Orthogonal robot control system and method based on PLC

Abstract

The invention provides an orthogonal robot control system and method based on a PLC. The system comprises a data processing assembly, a motion control assembly and a man-machine interaction assembly. The method comprises the steps that a plurality of sensors are used for collecting data in the working environment of the orthogonal robot, the data of the sensors are obtained in real time through a programmable logic controller, and filtering, calibration, error compensation and other processing are carried out; receiving the processed data, and generating a control signal through a programmable logic controller to drive a servo motor or a stepping motor so as to realize the movement of the robot on three orthogonal axes of X, Y and Z; data interaction is carried out through a programmable logic controller and an upper computer or a touch screen, and the functions of parameter setting, state display, fault diagnosis and the like of the orthogonal robot are achieved. The operation state of the orthogonal robot is monitored through the graphical interface, and when the operation of the orthogonal robot is abnormal, an alarm and a fault prompt are given out. According to the invention, efficient, accurate and reliable control of the orthogonal robot is realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent robot control technology, and in particular to a PLC-based orthogonal robot control system and method. Background Technology

[0002] A Programmable Logic Controller (PLC) is an electronic system specifically designed for numerical computation in industrial applications. Using programmable memory, it controls various types of machines or products, performing tasks such as logic operations, program control, timing, counting, and arithmetic. PLCs are widely used in industrial automatic control systems due to their high reliability, flexibility, and ease of expansion. Orthogonal robots are a new type of robotic arm developed based on the orthogonality of robotic arms. They use multiple linear spindles instead of conventional rotary joints, improving the flexibility and efficiency of the workspace; they also feature simple mechanisms, high rigidity, speed, and the ability to achieve high-precision positioning. Robots have wide applications in assembly, welding, and inspection. In today's society, due to improved production processes and rising labor costs, automation and intelligence have become increasingly prevalent in production. In this context, programmable logic controllers and orthogonal robotic arms are key technologies for achieving industrial automation and intelligent manufacturing. PLCs, with their flexible programming capabilities, high reliability, and high adaptability, are widely used in numerous industrial automatic control applications. Orthogonal robots, with their high precision, high speed, and high flexibility, have broad application prospects in industrial production, logistics, and medical devices. Organically integrating programmable logic controllers (PLCs) with orthogonal robot control technology can make production processes more complex and efficient, while also improving production efficiency, reducing production costs, and enhancing product quality. Therefore, it has significant practical implications and huge market demand in actual production. Currently, although both PLCs and orthogonal robotic arms have made considerable progress in their respective research directions, how to organically integrate the two to achieve more intelligent, efficient, and flexible industrial production remains a pressing problem to be solved.

[0003] Prior art 1, application number CN202211328006.2, discloses an electrical control method and distributed power supply system for an orthogonal joint snake robot. It includes: initializing the basic parameters of the orthogonal joint snake robot and setting the initial values ​​of the control parameters of the CPG model; determining the modal control parameters during the movement of the orthogonal joint snake robot; solving the model parameters and differential equations of the CPG model to obtain the control parameters; controlling the serial bus servo to perform actions based on the control parameters; updating the initial values ​​of the control parameters of the CPG model; obtaining the time-varying angle position of the serial bus servo; controlling the movement of the orthogonal joint snake robot; and providing a distributed power supply system based on this. While achieving multimodal movement of the snake robot in different motion scenarios improves its motion flexibility, stability, environmental adaptability, and endurance, reduces control difficulty, and features reasonable wiring, low circuit structure complexity, and high modularity, the use of the CPG model requires relatively accurate sample data for training, making it overly reliant on the accuracy of the model itself. This results in cumbersome usage and makes it difficult to change the control parameters.

[0004] Prior art two, application number CN202180047786.0, discloses a robot control device that allows the robot to move without the operator needing to directly re-enter the motion program when the robot is replaced by a new robot of a different size. The robot control device includes: a storage unit that stores motion programs; and a control unit that, based on the motion programs, causes the robot to move in an orthogonal three-axis robot coordinate system. The control unit includes: a movable range determination unit that determines whether there is an axis in the orthogonal three axes of the motion program that exceeds the robot's movable range; and a correction unit that, if the movable range determination unit determines that there is an axis exceeding the robot's movable range, rewrites the motion program by bringing the axis into the robot's movable range. While this provides a robot control device that allows the robot to move even when replaced by a new robot of a different size without the operator needing to directly re-enter the motion program, the control unit and motion program control method are complex, and equipment repair and maintenance are time-consuming and labor-intensive when problems occur.

[0005] Current technologies 1 and 2 have significant shortcomings in handling complex motion trajectories, multi-axis collaborative control, and real-time performance, making it difficult to achieve high-precision and high-efficiency motion control. Furthermore, they lack scalability and flexibility, making it difficult to adapt to changes in different production environments and needs. Therefore, this invention provides a PLC-based orthogonal robot control system and method. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a PLC-based orthogonal robot control system, comprising:

[0007] The data processing component is responsible for collecting data from the working environment of the orthogonal robot using multiple sensors and acquiring the sensor data in real time through a programmable logic controller.

[0008] The motion control component is responsible for receiving processed data and generating control signals through a programmable logic controller.

[0009] The human-machine interaction component is responsible for data interaction with the host computer or touch screen through the programmable logic controller, so as to realize the parameter setting, status display and fault diagnosis functions of the orthogonal robot.

[0010] Optional motion control components include:

[0011] The path planning module is responsible for using the programmable logic controller to plan the path based on the preprocessed data and calculate the best path for the robot from its current position to the target position.

[0012] The control signal generation module is responsible for generating control signals based on the path planning results. The control signals trigger the three-loop control program, which includes the position loop, speed loop, and current loop.

[0013] The motion execution and feedback module is responsible for transmitting the three-loop control program to the servo motor or stepper motor through the programmable logic controller, thereby driving the robot to move.

[0014] Optionally, during motor drive, the programmable logic controller in the motion execution and feedback module monitors the motor status in real time; during motion, sensors provide real-time feedback on the robot's position and speed information, and the programmable logic controller adjusts the control signal in real time based on the feedback information.

[0015] Optional, the route planning module includes:

[0016] The node search submodule is responsible for setting the robot's initial position as the root node of the tree and the target position; obtaining the map information of the current environment; finding the nearest node in the current tree; and randomly generating a point in the environment.

[0017] The collision detection submodule is responsible for expanding a new node by one step length from the nearest node to a randomly generated point in the environment. The step length is a preset constant used to control the expansion step size. It also checks whether the path of the new node from the nearest node collides with an obstacle.

[0018] The path processing submodule is responsible for checking whether a new node is close to the target location.

[0019] Optionally, in the path processing submodule, if the new node is within a certain range from the target position, a feasible path is found; starting from the target position, backtracking along the nodes of the tree to the initial position forms a path from the initial position to the target position, and the generated path is smoothed.

[0020] Optional, the control signal generation module includes:

[0021] The instruction corresponding submodule is responsible for receiving the path planning results and starting to generate the corresponding control signals. The control signals are the instructions that trigger the three-loop control program, corresponding to the position loop, speed loop and current loop respectively.

[0022] The position update submodule is responsible for activating the position loop and setting the robot's target position based on the path planning results; the velocity loop starts working and sets the robot's movement speed based on the output of the position loop, and the robot moves to the target position.

[0023] The current adjustment submodule is responsible for activating the current loop and adjusting the motor current according to the output of the speed loop, so that the robot moves according to the set speed and path.

[0024] Optionally, the instruction corresponds to a submodule, which includes:

[0025] The path planning result parsing unit is responsible for parsing the received path planning results. The path planning results contain a series of key points and path segments between each key point. Each path segment contains direction, distance and speed. The parsed path planning results are decomposed into a series of specific control commands, which will be used to trigger the three-loop control program.

[0026] The position loop control signal generation unit is responsible for instructing the corresponding submodule to generate the position loop control signal based on the parsed path planning results. The signal contains the robot's target position information and is represented in coordinate form. The generated position loop control signal is passed to the position update submodule to activate the position loop and set the robot's target position.

[0027] The velocity loop control signal generation unit is responsible for receiving the position loop control signal and generating the velocity loop control signal, which contains the robot's motion speed information on each path segment, with the support of the communication protocol. The velocity loop control signal is calculated based on the speed limit and path curvature factor in the path planning results.

[0028] The current loop control signal generation unit is responsible for receiving the speed loop control signal under the support of the communication protocol and generating the current loop control signal, which includes the current value required by the motor, to drive the robot to move according to the set speed and path; the current loop control signal is calculated based on the output of the speed loop and the torque-current relationship of the motor.

[0029] Optionally, the position loop control signal generation unit includes:

[0030] The route planning result receiving subunit is responsible for transmitting the route planning result to the route planning result parsing unit in the form of data packets via a communication protocol; it decodes the received data packets and extracts the data structure containing the route planning information.

[0031] The key point and path segment parsing subunit is responsible for extracting the coordinate information of each key point from the data structure and defining the turning points or target points of the path; for the path segments between each key point, the parsing unit extracts the direction, distance and speed information.

[0032] The path segment detailed information parsing subunit is responsible for parsing the direction information of the path segment and determining the robot's movement direction from the current key point to the next key point.

[0033] Optionally, the direction information is represented as a relative angle or an absolute angle; the distance information of the path segment is parsed to calculate the distance the robot needs to travel from the current key point to the next key point. The distance information is used to calculate the duration of the path segment; the speed information of the path segment is parsed to determine the robot's movement speed on the path segment. The speed information includes the maximum speed limit and speed adjustments related to the path curvature.

[0034] This invention provides a PLC-based orthogonal robot control method, comprising the following steps:

[0035] Multiple sensors are used to collect data from the working environment of the orthogonal robot, and the sensor data is acquired in real time through a programmable logic controller.

[0036] The system receives and processes the data, and then uses a programmable logic controller to generate control signals to drive the motor.

[0037] The orthogonal robot can interact with a host computer or touch screen via a programmable logic controller to achieve parameter setting, status display, and fault diagnosis.

[0038] The data processing component of this invention uses multiple sensors to collect data from the working environment of the orthogonal robot. The data is acquired in real time by a programmable logic controller (PLC) and processed through filtering, calibration, and error compensation. These sensors include photoelectric sensors, proximity switches, and encoders. The motion control component receives the processed data and generates control signals through the PLC to drive servo motors or stepper motors, enabling movement along the robot's X, Y, and Z orthogonal axes. The human-machine interface component interacts with a host computer or touchscreen via the PLC, enabling parameter setting, status display, and fault diagnosis for the orthogonal robot. A graphical interface monitors the robot's operating status, issuing alarms and fault prompts when abnormalities occur, alerting operators to take action. The data processing component in this solution, after PLC processing including filtering, calibration, and error compensation, ensures data accuracy and reliability. Significance: The data processing component ensures the robot can acquire information about its surrounding environment and make decisions based on accurate data, thereby improving the robot's operational accuracy and safety. After receiving processed data, the motion control component (PCC) generates control signals to drive servo motors or stepper motors, enabling precise movement of the robot along its three orthogonal axes (X, Y, and Z). Significance: The PCC ensures the robot moves along a predetermined path and speed, achieving precise operation and efficient workflow; it is particularly important for applications requiring high-precision positioning and rapid response. The human-machine interface (HMI) component interacts with a host computer or touchscreen via the PCC, providing a graphical interface for operators to set parameters, monitor status, and diagnose faults. It also promptly issues alarms and fault prompts when the robot malfunctions. Significance: The HMI component allows operators to intuitively monitor the robot's status, quickly respond to and handle abnormal situations, greatly improving operational convenience and system reliability.

[0039] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0040] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0041] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0042] Figure 1 This is a block diagram of the PLC-based orthogonal robot control system in Embodiment 1 of the present invention;

[0043] Figure 2 This is a block diagram of the data processing component in Embodiment 2 of the present invention;

[0044] Figure 3 This is a block diagram of the motion control component in Embodiment 3 of the present invention;

[0045] Figure 4 This is a block diagram of the path planning module in Embodiment 4 of the present invention;

[0046] Figure 5 This is a block diagram of the control signal generation module in Embodiment 5 of the present invention;

[0047] Figure 6 This is a block diagram of the sub-modules corresponding to the instructions in Embodiment 6 of the present invention;

[0048] Figure 7 This is a block diagram of the position loop control signal generation unit in Embodiment 7 of the present invention;

[0049] Figure 8 This is a block diagram of the human-computer interaction component in Embodiment 8 of the present invention;

[0050] Figure 9 This is a block diagram of the fault diagnosis and alarm notification module in Embodiment 9 of the present invention;

[0051] Figure 10 This is a flowchart of the PLC-based orthogonal robot control method in Embodiment 10 of the present invention. Detailed Implementation

[0052] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0053] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the embodiments of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0054] In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application. In the description of this application, it should be understood that the terms "first," "second," "third," etc., are used only to distinguish similar objects and are not necessarily used to describe a specific order or sequence, nor should they be construed as indicating or implying relative importance. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0055] Example 1: As Figure 1 As shown, this embodiment of the invention provides a PLC-based orthogonal robot control system, comprising:

[0056] The data processing component is responsible for collecting data from the working environment of the orthogonal robot using multiple sensors, acquiring the sensor data in real time through the programmable logic controller, and performing processing such as filtering, calibration, and error compensation.

[0057] The sensors include photoelectric sensors, proximity switches, and encoders, etc.

[0058] The motion control component is responsible for receiving and processing the data, generating control signals through a programmable logic controller to drive servo motors or stepper motors, thereby enabling the robot to move along its three orthogonal axes of X, Y, and Z.

[0059] The human-machine interface component is responsible for data interaction with the host computer or touch screen through the programmable logic controller, realizing functions such as parameter setting, status display and fault diagnosis of the orthogonal robot; it can monitor the operating status of the orthogonal robot through a graphical interface, and issue alarms and fault prompts when abnormalities occur in the operation of the orthogonal robot to remind the operator to handle the situation.

[0060] The working principle and beneficial effects of the above technical solution are as follows: The data processing component in this embodiment uses multiple sensors to collect data from the working environment of the orthogonal robot. The data is acquired in real time through a programmable logic controller (PLC) and processed, including filtering, calibration, and error compensation. The sensors include photoelectric sensors, proximity switches, and encoders. The motion control component receives the processed data and generates control signals through the PLC to drive servo motors or stepper motors, enabling the robot's X, Y, and Z orthogonal axes to move. The human-machine interface component interacts with a host computer or touchscreen through the PLC, enabling parameter setting, status display, and fault diagnosis for the orthogonal robot. A graphical interface monitors the robot's operating status, issuing alarms and fault prompts when abnormalities occur, alerting operators to take action. The data processing component in this solution, after PLC processing including filtering, calibration, and error compensation, ensures the accuracy and reliability of the data. Significance: The data processing component ensures the robot can acquire information about its surrounding environment and make decisions based on accurate data, thereby improving the robot's operational accuracy and safety. After receiving processed data, the motion control component (PCC) generates control signals to drive servo motors or stepper motors, enabling precise movement of the robot along its three orthogonal axes (X, Y, and Z). Significance: The PCC ensures the robot moves along a predetermined path and speed, achieving precise operation and efficient workflow; it is particularly important for applications requiring high-precision positioning and rapid response. The human-machine interface (HMI) component interacts with a host computer or touchscreen via the PCC, providing a graphical interface for operators to set parameters, monitor status, and diagnose faults. It also promptly issues alarms and fault prompts when the robot malfunctions. Significance: The HMI component allows operators to intuitively monitor the robot's status, quickly respond to and handle abnormal situations, greatly improving operational convenience and system reliability.

[0061] In summary, this embodiment achieves efficient, precise, and reliable control of the orthogonal robot through the collaborative work of its various components. This not only improves production efficiency but also enhances operational safety and convenience, which is of great significance to modern industrial automation. This embodiment achieves efficient and precise control of the orthogonal robot by introducing a PLC controller. The PLC controller has powerful computing and control capabilities, capable of handling complex motion trajectories and multi-axis collaborative control, meeting the high demands of modern industrial production. Furthermore, the PLC controller also possesses high scalability and flexibility, allowing for customization and adjustment according to different production environments and needs, thus improving the robot's adaptability and flexibility.

[0062] Example 2: Figure 2 As shown, based on Embodiment 1, the data processing component provided in this embodiment of the invention includes:

[0063] The data preprocessing module is responsible for capturing data from multiple sensors using multi-channel synchronous sampling; dynamically identifying the frequency response of the Kalman filter using adaptive spectrum analysis; separating the signal and noise in the data; suppressing interference signals; and obtaining noise-removed data.

[0064] Among them, adaptive spectrum analysis uses spectrum estimation methods and adaptive window adjustment strategies, involving the following short-time Fourier transform expressions:

[0065]

[0066] In the formula: S(n,ω) represents the spectrum estimation in the time-frequency domain, x(m) represents the m-th sample of the input signal, w(nm) represents the window function, and ω represents the angular frequency;

[0067] Adaptive window adjustment equation expression:

[0068]

[0069] In the formula: σ(n) represents the standard deviation of the adaptive window, which is dynamically adjusted according to the local characteristics of the signal; w(n) represents the adaptive window function;

[0070] Kalman filter equations, including extended Kalman filter and unscented Kalman filter;

[0071] The state prediction equation of the extended Kalman filter:

[0072]

[0073] In the formula: f represents the nonlinear state transition function; This represents the predicted state at time step k, estimated at time step k-1; u k Indicates control input;

[0074] Covariance prediction equation:

[0075]

[0076] Among them: A k-1 The Jacobian matrix representing the state transition function; P k|k-1 Q represents the covariance matrix of the state prediction. k-1 The covariance matrix represents the process noise;

[0077] Kalman gain equation:

[0078]

[0079] Where: H k The Jacobian matrix representing the observation function; Kk R represents the Kalman gain. k The covariance matrix representing the observation noise;

[0080] State update equation:

[0081]

[0082] In the formula: h represents the nonlinear observation function; z represents the state estimate at time step k. k This represents the observation at time step k;

[0083] Covariance update equation:

[0084] P k|k =(IK k H k )P k|k-1

[0085] In the formula, P k|k Let I represent the covariance matrix after the state update, and let I represent the identity matrix.

[0086] Sigma point generation for unscented Kalman filters:

[0087]

[0088] Where: χ k-1 Let λ represent the Sigma point, where λ = α 2 (n+κ)-n represents the scaling parameter, and α and k represent the parameters that control the distribution of Sigma points;

[0089] State prediction:

[0090] χ k|k-1 =f(χ) k-1 ,u k )

[0091] In the formula, χ k|k-1 This represents the predicted value of the Sigma point at time step k;

[0092] Covariance prediction:

[0093]

[0094] In the formula: W i (m) Indicates mean weight;

[0095] Observation and prediction:

[0096] γ k =h(χ k|k-1 )

[0097] In the formula, γ k Indicates the observed predicted value;

[0098] Kalman gain:

[0099]

[0100] In the formula: W represents the observation covariance. i (c) Indicates the covariance weight;

[0101] Status Update:

[0102]

[0103] In the formula, Indicates the observed predicted value;

[0104] Covariance update:

[0105]

[0106] Dynamic frequency response identification involves adjusting the parameters of the Kalman filter in real time to adapt to the frequency characteristics of the signal and noise. The frequency response adjustment equation is as follows:

[0107]

[0108] In the formula: α i and φ i The parameter ω represents the adjustment of the frequency response. i H(f) represents the angular frequency of the frequency component; H(f) represents the frequency response function; H0 represents the reference value of the frequency response; β represents the attenuation coefficient of the frequency response; f c Indicates the center frequency;

[0109] Adaptive Kalman filter parameter tuning equations:

[0110]

[0111] Where: δ j and ψ j The parameter ω represents the noise covariance during the adjustment process. j γ represents the angular frequency of the frequency component; Q(f) represents the process noise covariance, Q0 represents the baseline value of the process noise covariance, and γ represents the attenuation coefficient of the process noise covariance. Through the equation, more accurate adaptive spectrum analysis and dynamic adjustment of the Kalman filter can be achieved, thereby effectively separating the signal and noise, suppressing interference signals, and obtaining noise-removed data.

[0112] The calibration compensation module is responsible for equipping each sensor node with a miniature calibration unit, exchanging data with the calibration server of the human-machine interface component, generating calibration parameters, and the sensor nodes monitoring and adjusting their output in real time according to the calibration parameters; it also performs error compensation on the sensor output data according to its type to obtain the error-compensated data.

[0113] The storage and transmission module is responsible for storing the error-compensated data in layers. Real-time data is stored in high-speed memory, while historical and analytical data are stored in low-cost solid-state drives or cloud storage. The data is transmitted to the motion control components and human-computer interaction components via 5G.

[0114] The working principle and beneficial effects of the above technical solution are as follows: The data preprocessing module of this embodiment adopts multi-channel synchronous sampling to capture data from multiple sensors; it uses adaptive spectrum analysis to dynamically identify the frequency response of the Kalman filter, separates the signal and noise in the data, suppresses interference signals, and obtains noise-removed data; the calibration compensation module is equipped with a miniature calibration unit at each sensor node, which exchanges data with the calibration server of the human-machine interface component. The calibration server generates calibration parameters, and the sensor nodes monitor and adjust the output in real time according to the calibration parameters; error compensation is performed on the sensor output data according to its type to obtain error-compensated data; the storage and transmission module performs hierarchical storage of the error-compensated data, with real-time data stored in high-speed memory and historical and analysis data stored in low-cost solid-state drives or cloud storage; the data is transmitted to the motion control component and the human-machine interface component via 5G. The data preprocessing module of the above solution ensures the integrity and consistency of the data through multi-channel synchronous sampling; the application of adaptive spectrum analysis and dynamic identification of the Kalman filter effectively separates the signal and noise, suppresses interference, and significantly improves the quality of the data. Significance achieved: It provides clean and accurate basic data for subsequent data analysis and processing, ensuring the system's sensitivity and response speed to environmental changes. The miniature calibration unit in the calibration compensation module and the real-time data exchange with the calibration server ensure the accuracy and consistency of the sensor output; the error compensation mechanism further improves data precision. Significance: Real-time calibration and error compensation ensure the reliability of sensor data, reduce errors caused by sensor aging or environmental changes, and provide a guarantee for the long-term stable operation of the system. The layered storage strategy and 5G transmission technology in the storage and transmission module ensure rapid data storage and efficient transmission, meeting the needs of real-time data processing and historical data analysis. Significance: The combination of high-speed memory and low-cost storage ensures both data real-time performance and cost-effectiveness; 5G transmission ensures fast and stable data transmission, enabling remote control and real-time decision-making for the system.

[0115] In summary, this embodiment constructs an efficient, stable, and reliable data processing system, providing strong data support for various complex application scenarios.

[0116] Example 3: As Figure 3 As shown, based on Embodiment 1, the motion control component provided in this embodiment of the invention includes:

[0117] The path planning module is responsible for using the programmable logic controller to plan the path based on the preprocessed data and calculate the best path for the robot from its current position to the target position.

[0118] The control signal generation module is responsible for generating control signals based on the path planning results. The control signals trigger the three-loop control program, which includes the position loop, speed loop, and current loop.

[0119] The motion execution and feedback module is responsible for transmitting the three-loop control program to the servo motor or stepper motor through the programmable logic controller (PLC) to drive the robot to move along the three orthogonal axes of X, Y, and Z. During the motor drive process, the PLC monitors the motor status in real time. During the movement, the sensors provide real-time feedback on the robot's position and speed information, and the PLC adjusts the control signals in real time based on the feedback information.

[0120] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the path planning module, based on preprocessed data, uses a programmable logic controller (PLC) to perform path planning, calculating the optimal path for the robot from its current position to the target position. The control signal generation module generates control signals based on the path planning results, triggering a three-loop control program, including a position loop, a velocity loop, and a current loop. The motion execution and feedback module transmits the three-loop control program to a servo motor or stepper motor via the PLC, driving the robot to move along the X, Y, and Z orthogonal axes. During motor driving, the PLC monitors the motor's status in real time. During motion, sensors provide real-time feedback on the robot's position and speed information, and the PLC adjusts the control signals accordingly. The path planning module in this solution, based on preprocessed data, can quickly calculate the optimal path for the robot from its current position to the target position; this not only improves the efficiency of path planning but also ensures the optimality of the path. The module supports path planning by the PLC and can flexibly adjust the path planning algorithm according to different task requirements, adapting to various complex motion scenarios. Significance Achieved: Through precise path planning, the robot can reach the target position with the shortest path and minimal error, thereby improving overall motion accuracy and efficiency. The flexibility of the path planning module enables the robot to operate efficiently in different environments and tasks, enhancing the system's adaptability and versatility. Based on the path planning results, the control signal generation module generates precise control signals to trigger the three-loop control program (position loop, velocity loop, and current loop), ensuring the robot moves according to the predetermined path and speed. The module can dynamically adjust the control signals according to the real-time requirements of path planning, ensuring the robot maintains optimal performance throughout its movement. Significance Achieved: Through precise control signal generation, the robot can maintain stable posture and speed during movement, reducing vibration and jitter, and improving motion smoothness. Precise control signal generation can avoid unnecessary energy consumption, optimize the robot's energy efficiency, and extend the equipment's lifespan. The motion execution and feedback module monitors the motor status in real time through a programmable logic controller (PLC) and adjusts the control signals accordingly based on sensor feedback, ensuring the robot moves along a predetermined path and speed. The module can drive servo motors or stepper motors to move along the X, Y, and Z orthogonal axes, achieving high-precision motion control. The significance lies in the fact that real-time monitoring and feedback allow the module to adjust control signals promptly, ensuring the robot maintains high-precision position and speed control during movement; real-time monitoring of motor status and sensor feedback allows for timely detection and handling of anomalies, enhancing system safety and reliability.

[0121] In summary, this embodiment constitutes a highly efficient, stable, and flexible motion control system. Through precise path planning, accurate control signal generation, and real-time motion execution and feedback, the robot can operate efficiently and stably in various complex motion scenarios, meeting the needs of different tasks. This not only improves the robot's motion accuracy and efficiency but also enhances the system's adaptability and safety, providing strong support for industrial automation and intelligent manufacturing.

[0122] Example 4: Figure 4 As shown, based on Embodiment 3, the path planning module provided in this embodiment of the invention includes:

[0123] The node search submodule is responsible for setting the robot's initial position as the root node of the tree and the target position; obtaining map information of the current environment, including obstacle positions and free space; finding the nearest node in the current tree; and randomly generating a point in the environment.

[0124] The collision detection submodule is responsible for expanding a new node by a step size from the nearest node to a randomly generated point in the environment. The step size is a preset constant used to control the expansion step size. It checks whether the path of the new node from the nearest node collides with an obstacle. If a collision occurs, the expansion is abandoned and resampling is performed. If the path does not collide, the new node is added to the tree and connected to the nearest node to form a new branch.

[0125] The path processing submodule is responsible for checking whether a new node is close to the target location. If the new node is within a certain range from the target location, a feasible path is found. Starting from the target location, the system backtracks along the nodes of the tree to the initial location, forming a path from the initial location to the target location. The generated path is then smoothed.

[0126] The working principle and beneficial effects of the above technical solution are as follows: The node search submodule of this embodiment sets the robot's initial position as the root node of the tree and the target position; obtains the map information of the current environment, including obstacle positions and free space; searches for the nearest node in the current tree and randomly generates a point in the environment; the collision judgment submodule extends from the nearest node to the randomly generated point in the environment by one step, generating a new node. The step size is a preset constant used to control the extension stride; checks whether the path of the new node from the nearest node collides with an obstacle; if a collision occurs, the extension is abandoned and resampling is performed; if the path does not collide, the new node is added to the tree and connected to the nearest node to form a new branch; the path processing submodule checks whether the new node is close to the target position. If the new node is within a certain range from the target position, a feasible path is found; starting from the target position, the path is traced back along the nodes of the tree to the initial position to form a path from the initial position to the target position, and the generated path is smoothed. The node-finding submodule of the above scheme sets the robot's initial position as the root node of the tree and the target position, providing a clear start and end point for path planning; it acquires map information of the current environment, including obstacle locations and free space, providing necessary environmental data for path planning; it finds the nearest node in the current tree, ensuring the efficiency and accuracy of path expansion; and it randomly generates a point in the environment, increasing the diversity and exploratory nature of path planning. The significance of this is: ensuring that path planning has a clear direction and goal, avoiding aimless exploration; by acquiring environmental information, enabling path planning to adapt to different environmental conditions, improving the flexibility and practicality of planning; and by finding the nearest node and random sampling, improving the efficiency and success rate of path expansion and reducing unnecessary computational resource consumption. The collision detection submodule expands the path from the nearest node to a randomly generated point in the environment by a step size, creating a new node. This step size is a preset constant used to control the expansion's magnitude, ensuring the stability and controllability of the path expansion. It checks whether the path from the nearest node to the new node collides with obstacles, ensuring the generated path is safe. If there are no collisions, the new node is added to the tree and connected to the nearest node, forming new branches, gradually building a complete path tree. The benefits include: ensuring the generated path does not collide with obstacles through collision detection, improving path safety and reliability; optimizing the path construction process by progressively expanding and connecting nodes, making the path more rational and efficient; and avoiding invalid path expansions through collision detection, saving computational resources and improving planning efficiency.The path processing submodule checks if a new node is close to the target location. If the new node is within a certain range of the target location, a feasible path is found, ensuring the final goal of path planning is achieved. Starting from the target location, it backtracks along the tree nodes to the initial location, forming a path from the initial location to the target location, ensuring the path's continuity and integrity. The generated path is smoothed to reduce detours and redundancy, improving its smoothness and usability. The significance is: Target proximity detection ensures the successful achievement of the final goal of path planning, increasing the success rate of planning; path backtracking ensures the generated path is continuous and complete from start to finish, avoiding path interruptions or omissions; path smoothing optimizes the shape and direction of the path, making it smoother and more practical, improving the robot's navigation efficiency and user experience.

[0127] In summary, each component in this embodiment plays a specific role in path planning. Through efficient environmental perception, safe collision detection, and optimized path processing, it ensures that the robot can quickly, safely, and efficiently plan a path from the starting point to the destination, meeting the navigation requirements in practical applications.

[0128] Example 5: Figure 5 As shown, based on Embodiment 3, the control signal generation module provided in this embodiment of the invention includes:

[0129] The instruction corresponding submodule is responsible for receiving the path planning results and starting to generate the corresponding control signals. The control signals are the instructions that trigger the three-loop control program, corresponding to the position loop, speed loop and current loop respectively.

[0130] The position update submodule is responsible for activating the position loop and setting the robot's target position based on the path planning results; the velocity loop starts working and sets the robot's movement speed based on the output of the position loop, and the robot moves to the target position.

[0131] The current adjustment submodule is responsible for activating the current loop and adjusting the motor current according to the output of the speed loop, so that the robot moves according to the set speed and path.

[0132] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the instruction-corresponding submodule receives the path planning result and begins generating corresponding control signals. These control signals are instructions that trigger the three-loop control program, corresponding to the position loop, speed loop, and current loop, respectively. The position update submodule activates the position loop and sets the robot's target position based on the path planning result. The speed loop starts working, setting the robot's movement speed based on the position loop's output, and the robot moves to the target position. The current adjustment submodule activates the current loop, adjusting the motor current based on the speed loop's output, and the robot moves according to the set speed and path. The instruction-corresponding submodule in the above solution generates control signals that trigger the three-loop control program based on the path planning result, ensuring that the position loop, speed loop, and current loop are activated in the correct order and logic. The significance achieved is: by generating accurate control signals, it ensures that the robot can move according to the planned path; it guarantees coordinated operation between the various control loops, avoiding system malfunctions caused by signal errors. The position update submodule sets the robot's target position based on the path planning result and sets the robot's movement speed based on the position loop's output, ensuring that the robot can smoothly move to the target position. The benefits achieved include: ensuring the robot accurately follows the planned path, reducing positional deviations; and through reasonable speed settings, making the robot's movement smoother, reducing vibration and energy consumption. The current adjustment submodule adjusts the motor current based on the speed loop output, ensuring the robot moves at the set speed and path; real-time current adjustment allows the robot to quickly respond to path changes and external interference. The benefits also include: precise current control ensures the robot moves at the expected speed and path, reducing errors; and reasonable current adjustment optimizes energy use and extends the robot's operating time.

[0133] In summary, this embodiment constitutes a highly efficient and precise control system. Through the collaborative work of these sub-modules, the robot can not only accurately execute path planning but also maintain stability and efficiency during movement, ultimately reaching the expected target position. This embodiment not only improves the robot's working efficiency but also enhances its adaptability and stability in complex environments.

[0134] Example 6: As Figure 6 As shown, based on Embodiment 5, the instruction-corresponding submodule provided in this embodiment of the invention includes:

[0135] The path planning result parsing unit is responsible for parsing the received path planning results. The path planning results contain a series of key points and path segments between each key point. Each path segment contains information such as direction, distance, and speed. The parsed path planning results are decomposed into a series of specific control commands, which will be used to trigger the three-loop control program.

[0136] The position loop control signal generation unit is responsible for instructing the corresponding submodule to generate the position loop control signal based on the parsed path planning results. The signal contains the robot's target position information and is represented in coordinate form. The generated position loop control signal is passed to the position update submodule to activate the position loop and set the robot's target position.

[0137] The velocity loop control signal generation unit is responsible for receiving the position loop control signal and generating the velocity loop control signal, which contains the robot's motion speed information on each path segment, with the support of the communication protocol. The velocity loop control signal is calculated based on factors such as speed limits and path curvature in the path planning results.

[0138] The current loop control signal generation unit is responsible for receiving the speed loop control signal under the support of the communication protocol and generating the current loop control signal, which includes the current value required by the motor, to drive the robot to move according to the set speed and path; the current loop control signal is calculated based on the output of the speed loop and the torque-current relationship of the motor.

[0139] The working principle and beneficial effects of the above technical solution are as follows: The path planning result parsing unit of this embodiment parses the received path planning result. The path planning result includes a series of key points and path segments between each key point. Each path segment includes information such as direction, distance, and speed. The parsed path planning result is decomposed into a series of specific control commands, which are used to trigger the three-loop control program. The position loop control signal generation unit generates the position loop control signal according to the parsed path planning result and the corresponding sub-module of the command. The position loop control signal includes the robot's target position information, represented in coordinate form. The generated position loop control signal is transmitted to the position update sub-module to activate the position loop and set the robot's target position. The speed loop control signal generation unit receives the position loop control signal under the support of the communication protocol and generates the speed loop control signal, which includes the robot's movement speed information on each path segment. The speed loop control signal is calculated based on factors such as speed limits and path curvature in the path planning result. The current loop control signal generation unit receives the speed loop control signal under the support of the communication protocol and generates the current loop control signal, which includes the current value required by the motor to drive the robot to move according to the set speed and path. The current loop control signal is calculated based on the output of the speed loop and the torque-current relationship of the motor. The path planning result parsing unit of the above scheme parses the received path planning results, extracting information such as the direction, distance, and speed of key points and path segments; it decomposes the parsed path planning results into a series of specific control commands, which are used to trigger the three-loop control program. The significance is that by accurately parsing the path planning results, it ensures that the robot can move according to the planned path, avoiding deviations; decomposing complex path planning results into specific control commands improves the efficiency and accuracy of command execution. The position loop control signal generation unit generates the robot's target position information in coordinate form based on the parsed path planning results; it transmits the generated position loop control signal to the position update submodule to activate the position loop and set the robot's target position. The significance is that by setting a precise target position, it ensures that the robot can accurately reach the key points of each path segment; the generation and transmission of the position loop control signal enable the robot to adjust its position in real time during operation, adapting to the dynamic environment. The speed loop control signal generation unit receives the position loop control signal with the support of the communication protocol; based on factors such as speed limits and path curvature in the path planning results, it generates the robot's motion speed information on each path segment. Significance achieved: By rationally calculating speed information, the robot's speed is ensured to remain stable during movement, avoiding sudden acceleration or deceleration; the generation of speed loop control signals enables the robot to efficiently follow the path planning results, improving overall operational efficiency. The current loop control signal generation unit receives the speed loop control signals with the support of the communication protocol; based on the speed loop output and the torque-current relationship of the motor, it generates the required current value for the motor.Significance achieved: By generating precise current values, the motor can provide sufficient power to drive the robot to move at the set speed and path; the generation of current loop control signals enables the motor to provide optimal current output under different loads, improving energy efficiency and extending motor life.

[0140] In summary, this embodiment achieves precise control from path planning results to actual robot movement; it ensures accurate parsing of path planning results and decomposition of control commands, providing a foundation for subsequent control; the position loop control signal generation unit ensures accurate robot navigation by setting precise target positions; the velocity loop control signal generation unit ensures smooth and efficient robot movement by generating reasonable velocity information; and the current loop control signal generation unit ensures efficient motor drive of the robot by providing precise current values. This enables the robot to achieve precise, efficient, and smooth movement in complex environments, improving the overall system reliability and performance.

[0141] Example 7: As Figure 7 As shown, based on Embodiment 6, the position loop control signal generation unit provided in this embodiment of the invention includes:

[0142] The route planning result receiving subunit is responsible for transmitting the route planning result to the route planning result parsing unit in the form of data packets via a communication protocol; it decodes the received data packets and extracts the data structure containing the route planning information.

[0143] The key point and path segment parsing subunit is responsible for extracting the coordinate information of each key point from the data structure and defining the turning points or target points of the path. For the path segment between each key point, the parsing unit extracts information such as direction, distance and speed. The direction information is represented by angle or vector, the distance is in meters, and the speed is in meters per second.

[0144] The path segment detailed information parsing subunit is responsible for parsing the direction information of the path segment to determine the robot's movement direction from the current key point to the next key point; the direction information is expressed in relative angles or absolute angles; parsing the distance information of the path segment to calculate the distance the robot needs to travel from the current key point to the next key point, the distance information is used to calculate the duration of the path segment; parsing the speed information of the path segment to determine the robot's movement speed on the path segment, the speed information includes the maximum speed limit and speed adjustments related to the path curvature.

[0145] The working principle and beneficial effects of the above technical solution are as follows: The path planning result receiving subunit of this embodiment transmits the path planning result to the path planning result parsing unit in the form of data packets through a communication protocol; the received data packets are decoded to extract the data structure containing path planning information; the key point and path segment parsing subunit extracts the coordinate information of each key point from the data structure and defines the turning point or target point of the path; for the path segment between each key point, the parsing unit extracts information such as direction, distance and speed, where direction information is represented by angle or vector, distance is in meters, and speed is in meters per second; the path segment detailed information parsing subunit parses the direction information of the path segment to determine the robot's movement direction from the current key point to the next key point; the direction information is represented by relative angle or absolute angle; the distance information of the path segment is parsed to calculate the distance the robot needs to travel from the current key point to the next key point, and the distance information is used to calculate the duration of the path segment; the speed information of the path segment is parsed to determine the robot's movement speed on the path segment, and the speed information includes the maximum speed limit and speed adjustment related to the path curvature. The path planning result receiving subunit of the above scheme transmits the path planning results through a communication protocol and decodes the received data packets to ensure accurate transmission to the parsing unit. It extracts a data structure containing path planning information from the decoded data packets, providing basic data for the parsing process. The significance of this is: ensuring that the path planning results are not lost or damaged during transmission, guaranteeing the robot receives complete path information; and providing timely and accurate data support for subsequent path parsing and control signal generation through rapid decoding and data extraction, improving the system's response speed. The key point and path segment parsing subunit extracts the coordinate information of each key point from the data structure. These key points define the turning points or target points of the path, providing clear reference points for path planning. It also parses the path segments between each key point, extracting information such as direction, distance, and speed, providing detailed path parameters for the robot's motion control. The significance of this is: through clear key point and path segment information, ensuring the robot can move accurately along the predetermined path, reducing errors; and providing flexible motion control parameters for the robot based on the direction, distance, and speed information of the path segments, enabling it to adapt to different path requirements. The path segment details parsing subunit determines the robot's direction of movement from the current key point to the next key point. The direction information is expressed in relative or absolute angles to ensure that the robot can turn accurately. It calculates the distance the robot needs to travel from the current key point to the next key point. The distance information is used to calculate the duration of the path segment to ensure that the robot can move as planned. It determines the robot's speed on the path segment. The speed information includes the maximum speed limit and speed adjustments related to the path curvature to ensure that the robot can move safely and efficiently on different path segments.Significance achieved: Through detailed analysis, it ensures that the robot can move precisely along the predetermined path, avoiding deviation or collision; the analysis of speed information ensures that the robot can move safely and efficiently on different path segments, thus guaranteeing both safety and improving work efficiency.

[0146] In summary, the various subunits of the position loop control signal generation unit in this embodiment work collaboratively to achieve efficient and precise execution of path planning and control. This not only ensures the robot can move accurately along the predetermined path but also provides flexible motion control parameters, enabling the robot to adapt to different path requirements while balancing safety and efficiency. It significantly enhances the robot's autonomous navigation capabilities in complex environments, providing a solid technical guarantee for the successful completion of automated tasks.

[0147] Example 8: As Figure 8 As shown, based on Embodiment 1, the human-computer interaction component provided in this embodiment of the invention includes:

[0148] The parameter setting and configuration module is responsible for setting and configuring various parameters of the orthogonal robot through a graphical interface on a host computer or touch screen; for example, adjusting parameters such as the robot's movement speed, acceleration, and deceleration to adapt to different work requirements; the parameter setting interface is usually divided into multiple modules, such as "motion control parameters", "sensor configuration", and "alarm settings".

[0149] The status display and real-time monitoring module is responsible for displaying the operating status of the orthogonal robot in real time through a graphical interface via a host computer or touch screen. The status display interface includes multiple sub-windows, which display key parameters such as the robot's position, speed, acceleration, and sensor data. The monitoring interface displays the robot's motion trajectory and the changing trends of sensor data in real time.

[0150] The fault diagnosis and alarm module is responsible for automatically diagnosing faults when the orthogonal robot malfunctions. It analyzes the data collected by the programmable logic controller, identifies the cause of the abnormality, and displays the fault information on the monitoring interface, including the fault type, the time of occurrence, and the location of the fault.

[0151] The working principle and beneficial effects of the above technical solution are as follows: The parameter setting and configuration module of this embodiment sets and configures various parameters of the orthogonal robot through a graphical interface of a host computer or touch screen; for example, adjusting parameters such as the robot's motion speed, acceleration, and deceleration to adapt to different work requirements; the parameter setting interface is usually divided into multiple modules, such as "motion control parameters", "sensor configuration", "alarm settings", etc.; the status display and real-time monitoring module displays the running status of the orthogonal robot in real time through a graphical interface of a host computer or touch screen; the status display interface includes multiple sub-windows, which respectively display key parameters such as the robot's position, speed, acceleration, and sensor data; the monitoring interface displays the robot's motion trajectory and the changing trend of sensor data in real time; the fault diagnosis and alarm prompt module automatically performs fault diagnosis when the orthogonal robot's operation is abnormal, analyzes the data collected by the programmable logic controller, identifies the cause of the abnormality, and displays fault information on the monitoring interface, including the fault type, the time of the fault occurrence, and the fault location. The parameter setting and configuration module of the above solution uses a graphical interface, allowing operators to flexibly adjust various parameters of the orthogonal robot, such as motion speed, acceleration, and deceleration, to adapt to different work requirements. The parameter setting interface is divided into multiple modules, such as "Motion Control Parameters," "Sensor Configuration," and "Alarm Settings," each focusing on the configuration of a specific function, improving operational convenience and efficiency. It supports saving and loading parameter configurations, allowing operators to save the current parameter configuration as a template for quick loading and use in subsequent work, improving system repeatability and consistency. Significance achieved: Through flexible parameter settings and modular design, operators can quickly adjust robot parameters to adapt to different work scenarios, thereby improving work efficiency; the flexibility and savability of parameter configurations enable the system to adapt to various complex working environments, enhancing its adaptability and scalability. The status display and real-time monitoring module displays the orthogonal robot's operating status in real time through a graphical interface, including key parameters such as position, speed, acceleration, and sensor data, helping operators understand the robot's working status in real time. The monitoring interface displays the robot's motion trajectory and sensor data change trends in real time, allowing operators to intuitively observe the robot's operation through dynamic updates. The status display interface supports custom layouts of sub-windows, allowing operators to adjust the window size and position as needed for clear viewing of required information. Significance achieved: Real-time monitoring and visualization enable operators to clearly understand the robot's operating status, promptly identify potential problems, and improve operational transparency and controllability. Through real-time monitoring, the system can detect anomalies immediately and take timely measures, thereby improving system reliability and stability.The fault diagnosis and alarm module can automatically diagnose faults. By analyzing data collected by the PLC, it identifies the cause of the anomaly and displays detailed fault information on the monitoring interface, including the fault type, time of occurrence, and location. It supports multiple alarm notification methods, such as audible alarms, visual alarms, and pop-up alarms, ensuring operators receive alarm information promptly. The fault notification interface typically includes a detailed description of the fault and handling suggestions to help operators quickly locate and resolve problems. The significance is that automatic diagnosis and multi-mode alarms enable operators to detect and respond to faults immediately, improving the efficiency and speed of fault handling. Detailed fault information and handling suggestions allow operators to quickly locate and resolve problems, reducing the impact of faults on system operation and improving system availability and stability.

[0152] In summary, the various modules of the human-computer interaction component in this embodiment achieve comprehensive monitoring and management of the orthogonal robot through different technical means. The parameter setting and configuration module improves the system's flexibility and adaptability, the status display and real-time monitoring module enhances the system's transparency and reliability, and the fault diagnosis and alarm module improves fault response speed and processing efficiency. This not only improves the working efficiency and stability of the orthogonal robot but also enhances the system's maintainability and scalability, providing solid technical support for the widespread application of orthogonal robots in complex working environments.

[0153] Example 9: As Figure 9 As shown, based on Embodiment 8, the fault diagnosis and alarm prompting module provided in this embodiment of the invention includes:

[0154] The pattern recognition submodule is responsible for analyzing the features extracted from the data using the mean deviation method to identify abnormal patterns; matching the identified abnormal patterns with a predefined fault mode library to determine the specific type and cause of the abnormality.

[0155] The fault diagnosis submodule is responsible for fusing sensor data, controller data, and pattern recognition results from multiple sources and comprehensively analyzing the information from each data source. It employs a hierarchical diagnostic method to perform coarse-grained fault detection, followed by fine-grained fault localization, narrowing down the fault range until the specific fault point is determined.

[0156] The equation for multi-source data fusion is as follows: Assume there are n′ sensor data sources S1, S2, ..., S n′ And m′ controller data sources C1, C2, ..., C m′ The pattern recognition result is R, and the output of each data source can be represented as a vector or matrix.

[0157] Preprocess each data source:

[0158]

[0159] Calculate the weight of each data source based on its reliability and accuracy:

[0160]

[0161] w R =Weight(R)

[0162] A weighted average method is used to fuse sensor data and controller data:

[0163]

[0164] in: It is the result of fusion after weighted averaging;

[0165] Fuzzy logic is used to fuse the pattern recognition results to obtain the final comprehensive data output F(x): Assume there are k′ fuzzy rules, and the output of each rule is μ. k′ The result of fuzzy logic fusion can be expressed as:

[0166]

[0167] in: It is the output of the k′-th fuzzy rule, based on state estimation. and pattern recognition results μ k′ It is the membership degree of the k′-th fuzzy rule;

[0168] The final fusion result can be obtained by weighted summation:

[0169]

[0170] Wherein: α and β are weighting coefficients, which are determined according to the actual situation; the equation combines weighted averaging and fuzzy logic to comprehensively analyze sensor data, controller data and pattern recognition results, and obtain more comprehensive and accurate data fusion results.

[0171] The fault location submodule is responsible for analyzing the causal relationships between various parameters using causal analysis methods to determine the root cause of the anomaly. For example, it analyzes the causal relationship between speed anomalies and acceleration anomalies to locate the specific cause of the speed anomaly. Combining sensor data and the robot's motion trajectory, it determines the specific location where the fault occurred, such as a certain joint or a certain sensor.

[0172] The working principle and beneficial effects of the above technical solution are as follows: The pattern recognition submodule of this embodiment uses the mean deviation method to analyze the features extracted from the data and identify abnormal patterns; the identified abnormal patterns are matched with a predefined fault pattern library to determine the specific type and cause of the abnormality; the fault diagnosis submodule fuses sensor data, controller data, and pattern recognition results from multiple sources and comprehensively analyzes the information from each data source; a hierarchical diagnosis method is used to perform coarse-grained fault detection, followed by fine-grained fault localization, narrowing the fault range until the specific fault point is determined; the fault localization submodule uses causal analysis to analyze the causal relationship between various parameters and determine the root cause of the abnormality; for example, analyzing the causal relationship between speed abnormality and acceleration abnormality to locate the specific cause of speed abnormality; combining sensor data and the robot's motion trajectory, the specific location of the fault is determined, such as a certain joint or a certain sensor. The pattern recognition submodule of the above solution, through the mean deviation method, can quickly identify abnormal patterns in the data, ensuring that problems can be detected in the early stages of a fault; matching the identified abnormal patterns with a predefined fault pattern library can accurately determine the specific type and cause of the abnormality, providing a basis for diagnosis. Significance Achieved: The fault diagnosis submodule provides early warnings at the initial stage of a fault, preventing further deterioration and reducing losses. Through pattern matching, it quickly identifies the fault type, providing maintenance personnel with clear fault information and improving maintenance efficiency. The fault diagnosis submodule comprehensively analyzes sensor data, controller data, and pattern recognition results to gain a comprehensive understanding of the system's operating status. Through coarse-grained fault detection and fine-grained fault localization, it gradually narrows down the fault range, ultimately pinpointing the specific fault location. Significance Achieved: Multi-source data fusion provides more comprehensive fault information, avoiding the limitations of a single data source. Hierarchical diagnostic methods gradually narrow down the fault range, ultimately accurately locating the fault point and reducing unnecessary inspection and maintenance time. The fault localization submodule analyzes the causal relationships between various parameters to determine the root cause of the anomaly, avoiding misleading superficial phenomena. Combining sensor data and the robot's motion trajectory, it can determine the specific location of the fault, such as a joint or sensor. Significance Achieved: Causal analysis helps find the root cause of the problem, avoiding only addressing surface issues and ensuring a complete fault resolution. It accurately determines the specific location of the fault, providing maintenance personnel with clear maintenance goals and improving maintenance efficiency and accuracy.

[0173] In summary, this embodiment constitutes a complete fault diagnosis and alarm system. Through early warning, accurate diagnosis, and precise location, it can not only improve the reliability and stability of the system, but also significantly reduce maintenance time and costs, and improve overall operational efficiency.

[0174] Example 10: As Figure 10As shown, based on Embodiments 1-9, the PLC-based orthogonal robot control method provided in this embodiment of the invention includes the following steps:

[0175] S100: Uses multiple sensors to collect data from the working environment of the orthogonal robot, and obtains the sensor data in real time through a programmable logic controller, and performs filtering, calibration and error compensation, etc.

[0176] The sensors include photoelectric sensors, proximity switches, and encoders, etc.

[0177] S200: Receives processed data and generates control signals through a programmable logic controller to drive servo motors or stepper motors, enabling the robot to move along its three orthogonal axes of X, Y, and Z.

[0178] S300: Through a programmable logic controller, it interacts with a host computer or touch screen to realize functions such as parameter setting, status display, and fault diagnosis of orthogonal robots; it can monitor the operating status of orthogonal robots through a graphical interface, and issue alarms and fault prompts when abnormalities occur in the operation of orthogonal robots to remind operators to handle them.

[0179] The working principle and beneficial effects of the above technical solution are as follows: This embodiment first uses multiple sensors to collect data from the working environment of the orthogonal robot. The sensor data is acquired in real time by a programmable logic controller (PLC) and processed by filtering, calibration, and error compensation. The sensors include photoelectric sensors, proximity switches, and encoders. Secondly, the processed data is received, and the PLC generates control signals to drive servo motors or stepper motors, realizing the movement of the robot's three orthogonal axes (X, Y, and Z). Finally, the PLC interacts with a host computer or touchscreen to achieve functions such as parameter setting, status display, and fault diagnosis for the orthogonal robot. The operating status of the orthogonal robot can be monitored through a graphical interface. When abnormalities occur during the operation of the orthogonal robot, alarms and fault prompts are issued to remind the operator to handle the situation. Step S100 of the above solution, data acquisition and preprocessing, uses multiple sensors (such as photoelectric sensors, proximity switches, and encoders) to comprehensively collect data from the robot's working environment. The programmable logic controller (PLC) acquires this data in real time and performs filtering, calibration, and error compensation to ensure the accuracy and reliability of the data. Significance Achieved: Accurate sensor data and real-time processing ensure the robot can accurately execute tasks and reduce errors; through multi-sensor fusion, the robot can better adapt to complex working environments, improving work efficiency and safety. Step S200: Control Signal Generation and Motion Control. Control signals are generated by the PLC to drive servo motors or stepper motors, achieving precise movement of the robot's three orthogonal axes (X, Y, and Z). The PLC's high-speed processing capability ensures timely response to control signals, making the robot's movements rapid and accurate. Significance Achieved: Precise motion control enables the robot to complete complex tasks in a short time, improving production efficiency; automated motion control reduces human intervention and lowers the risk of operational errors. Step S300: Data Interaction and Monitoring. Through data interaction between the PLC and a host computer or touchscreen, the orthogonal robot's parameter settings, status display, and fault diagnosis functions are realized; the graphical interface allows operators to intuitively monitor the robot's operating status and promptly detect and handle abnormal situations. Significance Achieved: Real-time monitoring and fault diagnosis functions can promptly detect and handle problems, reducing downtime and improving system reliability; the graphical interface and alarm prompts allow operators to respond quickly, reducing operational complexity and improving work efficiency.

[0180] In summary, the control method of this embodiment, through precise data acquisition, real-time motion control, and a convenient monitoring system, not only improves the operational accuracy and work efficiency of the orthogonal robot, but also enhances the system's reliability and ease of operation, enabling the robot to perform tasks efficiently and safely in various complex environments.

[0181] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of equivalents of this invention, this invention is also intended to include these modifications and variations.

Claims

1. A PLC-based orthogonal robot control system, characterized by, Comprise: Data processing component, responsible for using multiple sensors to collect data in the orthogonal robot working environment, real-time acquisition of sensor data through programmable logic controller; Motion control component, responsible for receiving processed data, generating control signals through programmable logic controller; Human-computer interaction component, responsible for data interaction with host computer or touch screen through programmable logic controller, realizing parameter setting, state display and fault diagnosis function of orthogonal robot.

2. The PLC-based orthogonal robot control system of claim 1, wherein, Motion control component, comprising: Path planning module, responsible for path planning based on preprocessed data, programmable logic controller calculates the best path from the current position to the target position of the robot; Control signal generation module, responsible for generating control signals according to path planning results, control signals trigger three-ring control program, including position loop, speed loop and current loop; Motion execution and feedback module, responsible for passing three-ring control program to servo motor or stepper motor through programmable logic controller, driving robot to move.

3. The PLC-based orthogonal robot control system of claim 2, wherein, During motor driving, programmable logic controller in motion execution and feedback module monitors the state of motor in real time; During motion, the sensor feedbacks the position and speed information of the robot in real time, and the programmable logic controller adjusts the control signal in real time according to the feedback information.

4. The PLC-based orthogonal robot control system of claim 2, wherein, Path planning module, comprising: Node search sub-module, responsible for setting the initial position of the robot as the root node of the tree and the target position; Obtain the map information of the current environment; Find the nearest node in the current tree and generate a point in the environment randomly; Collision judgment submodule, responsible for expanding a step from the nearest node to a randomly generated point in the environment to generate a new node, the step is a preset constant used to control the step size of expansion; Check whether the path from the nearest node to the new node collides with the obstacle; Path processing submodule, responsible for checking whether the new node is close to the target position.

5. The PLC-based orthogonal robot control system of claim 4, wherein, If the new node is within a certain range of the target position in path processing submodule, a feasible path is found; Starting from the target position, the nodes of the tree are traced back to the initial position to form a path from the initial position to the target position, and the generated path is smoothed.

6. The PLC-based orthogonal robot control system of claim 2, wherein, Control signal generation module, comprising: Instruction corresponding submodule, responsible for receiving path planning results and starting to generate corresponding control signals, control signals are instructions to trigger three-ring control program, corresponding to position loop, speed loop and current loop respectively; Position update submodule, responsible for activating position loop, setting target position of robot according to path planning results; Speed loop starts to work, sets the moving speed of robot according to the output of position loop, and robot moves to target position; Current adjustment submodule, responsible for activating current loop, adjusting motor current according to the output of speed loop, and robot moving according to the set speed and path.

7. The PLC-based quadrature robot control system of claim 6, wherein, Instruction corresponding submodule, comprising: The path planning result analysis unit is responsible for analyzing the received path planning result, which contains a series of key points and path segments between each key point, each path segment containing direction, distance and speed. The analyzed path planning result is decomposed into a series of specific control instructions, which will be used to trigger the three-ring control program. The position loop control signal generation unit is responsible for generating the position loop control signal according to the analyzed path planning result, which contains the target position information of the robot in the form of coordinates. The generated position loop control signal is transmitted to the position update submodule to activate the position loop and set the target position of the robot. The speed loop control signal generation unit is responsible for receiving the position loop control signal under the support of the communication protocol, generating the speed loop control signal, which contains the motion speed information of the robot on each path segment. The speed loop control signal is calculated based on the speed limit and path curvature factors in the path planning result. The current loop control signal generation unit is responsible for receiving the speed loop control signal under the support of the communication protocol, generating the current loop control signal, which contains the required current value of the motor, used to drive the robot to move according to the set speed and path. The current loop control signal is calculated based on the output of the speed loop and the torque-current relationship of the motor.

8. The PLC-based quadrature robot control system of claim 7, wherein, The position loop control signal generation unit includes: The path planning result receiving submodule is responsible for transmitting the path planning result to the path planning result analysis unit in the form of a data packet through the communication protocol. It decodes the received data packet and extracts the data structure containing the path planning information. The key point and path segment analysis submodule is responsible for extracting the coordinate information of each key point from the data structure, defining the turning points or target points of the path. For each path segment between key points, the analysis unit extracts direction, distance and speed information. The path segment detail analysis submodule is responsible for analyzing the direction information of the path segment to determine the motion direction of the robot from the current key point to the next key point.

9. The PLC-based quadrature robotic control system of claim 8, wherein, The direction information is represented in relative or absolute angle. The distance information of the path segment is analyzed to calculate the distance the robot needs to travel from the current key point to the next key point, which is used to calculate the duration of the path segment. The speed information of the path segment is analyzed to determine the motion speed of the robot on the path segment, which includes the maximum speed limit and the speed adjustment related to the path curvature.

10. A PLC-based orthogonal robot control method, characterized by, The steps include: Using multiple sensors to collect data in the working environment of the orthogonal robot, and acquiring the data of the sensors in real time through the programmable logic controller; Receiving the processed data and generating control signals to drive the motor through the programmable logic controller; Through the programmable logic controller, data interaction is carried out with the upper computer or touch screen to realize the parameter setting, state display and fault diagnosis function of the orthogonal robot.

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