Agricultural machine control method, device and equipment based on humanoid robot and storage medium

Abstract

The embodiment of the invention provides an agricultural machine control method, device and equipment based on a humanoid robot and a storage medium, and relates to the technical field of robot control. The method comprises the following steps: respectively constructing a first optimization function corresponding to a steering wheel and a second optimization function corresponding to a pedal by taking the steering wheel of the agricultural machine as a first base system and the pedal of the agricultural machine as a second base system, solving the first optimization function to obtain a steering wheel control prediction parameter, and solving the second optimization function to obtain a pedal control prediction parameter. Control decoupling is carried out on control of a steering wheel and a pedal of the humanoid robot in an agricultural machinery driving scene, a first optimization function and a second optimization function are established according to the steering wheel and the pedal of the agricultural machinery respectively, mutual interference of the steering wheel and the pedal on control logic is avoided, and the robot can independently optimize control strategies of two key operation components; and the overall control efficiency is improved. In addition, independent optimization can also avoid center-of-gravity shift caused by cooperative action, and it is ensured that the robot is kept stable in the agricultural machinery driving operation process.

Description

Technical Field

[0001] This application relates to the field of robot control technology, and in particular to agricultural machinery control methods, devices, equipment and storage media based on humanoid robots. Background Technology

[0002] In the scenario of autonomous agricultural machinery operation using humanoid robots, relying on satellite positioning, sensors, and intelligent algorithms, humanoid robots can autonomously plan paths, avoid obstacles, and perform operations, improving efficiency and accuracy, reducing labor costs, and are particularly suitable for large-scale farmland, promoting the intelligent transformation of agricultural production. However, interference factors such as terrain changes and vehicle bumps reduce the robot's stability in maneuvering the steering wheel and pedals.

[0003] Motion control methods in related technologies typically utilize high-performance state estimation and high-quality motion planning to improve maneuverability. However, these methods are highly complex and nonlinear, which severely restricts the real-time performance and stability of robot motion control. Summary of the Invention

[0004] The main objective of this application is to propose a method, device, equipment, and storage medium for controlling agricultural machinery based on humanoid robots, thereby improving the motion control efficiency and stability of humanoid robots in agricultural machinery driving scenarios.

[0005] To achieve the above objectives, a first aspect of this application proposes an agricultural machinery control method based on a humanoid robot, wherein the humanoid robot includes a first-area robotic arm and a second-area robotic arm, and the method includes:

[0006] Using the agricultural machinery steering wheel as the first base coordinate system, the linkage inertial force is calculated based on the state parameters of the robotic arm in the first region according to the first base coordinate system. The linkage inertial torque is calculated based on the linkage inertial force. The resultant force of adjacent positions obtained by each robotic arm in the first region is calculated. The joint torque corresponding to each robotic arm in the first region is calculated based on the linkage inertial force, the linkage inertial torque and the resultant force to obtain the first state equation.

[0007] Obtain the state error weight and control input weight corresponding to the steering wheel, generate a first optimization function for steering wheel control based on the state error weight, the control input weight and the first state equation, solve the first optimization function to obtain steering wheel control prediction parameters, and the steering wheel control prediction parameters are used to indicate the control information of at least one robotic arm in the first area.

[0008] Using the agricultural machinery pedal as the second base coordinate system, a second state equation corresponding to the robotic arm in the second region is obtained based on the second base coordinate system. A first target subset and a second target subset are selected from the robotic arms in the second region. A second optimization function is constructed based on the second state equation, the first target subset, and the second target subset. The second optimization function is solved to obtain pedal control prediction parameters. The pedal control prediction parameters are used to indicate the control information of at least one robotic arm in the second region.

[0009] In some embodiments, calculating the link inertial force based on the state parameters of the robotic arm in the first region includes:

[0010] Select the first region robotic arm one by one as the current robotic arm, and obtain the position of the center of mass of the current robotic arm in the first base coordinate system, the current link angular velocity, the current link angular acceleration and the current link linear acceleration;

[0011] Calculate the current centroid linear acceleration based on the centroid position, the current link angular velocity, the current link angular acceleration, and the current link linear acceleration;

[0012] Obtain the current link mass of the current robotic arm, and calculate the link inertial force of the current robotic arm based on the product of the current link mass and the current centroidal acceleration. Calculate the link inertial force of all robotic arms in the first region in sequence.

[0013] In some embodiments, calculating the link inertial torque based on the link inertial force includes:

[0014] For the current robotic arm, obtain the current center of mass inertia tensor of the current robotic arm;

[0015] Calculate the first product based on the current center of mass inertia tensor and the current link angular acceleration, and calculate the second product based on the current link angular velocity and the current center of mass inertia tensor;

[0016] The link inertia torque of the current robotic arm is obtained by summing the first product and the second product, and the link inertia torque of all robotic arms in the first region is calculated sequentially.

[0017] In some embodiments, calculating the resultant force at adjacent positions acquired by the robotic arm in each of the first regions includes:

[0018] For the current robotic arm, obtain the backward rotation matrix of the first region robotic arm with respect to the current robotic arm;

[0019] Obtain the resultant force of the robotic arm in the first region, and calculate the third product based on the backward rotation matrix and the resultant force.

[0020] The resultant force of the current robotic arm is obtained based on the third product and the current inertial force, and the resultant force of all robotic arms in the first region is calculated sequentially.

[0021] In some embodiments, the step of calculating the joint torque corresponding to each first region of the robotic arm based on the link inertial force, the link inertial torque, and the resultant force to obtain the first state equation includes:

[0022] For the current robotic arm, obtain the next resultant torque and the next resultant force of the next robotic arm in the first region, and obtain the third relative value, the first relative value, and the first coordinate value of the previous robotic arm in the first region and the current robotic arm.

[0023] Calculate the fourth product of the subsequent resultant torque and the backward rotation matrix; calculate the fifth product of the center of mass position and the link inertial force; calculate the sixth product of the first relative value and the first value of the previous coordinate; calculate the seventh product between the third relative value, the forward rotation matrix, and the current third coordinate value; calculate the sum of the sixth and seventh products, and the eighth product between the backward rotation matrix and the subsequent resultant force;

[0024] The fourth product, the fifth product, the eighth product, and the link inertial torque are summed to obtain the joint torque of the current robotic arm. The joint torques of all robotic arms in the first region are calculated sequentially, and the first state equation is obtained based on all the joint torques.

[0025] In some embodiments, obtaining the state error weights and control input weights corresponding to the steering wheel, and generating a first optimization function for steering wheel control based on the state error weights, the control input weights, and the first state equation, includes:

[0026] The joint torque described in the first state equation is used as the control input, and the predicted joint torque at the next moment is used as the predicted state.

[0027] A first state value is obtained by multiplying the transpose of the predicted state, the state error weight, and the predicted state; a second state value is obtained by multiplying the transpose of the control input, the control input weight, and the control input; and the first optimization function is obtained by summing the first state value and the second state value.

[0028] In some embodiments, solving the first optimization function to obtain the steering wheel control prediction parameters includes:

[0029] Based on convex optimization, the first optimization function is transformed into a quadratic programming problem;

[0030] Constraints are generated based on the variation difference of the robotic arm in the first region. The quadratic programming problem is solved based on the constraints to obtain the solution of the predicted state, which is used as the steering wheel control prediction parameters. The steering wheel control prediction parameters include the predicted resultant force and predicted resultant torque corresponding to each robotic arm in the first region.

[0031] In some embodiments, selecting a first target subset and a second target subset from the second region robotic arm, and constructing a second optimization function based on the second state equation, the first target subset, and the second target subset, includes:

[0032] At least based on the corresponding regions of the ankle and knee joints, a first subset of targets is selected from the robotic arms in the second region, and the remaining robotic arms in the second region are used as a second subset of targets;

[0033] The first target subset is mapped to an output function, and the second derivative of the output function is taken to obtain a first function corresponding to the first target subset. Based on the first function, a second function corresponding to the second target subset is obtained. The second optimization function includes the first function and the second function.

[0034] To achieve the above objectives, a second aspect of this application provides an agricultural machinery control device based on a humanoid robot, wherein the humanoid robot includes a first-area robotic arm and a second-area robotic arm, and the device includes:

[0035] State equation construction module: It is used to calculate the link inertial force based on the state parameters of the first region manipulator, with the agricultural machinery steering wheel as the first base coordinate system, and calculate the link inertial torque based on the link inertial force. It calculates the resultant force of adjacent positions obtained by each manipulator in the first region, and calculates the joint torque corresponding to each manipulator in the first region based on the link inertial force, the link inertial torque and the resultant force, so as to obtain the first state equation.

[0036] Steering wheel control optimization module: used to obtain the state error weight and control input weight corresponding to the steering wheel, generate a first optimization function for steering wheel control based on the state error weight, the control input weight and the first state equation, solve the first optimization function to obtain steering wheel control prediction parameters, the steering wheel control prediction parameters are used to indicate the control information of at least one robotic arm in the first area;

[0037] The pedal control optimization module is used to obtain a second state equation corresponding to the robotic arm in the second region, based on the second coordinate system, using the agricultural machinery pedal as the second base coordinate system. It selects a first target subset and a second target subset from the robotic arms in the second region, constructs a second optimization function based on the second state equation, the first target subset, and the second target subset, solves the second optimization function, and obtains pedal control prediction parameters. The pedal control prediction parameters are used to indicate the control information of at least one robotic arm in the second region.

[0038] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0039] To achieve the above objectives, a fourth aspect of the present application provides a storage medium that stores a computer program, which, when executed by a processor, implements the method described in the first aspect.

[0040] The agricultural machinery control method, device, equipment, and storage medium based on a humanoid robot proposed in this application embodiment calculates the link inertial force according to the state parameters of the first region robotic arm, and calculates the link inertial torque based on the link inertial force. It then calculates the resultant force at adjacent positions of each first region robotic arm and calculates the joint torque corresponding to each first region robotic arm based on the link inertial force, link inertial torque, and resultant force, thus obtaining a first state equation. Finally, it obtains the state error weight and control input weight corresponding to the steering wheel and generates a control system based on the state error weight, control input weight, and the first state equation. A first optimization function for steering wheel control is generated, and the first optimization function is solved to obtain steering wheel control prediction parameters. These parameters are used to indicate the control information of at least one robotic arm in a first region. Using the agricultural machinery pedal as a second coordinate system, a second state equation corresponding to the robotic arm in the second region is obtained. A first target subset and a second target subset are selected from the robotic arms in the second region. A second optimization function is constructed based on the second state equation, the first target subset, and the second target subset. The second optimization function is solved to obtain pedal control prediction parameters, which are used to indicate the control information of at least one robotic arm in the second region. This embodiment decouples the control of the steering wheel and pedals of a humanoid robot in an agricultural machinery driving scenario. A first optimization function and a second optimization function are established for the steering wheel and pedals of the agricultural machinery, respectively, avoiding mutual interference in their control logic. This allows the robot to independently optimize the control strategies of the two key operating components, improving overall control efficiency. Furthermore, independent optimization also avoids center of gravity shifts caused by coordinated actions, ensuring the robot remains stable during agricultural machinery driving operations. Attached Figure Description

[0041] Figure 1 This is a flowchart of an agricultural machinery control method based on a humanoid robot provided in an embodiment of this application.

[0042] Figure 2 This is a schematic diagram of the coordinate system of the robotic arm in an embodiment of this application.

[0043] Figure 3 This is a flowchart of calculating the link inertial force based on the state parameters of the robotic arm in the first region, provided in an embodiment of this application.

[0044] Figure 4 This is a schematic diagram of the force state of the robotic arm provided in the embodiments of this application.

[0045] Figure 5 This is a flowchart of calculating the inertial torque of a link based on the inertial force of the link, provided in an embodiment of this application.

[0046] Figure 6This is a flowchart provided in an embodiment of the present application for calculating the resultant force at adjacent positions obtained by the robotic arm in each first region.

[0047] Figure 7 This is a flowchart provided in this application embodiment for calculating the joint torque corresponding to each first region of the robotic arm based on the link inertial force, link inertial torque and resultant force, to obtain the first state equation.

[0048] Figure 8 This is a flowchart of an embodiment of the present application, which describes the acquisition of the state error weight and control input weight corresponding to the steering wheel, and the generation of a first optimization function for steering wheel control based on the state error weight, control input weight, and first state equation.

[0049] Figure 9 This is a flowchart provided in the embodiments of this application for solving the first optimization function to obtain the steering wheel control prediction parameters.

[0050] Figure 10 This is a flowchart provided in an embodiment of the present application, showing how to select a first target subset and a second target subset from a second region robotic arm, and how to construct a second optimization function based on a second state equation, the first target subset, and the second target subset.

[0051] Figure 11 This is a structural block diagram of an agricultural machinery control device based on a humanoid robot, provided in another embodiment of this application.

[0052] Figure 12 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0054] It should be noted that although functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0056] In the scenario of autonomous agricultural machinery operation using humanoid robots, relying on satellite positioning, sensors, and intelligent algorithms, humanoid robots can autonomously plan paths, avoid obstacles, and perform operations, improving efficiency and accuracy, reducing labor costs, and are particularly suitable for large-scale farmland, promoting the intelligent transformation of agricultural production. However, interference factors such as terrain changes and vehicle bumps reduce the robot's stability in maneuvering the steering wheel and pedals.

[0057] Motion control methods in related technologies typically utilize high-performance state estimation and high-quality motion planning to improve maneuverability. However, these methods are highly complex and nonlinear, which severely restricts the real-time performance and stability of robot motion control.

[0058] Based on this, embodiments of this application provide a method, apparatus, device, and storage medium for controlling agricultural machinery based on a humanoid robot. The method decouples the control of the steering wheel and pedals of the humanoid robot in agricultural machinery driving scenarios. A first optimization function and a second optimization function are established for the steering wheel and pedals respectively, avoiding mutual interference in their control logic. This allows the robot to independently optimize the control strategies of the two key operating components, improving overall control efficiency. Furthermore, independent optimization also avoids center-of-gravity shifts caused by coordinated movements, ensuring the robot remains stable during agricultural machinery driving operations.

[0059] This application provides an agricultural machinery control method, apparatus, device, and storage medium based on a humanoid robot, which will be described in detail through the following embodiments. First, the agricultural machinery control method based on a humanoid robot in this application embodiment is described.

[0060] The agricultural machinery control method based on a humanoid robot provided in this application relates to the field of robot control technology. This method can be applied to a terminal, a server, or a computer program running on either the terminal or the server. For example, the computer program can be a native program or software module in an operating system; it can be a native application (APP), i.e., a program that needs to be installed in the operating system to run, such as a client supporting humanoid robot-based agricultural machinery control, i.e., a program that only needs to be downloaded to a browser environment to run; it can also be a small program that can be embedded into any APP. In short, the above-mentioned computer program can be any form of application, module, or plugin. The terminal communicates with the server via a network. This humanoid robot-based agricultural machinery control method can be executed by the terminal or the server, or by the terminal and the server working together.

[0061] In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, or smartwatch, etc. The server can be a standalone server, or a cloud server providing 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 (CDNs), and big data and artificial intelligence platforms; it can also be a service node in a blockchain system, where the service nodes form a peer-to-peer (P2P) network. The P2P protocol is an application layer protocol running on top of the Transmission Control Protocol (TCP). The terminal and server can connect via Bluetooth, Universal Serial Bus (USB), or a network, etc., and this embodiment does not impose any limitations.

[0062] This application 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 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. This application 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.

[0063] The following describes an agricultural machinery control method based on a humanoid robot in an embodiment of this application.

[0064] Figure 1 This is an optional flowchart of the agricultural machinery control method based on a humanoid robot provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps 110 to 130. It is also understood that this embodiment... Figure 1 The order of steps 110 to 130 is not specifically limited. The order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0065] Step 110: Taking the agricultural machinery steering wheel as the first base coordinate system, calculate the link inertial force according to the state parameters of the first region robotic arm based on the first base coordinate system, calculate the link inertial torque based on the link inertial force, calculate the resultant force of adjacent positions obtained by each first region robotic arm, and calculate the joint torque corresponding to each first region robotic arm based on the link inertial force, link inertial torque and resultant force to obtain the first state equation.

[0066] In one embodiment, the humanoid robot includes multiple robotic arms, which can be divided according to the positions of the upper and lower limbs. The area used to control the movement of the upper limbs is called the first region, and the robotic arm in this region is called the first region robotic arm. The robotic arm used to control the movement of the lower limbs is called the second region, and the robotic arm in this region is called the second region robotic arm. In this embodiment, the first region robotic arm controls the rotation of the agricultural machinery steering wheel, and the second region robotic arm controls the braking of the agricultural machinery pedals. It is understood that the first region robotic arm and the second region robotic arm may partially overlap. For example, the robotic arm related to the waist and hips may belong to either the first region or the second region. Therefore, the first region and the second region are divided according to the actual situation.

[0067] In one embodiment, under the background of autonomous driving of agricultural machinery, the system state and control behavior of the humanoid robot are transformed into a differentially flat process, thus transforming the humanoid robot system into a differentially flat system. This differentially flat process describes the relationship between states and inputs. If a system is called a differentially flat system, then a variable can be found whose finite derivative uniquely determines all states and inputs. Therefore, firstly, taking the agricultural machinery steering wheel as the first base coordinate system, the relevant state equations of the steering wheel control process are constructed corresponding to the first base coordinate system.

[0068] In one embodiment, reference is made to Figure 2 , Figure 2 This is a schematic diagram of the coordinate system of the robotic arm in an embodiment of this application.

[0069] Reference Figure 2 Taking the first region's robotic arm as an example, the first base coordinate system is defined as coordinate system 0. The robotic arms in the first region are marked in order from 1 to n according to their distance from the first base coordinate system, with the farthest distance being the nth robotic arm. The diagram uses the ith robotic arm and the (i-1)th robotic arm as examples. Each robotic arm includes at least one joint and a link. Different robotic arms are connected by links. One joint of each robotic arm is connected to a joint of the preceding robotic arm, and the other joint is connected to a joint of the following robotic arm. During calculation, one joint is selected for each robotic arm. (Refer to...) Figure 2 The coordinate system of the (i-1)th robotic arm can be represented as: The coordinate system of the i-th robotic arm can be represented as: For the coordinate axis X, the rotation angle between the coordinate system of the (i-1)th robot arm and the coordinate system of the ith robot arm is denoted as θ. i-1 Hereinafter, this rotation angle is referred to as the joint angle. The distance between the origin of the coordinate system of the (i-1)th robotic arm and the origin of the coordinate system of the ith robotic arm is denoted as the first relative value a. i-1 For the coordinate axis Z, the rotation angle between the coordinate system of the (i-1)th robot arm and the coordinate system of the ith robot arm is denoted as α. i-1 The distance between the origin of the coordinate system of the (i-1)th robotic arm and the origin of the coordinate system of the ith robotic arm is denoted as the third relative value d. i .

[0070] In one embodiment, a Modified DH coordinate system is used to establish the coordinate system of the robotic arm in the first region. The forward kinematics model of the robotic arm is derived based on the homogeneous transformation matrix, where n is the number of links in the robotic arm, and 0 is used as the first base coordinate system. The transformation matrix represents the nth first region joint of the robotic arm based on the first base coordinate system. The transformation matrix is ​​expressed as:

[0071]

[0072] visible, This represents the homogeneous transformation matrix from the 0th coordinate system (i.e., the first base coordinate system) to the nth coordinate system, used to describe the pose of the endpoint (the nth coordinate system) relative to the first base coordinate system. Specifically, it is achieved by "multiplying" the homogeneous transformation matrices between multiple adjacent coordinate systems. It enables the transformation and transfer from the first base coordinate system to the final coordinate system. Describe the pose transformation from the (i-1)th coordinate system to the ith coordinate system, which physically corresponds to the local transformation of the joints of the robotic arm.

[0073] Therefore, by using the links between the robotic arms in the first region, any robotic arm can be associated with the first base coordinate system, thereby realizing the mapping from joint angles to spatial positions and obtaining the transformation process of the coordinate system of each robotic arm in the first region relative to the first base coordinate system.

[0074] The relevant rotation matrix and position data can be obtained from the pose transformation from the (i-1)th coordinate system to the ith coordinate system, as follows:

[0075]

[0076] in, The rotation matrix from the (i-1)th coordinate system to the ith coordinate system is called the forward rotation matrix. This represents the position data from the (i-1)th coordinate system to the ith coordinate system. It can be understood that... Let represent the rotation matrix from the i-th coordinate system to the (i-1)-th coordinate system. This is the inverse of the forward rotation matrix and is called the forward rotation inverse matrix. Based on this, the rotation matrix and position data of each robotic arm relative to the preceding and following robotic arms can be obtained.

[0077] In one embodiment, the generation process of the first state equation is described based on the above. (Refer to...) Figure 3 , Figure 3 This is a flowchart of calculating the link inertial force based on the state parameters of the robotic arm in the first region, provided in an embodiment of this application. The flowchart specifically includes the following steps:

[0078] Step 310: Select the first region robotic arm one by one as the current robotic arm, and obtain the position of the center of mass of the current robotic arm in the first base coordinate system, the current link angular velocity, the current link angular acceleration, and the current link linear acceleration.

[0079] In one embodiment, reference is made to Figure 4 , Figure 4 This is a schematic diagram of the force state of the robotic arm provided in this application embodiment. First, for each link, it has a center of mass position within the current robotic arm. This center of mass position can be determined according to the actual situation and can be represented by a rotation matrix using coordinates in the first base coordinate system. For each link, its center of mass position is subjected to the resultant force f of the current robotic arm. i i Corresponding joint torque Linkage inertial force F i i And the link inertial torque generated by the action of the subsequent robotic arm. The impact.

[0080] Therefore, selecting the first region robotic arm one by one as the current robotic arm, taking the i-th first region robotic arm as the current robotic arm as an example, firstly, it is necessary to obtain the position of the centroid in the first base coordinate system. Current link angular velocity Current link angular acceleration and current link acceleration

[0081] Specifically, the current angular velocity of the connecting rod Represented as:

[0082]

[0083] in, Represents the forward rotation matrix. This represents the link angular velocity of the (i-1)th robotic arm in the first region. Let represent the joint angular velocity of the i-th robotic arm relative to the (i+1)-th robotic arm. This represents the Z-coordinate value of the centroid. It is understandable that... The initial value is the desired angular velocity of the steering wheel calculated by the agricultural machinery control system based on actual conditions, such as obstacle avoidance and special terrain.

[0084] Current link angular acceleration Represented as:

[0085]

[0086] in, Represents the forward rotation matrix. This represents the angular acceleration of the link of the (i-1)th robotic arm in the first region, calculated based on actual conditions. This represents the joint angular acceleration of the i-th robotic arm relative to the (i+1)-th robotic arm, calculated based on the actual situation.

[0087] Current link acceleration Represented as:

[0088]

[0089] in, This represents the X-coordinate value of the center of mass in the coordinate system of the previous robotic arm, denoted as the first value of the previous coordinate system. This represents the linear acceleration of the link of the preceding robotic arm. The initial value is the desired linear acceleration of the steering wheel calculated by the agricultural machinery control system based on actual conditions, such as obstacle avoidance and special terrain.

[0090] Step 320: Calculate the current center-of-mass linear acceleration based on the center-of-mass position, current link angular velocity, current link angular acceleration, and current link linear acceleration.

[0091] In one embodiment, based on the centroid position Setting, current link angular velocity Current link angular acceleration and current link acceleration Calculated current centroidal acceleration Represented as:

[0092]

[0093] Step 330: Obtain the current link mass of the current robotic arm, and calculate the link inertial force of the current robotic arm by multiplying the current link mass and the current centroidal acceleration. Calculate the link inertial force of all robotic arms in the first region in sequence.

[0094] In one embodiment, the current link mass of the current robotic arm is m. i Therefore, the current link inertial force F of the robotic arm is obtained by multiplying the current link mass and the current acceleration along the center of mass. i i , is represented as:

[0095]

[0096] Following the above process, calculate the link inertial forces of all robotic arms in the first region in sequence.

[0097] Next, refer to Figure 5 , Figure 5 This is a flowchart of calculating the inertial moment of a link based on the inertial force of the link, provided in an embodiment of this application. The flowchart specifically includes the following steps:

[0098] Step 510: For the current robotic arm, obtain the current center of mass inertia tensor of the current robotic arm.

[0099] In one embodiment, taking the i-th first region robotic arm as the current robotic arm as an example, the current centroid inertia tensor of the current robotic arm is obtained based on the actual situation or prior knowledge.

[0100] Step 520: Calculate the first product based on the current center of mass inertia tensor and the current link angular acceleration, and calculate the second product based on the current link angular velocity and the current center of mass inertia tensor.

[0101] In one embodiment, the first product is represented as: The second score is expressed as

[0102] Step 530: Obtain the link inertia torque of the current robotic arm based on the sum of the first product and the second product, and calculate the link inertia torque of all robotic arms in the first region in sequence.

[0103] In one embodiment, the link inertial torque Represented as:

[0104]

[0105] Following the above process, calculate the link inertia torque of all robotic arms in the first region in sequence.

[0106] Next, refer to Figure 6 , Figure 6 This is a flowchart illustrating the calculation of the resultant force at adjacent positions obtained by the robotic arm in each first region, provided in an embodiment of this application. The flowchart specifically includes the following steps:

[0107] Step 610: For the current robotic arm, obtain the backward rotation matrix of the next first region robotic arm with respect to the current robotic arm.

[0108] In one embodiment, taking the i-th first region robotic arm as the current robotic arm as an example, the backward rotation matrix of the next first region robotic arm, i.e., the (i+1)-th first region robotic arm, relative to the current robotic arm is obtained.

[0109] Step 620: Obtain the resultant force of the next first region robotic arm, and calculate the third product based on the backward rotation matrix and the resultant force.

[0110] In one embodiment, the subsequent resultant force of the robotic arm in the latter first region is The third product calculated based on the backward rotation matrix and the subsequent resultant force is expressed as: It is understandable that since the resultant force of the steering wheel calculated by the agricultural machinery control system based on the actual situation can be regarded as prior knowledge, it is possible to iteratively deduce the resultant force of the first robotic arm in the first region based on this prior knowledge.

[0111] Step 630: Calculate the resultant force of the current robotic arm based on the third product and the current inertial force, and then calculate the resultant force of all robotic arms in the first region in sequence.

[0112] In one embodiment, the resultant force f of the current robotic arm i i Represented as:

[0113]

[0114] Following the above process, calculate the resultant force of all robotic arms in the first region in sequence.

[0115] Next, in one embodiment, refer to Figure 7 , Figure 7 This application provides a flowchart for calculating the joint torques of the robotic arm in each first region based on the link inertial force, link inertial torque, and resultant force, to obtain the first state equation. The flowchart specifically includes the following steps:

[0116] Step 710: For the current robotic arm, obtain the next resultant torque and the next resultant force of the next first region robotic arm, and obtain the third relative value, the first relative value, and the first value of the previous coordinate between the previous first region robotic arm and the current robotic arm.

[0117] In one embodiment, taking the i-th first region robotic arm as the current robotic arm, the next first region robotic arm is the (i+1)-th robotic arm, and the previous first region robotic arm is the (i-1)-th robotic arm. At this time, the resultant torque of the next first region robotic arm is obtained. The next combined force The third relative value d between the previous first region robotic arm and the current robotic arm i First relative value a i-1 The first value of the previous coordinate

[0118] Step 720: Calculate the fourth product of the next resultant torque and the backward rotation matrix, calculate the fifth product of the center of mass position and the link inertial force, calculate the sixth product of the first relative value and the first value of the previous coordinate, calculate the seventh product between the third relative value, the forward rotation matrix, and the current third coordinate value, and calculate the sum of the sixth and seventh products and the eighth product between the backward rotation matrix and the next resultant torque.

[0119] In one embodiment, the fourth product is represented as: The fifth product is represented as: The sixth product is represented as: The seventh product is represented as: The eighth product is represented as:

[0120] Step 730: Accumulate the fourth product, fifth product, eighth product and link inertia torque to obtain the joint torque of the current robotic arm. Calculate the joint torque of all robotic arms in the first region in sequence, and obtain the first state equation based on all joint torques.

[0121] In one embodiment, the resultant force of the current robotic arm is obtained by summing the fourth, fifth, and eighth products and the link inertial torque. Represented as:

[0122]

[0123] Therefore, the joint torque τ i Represented as:

[0124]

[0125] in, This represents the Z-coordinate value of the joint position in the coordinate system of the i-th robotic arm.

[0126] It is understandable that since the robotic arms are connected to each other through joints and links, the transmission process of motion parameter calculations can be performed. The force and torque required for steering wheel control can be calculated by obstacle avoidance and control technologies. Therefore, the link inertial force, link inertial torque, resultant force, joint torque, etc. of each first region robotic arm can be calculated by backward iteration from end-to-base or forward iteration from base to end-to-base.

[0127] The above process describes the dynamic state of the links and joints of each first-region robotic arm from the perspective of force and torque, extending kinematic analysis to the force level, enabling precise coordinated control of the robotic arm and avoiding steering wheel overload.

[0128] In one embodiment, the total torque τ is obtained by summing the joint torques of all the first-region robotic arms. The total torque can then be expressed in the following form:

[0129]

[0130] The above equation is obtained by integrating the total torque equation. It can be seen that the variable ultimately affecting control can be characterized by the X-axis rotation angle of the joint in the coordinate system. Here, M is the mass matrix corresponding to the robotic arm, B represents centrifugal force, C represents the Coriolis force vector, and G is the gravity vector. The mass matrix characterizes the distribution of inertial drag during the coordinated rotation of the robotic arms. If a link in the robotic arm is heavy, the corresponding link mass is large, requiring a larger joint torque. The centrifugal force and Coriolis force vector are used to characterize the interference caused by the coupling of joint angular velocities between the robotic arms, while the gravity vector characterizes the downward torque of the robotic arm's own gravity on the steering wheel. It is necessary to ensure that the stability of the steering wheel is not affected during optimization.

[0131] Step 120: Obtain the state error weight and control input weight corresponding to the steering wheel. Generate the first optimization function for steering wheel control based on the state error weight, control input weight and the first state equation. Solve the first optimization function to obtain the steering wheel control prediction parameters.

[0132] In one embodiment, reference is made to Figure 8 , Figure 8 This is a flowchart illustrating the process of obtaining the state error weights and control input weights corresponding to the steering wheel, and generating a first optimization function for steering wheel control based on the state error weights, control input weights, and a first state equation, as provided in this application embodiment. The flowchart specifically includes the following steps:

[0133] Step 810: Use the joint torque in the first state equation as the control input, and use the predicted joint torque at the next moment as the predicted state.

[0134] In one embodiment, during the prediction process, in each control cycle, the system behavior over a future period (prediction time domain) is predicted using the current state and the system's dynamic parameters. This involves solving a finite-time optimization problem to obtain the optimal control sequence, which can be applied to the control of a robotic arm that manipulates a steering wheel. Specifically, the total torque τ is used as the control input. Where k represents the current time, k+i represents the prediction step, and the predicted joint torque for the next time step is... As a predicted state.

[0135] Step 820: Obtain the first state value by multiplying the transpose of the predicted state, the state error weight, and the predicted state; obtain the second state value by multiplying the transpose of the control input, the control input weight, and the control input; and accumulate the first state value and the second state value to obtain the first optimization function.

[0136] In one embodiment, the first state value is represented as: The second state value is represented as: Among them, Q i R represents the state error weight corresponding to the i-th robotic arm. i This represents the control input weight corresponding to the i-th robotic arm. The weight can be determined by prior knowledge or corrected during the actual prediction process.

[0137] Therefore, N represents the total number of prediction steps, and the first optimization function is expressed as:

[0138]

[0139] Next, refer to Figure 9 , Figure 9 This is a flowchart provided in this application embodiment for solving the first optimization function to obtain the steering wheel control prediction parameters, specifically including the following steps:

[0140] Step 910: Based on convex optimization, transform the first optimization function into a quadratic programming problem.

[0141] In one embodiment, the first state value in the first optimization function serves as a state deviation penalty, and the second state value serves as a control cost penalty. The predicted state can be represented as:

[0142]

[0143] Among them, A i Let X represent the state transition matrix. k This represents the state vector at the current time k, such as the position of the center of mass, angular velocity, angular acceleration, linear velocity, linear acceleration, and other parameters of the robotic arm in each first region. B i Represents the control input matrix. This represents the joint torque-related data starting from time k.

[0144] Therefore, the first optimization function becomes:

[0145]

[0146] Expanding and combining like terms yields the standard form of the quadratic programming problem, expressed as:

[0147] The first optimization function becomes:

[0148]

[0149] Where K represents a constant term, which can be ignored.

[0150] The final quadratic programming problem is expressed as:

[0151]

[0152]

[0153] at this time, H represents the quantity to be solved, H represents the coefficient of the quadratic penalty term, and E represents the penalty term for the coupling between state and control.

[0154] Step 920: Generate constraints based on the variation difference of the robotic arm in the first region, solve the quadratic programming problem based on the constraints, and obtain the solution of the predicted state as the steering wheel control prediction parameter.

[0155] In one embodiment, the constraint is expressed as:

[0156]

[0157] in, ΔU represents the difference in change between adjacent control steps. min ΔU represents the minimum permissible change value (to prevent sudden changes in the control quantity). max Indicates the maximum permissible variation value (to prevent actuator saturation), U min U represents the physical minimum value of a control quantity, such as the reverse torque limit of a motor. max This represents the physical maximum value of the control quantity, such as the positive torque limit of the motor. The physical quantity here is selected according to the actual requirements.

[0158] Next, the interior-point method or activity set method is used to solve the quadratic programming problem based on constraints, obtaining the solution for the predicted state, which serves as the steering wheel control prediction parameters. For example, first, the desired steering angle of the steering wheel is obtained based on the actual situation. The state vector at the current time k is determined based on this desired steering angle. Then, the optimization problem is solved based on the current joint torque, predicting the resultant force and resultant torque corresponding to each first region of the robotic arm, obtaining the predicted resultant force and predicted resultant torque.

[0159] This application embodiment adopts a two-handed collaborative mode to control the steering wheel of agricultural machinery using the range of motion of the robotic arm. The high-dimensional nonlinear autonomous driving robot system model is transformed into an equivalent first state equation using differential flatness analysis. Then, a first optimization algorithm is determined based on the first state equation. After solving it, an autonomous driving robot robotic arm motion control with good solution efficiency and strong anti-interference ability is achieved, realizing operations such as straight movement, left turn, and right turn.

[0160] Next, the control process of the agricultural machinery pedals will be described.

[0161] Step 130: Using the agricultural machinery pedal as the second base coordinate system, obtain the second state equation corresponding to the second region robotic arm based on the second base coordinate system, select the first target subset and the second target subset from the second region robotic arm, construct the second optimization function based on the second state equation, the first target subset and the second target subset, solve the second optimization function, and obtain the pedal control prediction parameters. The pedal control prediction parameters are used to indicate the control information of at least one second region robotic arm.

[0162] In one embodiment, firstly, following the method for generating the first state equation, using the agricultural machinery pedal as the second base coordinate system, the second state equation corresponding to the robotic arm in the second region is obtained based on the second base coordinate system. Next, optimization is performed based on the second state equation.

[0163] In one embodiment, reference is made to Figure 10 , Figure 10 This is a flowchart provided in this application embodiment of the process of selecting a first target subset and a second target subset from a second region robotic arm, and constructing a second optimization function based on a second state equation, the first target subset, and the second target subset. The flowchart specifically includes the following steps:

[0164] Step 1010: Select a first target subset from the second region robotic arms based at least on the corresponding regions of the ankle and knee joints, and use the remaining robotic arms in the second region robotic arms as the second target subset.

[0165] In one embodiment, since the primary objective of pedal control is the pedal's depressing angle and force, which are closely related to the ankle and knee joints, for the set of robotic arms in the second region, at least the robotic arms corresponding to the ankle and knee joints are considered as the set of robotic arms with the minimum control quantity corresponding to the primary objective, thus obtaining the first objective subset. The remaining robotic arms in the second region are considered as the second objective subset, corresponding to secondary objectives, such as hip joint posture adjustment. It is understood that since the motion of the robotic arm can be converted into a related representation of joint angles in the second state equation, the following representation relationship can be obtained: q = [q1, q2] = θ, where q1 represents the joint angle of the robotic arm in the first objective subset, and q2 represents the joint angle of the robotic arm in the second objective subset. Simultaneously, the corresponding angular velocity can be obtained from the joint angle q. Therefore, the state vector It can be represented as

[0166] Step 1020: Map the first target subset to an output function, take the second derivative of the output function to obtain the first function corresponding to the first target subset, and obtain the second function corresponding to the second target subset based on the first function.

[0167] In one embodiment, the first function and the second function constitute the second optimization function. A linearized system model is established, represented as:

[0168]

[0169] y = h(q)

[0170] in, This represents the error state, which is the transformed state vector. Let A' and B' represent the transformed control input vector, A' and B' represent the linearized system matrices, and y represent the system output function, such as the actual position corresponding to the actual angle of the pedal.

[0171] Next, the second derivative of the output function is taken to calculate the desired acceleration, which is represented by the first function:

[0172]

[0173] in, Indicates the final expected acceleration. This represents the initial expected acceleration planned based on actual conditions, and is considered prior knowledge. K represents the current actual speed. d K pThis represents the control gain matrix, which is set according to the actual situation. Specifically, it obtains the original expected acceleration generated by the motion planner, measures the current actual velocity and position, and then calculates the position error. and speed error Compensated acceleration is generated by controlling the gain matrix, and the final desired acceleration is obtained based on the original desired acceleration and the compensated acceleration.

[0174] Next, the second function for calculating the acceleration of the secondary target is expressed as:

[0175]

[0176] in, Indicates the expected acceleration of the secondary objective. Represents the generalized inverse Jacobian matrix. M represents the time derivative of the Jacobian matrix. 11 Let represent the link mass matrix corresponding to the first target subset, τ1 represent the total torque corresponding to the first target subset, and H1 represent the Jacobian matrix corresponding to the first target subset. Specifically, the dynamic coupling term is first calculated. Next, we calculate the velocity-related terms. Subtracting these two terms from the final expected acceleration calculated above, and then applying the generalized inverse Jacobian matrix... The results are mapped to the space corresponding to the second target subset, and finally the acceleration of the secondary target corresponding to the second target subset is output.

[0177] Based on the above process for the secondary target acceleration, the predicted parameters u for pedal control can be obtained, expressed as:

[0178]

[0179] Among them, M 22 M represents the matrix M representing the link mass corresponding to the second target subset. 21 =M 12 τ2 represents the link mass corresponding to the coupling terms of the first and second target subsets, and τ2 represents the total torque corresponding to the second target subset.

[0180] For example, when agricultural machinery needs to accelerate on a bumpy road, the ankle joint needs to press the accelerator pedal deeply. At this time, the total torque of the robotic arm corresponding to the ankle joint in the first target subset is used to control the accelerator pedal depth. In the second target subset, the robotic arm corresponding to the hip joint needs to change the corresponding total torque to achieve the secondary target acceleration. Then, by obtaining the matrix of the corresponding link mass, the corresponding pedal control prediction parameters can be obtained.

[0181] In the pedal control process of the above embodiment, based on the control of the current state quantity and motion planning reference state quantity by parameters such as the lateral error of the agricultural machinery, the speed error of the agricultural machinery, and the pose error of the robot's two legs end effectors, a mechanical leg motion control algorithm based on partial feedback linearization is designed by combining feedback control and the calculated robot state error. This transforms the nonlinear system into a linear system, thereby applying a linear control method to enable the mechanical leg to achieve relatively stable and accurate pedal control, realizing operations such as acceleration, deceleration, and stopping.

[0182] This application analyzes and decouples the robot's steering wheel control and pedal control processes. By studying the robot's kinematic characteristics, kinematic constraints, and minimum state description, it analyzes the robot's posture stability characteristics. Based on the obstacle avoidance control of humanoid robots in agricultural machinery, the control objectives can be divided into steering wheel control and pedal control requirements. The optimal steering wheel control and pedal control strategies are solved to design an autonomous driving motion planning scheme for the robot. First, based on the principle of differential flatness, the robot's kinematic model is analyzed for differential flatness, simplifying the complexity of the robot system and improving the feasibility of the controller design. Then, the relevant state equations of the robot are derived, and mathematical modeling of the motion control problem is performed, defining the motion control performance index function. Finally, optimization is performed based on this to obtain the optimal control strategies for the robot with respect to the steering wheel and pedals respectively. At the control theory level, the limitations of traditional single-stage state estimation are overcome, and a hierarchical predictive control architecture with time-varying constraint characteristics is designed to improve the stability of agricultural machinery motion control under complex disturbances.

[0183] The technical solution provided in this application embodiment uses the agricultural machinery steering wheel as the first base coordinate system. Corresponding to the first base coordinate system, it calculates the link inertial force based on the state parameters of the robotic arm in the first region, calculates the link inertial torque based on the link inertial force, calculates the resultant force at adjacent positions of each robotic arm in the first region, and calculates the joint torque corresponding to each robotic arm in the first region based on the link inertial force, link inertial torque, and resultant force, thus obtaining the first state equation. It then obtains the state error weight and control input weight corresponding to the steering wheel, and generates the first optimal steering wheel control based on the state error weight, control input weight, and the first state equation. The first optimization function is solved to obtain steering wheel control prediction parameters, which are used to indicate the control information of at least one robotic arm in the first region. Using the agricultural machinery pedal as the second coordinate system, a second state equation corresponding to the robotic arm in the second region is obtained. A first target subset and a second target subset are selected from the robotic arms in the second region. A second optimization function is constructed based on the second state equation, the first target subset, and the second target subset. The second optimization function is solved to obtain pedal control prediction parameters, which are used to indicate the control information of at least one robotic arm in the second region. This embodiment of the application decouples the control of the steering wheel and pedals of the humanoid robot in the agricultural machinery driving scenario. The first and second optimization functions are established for the steering wheel and pedals of the agricultural machinery, respectively, avoiding mutual interference in their control logic. This allows the robot to independently optimize the control strategies of the two key operating components, improving overall control efficiency. Furthermore, independent optimization also avoids center of gravity shifts caused by coordinated actions, ensuring the robot remains stable during agricultural machinery driving operations.

[0184] This application also provides an agricultural machinery control device based on a humanoid robot, which can implement the above-mentioned agricultural machinery control method based on a humanoid robot, referring to... Figure 11 The device includes:

[0185] State equation construction module 1110: It is used to calculate the link inertial force based on the state parameters of the first region robotic arm, with the agricultural machinery steering wheel as the first base coordinate system, and calculate the link inertial torque based on the link inertial force. It also calculates the resultant force of adjacent positions obtained by each first region robotic arm, and calculates the joint torque corresponding to each first region robotic arm based on the link inertial force, link inertial torque and resultant force, so as to obtain the first state equation.

[0186] Steering wheel control optimization module 1120: used to obtain the state error weight and control input weight corresponding to the steering wheel, generate the first optimization function of steering wheel control based on the state error weight, control input weight and the first state equation, solve the first optimization function to obtain steering wheel control prediction parameters, and the steering wheel control prediction parameters are used to indicate the control information of at least one first area robotic arm.

[0187] The pedal control optimization module 1130 is used to obtain the second state equation corresponding to the second region robotic arm based on the agricultural machinery pedal as the second base coordinate system, select the first target subset and the second target subset from the second region robotic arm, construct the second optimization function based on the second state equation, the first target subset and the second target subset, solve the second optimization function, and obtain the pedal control prediction parameters. The pedal control prediction parameters are used to indicate the control information of at least one second region robotic arm.

[0188] The specific implementation of the agricultural machinery control device based on humanoid robots in this embodiment is basically the same as the specific implementation of the agricultural machinery control method based on humanoid robots described above, and will not be repeated here.

[0189] This application also provides an electronic device, including:

[0190] At least one memory;

[0191] At least one processor;

[0192] At least one program;

[0193] The program is stored in a memory, and the processor executes the at least one program to implement the above-described agricultural machinery control method based on a humanoid robot. The electronic device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), and in-vehicle computers.

[0194] Please see Figure 12 , Figure 12 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0195] The processor 1201 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0196] The memory 1202 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1202 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1202 and is called and executed by the processor 1201 to execute the agricultural machinery control method based on a humanoid robot according to the embodiments of this application.

[0197] The input / output interface 1203 is used to implement information input and output;

[0198] The communication interface 1204 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0199] Bus 1205 transmits information between various components of the device (e.g., processor 1201, memory 1202, input / output interface 1203, and communication interface 1204);

[0200] The processor 1201, memory 1202, input / output interface 1203 and communication interface 1204 are connected to each other within the device via bus 1205.

[0201] This application embodiment also provides a storage medium that stores a computer program. When the computer program is executed by a processor, it implements the above-described agricultural machinery control method based on a humanoid robot.

[0202] Memory, as a non-transitory storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0203] The agricultural machinery control method, device, equipment, and storage medium based on a humanoid robot proposed in this application embodiment calculates the link inertial force according to the state parameters of the first region robotic arm, and calculates the link inertial torque based on the link inertial force. It then calculates the resultant force at adjacent positions of each first region robotic arm and calculates the joint torque corresponding to each first region robotic arm based on the link inertial force, link inertial torque, and resultant force, thus obtaining a first state equation. Finally, it obtains the state error weight and control input weight corresponding to the steering wheel and generates a control system based on the state error weight, control input weight, and the first state equation. A first optimization function for steering wheel control is generated, and the first optimization function is solved to obtain steering wheel control prediction parameters. These parameters are used to indicate the control information of at least one robotic arm in a first region. Using the agricultural machinery pedal as a second coordinate system, a second state equation corresponding to the robotic arm in the second region is obtained. A first target subset and a second target subset are selected from the robotic arms in the second region. A second optimization function is constructed based on the second state equation, the first target subset, and the second target subset. The second optimization function is solved to obtain pedal control prediction parameters, which are used to indicate the control information of at least one robotic arm in the second region. This embodiment decouples the control of the steering wheel and pedals of a humanoid robot in an agricultural machinery driving scenario. A first optimization function and a second optimization function are established for the steering wheel and pedals of the agricultural machinery, respectively, avoiding mutual interference in their control logic. This allows the robot to independently optimize the control strategies of the two key operating components, improving overall control efficiency. Furthermore, independent optimization also avoids center of gravity shifts caused by coordinated actions, ensuring the robot remains stable during agricultural machinery driving operations.

[0204] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0205] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0206] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; 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.

[0207] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0208] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0209] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0210] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0211] The units described above as separate components may or may not be physically separate. The components shown as units 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0212] Furthermore, the functional units in the various embodiments of this application 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 as a software functional unit.

[0213] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0214] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for controlling agricultural machinery based on a humanoid robot, characterized in that, The humanoid robot includes a first-area robotic arm and a second-area robotic arm, and the method includes: Using the agricultural machinery steering wheel as the first base coordinate system, the linkage inertial force is calculated based on the state parameters of the robotic arm in the first region according to the first base coordinate system. The linkage inertial torque is calculated based on the linkage inertial force. The resultant force of adjacent positions obtained by each robotic arm in the first region is calculated. The joint torque corresponding to each robotic arm in the first region is calculated based on the linkage inertial force, the linkage inertial torque and the resultant force to obtain the first state equation. Obtain the state error weight and control input weight corresponding to the steering wheel, generate a first optimization function for steering wheel control based on the state error weight, the control input weight and the first state equation, solve the first optimization function to obtain steering wheel control prediction parameters, and the steering wheel control prediction parameters are used to indicate the control information of at least one robotic arm in the first area. Using the agricultural machinery pedal as the second base coordinate system, a second state equation corresponding to the robotic arm in the second region is obtained based on the second base coordinate system. A first target subset and a second target subset are selected from the robotic arms in the second region. A second optimization function is constructed based on the second state equation, the first target subset, and the second target subset. The second optimization function is solved to obtain pedal control prediction parameters. The pedal control prediction parameters are used to indicate the control information of at least one robotic arm in the second region.

2. The agricultural machinery control method based on a humanoid robot according to claim 1, characterized in that, The calculation of the link inertial force based on the state parameters of the robotic arm in the first region includes: Select the first region robotic arm one by one as the current robotic arm, and obtain the position of the center of mass of the current robotic arm in the first base coordinate system, the current link angular velocity, the current link angular acceleration and the current link linear acceleration; Calculate the current centroid linear acceleration based on the centroid position, the current link angular velocity, the current link angular acceleration, and the current link linear acceleration; Obtain the current link mass of the current robotic arm, and calculate the link inertial force of the current robotic arm based on the product of the current link mass and the current centroidal acceleration. Calculate the link inertial force of all robotic arms in the first region sequentially.

3. The agricultural machinery control method based on a humanoid robot according to claim 2, characterized in that, The calculation of the link inertial torque based on the link inertial force includes: For the current robotic arm, obtain the current center of mass inertia tensor of the current robotic arm; Calculate the first product based on the current center of mass inertia tensor and the current link angular acceleration, and calculate the second product based on the current link angular velocity and the current center of mass inertia tensor; The link inertia torque of the current robotic arm is obtained by summing the first product and the second product, and the link inertia torque of all robotic arms in the first region is calculated sequentially.

4. The agricultural machinery control method based on a humanoid robot according to claim 3, characterized in that, The calculation of the resultant force at adjacent positions obtained by the robotic arm in each of the first regions includes: For the current robotic arm, obtain the backward rotation matrix of the first region robotic arm with respect to the current robotic arm; Obtain the resultant force of the robotic arm in the first region, and calculate the third product based on the backward rotation matrix and the resultant force. The resultant force of the current robotic arm is obtained based on the third product and the current inertial force, and the resultant force of all robotic arms in the first region is calculated sequentially.

5. The agricultural machinery control method based on a humanoid robot according to claim 4, characterized in that, The calculation of the joint torque corresponding to each first region of the robotic arm based on the link inertial force, the link inertial torque, and the resultant force yields the first state equation, including: For the current robotic arm, obtain the next resultant torque and the next resultant force of the next robotic arm in the first region, and obtain the third relative value, the first relative value, and the first coordinate value of the previous robotic arm in the first region and the current robotic arm. Calculate the fourth product of the subsequent resultant torque and the backward rotation matrix; calculate the fifth product of the center of mass position and the link inertial force; calculate the sixth product of the first relative value and the first value of the previous coordinate; calculate the seventh product between the third relative value, the forward rotation matrix, and the current third coordinate value; calculate the sum of the sixth and seventh products, and the eighth product between the backward rotation matrix and the subsequent resultant force; The joint torque of the current robotic arm is obtained by summing the fourth product, the fifth product, the eighth product, and the link inertia torque. The joint torques of all robotic arms in the first region are calculated sequentially, and the first state equation is obtained based on all the joint torques.

6. The agricultural machinery control method based on a humanoid robot according to claim 1, characterized in that, The step of obtaining the state error weight and control input weight corresponding to the steering wheel, and generating a first optimization function for steering wheel control based on the state error weight, the control input weight, and the first state equation includes: The joint torque described in the first state equation is used as the control input, and the predicted joint torque at the next moment is used as the predicted state. A first state value is obtained by multiplying the transpose of the predicted state, the state error weight, and the predicted state; a second state value is obtained by multiplying the transpose of the control input, the control input weight, and the control input; and the first optimization function is obtained by summing the first state value and the second state value.

7. The agricultural machinery control method based on a humanoid robot according to claim 6, characterized in that, Solving the first optimization function to obtain the steering wheel control prediction parameters includes: Based on convex optimization, the first optimization function is transformed into a quadratic programming problem; Constraints are generated based on the variation difference of the robotic arm in the first region. The quadratic programming problem is solved based on the constraints to obtain the solution of the predicted state, which is used as the steering wheel control prediction parameters. The steering wheel control prediction parameters include the predicted resultant force and predicted resultant torque corresponding to each robotic arm in the first region.

8. The agricultural machinery control method based on a humanoid robot according to claim 1, characterized in that, The step of selecting a first target subset and a second target subset from the second region robotic arm, and constructing a second optimization function based on the second state equation, the first target subset, and the second target subset includes: At least based on the corresponding regions of the ankle and knee joints, a first subset of targets is selected from the robotic arms in the second region, and the remaining robotic arms in the second region are used as a second subset of targets; The first target subset is mapped to an output function, and the second derivative of the output function is taken to obtain a first function corresponding to the first target subset. Based on the first function, a second function corresponding to the second target subset is obtained. The second optimization function includes the first function and the second function.

9. An agricultural machinery control device based on a humanoid robot, characterized in that, The humanoid robot includes a first-area robotic arm and a second-area robotic arm, and the device includes: State equation construction module: It is used to calculate the link inertial force based on the state parameters of the first region manipulator, with the agricultural machinery steering wheel as the first base coordinate system, and calculate the link inertial torque based on the link inertial force. It calculates the resultant force of adjacent positions obtained by each manipulator in the first region, and calculates the joint torque corresponding to each manipulator in the first region based on the link inertial force, the link inertial torque and the resultant force, so as to obtain the first state equation. Steering wheel control optimization module: used to obtain the state error weight and control input weight corresponding to the steering wheel, generate a first optimization function for steering wheel control based on the state error weight, the control input weight and the first state equation, solve the first optimization function to obtain steering wheel control prediction parameters, the steering wheel control prediction parameters are used to indicate the control information of at least one robotic arm in the first area; The pedal control optimization module is used to obtain a second state equation corresponding to the robotic arm in the second region, based on the second coordinate system, using the agricultural machinery pedal as the second base coordinate system. It selects a first target subset and a second target subset from the robotic arms in the second region, constructs a second optimization function based on the second state equation, the first target subset, and the second target subset, solves the second optimization function, and obtains pedal control prediction parameters. The pedal control prediction parameters are used to indicate the control information of at least one robotic arm in the second region.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the agricultural machinery control method based on a humanoid robot as described in any one of claims 1 to 8.

11. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the agricultural machinery control method based on a humanoid robot as described in any one of claims 1 to 8.

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