A trajectory planning method and system for a continuous synchronous mode of a robot arm

Abstract

The application provides a trajectory planning method and system of a mechanical arm continuous synchronization mode, relates to the technical field of mechanical arms, and collects operation related data of a mechanical arm system; a digital twin model is used to simulate the movement process of the mechanical arm along a preset movement trajectory, so as to generate a risk prediction result of the mechanical arm in the continuous synchronization mode; the preset movement trajectory is divided into multiple trajectory segments, and each trajectory segment is optimized and integrated based on the risk prediction result and the movement characteristic parameters of the mechanical arm, so as to form a preliminary operation trajectory; operation state data is analyzed, so as to generate data anomaly information; each mechanical arm in the mechanical arm system is controlled to move according to the preliminary operation trajectory, and real-time operation data of each mechanical arm is collected; the preliminary operation trajectory is adjusted in combination with the data anomaly information and the risk prediction result, so as to obtain a target operation trajectory of the mechanical arm, and safe, efficient and self-adaptive target trajectory operation of the mechanical arm in the continuous synchronization mode is realized.

Description

Technical Field

[0001] This application relates to the field of robotic arm technology, and in particular to a trajectory planning method and system for a continuous synchronous mode of a robotic arm. Background Technology

[0002] In automated automotive stamping production lines, robotic arms need to work in coordination with high-speed press slides to complete continuous loading and unloading tasks. This scenario requires the robotic arms to achieve high-precision and high-safety synchronous movement within a fixed cycle time. It is necessary to avoid spatial interference with the press slides while ensuring that the overall operating efficiency is not affected, which places stringent technical requirements on the real-time performance, safety, and dynamic adaptability of trajectory planning.

[0003] Existing solutions typically employ offline simulation-based pre-defined trajectory generation methods. These methods pre-calculate obstacle avoidance paths by constructing a motion model of the equipment and execute them in a fixed sequence during actual operation. However, this approach struggles to handle dynamic deviations caused by equipment wear, load variations, or fine-tuning of the cycle time. If the actual operating conditions deviate from the simulation, the pre-defined trajectory cannot be corrected in time, potentially leading to interference risks or cycle time instability. Furthermore, the lack of online identification and response capabilities for abnormal data in multi-arm collaborative motion limits the system's robustness and adaptability in complex, continuous operations. Summary of the Invention

[0004] The purpose of this application is to provide a trajectory planning method and system for a continuous synchronous mode of a robotic arm, so as to solve the problems of interference risk and cycle instability caused by the inability to respond online due to dynamic deviation and abnormal data in the prior art.

[0005] To address the aforementioned technical problems, in a first aspect, this application provides a trajectory planning method for a continuous synchronization mode of a robotic arm, comprising:

[0006] Collect operation-related data of the robotic arm system, including the relative angle data between the robotic arm and the press slide, the motion constraint parameters of the robotic arm, and the operation status data under a fixed production cycle.

[0007] The movement process of the robotic arm along a preset motion trajectory is simulated by a digital twin model to generate a risk prediction result of the robotic arm in continuous synchronous mode. The risk prediction result includes at least the predicted interference area between the robotic arm and the press slider.

[0008] The preset motion trajectory is divided into multiple trajectory segments by trajectory optimization technology. Based on the risk prediction results and the motion characteristic parameters of the robotic arm, each trajectory segment is optimized and integrated to form a preliminary running trajectory. The motion characteristic parameters are used to indicate: acceleration and avoidance should be performed for trajectory segments that pass through the predicted interference area, while the fixed production cycle should be maintained for other trajectory segments.

[0009] The operational status data is analyzed to generate data anomaly information;

[0010] The robotic arm system uses multi-axis control technology and distributed control technology to control each robotic arm to move according to the initial running trajectory. Real-time running data of each robotic arm during synchronous operation is collected. Combined with the abnormal data information and risk prediction results, the initial running trajectory is adjusted to obtain the target running trajectory of the robotic arm in continuous synchronous mode.

[0011] Optionally, the movement of the robotic arm along a preset trajectory is simulated using a digital twin model to generate risk prediction results for the robotic arm in a continuous synchronous mode, including:

[0012] The movement process of the robotic arm along the preset motion trajectory is simulated by a digital twin model, and the motion data of each robotic arm and its relative position information with the press slider are obtained during the movement.

[0013] Based on the relative position information and the relative angle data, motion conflict information is generated, which includes one or more predicted interference regions composed of risk location points;

[0014] The motion data is compared with the motion constraint parameters to obtain the comparison result;

[0015] Based on the motion conflict information and comparison results, abnormal motion phases that may overlap or do not conform to motion constraint parameters are identified. All abnormal motion phases are integrated to form the risk prediction result of the robotic arm in continuous synchronous mode.

[0016] Optionally, motion conflict information is generated based on the relative position information and the relative angle data, including:

[0017] Based on the external dimensions of each robotic arm and press slide, set the minimum safe distance and safe angle range between the robotic arm and the press slide;

[0018] Based on the actual distance and actual angle between the robotic arm and the press slider at each moment in the relative position information and the relative angle data, the target moment when the actual distance is less than the minimum safe distance or the actual angle exceeds the safe angle range, and the corresponding position information of the target robotic arm and the target press slider are identified.

[0019] The location information corresponding to each target at any given time is clustered in space to form one or more predictive interference regions that characterize the spatial range of collision risk.

[0020] By integrating the target time points, a risk period with collision risk is obtained. Combined with the predicted interference region, motion conflict information is formed.

[0021] Optionally, the preset motion trajectory is divided into multiple trajectory segments using trajectory optimization technology. Based on the risk prediction results and the motion characteristic parameters of the robotic arm, each trajectory segment is optimized and integrated to form a preliminary running trajectory, including:

[0022] The preset motion trajectory is divided into multiple trajectory segments, and priority is assigned to each trajectory segment according to the operation accuracy requirements;

[0023] Based on the risk prediction results, the target risk type is determined, and a first trajectory segment containing the target risk type and a second trajectory segment not containing the target risk type are identified; wherein, if the target risk type is predicted interference with the press slide, the corresponding first trajectory segment is marked as the trajectory segment that needs to be accelerated to avoid it;

[0024] Based on the target risk type corresponding to the first trajectory segment, the motion characteristic parameters of the robotic arm, and the load characteristic parameters of each robotic arm, determine the optimization direction and attitude adjustment parameters of the first trajectory segment.

[0025] Based on the optimization direction and attitude adjustment parameters of each first trajectory segment, the motion parameters of the first trajectory segments with different priorities are adapted and adjusted to obtain the third trajectory segment;

[0026] According to the preset motion trajectory, all third trajectory segments and second trajectory segments are spliced ​​and smoothed to form a preliminary running trajectory.

[0027] Optionally, the optimization direction of the first trajectory segment includes a first optimization direction and a second optimization direction;

[0028] Based on the target risk type corresponding to the first trajectory segment, the motion characteristic parameters of the robotic arm, and the load characteristic parameters of each robotic arm, the optimization direction and attitude adjustment parameters of the first trajectory segment are determined, including:

[0029] For the first trajectory segment with the target risk type of motion conflict anomaly, based on the motion characteristic parameters of the robotic arm, the working space range, and the load characteristic parameters of each robotic arm, the motion path nodes of each first trajectory segment that have the risk of collision with the press slider are analyzed to determine the first optimization direction. For the first trajectory segment with the target risk type of constraint over-limit anomaly, the reasons why the motion parameters of each first trajectory segment exceed the motion constraint parameters are analyzed to determine the second optimization direction.

[0030] Based on the first and second optimization directions, and combined with the motion characteristic parameters of the robotic arm, the attitude adjustment range of the end effector of the robotic arm and the coordinated motion range of each associated robotic arm are determined.

[0031] Based on the load characteristic parameters of each robotic arm, the attitude adjustment range and cooperative motion range are verified and corrected to generate attitude adjustment parameters for each first trajectory segment.

[0032] Optionally, multi-axis control technology and distributed control technology are used to control each robotic arm in the robotic arm system to move according to the initial running trajectory, and real-time running data of each robotic arm during synchronous operation is collected. Combined with the data anomaly information and risk prediction results, the initial running trajectory is adjusted to obtain the target running trajectory of the robotic arm in continuous synchronous mode, including:

[0033] The initial running trajectory is parsed into motion control commands corresponding to each robotic arm. Multi-axis control technology and distributed control technology are used to control each robotic arm to run synchronously according to the motion control commands, and real-time running data of each robotic arm during synchronous operation is collected.

[0034] Extract abnormal real-time data that matches the data anomaly information and abnormal stage information that matches the risk prediction results from the real-time operating data.

[0035] Based on the abnormal real-time data and the abnormal stage information, the preliminary running trajectory is adjusted to generate a candidate running trajectory;

[0036] Based on the risk prediction results and the motion constraint parameters, the candidate running trajectories are verified, and the candidate running trajectories that pass the verification are determined as the target running trajectory of the robotic arm in continuous synchronization mode.

[0037] Optionally, each robotic arm is controlled to operate synchronously according to the motion control commands using multi-axis control technology and distributed control technology, including:

[0038] A communication network between a multi-axis control center and controllers at various locations is constructed using distributed control technology. The communication addresses and data transmission protocols of the controllers at various locations are configured in the communication network to form a target communication link.

[0039] Based on the target communication link, the motion control command and the synchronization clock signal are sent to the corresponding part controller to calibrate the time reference of each part controller and obtain the calibrated time reference;

[0040] Based on the calibrated time reference, each robotic arm is controlled to operate synchronously according to the motion control commands.

[0041] Secondly, this application provides a trajectory planning system for a robotic arm in a continuous synchronous mode, comprising:

[0042] The data acquisition module is used to collect operation-related data of the robotic arm system. The operation-related data includes the relative angle data between the robotic arm and the press slide, the motion constraint parameters of the robotic arm, and the operation status data under a fixed production cycle.

[0043] The simulation module is used to simulate the movement process of the robotic arm along a preset motion trajectory through a digital twin model, so as to generate the risk prediction result of the robotic arm in continuous synchronization mode. The risk prediction result includes at least the predicted interference area between the robotic arm and the press slider.

[0044] The integration module is used to divide the preset motion trajectory into multiple trajectory segments through trajectory optimization technology, and optimize and integrate each trajectory segment based on the risk prediction results and the motion characteristic parameters of the robotic arm to form a preliminary running trajectory. The motion characteristic parameters are used to indicate: acceleration and avoidance should be performed for trajectory segments that pass through the predicted interference area, while other trajectory segments should maintain the fixed production cycle.

[0045] The analysis module is used to analyze the operating status data to generate data anomaly information;

[0046] The adjustment module is used to control each robotic arm in the robotic arm system to move according to the initial running trajectory through multi-axis control technology and distributed control technology, and to collect real-time running data of each robotic arm during synchronous operation. Combining the data anomaly information and risk prediction results, the module adjusts the initial running trajectory to obtain the target running trajectory of the robotic arm in continuous synchronous mode.

[0047] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement the steps of a trajectory planning method for a continuous synchronous mode of a robotic arm as described in the first aspect above.

[0048] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements the steps of a trajectory planning method for a continuous synchronous mode of a robotic arm as described in the first aspect above.

[0049] This application provides a trajectory planning method for a continuous synchronous mode of a robotic arm. By collecting multi-dimensional operational data between the robotic arm system and the press slider, a digital twin simulation environment that closely resembles actual working conditions is constructed, thereby identifying potential interference areas in advance. Based on this, the preset trajectory is segmented and optimized according to risk characteristics. An acceleration and avoidance strategy is implemented for high-risk sections while maintaining the cycle stability of other sections, ensuring both operational safety and production efficiency. Through anomaly analysis of operational status data and combined with multi-axis collaboration and distributed control architecture, feedback information is collected in real time during the synchronous execution of the robotic arm, and the initial trajectory is dynamically corrected. Finally, a target operating trajectory that can adapt to on-site disturbances and balance safety and cycle consistency is generated, improving the responsiveness and operational robustness in continuous synchronous operations.

[0050] Furthermore, by integrating the motion data of the robotic arm with the relative position and angle information of the press slider, the system accurately identifies spatial areas that may cause conflicts and determines whether there are any illegal motion phases by combining motion constraints, thereby systematically integrating comprehensive risk prediction results. This enhances the ability to predict potential interference behaviors in complex collaborative scenarios, so that trajectory planning no longer relies on static ideal assumptions, but is based on real-time modeling and evaluation of dynamic interaction relationships. This provides a reliable basis for subsequent trajectory optimization and online adjustment, overcoming the limitations of traditional offline solutions in terms of lack of foresight and adaptability when facing changes in working conditions. Attached Figure Description

[0051] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1 A flowchart illustrating a trajectory planning method for a continuous synchronization mode of a robotic arm, provided in an embodiment of this application;

[0053] Figure 2 A schematic diagram illustrating a specific implementation of a trajectory planning method for a continuous synchronization mode of a robotic arm, provided in an embodiment of this application;

[0054] Figure 3 This is a schematic diagram of the structure of a trajectory planning system for a continuous synchronous mode of a robotic arm, provided in an embodiment of this application. Detailed Implementation

[0055] To address the shortcomings of existing offline trajectory planning methods in terms of real-time response to dynamic disturbances such as equipment wear, load fluctuations, or cycle time adjustments, and the difficulty in balancing safety obstacle avoidance and cycle time stability, this application provides a trajectory planning method for a continuous synchronous mode of a robotic arm. The core idea is as follows: First, by collecting data on the relative angle, motion constraints, and operating status of the robotic arm and the press slider, a digital twin simulation environment reflecting real-world working conditions is established to identify potential interference risk areas in advance. Then, a segmented trajectory optimization strategy is introduced to implement targeted acceleration and avoidance in high-risk sections while ensuring the overall production cycle time. Simultaneously, a multi-axis collaborative and distributed control architecture is integrated to continuously acquire real-time data during synchronous operation, and the initial trajectory is corrected online based on anomaly information, thus forming a continuous synchronous trajectory planning mechanism that can dynamically adapt to changes in the field and coordinate safety and efficiency.

[0056] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0057] The core of this application is to provide a trajectory planning method for a continuous synchronous mode of a robotic arm, and a flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes:

[0058] Step 101: Collect operation-related data of the robotic arm system. The operation-related data includes the relative angle data between the robotic arm and the press slide, the motion constraint parameters of the robotic arm, and the operation status data under a fixed production cycle.

[0059] In this step, the relative angle data refers to the angle data of the robot arm and the press slide in space. This data includes the angle data between the robot arm end effector and the working surface of the press slide, the angle data between the robot arm boom and the movement direction of the press slide, etc.

[0060] Motion constraint parameters refer to various parameters that restrict the motion state of a robotic arm. These parameters include the maximum rotation angle of each joint of the robotic arm, the maximum motion speed, the maximum acceleration, and the upper limit of the load weight.

[0061] Fixed production cycle time can be understood as the fixed time set by the production line for the robotic arm to complete a single complete work cycle in order to meet the predetermined capacity requirements. It is determined based on the overall production plan and process coordination requirements of the production line.

[0062] In this embodiment, firstly, angle sensors and position sensors deployed on each joint of the robotic arm and the press slide are used to detect and acquire the spatial attitude information of the robotic arm and the press slide at different times in real time. Based on this information, the relative angle data between the robotic arm and the press slide is calculated. By consulting the manufacturer's technical parameter document of the robotic arm and the safety operation specifications of the production line, the motion constraint parameters of the robotic arm are extracted and determined. By continuously recording the start and stop time, speed change data of each motion stage, load change data, etc. of the robotic arm in multiple cycles of completing the preset work process, the operating status data under a fixed production cycle is statistically obtained.

[0063] Step 102: Simulate the movement process of the robotic arm along a preset motion trajectory using a digital twin model to generate a risk prediction result for the robotic arm in continuous synchronous mode. The risk prediction result includes at least the predicted interference area between the robotic arm and the press slider.

[0064] In this step, the preset motion trajectory refers to the complete motion path of the robotic arm based on the production line operation process, process coordination requirements and preset capacity planning. The motion trajectory includes the starting position, working position, path turning points, motion direction and preset motion parameters for each segment of the path.

[0065] Risk prediction results refer to the dataset formed after integrating motion conflict information and parameter comparison results. It includes at least the predicted interference area between the robotic arm and the press slider, and also covers abnormal motion stages and corresponding risk types.

[0066] The predicted interference area refers to the spatial range defined by spatially clustering risk points that are close to each other, and is used to identify the specific area where there is a risk of collision between the robotic arm and the press slider during the movement of the robotic arm.

[0067] In the embodiments of this application, such as Figure 2 As shown, step 102 specifically includes the following steps:

[0068] Step 201: Simulate the movement process of the robotic arm along the preset motion trajectory using a digital twin model, and obtain the motion data of each robotic arm during the movement process, as well as the relative position information with the press slider.

[0069] In this step, the motion data of the robotic arm refers to the collection of parameters such as the rotation angle, rotation speed, motion acceleration, and end effector position coordinates of each joint of the robotic arm at each moment during the simulated motion.

[0070] Relative position information refers to the spatial coordinate difference between various parts of the robotic arm and the press slider at different times during the simulation process. This information includes distance parameters in the horizontal, vertical and longitudinal directions.

[0071] It should be noted that this embodiment does not limit the structure and simulation process of the digital twin model, and can be set accordingly according to the actual situation.

[0072] In this embodiment, the preset motion trajectory is first imported into the constructed digital twin model to drive the robotic arm in the digital twin model to reproduce the entire motion process in the real operation scenario, and to record motion data such as rotation angle, speed, and acceleration of each joint of each robotic arm in real time. At the same time, the spatial positioning module built into the digital twin model is used to calculate and obtain the spatial coordinate difference between the robotic arm end, joints and other key parts and the press slider at each moment, forming relative position information.

[0073] Step 202: Generate motion conflict information based on the relative position information and the relative angle data. The motion conflict information includes one or more predicted interference regions composed of risk location points.

[0074] In this step, motion conflict information refers to the dataset formed by integrating risk periods, predicted interference areas, and corresponding target robotic arm and target press slider information, which is used to comprehensively reflect the collision risk between the robotic arm and the press slider.

[0075] Risk location points refer to the spatial location points of key parts of the robotic arm and the press slider at the target moment when the actual distance is less than the minimum safe distance or the actual angle exceeds the safe angle range. These are the basic units that constitute the predicted interference area.

[0076] In this embodiment of the application, 202 specifically includes the following steps:

[0077] Step 211: Based on the external dimensions of each robotic arm and press slide, set the minimum safe distance and safe angle range between the robotic arm and the press slide.

[0078] In this step, the minimum safe distance refers to a distance threshold set based on the external dimensions of the robotic arm and the press slider, after reserving sufficient safety margin. It is used to determine whether there is a risk of collision between the two. This embodiment does not limit the numerical design of the minimum safe distance, and it can be set according to the actual situation.

[0079] The safe angle range refers to the angular interval between the robotic arm and the press slide that will not cause attitude interference, based on the movement direction of the press slide and the working posture of the robotic arm.

[0080] In this embodiment, the external dimensions of each robotic arm body, end effector, and press slider are first measured and obtained to clarify the contour boundaries and limit dimensions of each component. Then, based on the motion characteristics of the robotic arm and the operating characteristics of the press, a safety margin for collision prevention is reserved, and the minimum safe distance is calculated and determined in combination with the component contour boundary data. At the same time, based on the vertical motion direction of the press slider and the operating posture range of the robotic arm, a safe angle range in which no posture interference will occur between the robotic arm and the press slider is defined.

[0081] Step 212: Based on the relative position information and the actual distance and actual angle between the robotic arm and the press slider at each moment in the relative angle data, identify the target moment when the actual distance is less than the minimum safe distance or the actual angle exceeds the safe angle range, and the corresponding position information of the target robotic arm and the target press slider.

[0082] In this step, the actual distance refers to the real spatial distance between the key parts of the robotic arm and the press slider at each moment, extracted from the relative position information.

[0083] The actual angle refers to the true angle of orientation between the robotic arm and the press slide at each moment, extracted from the relative angle data.

[0084] The target moment refers to the moment when the actual distance is less than the minimum safe distance or the actual angle exceeds the safe angle range; it is a time point where there is a risk of collision.

[0085] In this embodiment, the actual distance between the key parts of the robotic arm and the press slider at each moment is first extracted from the relative position information. Then, the actual angle between the two at each moment is extracted from the relative angle data. Next, the actual distance is compared with the minimum safe distance one by one, and the actual angle is compared with the safe angle range one by one. All moments when the actual distance is less than the minimum safe distance or the actual angle exceeds the safe angle range are selected. These moments are marked as target moments, and the spatial position information of the target robotic arm and the target press slider at the corresponding target moment is recorded simultaneously.

[0086] Step 213: Cluster the location information corresponding to each target at each time in space to form one or more predictive interference regions that characterize the spatial range of collision risk.

[0087] In this step, the collision risk spatial range refers to the specific spatial area that can characterize the possible collision between the robotic arm and the press slider, after spatially clustering the risk points that are close to each other.

[0088] In this embodiment, the position information of the target robotic arm and the target press slider corresponding to all target times is first organized into a set of spatial coordinates. Then, according to the distribution characteristics of the spatial coordinates, the risk location points with similar positions are grouped into one category by the nearest clustering method. The spatial range corresponding to each category of location points is delineated to clarify the spatial coverage area of ​​each type of risk location point. Finally, one or more predictive interference regions that can characterize the spatial range of collision risk are formed, thereby clearly locking the specific spatial location where there is a collision risk.

[0089] Step 214: Integrate the target time to obtain the risk period with collision risk, and combine it with the predicted interference area to form motion conflict information.

[0090] In this step, the risk period refers to the time interval obtained by merging consecutive or similar target times, which is the time range during the movement of the robotic arm where there is a risk of collision.

[0091] In this embodiment, all target moments are first arranged in chronological order, and target moments that are consecutive or have an interval less than a preset threshold are merged to obtain multiple risk periods with collision risk. Then, each risk period is associated with and bound to the corresponding predicted interference area, target robotic arm and target press slider information to form complete motion conflict information. This information comprehensively reflects the time range, spatial location and equipment involved in the collision risk.

[0092] Step 203: Compare the motion data with the motion constraint parameters to obtain the comparison result.

[0093] In this step, the comparison result refers to the detailed record formed after comparing the motion data of the robotic arm with the motion constraint parameters one by one. The result includes the judgment information on whether each parameter in the motion data meets the requirements of the motion constraint parameters, and the specific value of the parameter exceeding the limit when there is a parameter exceeding the limit.

[0094] In this embodiment, the motion data of the robotic arm is first compared with the motion constraint parameters item by item. Specifically, the joint rotation angle is compared with the maximum rotation angle, the joint rotation speed is compared with the maximum motion speed, the motion acceleration is compared with the maximum acceleration, and the load data is compared with the upper limit of the load weight. This is to record in detail whether each piece of motion data meets the motion constraint parameters, and to mark the specific out-of-limit values ​​of parameters that exceed the range of motion constraint parameters, forming a complete comparison result including compliant and non-compliant items.

[0095] Step 204: Based on the motion conflict information and comparison results, identify abnormal motion phases that may overlap or do not meet the motion constraint parameters, integrate all abnormal motion phases, and form the risk prediction result of the robotic arm in continuous synchronization mode.

[0096] In this step, the abnormal motion stage refers to the motion stage during which there is a risk of collision or the motion parameters exceed the motion constraint parameters. The abnormal motion stage includes independent stages with only a single risk and overlapping stages with both types of risks.

[0097] In this embodiment, the motion phases corresponding to the risk periods in the motion conflict information are first overlaid with the motion phases corresponding to parameter exceedances in the comparison results to identify overlapping abnormal phases that have both collision risks and parameter exceedances, as well as independent abnormal phases that have only collision risks or only parameter exceedances. Then, all abnormal motion phases are organized in chronological order and associated with the corresponding predicted interference areas, parameter exceedance types, and risk levels to form a complete risk prediction result. This provides a comprehensive and accurate risk reference for subsequent trajectory optimization, ensuring that the optimization work addresses various risks in a targeted manner.

[0098] The risk prediction results generated in this application provide a comprehensive and accurate risk basis for subsequent trajectory optimization, avoid the blindness of subsequent trajectory planning, improve the safety and rationality of trajectory planning, and ensure the stability of continuous synchronous operation of the robotic arm.

[0099] For example, in the collaborative operation scenario of the stamping process on production line A, in order to achieve continuous synchronous operation of multiple robotic arms and press model B, a digital twin of the production line is first constructed. The pre-planned operation path of robotic arm grasping-transferring-placement is imported into the digital twin model as a preset motion trajectory. The robotic arms in the model are driven to move along the preset trajectory, and motion data such as the rotation angle and speed of each robotic arm joint are collected in real time, as well as the spatial coordinate difference between the robotic arm end and the press slider, to obtain relative position information.

[0100] Next, the external dimensions of the robotic arm end effector and the press slide were measured, and a minimum safe distance was calculated with a 5cm safety margin. The safe angle range of 30°-150° was determined based on the vertical movement direction of the press slide. Then, the actual distance and angle at each moment were extracted, compared, and the target moments where the actual distance was less than the minimum safe distance and the actual angle exceeded the 30°-150° range were selected. The position information of the corresponding robotic arm and press slide was recorded.

[0101] Next, spatial clustering was performed on risk locations with similar positions to delineate two predicted interference regions. Then, consecutive target times were merged to obtain two risk time periods. These were then associated with the interference regions to form motion conflict information. Subsequently, the motion data was compared with motion constraint parameters such as the maximum rotation angle of the robotic arm joints (180°) and the maximum speed (5 rad / s), yielding a comparison result including two instances of joint speed exceeding limits. Finally, the risk time periods of motion conflict and the motion stages with parameter exceeding limits were overlaid and analyzed, identifying one overlapping abnormal stage and two independent abnormal stages. All abnormal stages, their corresponding interference regions, and exceedance types were integrated to form a complete risk prediction result.

[0102] Step 103: Divide the preset motion trajectory into multiple trajectory segments using trajectory optimization technology, and optimize and integrate each trajectory segment based on the risk prediction results and the motion characteristic parameters of the robotic arm to form a preliminary running trajectory. The motion characteristic parameters are used to indicate: accelerate and avoid trajectory segments that pass through the predicted interference area, while maintaining the fixed production rhythm for other trajectory segments.

[0103] In this step, a trajectory segment refers to several small segments of the preset motion trajectory after being divided according to the operation stage, path inflection point, or risk situation. Each trajectory segment includes an independent direction of motion, speed range, and operation target.

[0104] The motion characteristic parameters of a robotic arm refer to the core parameters that characterize the robotic arm's motion capabilities. These parameters include the response speed of each joint, acceleration performance, and attitude adjustment sensitivity.

[0105] The initial operating trajectory refers to the transitional motion trajectory formed after optimizing, splicing, and smoothing each trajectory segment. It has avoided major risks and maintained the production rhythm.

[0106] In this embodiment of the application, step 103 specifically includes the following steps:

[0107] Step 301: Divide the preset motion trajectory into multiple trajectory segments and assign priority to each trajectory segment according to the operation accuracy requirements.

[0108] In this step, the accuracy requirement refers to the specific standards for position and attitude accuracy of the task corresponding to each trajectory segment. The higher the accuracy requirement, the higher the priority of the trajectory segment.

[0109] Priority refers to the optimization order of trajectory segments based on the accuracy requirements of the operation. High-priority trajectory segments prioritize accuracy, while low-priority trajectory segments can be adapted and adjusted within the allowable range of the cycle time.

[0110] In this embodiment, the overall trajectory is first divided into multiple trajectory segments according to the operation stage and path inflection point of the preset motion trajectory, and each trajectory segment corresponds to a single operation action. Then, according to the production line operation standard, priority is assigned to each trajectory segment. For example, one allocation method is to assign the highest priority to the gripping segment and the placement segment because the workpiece needs to be accurately positioned, and assign medium priority to the trajectory segments in the risk-free area of ​​the transfer segment, so as to ensure that the core operation accuracy is prioritized during optimization.

[0111] Step 302: Based on the risk prediction results, determine the target risk type and identify the first trajectory segment where the target risk type exists and the second trajectory segment where the target risk type does not exist; wherein, if the target risk type is the predicted interference with the press slide, the corresponding first trajectory segment is marked as the trajectory segment that needs to be accelerated to avoid.

[0112] In this step, the target risk type refers to the core risk category extracted from the risk prediction results, which includes two categories: abnormal motion conflict with the press slide and abnormal motion parameter constraint exceeding the limit.

[0113] The first trajectory segment refers to the trajectory segment with a target risk type. If the risk is a predicted intervention, it is marked as a trajectory segment that needs to be accelerated to avoid. The second trajectory segment refers to the trajectory segment without any target risk type and does not require special avoidance treatment.

[0114] In this embodiment, the risk type corresponding to the abnormal motion stage is first extracted from the risk prediction results, and the target risk type is determined to be abnormal motion conflict or abnormal constraint exceedance. Then, each trajectory segment is matched with the time and space range of the abnormal motion stage, and the first trajectory segment in the abnormal stage and the second trajectory segment without risk are identified. Next, the first trajectory segment with the target risk type of predicted interference is clearly marked as the trajectory segment that needs to be accelerated to avoid, so as to make a mark for subsequent targeted optimization.

[0115] Step 303: Determine the optimization direction and attitude adjustment parameters of the first trajectory segment based on the target risk type corresponding to the first trajectory segment, the motion characteristic parameters of the robotic arm, and the load characteristic parameters of each robotic arm.

[0116] In this step, the load characteristic parameters refer to the parameters that characterize the load capacity of the robotic arm. These parameters include the upper limit of load weight, load inertia coefficient, and motion limits under different loads.

[0117] Optimize the adjustment criteria for the risk type setting of the directional pointer for the first trajectory segment, and clarify the adjustment logic of speed and attitude.

[0118] The attitude adjustment parameters refer to the angle and position adjustment values ​​of the end effector and each joint of the robotic arm.

[0119] In this embodiment of the application, step 303 specifically includes the following steps:

[0120] Step 311: For the first trajectory segment with the target risk type of motion conflict anomaly, based on the motion characteristic parameters of the robotic arm, the working space range, and the load characteristic parameters of each robotic arm, analyze the motion path nodes of each first trajectory segment that have a collision risk with the press slider to determine the first optimization direction. For the first trajectory segment with the target risk type of constraint over-limit anomaly, analyze the reasons why the motion parameters of each first trajectory segment exceed the motion constraint parameters to determine the second optimization direction.

[0121] In this step, the motion path node refers to the key location point on the first trajectory segment. This motion path node includes the point closest to the press slider, trajectory inflection point, etc., and is the core point of collision risk.

[0122] The first optimization direction refers to the optimization criteria set for abnormal motion conflicts, which is to maximize the motion speed to shorten the passage time while avoiding collision risks, under the premise of satisfying load constraints.

[0123] The second optimization direction refers to the optimization criteria set for constraint over-limit anomalies, that is, adjusting the motion parameters so that the trajectory segment meets the requirements of the motion constraint parameters.

[0124] In this embodiment, firstly, for the first trajectory segment with abnormal motion conflict, based on the robot arm's motion characteristic parameters, workspace boundary, and load characteristic parameters, the motion path node closest to the press slider on the trajectory segment is located. The first optimization direction is determined as follows: maximizing the motion speed and shortening the time to pass through the predicted interference area, while deviating from the collision path node, without exceeding the load-bearing limit and workspace range. Then, for the first trajectory segment with abnormal constraint exceedance, the reasons for parameter exceedance are analyzed, and the second optimization direction is determined as follows: lowering the corresponding motion parameters to ensure that the trajectory segment conforms to the motion constraint parameters, while trying to get as close as possible to the fixed production cycle.

[0125] Step 312: Based on the first and second optimization directions, and combined with the motion characteristic parameters of the robotic arm, determine the attitude adjustment range of the end effector of the robotic arm and the coordinated motion range of each associated robotic arm.

[0126] In this step, the attitude adjustment range of the end effector refers to the range of angles and positions that the end effector can adjust to achieve the optimized direction, ensuring that the attitude adapts to the avoidance requirements without affecting the operation.

[0127] The coordinated motion range of associated robotic arms refers to the adjustable motion range of other robotic arms when multiple robotic arms are working synchronously, in order to coordinate with the first trajectory segment of the target and avoid coordination interference.

[0128] In this embodiment, firstly, based on the first optimization direction and combined with the motion characteristic parameters of the robotic arm, the attitude adjustment range of the end effector is determined to ensure that the end effector attitude is stable during acceleration, the workpiece does not fall off, and it does not deviate from the predicted interference area. At the same time, the cooperative motion range of each associated robotic arm is defined, and its motion boundary is limited to avoid new cooperative collisions caused by the target robotic arm accelerating to avoid it. Finally, based on the second optimization direction, the attitude adjustment range of the end effector is correspondingly reduced to control the associated robotic arms to maintain the basic cooperative attitude and ensure motion stability.

[0129] Step 313: Based on the load characteristic parameters of each robotic arm, verify and correct the attitude adjustment range and cooperative motion range, and generate attitude adjustment parameters for each first trajectory segment.

[0130] In this embodiment, the determined attitude adjustment range and cooperative motion range are first verified based on the load characteristic parameters of each robotic arm: if the load inertia corresponding to a certain attitude adjustment angle exceeds the robotic arm's load-bearing limit, the adjustment range in that direction is reduced; if the associated robotic arm's cooperative range conflicts with the load motion trajectory, the cooperative boundary is corrected. Based on the verified range, the end effector attitude angle, joint rotation angle, and motion sequence corresponding to each first trajectory segment are calculated to generate accurate attitude adjustment parameters.

[0131] Step 304: Based on the optimization direction and attitude adjustment parameters of each first trajectory segment, the motion parameters of the first trajectory segments with different priorities are adapted and adjusted to obtain the third trajectory segment.

[0132] In this step, motion parameters refer to core parameters such as velocity, acceleration, and motion timing of the trajectory segment, while adaptation and adjustment refer to the process of balancing accuracy, velocity, and rhythm by combining priority and optimization direction.

[0133] The third trajectory segment refers to the first trajectory segment that, after adjustment of motion parameters, meets the requirements of optimization direction, load constraints, and priority, and has eliminated the corresponding risks.

[0134] In this embodiment, the motion parameters of the first trajectory segments are first adjusted according to their priority, prioritizing those with higher priority. One specific adjustment method is as follows: for acceleration and avoidance-type first trajectory segments, the parameters are adjusted based on the first optimization direction and attitude to increase motion speed, determine acceleration timing, and ensure attitude accuracy. For constraint-exceeding first trajectory segments, the motion parameters are lowered based on the second optimization direction to ensure compliance with constraints. For low-priority first trajectory segments, while adapting to the optimization direction, the motion timing of higher-priority trajectory segments is adapted as much as possible to avoid conflicts, ultimately resulting in the adjusted third trajectory segment.

[0135] Step 305: According to the preset motion trajectory, splice and smooth all the third trajectory segments and the second trajectory segments to form a preliminary running trajectory.

[0136] In this embodiment, all third and second trajectory segments are first sequentially spliced ​​together according to the original order of the preset motion trajectory. A smooth transition algorithm is used to correct the trajectory inflection points at the splicing points, adjusting the speed and attitude change rate at these points to avoid impact on the robotic arm's movement. The second trajectory segment maintains smooth motion synchronized with the fixed production cycle in the optimized direction, requiring no additional speed adjustment; it only ensures smooth connection with adjacent third trajectory segments, ultimately forming a preliminary operating trajectory that avoids risks, adapts to the cycle time, and ensures stable motion.

[0137] The embodiments of this application achieve accelerated avoidance of interference areas and correction of constraint over-limits, while ensuring the smoothness of motion and operational accuracy of the second trajectory segment. It can avoid collision and parameter over-limit risks, and take into account both fixed production rhythm and motion stability, laying a reliable foundation for subsequent real-time trajectory adjustment.

[0138] Step 104: Analyze the operating status data to generate data anomaly information.

[0139] In this step, the data anomaly information refers to the set of abnormal data that exceeds the normal fluctuation range and does not meet the operation standards after analyzing the operating status data under a fixed production cycle. This information includes the abnormal parameter type, abnormal characteristics, occurrence time and corresponding threshold.

[0140] In this embodiment, the operating status data under a fixed production cycle is first preprocessed by removing interference data collected by sensors using a denoising algorithm. Then, the data is standardized to unify the data format and units, resulting in processed data. Subsequently, the normal fluctuation range and anomaly judgment threshold for each operating parameter are set by combining the motion constraint parameters of the robotic arm, the fixed production cycle standard, and historical normal operation data. Next, the processed data is compared with the set thresholds one by one to filter out abnormal data that exceeds the normal range, and the abnormal parameter type, occurrence time, and specific value are labeled. Finally, the abnormal data is classified and organized according to type and occurrence time to form complete data anomaly information.

[0141] Step 105: Using multi-axis control technology and distributed control technology, control each robotic arm in the robotic arm system to move according to the preliminary running trajectory, and collect real-time running data of each robotic arm during synchronous operation. Combine the abnormal data information and risk prediction results, adjust the preliminary running trajectory to obtain the target running trajectory of the robotic arm in continuous synchronous mode.

[0142] In this step, real-time running data refers to the motion parameters of each joint, load data, relative position / angle data with the press slider, and equipment operating status data collected in real time when the robotic arm is actually running synchronously according to the initial running trajectory. It is used to reflect the actual running situation and is different from the motion data in the simulation stage.

[0143] The target trajectory refers to the continuous synchronous movement trajectory of the robotic arm that is finally determined after real-time data verification, adjustment and risk verification. It can be directly used for actual production line operations, taking into account safety, cycle time and stability.

[0144] In this embodiment of the application, step 105 specifically includes the following steps:

[0145] Step 501: The preliminary running trajectory is parsed into motion control commands corresponding to each robotic arm. Multi-axis control technology and distributed control technology are used to control each robotic arm to run synchronously according to the motion control commands, and real-time running data of each robotic arm during synchronous operation is collected.

[0146] In this step, motion control commands refer to the commands that the initial running trajectory is broken down into individual commands that the robotic arm can recognize and execute. These commands include joint rotation angles, speeds, accelerations, motion sequences, and coordinated action commands.

[0147] In this embodiment, the preliminary running trajectory is first analyzed. Based on the task assignment of each robotic arm, the trajectory parameters are broken down into motion control commands for each joint of the robotic arm, clarifying the motion angle, speed, timing, and coordination logic of each joint. Then, multi-axis control technology and distributed control technology are used to control each robotic arm to run synchronously according to the motion control commands, and real-time running data of each robotic arm during synchronous operation is collected. Specifically, "controlling each robotic arm to run synchronously according to the motion control commands using multi-axis control technology and distributed control technology" can include the following steps:

[0148] Step 511: Construct a communication network between the multi-axis control center and the controllers of each part using distributed control technology, and configure the communication address and data transmission protocol of each controller in the communication network to form a target communication link.

[0149] In this step, the explanation of distributed control technology can be found in relevant technologies, and will not be repeated here; the multi-axis control center refers to the core control unit that coordinates the movement of each robotic arm and joint axis, and is responsible for issuing commands and summarizing data.

[0150] The controllers for each part refer to the local controllers of each joint of the robotic arm or the entire robotic arm, which are responsible for executing control commands and feeding back data.

[0151] The target communication link refers to the dedicated communication channel that, once configured, allows for the stable transmission of commands and data between the multi-axis control center and the controllers at various locations, ensuring real-time and accurate transmission.

[0152] In this embodiment, a communication network is first built using distributed control technology. The network has a multi-axis control center as the core node and the joint controllers and overall controllers of each robotic arm as terminal nodes. A unique communication address is assigned to each terminal node in the network, and a real-time data transmission protocol adapted to the industrial scenario is configured to avoid data transmission delay or loss. Finally, a stable target communication link is formed to provide a channel for command issuance and data feedback.

[0153] Step 512: Based on the target communication link, send the motion control command and synchronization clock signal to the corresponding part controller to calibrate the time reference of each part controller and obtain the calibrated time reference.

[0154] In this step, the synchronization clock signal refers to the unified time reference signal issued by the multi-axis control center, which is used to unify the time dimension of the controllers in each part and ensure synchronous operation.

[0155] The calibrated time reference refers to the unified time standard formed by adjusting the time reference of each controller to be consistent with the multi-axis control center after receiving the synchronization clock signal.

[0156] In this embodiment, the multi-axis control center of the target communication link first sends motion control commands and synchronization clock signals to all part controllers simultaneously; after each part controller receives the signal, it corrects its own time deviation based on the synchronization clock signal, so that the time reference of all controllers is completely consistent, and obtains the calibrated time reference, ensuring that the timing of each robotic arm's movements is synchronized.

[0157] Step 513: Based on the calibrated time reference, control each robotic arm to run synchronously according to the motion control command.

[0158] In this step, synchronous operation means that each robotic arm and joint axis moves simultaneously according to the timing requirements of the motion control command based on a unified time reference, ensuring the coordinated operation of multiple robotic arms and the press slide.

[0159] In this embodiment, firstly, the controllers of each part accurately trigger the execution of motion control commands based on the calibrated time reference, controlling the corresponding joints to move at a set angle and speed; then, the multi-axis control center monitors the action status of each part in real time through the target communication link, dynamically fine-tunes the micro-time deviations, and ensures that the multiple robotic arms move synchronously according to the initial running trajectory, avoiding interference caused by time misalignment.

[0160] Step 502: Extract abnormal real-time data that matches the data anomaly information and abnormal stage information that matches the risk prediction result from the real-time operating data.

[0161] In this step, abnormal real-time data refers to data in the real-time operation data that matches the abnormal data information, reflecting the abnormal parameters in actual operation.

[0162] Abnormal phase information refers to the time and space information of abnormal movement phases in the corresponding risk prediction results in real-time operation data, reflecting the actual operation status of the risk area.

[0163] In this embodiment of the application, a data matching rule is first established. Based on the rule, the real-time running data is compared with the data anomaly information to extract abnormal real-time data that exceeds the normal fluctuation range and meets the abnormal characteristics. At the same time, the time and spatial coordinates of the real-time running data are matched with the abnormal movement stages in the risk prediction results to extract the abnormal stage information in the risk period and interference area, so as to clarify the risk points and abnormal parameters in actual operation.

[0164] Step 503: Adjust the preliminary running trajectory based on the abnormal real-time data and the abnormal stage information to generate a candidate running trajectory.

[0165] In this step, the candidate trajectory refers to the alternative trajectory obtained after adjusting the initial trajectory based on abnormal data and abnormal stage information. It needs to be verified later to be determined as the target trajectory.

[0166] In this embodiment, firstly, for abnormal parameters corresponding to abnormal real-time data, such as speed exceeding limits or load fluctuations, the trajectory segment in the preliminary running trajectory corresponding to the abnormal parameter is located, and the motion parameters of the trajectory segment are finely adjusted to eliminate the abnormal parameter problem by reducing the movement speed or adjusting the posture of the robotic arm; then, for the risk area corresponding to the abnormal stage information, the trajectory segment in the preliminary running trajectory that passes through the risk area is located, the motion path of the trajectory segment is optimized, and the avoidance distance between the robotic arm and the press slider is further increased or the acceleration sequence is adjusted to avoid the risk of collision.

[0167] Next, the adjusted trajectory segments obtained after targeted adjustment of the above two types are integrated with the trajectory segments in the preliminary running trajectory that do not show any abnormalities. They are then re-stitched according to the original operation sequence of the preliminary running trajectory, and the trajectory inflection points at the splicing points are smoothed and corrected to finally generate candidate running trajectories, ensuring that the adjusted trajectory is adapted to the actual operating conditions.

[0168] Step 504: Based on the risk prediction results and the motion constraint parameters, verify the candidate running trajectories, and determine the candidate running trajectories that pass the verification as the target running trajectory of the robotic arm in continuous synchronization mode.

[0169] In this embodiment, the candidate trajectory is first checked against the risk prediction results to ensure it completely avoids the predicted interference area and poses no new collision risk. Then, the motion parameters of the candidate trajectory are compared with the motion constraint parameters item by item to ensure that the angles, speeds, loads, etc., of each joint meet the corresponding constraint requirements and that there are no exceedances. If the check finds any risks or exceedances, the process returns to step 503 for readjustment. If the check finds no abnormalities, the candidate trajectory is determined to be the target trajectory and can be directly used for continuous synchronous operation of the robotic arm.

[0170] This application's embodiment solves the problem of mismatch between the initial trajectory and the actual working conditions, and avoids the risks of collisions and parameter exceeding limits. It also balances fixed production rhythm and operational stability, and finally outputs a precise trajectory that can be directly implemented. It can ensure that the robotic arm avoids interference while maintaining a fixed production rhythm for the entire line, thereby improving the reliability and efficiency of continuous synchronous operation of the robotic arm.

[0171] Figure 3 This is a schematic diagram illustrating a specific implementation of a trajectory planning system for a robotic arm in a continuous synchronization mode, as provided in this application. (Refer to...) Figure 3 The system may include:

[0172] The acquisition module 31 is used to acquire operation-related data of the robotic arm system. The operation-related data includes the relative angle data between the robotic arm and the press slide, the motion constraint parameters of the robotic arm, and the operation status data under a fixed production cycle.

[0173] The simulation module 32 is used to simulate the movement process of the robotic arm along a preset motion trajectory through a digital twin model, so as to generate a risk prediction result of the robotic arm in continuous synchronous mode. The risk prediction result includes at least the predicted interference area between the robotic arm and the press slider.

[0174] The integration module 33 is used to divide the preset motion trajectory into multiple trajectory segments through trajectory optimization technology, and optimize and integrate each trajectory segment based on the risk prediction result and the motion characteristic parameters of the robotic arm to form a preliminary running trajectory. The motion characteristic parameters are used to indicate: acceleration and avoidance should be performed for trajectory segments that pass through the predicted interference area, while other trajectory segments should maintain the fixed production cycle.

[0175] Analysis module 34 is used to analyze the operating status data to generate data anomaly information;

[0176] The adjustment module 35 is used to control each robotic arm in the robotic arm system to move according to the preliminary running trajectory through multi-axis control technology and distributed control technology, and to collect real-time running data of each robotic arm during synchronous operation. Combined with the abnormal data information and risk prediction results, the module adjusts the preliminary running trajectory to obtain the target running trajectory of the robotic arm in continuous synchronous mode.

[0177] This application provides a trajectory planning system for a continuous synchronous mode of a robotic arm, which is used to implement the aforementioned trajectory planning method for a continuous synchronous mode of a robotic arm. Therefore, the specific implementation of the trajectory planning system for a continuous synchronous mode of a robotic arm can be found in the embodiment section of the trajectory planning method for a continuous synchronous mode of a robotic arm described above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0178] This application also provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are to be invoked and executed by the processing component to implement the steps of the trajectory planning method for a continuous synchronous mode of a robotic arm as described above.

[0179] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, implements the steps of the trajectory planning method for a continuous synchronous mode of a robotic arm as described above.

[0180] In one exemplary embodiment, the computer storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0181] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in the trajectory planning method embodiments of any of the above-described continuous synchronization modes of a robotic arm.

[0182] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0183] The trajectory planning method and system for continuous synchronous mode of a robotic arm provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are only for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make several improvements and modifications to this application without departing from the principles of this application, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A trajectory planning method for a robotic arm in a continuous synchronous mode, characterized in that, include: Collect operation-related data of the robotic arm system, including the relative angle data between the robotic arm and the press slide, the motion constraint parameters of the robotic arm, and the operation status data under a fixed production cycle. The movement process of the robotic arm along a preset motion trajectory is simulated by a digital twin model to generate a risk prediction result of the robotic arm in continuous synchronous mode. The risk prediction result includes at least the predicted interference area between the robotic arm and the press slider. The preset motion trajectory is divided into multiple trajectory segments by trajectory optimization technology. Based on the risk prediction results and the motion characteristic parameters of the robotic arm, each trajectory segment is optimized and integrated to form a preliminary running trajectory. The motion characteristic parameters are used to indicate: acceleration and avoidance should be performed for trajectory segments that pass through the predicted interference area, while the fixed production cycle should be maintained for other trajectory segments. The operational status data is analyzed to generate data anomaly information; The robotic arm system uses multi-axis control technology and distributed control technology to control each robotic arm to move according to the initial running trajectory. Real-time running data of each robotic arm during synchronous operation is collected. Combined with the abnormal data information and risk prediction results, the initial running trajectory is adjusted to obtain the target running trajectory of the robotic arm in continuous synchronous mode. The movement of a robotic arm along a preset trajectory is simulated using a digital twin model to generate risk prediction results for the robotic arm in continuous synchronous mode, including: The movement process of the robotic arm along the preset motion trajectory is simulated by a digital twin model, and the motion data of each robotic arm and its relative position information with the press slider are obtained during the movement. Based on the relative position information and the relative angle data, motion conflict information is generated, which includes one or more predicted interference regions composed of risk location points; The motion data is compared with the motion constraint parameters to obtain the comparison result; Based on the motion conflict information and comparison results, abnormal motion phases that may overlap or do not conform to motion constraint parameters are identified. All abnormal motion phases are integrated to form the risk prediction result of the robotic arm in continuous synchronization mode. The preset motion trajectory is divided into multiple trajectory segments using trajectory optimization technology. Based on the risk prediction results and the motion characteristic parameters of the robotic arm, each trajectory segment is optimized and integrated to form a preliminary running trajectory, including: The preset motion trajectory is divided into multiple trajectory segments, and priority is assigned to each trajectory segment according to the operation accuracy requirements; Based on the risk prediction results, the target risk type is determined, and a first trajectory segment containing the target risk type and a second trajectory segment not containing the target risk type are identified; wherein, if the target risk type is predicted interference with the press slide, the corresponding first trajectory segment is marked as the trajectory segment that needs to be accelerated to avoid it; Based on the target risk type corresponding to the first trajectory segment, the motion characteristic parameters of the robotic arm, and the load characteristic parameters of each robotic arm, determine the optimization direction and attitude adjustment parameters of the first trajectory segment. Based on the optimization direction and attitude adjustment parameters of each first trajectory segment, the motion parameters of the first trajectory segments with different priorities are adapted and adjusted to obtain the third trajectory segment; According to the preset motion trajectory, all third trajectory segments and second trajectory segments are spliced ​​and smoothed to form a preliminary running trajectory.

2. The method according to claim 1, characterized in that, Based on the relative position information and the relative angle data, motion conflict information is generated, including: Based on the external dimensions of each robotic arm and press slide, set the minimum safe distance and safe angle range between the robotic arm and the press slide; Based on the actual distance and actual angle between the robotic arm and the press slider at each moment in the relative position information and the relative angle data, the target moment when the actual distance is less than the minimum safe distance or the actual angle exceeds the safe angle range, and the corresponding position information of the target robotic arm and the target press slider are identified. The location information corresponding to each target at any given time is clustered in space to form one or more predictive interference regions that characterize the spatial range of collision risk. By integrating the target time points, a risk period with collision risk is obtained. Combined with the predicted interference region, motion conflict information is formed.

3. The method according to claim 1, characterized in that, The optimization directions for the first trajectory segment include the first optimization direction and the second optimization direction; Based on the target risk type corresponding to the first trajectory segment, the motion characteristic parameters of the robotic arm, and the load characteristic parameters of each robotic arm, the optimization direction and attitude adjustment parameters of the first trajectory segment are determined, including: For the first trajectory segment with the target risk type of motion conflict anomaly, based on the motion characteristic parameters of the robotic arm, the working space range, and the load characteristic parameters of each robotic arm, the motion path nodes of each first trajectory segment that have the risk of collision with the press slider are analyzed to determine the first optimization direction. For the first trajectory segment with the target risk type of constraint over-limit anomaly, the reasons why the motion parameters of each first trajectory segment exceed the motion constraint parameters are analyzed to determine the second optimization direction. Based on the first and second optimization directions, and combined with the motion characteristic parameters of the robotic arm, the attitude adjustment range of the end effector of the robotic arm and the coordinated motion range of each associated robotic arm are determined. Based on the load characteristic parameters of each robotic arm, the attitude adjustment range and cooperative motion range are verified and corrected to generate attitude adjustment parameters for each first trajectory segment.

4. The method according to claim 1, characterized in that, The robotic arm system uses multi-axis control and distributed control technologies to control each robotic arm to move according to the initial running trajectory. Real-time running data of each robotic arm during synchronous operation is collected. Combined with data anomaly information and risk prediction results, the initial running trajectory is adjusted to obtain the target running trajectory of the robotic arm in continuous synchronous mode, including: The initial running trajectory is parsed into motion control commands corresponding to each robotic arm. Multi-axis control technology and distributed control technology are used to control each robotic arm to run synchronously according to the motion control commands, and real-time running data of each robotic arm during synchronous operation is collected. Extract abnormal real-time data that matches the data anomaly information and abnormal stage information that matches the risk prediction results from the real-time operating data. Based on the abnormal real-time data and the abnormal stage information, the preliminary running trajectory is adjusted to generate a candidate running trajectory; Based on the risk prediction results and the motion constraint parameters, the candidate running trajectories are verified, and the candidate running trajectories that pass the verification are determined as the target running trajectory of the robotic arm in continuous synchronization mode.

5. The method according to claim 4, characterized in that, The robotic arms are controlled to operate synchronously according to the motion control commands using multi-axis control technology and distributed control technology, including: A communication network between a multi-axis control center and controllers at various locations is constructed using distributed control technology. The communication addresses and data transmission protocols of the controllers at various locations are configured in the communication network to form a target communication link. Based on the target communication link, the motion control command and the synchronization clock signal are sent to the corresponding part controller to calibrate the time reference of each part controller and obtain the calibrated time reference; Based on the calibrated time reference, each robotic arm is controlled to operate synchronously according to the motion control commands.

6. A trajectory planning system for a continuous synchronous mode of a robotic arm, used in the trajectory planning method for a continuous synchronous mode of a robotic arm as described in any one of claims 1 to 5, characterized in that, include: The data acquisition module is used to collect operation-related data of the robotic arm system. The operation-related data includes the relative angle data between the robotic arm and the press slide, the motion constraint parameters of the robotic arm, and the operation status data under a fixed production cycle. The simulation module is used to simulate the movement process of the robotic arm along a preset motion trajectory through a digital twin model, so as to generate the risk prediction result of the robotic arm in continuous synchronization mode. The risk prediction result includes at least the predicted interference area between the robotic arm and the press slider. The integration module is used to divide the preset motion trajectory into multiple trajectory segments through trajectory optimization technology, and optimize and integrate each trajectory segment based on the risk prediction results and the motion characteristic parameters of the robotic arm to form a preliminary running trajectory. The motion characteristic parameters are used to indicate: acceleration and avoidance should be performed for trajectory segments that pass through the predicted interference area, while other trajectory segments should maintain the fixed production cycle. The analysis module is used to analyze the operating status data to generate data anomaly information; The adjustment module is used to control each robotic arm in the robotic arm system to move according to the initial running trajectory through multi-axis control technology and distributed control technology, and to collect real-time running data of each robotic arm during synchronous operation. Combining the data anomaly information and risk prediction results, the module adjusts the initial running trajectory to obtain the target running trajectory of the robotic arm in continuous synchronous mode.

7. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a trajectory planning method for a continuous synchronous mode of a robotic arm as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that, The system contains a computer program that, when executed by a computer, implements a trajectory planning method for a continuous synchronous mode of a robotic arm as described in any one of claims 1 to 5.

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