Foreign matter intelligent sorting robot control system based on AI recognition

Abstract

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

Description

Technical Field

[0001] This invention relates to the field of industrial robot control technology, specifically to a control system for an AI-based intelligent foreign object sorting robot. Background Technology

[0002] With the continuous improvement of industrial automation, intelligent sorting robots are increasingly widely used in manufacturing, logistics, and warehousing. Traditional sorting systems mainly rely on preset programs or simple vision guidance to complete sorting operations, but they face many challenges when handling foreign object sorting tasks in complex environments. Especially in industries such as electronic component assembly, food processing, and pharmaceutical production, it is necessary to accurately identify and sort foreign objects of different sizes and materials from high-speed production lines, which places extremely high demands on the robot's perception capabilities, decision-making speed, and execution accuracy. At the same time, in scenarios where multiple robotic arms work together, how to avoid mechanical interference and optimize operational efficiency has become a key technical problem that urgently needs to be solved.

[0003] The existing technology has the following shortcomings:

[0004] In high-speed sorting scenarios, when multiple robotic arm actuators (such as SCARA or six-axis robots) perform parallel sorting operations on target objects in the same work area based on a central scheduling system, traditional sorting systems are prone to mutual blocking of robotic arms due to the local optimization characteristics of path planning algorithms and communication delays when multiple robotic arms work together. This reduces overall efficiency and causes high-frequency oscillations in the end effectors of each robotic arm within a limited workspace, creating a risk of mechanical interference. Summary of the Invention

[0005] The purpose of this invention is to provide an AI-based intelligent foreign object sorting robot control system to solve the problems mentioned above.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] A control system for an AI-based intelligent foreign object sorting robot includes:

[0008] The dynamic spatial feature extraction module is used to collect spatial data of the intelligent foreign object sorting robot in the sorting working environment in real time, construct a three-dimensional dynamic spatial model with multi-dimensional topological features, and extract the connectivity features and ring obstacle features of the spatial structure through continuous homology analysis.

[0009] The spatial data includes depth image data, point cloud data, three-dimensional contour data, and dynamic obstacle motion trajectory data.

[0010] The robot motion state encoding module converts the real-time motion parameters of each robot into a dynamic system representation in the topological space and establishes a topological feature encoding matrix based on the motion trajectory.

[0011] The real-time motion parameters include end effector pose data, joint angle data, and motion speed data;

[0012] The collaborative conflict detection module is used to analyze the characteristic interaction of the motion trajectories of each robot arm in the topological space. When the intersection density of homology groups exceeds a predetermined threshold, a deadlock risk warning signal is generated.

[0013] The dynamic trajectory optimization module generates a dynamic motion trajectory with optimal collision avoidance characteristics based on the characteristic intensity of the deadlock risk warning signal, while maintaining the constraint of path topology invariance.

[0014] The execution control and adjustment module converts the optimized motion trajectory into a sequence of joint control parameters for the robotic arm, and dynamically adjusts the execution force and speed based on real-time environmental feedback.

[0015] As a further aspect of the present invention, the specific implementation process of the three-dimensional dynamic spatial model includes:

[0016] Spatial data is acquired by multi-sensor fusion, and the depth image data collected by the depth camera and the point cloud data collected by the lidar are spatiotemporally aligned by time stamp synchronization technology to establish an environmental geometric framework with millimeter-level accuracy.

[0017] Using a voxel-based 3D reconstruction algorithm, the original point cloud data is converted into an octree structure with topology preservation properties, and normal vector distribution features and curvature change features are embedded in each voxel node.

[0018] By calculating the lifetime of persistently homogeneous barcodes, key topological features in the environment are identified, including the extraction of persistent features of hole structures and connectivity features of channel structures, and a topological feature descriptor with multi-scale properties is constructed.

[0019] As a further aspect of the present invention: the extraction of connectivity features and annular obstacle features of the spatial structure specifically includes:

[0020] Topological clustering analysis is performed on the motion trajectories of dynamic obstacles. A clustering algorithm based on density peaks is used to identify the trajectory patterns of moving objects and establish a topological representation of the trajectory manifold.

[0021] Morse theory is used to perform critical point analysis on 3D contour data, and key topological structures in the workspace are extracted by calculating Reeb diagrams to identify potential mechanical interference areas.

[0022] By fusing the topology of the motion trajectory of dynamic obstacles with the topological features of the static environment, a spatiotemporal four-dimensional topological conflict prediction model is constructed. By calculating the intersection degree of homology groups, the potential collision risk of a group of robotic arms can be predicted in advance.

[0023] As a further aspect of the present invention: the establishment of the topological feature encoding matrix based on motion trajectory specifically includes:

[0024] Collect multi-dimensional motion parameters of the robot arm, including the six-degree-of-freedom pose data of the end effector, the real-time angle change data of each joint, and the motion acceleration data, and construct a complete description of the robot arm's motion state through time series alignment technology;

[0025] The robot's motion trajectory is mapped to a topological space. A nonlinear dimensionality reduction method based on manifold learning is used to extract key topological feature points in the trajectory, including extreme points of trajectory curvature and points of abrupt change in motion direction, to form a phase space representation of the dynamic system.

[0026] By calculating the homology invariants of the trajectory in the topological space, a persistent homology feature matrix of the motion trajectory is constructed, which includes the connectivity features of the trajectory loop and the distribution features of the motion obstacles.

[0027] The topological feature matrix is ​​coupled with the dynamic parameters of the robot to generate a topological encoding vector with motion constraints, which is used to characterize the dynamic behavior pattern of the robot in collaborative operation.

[0028] As a further aspect of the present invention: the specific implementation of analyzing the feature interactions of the robot's motion trajectory in the topological space includes:

[0029] A trajectory topological feature sequence based on time sliding window is established, and the topological evolution trend of the robot's motion trajectory is identified by calculating the homology group change rate of adjacent time segments.

[0030] A multi-scale coherence analysis method is used to detect the intensity of characteristic interactions between trajectories at both the macroscopic motion path and microscopic vibration levels. The spatial overlap of trajectory loops is analyzed at the macroscopic level, while the energy coupling of motion harmonics is detected at the microscopic level.

[0031] A dynamic density field model is constructed, and the probability of topological conflict of a group of robotic arms in a shared workspace is calculated in real time by quantifying the energy accumulation effect of the intersection region of homology groups.

[0032] As a further aspect of the present invention: the specific method for generating the deadlock risk warning signal includes:

[0033] An adaptive threshold adjustment mechanism based on historical operating data is set up to dynamically adjust the warning threshold of the intersection density of the homology group according to the workload density of the robot group.

[0034] A spatiotemporal deadlock pattern recognition model is established, and the development trend of deadlock risk is predicted by analyzing the propagation characteristics of topological conflicts in the time and space dimensions.

[0035] A tiered early warning mechanism is adopted to generate different levels of early warning signals based on the severity of the conflict, including local path adjustment suggestions and global task rescheduling instructions;

[0036] By connecting the early warning signal with the real-time decision-making module of the robotic arm control system, a closed-loop process from risk detection to control response is completed within 5 milliseconds.

[0037] As a further aspect of the present invention, the specific implementation of generating a dynamic motion trajectory with optimal collision avoidance characteristics includes:

[0038] The trajectory reconstruction technique based on topological constraints generates a set of candidate collision avoidance paths by introducing a virtual repulsion field to perform topological deformation on the conflict region while keeping the original path homology class unchanged.

[0039] A multi-objective optimization strategy is adopted to evaluate candidate paths, taking into account three dimensions: path length, manipulator energy consumption, and task timeliness, and an optimal path selection model based on the Pareto front is established.

[0040] The trajectory is smoothed by combining the dynamic characteristics of the manipulator, and the cubic spline interpolation algorithm is used to ensure that the optimized trajectory meets the joint motion velocity and acceleration constraints.

[0041] Implement a real-time trajectory verification mechanism and ensure that the optimized trajectory remains topologically equivalent to the original mission objective through topological persistence analysis.

[0042] As a further aspect of the present invention, the specific method for trajectory optimization under the constraint of preserving path topology invariance includes:

[0043] A path topology invariance verification model is constructed, and the degree of preservation of path topology features is quantitatively evaluated by calculating the change in Betti number of the trajectory before and after optimization.

[0044] A hierarchical optimization architecture is adopted, with the upper layer processing global topological constraints and the lower layer optimizing local geometric features, thereby achieving synergistic optimization of topological properties and motion performance;

[0045] An adaptive learning mechanism is introduced to dynamically adjust the topology constraint weight coefficients based on historical optimization results, balancing collision avoidance requirements with task completion quality.

[0046] Establish a closed-loop feedback mechanism for trajectory optimization, compare and analyze the actual execution data with the expected optimization target, and continuously improve the trajectory generation algorithm.

[0047] As a further aspect of the present invention: the specific implementation of converting the optimized motion trajectory into a sequence of joint control parameters includes:

[0048] A kinematic inverse model based on Lie group theory is established to decompose the pose trajectory of the end effector into independent motion components of each joint, ensuring that the conversion process meets the geometric constraints of the robotic arm.

[0049] An adaptive interpolation algorithm is used to process the key points of the trajectory, and the interpolation density is dynamically adjusted according to the current motion state of the robot, so as to reduce the computational load while ensuring the trajectory accuracy.

[0050] A joint space dynamics filter is constructed to compensate for the influence of inertial forces and Coriolis forces on motion accuracy in advance by predicting the load changes of each joint in the next control cycle.

[0051] A multi-level verification mechanism is implemented to confirm the safety of the generated joint control parameter sequence through both inverse kinematics verification and collision detection.

[0052] As a further aspect of the present invention: the specific method for dynamically adjusting the execution force and speed includes:

[0053] A force adjustment system based on fuzzy reasoning is established to dynamically adjust the clamping force parameters by real-time monitoring of the contact force signal between the end effector and the foreign object.

[0054] An environment-adaptive speed planning algorithm is adopted, which uses the maximum allowable speed in open areas and automatically reduces the speed in dense areas based on the obstacle density distribution characteristics of the workspace.

[0055] Construct an execution effect feedback network and, by comparing the deviation between the actual sorting trajectory and the planned trajectory, correct the adaptive adjustment strategy of the control parameters online;

[0056] It achieves coordinated control of force and speed, and automatically triggers the force-speed coupling adjustment mode when vulnerable foreign objects are detected, ensuring the safety of the sorting process.

[0057] The beneficial effects of this invention are:

[0058] (1) This invention utilizes a dynamic spatial feature extraction module and multi-sensor fusion technology to collect data such as depth images, point clouds, and motion trajectories of the intelligent foreign object sorting robot in its working environment in real time. This constructs a three-dimensional dynamic spatial model with multi-dimensional topological characteristics and employs continuous coherence analysis to accurately extract the connectivity features and annular obstacle features of the spatial structure. Based on this, the collaborative conflict detection module not only utilizes time-sliding window-based trajectory topological feature sequences and multi-scale coherence analysis to deeply probe the interaction features between the robot groups and between the robot and the environment at both macroscopic and microscopic levels, but also calculates the topological conflict probability within the shared workspace in real time by constructing a dynamic density field model. Furthermore, the system introduces a hierarchical early warning mechanism, adaptively adjusting the early warning threshold based on historical operating data to identify and handle potential deadlock risks, thereby achieving early warning and avoidance of collision risks under complex working conditions. This series of precise spatial perception, intelligent conflict prediction, and management strategies improves the safety and efficiency of collaborative robot operations, optimizes task execution paths, and ensures the efficient operation of the entire system. Through this multi-layered and comprehensive protection measure, the present invention reduces the probability of collisions during operation, while improving resource utilization and production efficiency.

[0059] (2) The execution control adjustment module of this invention deeply integrates Lie group theory and modern control algorithms. Addressing the motion requirements of multi-arm systems in complex, nonlinear, and strongly coupled spaces, it employs a kinematic inverse solution method based on Lie groups. This effectively avoids the singularity problem caused by coordinate system transformation in traditional Euclidean space modeling, achieving a high-fidelity mapping from joint space to task space. Based on this, the system introduces an adaptive interpolation algorithm, dynamically adjusting the interpolation step size and smoothness parameters in conjunction with real-time trajectory error feedback. This generates a highly continuous sequence of joint control parameters, ensuring that acceleration and jump during motion are within the optimal range, significantly improving motion stability and response speed. To further improve the trajectory tracking accuracy of the end effector, the module integrates a dynamic filter based on recursive least squares, which identifies and compensates for inertial forces, Coriolis forces, and gravitational disturbances caused by high-speed motion in real time, effectively suppressing the accumulation of dynamic errors. Meanwhile, the system innovatively integrates a fuzzy reasoning mechanism to achieve adaptive force adjustment. Based on the material, shape, and surface friction characteristics of the foreign object as reflected by visual recognition, it intelligently determines the optimal clamping torque to avoid damaging the material or causing gripping failure. Combined with the obstacle density map and dynamic risk level output by the environmental perception module, it implements an environmentally adaptive speed planning strategy. In high-density obstacle areas, it automatically reduces speed and enhances trajectory compliance; in open areas, it improves operational efficiency, achieving a dynamic balance between safety and speed. This multi-dimensional collaborative control mechanism not only ensures sub-millimeter-level spatial positioning accuracy and millisecond-level response capability but also endows the system with high robustness and intelligence in dynamic unstructured environments, fully meeting the stringent requirements of modern intelligent manufacturing for high-precision, high-flexibility, and high-safety automated sorting operations. Attached Figure Description

[0060] The invention will now be further described with reference to the accompanying drawings.

[0061] Figure 1 This is a flowchart of a foreign object intelligent sorting robot control system based on AI recognition, according to the present invention. Detailed Implementation

[0062] 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.

[0063] Example 1, please refer to Figure 1 As shown, the present invention is a control system for an AI-based intelligent foreign object sorting robot, comprising:

[0064] The dynamic spatial feature extraction module is used to collect spatial data of the intelligent foreign object sorting robot in the sorting working environment in real time, construct a three-dimensional dynamic spatial model with multi-dimensional topological features, and extract the connectivity features and ring obstacle features of the spatial structure through continuous homology analysis.

[0065] The spatial data includes depth image data, point cloud data, three-dimensional contour data, and dynamic obstacle motion trajectory data.

[0066] The robot motion state encoding module converts the real-time motion parameters of each robot into a dynamic system representation in the topological space and establishes a topological feature encoding matrix based on the motion trajectory.

[0067] The real-time motion parameters include end effector pose data, joint angle data, and motion speed data;

[0068] The collaborative conflict detection module is used to analyze the characteristic interaction of the motion trajectories of each robot arm in the topological space. When the intersection density of homology groups exceeds a predetermined threshold, a deadlock risk warning signal is generated.

[0069] The dynamic trajectory optimization module generates a dynamic motion trajectory with optimal collision avoidance characteristics based on the characteristic intensity of the deadlock risk warning signal, while maintaining the constraint of path topology invariance.

[0070] The execution control and adjustment module converts the optimized motion trajectory into a sequence of joint control parameters for the robotic arm, and dynamically adjusts the execution force and speed based on real-time environmental feedback.

[0071] In Example 2, the dynamic spatial feature extraction module first acquires spatial data of the sorting environment in real time through a multi-sensor fusion system. This system includes a high-precision depth camera and a LiDAR. The depth camera acquires depth image data of the environment at a frequency of 30 frames per second, while the LiDAR scans and generates point cloud data at a frequency of 10 Hz. To ensure spatiotemporal consistency of the data, the system uses a hardware synchronization signal to trigger synchronous acquisition by all sensors and uses precise timestamp alignment technology to unify the data from different sensors to the same time reference. In the preprocessing stage, the raw data undergoes denoising. Depth image data is processed using a bilateral filtering algorithm to eliminate measurement noise, while point cloud data is filtered for outlier measurements using a statistical outlier removal method.

[0072] Preprocessed spatial data enters the 3D reconstruction process. The system first registers depth image data and point cloud data in a unified coordinate system, employing an iterative nearest-point algorithm to achieve millimeter-level precision data fusion. The registered data is then converted into a voxel representation, with each voxel unit set to 5mm × 5mm × 5mm to control computational complexity while preserving environmental details. During voxelization, the system specifically preserves the topological characteristics of the original data to ensure that subsequent analysis accurately reflects the true connectivity of the workspace. In addition to storing basic spatial location information, each voxel node records the surface normal vector distribution characteristics and local curvature variation characteristics of the region, providing rich geometric features for subsequent topological analysis.

[0073] The system employs a persistent homology analysis method to extract key topological features from a 3D voxel model. This method first constructs a simplex complex representation of the voxel data, then calculates homology groups from 0D to 2D. By systematically changing filtering parameters, the generation and disappearance processes of each homology feature are recorded, generating corresponding persistent homology barcodes. Analyzing the lifetime of these barcodes, the system can identify persistent topological features in the environment: long-lived 0D homology classes correspond to connected components of the environment, 1D homology classes reflect ring structures such as holes, and 2D homology classes characterize cavity structures. Specifically, the system sets a lifetime threshold to retain only features with significant topological meaning, filtering out transient homology classes caused by measurement noise.

[0074] For dynamic obstacles in the environment, the system tracks and analyzes their trajectories in real time. Each trajectory is first represented as a time-series point set in three-dimensional space, and then different motion patterns are identified through a density peak-based clustering algorithm. This algorithm automatically determines the number of cluster centers and can effectively distinguish different trajectory types such as linear motion and curvilinear motion. The clustered trajectories are converted into a topological representation, and the system calculates the continuous homology features of each trajectory, paying particular attention to the loop structures and intersections formed by the trajectories. These topological features have a unified mathematical representation with the topological features of the static environment, facilitating subsequent fusion analysis.

[0075] The 3D contour data of the static environment is analyzed using Morse theory. The system first calculates the critical points of the 3D distance field, including minimum points, maximum points, and saddle points; these critical points constitute key features of the environment's topology. Based on these critical points, the system constructs a Reeb graph representation of the environment, simplifying the complex 3D shape into a graph structure that preserves topological properties. Nodes in the Reeb graph correspond to connected components in the contour tree, and edges represent the adjacency relationships between components. By analyzing the structural features of the Reeb graph, the system can identify important topological features in the workspace, such as narrow passages and potential interference regions.

[0076] The system fuses the trajectory topology of dynamic obstacles with the topological features of the static environment to construct a four-dimensional spatiotemporal topological model. This model adds a time dimension to the three-dimensional space, enabling a complete description of the spatiotemporal evolution of the workspace. During the fusion process, the system calculates the interrelationships of different topological features in space and time, particularly the variation patterns of homology groups on spatiotemporal slices. When multiple manipulator trajectory homology groups are detected to significantly intersect in the same spatiotemporal region, the system determines a potential conflict risk. The intersection degree calculation considers the area and duration of the intersection region, as well as the importance weights of the topological features, ultimately generating a quantified conflict probability prediction.

[0077] All extracted topological features are organized into a multi-layered feature database. The bottom layer stores raw topological feature data, including persistent coherence barcodes and Reeb graph structures; the middle layer stores abstracted feature descriptors, such as connectivity indices and ring structure parameters; and the top layer stores application-oriented semantic features, such as labels for "narrow passages" and "high-risk interference zones." The database is organized using a time-series approach, supporting rapid retrieval of historical topological features and providing a data foundation for the system's self-learning capabilities.

[0078] The system establishes a real-time update mechanism to ensure the timeliness of topological features. Upon receiving new sensor data, the system first assesses the degree of environmental change. For areas with significant changes, it updates the topological features locally, while retaining cached features for areas where changes are not observed. An incremental computation strategy is employed during the update process, recalculating only the topological features of the affected areas, significantly improving computational efficiency. Simultaneously, the system continuously optimizes feature extraction parameters. By analyzing the processing effects of historical data, it automatically adjusts key parameters such as voxel size and homology filtering threshold to maintain the accuracy of feature extraction.

[0079] The system is equipped with a comprehensive anomaly detection mechanism. Reasonableness checks are implemented at each stage of topological feature extraction, such as verifying the monotonicity of continuously homogeneous barcodes and checking the connectivity of the Reeb graph. When an anomaly is detected, the system automatically initiates a fault-tolerant processing procedure: first, the abnormal data is isolated, and then repaired using redundant sensor data or historical data to ensure the stability of the feature extraction process. All anomaly events are recorded and used to improve system robustness.

[0080] The dynamic spatial feature extraction module interacts with other modules in the system through standardized interfaces. Topological feature data employs a hierarchical transmission mechanism: basic topological data is transmitted in real-time via a high-speed data bus, while advanced semantic features are transmitted asynchronously via a message queue. The interface design considers data timeliness requirements, and critical conflict warning information uses an interrupt mechanism to immediately notify the motion planning module. Simultaneously, the interface retains sufficient scalability to support the incremental addition of new topological features.

[0081] To meet real-time requirements, the system employs multiple computational acceleration technologies. Topology feature computation tasks are allocated to dedicated computing units: continuous cohomology computation is executed in parallel on GPUs, while Reeb graph construction utilizes the parallel computing capabilities of multi-core CPUs. The system monitors the computational load in real time and dynamically adjusts the task scheduling strategy, prioritizing the processing of topology features in critical regions when computational resources are scarce. Memory management employs an intelligent caching strategy, retaining frequently accessed topology feature data in fast storage.

[0082] The system integrates comprehensive performance evaluation functions. By comparing actual robot arm movements with topology prediction results, the accuracy of feature extraction is continuously evaluated. Evaluation metrics include the accuracy of conflict prediction, false alarm rate, and the timeliness of topology features. Based on the evaluation results, the system automatically calibrates feature extraction parameters, forming a closed-loop optimization. Simultaneously, the system supports manual intervention calibration, allowing operators to fine-tune feature extraction for specific scenarios.

[0083] To meet the security requirements of industrial applications, the system implements multiple protection mechanisms. All topology feature data undergoes integrity verification, and critical control commands employ redundant verification designs. The system monitors the execution time of the feature extraction process in real time, automatically triggering a degradation processing scheme when the timeout occurs. Simultaneously, the system maintains complete operation logs, supporting post-event analysis and accountability.

[0084] The dynamic spatial feature extraction module adopts a highly modular design. Each functional component, such as data acquisition, 3D reconstruction, and topology analysis, is implemented as an independent module, interacting through a standard interface. This design facilitates functional expansion and maintenance; for example, the topology analysis algorithm can be upgraded independently without affecting other modules. The modular design also supports distributed deployment, allocating computationally intensive tasks to dedicated servers for execution.

[0085] In Example 3, the robot's motion state encoding module first acquires real-time motion parameters through high-precision encoders mounted on each joint of the robot and inertial measurement units mounted on the end effector. The joint encoders measure the joint angle changes of six degrees of freedom with a resolution of 0.1 degrees and a sampling frequency of 1 kHz. The pose data of the end effector is acquired through an optical tracking system mounted on the robot arm base, which outputs the position and attitude data of the end effector in three-dimensional space at a frequency of 100 frames per second. Motion acceleration data is measured by a triaxial accelerometer with a range covering ±10g, which can accurately capture the dynamic motion characteristics of the robot. All sensor data are synchronized at the microsecond level through hardware synchronization signals to ensure the spatiotemporal consistency of the motion state description.

[0086] The acquired multi-dimensional motion parameters are then used in the motion state description construction phase. The system employs a sliding time window technique to process continuous sensor data, with each window covering 200 milliseconds of motion, and adjacent windows overlapping by 50% to maintain continuity. Within each window, the system performs Kalman filtering on the raw data to eliminate measurement noise and compensate for sensor drift. The processed data is converted into a unified motion state description vector, containing the end effector's 3D position, 3D attitude quaternions, six joint angle values, and corresponding velocity and acceleration information. This description vector is updated at a frequency of 100Hz, providing complete temporal data for subsequent analysis.

[0087] The constructed motion state description vector is mapped to a topological space. The system first performs initial dimensionality reduction on the high-dimensional motion state vector using principal component analysis, retaining 90% of the original information. Then, a manifold learning method based on the t-SNE algorithm is used to further project the dimensionality-reduced data into a three-dimensional topological space. During the projection process, the system pays special attention to preserving the local and global topological properties of the motion trajectory, ensuring that the structural features of the original motion trajectory are accurately reflected in the low-dimensional space. The projected trajectory point set constitutes the phase space representation of the dynamic system, where each point corresponds to the motion state of the manipulator at a specific moment.

[0088] In the topological space, the system analyzes the geometric characteristics of the robot's motion trajectory to extract key feature points. First, it calculates the local curvature changes of the trajectory, identifying curvature extrema by the relative positions of adjacent motion state points; these points typically correspond to significant changes in the robot's motion direction. Simultaneously, the system detects abrupt changes in motion direction, using the direction cosine method to calculate the directional change between adjacent trajectory segments; when the change exceeds a preset threshold, it is marked as an abrupt change point. Furthermore, the system identifies dwell points and turning points in the trajectory, constructing a complete set of feature points. These feature points will serve as key nodes for subsequent topology analysis.

[0089] Based on the extracted key feature points, the system constructs a phase space representation of the dynamic system. This representation transforms the continuous motion trajectory of the manipulator into a discrete state transition diagram, where nodes correspond to feature points, and edges represent the transition relationships between feature points. Each node is accompanied by motion state parameters, including information such as velocity and acceleration when reaching that feature point. The edge weights reflect the ease or difficulty of the transition, taking into account factors such as joint torque constraints. This representation method preserves the topological properties of the motion trajectory while simplifying the computational complexity of subsequent analysis.

[0090] The system performs homology analysis on motion trajectories in topological space to extract deep topological features. Employing persistent homology theory, the system constructs a Vietoris-Rips complex sequence of point cloud data for the motion trajectory, generating a series of nested complex structures by changing the connectivity radius parameter. For each parameter value, the homology group of the trajectory is calculated, recording the generation and disappearance of homology classes. Particular attention is paid to the persistent existence of 1D homology classes (loop structures), which reflect the loop characteristics in the robot's motion trajectory. The analysis results are encoded as persistent homology barcodes, where long bars correspond to important topological features, while short bars may represent noise or transient structures.

[0091] Based on the persistent cohomology analysis results, the system constructs a topological feature matrix of the motion trajectory. The rows of this matrix correspond to different cohomology dimensions (0-dimensional, 1-dimensional, and 2-dimensional), and the columns record key parameters for each cohomology class, including birth radius, death radius, and persistence. The matrix specifically labels cohomology classes related to the robot's motion, such as the persistent 1-dimensional cohomology class representing periodic motion. The system also calculates the geometric center coordinates and spatial distribution characteristics for each cohomology class, enriching the matrix's descriptive capabilities. The final generated topological feature matrix is ​​updated at a frequency of 10Hz, reflecting the real-time changes in the topological characteristics of the robot's motion.

[0092] A deep coupling analysis is performed between the topological feature matrix and the manipulator's dynamic parameters. The system first establishes feature vectors for the dynamic parameters, including information such as torque output, energy consumption, and motion smoothness for each joint. Then, a specialized coupling algorithm is designed to correlate the dynamic feature vectors with corresponding rows of the topological feature matrix. For example, the persistence of 1D homology classes is correlated with the degree of joint torque fluctuation to identify motion patterns that may lead to manipulator overload. The coupling analysis uses a sliding time window, with the window size consistent with the motion state description construction stage to ensure temporal alignment.

[0093] Based on the coupling analysis results, the system generates the final topology encoding vector. This vector integrates the topological features and dynamic constraints of the motion trajectory, and includes the following main components: trajectory connectivity index, ring structure strength, motion obstacle distribution density, and dynamic constraint satisfaction. Each dimension in the vector is normalized, with values ​​ranging from 0 to 1, facilitating unified processing by subsequent modules. The encoding vector also includes a confidence index, reflecting the reliability of feature extraction. The system maintains an encoding vector sequence buffer, storing the motion encoding history for the most recent 30 seconds, supporting motion pattern analysis based on time context.

[0094] The motion state encoding module implements a real-time update mechanism to ensure encoding accuracy. The system continuously monitors encoding quality indicators, automatically triggering a recalculation process when a decrease in confidence is detected. Simultaneously, the module employs an incremental learning method, adjusting feature extraction parameters based on the robot's actual motion performance. For example, when certain topological features are found to continuously conflict with dynamic constraints, the weight of these features is automatically reduced. The update process is completed without interrupting real-time encoding, meeting the system's high availability requirements.

[0095] The module is equipped with a comprehensive anomaly detection mechanism. Reasonableness checks are implemented at every stage of the encoding process, such as verifying the positive definiteness of the topological feature matrix and checking the range of values ​​for the encoding vectors. When an anomaly is detected, the system takes different measures depending on the severity: minor anomalies trigger local recalculation, while severe anomalies activate a backup encoding scheme and issue an alarm. All anomaly events are recorded and used to improve system robustness, forming a closed-loop optimization mechanism.

[0096] The motion state encoding module collaborates with other modules in the system through standardized interfaces. Encoded vectors are transmitted in real-time to the collision detection module via a high-speed data bus, and simultaneously sent asynchronously to the learning and optimization module via a message queue. The interface design considers the different needs of various modules; for example, the collision detection module receives simplified real-time encodings, while the learning module obtains complete encoding sequences with historical context. The module also provides a query service, allowing other modules to obtain motion codes at specific points in time as needed.

[0097] To meet real-time requirements, the module employs an intelligent resource allocation strategy. Computationally intensive tasks (such as coherent analysis) are assigned to dedicated computing units, while tasks with high real-time requirements (such as encoding vector generation) receive priority access to CPU resources. The system dynamically monitors the computational load and automatically reduces the accuracy requirements of non-critical tasks when resources are scarce, ensuring the real-time response of core functions. Memory usage employs a combination of pre-allocation and dynamic adjustment to balance performance and resource utilization.

[0098] The system periodically evaluates the quality of motion state encoding. Evaluation metrics include the real-time performance of the encoding, the accuracy of feature extraction, and the degree of matching with subsequent modules. Evaluations are based on actual operational data, comparing the encoded prediction results with the actual motion state of the robotic arm. Evaluation results are used to automatically calibrate encoding parameters, such as adjusting feature point detection thresholds and optimizing coherence analysis parameters. The system supports manual calibration, allowing operators to perform targeted optimizations for specific motion patterns.

[0099] To ensure security for industrial applications, the module implements multiple protection measures. All coded data is accompanied by an integrity checksum to prevent transmission errors. Critical computation processes employ redundancy, with primary and backup computation paths running independently and cross-validating. The system monitors encoding latency in real time and automatically switches to simplified mode if the encoding fails to complete within a timeout period. Simultaneously, the module maintains detailed operation logs, recording the basis for all encoding decisions and supporting post-event auditing and analysis.

[0100] The motion state encoding module adopts a layered modular design. The bottom layer processes raw sensor data, the middle layer is responsible for feature extraction, and the top layer generates the encoding. Each layer interacts with the others through well-defined interfaces, supporting independent upgrades and expansions. For example, the topology analysis method in the middle layer can be replaced without affecting other parts. The module also reserves expansion interfaces to support the ingestion of new sensor data and the addition of new encoding features.

[0101] In Example 4, the collaborative conflict detection module first establishes a topological feature sequence of the robot's trajectory based on a time sliding window. The system sets a 200-millisecond sliding time window, with the window sliding forward in 50-millisecond steps to ensure the continuity of feature evolution. Within each window, the module receives topological feature vectors from the motion state encoding module, including parameters such as trajectory connectivity indices and ring structure strength. These feature vectors are arranged in chronological order to form a feature sequence. The sequence length is adaptively adjusted according to the robot's movement speed; shorter sequences are used for fast movement to ensure timeliness, while longer sequences are used for slow movement to improve analysis accuracy. The system standardizes the feature sequence to eliminate the influence of differences in the movement amplitude of different robots, establishing a unified benchmark for subsequent interactive analysis.

[0102] Based on the constructed feature sequence, the module calculates the rate of change of homology groups between adjacent time segments. For each topological feature dimension, the system compares the relative changes in feature values ​​in the current window with those in the previous window, and calculates the absolute value and first derivative of the rate of change. Particular attention is paid to changes in 1-dimensional homology classes (ring structures); the sudden appearance or disappearance of ring structures may indicate abnormal interactions in the robot's motion trajectory. The system maintains a historical database of rates of change, and by comparing the deviation of the current rate of change from the historical normal range, it preliminarily judges the degree of abnormality in the interaction behavior. The calculation process employs an incremental update strategy, recalculating only the changed features, significantly improving computational efficiency.

[0103] The module employs a multi-scale coherence analysis method to deeply detect the interaction characteristics between trajectories. At the macroscopic scale (centimeter level), the system analyzes the spatial overlap of the loop structures formed by the robot's motion paths. By calculating the intersection area and duration of different robot trajectory loops, the interaction intensity at the macroscopic level is quantified. At the microscopic scale (millimeter level), the system focuses on the harmonic characteristics generated by the robot's vibrations. It extracts characteristic frequency components from the motion spectrum of each robot using Fast Fourier Transform to analyze the coupling degree of vibration energy between different robots. The analysis results from both scales are fused into a unified interaction intensity index, reflecting both significant spatial interference and capturing subtle vibration disturbances.

[0104] Based on multi-scale analysis results, the module constructs a dynamic density field model to quantify conflict risk. The system discretizes the shared workspace into a 5mm resolution voxel grid, with each voxel recording two types of density values: spatial occupancy density, reflecting the distribution probability of the manipulator entity in the area, and interaction energy density, characterizing the intensity of interactions detected in the area. The density field is dynamically updated over time, with new observation data incorporated into the existing field through an exponentially decaying weighted approach, giving higher weight to recent data and gradually reducing the influence of earlier data. The system pays particular attention to the evolution trend of high-density areas, predicting the movement direction and diffusion speed of potential conflict zones by calculating density gradients and divergences.

[0105] The dynamic density field model outputs real-time topological conflict probabilities. The system defines the conflict probability as a normalized composite index, comprehensively considering the following factors: homology group intersection density, interaction energy intensity, historical conflict occurrence rate, and the robot's motion state. The probability calculation uses a moving average method to smooth instantaneous fluctuations and avoid false alarms. The calculation results are updated at a frequency of 100Hz and mapped as a visual heatmap overlaid on the 3D workspace model, allowing operators to intuitively understand the risk distribution. The system sets multiple probability thresholds, corresponding to different levels of risk states such as attention, warning, and danger.

[0106] The module implements adaptive threshold adjustment based on historical data. The system continuously records the statistical distribution of homology group intersection density under normal operating conditions, including parameters such as mean, variance, and extreme values. These statistical data are stored categorized by workload level, forming a threshold benchmark library. During actual operation, the module automatically selects the corresponding benchmark threshold based on the current load level and dynamically adjusts it considering recent system performance. For example, when a decline in robotic arm performance is detected (such as insufficient joint lubrication), the threshold is appropriately lowered to improve detection sensitivity. Threshold updates employ a gradual strategy to avoid sudden changes that could lead to system instability.

[0107] The system establishes a spatiotemporally correlated deadlock pattern recognition model. This model analyzes the propagation characteristics of topological conflicts in the temporal and spatial dimensions, identifying potential deadlocks through features such as the continuous expansion of the conflict region, closed-loop structures formed by multiple manipulators blocking each other, and failed attempts to resolve the conflict. The model uses a finite state machine to represent the deadlock development process, defining multiple intermediate states and transition conditions. In real-time monitoring, when a series of specific state transitions are observed, the deadlock risk is determined to have increased. The model also integrates manipulator dynamic constraints, enabling it to distinguish between temporary blockages and true deadlock states, reducing false positives.

[0108] Based on the severity of the conflict, the module implements a tiered early warning mechanism. Level 1 warnings target low-risk localized conflicts, sending only path fine-tuning suggestions to the relevant robotic arms; Level 2 warnings target medium-risk area conflicts, triggering local replanning; and Level 3 warnings target high-risk deadlocks, initiating global task rescheduling. Each warning level is accompanied by detailed contextual information, including the conflict location, involved robotic arms, and suggested actions. The system has an early warning escalation mechanism; if a lower-level warning fails to resolve the issue within a specified time, the warning level is automatically escalated. All warning events are recorded and used to refine the early warning strategy.

[0109] The early warning signal is transmitted to the real-time decision-making module of the robot control system via a high-speed communication interface. The system ensures that the end-to-end latency from risk detection to control response does not exceed 5 milliseconds. To achieve this, all processing steps on the critical path are scheduled with optimal priority, and network transmission uses time-triggered Ethernet protocol to ensure timeliness. Upon receiving the early warning, the decision-making module immediately sends an acknowledgment signal and provides feedback on the execution result within 10 milliseconds. The collaborative conflict detection module monitors the entire processing and activates a backup plan when the expected effect is not achieved. This closed-loop processing mechanism significantly improves the system's reliability and response speed.

[0110] The module employs multiple techniques to optimize performance. Computationally intensive tasks (such as density field updates) are allocated to GPU parallel processing; tasks with high real-time requirements (such as alert generation) are executed on dedicated CPU cores. The system dynamically monitors the load of each processing unit and achieves load balancing through task migration and priority adjustment. Memory access employs prefetching and caching optimization strategies to reduce data access latency. All optimization measures are implemented while ensuring functional integrity, guaranteeing that the accuracy of the analysis results is not affected.

[0111] The module is equipped with a robust exception handling mechanism. When a data processing exception is detected, the system first attempts automatic recovery by recalculating the affected data blocks. If automatic recovery fails, the appropriate degradation mode is activated based on the exception type: for non-critical exceptions, operation continues but data reliability is flagged as reduced; for critical exceptions, the system switches to simplified analysis mode and issues an alert. The system performs regular health checks to verify the functional integrity of each submodule and proactively address any issues. All exception events are logged in detail, supporting post-event analysis and system improvement.

[0112] The collaborative conflict detection module integrates with other parts of the system through well-defined interfaces. The input interface receives real-time data from the motion state coding module, while the output interface provides early warning signals and conflict analysis reports. The module also offers a query service, allowing other modules to retrieve conflict analysis results on demand. The interface design considers scalability, reserving access points for adding new data types and functions. Integration testing verifies that the module can seamlessly integrate into the existing architecture without affecting the overall system performance.

[0113] In Example 5, the dynamic trajectory optimization module first receives a deadlock risk warning signal from the cooperative conflict detection module. The system performs multi-dimensional analysis on the warning signal, extracting key feature parameters including: the spatial coordinate range of the conflict area, risk level score, urgency index, and predicted conflict time window. These parameters are converted into a standardized representation to eliminate output differences between different detection modules. The system pays particular attention to the spatial relationship between the conflict area and the robot's current motion path, quantifying the potential risk level faced by each path point by calculating the intersection depth between the path segment and the risk area. The analysis process employs a priority strategy, processing high-risk signals immediately while low-risk signals are queued and buffered.

[0114] Based on risk characteristic analysis, the system constructs a virtual repulsion field in the conflict area for trajectory deformation. The intensity distribution of the repulsion field is set according to the risk level, with the intensity at the center of high-risk areas set to 90% of the maximum value, and the intensity at the edges decreasing according to a Gaussian distribution. The direction of the field's action considers the robot's movement trend, prioritizing pushing the path towards low-density working areas. The system configures differentiated field parameters for different types of conflicts: for hard collision risks, a short-range strong field is used for rapid avoidance; for soft disturbances, a long-range weak field is used for smooth adjustment. All field parameters are normalized to ensure consistency under different conflict scenarios.

[0115] Under the influence of a repulsive field, the system reconstructs a trajectory while preserving its topological properties. The algorithm first identifies key topological features of the original path, including the start point, end point, waypoints, and key turning points. These points are categorized into critical points whose positions must be preserved and ordinary points that can be adjusted moderately. The reconstruction process employs an iterative optimization method. While ensuring the positions of critical points remain unchanged, the spatial coordinates of ordinary points are adjusted using a gradient descent algorithm, allowing the overall path to avoid conflict regions. After each iteration, the system verifies the topological equivalence of the path, ensuring that no new loop structures are generated or existing loop structures disappear. The reconstruction process typically converges within 3–5 iterations, meeting real-time requirements.

[0116] The trajectory reconstruction algorithm outputs a set of candidate paths, containing 3-5 topologically equivalent but geometrically different collision avoidance paths. Each candidate path comes with complete performance parameters, including path length, estimated execution time, and maximum curvature. The system performs an initial screening of the candidate paths, eliminating schemes that clearly do not meet the dynamic constraints, and retaining 2-3 optimal candidates for the next stage of evaluation. The screening process employs a rapid evaluation strategy, focusing on checking the continuity and executability of the paths, without performing detailed calculations to ensure response speed.

[0117] The system establishes a multi-objective optimization model to evaluate candidate paths. The model defines three core optimization objectives: shortest path length, lowest manipulator energy consumption, and optimal task timeliness. The weights of each objective are dynamically adjusted based on the current system state: timeliness is weighted more in time-sensitive scenarios, while energy consumption optimization is emphasized in energy-saving modes. The model also includes a series of hard constraints, including joint angle limits, speed limits, and acceleration limits. These constraints are expressed in inequalities to ensure that all generated solutions satisfy the physical limitations of the manipulator. The optimization model adopts a standardized design, converting all objectives and constraints into dimensionless forms for easy comprehensive comparison.

[0118] The multi-objective optimization problem is solved using the Pareto front method. The system plots the distribution of candidate paths in the objective space and searches for a front formed by the non-dominated solution set. The solution process employs an improved NSGA-II algorithm, progressively optimizing the solution set quality by simulating biological evolution. The algorithm can obtain a satisfactory approximate front after 5-10 iterations, meeting real-time response requirements. The system analyzes the morphological characteristics of the front; when a clear trade-off is detected, it automatically recommends a balance point scheme; if the front is biased towards a certain objective, it selects the optimal point in that direction. The final selected scheme guarantees multi-objective overall optimality under the existing constraints.

[0119] The selected optimized path undergoes dynamic adaptation. The system analyzes the degree of matching between the path's geometric characteristics and the robot's dynamic parameters, identifying high-curvature sections that may require adjustment. These sections are smoothed using a cubic spline interpolation algorithm to ensure the trajectory is continuous and executable across all derivatives. The smoothing process is strictly constrained: positional deviation does not exceed 5mm, and the rate of velocity change is limited to within 10%. The processed trajectory is discretized into a dense sequence of path points, each point carrying complete position, velocity, and acceleration information, providing a precise reference for subsequent execution control.

[0120] The system performs rigorous topological equivalence verification on the optimized trajectory. The verification process compares the topological invariants of the original and optimized paths, including calculating the 0-dimensional and 1-dimensional Betti numbers of the two paths, examining the relative positional relationships of key feature points, and analyzing changes in the connectivity of path loops. A tolerance range is set for the verification metrics, allowing for moderate geometric deformation while maintaining the topological structure. When a fundamental change in the topology is detected, the system immediately initiates a re-optimization process and adjusts the repulsion field parameters to prevent further failure. All verification results are recorded for use in improving subsequent optimization algorithms.

[0121] The system employs a layered architecture for efficient optimization. The upper-layer optimizer handles global topological constraints, quickly determining feasible topological structures through simplified path representations; the lower-layer optimizer is responsible for geometric detail adjustments, optimizing local path shapes within the global framework. The two optimizers interact through well-defined interfaces: the upper layer passes constraints to the lower layer, and the lower layer feeds back optimization results to the upper layer. This architecture ensures both strict preservation of topological properties and fine-grained optimization of local paths.

[0122] The system continuously improves and optimizes its performance through adaptive learning. After each trajectory optimization, the system records the difference between the actual execution effect and the expected goal, including indicators such as path tracking accuracy, execution time deviation, and energy consumption changes. This data is used to dynamically adjust the parameters of the optimization model: for goals that consistently perform poorly, their weights are appropriately reduced; for constraints that can always be met, the restrictions are gradually tightened. The learning process employs a sliding window strategy, considering only the most recent 100 optimization records to ensure that the system can quickly adapt to changes in the working environment. The learning rate is automatically adjusted based on the stability of the scenario; in a stable environment, the learning rate is reduced to maintain stability, and in a changing environment, the learning rate is increased to accelerate adaptation.

[0123] The dynamic trajectory optimization module constructs a complete closed-loop feedback system. The optimized trajectory is monitored in real time during execution, and the system collects deviation information between the actual motion data and the planned trajectory. This information is categorized and analyzed: short-term deviations are used to correct the current trajectory online, while medium- and long-term trends are fed back to the optimization algorithm to improve subsequent planning. The feedback system pays particular attention to recurring optimization failure modes; when persistently poor performance is detected in a specific scenario, a dedicated optimization process is triggered. All feedback data comes with complete contextual information, including environmental conditions, robot configuration, and task requirements, supporting multi-dimensional correlation analysis.

[0124] To ensure real-time performance, the module implements several optimization measures. Computationally intensive tasks (such as Pareto front solving) employ pre-computation and caching strategies, while repetitive scenarios directly utilize historical solutions. Real-time critical paths (such as topology verification) use dedicated computing resources to guarantee response speed. The system dynamically monitors processing latency and automatically switches to a simplified algorithm mode when a timeout is predicted. Resource allocation employs an optimal priority strategy, ensuring that highly important tasks receive computing resources first. These measures enable the module to maintain an average optimized latency of less than 50 milliseconds under typical workloads, meeting the real-time requirements of most industrial scenarios.

[0125] The module is equipped with a comprehensive anomaly handling system. During optimization, the system continuously monitors the algorithm's status. When anomalies are detected (such as non-convergence or constraint conflicts), different measures are taken based on the anomaly level: minor anomalies attempt automatic repair and continue optimization; severe anomalies abandon the current optimization cycle and revert to a safe trajectory. The system maintains multiple backup optimization strategies, automatically switching when the main algorithm fails. All anomalies are logged in detail, including input data, intermediate states, and error messages, supporting offline analysis and algorithm improvement. The fault tolerance mechanism ensures that even under anomaly conditions, the system can provide a feasible safe trajectory, protecting device safety.

[0126] The optimized trajectory is transmitted to the robot control system via a high-speed interface. The interface data contains a complete sequence of trajectory points, each point defining parameters such as six-dimensional pose, joint angle, and motion velocity. The system supports multiple control modes: in standard mode, dense trajectory points are provided for precise control; in high-speed mode, sparse key points are provided for interpolation by the underlying controller. The interface design considers real-time requirements; critical control commands are transmitted immediately via interrupts, while auxiliary information is updated asynchronously through shared memory. Integration testing shows that this interface can complete trajectory data transmission within 1 millisecond, meeting the requirements of high dynamic control.

[0127] In Example 6, the execution control and adjustment module first receives optimized motion trajectory data from the dynamic trajectory optimization module. This data contains a dense sequence of trajectory points, each point defining the position and attitude information of the end effector in three-dimensional space, with a sampling interval typically of 10 milliseconds. The system performs integrity verification on the input trajectory, checking data continuity, range rationality, and timing correctness. Possible numerical fluctuations are eliminated through moving average filtering while maintaining the geometric characteristics of the trajectory. The preprocessed trajectory is cached in a circular buffer to ensure that subsequent processing modules can continuously acquire a smooth trajectory data stream. The system specifically handles the connection between the trajectory starting point and the current position of the robot arm, using an S-shaped acceleration / deceleration curve to achieve a smooth transition and avoid initial impact.

[0128] The system constructs an inverse kinematics model based on Lie group theory. This model represents the pose of the end effector as elements of an SE(3) group, decomposing continuous motion into minute displacements in joint space through exponential mapping. The inverse kinematics process considers the geometric constraints of the robotic arm, including joint angle limits, singularity avoidance, and link interference checks. For redundant robotic arms, the system uses gradient projection to optimize self-motion parameters, ensuring the uniqueness and rationality of the solution. The inverse kinematics calculation is performed at a frequency of 1 kHz, generating precise target values ​​for joint angles in each control cycle. During the calculation, the condition number of the Jacobian matrix is ​​monitored in real time, and an anti-singularity strategy is automatically activated when the configuration approaches a singularity.

[0129] During the joint space trajectory generation stage, the system employs an adaptive interpolation algorithm to optimize computational efficiency. The algorithm dynamically analyzes the trajectory curvature characteristics, automatically increasing the interpolation point density in high-curvature regions (minimum interval of 1 millisecond) and decreasing the density in straight sections (maximum interval of 20 milliseconds). A fifth-order polynomial is used for interpolation to ensure the continuity of position, velocity, and acceleration. The system establishes an interpolation quality evaluation mechanism, dynamically adjusting interpolation parameters by comparing the deviation between the interpolated trajectory and the original trajectory. When a key feature point is detected as potentially missing, sampling points are immediately added to that region.

[0130] The system constructs a joint space dynamics filter to achieve precise control. Based on the robot arm's dynamic model, the filter predicts the inertial forces, Coriolis forces, and gravitational torques that each joint will experience in the next control cycle. These predicted values ​​are converted into feedforward compensation values ​​and superimposed on the basic control commands. The filter parameters are dynamically adjusted according to the robot arm's actual load, and key parameters such as mass and center of mass are updated through an online identification algorithm. The compensation values ​​are limited to avoid exceeding the actuator's capability range. The system also monitors the compensation effect, and automatically triggers a dynamic parameter re-identification process when a persistent deviation between the actual motion and the expected motion is detected.

[0131] The generated joint control parameters undergo rigorous multi-level verification. The first level of verification uses inverse kinematics to convert joint angles back to end-effector pose and check if the deviation from the original trajectory is within acceptable limits (position error < 0.5 mm, posture error < 0.5 degrees). The second level of verification implements collision detection, using a bounding box hierarchical tree algorithm to quickly determine the safe distance between each link of the robotic arm and environmental obstacles. The third level of verification assesses whether the joint torque requirements exceed the motor's capacity. All verifications are executed in parallel; failure of any verification triggers a regeneration process of control parameters. The verification results form a safety score for reference in subsequent control strategies.

[0132] For foreign objects with different characteristics, the system implements force adjustment based on fuzzy inference. The end effector is equipped with a six-dimensional force sensor to monitor the contact force signal with the foreign object in real time. The fuzzy system input includes the magnitude of the contact force, the rate of force change, and the material characteristics of the foreign object, and the output is the optimal clamping force. The rule base contains more than 50 expert experience rules, such as "if the contact force increases rapidly and the material is fragile, the force should be significantly reduced." The system learns the operator's adjustment preferences online and gradually personalizes the force curve. For particularly sensitive foreign objects, a safety mode can be activated, limiting the maximum clamping force to 30% of the normal value.

[0133] The workspace is dynamically divided into different speed zones. The system analyzes obstacle density distribution in real time, allowing the robotic arm to operate at 100% of its maximum design speed in open areas; limiting the speed to 70% in medium-density areas; and further reducing it to 30% in high-density areas. Speed ​​switching uses an S-curve transition to ensure continuous acceleration changes. The system predicts the type of area the robotic arm will enter in the next 3 seconds and begins speed adjustment in advance to avoid vibrations caused by sudden deceleration. An emergency braking strategy is in place to address the sudden appearance of obstacles, reducing the speed to a safe range within 100 milliseconds.

[0134] The system employs a closed-loop feedback network to continuously optimize control performance. A high-precision encoder collects real-time motion data from each joint and compares it with the planned trajectory. Position deviations are corrected by a PID controller; persistent deviation patterns trigger adaptive adjustments to control parameters. The feedback network pays particular attention to the phase characteristics of trajectory tracking, adjusting the feedforward compensation when systematic lag or lead is detected. All corrections are recorded in the performance log for offline analysis and algorithm improvement. The network learning rate automatically adjusts based on environmental stability, improving adaptability in rapidly changing scenarios and enhancing robustness in stable scenarios.

[0135] For fragile foreign objects, the system employs a force-speed coupling control mode. This mode establishes a correlation rule between force and speed: when the clamping force approaches its upper limit, the movement speed is automatically reduced; conversely, the speed is allowed to increase within a safe range. The coupling coefficient is set according to the fragility level of the foreign object, and the optimal parameters are obtained through experimental calibration. The system monitors the state feedback of the foreign object in real time (such as deformation detection) and dynamically adjusts the coupling strength. In special circumstances, a safety interlock can be activated, immediately pausing movement when abnormal contact force is detected. All parameters of the coupling control are normalized to ensure consistency between different robotic arms.

[0136] The execution control and adjustment module operates in a strictly real-time cyclic manner. The basic control cycle is set to 1 millisecond, and within each cycle, the following are executed sequentially: trajectory point acquisition, inverse kinematics solution, dynamic compensation, safety verification, and control command output. High-priority tasks (such as safety verification) respond immediately using interrupt service. The system monitors the timing performance of the cyclic execution and automatically simplifies non-critical calculation processes when a potential timeout is predicted. Critical control data is stored with triple redundancy to ensure that any single point of failure will not lead to control failure. All control commands are cross-validated before output to prevent transmission errors.

[0137] The module implements a multi-layered anomaly detection mechanism. At the hardware level, it monitors the validity of sensor data, and at the software level, it checks the consistency of the algorithm's internal state. Anomalies are categorized into three levels: minor anomalies (such as a single verification failure) trigger a retry mechanism; moderate anomalies (such as continuous tracking deviation) initiate a degradation control mode; and severe anomalies (such as collision detection) immediately execute an emergency stop. Recovery strategies are customized based on anomaly type: transient faults attempt automatic recovery; persistent faults require manual confirmation. The system maintains an anomaly knowledge base, recording the characteristics and handling experience of various anomalies to improve its autonomous recovery capabilities.

[0138] The control parameters are adjustable online. The base PID gain automatically adjusts based on the robot's current load: increasing gain under heavy loads to ensure stiffness, and decreasing gain under light loads to avoid oscillations. Filter parameters change with motion speed: strengthening filtering at high speeds to suppress vibration, and weakening filtering at low speeds to maintain response speed. The adjustment strategy is based on a pre-defined parameter mapping table, combined with real-time performance feedback for fine-tuning. Adjustments to critical parameters must undergo safety verification to ensure they do not cause instability. The system records all parameter change history, supporting fault tracing and strategy optimization.

[0139] The module interacts with external systems via standard interfaces. The drive interface outputs PWM or analog control signals with a sampling rate of at least 10kHz. The status monitoring interface collects real-time data such as motor current and temperature. The safety interface connects to emergency stop circuits and protective devices such as light curtains. All interfaces are electrically isolated and equipped with overvoltage and overcurrent protection. The system supports mainstream industrial bus protocols such as EtherCAT and PROFINET, facilitating integration into existing automated production lines. Strictly defined interface data formats ensure compatibility with equipment from different manufacturers.

[0140] The system offers a wealth of human-computer interaction functions. The operation panel displays real-time motion status, control parameters, and abnormal alarms. The debugging interface allows authorized engineers to adjust underlying control parameters. The training mode can record the control techniques of skilled operators and convert them into optimization strategies. All interactive operations are subject to authorization verification, and modifications to key parameters require double confirmation. The interface design conforms to ergonomic principles, highlighting key information and presenting an intuitive and concise operation process.

[0141] The working principle of this invention: This invention achieves efficient and precise intelligent sorting operations through multi-module collaboration. First, the system integrates spatial data through a dynamic spatial feature extraction module to construct a three-dimensional dynamic spatial model with multi-dimensional topological features, and extracts spatial connectivity and circular obstacle features. The robotic arm motion state encoding module converts end-effector pose, joint angles, and other motion parameters into a dynamic system representation in the topological space, establishing a topological feature encoding matrix for the motion trajectory. The collaborative conflict detection module analyzes the interaction of the robotic arm trajectory in the topological space, calculates the conflict probability in real time through multi-scale coherence analysis and dynamic density field modeling, and generates graded early warning signals based on adaptive thresholds. The dynamic trajectory optimization module, while maintaining path topological invariance, uses virtual repulsion field technology and a multi-objective optimization strategy to generate the optimal collision avoidance trajectory, and ensures the coordinated optimization of topological characteristics and motion performance through a hierarchical optimization architecture. The execution control adjustment module converts the optimized trajectory into joint control parameters, achieves precise control based on Lie group theory kinematic inverse solution and dynamic filtering, and dynamically adjusts the execution parameters through fuzzy inference force adjustment and environmental adaptive speed planning. Each module collaborates efficiently through standardized interfaces, forming a complete closed loop from environmental perception and conflict prediction to motion generation and execution control. This achieves sub-millimeter sorting accuracy and millisecond-level real-time response, significantly improving the safety, efficiency, and adaptability of multi-robot collaborative operations.

[0142] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A control system for an AI-based intelligent foreign object sorting robot, characterized in that, include: The dynamic spatial feature extraction module is used to collect spatial data of the intelligent foreign object sorting robot in the sorting working environment in real time, construct a three-dimensional dynamic spatial model with multi-dimensional topological features, and extract the connectivity features and ring obstacle features of the spatial structure through continuous homology analysis. The spatial data includes depth image data, point cloud data, three-dimensional contour data, and dynamic obstacle motion trajectory data. The robot motion state encoding module converts the real-time motion parameters of each robot into a dynamic system representation in the topological space and establishes a topological feature encoding matrix based on the motion trajectory. The real-time motion parameters include end effector pose data, joint angle data, and motion speed data; The collaborative conflict detection module is used to analyze the characteristic interaction of the motion trajectories of each robot arm in the topological space. When the intersection density of homology groups exceeds a predetermined threshold, a deadlock risk warning signal is generated. The dynamic trajectory optimization module generates a dynamic motion trajectory with optimal collision avoidance characteristics based on the characteristic intensity of the deadlock risk warning signal, while maintaining the constraint of path topology invariance. The execution control and adjustment module converts the optimized motion trajectory into a sequence of joint control parameters for the robotic arm, and dynamically adjusts the execution force and speed based on real-time environmental feedback.

2. The AI-based intelligent foreign object sorting robot control system according to claim 1, characterized in that, The specific implementation process of the three-dimensional dynamic spatial model includes: Spatial data is acquired by multi-sensor fusion, and the depth image data collected by the depth camera and the point cloud data collected by the lidar are spatiotemporally aligned by time stamp synchronization technology to establish an environmental geometric framework with millimeter-level accuracy. Using a voxel-based 3D reconstruction algorithm, the original point cloud data is converted into an octree structure with topology preservation properties, and normal vector distribution features and curvature change features are embedded in each voxel node. By calculating the lifetime of persistently homogeneous barcodes, key topological features in the environment are identified, including the extraction of persistent features of hole structures and connectivity features of channel structures, and a topological feature descriptor with multi-scale properties is constructed.

3. The AI-based intelligent foreign object sorting robot control system according to claim 1, characterized in that, The extraction of connectivity features and annular barrier features of the spatial structure specifically includes: Topological clustering analysis is performed on the motion trajectories of dynamic obstacles. A clustering algorithm based on density peaks is used to identify the trajectory patterns of moving objects and establish a topological representation of the trajectory manifold. Morse theory is used to perform critical point analysis on 3D contour data, and key topological structures in the workspace are extracted by calculating Reeb diagrams to identify potential mechanical interference areas. By fusing the topology of the motion trajectory of dynamic obstacles with the topological features of the static environment, a spatiotemporal four-dimensional topological conflict prediction model is constructed. By calculating the intersection degree of homology groups, the potential collision risk of a group of robotic arms can be predicted in advance.

4. The AI-based intelligent foreign object sorting robot control system according to claim 1, characterized in that, The establishment of the topological feature encoding matrix based on motion trajectory specifically includes: Collect multi-dimensional motion parameters of the robot arm, including the six-degree-of-freedom pose data of the end effector, the real-time angle change data of each joint, and the motion acceleration data, and construct a complete description of the robot arm's motion state through time series alignment technology; The robot's motion trajectory is mapped to a topological space. A nonlinear dimensionality reduction method based on manifold learning is used to extract key topological feature points in the trajectory, including extreme points of trajectory curvature and points of abrupt change in motion direction, to form a phase space representation of the dynamic system. By calculating the homology invariants of the trajectory in the topological space, a persistent homology feature matrix of the motion trajectory is constructed, which includes the connectivity features of the trajectory loop and the distribution features of the motion obstacles. The topological feature matrix is ​​coupled with the dynamic parameters of the robot to generate a topological encoding vector with motion constraints, which is used to characterize the dynamic behavior pattern of the robot in collaborative operation.

5. The AI-based intelligent foreign object sorting robot control system according to claim 1, characterized in that, The specific implementation of analyzing the feature interactions of the robot arm's motion trajectory in the topological space includes: A trajectory topological feature sequence based on time sliding window is established, and the topological evolution trend of the robot's motion trajectory is identified by calculating the homology group change rate of adjacent time segments. A multi-scale coherence analysis method is used to detect the intensity of characteristic interactions between trajectories at both the macroscopic motion path and microscopic vibration levels. The spatial overlap of trajectory loops is analyzed at the macroscopic level, while the energy coupling of motion harmonics is detected at the microscopic level. A dynamic density field model is constructed, and the probability of topological conflict of a group of robotic arms in a shared workspace is calculated in real time by quantifying the energy accumulation effect of the intersection region of homology groups.

6. The AI-based intelligent foreign object sorting robot control system according to claim 1, characterized in that, The specific method for generating deadlock risk warning signals includes: An adaptive threshold adjustment mechanism based on historical operating data is set up to dynamically adjust the warning threshold of the intersection density of the homology group according to the workload density of the robot group. A spatiotemporal deadlock pattern recognition model is established, and the development trend of deadlock risk is predicted by analyzing the propagation characteristics of topological conflicts in the time and space dimensions. A tiered early warning mechanism is adopted to generate different levels of early warning signals based on the severity of the conflict, including local path adjustment suggestions and global task rescheduling instructions; By connecting the early warning signal with the real-time decision-making module of the robotic arm control system, a closed-loop process from risk detection to control response is completed within 5 milliseconds.

7. The AI-based intelligent foreign object sorting robot control system according to claim 1, characterized in that, The specific implementation of generating a dynamic motion trajectory with optimal collision avoidance characteristics includes: The trajectory reconstruction technique based on topological constraints generates a set of candidate collision avoidance paths by introducing a virtual repulsion field to perform topological deformation on the conflict region while keeping the original path homology class unchanged. A multi-objective optimization strategy is adopted to evaluate candidate paths, taking into account three dimensions: path length, manipulator energy consumption, and task timeliness, and an optimal path selection model based on the Pareto front is established. The trajectory is smoothed by combining the dynamic characteristics of the manipulator, and the cubic spline interpolation algorithm is used to ensure that the optimized trajectory meets the joint motion velocity and acceleration constraints. Implement a real-time trajectory verification mechanism and ensure that the optimized trajectory remains topologically equivalent to the original mission objective through topological persistence analysis.

8. The AI-based intelligent foreign object sorting robot control system according to claim 1, characterized in that, The specific methods for trajectory optimization under the constraint of preserving path topology invariance include: A path topology invariance verification model is constructed, and the degree of preservation of path topology features is quantitatively evaluated by calculating the change in Betti number of the trajectory before and after optimization. A hierarchical optimization architecture is adopted, with the upper layer processing global topological constraints and the lower layer optimizing local geometric features, thereby achieving synergistic optimization of topological properties and motion performance; An adaptive learning mechanism is introduced to dynamically adjust the topology constraint weight coefficients based on historical optimization results, balancing collision avoidance requirements with task completion quality. Establish a closed-loop feedback mechanism for trajectory optimization, compare and analyze the actual execution data with the expected optimization target, and continuously improve the trajectory generation algorithm.

9. The AI-based intelligent foreign object sorting robot control system according to claim 1, characterized in that, The specific implementation of converting the optimized motion trajectory into a sequence of joint control parameters includes: A kinematic inverse model based on Lie group theory is established to decompose the pose trajectory of the end effector into independent motion components of each joint, ensuring that the conversion process meets the geometric constraints of the robotic arm. An adaptive interpolation algorithm is used to process the key points of the trajectory, and the interpolation density is dynamically adjusted according to the current motion state of the robot, so as to reduce the computational load while ensuring the trajectory accuracy. A joint space dynamics filter is constructed to compensate for the influence of inertial forces and Coriolis forces on motion accuracy in advance by predicting the load changes of each joint in the next control cycle. A multi-level verification mechanism is implemented to confirm the safety of the generated joint control parameter sequence through both inverse kinematics verification and collision detection.

10. A foreign object intelligent sorting robot control system based on AI recognition according to claim 1, characterized in that, The specific methods for dynamically adjusting the execution intensity and speed include: A force adjustment system based on fuzzy reasoning is established to dynamically adjust the clamping force parameters by real-time monitoring of the contact force signal between the end effector and the foreign object. An environment-adaptive speed planning algorithm is adopted, which uses the maximum allowable speed in open areas and automatically reduces the speed in dense areas based on the obstacle density distribution characteristics of the workspace. Construct an execution effect feedback network and, by comparing the deviation between the actual sorting trajectory and the planned trajectory, correct the adaptive adjustment strategy of the control parameters online; It achieves coordinated control of force and speed, and automatically triggers the force-speed coupling adjustment mode when vulnerable foreign objects are detected, ensuring the safety of the sorting process.

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