Control method and system for cooperative carrying of double SCARA robots

Abstract

The invention discloses a control method for cooperative carrying of double SCARA robots, and further comprises a system of a first SCARA robot and a system of a second SCARA robot, and the first SCARA robot and the second SCARA robot jointly clamp a workpiece through an end effector. The method comprises the steps that a cooperative control mode is adopted, a first robot independently moves according to a preset track, and a second robot keeps a relative position; acquiring an expected position and an acting force according to environmental constraints; acquiring positions and acting force of the two robots in real time; the first robot adopts a position control strategy and applies a proportional differential control law output acting force based on a position error through position feedback; and the second robot adopts a damping control strategy, applies a force error-based proportional differential control law through acting force feedback, and outputs a speed correction amount. Decoupling of the position and force is achieved through differential control, and the carrying stability, precision and adaptability are improved. The master-slave architecture avoids position grabbing or mutual dragging, reduces dependence on positioning precision, and is suitable for carrying precise or vulnerable workpieces.

Description

TECHNICAL FIELD

[0001] The application relates to a control method and system for cooperative carrying of a double SCARA robot. BACKGROUND

[0002] With the development of intelligent manufacturing towards high precision, high efficiency and flexibility, industrial robots are increasingly applied in high-end manufacturing fields such as electronic assembly, semiconductor manufacturing and new energy battery. SCARA robots are widely used in dispensing, locking, sorting and carrying tasks due to their selective compliant structure, high speed and high repeatability. However, in the face of growing demand for carrying large workpieces (such as large-size display screens, battery modules and PCB panels), a single SCARA robot is difficult to meet the actual demand in terms of load capacity, working range and dynamic balance, prompting the development of a double SCARA or multi-robot cooperative system. Such a system can significantly improve the carrying capacity and system flexibility by having two robots jointly hold the same workpiece and realize load sharing and motion coordination, but it also brings complex motion synchronization and torque coordination problems.

[0003] In the process of cooperative carrying of double robots, if both robots use the traditional position control mode and strictly track the same trajectory, it is easy to cause unbalanced distribution of clamping force due to mechanical errors, installation deviations, joint clearances or uneven workpiece stiffness, resulting in internal stress, which may affect the carrying stability, or even cause workpiece deformation, scratching or robot overload damage. Therefore, researchers have proposed various force / position hybrid control strategies, such as impedance control, admittance control and adaptive force control. For example, the robot force compliance control method based on variable speed impedance control disclosed in the patent CN116852356B uses impedance control to actively adjust and respond to contact force. However, these methods usually rely on accurate robot dynamics models or external six-axis force sensors, have high computational complexity, poor real-time performance and high requirements for system calibration accuracy, making them difficult to be widely used in high-speed and low-cost industrial production lines. In addition, although fully distributed control or centralized optimization control can theoretically achieve global optimization, they have problems such as communication delay, heavy computational burden and poor system scalability, limiting their engineering practicability.

[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 adopt different control strategies; the first SCARA robot adopts a position control strategy, applies a proportional-differential control law based on a position error through a position feedback signal, and outputs an acting force; and the second SCARA robot adopts a damping control strategy, applies a proportional-differential control law based on an acting force error through an acting force feedback signal, and outputs a speed correction of an end effector relative to a workpiece.

[0012] In the dual-SCARA robot collaborative handling system, the first SCARA robot serves as a master robot, adopts a position control strategy, and independently runs according to a preset trajectory to ensure the accuracy of the overall motion path; and the second SCARA robot serves as a slave robot, adopts a damping control strategy, detects a clamping force error in real time, adjusts the speed of an end effector relative to a workpiece through force feedback, and realizes compliant compensation. The system sets expected positions and acting forces based on environmental constraints, and performs closed-loop correction on real-time collected position and force signals through an improved PID algorithm to realize dynamic adjustment.

[0013] As a preferred mode, in the position control strategy of the first SCARA robot, a fuzzy adaptive control algorithm is adopted to optimize the proportional coefficient and the differential coefficient in the position control strategy according to the position error and the rate of change of the position error; the input language quantity of the fuzzy adaptive control algorithm is the position error and the rate of change of the position error , the output control quantity is the proportional coefficient correction quantity and the differential coefficient correction quantity ; the initial parameters are set as and , and the output rules of the parameters are set according to different value ranges of and .

[0014] As a preferred mode, in the damping control strategy of the second SCARA robot, an optimized bacterial foraging algorithm is adopted to perform iterative operations of chemotaxis, aggregation, reproduction, elimination and diffusion on the proportional coefficient and the differential coefficient in the damping control strategy to realize parameter optimization; the fitness function of the bacterial foraging algorithm is based on time-domain performance indicators of an acting force response curve, and the evaluation indicators include an error absolute value , a rise time , an overshoot and an oscillation time ; the fitness function is specifically designed as: fitness function , wherein and are weighting coefficients of the error absolute value, the oscillation time, the rise time and the overshoot, respectively, and are 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 cooperative handling of a workpiece by a dual SCARA robot, the system comprising a first SCARA robot and a second SCARA robot, both of which include an end effector for jointly holding the workpiece to be handled;

[0022] The system further comprises a trajectory control module, an expectation acquisition module, and an improved PID control module; the trajectory control module is configured to control the first SCARA robot to move independently according to a preset working condition trajectory, and to set the second SCARA robot at a relative position of the first SCARA robot; the expectation acquisition module is configured to acquire desired positions and desired forces of the first SCARA robot and the second SCARA robot relative to the workpiece according to environmental constraints; and the improved PID control module is configured to collect real-time positions and forces of the first SCARA robot and the second SCARA robot.

[0023] The improved PID control module comprises a position controller for the first SCARA robot, and a damping controller for the second SCARA robot; the position controller applies a proportional and differential control law based on a position error through a position feedback signal, and outputs a force; and the damping controller applies a proportional and differential control law based on a force error through a force feedback signal, and outputs a velocity correction of the end effector relative to the workpiece.

[0024] As a preferred mode, the position controller comprises a basic position controller module and a fuzzy adaptive control algorithm module; the fuzzy adaptive control algorithm module optimizes proportional and differential coefficients in a position control strategy according to a position feedback error and a rate of change of the error, and obtains a dynamic optimization parameter set in a motion process in real time; input language quantities of the fuzzy adaptive control algorithm are a position error and a rate of change of the position error , and output control quantities are a proportional coefficient correction and a differential coefficient correction ; initial parameters are set as and , and output rules of the parameters are set according to different value ranges of and ;

[0025] The damping controller comprises a basic damping controller module and an optimized bacterial foraging algorithm module; the optimized bacterial foraging algorithm module performs iterative operations of chemotaxis, aggregation, reproduction, elimination and diffusion on proportional and differential coefficients in a damping control strategy according to a designed fitness function, and realizes parameter optimization; the fitness function of the bacterial foraging algorithm is based on time domain performance indexes of a force response curve, and evaluation indexes include an absolute value of an error , rise time , overshoot and oscillation time ; the fitness function is specifically designed as: fitness function , wherein, and are the weighted coefficients of the absolute value of error, oscillation time, rise time and overshoot, respectively, for adjusting the importance of different indicators in the fitness function.

[0026] The present application has at least the following beneficial effects: the dual SCARA robot cooperative carrying control method proposed in the present application realizes efficient decoupling control of position and force through the cooperative operation of differentiated control strategies, significantly improving the stability, precision and adaptability of the carrying process. Among them, the first SCARA robot as the master unit adopts a position control strategy, independently runs according to the preset working condition trajectory, and implements proportional differential control based on position error through position feedback, ensuring the accuracy and predictability of the overall motion path; the second SCARA robot as the cooperative unit adopts a damping control strategy, applies proportional differential control law based on force error according to the real-time collected force feedback, and outputs the speed correction amount of the end effector relative to the workpiece, thereby actively adjusting the clamping state and compensating for the internal stress caused by assembly deviation, workpiece deformation or external disturbance.

[0027] The master-slave, position-force complementary control architecture not only avoids the common "position grabbing" or "mutual dragging" phenomenon in traditional rigid synchronous control, but also effectively reduces the dependence on the absolute positioning accuracy and mechanical calibration of the robot, especially suitable for the carrying scene of precision or fragile workpieces sensitive to clamping force. Further, the system introduces an improved PID algorithm for joint closed-loop correction of position and force, combining feedforward compensation and adaptive gain adjustment mechanism, improving the dynamic response speed, suppressing overshoot and oscillation, realizing the cooperative operation of the dual robots, and having good industrial application prospect and popularization value. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show the embodiments of the present application, and therefore should not be considered as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0029] Figure 1 is a flowchart of the present application;

[0030] Figure 2 is a control structure diagram of the dual SCARA robot cooperative carrying. DETAILED DESCRIPTION

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

[0032] It should be understood that in the following description specific details are set forth to provide a thorough understanding of the example embodiments. However, one of ordinary skill in the art will understand that the example embodiments can be practiced without these specific details. For example, systems can be shown in block diagram form to avoid obscuring the examples. In other instances, well-known processes, structures and techniques have not been shown in detail in order not to obscure the examples.

[0033] As shown in Figure 1 A control method of dual SCARA robot cooperative handling, the method is applied to a dual SCARA robot cooperative system, the system includes a first SCARA robot and a second SCARA robot, the first SCARA robot and the second SCARA robot both contain an end effector used to jointly hold a workpiece to be handled, the method is used to control the position and force of the end effector 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 arranged at a relative position of the first SCARA robot;

[0035] According to environmental constraints, the expected position and expected force of the first SCARA robot and the second SCARA robot relative to the workpiece are obtained;

[0036] The real-time position and force of the first SCARA robot and the second SCARA robot are collected;

[0037] The first SCARA robot and the second SCARA robot adopt different control strategies; the first SCARA robot adopts a position control strategy, applies a proportional and differential control law based on a position error through a position feedback signal, and outputs a force; the second SCARA robot adopts a damping control strategy, applies a proportional and differential control law based on a force error through a force feedback signal, and outputs a speed correction amount of the end effector relative to the workpiece.

[0038] The first SCARA robot adopts a position control strategy, with position feedback signals as control inputs and forces as outputs. This strategy is based on a proportional-derivative control law for position errors, which calculates and applies corresponding forces to drive the robot to accurately track the preset trajectory, ensuring the accuracy of the overall motion path. The second SCARA robot adopts a damping control strategy, with force feedback signals as control inputs and velocity corrections of the end effector relative to the workpiece as outputs. This strategy is based on a proportional-derivative control law for force errors, which dynamically adjusts the motion speed of the end effector to achieve compliant compensation, ensuring the stability of the contact force.

[0039] In scenarios requiring high-precision handling, such as electronic manufacturing, the dual-SCARA robot cooperative handling control method is applied to a cooperative system composed of a first SCARA robot and a second SCARA robot. Both robots are equipped with end effectors for jointly holding the workpiece to be handled. The core lies in accurately controlling the position and force of the end effector relative to the workpiece. Specifically, the first SCARA robot moves independently according to the preset working condition trajectory, while the second SCARA robot is placed at the relative position of the first SCARA robot, and both work cooperatively. 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, weight of the workpiece, and obstacles on the handling path. At the same time, the position and force information of the two robots are collected in real time and corrected based on an improved PID algorithm, thereby obtaining the actual output position and force, ensuring the accuracy of the handling process.

[0040] The first SCARA robot and the second SCARA robot adopt different control strategies but need to achieve high coordination to ensure stable handling of the workpiece. The first robot mainly adopts a position control strategy, dynamically adjusts the motion trajectory based on position feedback signals, ensures accurate operation according to the preset path, and thus improves the precision and efficiency of handling. At the same time, the system collects force information in real time through force sensors to monitor the clamping state, achieve dynamic adjustment of clamping force, and timely warn or trigger safety mechanisms when the clamping force is abnormal (such as workpiece loosening), effectively enhancing the reliability of the system. The second SCARA robot adopts a damping control strategy, adjusts the motion speed and clamping force based on force feedback to maintain stable contact between the end effector and the workpiece. When the clamping force changes cause a position deviation, the system dynamically corrects its position through real-time position feedback to ensure consistent relative position with the first robot, preventing the workpiece from shaking or being damaged. If the shape or weight of the workpiece changes, the system can also adjust the relative position accordingly to adapt to the new working condition.

[0041] It should be noted that the first SCARA robot and the second SCARA robot adopt different control strategies. The first SCARA robot adopts a position control strategy, and outputs an acting force according to a proportional-derivative control law of a position error through a position feedback signal; the second SCARA robot adopts a damping control strategy, and outputs a speed correction of an end effector relative to a workpiece according to a proportional-derivative control law of an acting force error according to an acting force feedback signal, so that the acceleration of the end effector can be changed to be closer to an expected acting force or a force to be adjusted.

[0042] In an electronic manufacturing workshop, a relatively fragile PCB needs to be accurately transported from one workbench to another. In order to ensure the position accuracy and clamping force during the transportation process, and avoid damage to the board due to vibration, misalignment or uneven force, a double SCARA robot cooperative operation mode is adopted to complete the task. The entire transportation process is uniformly scheduled by an intelligent cooperative control system, which fully utilizes the complementary advantages of the two robots to realize high-precision, soft and stable automatic operation.

[0043] Before the task starts, the first SCARA robot (master robot) and the second SCARA robot (slave robot) are both at the preset starting position, and the end effector is in standby state. Through the vision system deployed in the workshop, the control system has accurately obtained the position and attitude information of the PCB on the starting workbench, and taken it as the initial input parameter for cooperative transportation, to prepare for subsequent accurate grabbing.

[0044] After the transportation is started, the first SCARA robot as the master control unit starts to move according to the pre-planned trajectory (such as a straight line or a smooth curve path from the starting point to the target point) first. It adopts a position control strategy, and collects the position data of each joint and the end effector of itself in real time through the built-in position sensor, and compares it with the expected trajectory to calculate the current position error. Based on the error, the system applies a proportional-derivative (PD) control law to dynamically adjust the movement: the proportional term is used to quickly reduce the position deviation, and the derivative term is used to suppress overshoot and oscillation in the movement process, so that the action is more stable. After completing the positioning, the clamping force of the first robot grabs one end of the PCB, and the acting force is indirectly adjusted by the position control to ensure stable clamping and no damage to the workpiece.

[0045] Meanwhile, the second SCARA robot initiates a damping control strategy as a collaborative unit. It does not completely follow a fixed trajectory independently, but dynamically adjusts its motion according to the real-time position of the master robot and the overall posture of the workpiece. That is, while the first SCARA robot is moving, the second SCARA robot adjusts its position according to the position of the first robot and the desired position of the workpiece, and performs collaborative clamping. Its end is equipped with a force feedback device (such as a force sensor or a virtual force sensor based on current estimation), which can monitor the actual force when it contacts the PCB in real time. The system compares the actual force with the preset desired clamping force to obtain the force error. Based on the error, the second robot calculates a speed correction through another proportional-derivative control law, which is used to fine-tune the movement speed of its end effector relative to the workpiece. For example, if the clamping force is detected to be too large, it may indicate a risk of extrusion, and the system will instruct it to slow down slightly or retreat; if the clamping force is too small, it will appropriately speed up or advance to compensate for the clamping stability.

[0046] Throughout the entire handling process, the two robots always act in coordination: the master robot dominates the overall trajectory, ensuring accurate paths; the slave robot adjusts its motion in real time through force feedback, acting as a "compliant compensation" to effectively absorb the effects of mechanical errors, minor deformations, or external disturbances. The system also introduces an improved PID algorithm to fuse position and force signals, further improving response speed. Even if the workpiece weight changes slightly or there are minor disturbances in the path, the control system can quickly respond and adjust to ensure a smooth and reliable handling process.

[0047] When the first SCARA robot approaches the target workbench, both robots slow down synchronously, gradually adjusting their postures to ensure that the PCB is placed horizontally and accurately aligned with the target position. After reaching the final target point, both robots release the clamping jaws in coordination, smoothly releasing the PCB onto the target workbench. The entire placement process is force-controllable, avoiding impact or deviation.

[0048] After completing the task, both robots return to standby positions, ready to perform the next round of handling. Through this master-slave coordination and position-force division control method, the dual-SCARA robot system not only achieves safe handling of fragile workpieces, but also significantly improves automation level and production efficiency, reducing manual intervention, and providing 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 calculate the desired position and desired force of both 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 clamping position. Assuming the center coordinates of the workpiece are , its length is , and its width is , the two robot's gripping points should be reasonably distributed on both sides of the workpiece to achieve balanced carrying. Typically, the desired gripping position of the first SCARA robot is set as , and the second SCARA robot is set as

[0050] , i.e., respectively on the upper and lower edges of the workpiece center, ensuring symmetrical gripping and improving the stability of the posture during carrying.

[0051] The weight of the workpiece directly affects the gripping force required by the robot. Let the mass of the workpiece be , the acceleration of gravity be , and the safety factor be , which is generally between 1.2 and 1.5 to cope with dynamic acceleration or unexpected disturbances. The desired gripping force applied by each robot is , i.e., sharing the weight of the workpiece equally and leaving a safety margin to prevent slipping due to gripping that is too loose or damage to the workpiece due to gripping that is too tight. In addition, obstacle information on the carrying path must also be considered in trajectory planning. If the center of the obstacle is located at , and the size is , the system needs to segment the overall path and design a safe detour trajectory, such as a semicircular path with a radius of to avoid the obstacle area, ensuring that the robot avoids collisions while maintaining smooth and continuous motion during movement. By considering the geometric characteristics of the workpiece, weight properties, and obstacle distribution in the path environment, the system can accurately calculate the desired gripping positions of the two SCARA robots and the required applied force, and plan a safe and efficient collaborative motion trajectory accordingly. This fine design based on environmental constraints not only improves the accuracy and reliability of the carrying operation, but also enhances the adaptability and safety of the system in complex industrial scenarios.

[0052] In a preferred embodiment, in the position control strategy of the first SCARA robot, a fuzzy adaptive control algorithm is used to optimize the proportional coefficient and the differential coefficient in the position control strategy according to 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 the position error , and the output control quantity is the proportional coefficient correction amount and the differential coefficient correction amount ; the initial parameters are set as and , and the output rules of the parameters are set according to different value ranges of and .

[0053] The position control strategy of the first SCARA robot introduces a fuzzy adaptive control algorithm. This algorithm dynamically optimizes the proportional and derivative coefficients based on the position error and its rate of change. The input linguistic variables are the position error and its rate of change, and the output control variables are the proportional and derivative coefficient corrections. The initial parameters are set to and , and the output rules are flexibly set according to different ranges of the position error and its rate of change, achieving more precise and flexible position control and effectively improving the stability and reliability of the handling process, meeting the strict requirements of the electronic manufacturing industry for high-precision handling.

[0054] In the dual SCARA robot collaborative handling system, the first SCARA robot adopts a fuzzy adaptive control algorithm. The core of this algorithm is to dynamically adjust the control parameters according to the real-time position error and its rate of change, thereby achieving more precise and flexible position control. Specifically, the algorithm's input is the position error and its rate of change. These two parameters can reflect the deviation between the robot's current position and the desired position, as well as the trend of the deviation. Based on these input information, the fuzzy adaptive control algorithm outputs the proportional and derivative coefficient corrections. This means that the algorithm will automatically adjust the values of the proportional and derivative coefficients and based on the current position error and error rate to optimize control effectiveness. Initially, the proportional and derivative coefficients are set to and , which are pre-set based on the basic performance of the robot and the general requirements of the handling task. However, during the actual handling process, the weight, shape of the workpiece, and various factors on the handling path may change, so real-time adjustment of these control parameters is needed. The fuzzy adaptive control algorithm adjusts the and flexibly according to the different ranges of the position error and its rate of change through pre-set output rules. and .

[0055] According to different states of the position error and its rate of change, the fuzzy adaptive control algorithm optimizes the control performance by dynamically adjusting the proportional and derivative coefficients. When the position error is large, the system focuses on quickly reducing the error, so the proportional coefficient is increased to enhance the response speed. If the error is increasing at this time, the derivative coefficient is also increased to suppress overshoot, and if the error is decreasing, the derivative action is appropriately reduced to avoid excessive suppression. When the position error is small, the proportional coefficient is reduced to prevent excessive regulation. If the error has 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 also reduced to maintain the flexibility of movement. 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, improving the stability and reliability of handling. Even in the face of unexpected situations or environmental changes, it can quickly respond and adjust the control strategy to ensure smooth handling.

[0056] In a preferred embodiment, in the damping control strategy of the second SCARA robot, the chemotaxis, aggregation, reproduction, elimination and diffusion of the proportional coefficient and the derivative coefficient in the damping control strategy are iterated by using the optimized bacterial foraging algorithm to realize parameter optimization; the fitness function of the bacterial foraging algorithm is based on the time domain performance indicators of the force response curve, and the evaluation indicators include the absolute value of the error , the rise time , the overshoot and the oscillation time ; the fitness function is specifically designed as: fitness function

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

[0058] In the dual SCARA robot cooperative handling system, the second SCARA robot adopts a damping control strategy to ensure that the force between the end effector and the workpiece remains stable during handling. The core of this strategy is to finely adjust the control parameters using the optimized bacterial foraging algorithm to achieve the best control effect.

[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 quantity and the actual position quantity. This difference reflects the deviation between the current actual position and the desired position of the robot. To correct this deviation, the robot control system calculates a correction quantity, which is based on a proportional coefficient and a derivative coefficient. The proportional coefficient adjusts according to the size of the position error, if the error is large, a larger adjustment is needed to quickly correct the deviation; while the derivative coefficient considers the rate of change of the error, that is, how the error changes over time, which helps to smooth the motion of the robot, avoiding shaking or instability caused by rapid adjustment.

[0067] The actual output force is composed of the desired force plus the correction quantity. The desired force is pre-set according to the handling task, which represents the ideal force that the robot should exert on the workpiece. In this way, the first SCARA robot can dynamically adjust its motion and force according to real-time position feedback, ensuring accurate position control throughout the handling process, thus smoothly and accurately completing the handling task. 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 difference between the desired force and the actual force , the controller outputs a velocity correction quantity ; the damping control of the second SCARA robot is represented as: , where is the velocity correction quantity of the end effector relative to the workpiece, and are the proportional coefficient and the derivative coefficient, respectively.

[0069] In the dual SCARA robot cooperative handling system, the damping control strategy of the second SCARA robot is the key mechanism to ensure the stability of the force between the end effector and the workpiece during handling. The core of this strategy is to adjust the motion speed of the end effector by comparing the difference between the desired force and the actual force, so as to achieve precise force control.

[0070] The damping control strategy of the second SCARA robot 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 of the end effector. To correct this deviation, the controller calculates a velocity correction, which is based on a proportional coefficient and a derivative coefficient. The proportional coefficient adjusts according to the size of the force error, if the error is large, it needs a larger adjustment to quickly correct the deviation; while the derivative coefficient considers the rate of change of the error, that is, how the error changes over time, which helps to smooth the motion of the end effector, avoiding shaking or instability caused by rapid adjustment.

[0071] The velocity correction of the end effector relative to the workpiece is calculated by multiplying the proportional coefficient by the force error and adding the derivative coefficient multiplied by the rate of change of the force error In this way, the second SCARA robot can dynamically adjust the motion speed of its end effector according to real-time force feedback, ensuring that a stable force is maintained throughout the handling process, so that the handling task can be completed 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, in the cooperative control mode, the first SCARA robot and the second SCARA robot maintain consistency in relative position and velocity during handling, and through real-time communication and data interaction, coordinate their motion trajectories and actions to ensure stable handling of the workpiece.

[0073] In the double SCARA robot cooperative handling system, the cooperative control mode is the key to ensure the smooth progress of the handling process. In this mode, the first SCARA robot and the second SCARA robot need to work closely and maintain consistency in relative position and velocity. This means that the motion trajectories and actions of the two robots must be highly coordinated to ensure that the workpiece remains stable throughout the handling process. The first SCARA robot moves independently according to the preset working condition trajectory, while the second SCARA robot adjusts its position and velocity in real time according to the position and velocity of the first robot to maintain a constant relative position with the first robot. This relative position is maintained through real-time communication and data interaction. The control system continuously collects position and velocity information from the two robots, then calculates the necessary adjustment instructions based on this information, and sends them back to the robots.

[0074] If the first SCARA robot slightly speeds up during the handling process, the control system will immediately detect this change and adjust the speed of the second SCARA robot accordingly to ensure that the relative position between the two robots remains unchanged. Similarly, if the position of the first robot has a slight deviation, the second robot will also make corresponding adjustments according to the instructions of the control system. This real-time adjustment and coordination is achieved through a high-speed communication network and precise data processing, ensuring that the two robots work together like a whole during the handling process. Through this collaborative control mode, the dual SCARA robot can effectively avoid the possible shaking or position deviation of the workpiece during handling, thereby ensuring the stability and accuracy of the handling. This high degree of coordination not only improves the handling efficiency, but also reduces the risk of workpiece damage due to inconsistent robot actions, making the entire handling process more reliable and efficient.

[0075] In a preferred embodiment, the environmental constraints include the shape, size, weight of the workpiece, and obstacle information on the handling path, etc. According to these constraints, the desired position and desired force of the first SCARA robot and the second SCARA robot relative to the workpiece are calculated to meet the requirements of the handling task.

[0076] In the dual SCARA robot collaborative handling system, environmental constraints are important factors to ensure the smooth progress of the handling task. These constraints include the shape, size, weight of the workpiece, and obstacle information on the handling path, etc. These information are crucial for calculating the desired position and desired force of the first SCARA robot and the second SCARA robot relative to the workpiece, as they directly determine the motion trajectory and control strategy of the robots during handling.

[0077] Firstly, the shape and size of the workpiece determine the clamping method and position of the robot end effector. For example, if the workpiece is a rectangular PCB board, the robot needs to adjust the clamping point according to its length and width dimensions to ensure stable clamping and prevent damage to the workpiece. The weight of the workpiece affects the force that the robot needs to exert. A heavier workpiece requires more clamping force to maintain stability and more power to move during handling. Secondly, obstacle information on the handling path is crucial for planning the motion trajectory of the robot. By knowing the position and size of the 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 handling path, the robot needs to adjust the motion trajectory according to its position to ensure that no collision occurs during handling.

[0078] According to these environmental constraints, the control system calculates the desired position and desired force of the first SCARA robot and the second SCARA robot relative to the workpiece. The desired position refers to the position that the robot should maintain during the handling process to ensure stable handling of the workpiece. The desired force refers to the force that the robot should exert on the workpiece during the handling process to ensure that the workpiece does not slip or damage. These desired values are calculated by considering factors such as the shape, size, weight of the workpiece, and obstacle information, with the aim of meeting the requirements of the handling task and ensuring smooth handling process. In this way, the dual SCARA robot collaborative handling system can flexibly adjust its control strategy according to specific environmental conditions, thereby achieving efficient and stable handling operation. This comprehensive consideration and accurate calculation of environmental constraints enable the robot to safely and reliably complete various handling tasks in complex industrial environments.

[0079] In the electronic manufacturing scenario, a rectangular PCB board needs to be transported from one workbench to another. The workpiece is fragile and easily damaged, and there are fixed obstacles in the transportation path, requiring the robot system to have high-precision path planning and compliant control capabilities. The system first obtains the position and attitude of the workpiece through a vision device, and plans a safe bypass path accordingly. The master robot is responsible for precise motion according to the preset trajectory, ensuring accurate and smooth overall path; the slave robot adjusts the clamping state in real time through force feedback, dynamically compensates for force changes caused by errors or disturbances, and realizes compliant adaptive control. The two robots work together, with the master robot controlling the position and the slave robot adjusting the clamping force, ensuring that the workpiece is evenly stressed and stable in attitude during handling.

[0080] Throughout the process, the position and force signals are combined for collaborative control, the master robot ensures motion accuracy, and the slave robot generates a speed correction based on the sensed clamping force to avoid over-tightening or over-looseness. When approaching the target, it slows down to accurately position and release the clamping after placing the workpiece smoothly. This method not only avoids obstacles, but also realizes safe and efficient handling of precision workpieces, demonstrating good stability and adaptability.

[0081] In a preferred embodiment, the acquisition of real-time position and force of the first SCARA robot and the second SCARA robot is realized by sensors installed on the robot body, which include position sensors and force sensors that can acquire real-time position and force information of the robot and transmit these information to the control module for processing and analysis.

[0082] In a dual SCARA robot collaborative handling system, real-time position and force information of both robots need to be acquired to ensure the accuracy and stability of the handling process. This function is achieved through sensors installed on the robot body, including position sensors and force sensors. The main role of the position sensor is to monitor the position information of the robot in real time. During the handling process, the position of the robot will change constantly, and the position sensor can accurately capture these changes and transmit the position data to the control module in real time. After receiving these data, the control module will process and analyze them to ensure that the robot moves accurately along the preset path. For example, if the position sensor detects that the robot deviates from the predetermined trajectory, the control module will immediately issue adjustment instructions to make the robot return to the correct path. The force sensor is responsible for monitoring the force between the robot's end effector and the workpiece. During the handling process, the size and direction of the force are crucial to the stability and safety of the workpiece. The force sensor can measure these forces in real time and transmit the data to the control module. Based on these data, the control module adjusts the robot's movement speed and clamping force to ensure that the workpiece does not suffer from 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 cooperation of position sensors and force sensors, dual SCARA robots can achieve accurate 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 adopt a replaceable design, which can quickly replace the corresponding clamps or tools according to different handling tasks and workpiece types, improving the versatility and flexibility of the robot.

[0085] In the dual SCARA robot collaborative handling system, in order to adapt to different types of handling tasks and workpieces, the end effectors of the first SCARA robot and the second SCARA robot adopt a replaceable design, which can quickly adapt to various handling needs. The end effector is the part of the robot that directly contacts the workpiece. In practical applications, different workpieces may require different shapes and functions of clamps or tools for handling. For example, for a PCB board, an end effector with a vacuum chuck is needed to achieve handling; while for a heavy metal part, an end effector with mechanical clamps may be needed to ensure firm clamping. Through this replaceable design, the dual SCARA robot can quickly adapt to various handling tasks without the need to design and manufacture a robot for each task. This not only reduces costs, but also improves the efficiency and flexibility of the robot, enabling it to play the greatest role in a changing industrial environment.

[0086] In a preferred embodiment, the method further includes monitoring and handling abnormal situations during handling, when an abnormal situation is detected, such as workpiece falling, robot failure, etc., immediately start the emergency braking program, stop the movement of the robot, and issue an alarm signal, while recording abnormal information for subsequent analysis and processing.

[0087] In order to ensure the safety and reliability of the handling process, the system has the ability to monitor and handle abnormal situations. By integrating various sensors and monitoring mechanisms in the robot system, various key parameters and states during handling are monitored in real time. For example, the system continuously monitors the motion state of the robot, the clamping state of the end effector, and the position and attitude of the workpiece, etc. If the workpiece shows signs of loosening or falling during handling, the force sensor and position sensor installed on the end effector will immediately detect such abnormal changes. Similarly, if the robot itself has a fault, such as motor overheating, joint jamming, etc., the fault detection module in the system will also quickly capture these abnormal signals. Once any abnormal situation is detected, the system will immediately start the emergency braking program. This means that the robot will immediately stop all movements to prevent the workpiece from falling further or the robot from having more serious faults. At the same time, the system will issue an alarm signal to notify the operator or maintenance personnel of the occurrence of the abnormal situation. In addition, the system will automatically record various information at the time of the abnormal occurrence, including time, abnormal type, sensor data, etc. These recorded information is crucial for subsequent analysis and processing, which can help technicians quickly locate the cause of the problem and take appropriate measures to repair and improve.

[0088] A control system for dual SCARA robot collaborative handling (see Figure 2), the system comprises a first SCARA robot and a second SCARA robot, both of which contain end effectors used to jointly hold a workpiece to be transported;

[0089] The system further comprises a trajectory control module, an expectation 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 set the second SCARA robot at a relative position of the first SCARA robot; the expectation acquisition module is used to acquire a desired position and a desired force of the first SCARA robot and the second SCARA robot relative to the workpiece according to environmental constraints; and the improved PID control module is used to collect real-time positions and forces of the first SCARA robot and the second SCARA robot.

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

[0091] The dual-SCARA robot cooperative transportation control system is a highly automated and precise transportation solution designed to complete the transportation task of a workpiece through two SCARA robots. The core of this system lies in its precise control mechanism, which enables it to efficiently and stably transport various workpieces while ensuring the safety and reliability of the transportation process.

[0092] The system consists of a first SCARA robot and a second SCARA robot, each equipped with an end effector used to jointly hold a workpiece to be transported. This dual-robot cooperative working method not only improves the efficiency of transportation, but also enhances the flexibility and adaptability of the system, enabling it to cope with various complex transportation tasks.

[0093] The trajectory control module in the control system is responsible for planning and controlling the motion trajectory of the first SCARA robot. It accurately guides the first SCARA robot to move along the predetermined path according to the preset working condition. At the same time, this module also sets the second SCARA robot at the relative position of the first robot, ensuring that the two robots maintain coordinated actions during the transportation process. This precise trajectory control is the basis for efficient transportation, which guarantees the motion accuracy and stability of the robots in complex environments.

[0094] The expected acquisition module is another key part of the system, which acquires the desired position and force of the first and second SCARA robots relative to the workpiece based on environmental constraints such as the shape, size, weight of the workpiece, and obstacle information on the handling path. These desired values are calculated based on the characteristics of the workpiece and the requirements of the handling task, which provide targets and references for the motion control of the robots. By accurately acquiring these desired values, the system can ensure that the robots maintain the best motion state and force during handling, achieving smooth and safe handling.

[0095] The PID control module is the core component of the control system, responsible for collecting real-time position and force information of the first and second SCARA robots. Based on these real-time data, the improved PID algorithm accurately corrects the position and force, thus 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 proportional and derivative control laws based on position error through position feedback signals, thus outputting accurate force. If there is a deviation between the actual position of the robot and the desired position, the position controller will quickly adjust the robot's motion according to the size and trend of this deviation, making it return to the correct path. This position error-based control method can effectively reduce position deviation and improve handling accuracy. The damping controller for the second SCARA robot applies proportional and derivative control laws based on force error through force feedback signals, outputting the speed correction of the end effector relative to the workpiece. This means that if there is a difference between the force between the end effector and the workpiece and the desired value, the damping controller will adjust the speed of the end effector according to this difference to ensure the stability of the force. This force error-based control mechanism helps to reduce vibration and impact during handling, protects the workpiece from damage, and improves the stability of handling.

[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 is composed of a basic position controller module and a fuzzy adaptive control algorithm module. The basic position controller is responsible for the preliminary control of the robot's movement according to the preset trajectory and target position. The fuzzy adaptive control algorithm module further optimizes the control effect on this basis. It dynamically adjusts the proportional coefficient and the differential coefficient by analyzing the position feedback error and the error change rate. This adjustment is based on fuzzy logic, which allows the controller to make reasonable decisions even in complex and difficult-to-describe situations with precise mathematical models. For example, when the position error is large and changes rapidly, the fuzzy adaptive control algorithm will increase the proportional coefficient to quickly reduce the error, while appropriately adjusting the differential coefficient to avoid instability caused by excessive adjustment. In this way, the position controller can always maintain optimal control performance under different working conditions.

[0101] The damping controller includes a basic damping controller module and an optimized bacterial foraging algorithm module. The basic damping controller is responsible for the preliminary adjustment of the end effector's speed according to the force feedback signal to reduce the fluctuation of the force. The optimized bacterial foraging algorithm module then simulates the behavior of bacterial foraging to optimize the control parameters. This process includes four steps: chemotaxis, aggregation, reproduction, and elimination and dispersion. In the chemotaxis stage, the algorithm will find the optimal control parameters according to the current force error. The aggregation stage simulates the behavior of bacteria gathering together, and the algorithm explores the better area in the parameter space in this way. The reproduction stage allows the algorithm to copy the well-performing parameter settings, while the elimination and dispersion stage introduces randomness to help the algorithm jump out of the local optimal solution and find the global optimal solution. Through these steps, the optimized bacterial foraging algorithm can evaluate the control effect according to the time domain performance indicators of the force response curve, such as absolute error, rise time, overshoot, and oscillation time, and adjust the proportional coefficient and the differential coefficient accordingly. This biological-inspired optimization algorithm enables the damping controller to achieve precise force control in complex dynamic environments, ensuring the smoothness of the handling process.

[0102] By combining the fuzzy adaptive control and the optimized bacterial foraging algorithm control strategy, 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 abnormality monitoring module for real-time monitoring and processing of abnormal situations during handling; when an abnormal situation is detected, such as workpiece falling, robot failure, etc., the emergency braking program is immediately started to stop the movement of the robot, and an alarm signal is issued, 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 output of the predictive model is 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 predictive control strategy based on machine learning 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 comprises a human-machine interaction module for providing a user interface to enable an operator to set and adjust the handling task parameters, monitor the handling process, and receive system feedback information; the human-machine interaction module supports path planning parameter setting, simulation preview, and real-time monitoring functions, improving the ease of use and operational convenience of the system.

[0111] The human-machine interaction module in the dual-SCARA robot collaborative handling control system provides an intuitive and convenient user interface for the operator, making the setting, adjustment, and monitoring of handling tasks simple and efficient. The operator can easily set the parameters of the handling task, such as speed, clamping force, and path planning, through this module to meet the needs of different workpieces and tasks. At the same time, the real-time monitoring function allows the operator to monitor the robot state and workpiece information at any time during the handling process, ensuring the smooth progress of the task. In addition, the simulation preview function allows the operator to simulate the handling path before actual execution, to discover and optimize potential problems in advance, improving handling efficiency and safety. The system also feeds back real-time information to the operator, including task progress and system status, to make timely adjustments. The integration of these functions greatly improves the ease of use and operational convenience of the system, allowing even operators without professional backgrounds to quickly get started and ensure efficient and stable handling processes.

[0112] The present application proposes a dual-SCARA robot collaborative handling control method and system, which realizes high-precision and high-stability collaborative control of position and force in the handling process by integrating advanced control theory and intelligent algorithms. The system adopts a master-slave collaborative architecture, combining improved PID algorithm and proportional differential control law, to enable the master robot to accurately track the preset trajectory and the slave robot to dynamically adjust the clamping state based on force feedback, effectively solving the motion synchronization and internal force balance problems in multi-robot collaboration. Further introduction of fuzzy adaptive control and optimized bacterial foraging algorithm enhances the system's parameter self-tuning ability and robustness under complex working conditions; combined with machine learning-based predictive control strategy, it can analyze running data in real time, predict abnormalities and intervene in advance, significantly improving job safety and reliability. At the same time, the system integrates an exception monitoring and human-machine interaction module, strengthening real-time monitoring capability and operational convenience. This method combines the advantages of multiple control technologies, not only achieving efficient, compliant, and safe collaborative handling, but also having good environmental adaptability and engineering practicality, providing strong technical support for intelligent manufacturing, and having wide application prospects and promotional value in high-end industrial fields such as electronic manufacturing, semiconductor packaging, and new energy batteries that require high precision and stability.

[0113] Although the preferred embodiments of the application have been described in detail, those skilled in the art will appreciate that various modifications and alterations to those embodiments can be made within the scope of the application. Accordingly, the appended claims are intended to cover all such modifications and alterations as fall within the scope of the application. The foregoing is considered as illustrative only of the principles of the application. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the application to the exact construction and operation shown. Therefore, accordingly, all such variations are intended to be included within the scope of the application as defined in the following claims.

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 the 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 the position error through the position feedback signal to output the force. The second SCARA robot uses a damping control strategy, applying a proportional-derivative control law based on the force error through the force feedback signal to output the speed correction of the end effector relative to the workpiece.

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, characterized in that, 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 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.

4. 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.

5. The control method for cooperative handling by dual SCARA robots according to claim 1 or 3, 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.

6. 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.

7. 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.

8. 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.

9. 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 the workpiece to be transported. 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. 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 the speed correction amount of the end effector relative to the workpiece.

10. The control system for cooperative handling of dual SCARA robots according to claim 9, 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 Set 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, 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.

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