Control method and system for collaborative handling by dual scara robots

Abstract

The application discloses a control method for cooperative carrying of a double SCARA robot, and also discloses a system of the first and second SCARA robots which jointly hold a workpiece through end effectors. The method comprises the following steps: in a cooperative control mode, the first robot moves independently according to a preset trajectory, and the second robot keeps a relative position with the first robot; a desired position and force are obtained according to environmental constraints; positions and forces of the two robots are collected in real time; the first robot adopts a position control strategy, and outputs a force through a proportional differential control law based on a position error according to position feedback; and the second robot adopts a damping control strategy, and outputs a speed correction amount through a proportional differential control law based on a force error according to force feedback. The application realizes decoupling of the position and the force through differentiated control, and improves carrying stability, precision and adaptability. The master-slave architecture avoids position grabbing or mutual dragging, reduces dependence on positioning precision, and is suitable for carrying of precise or fragile workpieces.

Description

Technical Field

[0001] This invention relates to a control method and system for collaborative handling by two SCARA robots. Background Technology

[0002] As intelligent manufacturing evolves towards higher precision, higher efficiency, and greater flexibility, the application of industrial robots in high-end manufacturing fields such as electronic assembly, semiconductor manufacturing, and new energy batteries is continuously deepening. SCARA robots, due to their selective compliant structure, high speed, and high repeatability, are widely used in tasks such as dispensing, clamping, sorting, and handling. However, facing the increasing demand for handling large workpieces (such as large-size displays, battery modules, and PCB panels), a single SCARA robot can no longer meet actual needs in terms of load capacity, working range, and dynamic balance, making dual-SCARA or multi-robot collaborative operation systems an inevitable trend in technological development. Such systems achieve load sharing and motion coordination by having two robots jointly grip the same workpiece, significantly improving handling capacity and system flexibility, but also bringing complex challenges in motion synchronization and torque coordination.

[0003] In collaborative handling processes involving two robots, if both robots employ traditional position control modes and strictly track the same trajectory, imbalances in the clamping force distribution can easily occur due to factors such as mechanical errors, installation deviations, joint clearances, or uneven workpiece stiffness. This can generate internal stress, affecting handling stability to the point of causing workpiece deformation, scratches, or even robot overload damage. To address this, researchers have proposed various force / position hybrid control strategies, such as impedance control, admittance control, and adaptive force control. For example, the invention patent with publication number CN116852356B discloses a robot force compliance control method based on variable speed impedance control, which employs impedance control to achieve active adjustment and compliant response to contact forces. However, these methods typically rely on precise robot dynamics models or external six-dimensional force sensors, resulting in high computational complexity, poor real-time performance, and stringent requirements for system calibration accuracy, making large-scale application in high-speed, low-cost industrial production lines difficult. Furthermore, while fully distributed control or centralized optimization control can theoretically achieve global optimization, it suffers from communication delays, heavy computational burdens, and poor system scalability, limiting its engineering practicality.

[0004] In recent years, master-slave cooperative control architecture has become a hot research topic in multi-robot systems due to its advantages such as clear structure, control decoupling, and ease of implementation. This architecture designates one robot as the "master" robot responsible for trajectory planning and position tracking, while the other, as the "slave" robot, dynamically adjusts its motion behavior based on environmental feedback (such as force and displacement), thereby achieving compliant force control while ensuring path accuracy. However, existing master-slave control schemes mostly focus on force tracking or impedance regulation, lacking comprehensive optimization design for both position and force objectives. Especially when facing unstructured environments, dynamic disturbances, or unknown workpiece characteristics, problems such as response lag, insufficient stability, and unsmooth control switching still exist. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a control method and system for collaborative handling by dual SCARA robots, which solves the problems of uneven clamping force and asynchronous movement caused by factors such as mechanical errors, assembly deviations or workpiece deformation during collaborative handling by dual SCARA robots.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] A control method for collaborative handling by two SCARA robots is disclosed. The method is applied to a collaborative system of two SCARA robots, the system comprising a first SCARA robot and a second SCARA robot. Both the first and second SCARA robots include an end effector for jointly gripping a workpiece to be handled. The method controls the position and force of the end effector relative to the workpiece. The method includes:

[0008] The first SCARA robot and the second SCARA robot adopt a cooperative control mode. The first SCARA robot moves independently according to a preset working condition trajectory, and the second SCARA robot is set at the relative position of the first SCARA robot.

[0009] Based on environmental constraints, obtain the desired positions and desired forces of the first and second SCARA robots relative to the workpiece;

[0010] Collect the real-time position and force of the first and second SCARA robots;

[0011] The first SCARA robot and the second SCARA robot employ different control strategies. The first SCARA robot uses a position control strategy, applying a proportional-derivative control law based on the position error through a position feedback signal to output a force. The second SCARA robot uses a damping control strategy, applying a proportional-derivative control law based on the force error through a force feedback signal to output a speed correction amount of the end effector relative to the workpiece.

[0012] In a dual-SCARA robot collaborative handling system, the first SCARA robot, acting as the master robot, employs a position control strategy and operates independently along a preset trajectory to ensure the accuracy of the overall motion path. The second SCARA robot, acting as the slave robot, uses a damping control strategy. By detecting clamping force errors in real time and utilizing force feedback, it adjusts the speed of its end effector relative to the workpiece to achieve compliance compensation. The system sets the desired position and force based on environmental constraints and uses an improved PID algorithm to perform closed-loop correction on the real-time acquired position and force signals, achieving dynamic adjustment.

[0013] As a preferred embodiment, the position control strategy of the first SCARA robot employs a fuzzy adaptive control algorithm to optimize the proportional and derivative coefficients in the position control strategy based on the position error and its rate of change; the input linguistic quantity of the fuzzy adaptive control algorithm is the position error. and the rate of change of position error The output control quantity is the proportional coefficient correction quantity. and differential coefficient correction amount ; Set initial parameters as and The output rules of the parameters are based on and Set different value ranges.

[0014] As a preferred approach, the damping control strategy of the second SCARA robot employs an optimized bacterial foraging algorithm to iteratively perform four steps—chemotaxis, aggregation, reproduction, elimination, and diffusion—on the proportional and differential coefficients in the damping control strategy to achieve parameter optimization. The fitness function of the bacterial foraging algorithm is based on the time-domain performance index of the force response curve, and the evaluation index includes the absolute value of error. Ascent time Overshoot With oscillation time The fitness function is specifically designed as follows: Fitness Function ,in, and These are the weighting coefficients for the absolute value of error, oscillation time, rise time, and overshoot, respectively, used to adjust the importance of different indicators in the fitness function.

[0015] As a preferred approach, the position control strategy of the first SCARA robot is based on the desired position quantity. With actual position quantity The difference The position control of the first SCARA robot is represented as follows: ,

[0016] in, The actual output force, For the desired force, and These are the proportional coefficient and the differential coefficient, respectively.

[0017] As a preferred approach, the damping control strategy of the second SCARA robot is based on the desired force. With actual force The difference Controller output speed correction amount The damping control of the second SCARA robot is expressed as follows: ,in, This is the speed correction amount of the end effector relative to the workpiece. and These are the proportional coefficient and the differential coefficient, respectively.

[0018] As a preferred approach, in the cooperative control mode, the first SCARA robot and the second SCARA robot maintain consistency in relative position and speed during the handling process. Through real-time communication and data interaction, they coordinate their respective motion trajectories and actions to ensure stable handling of the workpiece.

[0019] As a preferred approach, the environmental constraints include the shape, size, weight of the workpiece, and information on obstacles along the transport path. Based on these constraints, the desired positions and desired forces of the first and second SCARA robots relative to the workpiece are calculated to meet the requirements of the transport task.

[0020] As a preferred method, the real-time position and force of the first SCARA robot and the second SCARA robot are collected by sensors installed on the robot body. The sensors include position sensors and force sensors, which can acquire the position and force information of the robot in real time and transmit this information to the control module for processing and analysis.

[0021] A control system for collaborative handling by two SCARA robots, the system comprising a first SCARA robot and a second SCARA robot, both of which include an end effector for jointly gripping the workpiece to be handled.

[0022] The system further includes a trajectory control module, a target acquisition module, and an improved PID control module. The trajectory control module is used to control the first SCARA robot to move independently according to a preset working condition trajectory and to position the second SCARA robot at a relative position to the first SCARA robot. The target acquisition module is used to acquire the target position and target force of the first and second SCARA robots relative to the workpiece based on environmental constraints. The improved PID control module is used to collect the real-time position and force of the first and second SCARA robots.

[0023] The improved PID control module includes a position controller for the first SCARA robot and a damping controller for the second SCARA robot. The position controller applies a proportional-derivative control law based on the position error through the position feedback signal and outputs a force. The damping controller applies a proportional-derivative control law based on the force error through the force feedback signal and outputs a speed correction amount of the end effector relative to the workpiece.

[0024] In a preferred embodiment, the position controller includes a basic position controller module and a fuzzy adaptive control algorithm module. The fuzzy adaptive control algorithm module optimizes the proportional and derivative coefficients in the position control strategy based on the position feedback error and the error change rate, obtaining a dynamic optimization parameter set in real time during the action process. The input linguistic quantity of the fuzzy adaptive control algorithm is the position error. and the rate of change of position error The output control quantity is the proportional coefficient correction quantity. and differential coefficient correction amount ; Set initial parameters as and The output rules of the parameters are based on and Set different value ranges;

[0025] The damping controller includes a basic damping controller module and an optimized bacterial foraging algorithm module. The optimized bacterial foraging algorithm module, based on a designed fitness function, iterates through four steps—chemotaxis, aggregation, reproduction, elimination, and diffusion—on the proportional and differential coefficients in the damping control strategy to optimize the parameters. The fitness function of the bacterial foraging algorithm is based on the time-domain performance index of the force response curve, and the evaluation index includes the absolute value of error. Ascent time Overshoot With oscillation time The fitness function is specifically designed as follows: Fitness Function ,in, and These are the weighting coefficients for the absolute value of error, oscillation time, rise time, and overshoot, respectively, used to adjust the importance of different indicators in the fitness function.

[0026] The present invention has at least the following beneficial effects: The dual SCARA robot cooperative handling control method proposed in this invention achieves efficient decoupling control of position and force through the coordinated cooperation of differentiated control strategies, significantly improving the stability, accuracy, and adaptability of the handling process. Specifically, the first SCARA robot, as the master control unit, adopts a position control strategy, operates independently according to a preset working condition trajectory, and implements proportional-derivative control based on position error through position feedback to ensure the accuracy and predictability of the overall motion path. The second SCARA robot, as the cooperative unit, adopts a damping control strategy, applies a proportional-derivative control law based on force error according to real-time force feedback, and outputs a speed correction amount for the end effector relative to the workpiece, thereby actively adjusting the clamping state and compensating for internal stress caused by assembly deviations, workpiece deformation, or external disturbances.

[0027] The master-slave, position-force complementary control architecture not only avoids the "position grabbing" or "mutual dragging" phenomena common in traditional rigid synchronous control, but also effectively reduces the dependence on the robot's absolute positioning accuracy and mechanical calibration. It is particularly suitable for handling precision or fragile workpieces sensitive to clamping forces. Furthermore, the system introduces an improved PID algorithm for joint closed-loop correction of position and force, integrating feedforward compensation and adaptive gain adjustment mechanisms to enhance dynamic response speed, suppress overshoot and oscillation, and achieve collaborative operation of two robots. This demonstrates promising industrial application prospects and widespread value. Attached Figure Description

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show the embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of the process of the present invention;

[0030] Figure 2 This is a schematic diagram of the control structure for collaborative handling by two SCARA robots. Detailed Implementation

[0031] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings, but the scope of protection of the present invention is not limited to the following description.

[0032] It should be understood that specific details are provided in the following description to facilitate a complete understanding of the exemplary embodiments. However, those skilled in the art will understand that the exemplary embodiments can be implemented without these specific details. For example, the system may be shown in block diagrams to avoid obscuring the example with unnecessary details. In other instances, well-known processes, structures, and techniques may be shown without non-essential details to avoid obscuring the example.

[0033] like Figure 1 As shown, a control method for collaborative handling by two SCARA robots is disclosed. The method is applied to a collaborative system of two SCARA robots, the system comprising a first SCARA robot and a second SCARA robot. Both the first and second SCARA robots include end effectors for jointly gripping a workpiece to be handled. The method is used to control the position and force of the end effectors relative to the workpiece. The method includes:

[0034] The first SCARA robot and the second SCARA robot adopt a cooperative control mode. The first SCARA robot moves independently according to a preset working condition trajectory, and the second SCARA robot is set at the relative position of the first SCARA robot.

[0035] Based on environmental constraints, obtain the desired positions and desired forces of the first and second SCARA robots relative to the workpiece;

[0036] Collect the real-time position and force of the first and second SCARA robots;

[0037] The first SCARA robot and the second SCARA robot employ different control strategies. The first SCARA robot uses a position control strategy, applying a proportional-derivative control law based on the position error through a position feedback signal to output a force. The second SCARA robot uses a damping control strategy, applying a proportional-derivative control law based on the force error through a force feedback signal to output a speed correction amount of the end effector relative to the workpiece.

[0038] The first SCARA robot employs a position control strategy, with position feedback signals as the control input and force as the output. This strategy is based on the proportional-derivative (PD) law of position error control. By measuring the deviation between the robot's current position and the desired trajectory in real time, it calculates and applies the corresponding force to drive the robot to accurately track the preset working condition trajectory, ensuring the accuracy of the overall motion path. The second SCARA robot employs a damping control strategy, with force feedback signals as the control input and the speed correction of the end effector relative to the workpiece as the output. This strategy is based on the PDR law of force error control. By detecting the deviation between the actual force and the desired force between the end effector and the workpiece, it dynamically adjusts the speed of the end effector to achieve compliance compensation and ensure stable contact force.

[0039] In scenarios requiring high-precision material handling, such as electronics manufacturing, the dual-SCARA robot cooperative handling control method is applied to a collaborative system consisting of a first SCARA robot and a second SCARA robot. Both robots are equipped with end effectors to jointly grip the workpiece to be handled. The core of this method lies in the precise control of the position and force of the end effector relative to the workpiece. Specifically, the first SCARA robot moves independently according to a pre-set working trajectory, while the second SCARA robot is positioned relative to the first, and the two work collaboratively. During this process, the system obtains the desired position and force of the two robots relative to the workpiece based on environmental constraints, such as the shape, size, and weight of the workpiece, as well as obstacles in the handling path. Simultaneously, the system collects the position and force information of the two robots in real time and makes corrections based on an improved PID algorithm to obtain the actual output position and force, ensuring the accuracy of the handling process.

[0040] The first and second SCARA robots employ different control strategies, but they must achieve a high degree of coordination to ensure stable workpiece handling. The first robot primarily uses a position control strategy, dynamically adjusting its motion trajectory based on position feedback signals to ensure precise operation along a preset path, thereby improving handling accuracy and efficiency. Simultaneously, the system uses force sensors to collect real-time force information to monitor the clamping status, dynamically adjust the clamping force, and provide timely warnings or trigger safety mechanisms when the clamping force is abnormal (e.g., workpiece loosening), effectively enhancing system reliability. The second SCARA robot uses a damping control strategy, adjusting its motion speed and clamping force based on force feedback to maintain stable contact between the end effector and the workpiece. When changes in clamping force cause positional shifts, the system dynamically corrects its position through real-time position feedback, ensuring consistency with the first robot's relative position and preventing workpiece wobbling or damage. If the workpiece's shape or weight changes, the system can also adjust its relative position accordingly to adapt to the new working conditions.

[0041] It is important to note that the first and second SCARA robots employ different control strategies. The first SCARA robot uses a position control strategy, outputting force based on the proportional-derivative (PD) law of position error using position feedback signals. The second SCARA robot, on the other hand, uses a damping control strategy, outputting a speed correction amount for the end effector relative to the workpiece based on the force feedback signal and the PPD law of force error. This allows the robot to change the acceleration of the end effector, making it closer to the desired force or the force that needs adjustment.

[0042] In an electronics manufacturing workshop, a relatively fragile PCB board needs to be precisely moved from one workbench to another. To ensure positional accuracy and clamping force during the transfer process and to avoid damage to the board due to vibration, misalignment, or uneven force, a dual SCARA robot collaborative operation is used to complete this task. The entire transfer process is uniformly scheduled by an intelligent collaborative control system, which fully leverages the complementary advantages of the two robots to achieve high-precision, smooth, and stable automated operation.

[0043] Before the task begins, both the first SCARA robot (master robot) and the second SCARA robot (slave robot) are in their preset starting positions, and the end effectors are in standby mode. Through the vision system deployed in the workshop, the control system has accurately acquired the position and orientation information of the PCB board on the starting worktable and used it as the initial input parameters for collaborative handling, preparing for subsequent precise gripping.

[0044] After the transport process begins, the first SCARA robot, acting as the main control unit, starts moving along a pre-planned trajectory (such as a straight line or smooth curve path from the starting point to the target point). It employs a position control strategy, using built-in position sensors to collect real-time position data of its joints and end effectors, comparing this data with the desired trajectory to calculate the current position error. Based on this error, the system uses a proportional-derivative (PD) control law to dynamically adjust the motion: the proportional term quickly reduces positional deviations, while the derivative term suppresses overshoot and oscillations during movement, resulting in smoother motion. After positioning is complete, the first robot grips one end of the PCB board; this force is indirectly regulated by position control to ensure stable gripping without damaging the workpiece.

[0045] Simultaneously, the second SCARA robot, acting as a collaborative unit, initiates a damping control strategy. It does not operate independently along a fixed trajectory but dynamically adjusts its own motion based on the real-time position of the main robot and the overall posture of the workpiece. That is, while the first SCARA robot moves, the second SCARA robot adjusts its position according to the position of the first robot and the desired position of the workpiece for collaborative clamping. Its end effector is equipped with a force feedback device (such as a force sensor or a virtual force sensor based on current estimation) to monitor the actual force applied when in contact with the PCB board in real time. The system compares this actual force with the preset desired clamping force to determine the force error. Based on this error, the second robot calculates a speed correction using a different proportional-derivative control law to fine-tune the speed of its end effector relative to the workpiece. For example, if the clamping force is detected to be too large, indicating a potential risk of crushing, the system will instruct it to slightly decelerate or retreat; if the clamping force is too small, it will appropriately accelerate or advance to compensate for clamping stability.

[0046] Throughout the handling process, the two robots maintain coordinated movements: the master robot guides the overall trajectory, ensuring path accuracy; the slave robot adjusts its own movement in real time through force feedback, acting as a "compliant compensation" mechanism to effectively absorb the effects of mechanical errors, minor deformations, or external disturbances. The system also incorporates an improved PID algorithm to fuse position and force signals, further enhancing response speed. Even with slight changes in workpiece weight or minor disturbances in the path, the control system can respond and adjust rapidly, ensuring a smooth and reliable handling process.

[0047] As the first SCARA robot approaches the target worktable, both robots decelerate synchronously and gradually adjust their posture to ensure the PCB board is level and precisely aligned with its placement position. Upon reaching the final target point, both robots work together to release their grippers, smoothly placing the PCB board onto the target worktable. The entire placement process is controlled by force, avoiding impact or tilting.

[0048] After completing the task, both robots return to their standby positions, ready to perform the next round of handling. Through this master-slave collaborative, position and force-based control method, the dual SCARA robot system not only achieves the safe handling of fragile workpieces, but also significantly improves automation levels and production efficiency, reduces human intervention, and provides reliable technical support for high-precision assembly and transmission in the electronics manufacturing field.

[0049] In simple terms, to ensure the accuracy and stability of the handling process, the system needs to comprehensively calculate the desired positions and desired forces of the two robots relative to the workpiece based on specific environmental constraints. First, the shape and size of the workpiece are key factors in determining the robot's gripping position. Assume the workpiece's center coordinates are... Its length is Width is Therefore, the gripping points of the two robots should be reasonably distributed on both sides of the workpiece to achieve balanced handling. The desired gripping position of the first SCARA robot is typically set as... The second SCARA robot is set as

[0050] These are located on the upper and lower edges of the workpiece center to ensure symmetrical clamping and improve posture stability during handling.

[0051] The weight of the workpiece directly affects the clamping force required by the robot. Let the mass of the workpiece be... Gravitational acceleration Safety factor A value between 1.2 and 1.5 is typically chosen to handle dynamic acceleration or unexpected disturbances. The desired clamping force that each robot should apply is... This means distributing the workpiece weight evenly while allowing for a safety margin, preventing slippage due to excessively loose clamping or damage to the workpiece due to excessively tight clamping. Furthermore, information on obstacles along the transport path must also be considered in trajectory planning. If the center of the obstacle is located... The size is The system needs to segment the overall path and design a safe detour trajectory, for example, using a radius of... The semi-circular path bypasses obstacle areas, ensuring that the robot avoids collisions while maintaining smooth and continuous motion. By comprehensively considering the geometric features and weight properties of the workpiece, as well as the distribution of obstacles in the path environment, the system can accurately calculate the desired gripping positions and required forces of the two SCARA robots, and plan a safe and efficient cooperative motion trajectory accordingly. This refined design based on environmental constraints not only improves the accuracy and reliability of handling operations, but also enhances the system's adaptability and safety in complex industrial scenarios.

[0052] In a preferred embodiment, the position control strategy of the first SCARA robot employs a fuzzy adaptive control algorithm to optimize the proportional and derivative coefficients in the position control strategy based on the position error and its rate of change; the input of the fuzzy adaptive control algorithm is the position error. and the rate of change of position error The output control quantity is the proportional coefficient correction quantity. and differential coefficient correction amount ; Set initial parameters as and The output rules of the parameters are based on and Set different value ranges.

[0053] The first SCARA robot's position control strategy incorporates a fuzzy adaptive control algorithm. This algorithm dynamically optimizes the proportional and derivative coefficients based on the position error and its rate of change. Its input parameters are the position error and its rate of change, while the output control parameters are the proportional and derivative coefficient corrections. The initial parameters are set as follows: and The output rules of the parameters are flexibly set according to the different value ranges of position error and position error change rate, thereby achieving more accurate and flexible position control, effectively improving the stability and reliability of the handling process, and meeting the strict requirements of high-precision handling in industries such as electronics manufacturing.

[0054] In the dual SCARA robot collaborative handling system, the first SCARA robot employs a fuzzy adaptive control algorithm. The core of this algorithm lies in its ability to dynamically adjust control parameters based on real-time position errors and their rate of change, thereby achieving more precise and flexible position control. Specifically, the algorithm's inputs are the position error and the rate of change of that error. These two parameters reflect in real-time the deviation between the robot's current position and the desired position, as well as the trend of this deviation. Based on this input information, the fuzzy adaptive control algorithm outputs proportional coefficient correction and derivative coefficient correction. This means that the algorithm automatically adjusts the proportional coefficient according to the current position error and its rate of change. and differential coefficients The values ​​of are adjusted to optimize control performance. Initially, the proportional and derivative coefficients are set to . and These initial parameters are preset based on the robot's basic performance and the general requirements of the handling task. However, during actual handling, the weight and shape of the workpiece, as well as various factors along the handling path, may change, necessitating real-time adjustment of these control parameters. The fuzzy adaptive control algorithm, through preset output rules, adjusts these parameters according to... and The different value ranges can be flexibly adjusted. and .

[0055] Based on different states of position error and its rate of change, the fuzzy adaptive control algorithm optimizes control performance by dynamically adjusting the proportional and derivative coefficients. When the position error is large, the system focuses on rapidly reducing the error, thus increasing the proportional coefficient to enhance response speed. If the error is increasing, the derivative coefficient is simultaneously increased to suppress overshoot; if the error is decreasing, the derivative action is appropriately reduced to avoid over-suppression. When the position error is small, the proportional coefficient is reduced to prevent over-adjustment; if the error shows a tendency to increase, the derivative coefficient is increased to suppress its change in advance; if the error continues to decrease, the derivative coefficient is simultaneously reduced to maintain compliant motion. This strategy achieves a good balance between response speed and stability by adaptively adjusting the control gain according to the dynamic characteristics of the error. This dynamic adjustment mechanism enables the first SCARA robot to better adapt to complex handling tasks, improve the stability and reliability of handling, and react quickly to adjust the control strategy even in the face of sudden situations or environmental changes, ensuring the smooth progress of the handling task.

[0056] In a preferred embodiment, the damping control strategy of the second SCARA robot employs an optimized bacterial foraging algorithm to iteratively perform four steps—chemotaxis, aggregation, reproduction, elimination, and diffusion—on the proportional and differential coefficients of the damping control strategy to achieve parameter optimization. The fitness function of the bacterial foraging algorithm is based on the time-domain performance index of the force response curve, and the evaluation index includes the absolute value of error. Ascent time Overshoot With oscillation time The fitness function is specifically designed as follows: Fitness Function

[0057] ,in, and These are the weighting coefficients for the absolute value of error, oscillation time, rise time, and overshoot, respectively, used to adjust the importance of different indicators in the fitness function.

[0058] In the dual-SCARA robot collaborative handling system, the second SCARA robot employs a damping control strategy to ensure stable forces between the end effector and the workpiece during handling. The core of this strategy lies in using an optimized bacterial foraging algorithm to finely adjust control parameters, thereby achieving optimal control performance.

[0059] Specifically, the bacterial foraging algorithm simulates the behavior of bacteria in the process of searching for food, including four steps: chemotaxis, aggregation, reproduction, and elimination / diffusion. In this process, the algorithm treats the proportionality coefficient and differential coefficient as parameters to be optimized, and finds the optimal parameter combination through iterative operations. In the chemotaxis phase, the algorithm evaluates the control effect based on the current parameter settings, similar to bacteria sensing the food concentration in their surroundings. The aggregation phase simulates the behavior of bacteria gathering together, allowing the algorithm to explore better regions in the parameter space. The reproduction phase allows the algorithm to replicate well-performing parameter settings, while the elimination / diffusion phase introduces randomness, helping the algorithm escape local optima and find the global optimum.

[0060] To evaluate the quality of the parameter settings, the algorithm uses a fitness function. This fitness function is based on the time-domain performance metrics of the force response curve, primarily considering four evaluation metrics: absolute error, rise time, overshoot, and oscillation time. The absolute error measures the deviation between the actual and expected force, while rise time, overshoot, and oscillation time reflect the speed and stability of the system response. The fitness function integrates these metrics, combining the integral of the absolute error and the oscillation time in a weighted manner. and These are the weighting coefficients for the absolute value of the error and the oscillation time, used to balance the importance of different indicators.

[0061] Specifically, in optimizing the control parameters of a SCARA robot, the first step is to initialize key parameters, including the proportional gain. Differential coefficients and weighting coefficients .in, and The weighting coefficients determine the basic response characteristics of the control system, while the weighting coefficients are used to construct the fitness function to balance the relative importance of performance indicators such as force error, oscillation time, rise time, and overshoot. The core process of the algorithm includes stages such as chemotaxis, clustering, reproduction, and elimination and diffusion. In the chemotaxis stage, based on the current... and Calculate the fitness function This function comprehensively evaluates the control performance of the system through a weighted summation method.

[0062] Subsequently, the algorithm enters the clustering and reproduction phases, simulating the behavior of bacterial aggregation and natural selection, searching for better solutions in the parameter space. During the clustering process, the algorithm explores neighboring regions; if a new parameter combination lowers the fitness value, it accepts that optimization. In the reproduction phase, it replicates parameter combinations with lower fitness (but better performance), retaining superior genes. Finally, random perturbations are introduced in the elimination and diffusion phases, randomly resetting some parameters to help the algorithm escape local optima and enhance its global search capability. The entire optimization process involves continuous iterative adjustments. and The fitness function is continuously evaluated, and the parameter combination with the lowest fitness value is finally selected as the optimal control strategy to achieve high-precision and high-stability control of the robot system.

[0063] Through this optimization process, the second SCARA robot can dynamically adjust the proportional and derivative coefficients in its damping control strategy to adapt to different handling tasks and workpiece characteristics. This allows the robot to control forces more precisely during handling, reducing vibration and impact, thereby improving the stability and reliability of handling. This control strategy based on optimization algorithms not only improves the system's automation level but also enhances its adaptability to complex tasks.

[0064] In a preferred embodiment, the position control strategy of the first SCARA robot is based on the desired position quantity. With actual position quantity The difference The position control of the first SCARA robot is represented as follows: ,in, The actual output force, For the desired force, and These are the proportional coefficient and the differential coefficient, respectively.

[0065] In a dual-SCARA robot collaborative handling system, the position control strategy of the first SCARA robot is a crucial element in ensuring precise handling. The core of this strategy lies in adjusting the robot's movement by comparing the desired position with the actual position, thereby achieving precise position control.

[0066] Specifically, the position control strategy of the first SCARA robot is based on the difference between the desired position and the actual position. This difference reflects the deviation between the robot's current actual position and the desired position. To correct this deviation, the robot control system calculates a correction factor based on a proportional coefficient and a derivative coefficient. The proportional coefficient is adjusted according to the magnitude of the position error; if the error is large, a larger adjustment force is needed to quickly correct the deviation. The derivative coefficient, on the other hand, considers the rate of change of the error, i.e., how the error changes over time. This helps to smooth the robot's movement and avoid jitter or instability caused by rapid adjustments.

[0067] The actual output force consists of the desired force plus a correction factor. The desired force is pre-set according to the handling task and represents the force that the robot should ideally apply to the workpiece. In this way, the first SCARA robot can dynamically adjust its motion and force based on real-time position feedback, ensuring precise position control throughout the handling process, thus completing the handling task smoothly and accurately. This control strategy based on position error and its rate of change enables the robot to adapt to different handling environments and task requirements, improving the stability and reliability of handling.

[0068] In a preferred embodiment, the damping control strategy of the second SCARA robot is based on the desired force. With actual force The difference Controller output speed correction amount The damping control of the second SCARA robot is expressed as follows: ,in, This is the speed correction amount of the end effector relative to the workpiece. and These are the proportional coefficient and the differential coefficient, respectively.

[0069] In a dual-SCARA robot collaborative handling system, the damping control strategy of the second SCARA robot is a key mechanism to ensure stable force between the end effector and the workpiece during handling. The core of this strategy lies in adjusting the end effector's speed by comparing the difference between the desired force and the actual force, thereby achieving precise force control.

[0070] The second SCARA robot's damping control strategy is based on the difference between the desired force and the actual force. This difference reflects the deviation between the actual force and the desired force exerted by the end effector. To correct this deviation, the controller calculates a speed correction based on proportional and derivative coefficients. The proportional coefficient is adjusted according to the magnitude of the force error; a larger error requires a greater adjustment to quickly correct the deviation. The derivative coefficient, on the other hand, considers the rate of change of the error—how the error changes over time—which helps smooth the end effector's movement and avoids jitter or instability caused by rapid adjustments.

[0071] The speed correction of the end effector relative to the workpiece is calculated by multiplying the proportional coefficient by the force error and adding the differential coefficient by the rate of change of the force error. This is achieved through a combination of methods. In this way, the second SCARA robot can dynamically adjust the speed of its end effector based on real-time force feedback, ensuring a stable force throughout the handling process, thus completing the handling task smoothly and accurately. This control strategy based on force error and its rate of change allows the robot to adapt to different handling environments and task requirements, improving the stability and reliability of handling.

[0072] In a preferred embodiment, under the cooperative control mode, the first SCARA robot and the second SCARA robot maintain consistency in relative position and speed during the handling process. Through real-time communication and data interaction, they coordinate their respective motion trajectories and actions to ensure stable handling of the workpiece.

[0073] In a dual-SCARA robot collaborative handling system, collaborative control is crucial for ensuring smooth operation. In this mode, the first and second SCARA robots need to work closely together, maintaining consistency in their relative positions and speeds. This means that the motion trajectories and actions of the two robots must be highly coordinated to ensure the workpiece remains stable throughout the handling process. The first SCARA robot moves independently along a preset trajectory, while the second SCARA robot adjusts its own position and speed in real time based on the first robot's position and speed to maintain a constant relative position. This maintenance of relative position is achieved through real-time communication and data interaction. The control system continuously collects position and speed information from both robots, calculates necessary adjustment commands based on this information, and sends these commands back to the robots.

[0074] If the first SCARA robot slightly increases its speed during transport, the control system immediately detects this change and adjusts the speed of the second SCARA robot accordingly to ensure the relative position between the two robots remains constant. Similarly, if the first robot's position shifts slightly, the second robot will adjust accordingly based on the control system's instructions. This real-time adjustment and coordination is achieved through a high-speed communication network and precise data processing, ensuring that the two robots work collaboratively as a single unit during transport. Through this collaborative control mode, dual SCARA robots can effectively avoid workpiece swaying or positional deviations during transport, thus ensuring the stability and accuracy of the transport. This high degree of coordination not only improves transport efficiency but also reduces the risk of workpiece damage due to inconsistent robot movements, making the entire transport process more reliable and efficient.

[0075] In a preferred embodiment, the environmental constraints include the shape, size, weight of the workpiece, and information on obstacles along the transport path. Based on these constraints, the desired positions and desired forces of the first and second SCARA robots relative to the workpiece are calculated to meet the requirements of the transport task.

[0076] In a dual SCARA robot cooperative handling system, environmental constraints are crucial for ensuring the smooth execution of the handling task. These constraints include the shape, size, and weight of the workpiece, as well as information about obstacles along the handling path. This information is essential for calculating the desired position and desired force of the first and second SCARA robots relative to the workpiece, as they directly determine the robot's motion trajectory and control strategy during the handling process.

[0077] First, the shape and size of the workpiece determine the gripping method and position of the robot's end effector. For example, if the workpiece is a rectangular PCB board, the robot needs to adjust the gripping points according to its length and width to ensure stable gripping without damaging the workpiece. The weight of the workpiece affects the force the robot needs to apply. A heavier workpiece requires a greater gripping force to maintain stability and also requires more power to move during transport. Second, information about obstacles along the transport path is crucial for planning the robot's motion trajectory. By knowing the location and size of obstacles in advance, the control system can plan a safe path for the robot to avoid collisions. If there is a fixed device on the transport path, the robot needs to adjust its trajectory according to its position to ensure that a collision does not occur during transport.

[0078] Based on these environmental constraints, the control system calculates the desired positions and desired forces of the first and second SCARA robots relative to the workpiece. The desired position refers to the position the robots should maintain during transport to ensure stable workpiece handling. The desired force refers to the force the robots should apply to the workpiece during transport to prevent slippage or damage. These desired values ​​are calculated by comprehensively considering factors such as the workpiece's shape, size, weight, and obstacle information, aiming to meet the requirements of the transport task and ensure smooth transport. In this way, the dual SCARA robot cooperative transport system can flexibly adjust its control strategy according to specific environmental conditions, thereby achieving efficient and stable transport operations. This comprehensive consideration and precise calculation of environmental constraints enables the robots to safely and reliably complete various transport tasks in complex industrial environments.

[0079] In an electronics manufacturing scenario, a rectangular PCB board needs to be moved from one workbench to another. This workpiece is brittle and fragile, and there are fixed obstacles along the transport path, requiring the robot system to possess high-precision path planning and compliant control capabilities. The system first acquires the workpiece's position and orientation through a vision device and plans a safe detour path accordingly. The master robot is responsible for precise movement along a preset trajectory, ensuring the overall path is accurate and stable; the slave robot adjusts its gripping state in real time through force feedback, dynamically compensating for force changes caused by errors or disturbances, achieving compliant adaptive control. The two robots work collaboratively, with the master robot controlling the position and the slave robot adjusting the gripping force to ensure uniform force and stable orientation of the workpiece during transport.

[0080] Throughout the process, position and force signals are coordinated for control. The main robot ensures motion accuracy, while the slave robot generates speed corrections by sensing the clamping force to avoid excessive tightness or looseness. Upon approaching the target, synchronous deceleration ensures precise positioning, and the clamp is released after the workpiece is smoothly placed. This method achieves safe and efficient handling of precision workpieces while avoiding obstacles, demonstrating good stability and adaptability.

[0081] In a preferred embodiment, the real-time position and force of the first SCARA robot and the second SCARA robot are acquired by sensors installed on the robot body. The sensors include position sensors and force sensors, which can acquire the robot's position and force information in real time and transmit this information to the control module for processing and analysis.

[0082] In a dual SCARA robot collaborative handling system, real-time acquisition of the position and force information of both robots is crucial for ensuring the accuracy and stability of the handling process. This is achieved through sensors mounted on the robot bodies, including position sensors and force sensors. The primary function of the position sensors is to monitor the robot's position in real time. During handling, the robot's position constantly changes, and the position sensors accurately capture these changes and transmit the position data to the control module in real time. Upon receiving this data, the control module processes and analyzes it to ensure the robot moves precisely along the preset path. For example, if the position sensor detects that the robot has deviated from the predetermined trajectory, the control module immediately issues an adjustment command to return the robot to the correct path. The force sensors monitor the force between the robot's end effector and the workpiece. During handling, the magnitude and direction of this force are critical to the workpiece's stability and safety. The force sensors measure these forces in real time and transmit the data to the control module. Based on this data, the control module adjusts the robot's speed and clamping force to ensure the workpiece is not subjected to excessive stress or vibration during handling. If the force sensor detects that the force is too large, the control module will immediately reduce the robot's movement speed or adjust the clamping force to prevent damage to the workpiece.

[0083] Through the coordinated operation of position and force sensors, dual SCARA robots achieve precise position control and stable force management. This real-time feedback mechanism enables the robot to maintain high adaptability and flexibility in complex handling tasks, ensuring the smooth progress of the handling process. This advanced sensor technology not only improves the efficiency and quality of handling but also enhances the reliability and safety of the system.

[0084] In a preferred embodiment, the end effectors of the first and second SCARA robots are designed to be interchangeable, allowing for quick replacement of appropriate grippers or tools according to different handling tasks and workpiece types, thereby improving the versatility and flexibility of the robots.

[0085] In a dual-SCARA robot collaborative handling system, to adapt to different types of handling tasks and workpieces, the end effectors of both the first and second SCARA robots employ a replaceable design, enabling them to quickly adapt to various handling requirements. The end effector is the part of the robot that directly contacts the workpiece. In practical applications, different workpieces may require grippers or tools of different shapes and functions for handling. For example, a PCB board requires an end effector with a vacuum suction cup for handling; while a heavier metal component may require an end effector with mechanical grippers to ensure secure clamping. Through this replaceable design, the dual-SCARA robot can quickly adapt to various handling tasks without needing to design and manufacture a separate robot for each task. This not only reduces costs but also improves the robot's efficiency and flexibility, allowing it to play a maximum role in diverse industrial environments.

[0086] In a preferred embodiment, the method further includes monitoring and handling abnormal situations during the handling process. When an abnormal situation is detected, such as a workpiece falling or a robot malfunction, an emergency braking procedure is immediately initiated to stop the robot's movement and issue an alarm signal. At the same time, abnormal information is recorded for subsequent analysis and processing.

[0087] To ensure the safety and reliability of the handling process, the system is equipped with the ability to monitor and handle abnormal situations. By integrating multiple sensors and monitoring mechanisms into the robot system, various key parameters and states during the handling process are monitored in real time. For example, the system continuously monitors the robot's motion status, the gripping status of the end effector, and the position and orientation of the workpiece. If the workpiece shows signs of loosening or falling during handling, the force and position sensors installed on the end effector will immediately detect this abnormal change. Similarly, if the robot itself malfunctions, such as motor overheating or joint jamming, the system's fault detection module will quickly capture these abnormal signals. Once any abnormality is detected, the system immediately initiates an emergency braking procedure. This means the robot will immediately stop all movement to prevent the workpiece from falling further or the robot from experiencing more serious malfunctions. Simultaneously, the system will issue an alarm signal to notify operators or maintenance personnel of the abnormality. Furthermore, the system automatically records various information when an abnormality occurs, including time, abnormality type, and sensor data. This recorded information is crucial for subsequent analysis and processing, helping technicians quickly locate the cause of the problem and take appropriate measures for repair and improvement.

[0088] A control system for cooperative handling by dual SCARA robots (see...) Figure 2The system includes a first SCARA robot and a second SCARA robot, both of which contain end effectors for jointly gripping the workpiece to be transported.

[0089] The system further includes a trajectory control module, a target acquisition module, and an improved PID control module. The trajectory control module is used to control the first SCARA robot to move independently according to a preset working condition trajectory and to position the second SCARA robot at a relative position to the first SCARA robot. The target acquisition module is used to acquire the target position and target force of the first and second SCARA robots relative to the workpiece based on environmental constraints. The improved PID control module is used to collect the real-time position and force of the first and second SCARA robots.

[0090] The improved PID control module includes a position controller for the first SCARA robot and a damping controller for the second SCARA robot. The position controller applies a proportional-derivative control law based on the position error through the position feedback signal and outputs a force. The damping controller applies a proportional-derivative control law based on the force error through the force feedback signal and outputs a speed correction amount of the end effector relative to the workpiece.

[0091] The dual SCARA robot collaborative handling control system is a highly automated and precise handling solution designed to use two SCARA robots to jointly complete workpiece handling tasks. The core of this system lies in its precise control mechanism, which enables it to efficiently and stably handle various workpieces while ensuring the safety and reliability of the handling process.

[0092] The system consists of a first SCARA robot and a second SCARA robot, each equipped with an end effector. These end effectors work together to grip the workpiece to be transported. This dual-robot collaborative approach not only improves transport efficiency but also enhances the system's flexibility and adaptability, enabling it to handle various complex transport tasks.

[0093] The trajectory control module in the control system is responsible for planning and controlling the motion trajectory of the first SCARA robot. Based on preset working conditions, it precisely guides the first SCARA robot along a predetermined path. Simultaneously, this module also positions the second SCARA robot relative to the first robot, ensuring that the two robots maintain coordinated movements during the handling process. This precise trajectory control is the foundation for efficient handling, guaranteeing the robot's motion accuracy and stability in complex environments.

[0094] The expected acquisition module is another key part of the system. Based on environmental constraints, such as the shape, size, and weight of the workpiece, as well as information about obstacles along the transport path, it acquires the desired positions and forces of the first and second SCARA robots relative to the workpiece. These expected values ​​are calculated based on the characteristics of the workpiece and the requirements of the transport task, providing targets and references for the robot's motion control. By accurately acquiring these expected values, the system can ensure that the robots maintain optimal motion and forces throughout the transport process, thereby achieving smooth and safe transport.

[0095] The PID control module is the core component of the control system. It is responsible for acquiring real-time position and force information of the first and second SCARA robots. Based on this real-time data, the improved PID algorithm precisely corrects the position and force, thereby generating the actual output position and force of the robots. This real-time feedback and correction mechanism enables the robots to dynamically adjust according to the actual handling situation, ensuring the accuracy and stability of the handling process.

[0096] In the improved PID control module, the position controller for the first SCARA robot applies a proportional-derivative control law based on position error via position feedback signal, thereby outputting a precise force. If there is a deviation between the robot's actual position and the desired position, the position controller will quickly adjust the robot's movement according to the magnitude and trend of this deviation, bringing it back to the correct path. This position error-based control method effectively reduces position deviation and improves handling accuracy. The damping controller for the second SCARA robot applies a proportional-derivative control law based on force error via force feedback signal, outputting a speed correction for the end effector relative to the workpiece. This indicates that if the force between the end effector and the workpiece differs from the desired value, the damping controller will adjust the end effector's speed according to this difference to ensure stable force. This force error-based control mechanism helps reduce vibration and impact during handling, protecting the workpiece from damage and improving handling stability.

[0097] The trajectory control module ensures the robot's movement, the target acquisition module provides the target and reference for the robot's movement, and the improved PID control module guarantees the accuracy and stability of the handling process through real-time feedback and correction. This collaborative approach enables dual SCARA robots to safely and reliably complete various handling tasks in complex industrial environments. The trajectory control module is responsible for planning the robot's motion trajectory and relative position, establishing the basic motion framework for the system; while the improved PID control module dynamically compensates for and finely adjusts the robot's movement based on the deviation between the real-time acquired data and the expected value, thereby improving the accuracy and stability of control.

[0098] In a preferred embodiment, the position controller includes a basic position controller module and a fuzzy adaptive control algorithm module. The fuzzy adaptive control algorithm module optimizes the proportional coefficient and derivative coefficient in the position control strategy based on the position feedback error and the error change rate, and obtains a dynamic optimization parameter set in real time during the action process. The input linguistic quantity of the fuzzy adaptive control algorithm is the position error. and the rate of change of position error The output control quantity is the proportional coefficient correction quantity. and differential coefficient correction amount ; Set initial parameters as and The output rules of the parameters are based on and Set different value ranges;

[0099] The damping controller includes a basic damping controller module and an optimized bacterial foraging algorithm module. The optimized bacterial foraging algorithm module, based on a designed fitness function, iterates through four steps—chemotaxis, aggregation, reproduction, elimination, and diffusion—on the proportional and differential coefficients in the damping control strategy to optimize the parameters. The fitness function of the bacterial foraging algorithm is based on the time-domain performance index of the force response curve, and the evaluation index includes the absolute value of error. Ascent time Overshoot With oscillation time The fitness function is specifically designed as follows: Fitness Function ,in, and These are the weighting coefficients for the absolute value of error, oscillation time, rise time, and overshoot, respectively, used to adjust the importance of different indicators in the fitness function.

[0100] The position controller consists of a basic position controller module and a fuzzy adaptive control algorithm module. The basic position controller is responsible for the initial control of the robot's motion based on the preset trajectory and target position. The fuzzy adaptive control algorithm module further optimizes the control effect based on this. It dynamically adjusts the proportional and derivative coefficients by analyzing the position feedback error and the rate of change of the error. This adjustment is based on fuzzy logic, which allows the controller to make reasonable decisions even when faced with complex situations that are difficult to describe with precise mathematical models. For example, when the position error is large and changes rapidly, the fuzzy adaptive control algorithm increases the proportional coefficient to quickly reduce the error, while appropriately adjusting the derivative coefficient to avoid instability caused by over-adjustment. In this way, the position controller can maintain optimal control performance under different operating conditions.

[0101] The damping controller comprises a basic damping controller module and an optimized bacterial foraging algorithm module. The basic damping controller is responsible for making initial adjustments to the speed of the end effector based on the force feedback signal to reduce force fluctuations. The optimized bacterial foraging algorithm module, building upon this, optimizes the control parameters by simulating bacterial foraging behavior. This process includes four steps: chemotaxis, aggregation, reproduction, and elimination / diffusion. In the chemotaxis phase, the algorithm searches for optimal control parameters based on the current force error. The aggregation phase simulates the behavior of bacteria clustering together, allowing the algorithm to explore better regions in the parameter space. The reproduction phase allows the algorithm to replicate well-performing parameter settings, while the elimination / diffusion phase introduces randomness, helping the algorithm escape local optima and find the global optimum. Through these steps, the optimized bacterial foraging algorithm can evaluate the control effect based on the time-domain performance indicators of the force response curve, such as absolute error, rise time, overshoot, and oscillation time, and adjust the proportional and derivative coefficients accordingly. This bio-inspired optimization algorithm enables the damping controller to achieve precise force control in complex dynamic environments, ensuring the smoothness of the transport process.

[0102] By combining fuzzy adaptive control and optimized bacterial foraging algorithm control strategies, the dual SCARA robot cooperative handling control system can achieve high-precision position control and stable force control in various complex handling tasks, thereby improving handling efficiency and quality.

[0103] In a preferred embodiment, the system further includes an anomaly monitoring module for real-time monitoring and handling of abnormal situations during the handling process; when an abnormal situation is detected, such as workpiece falling or robot malfunction, an emergency braking program is immediately initiated to stop the robot's movement and issue an alarm signal, while recording the abnormal information for subsequent analysis and processing.

[0104] In the dual SCARA robot collaborative handling control system, the anomaly monitoring module continuously monitors the handling process, collecting data such as robot motion status, end effector gripping force, and workpiece position and orientation through sensors. Once an anomaly is detected, such as a workpiece falling or robot malfunction, the module immediately initiates an emergency braking procedure to stop the robot's movement and prevent the accident from escalating. Simultaneously, the system issues an alarm signal to alert the operator and records anomaly information, including time, type, and sensor data, for subsequent cause analysis and system optimization to ensure the safety and stability of the handling process.

[0105] To further improve the system's adaptability, this embodiment introduces a predictive control strategy based on machine learning. This strategy analyzes historical and real-time data to predict potential anomalies and adjusts control parameters in advance to avoid these problems. Specifically, a machine learning module is added to the system. This module is responsible for collecting and analyzing various data during the handling process, including the robot's position, speed, forces, and the workpiece's state. Using this data, the machine learning module can train a predictive model to predict possible anomalies, such as workpiece falling or robot malfunction.

[0106] The predictive model outputs a risk score R, representing the probability of an anomaly occurring under the current handling conditions. The formula for calculating the risk score is as follows:

[0107]

[0108] in, It is a positional error. It is the error of the applied force. It is the maximum value of the position error. It is the maximum value of the force error. and These are weighting coefficients used to adjust the impact of different errors on risk scores. It is a bias term (unitless) used to adjust the baseline of the risk score.

[0109] Risk Score The value can be any real number; a larger value indicates a higher risk of an anomaly. When the risk score exceeds a preset threshold... In such cases, the system will proactively implement preventative measures, such as adjusting control parameters, reducing robot speed, or issuing warning signals. This machine learning-based predictive control strategy not only improves the system's robustness and adaptability but also enhances its predictive capabilities when facing complex tasks, further improving the safety and reliability of the handling process.

[0110] In a preferred embodiment, the system further includes a human-computer interaction module for providing a user interface, enabling operators to set and adjust handling task parameters, monitor the handling process, and receive system feedback information; the human-computer interaction module supports path planning parameter setting, simulation preview, and real-time monitoring functions, improving the system's usability and ease of operation.

[0111] The human-machine interface module in the dual SCARA robot collaborative handling control system provides operators with an intuitive and convenient user interface, making the setting, adjustment, and monitoring of handling tasks simple and efficient. Operators can easily set handling task parameters, such as speed, clamping force, and path planning, through this module to meet the needs of different workpieces and tasks. Simultaneously, the real-time monitoring function allows operators to monitor the robot's status and workpiece information at any time during the handling process, ensuring smooth task execution. Furthermore, the simulation preview function allows operators to simulate the handling path before actual execution, identifying and optimizing potential problems in advance, improving handling efficiency and safety. The system also provides operators with real-time feedback, including task progress and system status, enabling timely adjustments. The integration of these functions greatly enhances the system's usability and operational convenience, allowing even operators without professional backgrounds to quickly get started and ensure efficient and stable handling processes.

[0112] This invention proposes a control method and system for collaborative handling using dual SCARA robots. By integrating advanced control theory and intelligent algorithms, it achieves high-precision and high-stability collaborative control of position and force during the handling process. The system adopts a master-slave collaborative architecture, combining an improved PID algorithm and proportional-derivative control law, enabling the master robot to accurately track a preset trajectory and the slave robot to dynamically adjust its gripping state based on force feedback. This effectively solves the challenges of motion synchronization and internal force balance in multi-robot collaboration. Furthermore, the introduction of fuzzy adaptive control and an optimized bacterial foraging algorithm enhances the system's parameter self-tuning capability and robustness under complex working conditions. Combined with a machine learning-based predictive control strategy, it can analyze operational data in real time, predict anomalies, and intervene in advance, significantly improving operational safety and reliability. Simultaneously, the system integrates anomaly monitoring and human-machine interaction modules, strengthening real-time monitoring capabilities and ease of operation. This method integrates the advantages of multiple control technologies, achieving not only efficient, compliant, and safe collaborative handling but also good environmental adaptability and engineering practicality. It provides strong technical support for intelligent manufacturing and has broad application prospects and promotional value in high-end industrial fields with high precision and stability requirements, such as electronics manufacturing, semiconductor packaging, and new energy batteries.

[0113] Although preferred embodiments of the present invention have been described in detail, those skilled in the art, once they grasp the basic inventive concept, will be able to make further adjustments and improvements to these embodiments. The appended claims are intended to cover the preferred embodiments as well as all variations and modifications falling within the scope of this invention. The foregoing is merely an illustration of preferred embodiments of the invention and is not intended to limit its scope. Any modifications, equivalent substitutions, or improvements made under the guidance of the spirit and principles of this invention should be included within the protection scope of this invention.

Claims

1. A control method for cooperative handling by dual SCARA robots, characterized in that, The method is applied to a dual SCARA robot collaborative system, the system including a first SCARA robot and a second SCARA robot, both of which include an end effector for jointly gripping a workpiece to be transported. The method is used to control the position and force of the end effector relative to the workpiece; the method includes: The first SCARA robot and the second SCARA robot adopt a cooperative control mode. The first SCARA robot moves independently according to a preset working condition trajectory, and the second SCARA robot is set at the relative position of the first SCARA robot. Based on environmental constraints, obtain the desired positions and desired forces of the first and second SCARA robots relative to the workpiece; The system collects real-time position and force data of the first and second SCARA robots. The first and second SCARA robots employ different control strategies. The first SCARA robot uses a position control strategy, applying a proportional-derivative control law based on position error through position feedback signals to output a force. This force is indirectly adjusted by position control to ensure stable clamping without damaging the workpiece. The second SCARA robot uses a damping control strategy, applying a proportional-derivative control law based on force error through force feedback signals to output a speed correction amount for the end effector relative to the workpiece. This can change the acceleration of the end effector to make it closer to the desired force or the force that needs adjustment. In the damping control strategy of the second SCARA robot, an optimized bacterial foraging algorithm is used to iteratively perform four steps—chemotaxis, aggregation, reproduction, elimination, and diffusion—on the proportional and differential coefficients in the damping control strategy to achieve parameter optimization. The fitness function of the bacterial foraging algorithm is based on the time-domain performance index of the force response curve, and the evaluation index includes the absolute value of error. Ascent time Overshoot With oscillation time The fitness function is specifically designed as follows: Fitness Function ,in, and These are weighting coefficients for the absolute value of the error, oscillation time, rise time, and overshoot, used to adjust the importance of different indicators in the fitness function. The second SCARA robot does not operate independently along a fixed trajectory; instead, it adjusts its position based on the real-time position of the first SCARA robot and the desired position of the workpiece for collaborative clamping. The workpiece center coordinates are Its length is Width is Therefore, the gripping points of the two robots should be reasonably distributed on both sides of the workpiece to achieve balanced handling; Set the desired gripping position of the first SCARA robot as The second SCARA robot is set as Located on the upper and lower edges of the workpiece center respectively, ensuring symmetrical clamping and improving posture stability during handling; The expected clamping force that each robot should apply is The weight of the workpiece is evenly distributed and a safety margin is left to prevent slippage caused by excessively loose clamping or damage to the workpiece caused by excessively tight clamping. A machine learning module is added to the system. This module is responsible for collecting and analyzing various data during the handling process, including the robot's position, speed, forces, and the workpiece's state. Using this data, the machine learning module trains a predictive model to predict potential anomalies. The output of the predictive model is a risk score R, representing the probability of an anomaly occurring under the current handling condition. The formula for calculating the risk score R is as follows: in, It is a positional error. It is the error of the applied force. It is the maximum value of the position error. It is the maximum value of the force error. and These are weighting coefficients used to adjust the impact of different errors on the risk score. It is a bias term used to adjust the baseline of the risk score; When the risk score R exceeds the preset threshold In such cases, the system will initiate preventative measures in advance, including adjusting control parameters, reducing robot speed, or issuing warning signals.

2. The control method for cooperative handling by dual SCARA robots according to claim 1, characterized in that, In the position control strategy of the first SCARA robot, a fuzzy adaptive control algorithm is adopted to optimize the proportional and derivative coefficients in the position control strategy based on the position error and its rate of change. The input linguistic quantity of the fuzzy adaptive control algorithm is the position error. and the rate of change of position error The output control quantity is the proportional coefficient correction quantity. and differential coefficient correction amount ; Set initial parameters as and The output rules of the parameters are based on and Set different value ranges.

3. The control method for cooperative handling by dual SCARA robots according to claim 1 or 2, characterized in that, The first SCARA robot's position control strategy is based on the desired position quantity. With actual position quantity The difference The position control of the first SCARA robot is represented as follows: ,in, The actual output force, For the desired force, and These are the proportional coefficient and the differential coefficient, respectively.

4. The control method for cooperative handling by dual SCARA robots according to claim 1, characterized in that, The damping control strategy of the second SCARA robot is based on the desired force. With actual force The difference Controller output speed correction amount The damping control of the second SCARA robot is expressed as follows: ,in, This is the speed correction amount of the end effector relative to the workpiece. and These are the proportional coefficient and the differential coefficient, respectively.

5. The control method for cooperative handling by dual SCARA robots according to claim 1, characterized in that, In the cooperative control mode, the first SCARA robot and the second SCARA robot maintain consistency in relative position and speed during the handling process. Through real-time communication and data interaction, they coordinate their respective motion trajectories and actions to ensure stable handling of the workpiece.

6. The control method for cooperative handling by dual SCARA robots according to claim 1, characterized in that, The environmental constraints include the shape, size, weight of the workpiece, and information on obstacles along the transport path. Based on these constraints, the desired positions and desired forces of the first and second SCARA robots relative to the workpiece are calculated to meet the requirements of the transport task.

7. The control method for cooperative handling by dual SCARA robots according to claim 1, characterized in that, The real-time position and force data of the first and second SCARA robots are collected by sensors installed on the robot bodies. These sensors include position sensors and force sensors, which can acquire the robot's position and force information in real time and transmit this information to the control module for processing and analysis.

8. A control system for collaborative handling by two SCARA robots, characterized in that, The system includes a first SCARA robot and a second SCARA robot, both of which contain end effectors for jointly gripping a workpiece to be transported. The system also includes a trajectory control module, a target acquisition module, and an improved PID control module. The trajectory control module controls the first SCARA robot to move independently according to a preset working trajectory, and positions the second SCARA robot relative to the first SCARA robot. The target acquisition module acquires the target position and target force of the first and second SCARA robots relative to the workpiece based on environmental constraints. The improved PID control module collects the real-time position and force of the first and second SCARA robots. The improved PID control module includes a position controller for the first SCARA robot and a damping controller for the second SCARA robot. The position controller applies a proportional-derivative control law based on position error through position feedback signals and outputs the force. The damping controller applies a proportional-derivative control law based on the force error through the force feedback signal, and outputs the speed correction amount of the end effector relative to the workpiece.

9. The control system for cooperative handling of dual SCARA robots according to claim 8, characterized in that, The position controller includes a basic position controller module and a fuzzy adaptive control algorithm module. The fuzzy adaptive control algorithm module optimizes the proportional and derivative coefficients in the position control strategy based on the position feedback error and the error change rate, obtaining a dynamic optimization parameter set in real time during the action process. The input linguistic quantity of the fuzzy adaptive control algorithm is the position error. and the rate of change of position error The output control quantity is the proportional coefficient correction quantity. and differential coefficient correction amount ; Set initial parameters as and The output rules of the parameters are based on and The damping controller is set to different value ranges; the damping controller includes a basic damping controller module and an optimized bacterial foraging algorithm module. The optimized bacterial foraging algorithm module performs iterative operations on the proportional coefficient and differential coefficient in the damping control strategy in four steps: chemotaxis, aggregation, reproduction, elimination, and diffusion, based on the designed fitness function, to achieve parameter optimization; the fitness function of the bacterial foraging algorithm is based on the time-domain performance index of the force response curve, and the evaluation index includes the absolute value of error. Ascent time Overshoot With oscillation time The fitness function is specifically designed as follows: Fitness Function ,in, and These are the weighting coefficients for the absolute value of error, oscillation time, rise time, and overshoot, respectively, used to adjust the importance of different indicators in the fitness function.

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