Servo control system of multi-degree-of-freedom mechanical arm

Abstract

The invention discloses a servo control system of a multi-degree-of-freedom mechanical arm, and belongs to the technical field of automatic control. Comprising the following modules: a task scheduling module for executing real-time task feature analysis and priority evaluation and generating a priority-based dynamic scheduling strategy; the workspace management module is used for realizing efficient utilization and safety management of the workspace through real-time planning of space partitions and collision detection; the motion planning module is used for generating an optimal track meeting the precision requirement and realizing real-time optimization and adjustment of the track; the self-adaptive servo control module is used for realizing high-precision control on the position and force output of the mechanical arm through self-adaptive control and real-time feedback correction; the cooperative synchronous control module is used for coordinating the synchronous movement among the mechanical arms, correcting the movement error and improving the cooperative performance; and the state monitoring module monitors key system parameters in real time, executes fault diagnosis and operation state evaluation, and ensures operation safety and reliability.

Description

Technical Field

[0001] This application relates to the field of automation control technology, and more specifically, to a servo control system for a multi-degree-of-freedom robotic arm. Background Art

[0002] With the continuous progress of automation technology and the increasing demand for precision and efficient production in the manufacturing industry, multi-degree-of-freedom robotic arms (hereinafter referred to as robotic arms) have been widely used in industrial production, medical treatment, scientific research and other fields. As an efficient execution device, the robotic arm can complete various complex operation tasks, including welding, assembly, handling, spraying, etc. However, with the increasing complexity of task requirements and the diversity of environmental changes, the existing robotic arm control systems face many challenges.

[0003] First of all, when the robotic arm executes multi-tasks and complex operations, it needs to handle multi-dimensional resource scheduling problems. The dependency relationships and priority differences between tasks make it difficult for traditional scheduling methods to cope with the dynamically changing task requirements and cannot fully optimize task allocation and resource utilization efficiency. In addition, the working space of the robotic arm usually contains multiple obstacles and dynamic objects, and how to execute tasks efficiently and safely in a limited space becomes an important issue. The existing space management methods often lack sufficient flexibility and real-time performance and cannot respond to the dynamically changing factors in the environment in a timely manner.

[0004] Secondly, the motion planning and control of the robotic arm face complex constraint conditions. In addition to the dynamic constraints of the robotic arm itself, task requirements, space constraints and mechanical requirements also pose higher precision and optimization requirements for trajectory planning. Most of the existing motion planning methods focus on trajectory optimization in a static environment and lack dynamic adjustment and real-time optimization capabilities, and cannot cope with trajectory errors caused by environmental changes and task deviations.

[0005] Furthermore, the precise control of the robotic arm is the key to achieving efficient work. Traditional servo control methods are easily affected by external disturbances and system errors in practical applications, resulting in the motion accuracy and force control accuracy of the robotic arm being difficult to meet the requirements of high-demand tasks. The existing control systems often lack sufficient adaptability and are difficult to achieve real-time feedback correction and high-precision control in practical applications.

[0006] In addition, when multiple robotic arms work together, how to ensure efficient synchronization and cooperation between different robotic arms is an urgent problem to be solved. The existing cooperative control methods often have difficulty in real-time monitoring the operating states of multiple robotic arms and cannot effectively coordinate their actions, resulting in low task execution efficiency or resource conflicts.

[0007] In summary, how to achieve efficient task scheduling, precise motion planning and control, safe space management, and collaborative operation among multiple robotic arms in a dynamic and complex environment has become a technical problem that urgently needs to be solved. Summary of the Invention

[0008] To overcome a series of defects existing in the prior art, the purpose of this application is to provide a servo control system for a robotic arm with multiple degrees of freedom, including the following modules in response to the above problems:

[0009] A task scheduling module that performs real-time task feature analysis and priority evaluation to generate a dynamic scheduling strategy based on priorities to optimize task allocation and resource utilization;

[0010] A workspace management module that realizes efficient utilization and safe management of the workspace through real-time planning of space partitioning and collision detection;

[0011] A motion planning module that generates an optimal trajectory meeting the accuracy requirements based on task constraints and dynamic characteristics to achieve real-time optimization and adjustment of the trajectory;

[0012] An adaptive servo control module that realizes high-precision control of the robotic arm's position and force output through adaptive control and real-time feedback correction;

[0013] A cooperative synchronization control module that coordinates the synchronous motion among multiple robotic arms, corrects motion errors, and improves collaborative performance to ensure the consistency and reliability of task execution;

[0014] A status monitoring module that real-time monitors key system parameters, performs fault diagnosis and operating status evaluation to ensure operating safety and reliability.

[0015] Furthermore, the task scheduling module includes the following components:

[0016] A task feature acquisition unit responsible for real-time acquisition of the feature data of all current tasks, including task type, execution duration, required resources, and priority requirements;

[0017] A task status analysis unit that real-time analyzes the execution status of each task to dynamically update the priority evaluation of the tasks;

[0018] A resource demand prediction unit that predicts the demand of each task for various resources by analyzing task features and system resource status to provide a basis for task scheduling;

[0019] A task priority evaluation unit that, based on task features and status analysis results, combines predefined priority rules to calculate the priority value of each task in real time to achieve dynamic priority evaluation of the tasks;

[0020] The task management and allocation unit is responsible for analyzing, decomposing, and allocating task dependencies, ensuring that the task execution order complies with dependency rules and avoiding conflicts or resource contention between tasks;

[0021] The scheduling strategy generation unit generates an optimized task scheduling strategy based on the task priority assessment and resource requirement prediction results, ensuring that tasks are efficiently allocated and executed according to priorities.

[0022] Furthermore, the workspace management module includes the following components:

[0023] The environment modeling unit is responsible for collecting and constructing the environment model of the robotic arm's working area in real time, including the positions and shapes of static obstacles and dynamic objects;

[0024] The space partition planning unit manages the partition of the workspace based on task requirements and the environment model, dividing it into safe areas, task areas, and restricted access areas to optimize space utilization efficiency;

[0025] The dynamic object tracking unit monitors the movement trajectories of dynamic objects in the workspace in real time, providing dynamic data support for collision detection and path planning;

[0026] The collision detection and avoidance unit detects potential collisions between the robotic arm and objects in the environment in real time, provides safety alerts, and generates avoidance strategies;

[0027] The space safety management unit monitors the movement state of the robotic arm in real time, issues alerts or automatically adjusts movements that exceed the safe range, and combines the space partition strategy to ensure the safety and optimal utilization of the workspace.

[0028] Furthermore, the avoidance strategy is implemented through the following formula: where, P new (t) is the new position of the end effector of the robotic arm at time t after being adjusted by the avoidance strategy; P is the possible candidate position of the end effector of the robotic arm during the obstacle avoidance process; α is the weight coefficient for controlling the position adjustment; P arm (t) is the current position of the end effector of the robotic arm at time t; β is the weight coefficient for controlling the obstacle avoidance strategy; γ is the weight coefficient for controlling the target position; P goal is the target position of the robotic arm task; f obstacle (P) is the penalty function for obstacle avoidance, used to represent the distance between the current candidate position and the obstacle, and the formula is: P obstacle is the position of the obstacle; obstacle represents the obstacles in the environment.

[0029] Furthermore, the motion planning module includes the following components:

[0030] The task constraint parsing unit is responsible for parsing the task objectives and constraints, providing initial conditions and boundary parameters for motion planning;

[0031] The dynamics modeling unit, based on the dynamic characteristics of the robotic arm, establishes a dynamics model to constrain the physical feasibility of trajectory planning;

[0032] The trajectory planning unit, according to the task constraints and the dynamics model, combines the environmental information provided by the workspace management module to generate a collision-free trajectory that meets the accuracy requirements;

[0033] The real-time optimization unit, during the task execution, dynamically adjusts the generated trajectory according to the real-time feedback data, optimizing the trajectory to adapt to environmental changes or system deviations;

[0034] The path smoothing processing unit smooths the initially generated trajectory, optimizes the continuity of joint motion, and reduces the jitter and wear of the robotic arm;

[0035] The multi-objective optimization unit, in trajectory planning, generates the optimal motion path by comprehensively optimizing the objective function and considering multiple optimization objectives.

[0036] Furthermore, the formula for the comprehensive optimization objective function is: where F(x) is the comprehensive optimization objective function; w 1 represents the weight coefficient of the path shortening objective in the total objective; T is the time range of trajectory planning; is the position velocity of the end effector of the robotic arm at time t; dt is the time step, which represents the tiny increment of time in numerical integration calculations; w 2 represents the weight coefficient of the minimum energy consumption objective in the total objective; τ(t) is the joint torque of the end effector of the robotic arm at time t; w 3 represents the weight coefficient of the trajectory smoothness optimization objective in the total objective; is the position acceleration of the end effector of the robotic arm at time t; w 4 represents the weight coefficient of the collision avoidance objective in the total objective; penalty(x) is the collision penalty function, which is used to calculate the collision risk between the trajectory and the obstacle.

[0037] Furthermore, the adaptive servo control module includes the following components:

[0038] The state data interface unit is responsible for obtaining the key state data of the robotic arm from the state monitoring module, including position, velocity, acceleration, torque, and external environmental disturbances;

[0039] An adaptive control strategy generation unit that dynamically adjusts control parameters to adapt to system state changes and external disturbances;

[0040] A real-time force control unit that achieves precise control of the force output at the end of the robotic arm to ensure that the interaction process with the environment meets the mechanical requirements;

[0041] A position servo control unit that precisely controls the movement positions of the robotic arm joints and the end to ensure that the robotic arm operates according to the set trajectory and posture;

[0042] A real-time feedback correction unit that online adjusts the control strategy based on real-time feedback data, corrects the deviations during the execution process, and ensures high-precision motion and force output.

[0043] Furthermore, the cooperative synchronization control module includes the following components:

[0044] A cooperative task interface unit that obtains the decomposed subtask information from the task scheduling module to achieve the cooperative execution of multiple robotic arms;

[0045] A cooperative motion planning unit that, based on the trajectory planning service provided by the motion planning module, ensures the consistency of multiple robotic arms in time and space;

[0046] An error detection and correction unit that real-time monitors the motion errors of each robotic arm and corrects the deviations in position, speed, or force to ensure synchronization and accuracy;

[0047] A load balancing control unit that dynamically allocates task loads, balances the workloads of each robotic arm, and avoids the problems of overload or efficiency decline of a single robotic arm;

[0048] A cooperative communication unit that is responsible for the real-time data exchange between robotic arms, transmits motion states, task progress, and cooperation requirements, and ensures information sharing and quick response within the system;

[0049] A dynamic conflict resolution unit that detects possible motion conflicts or resource contentions and dynamically resolves the conflicts by adjusting paths or priorities to ensure the continuity of the cooperation process;

[0050] A system cooperative optimization unit that optimizes the cooperative strategy based on a unified performance optimization framework to improve the overall cooperation performance and task completion reliability.

[0051] Furthermore, dynamically allocating task loads, balancing the workloads of each robotic arm, and avoiding the problems of overload or efficiency decline of a single robotic arm include the following steps:

[0052] Continuously collect the key operation data of each robotic arm, including the current load, real-time operation speed, temperature, energy consumption, and remaining processing capacity;

[0053] Quickly calculate and evaluate the working saturation of each robotic arm, identify robotic arms with current overload or inefficient utilization, and predict potential load change trends in the short term;

[0054] Based on load prediction, reallocate part of the tasks of high-load robotic arms to low-load robotic arms in real time;

[0055] According to different stages and task characteristics, dynamically adjust the task allocation weights;

[0056] Build a health status monitoring and warning mechanism for robotic arms to timely identify robotic arms that may experience fatigue or performance degradation.

[0057] Furthermore, the status monitoring module includes the following components:

[0058] The central data acquisition unit uniformly collects and manages the key parameters of the robotic arms and the system, and provides data services for each functional module;

[0059] The health status evaluation unit analyzes the collected data, evaluates the overall health status of the system, and identifies potential fault risks or signs of performance decline;

[0060] The fault diagnosis unit, based on a preset diagnosis model and real-time data, identifies and locates system faults, and provides analysis of fault causes and recommended repair measures;

[0061] The performance monitoring unit monitors the performance of each robotic arm in real time to ensure that the system operates according to the predetermined parameters and requirements;

[0062] The anomaly detection and alarm unit detects system anomalies in real time. If any situation deviating from the normal operating state is found, it immediately triggers an alarm and takes corresponding preventive measures;

[0063] The operation status reporting unit regularly generates and updates operation status reports, records key events and abnormal situations, so as to provide maintenance references;

[0064] The system optimization management unit, as the core of the unified optimization framework, coordinates the optimization requirements of each module, and formulates and implements system-level optimization strategies.

[0065] Compared with the prior art, the present application has the following beneficial effects:

[0066] By integrating task scheduling, workspace management, motion planning, adaptive servo control, cooperative synchronization control, and status monitoring modules, the present application realizes efficient, safe, and precise control and management of multi-degree-of-freedom robotic arms; it can dynamically optimize task allocation and resource utilization, provide high-precision trajectory planning and real-time adjustment, ensure synchronization and cooperation between multiple robotic arms, and improve the overall work efficiency and reliability. Description of the Drawings

[0067] Figure 1 This is a schematic structural diagram of a servo control system for a multi-degree-of-freedom robotic arm disclosed in an embodiment of the present application. Detailed implementation manners

[0068] To make the objectives, technical solutions, and advantages of the implementation of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present invention.

[0069] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0070] The embodiments described below with reference to the accompanying drawings and directional terms are exemplary only and are intended to explain the present invention and should not be construed as limiting the present invention.

[0071] As Figure 1 shown, a servo control system for a multi-degree-of-freedom robotic arm includes the following modules:

[0072] A task scheduling module that performs real-time task feature analysis and priority evaluation to generate a dynamic scheduling strategy based on priorities to optimize task allocation and resource utilization;

[0073] A workspace management module that realizes the efficient utilization and safety management of the workspace through real-time planning of space partitioning and collision detection;

[0074] A motion planning module that generates an optimal trajectory meeting accuracy requirements based on task constraints and dynamic characteristics to achieve real-time optimization and adjustment of the trajectory;

[0075] An adaptive servo control module that realizes high-precision control of the robotic arm position and force output through adaptive control and real-time feedback correction;

[0076] A cooperative synchronization control module that coordinates the synchronous motion between multiple robotic arms, corrects motion errors, and improves the collaboration performance to ensure the consistency and reliability of task execution;

[0077] A status monitoring module that real-time monitors key system parameters, performs fault diagnosis and operation status evaluation to ensure operation safety and reliability.

[0078] The task scheduling module plays a crucial role in the servo control system of a multi - degree - of - freedom robotic arm. Its main function is to dynamically optimize task allocation and resource utilization based on the characteristics and priorities of real - time tasks. In this module, by analyzing the current task requirements and considering factors such as task complexity, urgency, and resource requirements, a priority - based scheduling strategy can be generated. This not only ensures the timely execution of tasks but also avoids resource conflicts and waste. The analysis of real - time task characteristics usually involves the detailed identification of task time, space, load, etc. requirements, and adjusts the order of task scheduling based on these analysis results to optimize the overall efficiency. In addition, the task scheduling module also dynamically adjusts priorities according to the dependencies between tasks to adapt to environmental changes and task delays. In complex multi - task scenarios, this module can achieve a reasonable arrangement between tasks, ensuring efficient operation with limited resources. By real - time evaluating and optimizing the scheduling strategy, the task scheduling module greatly improves the response speed and resource utilization rate, enabling the multi - degree - of - freedom robotic arm to execute tasks more flexibly and reliably. Especially when facing uncertainties and emergencies, it can quickly adjust and adapt.

[0079] The workspace management module is a core component of the servo control system of a multi - degree - of - freedom robotic arm, responsible for achieving the efficient utilization and safety management of the workspace. The workspace of the robotic arm is usually a multi - dimensional area. During task execution, it is necessary to avoid interference between different components and ensure that the movement of the robotic arm in a complex environment does not collide. This module can dynamically adjust the layout of the workspace and task allocation by real - time planning space partitioning, combined with sensor data and collision detection algorithms. In actual operation, the workspace management module can generate a real - time updated workspace map. By calculating factors such as the movement trajectory of the robotic arm, task positions, and obstacle positions, it avoids collisions between the robotic arm and the surrounding environment. The application of collision detection technology in this module is crucial. It monitors every possible collision point in the workspace through high - precision sensor data and adjusts the movement trajectory of the robotic arm or stops the movement in real - time to avoid accidents. In addition, the workspace management module also involves multi - task coordination, ensuring that the movement of the robotic arm does not exceed the predetermined space range, guaranteeing the smooth progress of various tasks, and maximizing the utilization of the workspace to improve work efficiency.

[0080] The motion planning module in the servo control system of a multi-degree-of-freedom robotic arm is responsible for generating an optimal trajectory that meets the accuracy requirements based on task constraints and the dynamic characteristics of the robotic arm. According to different task requirements and environmental conditions, this module plans the motion trajectory to ensure that the robotic arm can complete the specified actions along an accurate trajectory, while minimizing energy consumption, time delay, and other potential performance issues. Motion planning generally needs to comprehensively consider kinematic and dynamic characteristics, such as factors like speed, acceleration, torque, etc., as well as physical constraints of the task, such as position accuracy, path curvature, etc. In a complex operating environment, the motion planning module also needs to have real-time optimization and adjustment capabilities, and be able to adjust the original trajectory according to changes in the environment or task variations. To cope with the dynamic changes during the motion of the robotic arm, the motion planning module often adopts advanced optimization algorithms, such as model-based predictive control (MPC) and heuristic algorithms, to find the optimal trajectory path. The optimization process of this module can not only improve the motion efficiency of the robotic arm, but also ensure accuracy and stability. Especially when performing high-precision tasks, it can effectively reduce errors and ensure the successful execution of the task.

[0081] The adaptive servo control module is a key component to ensure the high-precision motion of a multi-degree-of-freedom robotic arm. Through adaptive control algorithms, this module can automatically adjust control parameters according to the real-time state of the robotic arm and changes in the external environment to achieve precise control of the position and force output of the robotic arm. Traditional servo control systems usually rely on fixed control parameters. However, in a complex task environment, the motion of the robotic arm may be affected by many unforeseen factors, such as load changes, friction changes, or interference in the working environment. The adaptive servo control module can automatically adjust the parameters in the control system through a real-time feedback mechanism to ensure that the robotic arm can always stay on the predetermined trajectory. The feedback correction technology adopted by this module can continuously obtain feedback signals during the motion of the robotic arm and correct key parameters such as position, speed, and torque to achieve the effect of high-precision control. Adaptive control technology can also improve the robustness of the system, enabling the robotic arm to maintain stable operating performance under various changing conditions, thereby enhancing the reliability and efficiency of the entire servo control system.

[0082] The collaborative synchronization control module in the multi-degree-of-freedom robotic arm servo control system is responsible for coordinating the synchronous movements between multiple robotic arms, ensuring that each robotic arm can work precisely in collaboration when performing joint tasks. Multi-robotic arm systems often need to execute multiple tasks simultaneously, which requires the movements between each robotic arm to be precisely synchronized to avoid task failure or inefficiency caused by motion errors. The collaborative synchronization control module ensures that the movements of each robotic arm can be completed on time and without interference by formulating precise synchronization control strategies. This module coordinates the control signals of different robotic arms, corrects possible errors during the movement process, and ensures that each robotic arm remains consistent in space and time. When performing high-precision tasks, collaborative synchronization control can further improve the precision and efficiency of task execution by optimizing the motion trajectory and adjusting the motion time. Through collaborative movement, multi-robotic arms can complete complex tasks in a shorter time, improving the overall working efficiency of the system. Especially in industrial production and precision operations, the collaborative synchronization control module can significantly improve the automation level and working precision of the production line.

[0083] The status monitoring module is a safeguard system in the multi-degree-of-freedom robotic arm servo control system, which monitors key system parameters in real time, performs fault diagnosis and operating status evaluation to ensure the safety and reliability of the system. This module monitors various operating indicators of the robotic arm in real time, such as position, speed, torque, temperature, etc., and promptly discovers any abnormal situations. By analyzing historical data and comparing real-time data, it can predict and diagnose potential fault risks and provide corresponding warning information so that maintenance or replacement measures can be taken in a timely manner to avoid downtime and losses caused by faults. Operating status evaluation not only helps to determine whether the robotic arm is within the normal working range but also can evaluate its performance degradation and reliability, thus realizing preventive maintenance of the equipment. By integrating advanced diagnostic algorithms and machine learning technologies, the status monitoring module can continuously optimize the monitoring strategy, improve the accuracy of fault prediction and diagnosis, and ensure long-term stable operation in an uninterrupted working state.

[0084] In summary, the servo control system of this multi-degree-of-freedom robotic arm ensures the efficient execution and precise control of the robotic arm in complex tasks through a series of precise module designs. The task scheduling module, workspace management module, motion planning module, adaptive servo control module, collaborative synchronization control module, and status monitoring module each perform their own functions and work closely together to jointly improve the overall performance of the system. By optimizing resource allocation, improving motion precision, enhancing system robustness, and ensuring operating safety, this system can meet the execution requirements of various high-demand tasks.

[0085] Furthermore, the task scheduling module includes the following components:

[0086] The task feature acquisition unit is responsible for real-time acquisition of the feature data of all current tasks, including task type, execution duration, required resources, and priority requirements;

[0087] The task status analysis unit analyzes the execution status of each task in real time to dynamically update the task priority assessment;

[0088] The resource demand prediction unit predicts the demand for various resources of each task by analyzing task features and system resource status, providing a basis for task scheduling;

[0089] The task priority assessment unit calculates the priority value of each task in real time according to task features and status analysis results, combined with predefined priority rules, to achieve dynamic priority assessment of tasks;

[0090] The task management and allocation unit is responsible for analyzing, decomposing, and allocating task dependencies, ensuring that the task execution order complies with dependency rules and avoiding conflicts or resource contention between tasks;

[0091] The scheduling strategy generation unit generates an optimized task scheduling strategy based on task priority assessment and resource demand prediction results, ensuring efficient allocation and execution of tasks according to priority.

[0092] In summary, the task scheduling module ensures the efficiency of task execution and the reasonable utilization of system resources through the collaborative work of a series of precise components. From the acquisition of task features, status analysis, to resource demand prediction and priority assessment, then to task management and allocation, and finally to the generation of scheduling strategies, each component plays a crucial role in the refined management of task execution. The task scheduling module can not only make real-time adjustments according to the dynamic changes of tasks, but also generate the optimal scheduling strategy according to complex task dependencies and resource constraints, ensuring that the robotic arm can complete tasks stably and reliably in various task environments.

[0093] Furthermore, the workspace management module includes the following components:

[0094] The environment modeling unit is responsible for real-time acquisition and construction of the environment model of the robotic arm's working area, including the positions and shapes of static obstacles and dynamic objects;

[0095] The space partition planning unit conducts partition management of the workspace based on task requirements and the environment model, dividing it into safe areas, task areas, and restricted areas to optimize space utilization efficiency;

[0096] The dynamic object tracking unit monitors the movement trajectories of dynamic objects in the workspace in real time, providing dynamic data support for collision detection and path planning;

[0097] Collision detection and obstacle avoidance unit, which conducts real-time detection of potential collisions between the robotic arm and objects in the environment, provides safety alerts and generates obstacle avoidance strategies;

[0098] Spatial safety management unit, which monitors the motion state of the robotic arm in real time, issues alerts or makes automatic adjustments for motions beyond the safe range, and combines the spatial zoning strategy to ensure the safety and optimal utilization of the working space.

[0099] In summary, the working space management module ensures that the multi-degree-of-freedom robotic arm can perform tasks efficiently and safely in a complex environment through the collaboration of multiple components. The environmental modeling unit provides real-time environmental data, the spatial zoning planning unit optimizes the utilization efficiency of the working space, and the dynamic object tracking unit and the collision detection and obstacle avoidance unit jointly ensure the safety of the robotic arm's motion. The spatial safety management unit plays a crucial role in ensuring that the robotic arm does not exceed the safe range. Through the close cooperation of these functional components, the working space management module can perceive and respond to the dynamically changing environment in real time, ensuring that every step in the task execution process is within the controllable range.

[0100] Furthermore, the obstacle avoidance strategy is implemented through the following formula: where, P new (t) is the new position of the end effector of the robotic arm after obstacle avoidance strategy adjustment at time t; P is the possible candidate position of the end effector of the robotic arm during obstacle avoidance; α is the weight coefficient for controlling position adjustment; P arm (t) is the current position of the end effector of the robotic arm at time t; β is the weight coefficient for controlling the obstacle avoidance strategy; γ is the weight coefficient for controlling the target position; P goal is the target position of the robotic arm task; f obstacle (P) is the penalty function for obstacle avoidance, which is used to represent the distance between the current candidate position and the obstacle, and the formula is: P obstacle is the position of the obstacle; obstacle represents the obstacles in the environment.

[0101] Furthermore, the motion planning module includes the following components:

[0102] Task constraint parsing unit, which is responsible for parsing task objectives and constraint conditions, and providing initial conditions and boundary parameters for motion planning;

[0103] Dynamics modeling unit, which based on the dynamic characteristics of the robotic arm, establishes a dynamics model to constrain the physical feasibility of trajectory planning;

[0104] The trajectory planning unit generates a collision-free trajectory that meets the accuracy requirements according to the task constraints and the dynamic model, in combination with the environmental information provided by the workspace management module;

[0105] The real-time optimization unit dynamically adjusts the generated trajectory during the task execution according to the real-time feedback data, and optimizes the trajectory to adapt to environmental changes or system deviations;

[0106] The path smoothing unit smooths the initially generated trajectory, optimizes the continuity of joint motion, and reduces the jitter and wear of the robotic arm;

[0107] The multi-objective optimization unit generates the optimal motion path by comprehensively optimizing the objective function and considering multiple optimization objectives during the trajectory planning.

[0108] In summary, the motion planning module plays an important role in the control system of the multi-degree-of-freedom robotic arm. Through the collaborative work of multiple components such as task constraint parsing, dynamic modeling, trajectory planning, real-time optimization, path smoothing, and multi-objective optimization, it ensures that the robotic arm can execute tasks efficiently and safely in complex environments. The task constraint parsing unit provides the starting conditions and boundary parameters for the trajectory planning, while the dynamic modeling unit ensures that the planned trajectory is physically feasible. The trajectory planning unit generates an accurate motion trajectory by combining the task requirements and environmental information, and the real-time optimization unit and the path smoothing unit ensure that the trajectory is continuously adjusted and optimized during the execution to cope with the dynamically changing environment and system deviations. The multi-objective optimization unit generates the optimal motion path by comprehensively considering multiple optimization objectives.

[0109] Furthermore, the formula for the comprehensive optimization objective function is: where \(F(x)\) is the comprehensive optimization objective function; \(w\) 1 represents the weight coefficient of the path shortestization objective in the total objective; \(T\) is the time range of the trajectory planning; is the position velocity of the end effector of the robotic arm at time \(t\); \(dt\) is the time step, which represents the tiny increment of time in the numerical integration calculation; \(w\) 2 represents the weight coefficient of the energy consumption minimization objective in the total objective; \(\tau(t)\) is the joint torque of the end effector of the robotic arm at time \(t\); \(w\) 3 represents the weight coefficient of the trajectory smoothness optimization objective in the total objective; is the position acceleration of the end effector of the robotic arm at time \(t\); \(w\) 4 represents the weight coefficient of the collision avoidance objective in the total objective; penalty\((x)\) is the collision penalty function, which is used to calculate the collision risk between the trajectory and the obstacle.

[0110] Furthermore, the adaptive servo control module includes the following components:

[0111] The status data interface unit is responsible for obtaining the key status data of the robotic arm from the status monitoring module, including position, speed, acceleration, torque, and external environmental disturbances;

[0112] The adaptive control strategy generation unit dynamically adjusts the control parameters to adapt to system state changes and external disturbances;

[0113] The real-time force control unit achieves precise control of the force output at the end of the robotic arm to ensure that the interaction process with the environment meets the mechanical requirements;

[0114] The position servo control unit precisely controls the movement positions of the robotic arm joints and the end effector, ensuring that the robotic arm operates according to the set trajectory and posture;

[0115] The real-time feedback correction unit makes online adjustments to the control strategy based on real-time feedback data, corrects the deviations during the execution process, and ensures high-precision motion and force output.

[0116] In summary, the adaptive servo control module plays a crucial role in the multi-degree-of-freedom robotic arm control system. By obtaining key status data in real time through the status data interface unit, the adaptive control strategy generation unit can dynamically adjust the control parameters according to changes in the system state, thereby ensuring that the system remains stable and precise in the face of environmental disturbances or uncertainties. The real-time force control unit guarantees the mechanical requirements when the robotic arm interacts with the environment, avoiding operation failures caused by excessive or insufficient force. The position servo control unit ensures that the robotic arm can precisely execute tasks according to the set trajectory and posture, while the real-time feedback correction unit corrects the deviations during the execution process through real-time correction to ensure the high-precision completion of tasks. Through the synergistic effect of these functions, the adaptive servo control module enhances the adaptability of the robotic arm in complex environments, improves the accuracy and stability of its task execution, and enables the robotic arm to efficiently and safely complete various tasks in dynamic and complex working environments.

[0117] Furthermore, the collaborative synchronization control module includes the following components:

[0118] The collaborative task interface unit obtains the decomposed subtask information from the task scheduling module to achieve the collaborative execution of multiple robotic arms;

[0119] The collaborative motion planning unit, based on the trajectory planning service provided by the motion planning module, ensures the consistency of multiple robotic arms in time and space;

[0120] The error detection and correction unit monitors the motion errors of each robotic arm in real time, corrects the deviations in position, speed, or force, and ensures synchronization and accuracy;

[0121] A load balancing control unit that dynamically allocates task loads, balances the workloads of each robotic arm, and avoids overloading or efficiency degradation of a single robotic arm;

[0122] A collaborative communication unit that is responsible for real-time data exchange between robotic arms, transmitting motion states, task progress, and collaboration requirements, and ensuring information sharing and rapid response within the system;

[0123] A dynamic conflict resolution unit that detects possible motion conflicts or resource contentions and dynamically resolves conflicts by adjusting paths or priorities to ensure the continuity of the collaboration process;

[0124] A system collaborative optimization unit that optimizes collaborative strategies based on a unified performance optimization framework to improve the overall collaboration performance and task completion reliability.

[0125] In summary, the collaborative synchronization control module is a key component in a multi-robotic arm collaboration system, whose purpose is to ensure that multiple robotic arms can maintain synchronization in space and time and complete tasks efficiently and accurately. Through the coordinated work of multiple components such as the collaborative task interface unit, collaborative motion planning unit, error detection and correction unit, etc., it can dynamically allocate tasks, adjust paths, correct motion errors, and resolve possible conflicts, thus ensuring the efficient collaboration of the system. In addition, the collaborative effects of the load balancing control unit, collaborative communication unit, and dynamic conflict resolution unit further improve the overall efficiency and task completion reliability. Under the guidance of the system collaborative optimization unit, the entire multi-robotic arm system can continuously optimize collaborative strategies in the face of complex tasks and environmental changes to ensure the smooth completion of tasks and maximize the working efficiency of the system.

[0126] Furthermore, dynamically allocating task loads, balancing the workloads of each robotic arm, and avoiding overloading or efficiency degradation of a single robotic arm includes the following steps:

[0127] Continuously collect key operation data of each robotic arm, including current load, real-time operation speed, temperature, energy consumption, and remaining processing capacity;

[0128] Quickly calculate and evaluate the work saturation of each robotic arm, identify robotic arms with current overload or inefficient utilization, and predict potential load change trends in the short term;

[0129] Based on the load prediction, reallocate some tasks of high-load robotic arms to low-load robotic arms in real time;

[0130] Dynamically adjust the task allocation weights according to different stages and task characteristics;

[0131] Build a monitoring and warning mechanism for the health status of robotic arms to timely identify robotic arms that may experience fatigue or performance degradation.

[0132] In summary, dynamically allocating task loads and balancing the workloads of each robotic arm are the keys to ensuring the efficient operation of a multi-robotic-arm system. By collecting the key operation data of each robotic arm in real time, it is possible to accurately evaluate the load status and work saturation of each robotic arm, and predict the load change trend. On this basis, the task allocation can be dynamically adjusted, and the tasks of high-load robotic arms can be reasonably reallocated to avoid problems such as overload or efficiency decline. In addition, the dynamic adjustment of task allocation weights and the construction of a health status monitoring and warning mechanism further optimize the load allocation strategy, ensuring the balance and long-term stability of the robotic arm system. Through these methods, not only can the working efficiency of a single robotic arm be improved, but also the overall collaboration performance can be optimized to achieve the efficient and accurate completion of tasks.

[0133] Furthermore, the status monitoring module includes the following components:

[0134] A central data acquisition unit that uniformly collects and manages the key parameters of the robotic arm and the system, providing data services for each functional module;

[0135] A health status evaluation unit that analyzes the collected data, evaluates the overall health status of the system, and identifies potential fault risks or signs of performance decline;

[0136] A fault diagnosis unit that, based on a preset diagnosis model and real-time data, identifies and locates system faults, and provides analysis of the cause of the fault and recommended repair measures;

[0137] A performance monitoring unit that monitors the performance of each robotic arm in real time to ensure that the system operates according to the predetermined parameters and requirements;

[0138] An anomaly detection and alarm unit that detects system anomalies in real time. If any situation deviating from the normal operating state is found, it immediately triggers an alarm and takes corresponding preventive measures;

[0139] An operation status reporting unit that regularly generates and updates operation status reports, records key events and abnormal situations, for providing maintenance references;

[0140] A system optimization management unit, as the core of a unified optimization framework, coordinates the optimization requirements of each module, formulates and implements system-level optimization strategies.

[0141] In summary, the status monitoring module plays a crucial role in the multi-robot arm system. By means of real-time data acquisition, health status analysis, fault diagnosis, performance monitoring, anomaly detection, etc., it ensures the normal operation of the system. Sub-units such as health status assessment, fault diagnosis, and performance monitoring provide detailed real-time feedback to the operators, which helps to identify potential problems in a timely manner and take effective measures. And anomaly detection and alarm, operating status reporting, and system optimization management further improve the reliability and response speed of the system, enabling the system to operate efficiently and stably, and greatly reducing the probability of faults. Through these refined monitoring means, the status monitoring module not only ensures the stability of the system under high load and complex environments, but also provides an important basis for the maintenance and optimization of the robot arms.

[0142] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A servo control system for a multi-degree-of-freedom robotic arm, characterized in that: Includes the following modules: Task scheduling module, which performs real-time task feature analysis and priority evaluation, and generates priority-based dynamic scheduling strategies to optimize task allocation and resource utilization; The workspace management module realizes efficient utilization and safe management of the workspace through real-time planning of space partitioning and collision detection; The motion planning module generates the optimal trajectory that meets the accuracy requirements based on task constraints and dynamic characteristics, and realizes real-time optimization and adjustment of the trajectory; Adaptive servo control module, which achieves high-precision control of the robot arm position and force output through adaptive control and real-time feedback correction; The collaborative synchronization control module coordinates the synchronous motion between multiple robotic arms, corrects motion errors and improves collaborative performance to ensure the consistency and reliability of task execution; The status monitoring module monitors key system parameters in real time, performs fault diagnosis and operation status assessment to ensure operational safety and reliability.

2. The servo control system of a multi-degree-of-freedom robotic arm according to claim 1, characterized in that: The task scheduling module includes the following components: The task feature collection unit is responsible for collecting the feature data of all current tasks in real time, including task type, execution time, required resources and priority requirements; Task status analysis unit, which analyzes the execution status of each task in real time to dynamically update the priority assessment of the task; The resource demand prediction unit predicts the demand for various resources of each task by analyzing the task characteristics and system resource status, providing a basis for task scheduling; The task priority evaluation unit calculates the priority value of each task in real time according to the task characteristics and status analysis results combined with the predetermined priority rules to achieve dynamic priority evaluation of the task; The task management and allocation unit is responsible for analyzing, decomposing and allocating task dependencies, ensuring that the order of task execution complies with dependency rules and avoiding conflicts or resource contention between tasks; The scheduling strategy generation unit generates an optimized task scheduling strategy based on the task priority evaluation and resource demand prediction results to ensure that tasks are efficiently allocated and executed according to priority.

3. The servo control system of a multi-degree-of-freedom robotic arm according to claim 1, characterized in that: The workspace management module includes the following components: The environment modeling unit is responsible for collecting and building the environment model of the robot's working area in real time, including the position and shape of static obstacles and dynamic objects; The space partition planning unit manages the workspace based on task requirements and environmental models, dividing it into safe areas, task areas, and prohibited areas to optimize space utilization efficiency. Dynamic object tracking unit, which monitors the motion trajectory of dynamic objects in the workspace in real time and provides dynamic data support for collision detection and path planning; The collision detection and obstacle avoidance unit detects potential collisions between the robot arm and objects in the environment in real time, provides safety alerts and generates obstacle avoidance strategies; The space safety management unit monitors the motion status of the robot arm in real time, issues alarms or automatically adjusts for movements beyond the safety range, and combines the space zoning strategy to ensure the safety and optimal use of the workspace.

4. The servo control system of a multi-degree-of-freedom robotic arm according to claim 3, characterized in that: The obstacle avoidance strategy is implemented by the following formula: Among them, P new (t) is the new position of the end effector of the manipulator at time t after the obstacle avoidance strategy is adjusted; P is the possible candidate position of the end effector of the manipulator during the obstacle avoidance process; α is the weight coefficient for controlling the position adjustment; P arm (t) is the current position of the end effector of the robot arm at time t; β is the weight coefficient of the obstacle avoidance strategy; γ is the weight coefficient of the target position; P goal is the target position of the robot task; f obstacle (P) is the penalty function for obstacle avoidance, which is used to represent the distance between the current candidate position and the obstacle. The formula is: P obstacle is the location of the obstacle; obstacle represents the obstacle in the environment.

5. The servo control system of a multi-degree-of-freedom robotic arm according to claim 1, characterized in that: The motion planning module includes the following components: The task constraint parsing unit is responsible for parsing the task objectives and constraints, and providing initial conditions and boundary parameters for motion planning; The dynamics modeling unit builds a dynamics model based on the dynamic characteristics of the robot arm to constrain the physical feasibility of trajectory planning; The trajectory planning unit generates a collision-free trajectory that meets the accuracy requirements based on the task constraints and dynamics model combined with the environmental information provided by the workspace management module; The real-time optimization unit dynamically adjusts the generated trajectory according to the real-time feedback data during the task execution, optimizing the trajectory to adapt to environmental changes or system deviations; The path smoothing unit smoothes the initially generated trajectory, optimizes the continuity of joint motion, and reduces the vibration and wear of the robot arm; The multi-objective optimization unit, in trajectory planning, generates the optimal motion path by comprehensively optimizing the objective function and considering multiple optimization objectives.

6. The servo control system of a multi-degree-of-freedom robotic arm according to claim 5, characterized in that: The formula of the comprehensive optimization objective function is: Among them, F(x) is the comprehensive optimization objective function; w1 represents the weight coefficient of the path shortest goal in the overall goal; T is the time range of trajectory planning; is the position velocity of the end effector of the manipulator at time t; dt is the time step, which is used to represent the small increment of time in the numerical integration calculation; w2 is the weight coefficient of the energy consumption minimization target in the overall target; τ(t) is the joint torque of the end effector of the manipulator at time t; w3 is the weight coefficient of the trajectory smoothness optimization target in the overall target; is the position acceleration of the end effector of the robot arm at time t; w4 represents the weight coefficient of the collision avoidance target in the total target; penalty(x) is the collision penalty function, which is used to calculate the collision risk between the trajectory and the obstacle.

7. The servo control system of a multi-degree-of-freedom robotic arm according to claim 1, characterized in that: The adaptive servo control module includes the following components: The state data interface unit is responsible for obtaining the key state data of the robot arm from the state monitoring module, including position, speed, acceleration, torque and external environmental interference; Adaptive control strategy generation unit, dynamically adjusts control parameters to adapt to system state changes and external disturbances; Real-time force control unit to achieve precise control of the force output at the end of the robot arm, ensuring that the interaction process with the environment meets the mechanical requirements; The position servo control unit precisely controls the motion position of the robot arm joints and ends to ensure that the robot arm runs according to the set trajectory and posture; The real-time feedback correction unit adjusts the control strategy online based on real-time feedback data, corrects deviations during execution, and ensures high-precision motion and force output.

8. The servo control system of a multi-degree-of-freedom robotic arm according to claim 1, characterized in that: The collaborative synchronization control module includes the following components: The collaborative task interface unit obtains the decomposed subtask information from the task scheduling module to achieve the collaborative execution of multiple robotic arms; Collaborative motion planning unit, based on the trajectory planning service provided by the motion planning module, ensures the consistency of multiple robotic arms in time and space; The error detection and correction unit monitors the motion errors of each robot arm in real time and corrects the deviations in position, speed or force to ensure synchronization and accuracy; The load balancing control unit dynamically distributes task loads and balances the workload of each robotic arm to avoid overload or efficiency reduction of a single robotic arm. The collaborative communication unit is responsible for real-time data exchange between the robotic arms, transmitting motion status, task progress and collaboration requirements, ensuring information sharing and rapid response within the system; Dynamic conflict resolution unit detects possible movement conflicts or resource competition, and dynamically resolves conflicts by adjusting paths or priorities to ensure the continuity of the collaboration process; The system collaborative optimization unit optimizes the collaborative strategy based on a unified performance optimization framework to improve the overall collaborative performance and task completion reliability.

9. The servo control system of a multi-degree-of-freedom robotic arm according to claim 8, characterized in that: Dynamically distribute task loads and balance the workload of each robot arm to avoid overload or efficiency reduction of a single robot arm. The following steps are included: Continuously collect key operating data of each robot, including current load, real-time operating speed, temperature, energy consumption, and remaining processing capacity; Quickly calculate and evaluate the working saturation of each robot arm, identify the currently overloaded or inefficiently utilized robot arms, and predict potential load change trends in the short term; Based on load prediction, some tasks of the high-load robot arm are reallocated to the robot arm with lower load in real time; Dynamically adjust task allocation weights according to different stages and task characteristics; Build a robot arm health status monitoring and early warning mechanism to promptly identify robot arms that may be fatigued or have performance degradation.

10. The servo control system of a multi-degree-of-freedom robotic arm according to claim 1, characterized in that: The status monitoring module includes the following components: The central data acquisition unit uniformly collects and manages the key parameters of the robot arm and system, and provides data services for each functional module; The health status assessment unit analyzes the collected data, evaluates the overall health status of the system, and identifies potential failure risks or performance degradation signs; Fault diagnosis unit, based on preset diagnostic models and real-time data, identifies and locates system faults, provides fault cause analysis and recommended repair measures; Performance monitoring unit, which monitors the performance of each robot arm in real time to ensure that the system operates according to the predetermined parameters and requirements; Abnormal detection and alarm unit detects system abnormalities in real time. If any deviation from normal operation is found, an alarm is triggered immediately and corresponding preventive measures are taken; Operation status reporting unit, which regularly generates and updates operation status reports, records key events and abnormal conditions, and provides maintenance references; The system optimization management unit, as the core of the unified optimization framework, coordinates the optimization requirements of each module and formulates and implements system-level optimization strategies.

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