Precise control method, device and equipment for truss robot and medium

Abstract

The invention relates to the field of truss robot control, in particular to an accurate control method, device and equipment for a truss robot and a medium. The method comprises the steps that operation data (including the current position and the movable angle of a mechanical arm) of each target robot corresponding to a target area and a workpiece transportation plan (including the workpiece transportation position and the workpiece transportation speed) corresponding to the target area are acquired, and the workpiece transportation position and the workpiece transportation speed are calculated on the basis of the workpiece transportation plan and the operation data of each target robot; and determining a grabbing workpiece corresponding to each target robot, and formulating and executing a grabbing plan of each target robot according to the grabbing workpiece corresponding to each target robot and the operation data. Accurate control over the actions of the robot is achieved through the step, and the workpiece grabbing accuracy of the truss robot is improved.

Description

Technical Field

[0001] The present application relates to the field of truss robot control, and in particular to a truss robot precision control method, device, equipment and medium. Background Art

[0002] Gantry robots play an important role in modern industrial production, especially in the material handling and processing on automated production lines. With the continuous development of the manufacturing industry, the application scope of gantry robots has gradually expanded, which not only improves production efficiency but also significantly reduces labor costs. By precisely controlling the movement of gantry robots, high-precision workpiece handling and assembly can be achieved, thereby improving product quality and consistency. In addition, the adaptability and flexibility of gantry robots in complex environments also make them the preferred equipment for many factories and enterprises.

[0003] In practical applications, in order to achieve efficient operation of truss robots, some technical means are usually used to optimize their control strategies. For example, the position and posture information of the robot can be collected in real time through sensors, and the corresponding motion path and grasping plan can be formulated in combination with the pre-set workpiece transportation plan. Common control methods include PID control, fuzzy control, adaptive control, etc. These methods can effectively cope with dynamic changes in different working environments and ensure the stable operation of the robot. At the same time, dynamic modeling and simulation technology can be used to further optimize the robot's motion performance, reduce energy consumption, and extend its service life.

[0004] However, existing truss robot control methods have certain limitations when dealing with multi-robot collaborative operations. Specifically, when faced with complex workpiece transportation plans and multi-robot collaborative operations, traditional control methods are difficult to achieve accurate grasping and transportation, which can easily lead to workpiece position deviation and low transportation efficiency. Therefore, a method that can effectively solve the above grasping accuracy problem is urgently needed. Summary of the invention

[0005] In order to improve the grasping accuracy of a truss robot, the present application provides a truss robot precision control method, device, equipment and medium.

[0006] In the first aspect, the present application provides a truss robot precision control method, which adopts the following technical solution: A truss robot precision control method, comprising: Acquire operation data of each target robot corresponding to the target area, and acquire a workpiece transportation plan corresponding to the target area, wherein the target area corresponds to at least two target robots, and one target robot corresponds to at least one robotic arm, wherein the operation data includes the current position of the target robot and the movable angle of the robotic arm of each target robot, and the workpiece transportation plan includes the workpiece transportation position and the workpiece transportation speed; Determine the grasped workpiece corresponding to each target robot based on the workpiece transportation plan and the operation data of each target robot; Based on the grasped workpiece and operation data corresponding to each of the target robots, a grasping plan corresponding to each of the target robots is determined, and the corresponding target robot is controlled according to the grasping plan.

[0007] By adopting the above technical solution, by obtaining the operation data of each target robot corresponding to the target area (including the current position and the movable angle of the robot arm), and the workpiece transportation plan corresponding to the target area (including the workpiece transportation position and speed), based on the workpiece transportation plan and the operation data of each target robot, the grasping workpiece corresponding to each target robot is determined, and according to the grasping workpiece and operation data corresponding to each target robot, the grasping plan of each target robot is formulated and executed. This step realizes the precise control of the robot's movements and improves the accuracy of the truss robot's grasping of workpieces.

[0008] In a possible implementation, the operation data further includes the current speed, current acceleration, and current robot arm angle of each target robot. Based on the workpiece transportation plan and the operation data of each target robot, determining the grasped workpiece corresponding to each target robot includes: Receiving operation data corresponding to each target robot in real time, and establishing a dynamic model corresponding to the target area based on the operation data corresponding to each target robot; Based on the workpiece transportation plan and the dynamic model corresponding to the target area, the grasped workpiece corresponding to each target robot is determined.

[0009] By adopting the above technical solutions, the efficiency and accuracy of the robot's work are significantly improved, and the error rate and cost are reduced by collecting operation data in real time, building a dynamic model and accurately determining the grasped workpiece.

[0010] In a possible implementation, a dynamic model corresponding to the target area is established based on the operation data corresponding to each target robot, including: Based on the operation data corresponding to each of the target robots, a state equation corresponding to each of the target robots is established, and a variable relationship between each state equation and the operation data is determined; Establish the measurement equation corresponding to each variable relationship; Based on each of the measurement directions and each of the state equations, a dynamic model corresponding to the target area is established.

[0011] By adopting the above technical solution, a target area dynamics model is established based on the operating data of each target robot, which not only improves the accuracy and practicality of the model, but also provides a comprehensive and systematic method to understand and control the dynamic changes in the target area, which helps to improve the working efficiency and accuracy of the truss robot.

[0012] In a possible implementation, based on the operation data corresponding to each of the target robots, a state equation corresponding to each of the target robots is established. The process of establishing the state equation corresponding to each of the target robots includes: Generate a state vector corresponding to the operating data, and determine a target derivative corresponding to the state vector, wherein the target derivative is a derivative of the state vector with respect to time; Determine the basic form of the state equation corresponding to the operating data based on the Newton-Euler equation; Obtaining control features corresponding to the target robot; Based on the control characteristics, target derivatives and basic types corresponding to the target robot, a state equation corresponding to the target robot is established.

[0013] By adopting the above technical solution, the process of establishing the corresponding state equation of each target robot based on its operating data not only improves the accuracy and reliability of the model, but also provides a comprehensive and systematic method to control the robot's motion state, thereby helping to improve the robot's work efficiency and performance.

[0014] In a possible implementation, determining the grasped workpiece corresponding to each target robot based on the workpiece transportation plan and the dynamic model corresponding to the target area includes: Obtaining the workpiece demand and the number of robot arms corresponding to each target robot, and identifying the transportation sub-plan corresponding to each workpiece in the workpiece transportation plan, wherein the transportation sub-plan includes the start transportation time, transportation speed, and transportation location of the workpiece; Determine the gripping robot arm corresponding to each workpiece based on the operation data corresponding to each target robot, the workpiece demand, the number of robot arms, and the transportation sub-plan corresponding to each workpiece; The grasping workpiece corresponding to each target robot is determined based on the grasping robot arm corresponding to each workpiece.

[0015] By adopting the above technical solution, the process of grasping the workpiece corresponding to each target robot is determined based on the workpiece transportation plan and the target area dynamics model, which not only improves the accuracy of resource allocation and task execution, but also ensures the rationality and efficiency of grasping decisions.

[0016] In a possible implementation, based on the grasped workpiece and operation data corresponding to each of the target robots, a grasping plan corresponding to each of the target robots is determined, and the process of determining the grasping plan corresponding to each of the target robots includes: According to the grasped workpiece and operation data corresponding to the target robot, predict the motion trajectory and grasping action of the mechanical arm corresponding to the target robot, and determine the time node and grasping force of the grasping action of the mechanical arm corresponding to the target robot; Using an inverse kinematics algorithm and according to the position of the workpiece corresponding to the target robot and the operation data corresponding to the target robot, the motion paths of the respective joints of the mechanical arm corresponding to the target robot are calculated; Based on the motion trajectory, grasping action, time nodes and grasping force of the mechanical arm corresponding to the target robot and the motion paths of each joint, a grasping plan corresponding to the target robot is generated.

[0017] By adopting the above technical solution, the motion trajectory and grasping action of the target robot's mechanical arm can be predicted according to the grasping workpiece and operation data corresponding to the target robot, which not only ensures the accuracy and reliability of the grasping action, but also provides key information about the grasping action time node and grasping force. At the same time, by using the inverse kinematics algorithm and combining the workpiece position and operation data corresponding to the target robot, the motion path of each joint of the target robot's mechanical arm can be calculated, thereby ensuring that when the robot performs the grasping task, each joint can move according to the predetermined path and speed, thereby improving the accuracy and stability of the grasping task. Based on the motion trajectory, grasping action, grasping action time node and grasping force of the grasping action and the motion path of each joint of the target robot's mechanical arm, a grasping plan corresponding to the target robot is generated.

[0018] In a possible implementation, controlling a corresponding target robot according to the grasping plan includes: Based on the motion trajectory, grasping action, time node and grasping force of the mechanical arm corresponding to each target robot and the motion path of each joint, a control signal corresponding to each target robot at each moment is generated; Using a cross-coupling control algorithm to generate a compensation signal, and adding the compensation signal to a corresponding control signal to obtain a target control signal corresponding to each target robot at each moment; The target control signal corresponding to each target robot at each moment is sent to the corresponding target robot to control each target robot to operate according to the grasping plan.

[0019] By adopting the above technical solution, the process of controlling the corresponding target robot based on the grasping plan is significantly improved by accurately generating control signals, using cross-coupling control algorithms for compensation adjustments, and sending target control signals in real time.

[0020] In a second aspect, the present application provides a truss robot precision control device, which adopts the following technical solution: A truss robot precision control device, comprising: An acquisition module, used to acquire operation data of each target robot corresponding to a target area, and acquire a workpiece transportation plan corresponding to the target area, wherein the target area corresponds to at least two target robots, and one target robot corresponds to at least one mechanical arm, wherein the operation data includes a current position of the target robot and a movable angle of the mechanical arm of each target robot, and the workpiece transportation plan includes a workpiece transportation position and a workpiece transportation speed; A determination module, configured to determine a grasped workpiece corresponding to each target robot based on the workpiece transportation plan and the operation data of each target robot; The control module is used to determine the grasping plan corresponding to each target robot based on the grasped workpiece and operation data corresponding to each target robot, and control the corresponding target robot according to the grasping plan.

[0021] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device, comprising: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the truss robot precision control method described in the first aspect above.

[0022] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program that can be loaded by a processor and execute the truss robot precision control method described in the first aspect.

[0023] In summary, this application includes the following beneficial technical effects: By obtaining the operation data of each target robot corresponding to the target area (including the current position and the movable angle of the robot arm), and the workpiece transportation plan corresponding to the target area (including the workpiece transportation position and speed), based on the workpiece transportation plan and the operation data of each target robot, the grasping workpiece corresponding to each target robot is determined, and the grasping plan of each target robot is formulated and executed according to the grasping workpiece and operation data corresponding to each target robot. This step realizes the precise control of the robot's movements and improves the accuracy of the truss robot's grasping of workpieces. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is a schematic diagram of interaction between an electronic device and a target robot in a target area provided by an embodiment of the present application; Figure 2 It is a flow chart of a truss robot precision control method provided in an embodiment of the present application; Figure 3 It is a block diagram of a truss robot precision control device provided in an embodiment of the present application; Figure 4 It is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The following is combined with Figure 1 -Attached Figure 4 This application is described in further detail.

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In order to facilitate understanding of the technical solution proposed in this application, several elements introduced in the description of this application are first introduced here. It should be understood that the following introduction is only for the convenience of understanding these elements, so as to understand the content of the embodiments of this application, and does not necessarily cover all possible situations.

[0028] Truss robots are usually used for batch production and processing on industrial automation production lines. They can move along the rails of the beam or gantry frame and are used in factories for automated handling, loading and unloading, welding, assembly and other processes. Truss robots usually consist of the following parts: Truss structure: provides support and motion track for the robot; Guide rail: usually adopts high-strength steel rail or aluminum alloy structure to ensure the robot's high-speed, high-load, high-precision and long-life movement; Drive device: can be servo motor, stepper motor or hydraulic motor, etc., select the appropriate drive mode according to the requirements of the production process; Transmission device: including gear rack, chain, slider and other components, transmit the power of the drive device to the truss structure and realize movement; Control system: is the core part of the truss robot, responsible for controlling the robot's motion state, speed, position and other parameters; Operation panel: provides the operator with operation control, monitoring operation status and troubleshooting functions.

[0029] The existing truss robot control method has certain limitations when dealing with multi-robot collaborative operations. Specifically, when faced with complex workpiece transportation plans and multi-target robot collaborative operations, traditional control methods are difficult to achieve accurate grasping and transportation, which easily leads to workpiece position deviation and low transportation efficiency. Therefore, a method that can effectively solve the above grasping accuracy problem is urgently needed.

[0030] In view of this, the present application embodiment provides a truss robot precision control method, see Figure 1 The electronic device is electrically connected to each target robot in the target area, and the electronic device can send a control signal to the target robot to control the target robot to run, stop or grab. Among them, multiple machine tools and multiple truss robots are installed in the factory, and the machine room is divided into multiple areas according to a preset division method. The target area is one of the multiple areas. Among them, one airport corresponds to at least one truss robot, and one truss robot is equipped with at least one mechanical arm.

[0031] The present application embodiment provides a truss robot precision control method, such as Figure 1 As shown, the method provided in the embodiment of the present application is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S201-S203, wherein: Step S201: Acquire the operation data of each target robot corresponding to the target area, and acquire the workpiece transportation plan corresponding to the target area.

[0032] Among them, there are at least two target robots corresponding to the target area, and one target robot corresponds to at least one robotic arm. The operation data includes the current position of the target robot and the movable angle of the robotic arm of each target robot. The workpiece transportation plan includes the workpiece transportation position and the workpiece transportation speed. Specifically, the operation data reflects the information set of the current working state of the target robot, including the coordinate position of the robot in the target area and the angle range in which each joint of the robotic arm can move, etc. Each robot corresponds to a maximum movable range (the maximum movable range corresponding to different models of truss robots may be different), that is, the movable angle corresponding to each robot. The electronic device can obtain the maximum movable range corresponding to each target robot from the database corresponding to the target area to obtain the movable angle of the robotic arm of each target robot.

[0033] The workpiece transportation plan is a pre-planning of the workpiece handling process in the target area, which clarifies the location where the workpiece needs to be transported and the speed setting during the transportation process. Specifically, the workpiece is generally transported by a conveyor belt during the process of being transported from the initial location to the location to be transported. During the transportation of the workpiece on the conveyor belt, the transportation speed of the workpiece is consistent with the speed of the conveyor belt. Therefore, the initial location corresponding to each workpiece can be obtained from the database corresponding to the target area to obtain the workpiece transportation location, and the transportation speed of the conveyor belt corresponding to each workpiece can be obtained to obtain the workpiece transportation speed.

[0034] Furthermore, the electronic device acquires the current location information of the target robot by establishing a communication connection with a GPS or indoor positioning system installed on the target robot.

[0035] Step S202: Determine the grasped workpiece corresponding to each target robot based on the workpiece transportation plan and the operation data of each target robot.

[0036] Among them, the grasped workpiece is a single workpiece among many workpieces that need to be transported, which is assigned to a specific target robot to grasp according to various conditions.

[0037] Specifically, according to the workpiece transportation position information in the acquired workpiece transportation plan, the theoretical path length from each workpiece to the target robot is calculated, and based on the workpiece transportation speed information of each workpiece, the theoretical time from each workpiece to the target robot can be calculated, and based on the theoretical time, the grasping workpiece corresponding to each target robot is determined. More specifically, the theoretical path length from each workpiece to the target robot can be calculated by a map model of the target area (stored in the electronic device memory or a connected storage device in the form of electronic map data) and a path planning algorithm (such as the Dijkstra algorithm). The algorithm will calculate the shortest path length from each workpiece position to each target robot based on the current position corresponding to each workpiece in the map and the connection relationship and distance information between the positions corresponding to each target robot, so as to obtain the theoretical path length corresponding to each workpiece.

[0038] Furthermore, after obtaining the theoretical path length from each workpiece position to each target robot, the theoretical time corresponding to each workpiece is calculated based on the theoretical time = theoretical path length / workpiece transportation speed. After obtaining the theoretical time corresponding to each workpiece, the adaptability of each robot arm to grasp the workpiece is evaluated in combination with the operation data of the target robot. Specifically, the current movable angle information of the robot arm is used to determine whether the robot arm can reach the workpiece position and perform a grasping operation, that is, to determine whether the grasping angle corresponding to the position of the workpiece closest to the robot arm during transportation belongs to the movable angle of the robot arm. If the movable angle of the robot arm cannot meet the extension and rotation requirements required to grasp a workpiece, the feasibility of the robot grasping the workpiece is 0; if the movable angle of the robot arm can meet the extension and rotation requirements required to grasp a workpiece, the feasibility of the robot grasping the workpiece is 1.

[0039] Furthermore, an optimization allocation algorithm (such as a priority-based allocation algorithm) is used to comprehensively consider the theoretical time of the workpiece and the feasibility of grasping, and determine the corresponding grasped workpiece for each target robot. Specifically, for each target robot, the corresponding grasping number of the target robot is obtained, and the workpiece with a feasibility of 1 is selected from multiple workpieces as the first workpiece, and the grasping number of workpieces is selected from the first workpiece in the order of transportation time from long to short as the second workpiece, and the second workpiece is the grasping workpiece corresponding to the target robot.

[0040] Step S203: determining a grasping plan corresponding to each target robot based on the grasped workpiece and operation data corresponding to each target robot, and controlling the corresponding target robot according to the grasping plan.

[0041] Among them, the grasping plan is the operation plan for the target robot to grasp a specific workpiece.

[0042] After obtaining the grasped workpiece corresponding to the target robot and the corresponding operation data of the target robot, a grasping plan can be formulated according to the grasped workpiece corresponding to the target robot and its operation data. Specifically, the inverse kinematics algorithm of the manipulator is obtained from the database corresponding to the target area, and the kinematics algorithm of the manipulator is used to calculate the motion paths of each joint required for the manipulator to move from the current position to the grasping position of the workpiece according to the position and posture of the workpiece, the current position of the robot and the state of the manipulator, and determine the target angle and position of the manipulator at different time points, so as to plan a smooth and accurate motion trajectory.

[0043] For the grasping action, the appropriate grasping force and grasping time are set according to the physical characteristics of the workpiece (such as weight, shape, material, etc., which can be obtained from the database corresponding to the target area). For example, for workpieces with soft texture or fragile surface, the preset gentle grasping parameter table will be referred to to determine a smaller grasping force and a relatively longer grasping time to avoid damage to the workpiece. Furthermore, based on the running time and motion trajectory of the robot arm corresponding to the target robot and the grasping action, the grasping plan corresponding to the target robot is obtained.

[0044] Furthermore, the prepared grasping plan is converted into a control instruction format that can be recognized by the target robot controller. For example, the motion parameters of the robot arm joint are converted into the duty cycle, frequency and other parameters of the pulse width modulation (PWM) signal required by the controller to control the speed and direction of the motor, thereby achieving precise control of the robot arm movement. Then, the control instructions are sent to the controller of the target robot through a communication bus (such as industrial Ethernet, CAN bus, etc.). After receiving the instructions, the controller drives the robot to execute the operations in the grasping plan.

[0045] The embodiment of the present application provides a method for precise control of a truss robot, which obtains the operation data (including the current position and the movable angle of the robot arm) of each target robot corresponding to the target area, and the workpiece transportation plan (including the workpiece transportation position and speed) corresponding to the target area, and determines the grasping workpiece corresponding to each target robot based on the workpiece transportation plan and the operation data of each target robot, and formulates and executes the grasping plan of each target robot according to the grasping workpiece and operation data corresponding to each target robot. This step realizes precise control of the robot's movements and improves the accuracy of the truss robot in grasping workpieces.

[0046] A possible implementation of the embodiment of the present application, in the above step S202, when the operation data also includes the current speed, current acceleration and current robot arm angle of each target robot, based on the workpiece transportation plan and the operation data of each target robot, determining the grasped workpiece corresponding to each target robot includes: Receive the operation data corresponding to each target robot in real time, and establish a dynamic model corresponding to the target area based on the operation data corresponding to each target robot; Based on the workpiece transportation plan and the dynamic model corresponding to the target area, the grasped workpiece corresponding to each target robot is determined.

[0047] Among them, the electronic device establishes a stable data transmission link with various sensors equipped on the target robot through wired (such as Ethernet, RS485 and other communication cables) or wireless (such as Wi-Fi, Bluetooth, Zigbee and other communication protocols) communication methods. For example, the speed sensor (will send the current speed data detected in real time to the electronic device; the acceleration sensor will transmit the current acceleration information to the electronic device; and the robot arm angle sensor (such as the high-precision encoder at the joint) will continuously feedback the current angle data of each joint of the robot arm.

[0048] After obtaining the operation data corresponding to each target robot, the dynamic model corresponding to the target area can be constructed. Specifically, according to the number of rigid bodies of the target robot in the target area (the various connecting rods of the robot arm, the mobile platform of the robot, etc. can be regarded as rigid bodies), the connection mode between each rigid body (such as rotating joints, mobile joints, etc.) and the corresponding joint parameters (such as joint rotation range, transmission ratio, etc.) are identified, and the dynamic equations of each rigid body are established based on the basic principles of mechanics (such as Newton's second law, momentum theorem, etc.). For the robot arm part, considering factors such as the mass, center of mass position, and moment of inertia of the connecting rod, combined with the received current robot arm angle, speed, acceleration and other operation data, the Lagrange equation or Newton-Euler equation is used to construct a dynamic sub-model describing the movement of the robot arm. For the moving part of the robot (such as the movement of the trolley of the truss robot with a trolley), according to the mass of the trolley and the driving force (such as the thrust provided by the motor), its dynamic sub-model is established through Newton's second law. Furthermore, the respective dynamic sub-models of each part are integrated according to the overall structure and interaction relationship of the robot system to form a dynamic model that can describe the motion of the target robot system in the entire target area.

[0049] Furthermore, according to the information of the workpiece transportation position and the workpiece transportation speed in the workpiece transportation plan, the dynamic model corresponding to the established target area is combined for analysis. The grasping range corresponding to each robot arm is determined through the current movable angle information of the robot arm and the dynamic model, and based on the grasping range, it is judged whether the robot arm can reach the workpiece position and perform the grasping operation, that is, it is judged whether the grasping angle corresponding to the position of the workpiece closest to the robot arm during the transportation process belongs to the movable angle of the robot arm. If the movable angle of the robot arm cannot meet the extension and rotation requirements required for grasping a certain workpiece, the feasibility of the robot grasping this workpiece is 0; if the movable angle of the robot arm can meet the extension and rotation requirements required for grasping a certain workpiece, the feasibility of the robot grasping this workpiece is 1. Furthermore, an optimization allocation algorithm (such as a priority-based allocation algorithm) is used to comprehensively consider the theoretical duration of the workpiece and the grasping feasibility, and determine the corresponding grasping workpiece for each target robot. Specifically, for each target robot, the corresponding grasping number of the target robot is obtained, and the workpiece with a feasibility of 1 is screened out from multiple workpieces as the first workpiece, and the grasping number of workpieces is randomly selected from the first workpiece as the second workpiece. The second workpiece is the grasping workpiece corresponding to the target robot.

[0050] A possible implementation of the embodiment of the present application, in the above embodiment, based on the operation data corresponding to each target robot, a dynamic model corresponding to the target area is established, including: Based on the operation data corresponding to each target robot, a state equation corresponding to each target robot is established, and a variable relationship between each state equation and the operation data is determined; Establish the measurement equation corresponding to each variable relationship; Based on each measurement direction and each state equation, a dynamic model corresponding to the target area is established.

[0051] After obtaining the operation data corresponding to each target robot, the state variables of the target robot can be determined. Specifically, for each target robot, the spatial coordinate system corresponding to the target area is obtained from the database corresponding to the target area (the x-axis, y-axis, and positive direction of the z-axis are all preset, which will not be described in detail in the present embodiment of the application), and the coordinate position (set as x, y, z) and speed along each coordinate axis (set as ) and the angles of each joint of the robot arm (set to ) and the corresponding angular velocity (set to ) as state variables, forming the state vector . Furthermore, the state equation is constructed based on the robot's kinematics and dynamics principles. Specifically, for the robot's translational motion, according to Newton's second law F=ma (where F is the resultant force, m is the mass, and a is the acceleration), the driving force, friction, and other external forces acting on the target robot are considered, and the state equation is established. ( is the velocity in the x direction, , The control input is the driving force provided by the motor, which is used to describe the change of direction and position over time. For the rotational motion of the robot arm, the Euler equation or Lagrange equation is used to combine the moment of inertia and torque at the joint to construct (i=1, 2, ..., n) to describe the change law of joint angle. Furthermore, based on the change of direction position over time and the change law of joint angle, a complete state equation is formed. u is the control input vector). Among them, the position in the state variable can directly correspond to the current position operation data of the target robot obtained by the positioning sensor (such as laser positioning device, GPS, etc.); the speed can be associated with the data measured by the speed sensor (such as speed encoder, etc.); the robot arm joint angle and the current robot arm angle operation data obtained by the angle encoder installed at the joint establish a corresponding relationship. It is worth noting that the truss robot can only move in the x-axis direction, so, , Both are 0.

[0052] Furthermore, for each state variable, the associated sensors and their measurement principles are analyzed to establish a measurement equation. Specifically, for the position of the robot in the x direction in the state variable, a laser ranging sensor is used to measure the distance value z. Due to factors such as the sensor installation position and measurement error, the measurement equation can be expressed as z=h(a, v), where h is the measurement function, which is used to characterize the relationship between the measured value and the actual state variable x, and v is the measurement noise, which reflects the uncertainty in the measurement process (such as errors caused by environmental interference during laser ranging, sensor accuracy limitations, etc.). For a certain joint angle of the robotic arm , measured by the angle encoder installed at the joint, assuming the measured angle value is , and its measurement equation is in is the measurement function associated with the angle encoder measurement mechanism, is the corresponding measurement noise (caused by encoder accuracy, mechanical installation errors, etc.).

[0053] After obtaining the state equation corresponding to each target robot, the state equations of each target robot are combined to obtain the dynamic model corresponding to the target area. Specifically, if there are N target robots in the target area, each robot has its corresponding state equation (i=1, 2, ..., N, is the state vector of the th robot, is its control input vector), these state equations are combined to form a total state equation group, which is used to describe the state change law of the entire robot system. Furthermore, the measurement equations corresponding to each target robot are also integrated to construct a complete dynamic model corresponding to the target area.

[0054] A possible implementation of the embodiment of the present application, in the above embodiment, based on the operation data corresponding to each target robot, a state equation corresponding to each robot is established, and the process of establishing the state equation corresponding to each target robot includes: Generate a state vector corresponding to the operating data, and determine a target derivative corresponding to the state vector, where the target derivative is the derivative of the state vector with respect to time; Determine the basic form of the state equation corresponding to the operating data based on the Newton-Euler equation; Obtain the control characteristics corresponding to the target robot; Based on the control characteristics, target derivatives and basic types corresponding to the target robot, the state equation corresponding to the target robot is established.

[0055] The control feature is a control input mode and related control parameters that the target robot can accept during actual operation. Specifically, the control feature corresponding to the target robot can be obtained from a database corresponding to the target area.

[0056] Specifically, the coordinate position of the target robot in the target area, the speed along each coordinate axis, and the angle and corresponding angular velocity of each joint of the manipulator are obtained from the database corresponding to the target area as state variables, and are arranged in a set order to form a state vector. Further, the target derivative corresponding to the state vector is determined according to the preset derivation rule. Specifically, the position variable is differentiated with respect to time to obtain the velocity variable, and the derivative of the velocity variable with respect to time is the acceleration along each coordinate axis. For the manipulator joint angle variable, its derivative with respect to time is the corresponding angular velocity, wherein the derivative of the angular velocity with respect to time is the angular acceleration. Arrange these derivatives in the order corresponding to the state vector elements to obtain the target derivative corresponding to the state vector.

[0057] Furthermore, the Newton-Euler equations are used to construct the basic form for different parts of the target robot (such as the robot arm and the mobile platform). For the translational motion part of the robot (such as the trolley movement of the truss robot with a trolley), according to Newton's second law, the resultant force is expressed as a function form related to the robot's position, velocity and other state variables. Furthermore, for the rotational motion part of the robot arm, the Euler equations are used, combined with factors such as the moment of inertia of the robot arm link and the torque at the joint. The angular acceleration of the joint is expressed as a function form related to the joint angle, angular velocity and the torque acting on the joint (including motor output torque, external load torque, etc.), and a method for describing the change law of the joint angle is constructed to obtain the basic form of the state equation.

[0058] Furthermore, the acquired control characteristics are integrated into the basic form of the state equation previously determined based on the Newton-Euler equations to improve and refine the basic form of the equation and obtain the state equation corresponding to the complete target robot. This state equation accurately describes the change pattern of the state vector of the target robot over time under the influence of factors such as control input and its own mechanical properties, thereby completing the establishment of the state equation and laying the foundation for subsequent model-based analysis and control operations.

[0059] A possible implementation of the embodiment of the present application, in the above embodiment, based on the workpiece transportation plan and the dynamic model corresponding to the target area, determining the grasped workpiece corresponding to each target robot includes: Obtain the workpiece demand and the number of robotic arms corresponding to each target robot, and identify the transportation sub-plan corresponding to each workpiece in the workpiece transportation plan, the transportation sub-plan including the start transportation time, transportation speed and transportation location of the workpiece; Determine the gripping robot arm corresponding to each workpiece based on the operation data corresponding to each target robot, the workpiece demand, the number of robot arms, and the transportation sub-plan corresponding to each workpiece; The grasping workpiece corresponding to each target robot is determined based on the grasping robot arm corresponding to each workpiece.

[0060] Different production plans will result in different workpiece quantity requirements for different machine tools. Therefore, the workpiece quantity required for each target robot and the number of robotic arms corresponding to each target robot can be obtained from the database corresponding to the target area. Furthermore, the transportation sub-plan information corresponding to each workpiece is extracted from the workpiece transportation plan. Specifically, the transportation start time, transportation conveyor belt and transportation initial position corresponding to each component in the workpiece transportation plan can be obtained, and the speed of the transportation conveyor belt can be used as the transportation speed of the workpiece, so as to obtain the transportation sub-plan corresponding to the workpiece.

[0061] Furthermore, according to the current position in the operation data of the target robot and the transport position in the transport sub-plan corresponding to the workpiece, the distance from each workpiece position to each target robot is calculated, and based on the distance and the movable range corresponding to each robot arm, the corresponding robot arm of each workpiece is determined. Specifically, for each robot arm of each target robot, the current movable angle information of the robot arm is used to determine whether the robot arm can reach the workpiece position and perform a grasping operation (specifically, for each component, the transport conveyor belt corresponding to the component can obtain the transport path, that is, the path between the transport start and the transport end of the transport conveyor belt, and the shortest distance between each position in the path and the robot arm is used to determine whether the shortest distance belongs to the movable range of the robot arm), that is, to determine whether the grasping angle corresponding to the position of the workpiece closest to the robot arm during transportation belongs to the movable angle of the robot arm. If the movable angle of the robot arm cannot meet the extension and rotation requirements for grasping a workpiece, the feasibility of the robot grasping the workpiece is 0; if the movable angle of the robot arm can meet the extension and rotation requirements for grasping a workpiece, the feasibility of the robot grasping the workpiece is 1. Further, the number of grasps corresponding to the target robot is obtained, and the workpiece with a feasibility of 1 is selected from multiple workpieces as the first workpiece, and the workpiece with the number of grasps is randomly selected from the first workpiece as the second workpiece, and the grasping robot arm corresponding to the second workpiece is the robot arm corresponding to the target robot.

[0062] Furthermore, after the grasping robot arm corresponding to each workpiece is determined, since the grasping robot arm belongs to a specific target robot, statistics and sorting can be performed based on this belonging relationship to determine the grasping workpiece corresponding to each target robot.

[0063] In a possible implementation of the embodiment of the present application, in the above step S203, based on the grasped workpiece and operation data corresponding to each target robot, a grasping plan corresponding to each target robot is determined, and the process of determining the grasping plan corresponding to each target robot includes: According to the grasped workpiece and operation data of the target robot, the motion trajectory and grasping action of the mechanical arm corresponding to the target robot are predicted, and the time node and grasping force of the grasping action of the mechanical arm corresponding to the target robot are determined; Using the inverse kinematics algorithm, and based on the position of the workpiece corresponding to the target robot and the operation data corresponding to the target robot, the motion paths of the respective joints of the robotic arm corresponding to the target robot are calculated; Based on the motion trajectory, grasping action, time nodes and grasping force of the manipulator arm corresponding to the target robot and the motion path of each joint, a grasping plan corresponding to the target robot is generated.

[0064] Among them, the time node is the specific time point corresponding to each key action (such as starting to move, approaching the workpiece, starting to grasp, completing grasping, etc.) during the entire grasping process of the robot arm.

[0065] Specifically, the current position and posture of the robot arm are obtained according to the operation data of the target robot, and the position of the workpiece is determined from the relevant information of the grasped workpiece (the coordinate position, shape and size of the workpiece in the target area). Based on the spatial layout of the target area (stored in the database corresponding to the target area in the form of an electronic map or a spatial coordinate system) and the distribution of obstacles in the robot's working environment (which can be obtained and recorded in advance by scanning the environmental perception sensor), a path planning algorithm (such as the A* algorithm, etc.) is used to plan a collision-free and efficient spatial path from the current position of the robot arm to the grasped workpiece position, which is used as the initial motion trajectory of the robot arm.

[0066] Furthermore, a grasping action corresponding to the shape of the workpiece is obtained from the database corresponding to the target area. For example, if the workpiece is in the shape of a cuboid and has a relatively flat surface, it can be planned to clamp it from both sides with parallel jaws; if it is a spherical workpiece, it may be necessary to use special embracing jaws for grasping. According to the start transportation time, transportation speed and transportation position of the grasped workpiece, the transportation distance between the transportation position and the current position of the corresponding target robot is calculated, and the transportation duration is obtained based on the transportation speed and the transportation distance, and the time node corresponding to the grasping element is determined based on the transportation duration and the start transportation time. The weight of the grasped workpiece is obtained from the database corresponding to the target area, and the grasping force corresponding to the weight is obtained to obtain the grasping force corresponding to the target robot.

[0067] Based on the structure of the robot arm (such as the length of the connecting rod, whether the joint type is a rotational joint or a translational joint, etc., these parameters are obtained from the design drawings or configuration files of the robot), the mathematical relationship between the position and posture of the end effector of the robot arm and the variables of each joint (angle or displacement) is established to construct the kinematic model of the target robot arm. Further, the position of the grasped workpiece (as the target position to be reached by the end effector) and the current joint angles of the robot arm in the target robot operation data are substituted into the inverse kinematics algorithm. For common robot arms with multiple rotational joints, the inverse kinematics algorithm may involve mathematical methods such as trigonometric functions and matrix operations to solve. For example, through continuous iterative calculations (such as using numerical calculation methods such as the Newton-Raphson iteration method or analytical solutions based on geometric relationships, and selecting appropriate methods according to the complexity of the robot arm), the angle sequence that each joint needs to rotate in sequence from the current angle in order for the end effector to accurately reach the workpiece position is gradually determined, and the change of this angle sequence over time constitutes the motion path of each joint.

[0068] Furthermore, the information such as the motion trajectory of the robot arm, the grasping action, the time node of the grasping action, the grasping force and the motion path of each joint determined in the previous steps are integrated to form a complete grasping plan corresponding to the target robot.

[0069] A possible implementation of the embodiment of the present application, in the above embodiment, controlling the corresponding target robot according to the grasping plan includes: Based on the motion trajectory, grasping action, time node and grasping force of the manipulator arm corresponding to each target robot and the motion path of each joint, a control signal corresponding to each target robot at each moment is generated; A cross-coupling control algorithm is used to generate compensation signals, and the compensation signals are added to the corresponding control signals to obtain the target control signals corresponding to each target robot at each moment; The target control signal corresponding to each target robot at each moment is sent to the corresponding target robot to control each target robot to operate according to the grasping plan.

[0070] After obtaining the motion trajectory, grasping action, grasping action time node and grasping force of the manipulator corresponding to each target robot, as well as the motion path of each joint, a control signal is generated. Specifically, according to the difference between the current angle of the joint and the target angle, the kinematic parameters of each joint, and the control characteristics of the motor (such as the relationship between the rotation speed and the control voltage or pulse frequency, etc.), the control signal required by the corresponding motor is calculated (for example, when it is an analog voltage signal, determine the specific voltage value; if a pulse width modulation signal is used to control the motor, determine the corresponding pulse frequency and duty cycle, etc.), so that the motor rotates to drive the joint to move to the target angle as required. For the end effector control involved in the grasping action (such as controlling the gripper motor to open and close the gripper), the corresponding control signal is also generated according to the grasping action requirements and the corresponding force setting to drive the end effector to perform the correct grasping operation.

[0071] Furthermore, based on the structural characteristics and motion characteristics of the target robot, the coupling relationship between the motion of each joint and the overall motion of the robot is analyzed. Specifically, for a robotic arm with multiple degrees of freedom, the motion of a joint may affect the motion accuracy of other joints due to factors such as force transmission and inertia. At the same time, the movement of the robot may also affect the posture of the robotic arm. By establishing the robot's kinematic and dynamic models (previously constructed to analyze the system status) and referring to past experimental data or error records in actual operation, the mutual influence of motion errors between axes is determined. Furthermore, when the robot is running according to the grasping plan, the actual position, speed and other feedback information of each joint are obtained in real time (obtained through sensors installed at the joints, such as encoders, etc.), and compared with the corresponding target position and speed (from the requirements of each moment set in the grasping plan), the motion error of each axis is calculated, and based on the coupling coefficient preset in the cross-coupling control algorithm (the coupling coefficient can be determined through theoretical analysis and experimental debugging based on the characteristics of the robot system, reflecting the degree of correlation between the errors of each axis) and the motion error of each axis, a compensation signal is generated through certain mathematical operations (for example, according to the calculation formula of the cross-coupling control algorithm, involving matrix operations, weighted summation, etc., the specific form varies depending on different robot systems and algorithm implementations). Among them, the content of the compensation signal corresponds to the relevant parameters of the control motor (such as voltage compensation, pulse frequency or duty cycle adjustment, etc.), which is used to compensate and correct the control signal of each axis.

[0072] Furthermore, the generated compensation signal is added to the corresponding original control signal according to the corresponding rules (for example, for analog voltage control signals, the compensation voltage value is directly added to the original voltage value; for pulse signals, they are synthesized according to the duty cycle, frequency and other adjustment rules), and the target control signal corresponding to each target robot at each moment is obtained, so that the control signal is more in line with the actual motion of the robot, the motion control accuracy is improved, and the accurate execution of the grasping plan is guaranteed. A stable communication link is established with each target robot through communication. At each moment, the electronic device packages and sends the corresponding target control signal to the corresponding target robot according to the established communication protocol and data format. At the same time, the electronic device continuously receives the operating status information fed back by the target robot (such as the actual position of each joint, the operating status of the motor, etc.), and monitors in real time whether the robot is running accurately according to the grasping plan. If a deviation is found in the operation of the robot (judged by comparing the feedback information with the requirements in the grasping plan), the electronic device can adjust the target control signal at the subsequent moment in time to ensure that the robot can always complete the workpiece grasping task in an orderly and accurate manner according to the grasping plan.

[0073] The above-mentioned embodiment introduces a truss robot precision control method from the perspective of method flow, and the following embodiment introduces a truss robot precision control device from the perspective of a virtual module or a virtual unit. Please refer to the following embodiment for details.

[0074] See also Figure 3 The truss robot precision control device 30 may specifically include: an acquisition module 301, a determination module 302 and a control module 303, wherein: A truss robot precision control device 30, comprising: An acquisition module is used to acquire the operation data of each target robot corresponding to the target area, and acquire the workpiece transportation plan corresponding to the target area. There are at least two target robots corresponding to the target area, and one target robot corresponds to at least one mechanical arm. The operation data includes the current position of the target robot and the movable angle of the mechanical arm of each target robot. The workpiece transportation plan includes the workpiece transportation position and the workpiece transportation speed. A determination module, used for determining the grasped workpiece corresponding to each target robot based on the workpiece transportation plan and the operation data of each target robot; The control module is used to determine the grasping plan corresponding to each target robot based on the grasped workpiece and operation data corresponding to each target robot, and control the corresponding target robot according to the grasping plan.

[0075] In a possible implementation of the embodiment of the present application, the operation data further includes the current speed, current acceleration, and current robot arm angle of each target robot. When the determination module 302 determines the grasped workpiece corresponding to each target robot based on the workpiece transportation plan and the operation data of each target robot, it can be specifically used to: Receive the operation data corresponding to each target robot in real time, and establish a dynamic model corresponding to the target area based on the operation data corresponding to each target robot; Based on the workpiece transportation plan and the dynamic model corresponding to the target area, the grasped workpiece corresponding to each target robot is determined.

[0076] In a possible implementation of the embodiment of the present application, when the determination module 302 establishes a dynamic model corresponding to the target area based on the operation data corresponding to each target robot, it can be specifically used to: Based on the operation data corresponding to each target robot, a state equation corresponding to each target robot is established, and a variable relationship between each state equation and the operation data is determined; Establish the measurement equation corresponding to each variable relationship; Based on each measurement direction and each state equation, a dynamic model corresponding to the target area is established.

[0077] In a possible implementation of the embodiment of the present application, the determination module 302 establishes a state equation corresponding to each robot based on the operation data corresponding to each target robot. The establishment process of the state equation corresponding to each target robot can be specifically used to: Generate a state vector corresponding to the operating data, and determine a target derivative corresponding to the state vector, where the target derivative is the derivative of the state vector with respect to time; Determine the basic form of the state equation corresponding to the operating data based on the Newton-Euler equation; Obtain the control characteristics corresponding to the target robot; Based on the control characteristics, target derivatives and basic types corresponding to the target robot, the state equation corresponding to the target robot is established.

[0078] In a possible implementation of the embodiment of the present application, when the determination module 302 determines the grasped workpiece corresponding to each target robot based on the workpiece transportation plan and the dynamic model corresponding to the target area, it can be specifically used to: Obtain the workpiece demand and the number of robotic arms corresponding to each target robot, and identify the transportation sub-plan corresponding to each workpiece in the workpiece transportation plan, the transportation sub-plan including the start transportation time, transportation speed and transportation location of the workpiece; Determine the gripping robot arm corresponding to each workpiece based on the operation data corresponding to each target robot, the workpiece demand, the number of robot arms, and the transportation sub-plan corresponding to each workpiece; The grasping workpiece corresponding to each target robot is determined based on the grasping robot arm corresponding to each workpiece.

[0079] In a possible implementation of the embodiment of the present application, the determination module 302 determines the grasping plan corresponding to each target robot based on the grasped workpiece and operation data corresponding to each target robot. The grasping plan corresponding to each target robot can be specifically used to: According to the grasped workpiece and operation data of the target robot, the motion trajectory and grasping action of the mechanical arm corresponding to the target robot are predicted, and the time node and grasping force of the grasping action of the mechanical arm corresponding to the target robot are determined; Using the inverse kinematics algorithm, and based on the position of the workpiece corresponding to the target robot and the operation data corresponding to the target robot, the motion paths of the respective joints of the robotic arm corresponding to the target robot are calculated; Based on the motion trajectory, grasping action, time nodes and grasping force of the manipulator arm corresponding to the target robot and the motion path of each joint, a grasping plan corresponding to the target robot is generated.

[0080] In a possible implementation of the embodiment of the present application, when the control module 303 controls the corresponding target robot according to the grasping plan, it can be specifically used to: Based on the motion trajectory, grasping action, time node and grasping force of the manipulator arm corresponding to each target robot and the motion path of each joint, a control signal corresponding to each target robot at each moment is generated; A cross-coupling control algorithm is used to generate compensation signals, and the compensation signals are added to the corresponding control signals to obtain the target control signals corresponding to each target robot at each moment; The target control signal corresponding to each target robot at each moment is sent to the corresponding target robot to control each target robot to operate according to the grasping plan.

[0081] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0082] join Figure 4 , the embodiment of the present application also introduces an electronic device from the perspective of a physical device, such as Figure 4 As shown, Figure 4 The electronic device 400 shown includes: a processor 401 and a memory 403. The processor 401 and the memory 403 are connected, such as through a bus 402. Optionally, the electronic device 400 may also include a transceiver 404. It should be noted that in actual applications, the transceiver 404 is not limited to one, and the structure of the electronic device 400 does not constitute a limitation on the embodiments of the present application.

[0083] Processor 401 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 401 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0084] The bus 402 may include a path to transmit information between the above components. The bus 402 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 402 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0085] The memory 403 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0086] The memory 403 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 401. The processor 401 is used to execute the application code stored in the memory 403 to implement the contents shown in the above method embodiment.

[0087] The electronic devices include but are not limited to: mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc., and can also be servers, etc. Figure 4 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0088] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0089] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0090] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A truss robot precision control method, characterized in that: include: Acquire operation data of each target robot corresponding to the target area, and acquire a workpiece transportation plan corresponding to the target area, wherein the target area corresponds to at least two target robots, and one target robot corresponds to at least one robotic arm, wherein the operation data includes the current position of the target robot and the movable angle of the robotic arm of each target robot, and the workpiece transportation plan includes the workpiece transportation position and the workpiece transportation speed; Determine the grasped workpiece corresponding to each target robot based on the workpiece transportation plan and the operation data of each target robot; Based on the grasped workpiece and operation data corresponding to each of the target robots, a grasping plan corresponding to each of the target robots is determined, and the corresponding target robot is controlled according to the grasping plan.

2. The truss robot precision control method according to claim 1, characterized in that: The operation data also includes the current speed, current acceleration and current robot arm angle of each target robot. Based on the workpiece transportation plan and the operation data of each target robot, the grasped workpiece corresponding to each target robot is determined, including: Receiving operation data corresponding to each target robot in real time, and establishing a dynamic model corresponding to the target area based on the operation data corresponding to each target robot; Based on the workpiece transportation plan and the dynamic model corresponding to the target area, the grasped workpiece corresponding to each target robot is determined.

3. The truss robot precision control method according to claim 2, characterized in that: The step of establishing a dynamic model corresponding to the target area based on the operation data corresponding to each target robot includes: Based on the operation data corresponding to each of the target robots, a state equation corresponding to each of the target robots is established, and a variable relationship between each state equation and the operation data is determined; Establish the measurement equation corresponding to each variable relationship; Based on each of the measurement directions and each of the state equations, a dynamic model corresponding to the target area is established.

4. The truss robot precision control method according to claim 3, characterized in that: The state equation corresponding to each of the target robots is established based on the operation data corresponding to each of the target robots. The process of establishing the state equation corresponding to each of the target robots includes: Generate a state vector corresponding to the operating data, and determine a target derivative corresponding to the state vector, wherein the target derivative is a derivative of the state vector with respect to time; Determine the basic form of the state equation corresponding to the operating data based on the Newton-Euler equation; Obtaining control features corresponding to the target robot; Based on the control characteristics, target derivatives and basic types corresponding to the target robot, a state equation corresponding to the target robot is established.

5. The truss robot precision control method according to claim 3, characterized in that: The step of determining the grasped workpiece corresponding to each target robot based on the workpiece transportation plan and the dynamic model corresponding to the target area includes: Obtaining the workpiece demand and the number of robot arms corresponding to each target robot, and identifying the transportation sub-plan corresponding to each workpiece in the workpiece transportation plan, wherein the transportation sub-plan includes the start transportation time, transportation speed, and transportation location of the workpiece; Determine the gripping robot arm corresponding to each workpiece based on the operation data corresponding to each target robot, the workpiece demand, the number of robot arms, and the transportation sub-plan corresponding to each workpiece; The grasping workpiece corresponding to each target robot is determined based on the grasping robot arm corresponding to each workpiece.

6. The truss robot precision control method according to claim 1, characterized in that: The grasping plan corresponding to each target robot is determined based on the grasped workpiece and the operation data corresponding to each target robot. The process of determining the grasping plan corresponding to each target robot includes: According to the grasped workpiece and operation data corresponding to the target robot, predict the motion trajectory and grasping action of the mechanical arm corresponding to the target robot, and determine the time node and grasping force of the grasping action of the mechanical arm corresponding to the target robot; Using an inverse kinematics algorithm and according to the position of the workpiece corresponding to the target robot and the operation data corresponding to the target robot, the motion paths of the respective joints of the mechanical arm corresponding to the target robot are calculated; Based on the motion trajectory, grasping action, time nodes and grasping force of the mechanical arm corresponding to the target robot and the motion paths of each joint, a grasping plan corresponding to the target robot is generated.

7. The truss robot precision control method according to claim 6, characterized in that: The controlling the corresponding target robot according to the grasping plan includes: Based on the motion trajectory, grasping action, time node and grasping force of the mechanical arm corresponding to each target robot and the motion path of each joint, a control signal corresponding to each target robot at each moment is generated; Using a cross-coupling control algorithm to generate a compensation signal, and adding the compensation signal to a corresponding control signal to obtain a target control signal corresponding to each target robot at each moment; The target control signal corresponding to each target robot at each moment is sent to the corresponding target robot to control each target robot to operate according to the grasping plan.

8. A truss robot precision control device, characterized in that: include: An acquisition module, used to acquire operation data of each target robot corresponding to a target area, and acquire a workpiece transportation plan corresponding to the target area, wherein the target area corresponds to at least two target robots, and one target robot corresponds to at least one mechanical arm, wherein the operation data includes a current position of the target robot and a movable angle of the mechanical arm of each target robot, and the workpiece transportation plan includes a workpiece transportation position and a workpiece transportation speed; A determination module, configured to determine a grasped workpiece corresponding to each target robot based on the workpiece transportation plan and the operation data of each target robot; The control module is used to determine the grasping plan corresponding to each target robot based on the grasped workpiece and operation data corresponding to each target robot, and control the corresponding target robot according to the grasping plan.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the truss robot precision control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the truss robot precision control method according to any one of claims 1 to 7.

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