having any disturbances that exceed 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 mapping relationship between the expected current, expected angular velocity, and expected torque of each joint; Real-time detection of whether load change flags are received; When an asymmetrical fluctuation in the force on the supporting leg of the target mobile robot is detected, 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 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 any 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.

[0009] According to a preferred embodiment of the present invention, 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.

[0010] According to a preferred embodiment of the present invention, the step of confirming whether the target mobile robot has undergone 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.

[0011] According to a preferred embodiment of the present invention, the centroid reconstruction based on the estimated mass value of the new load and the estimated location value of the new load, to obtain 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.

[0012] According to a preferred embodiment of the present invention, the step of calling the model prediction control model and controlling the target mobile robot to adjust its posture based on the reconstruction result and the real-time motion and posture data includes: 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.

[0013] According to a preferred embodiment of the present invention, 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.

[0014] A mobile robot posture control device, the mobile robot posture control device comprising: The detection unit is used to detect in real time whether the target mobile robot experiences a load change in response to the attitude control command of 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.

[0015] A computer device, the computer device comprising: Memory, storing at least one instruction; and The processor executes the instructions stored in the memory to implement the mobile robot posture control method.

[0016] A computer-readable storage medium storing at least one instruction, which is executed by a processor in a computer device to implement the mobile robot posture control method.

[0017] As can be seen from the above technical solutions, this invention can detect whether the target mobile robot has experienced load changes in real time, and reconfirm whether the target mobile robot has experienced load changes based on the robot's dynamic equations, real-time motion and attitude data, thereby improving the accuracy of detection. It utilizes the extended Kalman filter algorithm and real-time motion and attitude data to model the new load, and reconstructs the center of gravity based on the estimated mass and position of the new load. The entire process requires no machine shutdown or sensor repositioning, improving compatibility. It calls the model predictive control model and controls the target mobile robot to adjust its attitude based on the reconstruction results and real-time motion and attitude data, enabling dynamic compensation for changes in the center of gravity. By adjusting the mobile robot's attitude, it effectively prevents instability, slippage, or falls caused by center of gravity drift, improving the stability of the mobile robot's task execution in variable and unpredictable environments. Attached Figure Description

[0018] Figure 1 This is a flowchart of a preferred embodiment of the mobile robot posture control method of the present invention; Figure 2 This is a functional block diagram of a preferred embodiment of the mobile robot posture control device of the present invention; Figure 3 This is a schematic diagram of the structure of a computer device that implements a preferred embodiment of the mobile robot posture control method of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] like Figure 1 The diagram shown is a flowchart of a preferred embodiment of the mobile robot posture control method of the present invention. The order of the steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements.

[0021] The mobile robot posture control method is applied to one or more computer devices. The computer device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0022] The computer device can be any electronic product that can interact with the user, such as a personal computer, tablet computer, smartphone, personal digital assistant (PDA), game console, interactive network television (IPTV), smart wearable device, etc.

[0023] The computer equipment may also include network equipment and / or user equipment. The network equipment includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0024] The server can be a standalone server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0025] Artificial intelligence (AI) is the theory, method, technology and application system that uses 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.

[0026] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0027] The network in which the computer device is located includes, but is not limited to, the Internet, wide area network, metropolitan area network, local area network, and virtual private network (VPN).

[0028] S10, in response to the attitude control command of the target mobile robot, detect in real time whether the target mobile robot has a load change.

[0029] In this embodiment, the target mobile robot may include, but is not limited to, a robot dog capable of carrying a load device.

[0030] The load device may include, but is not limited to, a PTZ camera, a fire extinguisher, a sensor module, and communication equipment.

[0031] In this embodiment, the attitude control command can be automatically triggered when the target mobile robot is detected to start, so as to achieve full control of the target robot.

[0032] In this embodiment, the real-time detection of whether the target mobile robot experiences a load change includes: The target mobile robot uses its own foot force sensors to detect in real time whether there is asymmetrical fluctuation in the force on the supporting foot of the target mobile robot; The inertial measurement unit (IMU) of the target mobile robot is used to detect in real time whether the torso posture of the target mobile robot continues to deviate within a first preset time period; The actual current, actual angular velocity, and actual torque of each joint of the target mobile robot are acquired in real time, and the target mobile robot is identified as having any disturbances that exceed 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 mapping relationship between the expected current, expected angular velocity, and expected torque of each joint; Real-time detection of whether load change flags are received; When an asymmetrical fluctuation in the force on the supporting leg of the target mobile robot is detected, 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 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 any 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.

[0033] The first preset duration can be configured according to the actual movement requirements of the target mobile robot.

[0034] The inertial measurement unit may include a three-axis accelerometer, a three-axis gyroscope, a three-axis magnetometer, etc.

[0035] For example, the triaxial accelerometer can be used to measure the linear acceleration of the target mobile robot body in the X-axis, Y-axis, and Z-axis directions to determine whether the body body tilts or undergoes inertial displacement; the triaxial gyroscope can be used to measure the angular velocity of the target mobile robot to determine angle changes over a short period of time; and the triaxial magnetometer can be used to detect the geomagnetic direction to determine the long-term stability of the heading angle.

[0036] In this context, when mobile robots such as robot dogs perform actions such as walking, turning, and climbing without external load, the current, angular velocity, and torque of each joint have a standard model, which is the existing motion model. The existing motion model can be pre-constructed through experiments. For example, the existing motion model can record the expected curves that reflect the mapping relationship between the expected current, expected angular velocity, and expected torque of each joint (such as the mapping curves of expected current-angular velocity-torque of each joint when the robot walks under no load, and can be stored as a standard model parameter table).

[0037] The actual current of each joint can be obtained through a Hall current loop, and the actual torque of each joint can be obtained by calculating the product of the actual current of each joint and the torque constant.

[0038] The load change flag can be obtained through a pre-configured modular interface. The load change flag is used to reflect changes in load and can be obtained directly through communication with the target mobile robot.

[0039] The above embodiments enable preliminary detection of load changes.

[0040] In this embodiment, 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.

[0041] The configured current value, the second preset duration, and the preset ratio can be optimal values ​​selected based on numerous experiments. For example, after conducting a large number of experiments, the configured current value can be configured to be within the range of 120%-150% of the rated current, the second preset duration can be configured to be 100-300 milliseconds, and the preset ratio can be configured to be 30% or 20%-25%, etc.

[0042] Through the above embodiments, it is possible to effectively identify whether the target mobile robot has disturbances that exceed the existing motion model, thereby assisting in determining whether the target mobile robot has experienced load changes.

[0043] S11, 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 equation and the real-time motion and attitude data.

[0044] In this embodiment, real-time motion and attitude data of the target mobile robot can be collected through various sensors, such as the target mobile robot's own inertial measurement unit and Hall current loop.

[0045] In this embodiment, 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.

[0046] The robot dynamics equations can be constructed based on the Lagrange algorithm or the Newton-Euler algorithm.

[0047] For example, the robot's dynamics equations can be expressed as follows: ; in, This represents the joint driving torque vector of the target mobile robot; This represents the mass matrix or inertia matrix of the target mobile robot; This indicates the joint positions of the target mobile robot; This represents the acceleration of the target mobile robot; Represents the Coriolis or centrifugal force matrix; This indicates the speed of the target mobile robot; This represents the gravitational torque vector of the target mobile robot.

[0048] Based on the above robot dynamics equations and the inverse kinematics-dynamics solution, inverse dynamics can be derived, which allows us to derive the body mass distribution and theoretical torque that the target mobile robot should generate under the current action.

[0049] The actual torque can be collected by a corresponding sensor.

[0050] The preset threshold can be configured based on experiments. For example, the preset threshold can be configured to be 5%-10% of the theoretical torque.

[0051] In the above embodiments, the comparison between the actual torque and the theoretical torque can further confirm whether the load has changed, thereby improving the accuracy of load change event detection.

[0052] This embodiment demonstrates strong dynamic load sensing capabilities and rapid adaptation to environmental changes. By integrating multi-source data such as IMU, joint current and torque sensors, and pose encoders, it can identify changes in external load (including load increases, transfers, and removals) in real time during the operation of the mobile robot, enabling rapid detection and response to sudden load disturbances without manual input or recalibration.

[0053] S12, when it is confirmed that the target mobile robot has a load change, the extended Kalman filter (EKF) algorithm and the real-time motion and attitude data are used to model the new load and obtain the estimated value of the new load mass and the estimated value of the new load position.

[0054] In this embodiment, the load can be modeled as an additional mass body (the state variables may include the mass of the load), and the dynamic response caused by it can be recovered using state estimation techniques.

[0055] For example, the target robot's posture, acceleration, torque, etc., can be used as inputs to predict the system state of the target mobile robot through the extended Kalman filter algorithm, correct measurement errors, and continuously iterate and converge to finally obtain the estimated value of the new load mass and the estimated value of the new load position.

[0056] S13, perform centroid reconstruction based on the estimated value of the new load quality and the estimated value of the new load location to obtain the reconstruction result.

[0057] In this embodiment, the centroid (CoM) reconstruction based on the estimated mass and location of the new load, to obtain 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 (IM) 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-moment point (ZMP) of the target mobile robot is updated based on the updated position, acceleration, and mass of each mass point to obtain the reconstructed zero-moment point; The reconstruction result is obtained by integrating the reconstructed centroid, the reconstructed mass matrix, and the reconstructed zero moment point.

[0058] In this process, by performing weighted calculations on the centroids of each module of the target mobile robot, centroid reconstruction can be achieved.

[0059] In this process, if the load is detected to have a significant impact through the reconstructed mass matrix, the inertial tensor of the target mobile robot also needs to be corrected.

[0060] In addition, after obtaining the reconstructed mass matrix, the robot dynamics equations can be updated synchronously to facilitate subsequent control.

[0061] The zero-moment point is a crucial reference point for maintaining the balance of the target mobile robot, reflecting the resultant force of the ground reaction force. If the zero-moment point extends beyond the supporting polygon, the target mobile robot may become unstable. Therefore, one of the control objectives for the target mobile robot is to keep the zero-moment point within the supporting area.

[0062] Through the above embodiments, the new dynamic state of the target mobile robot can be recorded in real time through centroid reconstruction.

[0063] This embodiment combines multiple algorithms to achieve online load modeling and adaptive centroid reconstruction. By identifying parameters such as the mass and eccentricity of the newly added load online, the mass distribution model, centroid position, and zero-moment point region of the entire mobile robot system are dynamically updated. This modeling process requires no downtime or sensor rearrangement and is compatible with robots of different models and structures.

[0064] S14, invoke the Model Predictive Control (MPC) model to control the target mobile robot to adjust its posture based on the reconstruction results and the real-time motion and posture data.

[0065] In this embodiment, the step of calling the model prediction control model to control the target mobile robot to adjust its posture based on the reconstruction result and the real-time motion and posture data includes: 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.

[0066] The gait characteristic parameters may include, but are not limited to, one or a combination of the following parameters: gait cycle, support phase ratio, stride length, stride frequency, trunk height, etc.

[0067] The attitude controller gain may include, but is not limited to, a combination of one or more of the following parameters: waist LQR (Linear Quadratic Regulator) control coefficient, self-balancing PID (Proportional Integral Derivative) parameters, etc.

[0068] The support surface strategy may include, but is not limited to, whether to switch the number of foot supports (e.g., whether to switch to bipedal support, hexapedal support, etc.) and whether to extend the hind foot support to equal the morphological change strategy of the mobile robot.

[0069] The optimization objectives may include, but are not limited to: minimizing the deviation between the centroid position and the ideal trajectory, minimizing the offset between the centroid position and the zero torque point region, and controlling torque redundancy.

[0070] The model predictive control model may also include constraints, such as keeping the zero-moment point inside the supporting polygon, the attitude angle not exceeding a set threshold, and energy consumption limits.

[0071] Specifically, the gait feature parameters, the attitude controller gain, the support surface strategy, the gait sequence, and the joint control signals can be sent to the underlying actuator to achieve attitude adjustment of the target mobile robot.

[0072] Experiments have shown that an adjustment can be completed in approximately 50ms, ensuring that the target mobile robot can stably transition to the new load state.

[0073] For example, in various scenarios such as industry, fire fighting, and logistics, mobile robots (such as bionic robot dogs) need to frequently carry different task modules or equipment (such as fire extinguishers, cameras, gimbals, etc.). This embodiment can autonomously adjust the mobile robot's posture control strategy according to changes in the task without human intervention, improving the stability and robustness of the mobile robot in complex task scenarios such as changing and unpredictable environments.

[0074] Through the above embodiments, adaptive optimization of mobile robot control parameters can be achieved, improving the stability of the mobile robot's posture. When a center of gravity shift caused by load is detected, core parameters such as gait control, joint impedance adjustment, and torso posture control are automatically updated. By minimizing the offset between the center of gravity position and the zero-torque point region or the redundancy of control torque, dynamic compensation for changes in the center of gravity can be achieved, effectively preventing instability, slippage, or falls caused by center of gravity drift.

[0075] In this embodiment, 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.

[0076] By training the model with rewards, the model output can be continuously optimized.

[0077] The preset time interval can be configured according to actual needs.

[0078] When a new load is detected to be similar to a historical load, the corresponding parameters and control commands can be quickly switched to avoid retraining.

[0079] This embodiment features a learning and memory function, supporting rapid identification of repetitive loads. By introducing a learning mechanism and task memory mode, it can record past load types and their corresponding control parameters. Once a similar load is identified, it can quickly switch to the historically optimal control strategy, reducing computational resource consumption and improving response speed.

[0080] This embodiment can achieve a complete closed loop, including load change perception, load modeling, mobile robot posture control adjustment, optimization feedback and experience accumulation. It can continuously enhance load adaptability and control robustness during the operation of the mobile robot, and significantly improve the task execution stability of the mobile robot in industrial applications, emergency rescue, and complex environment inspection.

[0081] As can be seen from the above technical solutions, this invention can detect whether the target mobile robot has experienced load changes in real time, and reconfirm whether the target mobile robot has experienced load changes based on the robot's dynamic equations, real-time motion and attitude data, thereby improving the accuracy of detection. It utilizes the extended Kalman filter algorithm and real-time motion and attitude data to model the new load, and reconstructs the center of gravity based on the estimated mass and position of the new load. The entire process requires no machine shutdown or sensor repositioning, improving compatibility. It calls the model predictive control model and controls the target mobile robot to adjust its attitude based on the reconstruction results and real-time motion and attitude data, enabling dynamic compensation for changes in the center of gravity. By adjusting the mobile robot's attitude, it effectively prevents instability, slippage, or falls caused by center of gravity drift, improving the stability of the mobile robot's task execution in variable and unpredictable environments.

[0082] like Figure 2The diagram shown is a functional block diagram of a preferred embodiment of the mobile robot posture control device of the present invention. 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 this invention is a series of computer program segments that can be executed by a processor and perform a fixed function, and which are stored in memory. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0083] The detection unit 110 is used to detect in real time whether the target mobile robot experiences a load change in response to the attitude control command of the target mobile robot. The confirmation unit 111 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 equation and the real-time motion and attitude data. The modeling unit 112 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 undergone 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 113 is used to perform centroid reconstruction based on the estimated value of the new load quality and the estimated value of the new load location to obtain the reconstruction result. The control unit 114 is used to call the model predictive control model and control the target mobile robot to adjust its posture according to the reconstruction result and the real-time motion and posture data.

[0084] As can be seen from the above technical solutions, this invention can detect whether the target mobile robot has experienced load changes in real time, and reconfirm whether the target mobile robot has experienced load changes based on the robot's dynamic equations, real-time motion and attitude data, thereby improving the accuracy of detection. It utilizes the extended Kalman filter algorithm and real-time motion and attitude data to model the new load, and reconstructs the center of gravity based on the estimated mass and position of the new load. The entire process requires no machine shutdown or sensor repositioning, improving compatibility. It calls the model predictive control model and controls the target mobile robot to adjust its attitude based on the reconstruction results and real-time motion and attitude data, enabling dynamic compensation for changes in the center of gravity. By adjusting the mobile robot's attitude, it effectively prevents instability, slippage, or falls caused by center of gravity drift, improving the stability of the mobile robot's task execution in variable and unpredictable environments.

[0085] like Figure 3 The diagram shown is a schematic diagram of the structure of a computer device for implementing the mobile robot posture control method of the present invention.

[0086] The computer device 1 may include a memory 12, a processor 13, and a bus (the arrow in the figure represents the bus), and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a mobile robot posture control program.

[0087] Those skilled in the art will understand that the schematic diagram is merely an example of computer device 1 and does not constitute a limitation on computer device 1. Computer device 1 can be either a bus topology or a star topology. Computer device 1 may also include more or fewer other hardware or software than shown in the diagram, or different component arrangements. For example, computer device 1 may also include input / output devices, network access devices, etc.

[0088] It should be noted that the computer device 1 described is merely an example. Other existing or future electronic products that are adaptable to this invention should also be included within the scope of protection of this invention and are incorporated herein by reference.

[0089] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the computer device 1, such as a portable hard drive of the computer device 1. In other embodiments, the memory 12 can be an external storage device of the computer device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device 1. Furthermore, the memory 12 can include both internal and external storage units of the computer device 1. The memory 12 can be used not only to store application software and various types of data installed on the computer device 1, such as the code of a mobile robot posture control program, but also to temporarily store data that has been output or will be output.

[0090] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control unit of the computer device 1, connecting various components of the computer device 1 via various interfaces and lines. It executes programs or modules stored in the memory 12 (e.g., executing a mobile robot posture control program) and calls data stored in the memory 12 to perform various functions of the computer device 1 and process data.

[0091] The processor 13 executes the operating system of the computer device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the various embodiments of the mobile robot posture control method described above, for example... Figure 1 The steps are shown.

[0092] For example, the computer program may 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 invention. The one or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, which describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into a detection unit 110, a verification unit 111, a modeling unit 112, a reconstruction unit 113, and a control unit 114.

[0093] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute portions of the mobile robot posture control method described in the various embodiments of the present invention.

[0094] If the modules / units integrated in the computer device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0095] The computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory, etc.

[0096] Furthermore, the computer-readable storage medium may primarily include a stored program area and a stored data area, wherein the stored program area may store the operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of blockchain nodes, etc.

[0097] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0098] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, in... Figure 3 The bus is represented by only one straight line, but this does not mean that there is only one bus or one type of bus. The bus is configured to enable communication between the memory 12 and at least one processor 13, etc.

[0099] Although not shown, the computer device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 13 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The computer device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0100] Furthermore, the computer device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the computer device 1 and other computer devices.

[0101] Optionally, the computer device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the computer device 1 and to display a visual user interface.

[0102] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0103] It will be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the computer device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0104] Combination Figure 1 The memory 12 in the computer device 1 stores multiple instructions to implement a mobile robot posture control method, and the processor 13 can execute the multiple instructions to achieve the following: In response to attitude control commands to the target mobile robot, the system detects in real time whether the target mobile robot experiences load changes. 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.

[0105] Specifically, the processor 13's implementation method for the above instructions can be found in [reference needed]. Figure 1 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0106] It should be noted that all data involved in this case was legally obtained. Software tools or components not belonging to this company that appear in the embodiments of this application are merely illustrative examples and do not represent actual use.

[0107] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0108] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0109] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0110] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0111] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0112] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0113] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in this invention can also be implemented by a single unit or device through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

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 the target mobile robot, the system detects in real time whether the target mobile robot experiences load changes. 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 real-time detection of whether the target mobile robot experiences a load change includes: The target mobile robot uses its own foot force sensors to detect in real time whether there is asymmetrical fluctuation in the force on the supporting foot of the target mobile robot; The inertial measurement unit of the target mobile robot itself is used to detect in real time whether the torso posture of the target mobile robot continues to deviate within a first preset time period; The actual current, actual angular velocity, and actual torque of each joint of the target mobile robot are acquired in real time, and the target mobile robot is identified as having any disturbances that exceed 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 mapping relationship between the expected current, expected angular velocity, and expected torque of each joint; Real-time detection of whether load change flags are received; When an asymmetrical fluctuation in the force on the supporting leg of the target mobile robot is detected, 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 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 any 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.

3. The mobile robot posture control method as described in claim 2, 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.

4. 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.

5. 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.

6. 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.

7. The mobile robot posture control method as described in claim 6, 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.

8. A mobile robot posture control device, characterized in that, The mobile robot posture control device includes: The detection unit is used to detect in real time whether the target mobile robot experiences a load change in response to the attitude control command of 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.

9. 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 7.

10. 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 7.

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