A multi-task robot control method and system

Abstract

This invention relates to the field of robot control technology, and more particularly to a multi-task robot control method and system. The method includes the following steps: obtaining a multi-task execution timing table; selecting the current task according to the multi-task execution timing table; obtaining an electrical contact resistance sequence during electrical contact coupling between the robot's quick-change interface and the tool corresponding to the current task, and determining the tool status identification code; generating a next task prediction deviation vector based on the tool status identification code; and pre-correcting control parameters by setting linear changes in the loading weights of the current and next tool parameters, combined with the next task prediction deviation vector, thereby enabling the robot's end effector to switch between multiple tasks and perform continuous operation. This invention is used to achieve rapid tool change control and continuous task execution, solving the problems of decreased positioning accuracy and prolonged task transition time after frequent tool changes in robots.

Description

Technical Field

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

[0002] Robots need to autonomously change between various tools such as grippers, suction cups, welding guns, grinding heads, and measuring probes using end effectors to perform a range of tasks, including assembly, handling, welding, and inspection. Current robot end effector changing systems generally combine purely mechanical locking with pneumatic / electric drives. This design requires the robot to move to a preset, fixed changing position and come to a complete stop before performing tool changes. The entire switching process, including robot movement, alignment, unlocking the old tool, removing it, aligning it with the new tool, and locking, is cumbersome and time-consuming. This forced "stop-and-go" mode results in significant unproductive waiting time, especially on high-mix production lines requiring frequent tool changes. This cumulative effect significantly lengthens the overall production cycle time, reduces overall equipment efficiency, and prevents robots from achieving high-precision, rapid tool changes and continuous task execution. Summary of the Invention

[0003] Based on this, the present invention provides a multi-task robot control method and system to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a multi-task robot control method includes the following steps:

[0005] Step S1: The robot receives multiple task instructions to be executed, arranges a multi-task priority queue according to the task instructions, and plans the start time and expected execution time of the tasks according to the multi-task priority queue to generate a multi-task execution sequence table.

[0006] Step S2: Select the current task according to the multi-task execution timing table. During the electrical contact coupling between the robot's quick-change interface and the tool corresponding to the current task, obtain the electrical contact resistance sequence. Determine the tool status identification code based on the electrical contact resistance sequence. Generate a spatial offset correction amount based on the tool status identification code and the deviation between the current tool's posture and the historical posture.

[0007] Step S3: Based on the tool type and location correlation between the current task and the next task in the multi-task execution time sequence table, adjust the spatial offset correction amount to the prediction bias vector of the next task.

[0008] Step S4: During tool switching, by setting the linear change of the loading weight of the current tool parameter and the loading weight of the next tool parameter, and combining the prediction deviation vector of the next task to pre-correct the control parameters, the robot's end effector can switch between multiple tasks and operate continuously.

[0009] The present invention also provides a multi-task robot control system that executes the multi-task robot control method described above. The multi-task robot control system includes:

[0010] The multi-task orchestration module is used by the robot to receive multiple task instructions to be executed, to orchestrate a multi-task priority queue according to the task instructions, and to plan the start time and expected execution duration of the tasks according to the multi-task priority queue, and generate a multi-task execution sequence table.

[0011] The tool status analysis module is used to select the current task according to the multi-task execution timing table, obtain the electrical contact resistance sequence during the electrical contact coupling between the robot's quick-change interface and the tool corresponding to the current task; determine the tool status identification code based on the electrical contact resistance sequence; and generate a spatial offset correction amount based on the tool status identification and the deviation between the current tool's posture and the historical posture.

[0012] The inter-task deviation prediction module is used to adjust the spatial offset correction amount into the prediction deviation vector for the next task based on the tool type and location correlation between the current task and the next task in the multi-task execution timeline.

[0013] The smooth switching control module is used to enable the robot's end effector to switch between multiple tasks and perform continuous operation during tool switching by setting the linear change of the loading weight of the current tool parameter and the loading weight of the next tool parameter, and combining the prediction deviation vector of the next task to pre-correct the control parameters.

[0014] The beneficial effects of this invention are:

[0015] On one hand, by acquiring the electrical contact resistance sequence and generating a tool status identification code in real time during the electrical contact coupling between the robot's quick-change interface and the tool, and then comparing the deviation between the current tool posture and the historical reference posture to generate a spatial offset correction, this process, based on the real-time electrical characteristics and posture perception at the moment of tool installation, can dynamically compensate for minor positioning errors introduced by factors such as mechanical wear and thermal expansion and contraction of the quick-change interface due to long-term use. Furthermore, this method integrates tool identification and posture calibration, completing high-precision online calibration at the moment of physical tool connection, completely eliminating the cumbersome process of complete shutdown and time-consuming recalibration required after tool replacement in traditional technologies. This real-time, dynamic compensation mechanism based on the actual installation state ensures high-precision positioning after each tool replacement, fundamentally solving the problem of irreversible accuracy degradation caused by hardware aging in traditional solutions, and significantly improving the quality consistency and reliability of multi-task execution.

[0016] On the other hand, based on a pre-arranged multi-task execution sequence table, this invention innovatively adopts a linear smooth transition strategy during tool switching, weighting the parameters of the current and next tools, and combining this with a predicted deviation vector for the next task generated based on the current offset to proactively and dynamically correct the control parameters. This design transforms the tool switching process from a breakpoint in a mechanical action into a continuous interval for a smooth transition of the control system. By predicting the tool type and positional deviation of the next task, the system can begin adjusting the robot's dynamic model and motion control parameters before the physical switch is completed, achieving "seamless switching" at the control level. This continuous operation mode of "moving, switching, and adjusting simultaneously" replaces the discrete operation process of "stopping first, then switching, and then calibrating" in existing technologies, greatly shortening the non-productive waiting time between tasks, achieving continuous task execution, and significantly improving the robot's overall utilization rate and overall production cycle time. It is particularly suitable for highly mixed production scenarios with stringent cycle time requirements.

[0017] On the other hand, through top-level planning of task priority queues and execution timing tables, this invention enables the robot to predictively arrange its work processes; through real-time sensing of electrical resistance sequences, the robot obtains precise end-effector state "tactile feedback"; and through the smooth transition of predicted deviation vectors and control parameters, the robot achieves "pre-adaptation" to future tasks. This multi-level collaborative control not only solves the problems of decreased accuracy and efficiency bottlenecks caused by tool switching in existing technologies, but also endows the robot with high flexibility and autonomous adaptability in complex and ever-changing production environments. It provides key technical support for achieving truly intelligent manufacturing and flexible production, effectively avoiding production safety hazards and quality risks caused by insufficient switching accuracy and process interruptions. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the steps of the multi-task robot control method of the present invention;

[0019] Figure 2 This is a block diagram of the multi-task robot control system of the present invention;

[0020] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] To achieve the above objectives, please refer to Figures 1 to 2 This invention provides a multi-task robot control method, comprising the following steps:

[0022] Step S1: The robot receives multiple task instructions to be executed, arranges a multi-task priority queue according to the task instructions, and plans the start time and expected execution time of the tasks according to the multi-task priority queue to generate a multi-task execution sequence table.

[0023] In this embodiment of the invention, the industrial robot control system receives multiple task instructions to be executed from the upper-level Manufacturing Execution System (MES) via an industrial Ethernet interface. These instructions are encapsulated in XML format and include information such as task ID, task type (e.g., welding, gripping, inspection), workpiece position coordinates, task priority (urgent, high, medium, low), estimated execution time, and latest completion time. A weighted shortest job first (SJF) algorithm is used to sort the tasks. This algorithm comprehensively considers the commercial value of the tasks (converted from priority and latest completion time) and the scale of the operation (estimated execution time). This time sequence table is loaded into the robot's main controller as the reference timeline for all subsequent actions.

[0024] Step S2: Select the current task according to the multi-task execution timing table. During the electrical contact coupling between the robot's quick-change interface and the tool corresponding to the current task, obtain the electrical contact resistance sequence. Determine the tool status identification code based on the electrical contact resistance sequence. Generate a spatial offset correction amount based on the tool status identification code and the deviation between the current tool's posture and the historical posture.

[0025] In some embodiments of the present invention, after the robot completes a preliminary task, it enters a tool switching phase according to a preset process. Taking switching from one tool (e.g., tool A) to another tool (e.g., tool B) as an example, the robot first returns the current tool to its storage location and then moves to the location of the target tool. The robot's quick-change interface has a number of probe-type electrical contacts with elastic elements arranged in a specific layout (e.g., ring), while the target tool end has a corresponding array of multiple conductive contacts. When the robot flange and the tool interface are physically coupled at a certain stage, these paired electrical contacts begin to make contact.

[0026] During coupling, a high-precision resistance measurement module built into the robot controller synchronously collects contact resistance values ​​through all these channels at a preset sampling frequency, forming an electrical contact resistance value sequence containing a large number of data points. The target tool may have a pre-set identification circuit composed of one or more precision components (such as resistors) with specific resistance values, connected between certain designated contact pairs. By reading the resistance value after stable contact, it is compared with a pre-stored feature library. Upon successful matching, the identity of the currently connected tool is determined, and a corresponding identification code is assigned. The dynamic resistance value sequence of all channels during coupling is compared with a pre-stored historical resistance value sequence representing an "ideal" or reference installation state. The ideal sequence is reference data collected and stored under brand-new, wear-free conditions after perfect alignment was ensured using a precision coordinate measuring machine. The difference vector between the current and historical sequences is input into a Jacobian matrix model pre-established through finite element simulation and experimental calibration. This model maps multidimensional resistance deviations to multi-degree-of-freedom spatial pose deviations.

[0027] Step S3: Based on the tool type and location correlation between the current task and the next task in the multi-task execution time sequence table, adjust the spatial offset correction amount to the prediction bias vector of the next task.

[0028] In this embodiment of the invention, adjustments are made based on the following logic: Mechanical wear has a certain systematicity and continuity; the deviation of one installation can provide a basis for prediction of the next. However, different tools have different weights, centers of gravity, and sizes, and the resulting pose offsets when coupled with the wear-prone quick-change interface are not entirely the same. For example, a heavy welding torch will exacerbate sinking in a certain direction, while a light gripper will have a smaller impact. An internally stored "tool-related state transition matrix M" is obtained through extensive offline experimental learning and describes the transmission relationship of offsets between different tool types. Based on the type of the current tool (welding torch) and the next tool (gripper), the corresponding transition matrix M_welding torch->gripper is selected. The current spatial offset correction is multiplied on the left by this matrix and linearly transformed. The next task prediction deviation vector = M_welding torch->gripper × spatial offset correction; this vector not only considers the wear offset that has already occurred but also makes fine adjustments based on the characteristics of the gripper (for example, predicting that it will have a slight upward tilt in the Z-axis direction due to its lighter weight), thus constituting an accurate prediction of the pose deviation generated during the next gripper installation.

[0029] Step S4: During tool switching, by setting the linear change of the loading weight of the current tool parameter and the loading weight of the next tool parameter, and combining the prediction deviation vector of the next task to pre-correct the control parameters, the robot's end effector can switch between multiple tasks and operate continuously.

[0030] In this embodiment of the invention, when the welding task is about to end, the robot does not stop completely, but instead plans a smooth transition trajectory from the last weld point to the tool exchange station. At the instant the robot begins executing this transition trajectory, the control system initiates a parameter smoothing switching program, dynamically adjusting the parameters applied to the robot's motion control algorithm. These parameters include: the dynamic parameters of the end effector load (mass, center of mass, moment of inertia), the PID control gain of each joint servo motor, and the acceleration (Jerk) limit in trajectory planning. The loading weights of the parameters change linearly with time, enabling the robot to smoothly adapt to changes in load during movement.

[0031] Crucially, when loading the parameters for the next tool (gripper), its standard preset parameters are not used directly. Instead, they are pre-corrected by incorporating the generated next task prediction deviation vector. Specifically, this correction involves: Tool Center Point (TCP) pre-compensation: the standard TCP coordinates of the gripper are superimposed with the predicted deviation vector. This means that when the robot plans its path to grasp the workpiece, its target point has already considered and compensated for the x-axis deviation that occurs after the gripper is installed; and dynamic model correction: the predicted attitude deviation is substituted into the dynamic model, fine-tuning the center of mass position and rotational inertia tensor to make the control torque output more precise. Therefore, the moment the robot reaches the tool rack, completes the physical replacement, and clamps the gripper, its control system has not only completely switched to the gripper's control parameters, but this parameter set is also "tailor-made" for the minor errors generated during this installation. The robot can directly and with high precision execute the grasping task without stopping for recalibration, achieving seamless and continuous operation between tasks.

[0032] Preferably, step S1, which involves arranging a multi-task priority queue according to the task instructions to be executed, includes:

[0033] Extract the tool type identifier, task target location coordinates, and task completion timeout timestamp from the task instructions to be executed;

[0034] Get the current system timestamp, calculate the time difference between the task completion timeout timestamp in each pending task instruction and the current system timestamp, and use it as the remaining time for the task.

[0035] The remaining time of the task is normalized according to its urgency to obtain the time urgency factor.

[0036] Calculate the three-dimensional spatial distance between the target position coordinates and the current robot end effector position coordinates, and denot it as the task space distance; divide the task space distance by the preset maximum workspace radius to obtain the space urgency factor;

[0037] Task priorities are scored based on time urgency factors and spatial urgency factors, and all tasks are sorted from highest to lowest priority score to generate a multi-task priority queue.

[0038] In this embodiment of the invention, for example, the robot control system receives two task instructions to be executed from the upper-level manufacturing execution system. The instruction format is JSON. The parsing program will traverse the received instructions, extract the values ​​of the above key fields, and store them in a temporary task list for subsequent calculations.

[0039] In this embodiment of the invention, the robot control system incorporates a clock module that is precisely synchronized with the server via Network Time Protocol (NTP) to ensure the accuracy of the timestamps. Specifically, the current system timestamp is obtained the instant the task orchestration program is started. Subsequently, the temporary task list is traversed to calculate the remaining time for each task.

[0040] In this embodiment of the invention, in order to unify the remaining time at different scales into a standard evaluation system, an inverse proportional function is used for normalization, so that the shorter the remaining time, the higher the time urgency factor.

[0041] In this embodiment of the invention, the position coordinates of the center of the end effector flange of the robot are obtained in real time by reading the encoder values ​​of each joint of the robot and performing forward kinematics calculation. Then, the Euclidean distance between the current end effector position and the position of each task target is calculated as the task space distance.

[0042] In another implementation of this invention, to normalize the spatial distance, a linear normalization method is used, so that the closer the distance, the higher the spatial urgency factor. The calculation formula is: Spatial urgency factor = 1 - (Task spatial distance / Maximum workspace radius); where "maximum workspace radius" is an inherent design parameter of the robot, obtained from its technical manual.

[0043] In this embodiment of the invention, a weighted summation method is used to comprehensively prioritize each task, balancing the urgency of both time and space dimensions. Specifically, the task priority score calculation model is: Task Priority Score = Time Weight Coefficient × Time Urgency Factor + Space Weight Coefficient × Space Urgency Factor. It should be noted that the time weight coefficient and space weight coefficient are pre-set according to the specific production strategy, and their sum is 1. For example, in scenarios emphasizing on-time delivery, the time weight coefficient will be set higher.

[0044] Of particular importance is the task priority scoring based on time urgency factors and space urgency factors, which includes:

[0045] Multiply the time urgency factor by the preset time weight coefficient, multiply the space urgency factor by the preset space weight coefficient, and add the two together to obtain the comprehensive urgency score.

[0046] Count the number of times each tool type appears in all pending task instructions. For tool types that appear more than a preset continuous use threshold, set the tool switching cost of their corresponding task to a low cost value. For tool types that appear only once, set the tool switching cost of their corresponding task to a high cost value.

[0047] Divide the overall urgency score by the tool switching cost to obtain the task priority score. Sort all tasks according to the task priority score from largest to smallest to generate a multi-task priority queue.

[0048] In this embodiment of the invention, preset time weighting coefficients and spatial weighting coefficients are loaded from a configuration file. These two coefficients are set by production line process engineers based on current production goals (e.g., pursuing maximum output or fastest order response). The formula for calculating the overall urgency score is: Overall Urgency Score = Time Weighting Coefficient × Time Urgency Factor + Spatial Weighting Coefficient × Spatial Urgency Factor.

[0049] It should be noted that the preset "continuous use threshold" is determined based on the ratio of the average time for tool switching to the average time for task execution, aiming to identify task clusters that can significantly save overall time through continuous execution. Based on the statistical results and the threshold, a tool switching cost is assigned to each task. It's important to note that the ratio of high-cost to low-cost values ​​reflects the relative importance of the time loss from a single tool switch relative to the task execution time; this ratio can be adjusted by the user according to actual production line cycle time requirements.

[0050] Specifically, the task priority score calculation model is: Task Priority Score = Overall Urgency Score / Tool Switching Cost. Based on this model, and combining the overall urgency score and tool switching cost calculated in the previous step, the final priority score for each task is calculated. All tasks are then sorted in descending order according to their final calculated task priority scores. The resulting multi-task priority queue not only considers the time and space urgency of the tasks themselves, but also, by reducing the cost of consecutive tasks, tends to group tasks using the same tools closer together in the order, thereby macroscopically optimizing the execution efficiency of the entire task sequence.

[0051] Preferably, in step S2, the current task is selected according to the multi-task execution timing table, and the electrical contact resistance sequence is obtained during the electrical contact coupling between the robot's quick-change interface and the tool corresponding to the current task, including:

[0052] The robot's quick-change interface is equipped with main power contact pairs and signal transmission contact pairs.

[0053] During the electrical contact coupling of the electrical contacts, the resistance values ​​of the main power contact pair and the signal transmission contact pair are measured multiple times at a preset sampling period. The resistance values ​​obtained by the main power contact pair are recorded as the main resistance sequence, and the resistance values ​​obtained by the signal transmission contact pair are recorded as the signal resistance sequence.

[0054] The system detects whether there are step features formed by the resistance values ​​of continuous sampling points within the preset step resistance value range in the main resistance sequence. If they exist, the step flag is set to normal contact state; otherwise, the step flag is set to abnormal contact state.

[0055] The system detects the number of fluctuation points in the resistance sequence where the resistance change during a single sampling interval exceeds a preset fluctuation threshold. When the number of fluctuation points reaches the preset fluctuation threshold, the fluctuation flag is set to an abnormal signal state. When the number of fluctuation points does not reach the preset fluctuation threshold, the fluctuation flag is set to a normal signal state.

[0056] The main resistance sequence and signal resistance sequence are integrated and associated with step identifier and fluctuation identifier to generate an electrical contact resistance sequence.

[0057] In some embodiments of the present invention, the quick-change interface flange of the robot integrates multiple spring-probe type electrical contacts. Specifically, some of these contacts are designed as high-current-carrying contacts, with probes having a relatively large diameter. The contact surface may also be plated with a layer of highly conductive, wear-resistant material, used to provide power to end-effectors (such as welding torches), and therefore can be defined as a power contact group. The remaining contacts have relatively small probe diameters and are mainly used to transmit low-current signals such as tool identification signals and sensor data, and therefore can be defined as a signal transmission contact group.

[0058] In some embodiments of the invention, the robot controller incorporates a high-precision resistance measurement module, such as a microohmmeter or a similar measuring unit. This module is triggered when the robot's quick-change interface begins physical coupling with the tool. The module measures the resistance of the power contact group and one or more designated signal transmission contact groups at a preset sampling frequency within a coupling window.

[0059] In one implementation of this invention, the sequence of resistance values ​​of the collected power contact group is recorded as the main resistance sequence. This sequence exhibits extremely high resistance when the contacts are not in contact, and quickly stabilizes at a lower value range after contact is made. Simultaneously, the sequence of resistance values ​​of a pair of signal transmission contact groups is recorded as the signal resistance sequence, which can be used for functions such as tool identification.

[0060] In this embodiment of the invention, a state machine is used to analyze the main resistance sequence to determine whether the power contacts are making good contact. It should be noted that the "step resistance range" is determined based on the contact material (e.g., beryllium copper plated with gold) and the theoretical contact resistance under rated contact pressure provided by the quick-connect interface manufacturer, combined with a large amount of experimental test data. Specifically, it checks whether there are several consecutive sampling points in the main resistance sequence whose resistance values ​​all fall within this preset range. If a sufficient number of consecutive sampling points meet the condition, it can be determined that a valid step feature has been formed, and a status flag (e.g., a step flag bit) is set to a value representing the normal state. If no step feature meeting the condition is detected, the flag bit is set to a value representing the abnormal state, and an alarm is triggered.

[0061] It is important to note that a preset fluctuation threshold is set by statistically analyzing the signal resistance sequence under healthy connection conditions and based on a multiple of its standard deviation, in order to effectively identify abnormal jumps that exceed the normal noise range.

[0062] In one implementation of this invention, the resistance change at adjacent sampling points in the signal resistance sequence is calculated point by point. Fluctuation points are filtered out using a preset threshold for the amount of change, and the entire sequence is iterated to count the number of all fluctuation points. If the final count of fluctuation points is small and does not reach a preset fluctuation threshold, another status flag (such as a fluctuation flag bit) is set to a value representing a normal signal. If the number of fluctuation points is equal to or exceeds the threshold, it is set to a value representing an abnormal signal.

[0063] In this embodiment of the invention, all collected and processed data are encapsulated into a structured data object, namely the final electrical contact resistance value sequence. The structure of this data object includes four fields: the first field stores the main resistance sequence; the second field stores the signal resistance sequence; the third field stores the step identifier; and the fourth field stores the fluctuation identifier.

[0064] Preferably, determining the tool status identification code based on the electrical contact resistance sequence in step S2 includes:

[0065] Electrical characteristic values ​​are calculated based on the electrical contact resistance sequence to obtain the main resistance characteristic value and the signal resistance characteristic value, respectively.

[0066] The electrical contact characteristic value is generated by weighting the sum of the main resistance characteristics and the sum of the signal resistance characteristics.

[0067] The timer starts when the locking mechanism in the robot receives the locking command and stops when all the position sensors of the locking pins on the robot's quick-change interface return the signal to be in place. The time value of the timer is recorded as the locking response time.

[0068] The preliminary tool condition identification code is determined based on the electrical contact characteristic value and the locking response time; the preliminary tool condition identification code includes three types of condition identification codes: precision, standard, or wear.

[0069] The initial tool status identification code is corrected based on the combined state of the step identifier and the fluctuation identifier in the electrical contact resistance value sequence, and a tool status identification code is generated.

[0070] In this embodiment of the invention, the electrical contact resistance value sequence is preprocessed to remove infinite or high resistance data points at the beginning of the sequence that represent an uncontacted state. Subsequently, the arithmetic mean of the sampling points representing stable contact in the latter part of the electrical contact resistance value sequence is calculated and recorded as the main resistance characteristic sum and the signal resistance characteristic sum, respectively.

[0071] In this embodiment of the invention, to comprehensively evaluate the overall health of the electrical connection, the characteristic values ​​of the main resistance and signal resistance are normalized and weighted. It should be noted that the ideal value and wear threshold used for normalization are obtained through statistical analysis of baseline data acquired by repeatedly inserting and removing brand-new quick-switch interfaces and aging data acquired by testing interfaces that have reached their design lifespan limit. Specifically, the health score of each characteristic value is first calculated using the following model: Health Score = 1 - (Actual Measured Value - Ideal Baseline Value) / (Wear Threshold - Ideal Baseline Value).

[0072] In this embodiment of the invention, the robot's quick-change interface employs a pneumatic locking mechanism, with each locking pin equipped with a Hall effect position sensor. When the programmable logic controller (PLC) module in the robot controller outputs a high-level signal to the solenoid valve to drive the locking action, a high-precision hardware timer with a resolution of 1 microsecond is synchronously started. The controller continuously scans the input signals of the Hall sensors. When the signal states of all sensors change from "not in position" (low level) to "in position" (high level), the timer immediately stops, and this value is stored as the locking response time for this operation.

[0073] In this embodiment of the invention, a two-dimensional lookup table (LUT) is used to comprehensively determine the mechanical and electrical overall condition of the quick-change interface. This lookup table is constructed based on a large amount of full-lifecycle experimental data, dividing the electrical contact characteristic value and locking response time into different intervals. It is important to note that the interval division of this lookup table is based on the following principle: the higher the electrical contact characteristic value and the shorter the locking response time, the closer the interface condition is to brand new.

[0074] In this embodiment of the invention, the correction rule is set as follows: the preliminary tool status identification code is only confirmed as the final tool status identification code when the step identifier is "contact normal state" and the fluctuation identifier is "signal normal state". In any other case, regardless of the preliminary identification result, the final tool status identification code will be forcibly corrected to a specific error code.

[0075] Of particular importance is that the preliminary tool status identification code is determined based on electrical contact characteristic values ​​and locking response time, including:

[0076] The electrical contact characteristic values ​​are compared with a preset electrical contact standard baseline to evaluate the oxidation and contact stability of the electrical interface and generate the electrical interface wear degree.

[0077] The locking response time is compared with a preset mechanical locking standard baseline to evaluate the mating clearance and locking efficiency of the quick-change interface and generate the mechanical interface wear degree.

[0078] The wear degree of electrical interface and mechanical interface is weighted and combined, and compared with the preset precision condition judgment threshold and the preset wear condition judgment threshold to divide the tool condition into three levels: precision, standard or wear, and generate the tool wear condition level.

[0079] A preliminary tool condition identification code is set based on the tool wear level.

[0080] In this embodiment of the invention, a preset electrical contact standard baseline is loaded from a non-volatile memory. This baseline includes two key parameters: an ideal electrical contact characteristic value and an electrical failure characteristic value. It should be noted that these two baseline parameters are obtained by conducting cyclic insertion and removal experiments on a brand-new quick-switch interface, recording the electrical contact characteristic value each time, taking the average of the first 10% of the data as the "ideal electrical contact characteristic value," and taking the average of the last 10% of the data as the "electrical failure characteristic value."

[0081] Specifically, the calculation model for electrical interface wear is a linear mapping function: Electrical interface wear = (ideal electrical contact characteristic value - current electrical contact characteristic value) / (ideal electrical contact characteristic value - electrical failure characteristic value); This formula maps the current measurement value to the range of 0 to 1, and the larger the value, the more severe the wear.

[0082] In this embodiment of the invention, a preset mechanical locking standard baseline is also loaded. This baseline includes two parameters: ideal locking response time and mechanical failure response time. It is important to note that these two baseline parameters are obtained in a similar manner to the electrical baseline. During the entire lifecycle experiment, the locking response time is recorded each time, and the average of the first 10% is taken as the "ideal locking response time," while the average of the last 10% is taken as the "mechanical failure response time." Specifically, the calculation model for mechanical interface wear is also a linear mapping: Mechanical interface wear = (current locking response time - ideal locking response time) / (mechanical failure response time - ideal locking response time).

[0083] Specifically, the status classification rules are as follows: If the overall wear degree is less than or equal to the precision status judgment threshold, the tool wear status level is "precision". If the precision status judgment threshold is less than the overall wear degree and less than the wear status judgment threshold, the tool wear status level is "standard". If the overall wear degree is greater than the wear status judgment threshold, the tool wear status level is "wear".

[0084] Of particular importance is the correction of the initial tool status identification code based on the combined state of the step identifier and fluctuation identifier in the electrical contact resistance value sequence, including:

[0085] The preliminary tool status identification code is corrected based on the combined state of the step identifier and the fluctuation identifier in the electrical contact resistance sequence. When both identifiers are in a normal state, the original preliminary tool status identification code remains unchanged. When only the step identifier is in a contact abnormal state, a first status correction value is added to the preliminary tool status identification code. When only the fluctuation identifier is in a signal abnormal state, a second status correction value is added to the preliminary tool status identification code. When both identifiers are in an abnormal state, a third status correction value is added to the preliminary tool status identification code.

[0086] Based on the revised preliminary tool condition identification code, determine the precision, standard, or wear condition, and generate a tool condition identification code.

[0087] In this embodiment of the invention, a preliminary tool state identification code is finely adjusted based on a logical correction matrix to reflect anomalies in instantaneous contact quality. It should be noted that the "first state correction value," "second state correction value," and "third state correction value" are preset integer constants used to offset the preliminary identification code, thereby generating a new identification code that carries more state information.

[0088] Specifically, the correction values ​​are set as follows, including but not limited to: First state correction value: When the main power supply contact is abnormal (step indicator bit abnormal), it means there is a serious physical contact problem, which has a significant impact on safety and power transmission, so a large correction value is given. Second state correction value: When the signal contact is abnormal (fluctuation indicator bit abnormal), it affects the reliability of data transmission. The problem is relatively minor, but still needs attention, so a medium correction value is given. Third state correction value: When both the main power supply and signal contact are abnormal, it indicates an extremely poor connection status, which is a superposition of the first and second abnormal situations, so it is set to the sum of the first and second state correction values.

[0089] In this embodiment of the invention, an extended state mapping table is maintained. This table maps all corrected code values ​​to the final tool state identification code, thereby transforming code values ​​containing transient fault information into explicit state codes that can be used by upper-layer applications. It is important to note that this mapping table not only includes basic states but also adds sub-states to describe specific anomaly types.

[0090] Preferably, before generating the spatial offset correction amount based on the tool status identification code and the deviation between the current tool's attitude and the historical attitude in step S2, the method further includes:

[0091] When the tool status identification code is in the precision state, set the angle conversion coefficient to the precision state coefficient and set the safety redundancy to zero.

[0092] When the tool status identification code is in the standard state, set the angle conversion coefficient to the standard state coefficient and set the safety redundancy to zero.

[0093] When the tool status identification code indicates a worn state, the angle conversion coefficient is set to the wear state coefficient, and the safety redundancy is set to the preset wear redundancy value. At the same time, a tool maintenance reminder signal is generated.

[0094] The angle conversion coefficient and safety redundancy are correlated to generate a tool status parameter group.

[0095] In this embodiment of the invention, when the received tool status identification code indicates that the current quick-change interface is in a precise state, a set of optimal conversion parameters is loaded from the parameter library. Specifically, the "angle conversion coefficient" used to calculate the attitude deviation is set as the precision state coefficient. It should be noted that the angle conversion coefficient is a key parameter in the spatial offset correction model, defining the proportional relationship between the unit resistance deviation value and the tool attitude angle deviation (e.g., the rotation angle around the X-axis). The precision state coefficient is obtained by performing thousands of high-precision calibration experiments on the quick-change interface, using the least squares method to fit the functional relationship between resistance change and actual attitude change, and finally statistically calculating the average slope, which represents the most ideal conversion efficiency.

[0096] In one implementation of this invention, since the positioning accuracy and repeatability of the interface are extremely high in precision conditions, its attitude deviation is considered highly predictable, and no additional safety protection is required. Therefore, the "safety redundancy" is set to zero, i.e., 0.0 mm.

[0097] In this embodiment of the invention, when the tool status identification code indicates that the interface is in a standard state (i.e., with slight signs of use but stable performance), another set of adapted conversion parameters is loaded. Specifically, the angle conversion coefficient is set to the standard state coefficient. It should be noted that this coefficient is larger than that in the precision state because with slight wear of the interface, the pressure distribution and conductive path at the contact points will change slightly, making the resistance change more sensitive to attitude deviations.

[0098] In another implementation of this invention, under this standard condition, the evaluation concludes that the spatial offset correction calculated by the model is sufficient to compensate for the deviation caused by wear, and the positioning accuracy is still within the tolerance range required by the process. Therefore, the safety redundancy is still set to zero.

[0099] In this embodiment of the invention, when the tool status identification code indicates that the interface has entered a worn state, a more conservative parameter setting is adopted and an early warning is triggered. Specifically, the angle conversion coefficient is set to a higher wear state coefficient. This value reflects that under severe wear conditions, the relationship between resistance and attitude deviation becomes more nonlinear or amplified, requiring a larger coefficient for approximate compensation. Simultaneously, the safety redundancy is set to a preset wear redundancy value. It should be noted that the safety redundancy is a safety compensation distance used in robot path planning. When this value is not zero, when the robot executes any movement commands approaching the workpiece, fixture, or other obstacles, its target point will automatically increase this distance in the outer normal direction to avoid collisions caused by incompletely compensated positioning errors due to wear. The wear redundancy value is set based on the production line's safety risk assessment report, aiming to cover the worst-case positioning accuracy loss. Furthermore, the control system immediately generates a tool maintenance reminder signal, which publishes a message with the subject "MaintenanceAlert" via the OPC UA protocol. The message content is "Quick-change interface #1 has reached the wear threshold; please arrange inspection and maintenance," which can be subscribed to by the upper-level monitoring system or human-machine interface and used to alert the operator.

[0100] In this embodiment of the invention, after completing the above parameter selection based on different states, the currently selected angle conversion coefficient and safety redundancy will be encapsulated into a structured data unit, namely the tool state parameter group.

[0101] Preferably, the step S2, which generates the spatial offset correction amount based on tool state recognition and the deviation between the current tool's attitude and its historical attitude, includes:

[0102] At the moment of stillness after the tool has locked in place, the current roll angle, pitch angle and yaw angle are read by the three-axis attitude sensors on the robot's end effector to form the current attitude triplet.

[0103] Obtain the tool number of the current tool, and then query the roll angle, pitch angle and yaw angle at the last unload time corresponding to the tool number to form a stored attitude triplet;

[0104] The difference between the yaw angle in the current attitude triplet and the yaw angle in the stored attitude triplet is calculated to obtain the cable rotation angle difference.

[0105] Determine if the absolute value of the cable rotation angle difference is less than the cable influence threshold. If it is less, mark the cable influence level as low and set the cable correction base to zero. Otherwise, set the cable correction base to the cable rotation angle difference.

[0106] Multiply the cable correction base by the angle conversion coefficient in the tool status parameter group, and then add the safety redundancy in the tool status parameter group to obtain the total linear offset.

[0107] Based on the cable rotation angle difference, the total linear offset is decomposed into X-direction offset component, Y-direction offset component and Z-direction offset component, and the three components are combined to construct the spatial offset correction.

[0108] It should be added that, based on the current tool, the central axis around the robot's end flange is determined. Based on the projection direction of this central axis in the current tool coordinate system and the sign of the cable rotation angle difference, the three-dimensional offset direction vector is determined. Based on the three-dimensional offset direction vector, the total linear offset is decomposed into X-direction offset component, Y-direction offset component and Z-direction offset component. The three components are combined to construct the spatial offset correction amount.

[0109] In this embodiment of the invention, a microelectromechanical system (MEMS) inertial measurement unit (IMU) of model MPU-6050 is integrated on the quick-change interface flange of the robot. This unit includes a three-axis gyroscope and a three-axis accelerometer. After all the locking pins of the quick-change interface are in place and the robot arm is stationary for a preset time window, the attitude data fused by the Kalman filter algorithm inside the IMU is read through the I2C bus as the current attitude triplet.

[0110] In this embodiment of the invention, it should be noted that a tool history state database is maintained. Whenever a tool is unloaded from the robot, at the instant before it separates from the robot flange, the tool's attitude triplet is recorded and stored in the database along with the tool number and timestamp.

[0111] In this embodiment of the invention, the focus is on the change in yaw angle, as it directly reflects the degree of torsion of the power cable or air hose connected to the tool due to repeated insertion and removal of the tool. Specifically, the cable rotation angle difference is calculated as follows: Cable rotation angle difference = Current yaw angle - Stored yaw angle.

[0112] In this embodiment of the invention, a "cable influence threshold" is set to distinguish between normal angular fluctuations and significant angular changes requiring compensation caused by cable torsional stress. It should be noted that this threshold is determined experimentally, specifically by repeatedly installing the same tool without significant cable torsion and statistically analyzing the yaw angle change over a period of three times the standard deviation.

[0113] In this embodiment of the invention, the calculation model for the total linear offset is: total linear offset = cable correction base × angle conversion coefficient + safety redundancy; the total linear offset represents the equivalent total linear offset distance generated on the quick-change interface plane.

[0114] In this embodiment of the invention, the position and orientation of the cable bundle relative to the central axis of the robot end flange are determined based on the CAD model data of the current tool.

[0115] In one implementation of this invention, the final offset direction is determined by combining the sign of the cable rotation angle difference. Clockwise twisting generates a "tightening" force on the cable bundle, perpendicular to the cable's lead-out direction and pointing towards the flange center. A three-dimensional offset direction vector is obtained through vector cross product calculation. Finally, the total linear offset is decomposed according to this direction vector to construct the spatial offset correction for this installation. This vector will be used to correct the robot's tool coordinate system.

[0116] Preferably, step S3 includes the following steps:

[0117] Step S31: Extract the tool type identifiers of the current task node and the next task node from the multi-task execution sequence table, and determine whether they are the same. If they are the same, set the tool continuation coefficient to a high continuation value; if they are different, set the tool continuation coefficient to a low continuation value.

[0118] Step S32: Extract the tool storage location coordinates of the current task node and the task target location coordinates of the next task node from the multi-task execution time sequence table, calculate the distance between them in space, set the spatial correlation coefficient to a high correlation value when the distance is less than the preset neighboring task threshold, and set the spatial correlation coefficient to a low correlation value when the distance is greater than or equal to the preset neighboring task threshold.

[0119] Step S33: Multiply the tool continuation coefficient by the spatial correlation coefficient to obtain the inter-task deviation correlation coefficient;

[0120] Step S34: Multiply the X-direction offset component, Y-direction offset component and Z-direction offset component in the spatial offset correction by the inter-task deviation correlation coefficient to generate the prediction deviation vector for the next task.

[0121] In this embodiment of the invention, after the current task is completed, the core information of the current task node and the immediately following next task node is immediately read from the loaded multi-task execution sequence table. The tool type identifier of the current task and the tool type identifier of the next task T2 are extracted. The two identifiers are compared to determine if a different type of tool needs to be used. It should be noted that the high continuation value and low continuation value are preset floating-point numbers between 0 and 1, used to quantify the reference value of the currently measured deviation for the next installation. These two values ​​are obtained through statistical regression analysis of a large amount of historical task switching data.

[0122] In one implementation of this invention, because the tool types for the current task and the next task are different, the weight, center of gravity, and cable constraints of the tool will change, resulting in poor continuity of deviation. Therefore, the tool continuity factor is set to a low continuity value. If the tool types are determined to be the same, it means that the deviation mainly comes from the wear and tear of the quick-change interface itself, and the continuity is strong. In this case, the tool continuity factor is set to a high continuity value.

[0123] In this embodiment of the invention, the coordinates of the current welding torch tool's location on the tool holder, in millimeters, are extracted. Simultaneously, the coordinates of the target working position for the next task are extracted. The Euclidean distance between these two three-dimensional coordinate points is then calculated. It should be noted that the preset "proximity task threshold" is set based on the robot's workspace size and typical workflow, used to determine whether the robot is in a similar posture and load area during two task executions.

[0124] In one implementation of this invention, after the robot completes tool switching, its arm span posture does not change significantly. Therefore, systematic deviations caused by gravity, joint clearances, etc., are highly correlated. Accordingly, the spatial correlation coefficient is set to a high correlation value; otherwise, it will be set to a low correlation value. The high correlation value is greater than the low correlation value and can be manually adjusted.

[0125] In this embodiment of the invention, the two dimensions of tool continuity and spatial correlation are combined to generate a final coefficient that comprehensively reflects the correlation strength between the two installation deviations. The formula for calculating the inter-task deviation correlation coefficient is: Inter-task deviation correlation coefficient = Tool continuity coefficient × Spatial correlation coefficient.

[0126] In this embodiment of the invention, assuming the spatial offset correction calculated for the current welding torch installation, the translation components (the first three terms) of this vector are multiplied by the inter-task deviation correlation coefficient. The calculation of the next task prediction deviation vector is as follows: Predicted X-direction offset = X-direction offset component × inter-task deviation correlation coefficient. Predicted Y-direction offset = Y-direction offset component × inter-task deviation correlation coefficient. Predicted Z-direction offset = Z-direction offset component × inter-task deviation correlation coefficient. These three calculated prediction offset components are combined to generate the next task prediction deviation vector, which will be used to pre-correct the control parameters of the next tool (clamp).

[0127] Preferably, step S4 includes the following steps:

[0128] Step S41: During the execution of the current task by the current tool, monitor the completion progress of the current task in real time. When the completion progress reaches the preset trigger progress, trigger the parameter preparation process of the next tool.

[0129] Step S42: Obtain the tool number required for the next task from the multi-task execution timing table, start the tool status identification code generation process, spatial offset correction amount generation process and next task prediction deviation vector generation process corresponding to the tool number, and write the generated next task prediction deviation vector into the next tool parameter buffer in the controller memory.

[0130] Step S43: Divide the tool switching process from the end of the current tool task to the start of the next tool task into six consecutive time segments, and set the duration of each time segment according to the switching action type;

[0131] Step S44: In the first time segment, maintain the loading state of the current tool's control parameters in the robot control system, and the loading weight of the current tool parameters is the full loading weight;

[0132] Step S45: In the second to fourth time segments, perform the actions of lifting, moving to the tool library, unlocking and putting back the current tool, and simultaneously reduce the loading weight of the current tool parameters linearly from the full loading weight to the half loading weight;

[0133] Step S46: In the fifth time segment, the robot's end effector begins to move to the next tool storage position, reads the next task prediction deviation vector from the next tool parameter buffer, and begins to mix and load it into the robot control system with half-load weights;

[0134] Step S47: In the sixth time segment, the robot's end effector locks onto the next tool, reduces the current tool parameter load weight to zero, and increases the control parameter load weight of the next tool to full load weight, thus completing the complete switch of control parameters.

[0135] In this embodiment of the invention, the robot performs a welding task and updates the task completion progress in real time by monitoring the weld completion signal fed back by the welding controller. It should be noted that the preset "trigger progress" is a percentage value, which is set to ensure that the calculation time of the parameter preparation process is less than the remaining execution time of the task, so as to avoid the robot waiting. This value is determined by analyzing the average time of the parameter preparation process and the total task duration in historical data.

[0136] In this embodiment of the invention, after triggering the parameter preparation process, the tool required for the next task is first queried from the multi-task execution timing table, which indicates that the tool is a gripper. Specifically, the installation process for the tool gripper is then simulated and executed in the background, i.e., the complete calculation logic of steps S2 and S3 is called and executed. This simulation process utilizes the latest status information of the current quick-change interface (e.g., the latest locking response time, wear trends learned from the current welding torch installation, etc.) and combines it with the historical data of the gripper to finally generate a prediction deviation vector for the next task for the next gripper installation. This vector is immediately written into the next tool parameter buffer in the robot controller's memory, waiting to be called during the physical switchover process.

[0137] In this embodiment of the invention, the entire tool switching time is finely divided into six time segments of unequal length according to the physical actions the robot needs to perform. For example, the division and duration of the time segments are set as follows, and these durations are optimized values ​​obtained from robot kinematic simulation and actual measurement: First time segment (task completion); Second time segment (lifting); Third time segment (moving to storage location); Fourth time segment (placing the old tool); Fifth time segment (moving to the new tool and picking it up); Sixth time segment (locking and task initiation).

[0138] In this embodiment of the invention, after the last weld point of the welding task is completed, the robot performs a finishing action, such as safely lifting the welding torch a short distance from the workpiece surface. During this period, the control system fully utilizes the dynamic parameters and PID gain of the welding torch, with a load weight of 1.0 (i.e., full load weight), to ensure the accuracy and stability of the action.

[0139] In this embodiment of the invention, the robot performs a series of actions to unload the welding torch. During this process, the loading weight W_current(t) of the current tool (welding torch) parameters smoothly decreases from 1.0 to 0.5 according to a linear function. The calculation formula is: W_current(t) = 1.0 - 0.5 × (t - 0.5) / 3.0, where t is the total time during the switching process. When the welding torch is returned to the tool holder, its parameter weight has exactly decreased to 0.5.

[0140] In this embodiment of the invention, the robot moves unloaded to the storage location of the gripper and picks it up. At the beginning of this time segment, the system reads the next task prediction deviation vector from the memory address buffer. Simultaneously, the control parameters of the next tool (gripper) begin to be loaded, with its loading weight W_next(t) increasing linearly from 0. It should be noted that the control system is in a hybrid loading state at this time, with a total weight of 1.0 (0.5 from the attenuation weight of the current tool and 0.5 from the growth weight of the next tool). Furthermore, the loaded next tool parameters have already been pre-stacked with the predicted deviation vector, so that the robot's actions when approaching and picking up the new tool have already taken into account potential installation errors.

[0141] In this embodiment of the invention, during the robot's grasping and locking action on the gripper, the parameter loading weight of the current tool (welding torch) rapidly and linearly decreases from 0.5 to 0, while the parameter loading weight of the next tool (gripper) linearly increases from 0.5 to 1.0 (full loading weight).

[0142] In one implementation of this invention, the control parameters, corrected for predicted deviations, are fully and smoothly loaded into the robot control system the instant the gripper locks in. The robot can immediately begin performing the grasping task without any pauses or recalibration, thus achieving efficient and continuous task transition.

[0143] Preferably, during the fifth time segment, as the robot's end effector begins to move to the next tool storage location, the process further includes:

[0144] Calculate the three-dimensional straight-line distance between the current robot's end effector and the next tool storage location, and denot it as the switching path distance;

[0145] Determine whether the switching path distance is greater than the preset space margin threshold. If it is greater, set the space margin coefficient to a sufficient margin value. If it is less than or equal to the space margin coefficient, set it to a limited margin value.

[0146] The path correction coefficient is calculated based on the switching path distance and space margin coefficient, and the switching speed limit coefficient is set based on the path correction coefficient.

[0147] The switching speed limit coefficient is sent to the robot motion planning module to adjust the upper limit of movement speed during tool switching.

[0148] In this embodiment of the invention, at the beginning of the fifth time segment, that is, the instant the robot puts the old tool (welding torch) back, the spatial coordinates of the current end effector flange center are obtained through forward kinematics calculation. Simultaneously, the storage location coordinates of the next tool (gripper) are retrieved from the tool database. Specifically, the Euclidean distance between these two points is then calculated as the switching path distance.

[0149] In this embodiment of the invention, a "space margin threshold" is set to determine whether the robot has sufficient space to accelerate and decelerate during this unloaded movement. It should be noted that this threshold is determined based on the robot's maximum acceleration performance and safe stopping distance requirements, aiming to avoid overshoot or vibration caused by excessive speed during short-distance movement.

[0150] In one implementation of this invention, if the movement is determined to be a short-distance, spatially restricted switch, and the switch path distance is spatially restricted, the space margin coefficient is set to a restricted margin value. If the switch path distance has ample space, the space margin coefficient is set to a sufficient margin value.

[0151] In this embodiment of the invention, the path correction coefficient is calculated as follows: Path correction coefficient = (switching path distance / maximum switching distance) × space margin coefficient; where "maximum switching distance" is the distance between the two farthest tool positions in the tool library, which is a fixed system parameter. Next, based on the path correction coefficient, a nonlinear mapping function is used to set the final switching speed limit coefficient, which is designed to impose stricter speed limits on short-distance movements.

[0152] In one implementation of this invention, the mathematical expression of the mapping function is: switching speed limit coefficient = 1 - e^(-k × path correction coefficient), where k is a gain constant used to adjust the sensitivity of the speed limit, which is obtained through experimental tuning, and e is the base of the natural logarithm.

[0153] In this embodiment of the invention, before the robot begins to move to the next tool position, the calculated switching speed limit coefficient is sent to its underlying motion planning module. Specifically, when generating the motion trajectory of the fifth time segment (e.g., a PTP point-to-point motion), the motion planning module reads its default maximum speed parameter and multiplies it by the received switching speed limit coefficient. Therefore, the actual upper limit of the switching speed is dynamically adjusted, and the robot will perform movement according to this dynamically limited upper limit, thereby ensuring stability and safety during short-distance switching in confined space and effectively suppressing mechanical vibration.

[0154] The present invention also provides a multi-task robot control system 100, which executes the multi-task robot control method described above. The multi-task robot control system includes:

[0155] The multi-task orchestration module 101 is used for the robot to receive multiple task instructions to be executed, to orchestrate a multi-task priority queue according to the task instructions to be executed, to plan the start time and expected execution duration of the tasks according to the multi-task priority queue, and to generate a multi-task execution sequence table.

[0156] The tool status analysis module 102 is used to select the current task according to the multi-task execution timing table, obtain the electrical contact resistance sequence during the electrical contact coupling between the robot's quick-change interface and the tool corresponding to the current task; determine the tool status identification code according to the electrical contact resistance sequence; and generate a spatial offset correction amount according to the tool status identification code and the deviation between the current tool's posture and the historical posture.

[0157] The inter-task deviation prediction module 103 is used to adjust the spatial offset correction amount into the prediction deviation vector for the next task based on the tool type and location correlation between the current task and the next task in the multi-task execution time sequence table.

[0158] The smooth switching control module 104 is used to achieve the switching and continuous operation of the robot's end effector between multiple tasks by setting the linear change of the loading weight of the current tool parameter and the loading weight of the next tool parameter during tool switching, and combining the prediction deviation vector of the next task to pre-correct the control parameters.

[0159] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0160] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A multi-task robot control method, characterized in that, Includes the following steps: Step S1: The robot receives multiple task instructions to be executed; arranges a multi-task priority queue according to the task instructions to be executed; plans the start time and expected execution time of the tasks according to the multi-task priority queue, and generates a multi-task execution sequence table; Step S2: Select the current task according to the multi-task execution timing table. During the electrical contact coupling between the robot's quick-change interface and the tool corresponding to the current task, obtain the electrical contact resistance sequence. Determine the tool status identification code based on the electrical contact resistance sequence. Generate a spatial offset correction amount based on the tool status identification code and the deviation between the current tool's posture and the historical posture. Step S3: Based on the tool type and location correlation between the current task and the next task in the multi-task execution time sequence table, adjust the spatial offset correction amount to the prediction deviation vector of the next task; Step S4: During tool switching, by setting the linear change of the loading weight of the current tool parameter and the loading weight of the next tool parameter, and combining the prediction deviation vector of the next task to pre-correct the control parameters, the robot's end effector can switch between multiple tasks and perform continuous operation. Step S3 includes the following steps: Step S31: Extract the tool type identifiers of the current task node and the next task node from the multi-task execution sequence table, and determine whether they are the same. If they are the same, set the tool continuation coefficient to a high continuation value; if they are different, set the tool continuation coefficient to a low continuation value. Step S32: Extract the tool storage location coordinates of the current task node and the task target location coordinates of the next task node from the multi-task execution time sequence table, calculate the distance between them in space, set the spatial correlation coefficient to a high correlation value when the distance is less than the preset neighboring task threshold, and set the spatial correlation coefficient to a low correlation value when the distance is greater than or equal to the preset neighboring task threshold. Step S33: Multiply the tool continuation coefficient by the spatial correlation coefficient to obtain the inter-task deviation correlation coefficient; Step S34: Multiply the X-direction offset component, Y-direction offset component and Z-direction offset component in the spatial offset correction by the inter-task deviation correlation coefficient to generate the prediction deviation vector for the next task. Step S4 includes the following steps: Step S41: During the execution of the current task by the current tool, monitor the completion progress of the current task in real time. When the completion progress reaches the preset trigger progress, trigger the parameter preparation process of the next tool. Step S42: Obtain the tool number required for the next task from the multi-task execution timing table, start the tool status identification code generation process, spatial offset correction amount generation process and next task prediction deviation vector generation process corresponding to the tool number, and write the generated next task prediction deviation vector into the next tool parameter buffer in the controller memory. Step S43: Divide the tool switching process from the end of the current tool task to the start of the next tool task into six consecutive time segments, and set the duration of each time segment according to the switching action type; Step S44: In the first time segment, maintain the loading state of the current tool's control parameters in the robot control system, and the loading weight of the current tool parameters is the full loading weight; Step S45: In the second to fourth time segments, perform the actions of lifting, moving to the tool library, unlocking and putting back the current tool, and simultaneously reduce the loading weight of the current tool parameters linearly from the full loading weight to the half loading weight; Step S46: In the fifth time segment, the robot's end effector begins to move to the next tool storage position, reads the next task prediction deviation vector from the next tool parameter buffer, and begins to mix and load it into the robot control system with half-load weights; Step S47: In the sixth time segment, the robot's end effector locks onto the next tool, reduces the current tool parameter load weight to zero, and increases the control parameter load weight of the next tool to full load weight, thus completing the complete switch of control parameters.

2. The multi-task robot control method according to claim 1, characterized in that, Step S1, which involves arranging a multi-task priority queue based on the instructions to be executed, includes: Extract the tool type identifier, task target location coordinates, and task completion timeout timestamp from the task instructions to be executed; Get the current system timestamp, calculate the time difference between the task completion timeout timestamp in each pending task instruction and the current system timestamp, and use it as the remaining time for the task. The remaining time of the task is normalized according to its urgency to obtain the time urgency factor. Calculate the three-dimensional spatial distance between the target position coordinates and the current robot end effector position coordinates, and denot it as the task space distance; divide the task space distance by the preset maximum workspace radius to obtain the space urgency factor; Task priorities are scored based on time urgency factors and spatial urgency factors, and all tasks are sorted from highest to lowest priority score to generate a multi-task priority queue.

3. The multi-task robot control method according to claim 1, characterized in that, In step S2, the current task is selected according to the multi-task execution timing table. During the electrical contact coupling between the robot's quick-change interface and the tool corresponding to the current task, the electrical contact resistance sequence is obtained, including: The robot's quick-change interface is equipped with main power contact pairs and signal transmission contact pairs. During the electrical contact coupling of the electrical contacts, the resistance values ​​of the main power contact pair and the signal transmission contact pair are measured multiple times at a preset sampling period. The resistance values ​​obtained by the main power contact pair are recorded as the main resistance sequence, and the resistance values ​​obtained by the signal transmission contact pair are recorded as the signal resistance sequence. The system detects whether there are step features formed by the resistance values ​​of continuous sampling points within the preset step resistance value range in the main resistance sequence. If they exist, the step flag is set to normal contact state; otherwise, the step flag is set to abnormal contact state. The system detects the number of fluctuation points in the resistance sequence where the resistance change during a single sampling interval exceeds a preset fluctuation threshold. When the number of fluctuation points reaches the preset fluctuation threshold, the fluctuation flag is set to an abnormal signal state. When the number of fluctuation points does not reach the preset fluctuation threshold, the fluctuation flag is set to a normal signal state. The main resistance sequence and signal resistance sequence are integrated and associated with step identifier and fluctuation identifier to generate an electrical contact resistance sequence.

4. The multi-task robot control method according to claim 1, characterized in that, Step S2, which determines the tool status identification code based on the electrical contact resistance sequence, includes: Electrical characteristic values ​​are calculated based on the electrical contact resistance sequence to obtain the main resistance characteristic value and the signal resistance characteristic value, respectively. The electrical contact characteristic value is generated by weighting the sum of the main resistance characteristics and the sum of the signal resistance characteristics. The timer starts when the locking mechanism in the robot receives the locking command and stops when all the position sensors of the locking pins on the robot's quick-change interface return the signal to be in place. The time value of the timer is recorded as the locking response time. The preliminary tool condition identification code is determined based on the electrical contact characteristic value and the locking response time; the preliminary tool condition identification code includes three types of condition identification codes: precision, standard, or wear. The initial tool status identification code is corrected based on the combined state of the step identifier and the fluctuation identifier in the electrical contact resistance value sequence, and a tool status identification code is generated.

5. The multi-task robot control method according to claim 1, characterized in that, Before generating the spatial offset correction amount based on the tool status identification code and the deviation between the current tool attitude and the historical attitude in step S2, the following steps are also included: When the tool status identification code is in the precision state, set the angle conversion coefficient to the precision state coefficient and set the safety redundancy to zero. When the tool status identification code is in the standard state, set the angle conversion coefficient to the standard state coefficient and set the safety redundancy to zero. When the tool status identification code indicates a worn state, the angle conversion coefficient is set to the wear state coefficient, and the safety redundancy is set to the preset wear redundancy value. At the same time, a tool maintenance reminder signal is generated. The angle conversion coefficient and safety redundancy are correlated to generate a tool status parameter group.

6. The multi-task robot control method according to claim 1, characterized in that, Step S2, which generates the spatial offset correction based on the tool status identification code and the deviation between the current tool attitude and the historical attitude, includes: At the moment of stillness after the tool has locked in place, the current roll angle, pitch angle and yaw angle are read by the three-axis attitude sensors on the robot's end effector to form the current attitude triplet. Obtain the tool number of the current tool, and then query the roll angle, pitch angle and yaw angle at the last unload time corresponding to the tool number to form a stored attitude triplet; The difference between the yaw angle in the current attitude triplet and the yaw angle in the stored attitude triplet is calculated to obtain the cable rotation angle difference. Determine if the absolute value of the cable rotation angle difference is less than the cable influence threshold. If it is less, mark the cable influence level as low and set the cable correction base to zero. Otherwise, set the cable correction base to the cable rotation angle difference. Multiply the cable correction base by the angle conversion coefficient in the tool status parameter group, and then add the safety redundancy in the tool status parameter group to obtain the total linear offset. Based on the cable rotation angle difference, the total linear offset is decomposed into X-direction offset component, Y-direction offset component and Z-direction offset component, and the three components are combined to construct the spatial offset correction.

7. The multi-task robot control method according to claim 1, characterized in that, In the fifth time segment, as the robot's end effector begins to move to the next tool storage location, the following also occurs: Calculate the three-dimensional straight-line distance between the current robot's end effector and the next tool storage location, and denot it as the switching path distance; Determine whether the switching path distance is greater than the preset space margin threshold. If it is greater, set the space margin coefficient to a sufficient margin value. If it is less than or equal to the space margin coefficient, set it to a limited margin value. The path correction coefficient is calculated based on the switching path distance and space margin coefficient, and the switching speed limit coefficient is set based on the path correction coefficient. The switching speed limit coefficient is sent to the robot motion planning module to adjust the upper limit of movement speed during tool switching.

8. A multi-task robot control system, characterized in that, For performing the multi-task robot control method as described in claim 1, the multi-task robot control system includes: The multi-task orchestration module is used for the robot to receive multiple task instructions to be executed; to orchestrate a multi-task priority queue according to the task instructions to be executed; and to plan the start time and expected execution duration of the tasks according to the multi-task priority queue, and generate a multi-task execution sequence table. The tool status analysis module is used to select the current task according to the multi-task execution timing table, obtain the electrical contact resistance sequence during the electrical contact coupling between the robot's quick-change interface and the tool corresponding to the current task; determine the tool status identification code based on the electrical contact resistance sequence; and generate a spatial offset correction amount based on the tool status identification code and the deviation between the current tool's posture and the historical posture. The inter-task deviation prediction module is used to adjust the spatial offset correction amount into the prediction deviation vector of the next task based on the tool type and position correlation between the current task and the next task in the multi-task execution time sequence table. The smooth switching control module is used to enable the robot's end effector to switch between multiple tasks and perform continuous operation during tool switching by setting the linear change of the loading weight of the current tool parameter and the loading weight of the next tool parameter, and combining the prediction deviation vector of the next task to pre-correct the control parameters.

Images