Predictive synchronous cooperative control method of automatic working unit

Abstract

The invention discloses a predictive synchronous cooperative control method of an automatic working unit, and belongs to the technical field of industrial automatic control. The core of the method is that a predictive front starting time point Ts is accurately calculated by establishing a time model of a work cycle, so that the moving process of the mechanical arm and the machining process of equipment can be carried out in parallel, and seamless connection of operation of the mechanical arm and the machining process is achieved. The method further comprises the following steps: dynamically calculating a synchronous safety margin delta t based on historical data and communication delay; optimizing the operation energy consumption of the mechanical arm according to the production takt; active safety early warning is realized by monitoring the motor torque in real time and comparing the motor torque with a dynamic safety base line; and carrying out adaptive calibration on system parameters by utilizing a PID algorithm. According to the method, the production cycle can be remarkably shortened, the safety mechanism is promoted from post-event response to pre-warning, and meanwhile, the system is endowed with the self-optimization capability.

Description

Technical Field

[0001] This invention belongs to the field of industrial automation control technology, and particularly relates to a predictive synchronous collaborative control method for automated work units, which is used to optimize the collaborative operation efficiency between robotic arms and processing equipment and enhance process safety monitoring. Background Technology

[0002] In modern manufacturing, automated work units consisting of industrial robotic arms, CNC machine tools, injection molding machines, stamping machines, and testing equipment are key to achieving high-efficiency production. In traditional control logic, robotic arms and processing equipment typically employ a serial operating mode based on "request-response" or "signal handshake" to ensure absolute operational safety. However, this mode leads to significant efficiency bottlenecks: when the processing equipment is running, the robotic arm must remain idle in a safe position; conversely, when the robotic arm is performing loading and unloading operations, the processing equipment is in a stopped, waiting state. These large, unavoidable idle times accumulate linearly, not only extending the overall production cycle but also significantly reducing the return on investment for expensive automated equipment.

[0003] The aforementioned efficiency bottlenecks are prevalent in all automation scenarios, but the challenges they pose are particularly severe in fields with extreme requirements for safety and production cycle time, such as the processing of civil explosives, the manufacturing of precision medical devices, or the handling of high-value semiconductors. Taking the production process of propellant charges in the civil explosives industry as an example, the application of automation technology is a core development direction for improving the inherent safety level of production lines and ensuring product quality uniformity. According to industry standards such as GB50089-2018 "Safety Standard for Engineering Design of Civil Explosives," such production processes impose strict safety requirements from plant layout to specific operational procedures, aiming to maximize the separation of humans and machines, and the separation of humans and explosives, to reduce the risks to on-site operators.

[0004] To address these challenges, "replacing manpower with mechanization and reducing manpower with automation" has become an industry consensus. In recent years, using multi-axis industrial robotic arms (mechanical arms) to replace manual labor and construct automated work units has become the mainstream technological improvement path. In these existing automation solutions, their control logic generally follows a rigorous but inefficient sequential control mode. A typical workflow is as follows:

[0005] The control system (usually a PLC) first instructs the robotic arm to start from the waiting position, grab the workpiece (such as a pill) from the tray, and transport it to the CNC lathe for clamping. Only after confirming that the lathe chuck is clamped, the end effector of the robotic arm is released, and it has fully retracted to a preset safe waiting position does the control system issue a machining command to the lathe. After the lathe completes machining of the first end of the workpiece, the spindle stops and signals the control system that machining is complete. At this point, the control system reawakens the robotic arm from the waiting position and instructs it to move from the waiting position to the lathe operating position to perform subsequent actions such as picking up material, reversing direction, and secondary clamping.

[0006] This serial working mode based on "request-response" or "signal handshake" logically ensures that there will be no physical interference between the movements of the robotic arm and the lathe, thus ensuring basic operational safety. However, its inherent defects are also very obvious, namely, there is a large amount of unavoidable idle waiting time between the devices.

[0007] Therefore, how to design a new control method that can break the existing serial logic, realize intelligent collaboration and parallel operation between devices, and integrate a more proactive and predictive safety monitoring mechanism is a key problem that urgently needs to be solved in this technical field. Summary of the Invention

[0008] The present invention aims to solve the problems of low production efficiency and passive and unpredictable safety monitoring mechanisms in existing automated work units, especially in automated processing schemes for flammable and explosive materials, due to the use of serial control logic between robotic arms and processing equipment.

[0009] To achieve the above objectives, this invention provides a predictive synchronous collaborative control method for automated work units. This method departs from the traditional "wait-execute" sequential logic, instead establishing a time model of the work cycle to accurately predict the safe start point for the robotic arm, thereby enabling the robotic arm's movement and the equipment's processing to proceed in parallel. The specific technical solution is as follows:

[0010] This invention provides a predictive synchronous cooperative control method for an automated work unit, wherein the cooperative control operation is performed within a work unit comprising a robotic arm and a processing device, the robotic arm moving between a waiting position and an operating position. The method comprises the following steps:

[0011] S1. For a complete work cycle, establish a time model containing multiple time components, where each time component includes at least one known processing time T. m and a speed V of the robotic arm r The determined travel time;

[0012] S2. Based on a specific operating speed value V preset for the robotic arm r , calculate the corresponding specific travel time value T r ;

[0013] S3. Determine a predictive start time point T for the robotic arm s ;

[0014] where T s is obtained by subtracting the travel time T quantified in step S2 from the known processing time T m , and then subtracting a preset synchronization safety margin Δt; r

[0015] S4. During the process that the processing equipment is still performing its processing operation, at the predictive start time point T determined in step S3 s , instruct the robotic arm to start moving from its waiting position to the operating position;

[0016] S5. Control the arrival time of the robotic arm so that the time point when it reaches the operating position coincides with the time point when the processing equipment completes its processing operation.

[0017] Preferably, the processing equipment is one of a numerical control lathe, an injection molding machine, a stamping machine or an automated inspection equipment.

[0018] Preferably, in step S3, the synchronization safety margin Δt is dynamically calculated according to the following formula:

[0019] Δt = T l + k × σ r

[0020] where, T l is the average signal communication delay between the controller and the robotic arm;

[0021] σ r is the standard deviation of the travel time T measured in multiple historical cycles r ;

[0022] k is a preset safety factor.

[0023] Preferably, before step S3, an energy consumption optimization step is further included, where the operating speed V of the robotic arm r is determined according to the following optimization condition:

[0024] V r = min{v丨T m + T[[ID=​​​​​​

[0025] Where v is the velocity variable;

[0026] T t The preset production cycle time target;

[0027] T h For a fixed interaction time;

[0028] T r (v) represents the travel time as a function of speed v.

[0029] Preferably, the method further includes an enhanced safety monitoring step that operates during the parallel movement of step S4, monitoring the joint motor torque τ of the robotic arm in real time.

[0030] If τ exceeds the preset dynamic torque safety baseline τ lim If the parallel movement is stopped immediately, the robotic arm is instructed to return to the waiting position.

[0031] Preferably, the dynamic torque safety baseline τ lim It is determined based on each time point i along the robotic arm's movement trajectory using the following formula:

[0032] τ lim (i)=τ avg (i)+n×τ(i)

[0033] Where, τ avg (i) is the average torque curve measured under normal conditions;

[0034] τ(i) is the torque standard deviation curve;

[0035] n is the sensitivity factor.

[0036] Preferably, the method further includes an adaptive calibration step, which continuously monitors the actual cycle time T for N cycles. a And compare it with the theoretical periodic time T calculated according to the model. c Compare;

[0037] If the deviation |E| between the two means exceeds the preset deviation threshold E max Then, the synchronization safety margin Δt will be automatically adjusted in a compensatory manner.

[0038] Preferably, the compensatory adjustment of the synchronization safety margin Δt is implemented based on a PID control algorithm, and its calculation formula is as follows:

[0039] Δt new =Δt old +K p ×E+K i×∫Edt+K d ×dE / dt

[0040] Where, Δt new This is the adjusted new safety margin;

[0041] Δt old This refers to the old safety margin before the adjustment;

[0042] K p K i K d This is the control factor.

[0043] Preferably, in step S2, the travel time T r The determination is based on a kinematic model that incorporates the complete S-shaped acceleration and deceleration curve of the robotic arm from start to stop into the calculation.

[0044] Preferably, the method provides an interface on the touchscreen human-machine interface of the work unit, allowing the operator to adjust the safety factor k and sensitivity factor n online, and graphically displays various time parameters within the work cycle in real time.

[0045] Compared to existing technologies, this invention introduces predictive synchronization control, maximizing the overlap between the robotic arm's travel time and the equipment's processing time, which were originally linearly accumulated. This fundamentally breaks the inherent "mutual waiting" bottleneck of traditional serial logic. It significantly shortens the cycle time of each work cycle, resulting in a substantial increase in product output per unit time. Furthermore, this invention optimizes energy consumption, ensuring that efficiency improvements do not come at the expense of energy, thus achieving a higher level of green and efficient production.

[0046] More importantly, this invention goes beyond traditional post-fault response in its safety monitoring mechanism. Instead, by establishing a dynamic torque safety baseline, it shifts the safety protection focus to the "early warning" stage. This proactive monitoring method can predict potential collision or jamming risks by capturing subtle deviations from normal physical laws, thus allowing for intervention before physical damage or even safety accidents occur. This shift from "passive" to "proactive" significantly enhances the system's inherent safety when handling flammable and explosive materials, providing a higher level of safety assurance for the entire production process.

[0047] Finally, all these improvements are not one-off, static enhancements. This invention, by introducing an adaptive calibration mechanism, endows the entire automation unit with the ability to "learn" and "self-correct." This means that, faced with natural wear and tear or performance drift caused by long-term operation, the system can continuously fine-tune itself based on a PID algorithm, always maintaining itself in a near-optimal operating state. This high degree of intelligence and adaptability ensures that the comprehensive benefits brought about by improved efficiency and safety can be maintained stably and over the long term, ultimately elevating the entire automated work unit from a simple program executor into a highly efficient, safe, and self-evolving intelligent production system. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the structure of the automated work unit used in the embodiments of the present invention;

[0049] Figure 2 This is a schematic diagram of the S-shaped acceleration and deceleration curves of the speed and position of the robotic arm as it performs one stroke in this embodiment of the invention.

[0050] In the picture: 1. Robotic arm; 2. CNC lathe; 3. Double-layer belt conveyor line. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit its scope. The core ideas of this invention can be widely applied to any other situation involving the collaboration of robotic arms and automated equipment.

[0052] This embodiment provides a predictive synchronous collaborative control method for an automated work unit. This method is implemented within an automated work unit that includes a robotic arm and processing equipment through a unique control system and software logic running within it.

[0053] Reference Figure 1 The automated work unit mainly includes a six-axis industrial robotic arm 1, such as the Nachi MZ07L model robotic arm, which features high precision and fast cycle time, making it ideal for precision loading and unloading operations. The work unit also includes a CNC lathe 2 for machining workpieces, and a double-layer belt conveyor 3 for carrying and transporting material trays.

[0054] To illustrate with a specific application scenario: the workpiece to be processed is a cylindrical propellant column, and the processing operation involves the CNC lathe 2 precisely turning both ends of the column. To achieve efficient material flow, the robotic arm 1 is mounted on the side front of the CNC lathe 2, and its working range, after precise teaching, can completely cover the chuck operating position of the CNC lathe 2 and the upper material receiving area of ​​the double-layer belt conveyor 3.

[0055] As the main controller of the entire work unit, the PLC establishes a real-time communication network with the independent controller of robotic arm 1 and the independent CNC system of CNC lathe 2 via industrial Ethernet buses such as PROFINET. In this distributed control architecture, the PLC is responsible for coordinating and scheduling the operation of all equipment. Directly connected to the PLC is a touchscreen human-machine interface (HMI) as the window for the operator to interact with the system.

[0056] To achieve the enhanced safety monitoring function in this invention, the control system of robotic arm 1 integrates a torque monitoring unit, which can monitor the torque of the servo motors of each joint in real time and send the data to the PLC via the network. Optionally, to further improve safety, a visual safety sensor can also be installed in the key interaction area between robotic arm 1 and CNC lathe 2. This sensor is connected to the PLC via a high-speed digital I / O point to achieve a low-latency response to abnormal intrusions.

[0057] In this embodiment, the predictive synchronization optimization method is specifically implemented through a series of cooperative software functional modules and program logic running in the PLC. These modules translate the method steps into actual control actions, and their detailed execution flow is as follows:

[0058] First, step S1 is executed, which involves establishing a time model. When the automated process starts or a production product is switched, a time model establishment module in the PLC is invoked. This module's function is to establish a time model containing multiple key time components for a complete work cycle. This model serves as the logical foundation for all subsequent predictive calculations.

[0059] Specifically, the model includes at least a known, relatively fixed processing time T. m This value, read by the PLC from the CNC system of CNC lathe 2 via communication, represents the preset time required for the lathe to process a single cartridge. For example, for a cartridge of a specific size, this processing time T... m It can be set to 15 seconds. Simultaneously, the model also identifies a key variable component in the work cycle, namely, the operating speed V of robotic arm 1. rThe determined travel time. Here, travel time specifically refers to the time taken for robotic arm 1 to move along a specific path from its safe waiting position to the lathe operating position.

[0060] Next, step S2 is executed, which involves quantifying the travel time. After establishing the conceptual model in S1, this step aims to precisely quantify the variable component of travel time. Before the start of each work cycle, the PLC will determine the travel time based on a preset operating speed value V for robotic arm 1. r To calculate the corresponding travel time value T r The operating speed V here r It can be set directly by the operator through the HMI, or it can be automatically determined by the energy optimization steps that will be described later.

[0061] To improve the accuracy of the calculation, the travel time is determined based on a sophisticated kinematic model. See details in the [reference needed]. Figure 2 This model takes into account the complete S-shaped acceleration and deceleration curve of robotic arm 1 from start to stop. For example... Figure 2 As shown in the blue "speed change curve," the robotic arm's speed smoothly accelerates from 0 to the preset speed V. r After the normalized value of V is set to 1.0 and held for a period of time, it smoothly decelerates back to 0. The entire acceleration and deceleration phase exhibits a smooth S-shape, which effectively reduces mechanical shock and improves motion stability. The yellow "position change curve" in the figure is the integral of the velocity curve, representing the smooth movement of the robotic arm's position from the starting point 0.0 to the ending point 1.0 over time. This S-curve motion planning algorithm is embedded in the PLC controller and can be implemented based on the input V. r Based on preset path points, it quickly calculates travel time values ​​that closely approximate physical realities. For example, after calculation, at a set speed, the specific travel time value T is obtained. r It could be 2.5 seconds.

[0062] Then, step S3 is executed, which calculates the pre-start point. This step breaks the traditional "wait-execute" pattern through precise mathematical calculations. In this step, the PLC determines a predictive start time T for robotic arm 1, significantly earlier than the traditional method. s At time point T s The determination is made by using the known processing time T m Subtract the travel time T that was just quantized in step S2 from the end. r This is obtained by subtracting a crucial, pre-defined synchronization safety margin Δt. The existence of this Δt is key to balancing efficiency and safety in this method, providing a buffer for various signal delays and execution errors that may exist in the system.

[0063] As a preferred embodiment of this example, the synchronization safety margin Δt is itself dynamically calculated to adapt to the real-time operating conditions of the system. Its calculation formula is: Δt = T l + k × σ r . In this formula, T l represents the average signal communication delay between the PLC and the manipulator 1 controller, which is a physical parameter that can be measured by a network diagnostic tool, for example, 0.05 seconds. σ r is the standard deviation of the actual travel time T r calculated by the PLC in real time and statistically over the past N (for example, N = 100) historical working cycles, which objectively reflects the actual volatility of the manipulator's movement time. k is a preset safety factor that can be adjusted by the operator through the HMI. For example, it can be set to 3 at the initial stage of system debugging and can be appropriately reduced after the system runs stably.

[0064] In this way, Δt can be dynamically adjusted so that the system can maintain an optimal balance point under different operating conditions. For example, if T m = 15 seconds, T r = 2.5 seconds, T l = 0.05 seconds, σ r = 0.02 seconds, and k = 3, then Δt = 0.05 + 3 × 0.02 = 0.11 seconds. Then, the finally calculated predictive start time point T s = 15 - 2.5 - 0.11 = 12.39 seconds. The physical meaning of this value is that the manipulator 1 should start in advance at the 12.39th second after the lathe starts processing.

[0065] In addition, as another preferred method, before performing this step S3, the system can also execute an energy consumption optimization step. The goal of this step is to minimize the operating energy consumption of the manipulator as much as possible while ensuring production efficiency. Specifically, the operator can set a desired production beat target T[[ID=​​​​​​​​​​​​​​​​​​The minimum operating speed v is set, and this speed is used as the preset specific operating speed value V. r This is used for calculations in subsequent steps.

[0066] Next, step S4 is executed, which involves parallel movement. In this step, a high-precision timer inside the PLC starts counting from the moment the CNC system of the CNC lathe 2 sends the "machining start" signal. The PLC control program continuously monitors the value of this timer, and when the count reaches the predictive start time T calculated in step S3... s For example, at 12.39 seconds, the PLC immediately sends a start command to the controller of robotic arm 1 via the industrial Ethernet. At this moment, a crucial scenario, completely different from the traditional mode, occurs: the machining operation of the CNC lathe 2 continues, its spindle is still rotating at high speed, while robotic arm 1, following the command, begins to move smoothly from its distant waiting position along a preset trajectory towards the lathe's operating position. This step, through precise timing coordination, achieves parallel operation of the robotic arm's movement and the lathe's machining process, thereby converting the previously wasted waiting time into effective working time.

[0067] To ensure absolute safety in the high-risk materials processing environment during this parallel movement, this method also includes an enhanced safety monitoring step. Every millisecond of movement of robotic arm 1, the torque monitoring unit within its control system collects the real-time torque values ​​τ of all joint motors and reports them to the PLC via the network. Simultaneously, the PLC, based on the current movement trajectory position i of robotic arm 1, retrieves a corresponding dynamic torque safety baseline τ from a pre-stored torque model established through machine learning. lim This baseline is not a constant value, but rather varies according to the formula τ. lim (i)=τ avg (i) + n × τ(i) is dynamically determined. Where τ avg τ(i) and τ(i) are the mean torque curve and standard deviation curve, respectively, obtained through multiple "learning" and statistical analysis of normal, collision-free operation. n is a sensitivity factor that can be set by the operator on the HMI, for example, set to 5. As long as the real-time torque τ exceeds this dynamic baseline τ that closely matches the normal operating state... lim The PLC will then interpret this as an abnormal event indicating a risk of collision or jamming, and will immediately stop the current parallel movement, instructing robotic arm 1 to return to the waiting position along the original path. Simultaneously, detailed alarm information will pop up on the HMI. This monitoring mechanism, compared to traditional fixed threshold alarms, has extremely high sensitivity and predictability.

[0068] Finally, step S5 is executed, achieving timing synchronization. Without triggering any safety alarms, robotic arm 1 continues its parallel movement. Through precise calculations in S2 and S3, its final arrival time is precisely controlled. Ideally, at the exact moment the end-effector of robotic arm 1 reaches the lathe's operating position, the CNC lathe 2 completes all its machining operations, the spindle stops rotating, and the safety door slides open to one side. This seamless transition allows subsequent material handling and loading / unloading actions to be performed immediately, reducing the waiting time caused by robotic arm movement to almost zero.

[0069] To ensure that this method maintains optimal performance over a long period, the system also integrates an adaptive calibration step. The PLC continuously records the actual completion time T of the most recent N (e.g., N=100) cycles in the background. a And compared with the theoretical period time T calculated according to the model. c The two are compared. If the absolute value of the deviation between their means, |E|, exceeds a preset deviation threshold E, then... max For example, if it takes 0.1 seconds, the system will determine that the equipment's performance may have drifted, such as wear causing the robotic arm to slow down. At this point, a calibration program based on a PID control algorithm will be activated, which will adjust the calibration according to the formula Δt. new =Δt old +K p ×E+K i ×∫Edt+K d ×dE / dt automatically and smoothly makes small compensatory adjustments to the synchronization safety margin Δt, so that the actual operation of the system can always closely track the theoretical optimal state.

[0070] Finally, to facilitate operator management and maintenance, this method provides a feature-rich monitoring and configuration interface on the HMI. Operators can view T in real-time via a graphical interface. m T r T a T c The system displays dynamic curves or numerical values ​​for key time parameters, and allows for online adjustment of advanced parameters such as the safety factor k and sensitivity factor n via touch input, thus achieving a high degree of visualization and controllability of the entire optimization method.

[0071] In summary, the method disclosed in this embodiment, by constructing a time model, performing predictive calculations and parallel movements, and integrating advanced control strategies such as dynamic safety monitoring and adaptive calibration throughout the process, ultimately achieves a complete, efficient, and extremely safe automated processing optimization scheme for flammable and explosive materials.

Claims

1. A predictive synchronous collaborative control method for an automated work unit, wherein the collaborative control operation is performed within a work unit comprising a robotic arm (1) and a processing device, the robotic arm (1) moving between a waiting position and an operating position, characterized in that, The method includes the following steps: S1. For a complete work cycle, establish a time model containing multiple time components, where each time component includes at least one known processing time T. m and a speed V of the robotic arm (1) r The determined travel time; S2. Based on a specific operating speed value V preset for the robotic arm (1) r Calculate the corresponding specific travel time value T. r ; S3. Determine a predictive start-up time T for the robotic arm (1). s ; Where T s By using the known processing time T m Subtract the travel time T quantized in step S2 from the total travel time. r And then subtract a preset synchronization safety margin Δt to obtain the result; S4. While the processing equipment is still performing its processing operation, at the predictive start-up time point T determined in step S3. s The instruction is given to the robotic arm (1) to begin moving from its waiting position to its operating position; S5. Control the arrival time of the robotic arm (1) so that the time point when it arrives at the operation position coincides with the time point when the processing equipment completes its processing operation.

2. The predictive synchronous cooperative control method for automated work units according to claim 1, characterized in that: The processing equipment is one of the following: a CNC lathe (2), an injection molding machine, a stamping machine, or an automated testing equipment.

3. The predictive synchronous cooperative control method for automated work units according to claim 1, characterized in that, In step S3, the synchronization safety margin Δt is dynamically calculated according to the following formula: Δt=T l +k×σ r Among them, T l The average signal communication delay between the controller and the robotic arm (1); σ r The travel time T measured in multiple historical periods r Standard deviation; k is the preset safety factor.

4. The predictive synchronous cooperative control method for automated work units according to claim 1, characterized in that, Before step S3, an energy consumption optimization step is also included, wherein the operating speed V of the robotic arm (1) is... r It was determined based on the following optimization conditions: V r = min{v丨T m + T h + T r (v) ≤ T t} Where v is the velocity variable; T t The preset production cycle time target; T h For a fixed interaction time; T r (v) represents the travel time as a function of speed v.

5. The predictive synchronous cooperative control method for automated work units according to claim 1, characterized in that, The method also includes an enhanced safety monitoring step that runs during the parallel movement of step S4, monitoring the joint motor torque τ of the robotic arm (1) in real time. If τ exceeds the preset dynamic torque safety baseline τ lim If the parallel movement is stopped immediately, the robotic arm (1) is instructed to return to the waiting position.

6. The predictive synchronous cooperative control method for automated work units according to claim 5, characterized in that, The dynamic torque safety baseline τ lim It is determined by the following formula based on each time point i along the movement trajectory of the robotic arm (1): τ lim (i)=τ avg (i)+n×τ(i) Where, τ avg (i) is the average torque curve measured under normal conditions; τ(i) is the torque standard deviation curve; n is the sensitivity factor.

7. The predictive synchronous cooperative control method for automated work units according to claim 1, characterized in that, The method further includes an adaptive calibration step, which continuously monitors the actual cycle time T for N cycles. a And compare it with the theoretical periodic time T calculated according to the model. c Compare; If the deviation |E| between the two means exceeds the preset deviation threshold E max Then, the synchronization safety margin Δt will be automatically adjusted in a compensatory manner.

8. The predictive synchronous cooperative control method for automated work units according to claim 7, characterized in that, The compensatory adjustment of the synchronization safety margin Δt is implemented based on the PID control algorithm, and its calculation formula is as follows: Δt new =Δt old +K p ×E+K i ×∫Edt+K d ×dE / dt Where, Δt new This is the adjusted new safety margin; Δt old This refers to the old safety margin before the adjustment; K p K i K d This is the control factor.

9. The predictive synchronous cooperative control method for automated work units according to claim 1, characterized in that: In step S2, the travel time T r The determination is based on a kinematic model that incorporates the complete S-shaped acceleration and deceleration curve of the robotic arm (1) from start to stop into the calculation.

10. The predictive synchronous cooperative control method for automated work units according to claim 1, characterized in that: The method provides an interface on the touch screen human-machine interface of the working unit, allowing the operator to adjust the safety factor k and sensitivity factor n online, and displays various time parameters within the working cycle in a graphical manner in real time.

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