Automatic cable carrying and positioning system based on manipulator

Abstract

The invention relates to the technical field of cable carrying and positioning, and discloses an automatic cable carrying and positioning system based on a manipulator. The system comprises a data acquisition module, a state analysis module, an influence evaluation module, a synchronous association evaluation module and a position optimization adjustment module. The data acquisition module acquires position and state data during carrying in real time; the state analysis module analyzes actual and target position deviation and generates a position abnormal signal when the deviation exceeds a preset range; the influence evaluation module monitors the movement speed, calculates a movement influence value, and generates a large influence signal when the movement influence value exceeds a threshold value; the synchronous correlation evaluation module obtains a speed change trend and a position change similarity value according to the signal, and performs correlation calculation to obtain a synchronous correlation value; and the position optimization adjustment module combines the influence large signal and the current target position, and obtains an adjusted position by using a position adjustment coefficient. The system can realize whole-process monitoring, accurately adjust the position and adapt to different working conditions.

Description

Technical Field

[0001] This invention relates to the field of cable handling and positioning technology, specifically to an automatic cable handling and positioning system based on a robotic arm. Background Technology

[0002] In cable production, warehousing, and engineering construction, cable handling and positioning are crucial aspects of daily operations. With the advancement of industrial automation, robotic arms are gradually replacing manual labor as the primary equipment for cable handling. However, they still face many unresolved issues in practical applications. Traditional robotic cable handling methods largely rely on pre-set fixed programs for action execution, lacking effective real-time data collection and analysis during the handling process. During handling, due to uneven weight distribution and external environmental interference, the actual position of the cable is prone to deviating from the target position.

[0003] In existing technologies, while some robotic arm systems possess basic position detection capabilities, they can only issue alarm signals when deviations exceed a significant range, failing to quantify the impact of these deviations. For example, when the robotic arm's movement speed changes, it's impossible to determine the correlation between this speed change and the position deviation, or whether the current speed change will further exacerbate the position deviation, leading to a lack of targeted adjustments in subsequent modifications. Furthermore, existing systems often employ fixed adjustment parameters during position adjustments, neglecting the adaptability of adjustment coefficients under different operating conditions. This frequently results in over- or under-adjustment, impacting handling efficiency and potentially damaging cables, increasing production costs. Simultaneously, existing systems lack synchronous correlation analysis of speed change trends and position changes, making it difficult to grasp the dynamic patterns of the handling process as a whole. This hinders the ability to predict potential positional anomalies, leading to delayed fault handling and further reducing the stability and reliability of the entire handling system. Summary of the Invention

[0004] The purpose of this invention is to provide an automated cable handling and positioning system based on a robotic arm to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides an automated cable handling and positioning system based on a robotic arm, the system comprising: The data acquisition module is used to collect position data and handling status data of the robotic arm in real time during the cable handling process; The status analysis module is used to analyze the deviation between the actual position and the target position of the cable based on the location data, compare the deviation with the preset position range, and generate a position anomaly signal if the deviation exceeds the preset position range. The impact assessment module is used to monitor the movement speed data of the robot arm based on the position anomaly signal. It acquires speed data within the monitoring period, calculates the movement impact value, compares the movement impact value with the movement impact threshold, and generates an impact magnitude signal. If the movement impact value is greater than or equal to the movement impact threshold, a large impact signal is generated. The synchronization correlation evaluation module is used to obtain the velocity change trend value and the position change similarity value based on the large-influence signal, and to perform correlation calculation between the velocity change trend value and the position change similarity value to obtain the synchronization correlation value. The position optimization and adjustment module is used to obtain the position adjustment coefficient based on the large-influence signal, combine the current target position with the position adjustment coefficient to obtain the adjusted position, and complete the position adjustment.

[0006] Preferably, the data acquisition module acquires data in the following way: The robotic arm's handling path is divided into several handling areas. Position data within each handling area is obtained, and the position data of the handling areas are integrated to determine the comprehensive position value. Acquire the handling status data for each handling area, and merge the handling status data to generate a comprehensive status value.

[0007] Preferably, the analysis method of the state analysis module is as follows: Based on the comprehensive location value, calculate the difference distance between the actual location and the target location; Compare the difference distance with the maximum and minimum boundary values ​​of the preset position range respectively. If the difference distance is greater than the maximum boundary value or less than the minimum boundary value, it is determined that the deviation exceeds the preset position range. When generating a location anomaly signal, the overall state value is combined.

[0008] Preferably, the calculation method of the impact assessment module is as follows: Extract the fluctuation characteristics of speed data within the monitoring period; Based on fluctuation characteristics and location anomaly signals, the value of the movement impact is derived; The moving impact value is compared with the upper and lower limits of the moving impact threshold. If the moving impact value exceeds the upper limit or falls below the lower limit, a large impact signal is generated.

[0009] Preferably, the synchronous correlation evaluation module is obtained in the following way: Identify speed change trend values, which reflect the overall direction of speed data change within the monitoring period; Identify similarity values ​​for position changes, where the similarity values ​​reflect the degree of matching between position data and velocity data; The correlation value is obtained by linking the velocity change trend value with the position change similarity value.

[0010] Preferably, the coefficients of the position optimization adjustment module are obtained in the following way: Summarize the synchronization correlation values ​​corresponding to the major signals; Based on the synchronous correlation value, the position adjustment coefficient is derived; Combine the current target position with the position adjustment coefficient to output the adjusted position.

[0011] Preferably, the system further includes: The path update module is used to replan the handling path of the robot arm based on the synchronous association value; The path update module is planned as follows: extract the synchronization association value, map it to the key nodes of the transport path, and generate updated path data.

[0012] Preferably, the mapping method of the path update module is as follows: Identify positional offset features in key nodes; Based on location offset features and updated path data, the coordinates of key nodes are corrected; Output the corrected transport path.

[0013] Preferably, the system further includes: The material handling management module is used to coordinate the handling actions of the robotic arm based on the adjusted position and the corrected material handling path; The coordination method of the material handling management module is as follows: sort the priority sequence of material handling actions and allocate the execution time of the actions.

[0014] Preferably, the sorting method of the handling management module is as follows: Extract action conflict identifiers from the priority sequence; Based on action conflict identifiers and action execution times, the priority sequence is rearranged; Generate the final transport instructions.

[0015] Compared with the prior art, the beneficial effects of the present invention are: By setting up a data acquisition module, the position and handling status data of the robotic arm during cable handling can be collected in real time, realizing dynamic monitoring of the entire handling process. This breaks through the limitations of traditional systems that can only perform phased detection, and can promptly capture subtle changes in position and status, providing comprehensive and accurate data support for subsequent analysis and adjustment. The status analysis module analyzes the deviation between the actual position and the target position of the cable based on the location data, and compares it with the preset position range to generate a position anomaly signal. This enables the position anomaly to be quickly identified, avoiding serious problems caused by the accumulation of deviations. It allows operators or the system to know about the position deviation at the first time, creating conditions for timely intervention. Upon receiving a position anomaly signal, the impact assessment module monitors the robot's movement speed data, calculates the movement impact value, and compares it with a threshold to generate an impact magnitude signal. This allows for a quantitative assessment of the degree of impact of the position anomaly. Unlike traditional systems that can only determine the existence of an anomaly, this module can also clearly define the severity of its impact. When the movement impact value is greater than or equal to the threshold, generating a high-impact signal, the system can prioritize handling such high-impact anomalies, rationally allocate system resources, and improve the efficiency and targeted nature of fault handling. The synchronization correlation assessment module acquires the trend value of velocity change and the similarity value of position change based on the large signal, and performs correlation calculations to obtain the synchronization correlation value. This allows for in-depth exploration of the intrinsic relationship between velocity and position changes. Through this correlation analysis, the dynamic correlation pattern between the two can be clearly understood, revealing how velocity changes affect position changes and the synchronicity of their changes. This provides a more in-depth analytical basis for subsequent position adjustments, helping to identify the key factors causing positional anomalies at their root, rather than merely focusing on superficial deviation corrections. The position optimization and adjustment module acquires position adjustment coefficients based on the large-influence signal and combines this with the current target position to obtain the adjusted position, achieving personalized and precise position adjustment. Unlike traditional systems that use fixed adjustment parameters, this module can dynamically determine the adjustment coefficients according to the actual degree of influence, making the adjusted position more consistent with the current working conditions. This effectively avoids over- or under-adjustment and ensures positional accuracy during cable handling. Simultaneously, precise position adjustment reduces cable collisions and wear caused by positional deviations, extending cable lifespan and reducing production costs. Furthermore, the entire system, through the collaborative work of its modules, forms a complete closed loop from data acquisition, anomaly identification, impact assessment, correlation analysis to precise adjustment. This significantly improves the stability and reliability of the robotic arm's cable handling and positioning, ensuring smooth handling processes, increasing overall handling efficiency, and is suitable for different cable specifications and handling scenarios, demonstrating strong practicality and adaptability. Attached Figure Description

[0016] Figure 1 This is a timing diagram of the automatic cable handling and positioning system based on a robotic arm as described in this invention. Figure 2 The flowchart for data acquisition by the data acquisition module; Figure 3 A flowchart for the impact assessment module calculations; Figure 4 The flowchart is obtained for the synchronous correlation evaluation module. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 This invention provides an automated cable handling and positioning system based on a robotic arm. The system includes: a data acquisition module, a status analysis module, an impact assessment module, a synchronization correlation assessment module, and a position optimization and adjustment module. Specific implementation details are as follows: The data acquisition module collects real-time position and status data of the robotic arm during the handling process. The status analysis module calculates the deviation between the actual cable position and the target position based on the position data. If the deviation exceeds a preset range, a position anomaly signal is generated. The impact assessment module monitors the robotic arm's movement speed data based on the position anomaly signal, calculates the movement impact value, and generates an impact magnitude signal. The synchronization correlation assessment module obtains the speed change trend value and position change similarity value based on the impact magnitude signal, and obtains the synchronization correlation value through correlation calculation. The position optimization and adjustment module combines the position adjustment coefficient with the current target position to output the adjusted position, completing the position optimization.

[0019] Example 1: See Figure 2 In a robotic arm-based automated cable handling and positioning system, the data acquisition module first divides the entire handling path of the robotic arm into multiple logical regions. Each region is defined based on the robotic arm's motion characteristics, the cable's physical state, and the specific conditions of the handling environment. For example, in long-distance handling scenarios, the path might be divided into an initial acceleration zone, a constant-speed handling zone, and an end-deceleration zone; while in complex path handling, the regions might be divided based on turning angles or obstacle distribution. Within each handling region, the robotic arm's position data is acquired through encoders, laser rangefinders, or vision sensors installed at the robotic arm joints and end effector. The encoder records the angle changes of each joint, the laser rangefinder measures the distance between the robotic arm's end effector and a reference point, and the vision sensor captures the cable's real-time position using image recognition technology. This raw data, after coordinate transformation and unit unification, is then transmitted to the data processing unit.

[0020] Force sensors are mounted on the end effector of the robotic arm to monitor load changes in real time when gripping cables. When the cable swings or slips due to external forces, the load data will fluctuate accordingly. An inertial measurement unit records the robotic arm's acceleration and angular velocity during movement, reflecting the stability of the handling process. Environmental sensors detect external disturbances such as wind speed and vibration; these data are correlated with the robotic arm's motion state. For example, when wind speed increases, the cable's swing amplitude may increase, causing abnormal fluctuations in the load data. All state data, after being timestamped, is sent to a data fusion algorithm for processing.

[0021] The overall location value is calculated by weighting and averaging all location data within the area, with the weights dynamically adjusted based on data reliability and real-time performance. For example, visual sensors have higher weights in good lighting conditions, while data from encoders and laser rangefinders are relied upon in low-light conditions. The overall state value is generated by extracting and fusing features from multi-source state data using a neural network model. During the training phase, this model learns the correlation patterns between various state parameters under different handling scenarios.

[0022] The target position is preset by the handling task planning system, including three-dimensional spatial coordinates and attitude information. When the robot arm is disturbed during handling, its actual position will deviate from the target position, creating a discrepancy distance. The preset position range is determined based on the cable's diameter, flexibility, and handling accuracy requirements. For example, for large-diameter cables, the position range may be set wider to allow for moderate swaying; while for thin cables in precision applications, the range is correspondingly narrowed. When the discrepancy distance exceeds the preset boundary, the state analysis module combines information such as load changes and sway amplitude in the state composite value to determine the nature of the deviation. For example, if the load suddenly decreases while accompanied by large swaying, it may indicate cable slippage; while continuous load fluctuations may originate from abnormal transmission of the robot arm joints.

[0023] The generation of location anomaly signals depends not only on the magnitude of the deviation but also on its duration and trend. Instantaneous, brief deviations may be caused by random disturbances and are marked as low-priority anomalies by the system; while continuously increasing deviations are classified as high-priority anomalies, triggering rapid responses from subsequent modules. Environmental data in the overall state value is used to assist in determining the cause of the anomaly. For example, if a strong wind record is detected when an anomaly occurs, the system will attribute the deviation to environmental factors rather than mechanical failure. This multi-factor comprehensive analysis mechanism improves the accuracy of anomaly detection and avoids false triggering of adjustment actions.

[0024] The system records the frequency and severity of deviations in each area during each handling process, and identifies high-problem areas through cluster analysis. For areas with frequent anomalies, the system automatically narrows the area or increases the sensor sampling frequency, dynamically adjusting the monitoring density. The data acquisition cycle for handling status is also not fixed; when the status analysis module detects abnormal signs, it temporarily increases the data acquisition frequency to obtain a more detailed record of status changes. This adaptive acquisition mechanism effectively reduces the data processing load while ensuring system response speed.

[0025] For straight-line transport segments, the system uses linear interpolation to complete the data between sampling points; while for curved transport segments, it uses high-order polynomial fitting of the trajectory. This differentiated data processing approach better aligns with the actual motion patterns of the robotic arm, improving the accuracy of the comprehensive position value. The state data fusion algorithm is continuously updated, and the system periodically compares the actual transport results with the predicted state, adjusting the parameters of the neural network model to ensure that the comprehensive state value more accurately reflects the actual transport situation. The preset position range of the state analysis module is not globally uniform but rather sets differentiated standards for different transport stages. During acceleration and deceleration phases, a larger range of position fluctuations is allowed due to inertia; while during uniform speed transport, a stricter position tolerance standard is applied. This dynamic threshold management method conforms to the laws of physical motion, avoiding unnecessary adjustments. The deviation judgment algorithm also introduces fuzzy logic to handle boundary cases. When the difference distance approaches the boundary value, the system comprehensively considers historical data and the state of adjacent areas to make a more reasonable anomaly judgment.

[0026] The system records the processing results of each abnormal signal, establishing a correlation model between signal characteristics and the final adjustment effect. When similar characteristics reappear, the system can predict potential adjustment needs and prepare optimization solutions in advance. This predictive analysis shortens the system response time, especially when handling repetitive handling tasks. Abnormal signals also come with confidence indicators, reflecting the reliability of the judgment results for reference by subsequent modules. The actual effect after position adjustment is recorded and transmitted back to the data acquisition module for calibrating sensor parameters and optimizing area division strategies. The status analysis module also continuously optimizes the deviation judgment algorithm based on historical adjustment data, forming a continuously improving intelligent system. This closed-loop operation mode allows the system to gradually adapt to different handling environments and cable types, improving overall handling accuracy and stability.

[0027] The control commands include the expected position and status targets. The data acquisition module compares these theoretical values ​​with actual measurements to quickly identify anomalies. The results from the status analysis module are transmitted to subsequent processing units via a high-speed bus, ensuring real-time system response. All acquired data and analysis results are accurately timestamped to guarantee accurate data synchronization and causal relationships between multiple modules. The system also features a data caching mechanism to maintain basic functionality during brief communication interruptions, automatically synchronizing the latest status once communication is restored.

[0028] The data acquisition module features a redundant hardware configuration, with key sensors equipped with multiple backups, automatically switching to the backup channel when a signal from one channel fails. The status analysis module's algorithm implementation employs multi-threaded parallel computing to ensure real-time processing capabilities under high data throughput. The system periodically performs self-diagnostics, monitoring the operational status of each sensor and the effectiveness of the data analysis algorithm, promptly identifying and fixing potential problems. This highly reliable design ensures stable system operation in industrial environments.

[0029] Example 2: See Figure 3 In the automated cable handling and positioning system, the impact assessment module is responsible for analyzing the degree of influence of the robot's movement speed on positional deviation. Upon receiving a positional anomaly signal, this module immediately initiates a monitoring cycle, the duration of which is dynamically adjusted according to the handling stage. For example, during the precise cable positioning stage, the monitoring cycle is shortened to milliseconds to capture instantaneous changes; while during long-distance uniform speed handling, the cycle is extended to several seconds to observe trend effects. During the monitoring cycle, encoders installed at each joint of the robot arm record angular velocity data at a fixed sampling frequency, while a laser Doppler velocimeter directly measures the linear velocity of the end effector. These raw velocity data are then transformed into a unified three-dimensional velocity vector in the robot's working coordinate system.

[0030] The system first performs noise reduction on the original velocity sequence by eliminating high-frequency interference signals using a sliding window filter. Then, it calculates the standard deviation and coefficient of variation of the velocity in the time domain to reflect the overall amplitude of velocity fluctuations; in the frequency domain, it identifies periodic fluctuation components through spectral analysis, such as fixed-frequency vibrations caused by mechanical transmission systems. For non-periodic fluctuations, the system uses entropy analysis to quantify their randomness. All fluctuation characteristic parameters are aligned by timestamps to form a complete fluctuation characteristic vector.

[0031] The system constructs a three-dimensional state space, with coordinate axes representing position deviation, velocity fluctuation amplitude, and fluctuation frequency characteristics, respectively. The distribution of state point clouds formed by historical data reveals the mapping pattern among these three factors: when the position deviation increases, if it is accompanied by high-frequency, large-amplitude velocity fluctuations, the velocity impact is determined to be significant. In specific calculations, the system retrieves neighboring historical points in the current state space and determines the movement impact value through a weighted voting mechanism. The weight allocation considers a time decay factor, with more recent data having a higher weight than earlier data, enabling the model to adapt to the slow changes in system characteristics.

[0032] The baseline threshold is determined based on the robot's dynamic parameters, such as physical constraints like maximum allowable acceleration and joint torque limits. The dynamic threshold is adjusted through an online learning mechanism. The system continuously records the speed fluctuation range under normal handling conditions, and automatically increases the threshold sensitivity when the fluctuation characteristics continuously deviate from the baseline level. The threshold range is divided into three levels: safe zone, warning zone, and abnormal zone. When the movement impact value is within the safe zone, the system only records data without triggering an alarm; entering the warning zone activates an enhanced monitoring mode; if the boundary of the abnormal zone is breached, a large impact signal is immediately generated.

[0033] The system checks whether the signal occurs continuously within a short period to avoid false triggers caused by occasional interference. It also correlates with environmental sensor data to eliminate false positives caused by external factors (such as sudden strong winds). Signal strength is graded to indicate the degree of impact, with level one indicating a slight impact and level three indicating a severe impact. The signal is accompanied by a detailed characteristic fingerprint, including a spectral feature map of velocity fluctuations, time-domain waveform slices, and evolution curves of related positional deviations. This data provides a complete analytical background for subsequent modules.

[0034] When a significant signal is detected, the system automatically traces back two monitoring cycles to analyze the accumulation process of the anomaly. For example, before a cable detachment accident, there is usually a gradual increase in the amplitude of speed fluctuations. This retrospective analysis helps distinguish between sudden anomalies and gradual faults, providing a basis for maintenance decisions. Simultaneously, the system extends the monitoring cycle forward by one cycle to observe the response after adjustment measures are implemented, forming a complete closed-loop record of the anomaly event.

[0035] The speed data acquisition network employs a heterogeneous sensor fusion strategy. In addition to the joint encoder, vibration accelerometers are deployed at key locations on the robot arm to capture abnormal vibrations in the transmission system; high-speed cameras are installed at cable suspension points to indirectly calculate the swing speed through image analysis; and current sensors are installed in the control cabinet to infer speed changes from motor current fluctuations. Multi-source data is fused using a Kalman filter to eliminate measurement errors from individual sensors. When a sensor fails, the system automatically switches to a backup data source to ensure continuous monitoring.

[0036] The system periodically evaluates the feature extraction results. When a new fluctuation pattern is found that cannot be described by existing features, the feature expansion process is automatically initiated. New feature dimensions are added to the feature vector after correlation verification. For example, after discovering a low-frequency fluctuation pattern unique to cable swaying in a humid environment, the system added an energy proportion feature in the 0.5-2Hz frequency band. This dynamically evolving feature system enables the system to adapt to changes in complex operating conditions.

[0037] After each handling task is completed, the system compares the actual results with the predicted impact value and calculates the prediction deviation. When consecutive deviations occur in the same direction, the model parameters are automatically adjusted. Major abnormal events trigger the model reconstruction process, and the system retrains the state space mapping relationship using recent data. The model version management function records all change history, allowing rollback to a stable version when necessary. This continuous optimization mechanism improves the accuracy of impact value assessment over time. A transition interval is set near the threshold boundary; impact values ​​within this interval do not immediately trigger a signal but instead initiate verification measurements. The system temporarily increases the sensor sampling frequency for multiple rounds of verification measurements. Only when three consecutive verification results exceed the threshold is a large impact signal finally confirmed. This design effectively filters out transient interference and improves signal reliability. The threshold itself is also dynamically adjusted according to seasonal factors; for example, in low-temperature winter environments, the threshold is appropriately relaxed to account for the impact of increased lubricating oil viscosity.

[0038] Level 1 signals trigger the local controller to fine-tune speed parameters; Level 2 signals notify the host computer to optimize the handling path; Level 3 signals activate safety protocols, including deceleration, audible and visual alarms, and even emergency pauses. The choice of response strategy considers the importance of each handling stage; for example, even a Level 1 signal can trigger path replanning when cables pass through areas with precision equipment. Signal lifecycle management ensures that each signal has a clear start, confirmation, processing, and closure record, forming a traceable event chain.

[0039] Cases are automatically categorized by characteristic fingerprints, forming a fault mode knowledge base. When the matching degree between newly emerging signal characteristics and historical cases exceeds a set value, the system automatically retrieves the corresponding handling solution, shortening the response time. For new anomalies with unknown characteristics, the system activates a special recording mode to collect high-density data for subsequent in-depth analysis. The case library is periodically compressed and archived, retaining characteristic fingerprints and key parameters for long-term trend analysis. During implementation, the impact assessment module maintains bidirectional communication with the robot control system. The expected speed curve in the control commands is transmitted to the assessment module in real time as a theoretical benchmark. The assessment results are then fed back to the control system to adjust speed planning parameters. This closed-loop interaction allows the system to dynamically control the impact range of speed fluctuations while maintaining handling efficiency. All data exchanges are strictly synchronized via timestamps to ensure the consistency of analysis results with the timing of action execution.

[0040] Feature extraction tasks are distributed across multiple dedicated processors for parallel execution, while motion impact value calculation is handled by high-performance computing units, and threshold comparison and signal generation are performed by the real-time controller. The task scheduler dynamically allocates computing resources based on system load, prioritizing the real-time performance of core functions during peak periods. The data pipeline employs a circular buffer design to avoid processing delays caused by data backlog.

[0041] Example 3: See Figure 4 The synchronous correlation evaluation module in the automated cable handling and positioning system is responsible for establishing a dynamic relationship model between the robot's speed changes and position deviations. This module activates immediately upon receiving a significant signal, first retrieving the complete dataset for the current monitoring period from the system cache, including time-series velocity and position samples. The velocity data originates from the angular velocity sensor arrays at each joint of the robot arm; this raw data is converted into a linear velocity vector of the end effector in the three-dimensional workspace using forward kinematics. The position data comes from the fusion output of the laser tracker and the vision positioning system, reflecting the actual trajectory of the cable gripping point.

[0042] The calculation of the speed change trend value employs a trend decomposition algorithm, separating the speed time series into a trend term, a periodic term, and a random term. The trend term is extracted using a weighted moving average method, with the window width dynamically adjusted according to the transport stage: a narrower window is used during acceleration to capture rapid changes, while a wider window is used during constant speed to smooth noise. The extracted trend curve is normalized and transformed into a dimensionless trend strength index, which quantifies the persistence and directionality of speed changes. For example, when the robotic arm performs a deceleration action, the trend strength index shows a negative value, and its absolute value reflects the magnitude of the deceleration.

[0043] The system first constructs a sequence of position deviation vectors, each containing three-dimensional position offset and time derivative information. A dynamic time warping algorithm aligns the time axis of the velocity change curve with that of the position deviation curve, eliminating the phase difference between them. Similarity measurement considers two dimensions: shape similarity and amplitude matching. Shape similarity is evaluated using the correlation of curve derivatives, while amplitude matching compares the normalized fluctuation intensity. The final position change similarity value is the weighted harmonic mean of these two components, with the weighting coefficients configured according to the accuracy requirements of the handling task.

[0044] The synchronization correlation value is calculated using the following formula:

[0045] in: Indicates the value to be synchronized. It is the first The intensity of the velocity trend at each sampling point These are the similarity components of positional changes at corresponding times. It is a time decay weighting factor. This represents the total number of sampling points within the monitoring period. The time decay weighting factor in the formula assigns higher weight to recent data, and its value decreases exponentially with time. The synchronization correlation value ranges from [-1, 1], with positive values ​​indicating coordination between velocity and position changes, and negative values ​​indicating a contradiction between the two.

[0046] The system maintains a historical case library, where each case stores a sequence of synchronization correlation values ​​under specific working conditions and their corresponding successful adjustment parameters. When a new synchronization correlation value is input, the retrieval module searches the case library for the most similar correlation pattern and extracts the corresponding adjustment parameters as initial suggested values. These suggested values ​​are then corrected by a real-time optimizer, which considers real-time factors such as the current dynamic state of the robot, cable tension, and environmental interference. The final output position adjustment coefficient is a three-dimensional vector, with each component corresponding to an axial adjustment ratio in the working coordinate system.

[0047] During implementation, the system checks whether the adjusted position meets the workspace constraints of the robotic arm. When approaching joint limits, it automatically introduces a softening factor to prevent damage to the mechanical structure. The position adjustment command is sent to the motion controller via a real-time communication bus, along with a confidence index of the synchronization correlation value. The controller determines the execution intensity of the adjustment action based on the confidence level; a direct positioning method is used when the confidence level is high, while a progressive approximation strategy is activated when the confidence level is low.

[0048] The system simultaneously runs multiple trend extraction threads at different time scales, performing parallel computations from second-level trends to minute-level trends. The trend fusion algorithm selects the dominant scale based on the current transport stage; for example, it prioritizes short-term trends during precise positioning and emphasizes long-term trends during long-distance transport. This multi-scale analysis can simultaneously capture instantaneous anomalies and slow drifts, improving the comprehensiveness of trend judgment. The trend strength index also includes a reliability score, reflecting the degree to which the current data quality supports trend judgment, for reference by subsequent modules.

[0049] The system uses a deep neural network to construct a feature extractor for positional changes. This network is trained offline using a large amount of historical data to learn typical motion patterns during cable handling. During online operation, the network outputs a latent space representation of the position sequence in real time. Similarity calculations are performed in this low-dimensional space, improving both computational efficiency and feature expressiveness. Network parameters are updated periodically, incorporating new motion patterns through incremental learning.

[0050] The system continuously monitors the consistency between correlated values ​​and actual adjustment effects. When a systematic deviation is detected, the model parameter calibration process is initiated. The calibration process employs a sliding window least squares method, with the window width automatically adjusted based on the deviation characteristics: a wide window is used for slowly changing systematic deviations, while a narrow window is used for sudden anomalies for rapid response. The calibrated parameters undergo stability testing before being put into formal use to avoid overfitting noisy data.

[0051] The adjustment coefficients at adjacent time points must meet the smooth transition condition to avoid abrupt changes in the robot's movement. The system establishes a three-dimensional coefficient space, and each newly generated coefficient must be located within the feasible region determined by the preceding coefficients. When the coefficients suggested by the optimization algorithm exceed the feasible region, the constraint processor projects them onto the region boundary and records the violation event for subsequent analysis. This constraint mechanism ensures the continuity of movement during the handling process and reduces impact on the cables.

[0052] The system establishes a time delay model based on the dynamic response characteristics of the robotic arm to predict the actual effective time of adjustment commands. When sending a position adjustment command, the predicted effective time is simultaneously sent, allowing the motion controller to initiate compensation actions in advance. This feedforward control method effectively offsets the impact of system response delay, enabling the actual position to accurately track the adjustment target. The time delay model parameters are periodically updated through closed-loop identification to adapt to the slow changes in the mechanical system characteristics.

[0053] The addition of new cases undergoes multiple verification processes, including evaluating the effectiveness of adjustments, verifying the uniqueness of features, and resolving conflicts. Case retrieval employs an approximate nearest neighbor algorithm, and the index structure is periodically reorganized to maintain query efficiency. Expired cases are removed based on both usage frequency and timeliness, while their statistical characteristics are retained for trend analysis. The case library is version-controlled, supporting rollback to historical stable versions when necessary.

[0054] Through the above implementation methods, the synchronous correlation evaluation module constructs a precise correlation model between speed and position changes. Multi-scale trend analysis comprehensively captures dynamic characteristics, intelligent similarity evaluation accurately quantifies the degree of motion coordination, and rigorous mathematical formulas establish a standardized correlation calculation framework. The generation of position adjustment coefficients integrates historical experience and real-time optimization, while the execution process ensures accuracy through advanced predictive compensation. The systematic integration of these technical elements enables the module to effectively coordinate the relationship between the robot's speed and the cable position, providing crucial dynamic adjustment capabilities for automated handling systems.

[0055] Example 4: The path update module in the automated cable handling and positioning system is responsible for dynamically optimizing the robot's trajectory based on real-time monitoring data. This module receives processed synchronization correlation values ​​from the synchronization correlation evaluation module; these values ​​reflect the degree of coordination between changes in the robot's speed and cable position deviations. The module contains a path planning engine that decomposes the entire handling path into several key nodes, each containing attributes such as spatial coordinates, motion parameters, and tolerance range. The system defaults to using linear interpolation to connect adjacent nodes to form an initial path, but initiates a path replanning process when an abnormal synchronization correlation value is detected.

[0056] The system calculates the second derivative of the path and marks locations with curvature exceeding a set threshold as critical nodes. For cable handling tasks, typical nodes include start points, end points, obstacle avoidance points, and load direction change points. Each node has an influence radius parameter; when a synchronization correlation value anomaly occurs within the influence radius of a node, that node is marked as a node to be optimized. Node optimization priority is comprehensively evaluated based on the degree of anomaly and the importance of the location, as shown in Table 1.

[0057] Table 1: Example of node-optimized queues.

[0058] Node number Spatial coordinates (x, y, z) Synchronize associated values Radius of influence (mm) Optimize priority N203 (1250,780,320) -0.47 150 urgent N156 (980,1020,280) -0.32 200 high N341 (1530,650,350) -0.18 120 middle N422 (1420,880,310) -0.25 180 high Within the influence radius of the node to be optimized, the system activates a high-density monitoring mode, increasing the position sampling frequency to five times the normal value. By comparing the deviation sequence between theoretical and actual coordinates, the main direction and gradient of the offset are calculated. The offset direction is divided into two categories: axial offset and radial offset. Axial offset is along the direction of robot movement and is usually caused by improper speed control; radial offset is perpendicular to the direction of movement and is often caused by cable swaying or external interference. The system establishes an offset feature vector, which includes dimensional information such as offset type, duration, and spatial distribution pattern.

[0059] For nodes where axial offset is dominant, the system adjusts the node spacing while maintaining the path curvature, achieving position correction by changing the motion velocity distribution. Radial offset is compensated by introducing new transition nodes, the positions of which are calculated based on the offset characteristics, ensuring that the corrected path meets the joint angle limits of the robotic arm. Each update operation is verified through dynamic simulation to check for risks of torque overruns or resonance. Verified update data is encapsulated as a path segment descriptor, containing the start node, end node, array of intermediate path points, and motion constraints.

[0060] The system first calculates a preliminary correction vector based on the offset characteristics, and then applies this vector to the original node coordinates to obtain candidate positions. These candidate positions must meet reachability checks, including verification of the existence of inverse kinematics solutions and workspace boundary checks. For complex offset patterns, the system employs a step-by-step correction strategy, addressing the major offset components first and then processing the minor ones. After each correction, the synchronization correlation value is re-evaluated until it returns to the normal range or the maximum number of iterations is reached. The final determined node coordinate update scheme is recorded as a versioned revision, supporting rollback operations when necessary.

[0061] The system inserts multiple auxiliary points between adjacent key nodes and uses cubic spline curve fitting to generate a continuous and smooth trajectory. The second derivative of the curve is strictly controlled during interpolation to avoid abrupt acceleration changes. For load-sensitive areas, the density of path points is increased to ensure that the cable sway amplitude remains within allowable limits. The smoothed path is converted into a sequence of robot control commands, including the position, velocity, and acceleration parameters of each axis, and sent to each joint controller via a real-time communication bus.

[0062] After the path is updated, the system initiates dedicated monitoring to record the degree of consistency between the actual trajectory and the expected path. Evaluation metrics include position tracking error, speed following accuracy, and the degree of improvement in synchronization correlation values. This data is fed back to the path planning engine to adjust node influence radius parameters and optimization strategy weights. For path segments where problems repeatedly occur, the system automatically marks them as sensitive areas for preventative optimization in subsequent tasks.

[0063] Each node entry includes initial coordinates, records of all corrections, curves showing changes in related synchronous values, and an optimization performance score. The database supports multi-dimensional queries, allowing retrieval of relevant nodes by spatial region, time window, or anomaly type. Periodically performed database compression merges redundant records but retains metadata for all critical revisions for audit trail purposes. Any coordinate correction operation requires electronic signature verification to ensure the credibility of the instruction source. Update instruction transmission employs redundant checksum encoding to prevent erroneous operations due to communication errors. Before implementing path changes, the system briefly reduces the robot's operating speed to provide a safe time window for emergency stops. Major path adjustments also require manual confirmation; operators can review the change details through a visual interface before authorizing execution.

[0064] When a path update detects a continuous deterioration in the synchronization correlation value, the system automatically reverts to the previous stable version. For sudden severe anomalies, an emergency recovery path is provided. This path is simplified, retaining only the most critical safety nodes to ensure the robot can stop or return in the safest way. Detailed status data during the recovery process is recorded for post-event analysis and system improvement. The path update module enables dynamic optimization of the cable handling path. Critical node identification accurately captures path characteristics, offset feature analysis reveals the root cause of problems, iterative correction algorithms ensure adjustment accuracy, and a closed-loop evaluation mechanism continuously improves the optimization effect. The entire path update process is conducted under strict safety constraints, ensuring both adjustment flexibility and system reliability. This dynamic path optimization capability enables the robot to adapt to complex handling environment changes, ensuring the accuracy and stability of cable positioning.

[0065] The system maintains a global optimization goal to prevent local optimization from causing performance degradation in other segments. When correcting a single node, the impact on upstream and downstream nodes is assessed, and correlation adjustments are implemented as necessary. For long-distance transport tasks, a segmented optimization strategy is adopted, dividing the path into logical segments for parallel processing, and finally optimizing the connection between these segments. This holistic approach prevents secondary problems from arising during the optimization process.

[0066] The system provides a 3D path display interface, using different colors to indicate node status: red for abnormal nodes, yellow for nodes requiring optimization, and green for normal nodes. Operators can view analysis results such as path curvature contour maps and synchronous correlation value heatmaps through the interface. The visualization tool also supports path comparison, simultaneously displaying the differences between the original and corrected paths to help understand the optimization effect.

[0067] The system continuously monitors changes in environmental parameters. For example, ambient temperature affects the dimensional stability of the robotic arm, and airflow alters the cable's oscillation characteristics. These environmental factors are transformed into optimization constraints, enabling the generated path to automatically adapt to environmental changes. For periodic environmental disturbances, the system can also learn their patterns of change and proactively adjust path parameters to compensate.

[0068] Example 5: The handling management module plays a core role in motion coordination and task scheduling in the automated cable handling and positioning system. This module receives adjusted position data from the position optimization and adjustment module and corrected handling paths provided by the path update module, transforming this information into an executable sequence of robotic arm movements. The motion planning engine first analyzes the target requirements of the handling task, decomposing them into basic motion units. Each unit includes attributes such as motion type, target parameters, allowable error range, and execution time limit. The system establishes a motion resource model, quantifying the motion capabilities of each joint of the robotic arm, the load state of the end effector, and the physical constraints of the cable, providing a physical basis for motion scheduling.

[0069] The priority sequence is generated using a dynamic sorting algorithm. The system calculates an urgency score for each action unit, which comprehensively considers task schedule requirements, positional deviation risks, and equipment safety factors. For example, when a cable crosses a narrow passage, anti-collision actions receive the highest priority; while in the stable handling phase, position accuracy control actions take precedence over speed optimization actions. Conflict pre-detection is implemented during the sorting process to identify action combinations that may compete for the same mechanical resources. When a potential conflict is detected, the system automatically inserts buffer actions or adjusts the action timing to eliminate resource contention risks. The sorting results form an initial priority queue, which is continuously updated during task execution.

[0070] The system maintains a conflict pattern library, storing the characteristics of typical conflict scenarios that have occurred historically. It monitors the action parameter combinations in the action queue in real time, and immediately marks a conflict when a feature combination similar to historical conflict cases is detected. For novel conflict patterns, the system analyzes them using a rule-based reasoning engine, evaluating constraints such as joint angle limitations, torque distribution conflicts, and cable tension safety boundaries to determine the nature of the conflict. Each conflict marker is accompanied by a detailed description, including the conflict type, scope of impact, and recommended resolution level.

[0071] Based on the dynamic response characteristics of the robotic arm, the system calculates the theoretical minimum execution time for each motion unit. During actual allocation, it considers the execution margin of preceding actions and the buffering needs of subsequent actions, setting reasonable time margins. The time window allocation algorithm ensures that high-priority actions receive sufficient time resources while avoiding excessive delays for low-priority actions. For critical precision actions, the system automatically extends the execution time window to ensure positioning accuracy; while for non-critical transitional actions, the time window is compressed to improve overall efficiency.

[0072] When an action conflict is detected or a new adjustment instruction is received, the system prioritizes local adjustment strategies, modifying only the queue order near the conflicting actions. Global reordering is only initiated when local adjustments fail to resolve the issue. During reordering, the history of completed actions is preserved to ensure the continuity between the new sequence and executed actions. A virtual simulation is performed after each reordering to verify the feasibility of the new sequence and prevent secondary conflicts. A change impact assessment report is attached to the reordering decision for operators' reference.

[0073] Before the system converts the scheduling results into device-level control commands, it performs inverse kinematics verification to ensure that all target positions are within the robotic arm's workspace. Dynamic verification checks whether the joint torque requirements are within the motor output range to avoid overload risks. Cable condition verification assesses the bending radius and tension changes of the cable during handling to prevent damage to the cable insulation. After all verifications pass, the command compiler converts the high-level motion description into low-level control signals, including the target angles of each joint, motion curve parameters, and safety monitoring thresholds.

[0074] Control commands are encapsulated as timestamped, synchronized data packets and transmitted to each joint controller via a deterministic network. Each data packet contains information such as the target state, allowable error, and emergency stop conditions. The transmission system implements a redundancy check mechanism, automatically triggering retransmission when data anomalies are detected. The receiving controller executes commands strictly according to the timestamp sequence to ensure precise synchronization of multi-axis motion. The system reserves a command buffer to maintain basic motion control during brief network interruptions. Sensors at each joint provide feedback on actual position, velocity, and torque data, which are compared with the expected state. When the deviation exceeds a threshold, the monitoring system immediately sends an anomaly report to the handling management module. The system distinguishes between temporary and trend deviations, initiating fine-tuning compensation for temporary deviations and triggering a scheduling reassessment for trend deviations. Monitoring data is also used to update the robot's performance model, making subsequent scheduling decisions more consistent with the actual state of the equipment.

[0075] Upon receiving an emergency stop signal or detecting a major anomaly, the system immediately freezes the current action queue and initiates the interrupt handling procedure. The procedure first controls the robotic arm to enter a safety holding state to prevent cable fall or mechanical damage. It then analyzes the cause of the interruption and formulates a recovery plan. For recoverable interruptions, the system generates an optimized sequence to continue execution from the breakpoint; for unrecoverable interruptions, it plans a safe return path. All operations during the interruption event are recorded in detail, forming a complete incident report.

[0076] The scheduling decision support system accumulates historical operational data. After each task is completed, the system analyzes the differences between the actual execution trajectory and the planned sequence to evaluate the effectiveness of the scheduling strategy. The scheduling parameter self-learning module adjusts key parameters such as sorting weights and time window allocation ratios based on the evaluation results. For repetitive tasks, the system establishes an optimal scheduling template, significantly improving the planning efficiency of subsequent tasks. Long-term operational data is also used to identify performance degradation trends of the robotic arm, providing a basis for preventative maintenance.

[0077] Operators can view the current action queue, conflict marker distribution, and execution progress through a graphical interface. The interface supports manual priority adjustment, time allocation modification, and insertion of custom actions. All manual interventions require double confirmation and are automatically logged. The system provides operators with decision support information, including conflict resolution suggestions and parameter adjustment impact predictions, supporting efficient human-machine collaborative decision-making.

[0078] Through the above implementation methods, the material handling management module constructs an efficient and reliable motion coordination system. A dynamic priority ranking mechanism flexibly responds to changes in task requirements, intelligent conflict detection promptly resolves resource competition risks, and adaptive time allocation optimizes overall execution efficiency. Multi-level verification ensures the safety and feasibility of instructions, rigorous monitoring ensures controlled execution, and robust interrupt handling maintains system robustness. These functions work together to enable the robotic arm to precisely coordinate various handling actions, achieving smooth and efficient operation of automated cable handling.

[0079] The system periodically saves the current action queue state to non-volatile memory, allowing task execution to resume from the most recent save point after an unexpected power outage or system restart. The stored data includes the complete queue structure, executed action flags, and parameters for pending actions, ensuring consistency of the task state after recovery. The storage interval is dynamically adjusted based on task criticality, with increased storage frequency for critical stages. The material handling management module receives task instructions from the factory scheduling system and provides feedback on the task status after completing cable handling. The interface uses standard industrial communication protocols, supporting data exchange with material management systems and quality monitoring systems. When incoming material anomalies are detected or downstream processes are not fully prepared, the system automatically adjusts the handling rhythm to achieve cross-process collaboration.

[0080] The system continuously monitors environmental parameters such as temperature and humidity, and automatically adjusts motion parameters when environmental changes affect the robot's performance. For example, it extends the cooling action time window in high-temperature environments and reduces the allowable threshold for cable sway in humid environments. The correlation model between environmental parameters and scheduling decisions is continuously optimized through machine learning, improving the system's adaptability. For common material handling scenarios, such as linear handling, curved detours, and precision positioning, the system has pre-set optimized motion sequence templates. When a similar scenario is identified, the corresponding template is automatically called as the scheduling basis, significantly reducing planning time. The templates support parameterized customization, allowing adjustment of motion parameters according to specific task requirements. The library is regularly updated, incorporating excellent scheduling cases from actual operation.

[0081] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0082] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A robotic cable handling positioning system based on a robot, characterized in that, The method comprises the following steps: a data acquisition module is used to acquire position data and carrying state data in real time during the process of carrying the cable by the manipulator; a state analysis module is used to analyze the deviation of the actual position of the cable from the target position based on the position data, compare the deviation with a preset position range, and generate a position abnormality signal if the deviation exceeds the preset position range; an influence evaluation module is used to monitor the moving speed data of the manipulator based on the position abnormality signal, acquire the speed data within a monitoring period, calculate a moving influence value, compare the moving influence value with a moving influence threshold value, and generate an influence size signal; if the moving influence value is greater than or equal to the moving influence threshold value, an influence large signal is generated; a synchronous correlation evaluation module is used to acquire a speed change trend value and a position change similarity value based on the influence large signal, and perform correlation calculation on the speed change trend value and the position change similarity value to obtain a synchronous correlation value; a position optimization adjustment module is used to acquire a position adjustment coefficient based on the influence large signal, combine the current target position with the position adjustment coefficient, and obtain an adjusted position to complete position adjustment.

2. The robotic cable handling positioning system of claim 1, wherein, The acquisition mode of the data acquisition module is as follows: the carrying path of the manipulator is divided into a plurality of carrying areas, position data in each carrying area is acquired, and the position data of the carrying areas is integrated to determine a comprehensive position value; carrying state data in each carrying area is acquired, and the carrying state data is fused to generate a state comprehensive value.

3. The robotic cable handling positioning system of claim 2, wherein, The analysis mode of the state analysis module is as follows: the difference distance between the actual position and the target position is calculated based on the comprehensive position value; the difference distance is compared with the maximum boundary value and the minimum boundary value of the preset position range respectively, and it is determined that the deviation exceeds the preset position range if the difference distance is greater than the maximum boundary value or less than the minimum boundary value; when the position abnormality signal is generated, the state comprehensive value is combined.

4. The robotic cable handling positioning system of claim 3, wherein, The calculation mode of the influence evaluation module is as follows: the fluctuation characteristics of the speed data are extracted within the monitoring period; the moving influence value is derived based on the fluctuation characteristics and the position abnormality signal; the moving influence value is compared with the upper limit value and the lower limit value of the moving influence threshold value, and the influence large signal is generated if the moving influence value exceeds the upper limit value or is lower than the lower limit value.

5. The robotic cable handling positioning system of claim 4, wherein, The acquisition mode of the synchronous correlation evaluation module is as follows: the speed change trend value is identified, and the speed change trend value reflects the overall change direction of the speed data within the monitoring period; the position change similarity value is identified, and the position change similarity value reflects the matching degree of the position data and the speed data; the speed change trend value and the position change similarity value are correlated to obtain the synchronous correlation value.

6. The robotic cable handling positioning system of claim 5, wherein, The coefficient acquisition mode of the position optimization adjustment module is as follows: the synchronous correlation values corresponding to the influence large signals are summarized; the position adjustment coefficient is derived based on the synchronous correlation values; the current target position is combined with the position adjustment coefficient to output the adjusted position.

7. The robotic cable handling positioning system of claim 6, wherein, Further comprising: a path updating module is used to re-plan the carrying path of the manipulator based on the synchronous correlation value; the planning mode of the path updating module is as follows: the synchronous correlation value is extracted and mapped to the key nodes of the carrying path to generate updated path data.

8. The robotic cable handling positioning system of claim 7, wherein, The mapping mode of the path updating module is as follows: the position offset characteristics in the key nodes are identified; the coordinates of the key nodes are corrected based on the position offset characteristics and the updated path data. Output the corrected carrying path.

9. The robot-based cable handling positioning system of claim 8, wherein, Further comprising: A carrying management module, configured to coordinate the carrying operation of the robot hand based on the adjusted position and the corrected carrying path; The coordination manner of the carrying management module is to sort a priority sequence of the carrying operation and assign an action execution time.

10. The robot-based cable handling positioning system of claim 9, wherein, The sorting manner of the carrying management module is: Extract an action conflict identifier in the priority sequence; Re-arrange the priority sequence based on the action conflict identifier and the action execution time; Generate a final carrying instruction.

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