A robot dual-arm force control operation method and device adaptive to electric power tools, a terminal device, and a computer readable storage medium

Abstract

The application discloses a kind of electric power operation tool adaptive robot double-arm force control operation method, device, terminal equipment and computer readable storage medium, belong to robot control technical field.Method includes: obtaining multimodal state data, according to this control distribution has the role of double-arm robot double-arm to execute collaborative operation;When executing collaborative operation, state change variable is extracted from real-time multimodal state data;According to the parameter, identify abnormal precursor characteristics and analyze and extract current precursor type and stress level;According to this, the impedance adjustment amount of main operating arm and auxiliary stabilizing arm and stress distribution ratio are determined;And based on this, update control instruction to execute real-time force control correction.The application solves the problem that double-arm robot in prior art cannot realize real-time adaptive adjustment of force control parameters for uncertainty working conditions such as deflection and sticking due to reliance on rigid rules and neglecting dynamic interaction changes when executing operation.

Description

Technical Field

[0001] This invention relates to the field of robot control technology, and in particular to an adaptive robot dual-arm force control operation method, device, terminal equipment, and computer-readable storage medium for power operation tools. Background Technology

[0002] With the continuous development of robotics technology, dual-arm collaborative operation has been widely used in complex scenarios such as power operation and maintenance, becoming an important technical means to improve the stability and accuracy of dangerous operations.

[0003] Existing force control methods for dual-arm robots typically rely on pre-defined rules for rigid force distribution, neglecting the dynamic interactions between the tool and the object during the operation phase. This results in the robot being unable to perform real-time force control corrections when faced with uncertainties such as tool swaying or jamming, leading to frequent instability in collaborative dual-arm operations and a very high risk of task failure. Therefore, how to achieve real-time adaptive adjustment of collaborative force control based on the dynamic interaction state during the operation process has become a pressing technical problem to be solved in this field. Summary of the Invention

[0004] This invention provides an adaptive force control operation method, device, terminal equipment, and computer-readable storage medium for dual-arm robots in power operation tools. It can solve the problem that dual-arm robots in the prior art cannot achieve real-time adaptive adjustment of force control parameters for uncertain working conditions such as swaying and jamming because they rely on rigid rules and ignore dynamic interactive changes.

[0005] An embodiment of the present invention provides an adaptive robot dual-arm force control operation method for power operation tools, comprising: acquiring multimodal state data of the power operation tool and the work object, and controlling the robot dual arms, which are assigned dual-arm roles, to perform cooperative operation on the power operation tool according to the multimodal state data, wherein the dual-arm roles include a main operating arm and an auxiliary stabilizing arm; During the execution of the collaborative operation, real-time multimodal state data is continuously acquired, and state change parameters are extracted from the real-time multimodal state data; Based on the state change parameters, if abnormal precursor features are identified in the cooperative operation, the abnormal precursor features are analyzed to extract the corresponding current precursor type and force level. Based on the current precursor type and the force level, determine the impedance adjustment amount and force distribution ratio for the main operating arm and the auxiliary stabilizing arm; Based on the updated control command corresponding to the impedance adjustment amount and the force distribution ratio, the main operating arm and the auxiliary stabilizing arm are subjected to real-time force control correction according to the control command.

[0006] Furthermore, controlling the robot's two arms, each assigned a dual-arm role, to perform cooperative operations on the power work tool based on the multimodal state data includes: assigning dual-arm roles to the robot's two arms based on the multimodal state data; decomposing the total workload into a main operation output component corresponding to the main operating arm and an auxiliary support component corresponding to the auxiliary stabilizing arm based on the assigned dual-arm roles; and controlling the main operating arm and the auxiliary stabilizing arm to perform the cooperative operations on the power work tool based on the main operation output component and the auxiliary support component.

[0007] Furthermore, assigning roles to the robot's two arms based on the multimodal state data includes: extracting candidate contact relationships between the power operation tool and the work object based on the multimodal state data; constructing a contact semantic model based on the candidate contact relationships; and assigning the main operating arm and the auxiliary stabilizing arm to the robot's two arms based on the contact semantic model.

[0008] Further, based on the state change parameters, identifying abnormal precursor features occurring in the cooperative operation includes: extracting the tactile pressure distribution change rate and the contact center offset from the state change parameters, and performing a weighted calculation on the tactile pressure distribution change rate and the contact center offset to obtain real-time slip characteristics; extracting the torque change rate and the tool propulsion displacement stagnation from the state change parameters, and performing a weighted calculation on the torque change rate and the tool propulsion displacement stagnation to obtain real-time jamming characteristics; comparing the real-time slip characteristics with a preset slip precursor threshold, and determining a micro-slip precursor feature corresponding to the cooperative operation when the real-time slip characteristics are greater than the slip precursor threshold; comparing the real-time jamming characteristics with a preset jamming precursor threshold, and determining a local jamming precursor feature corresponding to the cooperative operation when the real-time jamming characteristics are greater than the jamming precursor threshold; and generating the abnormal precursor feature based on at least one of the determined micro-slip precursor features or local jamming precursor features.

[0009] Furthermore, based on the current precursor type and the force level, the impedance adjustment amount and force distribution ratio for the main operating arm and the auxiliary stabilizing arm are determined, including: when the current precursor type includes micro-slip precursor characteristics, a first impedance parameter adjustment range is determined based on the force level, and the first impedance parameter adjustment range is used as the impedance adjustment amount for the main operating arm and the auxiliary stabilizing arm; when the current precursor type includes local jamming precursor characteristics, a second impedance parameter adjustment range is determined based on the force level, and the second impedance parameter adjustment range is used as the impedance adjustment amount for the main operating arm and the auxiliary stabilizing arm; simultaneously, the current output torque is extracted from the multimodal state data, a proportional adjustment coefficient is calculated based on the current output torque, and the force distribution ratio is determined based on the proportional adjustment coefficient.

[0010] Furthermore, the control command corresponding to the impedance adjustment amount and the force distribution ratio is updated based on the following: when the current precursor type is the micro-slip precursor characteristic, the normal clamping force increment is determined according to the force level, and the first impedance parameter adjustment range is used as the impedance adjustment amount, and the damping parameter and stiffness parameter are increased based on the first impedance parameter adjustment range. Extract the contact center offset from the real-time multimodal state data, determine the direction correction compensation amount based on the contact center offset, and perform correction processing on the tangential driving force direction based on the direction correction compensation amount; The increased damping parameter, the stiffness parameter, the normal clamping force increment, and the corrected tangential driving force direction are combined and updated in the control command.

[0011] Furthermore, based on the current precursor type and the force level, the impedance adjustment amount and force distribution ratio for the main operating arm and the auxiliary stabilizing arm are determined, and the corresponding control command is updated based on the impedance adjustment amount and the force distribution ratio, including: when the current precursor type is the local jamming precursor feature, the upper limit constraint value of the torque is determined based on the force level, and the adjustment range of the second impedance parameter as the impedance adjustment amount is determined, and the damping parameter and stiffness parameter corresponding to the propulsion direction are reduced based on the adjustment range of the second impedance parameter, while the upper limit of the output torque is updated to the upper limit constraint value of the torque; Extract the current output torque from the real-time multimodal state data, calculate the proportional adjustment coefficient based on the current output torque, and update the force distribution ratio based on the proportional adjustment coefficient; The damping parameter and stiffness parameter after reduction, the updated upper limit of output torque, and the updated force distribution ratio are combined into the control command.

[0012] Another embodiment of the present invention provides an adaptive robot dual-arm force control operation device for power operation tools, including: a cooperative control module, a parameter extraction module, a precursor recognition module, an adjustment amount determination module, and a force control correction module; The collaborative control module is used to acquire multimodal state data of the power operation tool and the work object, and control the robot's two arms, which are assigned dual-arm roles, to perform collaborative operations on the power operation tool based on the multimodal state data. The dual-arm roles include a main operating arm and an auxiliary stabilizing arm. The parameter extraction module is used to continuously acquire real-time multimodal state data during the execution of the collaborative operation, and extract state change parameters from the real-time multimodal state data. The precursor identification module is used to analyze the abnormal precursor features and extract the corresponding current precursor type and force level when the abnormal precursor features are identified in the cooperative operation based on the state change parameters. The adjustment amount determination module is used to determine the impedance adjustment amount and force distribution ratio for the main operating arm and the auxiliary stabilizing arm based on the current precursor type and the force level. The force control correction module is used to update the corresponding control command based on the impedance adjustment amount and the force distribution ratio, and to perform real-time force control correction on the main operating arm and the auxiliary stabilizing arm according to the control command.

[0013] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the adaptive robot dual-arm force control operation method of the present invention for power operation tools.

[0014] Another embodiment of the present invention provides a computer-readable storage medium, including: a stored computer program, wherein, when the computer program is running, the device where the computer-readable storage medium is located executes the adaptive robot dual-arm force control operation method of the present invention for power operation tools.

[0015] The embodiments of the present invention have the following beneficial effects: This invention provides an adaptive force control method for a dual-arm robot operating system for power work tools. By acquiring multimodal state data of the power work tool and the work object, the method controls the robot's two arms (including a main operating arm and an auxiliary stabilizing arm) to perform collaborative operations on the power work tool based on this multimodal state data. Furthermore, addressing the shortcomings of existing dual-arm robots that cannot perform real-time force control correction when facing uncertain conditions such as tool swaying or jamming, resulting in frequent instability and a high risk of task failure during collaborative operation, this invention continuously acquires real-time multimodal state data during the collaborative operation and extracts state change parameters from this data. Then, based on these state change parameters, if abnormal precursor features are identified in the collaborative operation, the abnormal precursor features are analyzed to extract the corresponding current precursor type and force level. This precursor identification and analysis mechanism based on real-time continuous state change enables the control system to perform collaborative operations even under substantial physical deviations. Before a swing or jamming failure occurs, the invention captures and quantifies the microscopic abnormal change trends of the operational interaction state in advance, thereby effectively overcoming the lag response of traditional methods to uncertain working conditions. Furthermore, based on the current precursor type and the force level, the invention determines the impedance adjustment amount and force distribution ratio for the main operating arm and the auxiliary stabilizing arm, and updates the corresponding control commands based on the impedance adjustment amount and the force distribution ratio. Finally, based on the control commands, the invention performs real-time force control correction on the main operating arm and the auxiliary stabilizing arm. Through this closed-loop control command update mechanism, the invention transforms the originally difficult-to-handle open-loop rigid control into real-time dynamic intervention based on accurate feedback of precursor type and force state. This enables the robot to directly redistribute impedance and force distribution ratio at the underlying level when facing complex dynamic working conditions, effectively overcoming the problem of frequent instability in dual-arm operation caused by the lack or lag of force control correction in traditional methods, and significantly reducing the risk of task failure in power collaborative operations. Attached Figure Description

[0016] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating an adaptive robot dual-arm force control operation method for power operation tools according to an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of the structure of a device for an adaptive robot dual-arm force control operation method for power operation tools, provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0021] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0024] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0025] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0026] Specifically, in the step descriptions of the various method embodiments below, the executing entities involved are collectively referred to as the "system". It should be understood that the "system" does not specifically refer to a single physical computing device, but rather to the overall logical architecture for implementing the solution of this application. In actual physical deployment, the "system" encompasses a remote control terminal for power maintenance personnel to monitor and interact with tasks, an edge computing server deployed at the work site to process massive visual point clouds and high-frequency tactile / force data, and a robot main controller or real-time motion control cabinet directly integrated inside the robot body to perform millisecond-level inverse dynamics calculations, second-order impedance control, and multi-axis cooperative servo drives.

[0027] Those skilled in the art should understand that the following steps, including continuous acquisition and spatiotemporal synchronization of multimodal state data, dual-arm role allocation and total workload distribution based on contact relationships, weighted extraction and priority analysis of micro-slippage and local jamming anomaly precursor features, impedance parameter reconstruction based on precursors and force levels, dynamic adjustment of force distribution ratio and real-time issuance of underlying commands, etc., can be flexibly configured and executed in remote operation and maintenance scheduling center host, field edge collaborative processing node, robot main controller or distributed servo drivers of each joint, without departing from the core logic of this application, based on the actual high-voltage power operation site safety communication specifications, the computing power distribution of the robot body's heterogeneous computing unit, and the stringent requirements of high-frequency closed-loop force control on system communication latency, data throughput and execution real-time performance.

[0028] Therefore, the phrase "system performing an operation" used in the following text for the purpose of concise description includes both situations where a single hardware entity or underlying real-time operating system performs the operation independently, and situations where multiple devices, such as cloud-based operation and maintenance scheduling platforms, on-site edge computing servers, and robot-level motion controllers and servo drive units, perform the operation in a distributed and collaborative manner. It should not be narrowly interpreted as a strict limitation on the specific physical execution location of the control algorithm or a specific hardware entity.

[0029] To address the problem that existing dual-arm robots, relying on rigid rules and neglecting dynamic interaction changes, cannot achieve real-time adaptive adjustment of force control parameters for uncertain working conditions such as swaying and jamming, an embodiment of the present invention provides an adaptive force control operation method for a dual-arm robot in power operation tools, comprising: Step S1: Obtain multimodal state data of the power operation tool and the work object, and control the robot's two arms, which are assigned dual-arm roles, to perform cooperative operations on the power operation tool based on the multimodal state data. The dual-arm roles include the main operating arm and the auxiliary stabilizing arm. In a preferred embodiment, controlling the robot's two arms, each assigned a dual-arm role, to perform a collaborative operation on a power work tool based on the multimodal state data includes: assigning dual-arm roles to the robot's two arms based on the multimodal state data; decomposing the total workload into a main operating output component corresponding to the main operating arm and an auxiliary support component corresponding to the auxiliary stabilizing arm based on the assigned dual-arm roles; and controlling the main operating arm and the auxiliary stabilizing arm to perform the collaborative operation on the power work tool based on the main operating output component and the auxiliary support component.

[0030] Preferably, assigning roles to the robot's two arms based on the multimodal state data includes: extracting candidate contact relationships between the power operation tool and the work object based on the multimodal state data; constructing a contact semantic model based on the candidate contact relationships; and assigning the main operating arm and the auxiliary stabilizing arm to the robot's two arms based on the contact semantic model.

[0031] Preferably, acquiring multimodal state data of the power operation tool and the work object specifically includes: synchronously collecting data information from the work site through a sensor network installed on different parts of the robot body. The sensor network includes at least a vision sensor, a tactile sensor, and wrist force / torque sensors installed on the wrists of the robot's two arms; wherein the multimodal state data specifically includes: three-dimensional visual point cloud or image data of the appearance and spatial position of the power operation tool and the work object acquired by the vision sensor; tactile feature data of the magnitude of contact force and pressure distribution sensed and acquired by the tactile sensor; and mechanical state data of the three-dimensional force and three-dimensional torque of the robot's manipulator wrist measured by the wrist force / torque sensors. To eliminate the differences in sampling frequency and spatial installation location between different types of sensors, it is necessary to perform time synchronization and coordinate unification processing on the initial data collected: On the one hand, a hardware-triggered synchronization mechanism is used to perform timestamp correction on the output signals of the above-mentioned sensors, and the visual state data, tactile state data, and force state data are associated with the same working time on the time axis (for example, the timestamp error is strictly controlled within a preset time threshold of 1ms) to avoid the divergence of subsequent state estimation caused by asynchronous data; On the other hand, the spatial coordinate system is unified through the hand-eye calibration matrix.

[0032] Specifically, let the visual sensor coordinate system be... The base coordinate system of the robotic arm is Calculated based on the coordinate transformation formula: ; in, This indicates that the visual sensor is in the visual coordinate system. The coordinates of the three-dimensional homogeneous points collected below; This represents the rigid body transformation matrix (including translation and rotation matrices) from the visual coordinate system to the robot arm base coordinate system, calculated through the hand-eye calibration process. This indicates that the coordinates have been transformed to the robot arm's base coordinate system after matrix operations. A unified coordinate system is established. Based on this coordinate transformation logic, the scattered tool feature points, object feature points, and end-effector poses are uniformly mapped to the same reference coordinate system, thereby providing a position-aligned input source for subsequent construction of accurate spatial geometric relationships and contact semantic extraction.

[0033] Preferably, assigning the roles of the robot's two arms based on the multimodal state data specifically includes: First, dividing the power operation tool into functional areas based on its physical structure and operational attributes to obtain a tool functional area set including a clamping section, a force application section, a guiding section, and a limiting section; simultaneously, dividing the operational object into action interfaces to extract an object action area set including a plugging interface, a turning interface, a pressing interface, and a supporting interface. Then, based on the coordinate-unified multimodal state data, extracting candidate contact relationships between the tool and the object. Further, constructing a contact semantic model based on the candidate contact relationships. The contact semantic model is a mathematical expression structure used to describe the contact relationship between a tool and an object at the abstract control logic level. In this embodiment, the contact semantic model is specifically constructed as a quintuple set M: ; In the formula, This represents the set of tool function areas established above; This represents the set of object interface scopes established above; It is a set of contact methods (covering interaction forms such as rigid surface contact and flexible point contact); The set of allowable force directions (defining the set of three-dimensional force space vectors that the tool can safely withstand without slippage under a specific working interface); This includes the set of roles for both the main operating arm and the auxiliary stabilizing arm. Finally, based on the relationship chain defined by the aforementioned five-tuple contact semantic model, the roles of the two arms are assigned. The control system analyzes the current task... and The mapping relationship is as follows: the robotic arm that needs to output the main working force along the main working direction of the tool and perform large-range posture change operations (such as grasping, inserting, twisting and turning) is configured as the main operating arm; at the same time, the robotic arm that is responsible for providing reaction force support within a limited area, assisting in maintaining the relative spatial stability of the tool and the object, and providing lateral limit to form a mechanical closed loop is configured as the auxiliary stabilizing arm.

[0034] This role assignment mechanism, which is automatically generated based on the five-tuple semantic model, can generalize and adapt to various irregularly shaped electrical work tools such as fire extinguishers, maintenance keys, valve wrenches, and cable connectors without having to pre-set exclusive tooling and fixed program templates for specific objects.

[0035] Preferably, based on the assigned roles of the two arms, the total workload is decomposed into a main operating output component corresponding to the main operating arm and an auxiliary support component corresponding to the auxiliary stabilizing arm. Specifically, this includes: During the interaction process of the robot performing electrical work, the sum of gravity, operating reaction force, and frictional resistance borne by the entire system is collectively referred to as the total workload. To prevent a single arm from triggering an overload alarm on the servo motor or causing tool sway due to excessive load, a closed-loop force flow relationship between the two arms needs to be established, which satisfies the following force balance equation and torque balance equation: ; ; in, and These represent the total working force and the total working torque, respectively. The physical quantities on the right side of the equation are strictly defined according to the roles of the two arms described above: (Main control arm output force) and (The output torque of the main operating arm) together constitutes the main operating output component corresponding to the main operating arm; (Auxiliary stabilizer arm reaction force) and (Auxiliary stabilizer arm closing torque), together with the lateral stabilizing component derived from the relative attitude constraints of the two arms (i.e., lateral stabilizing force). With lateral stabilizing torque (To prevent lateral swaying of the arms in the horizontal plane), the above four items together constitute the auxiliary support component corresponding to the auxiliary stabilizing arm. Specifically, the force distribution ratio coefficient between the two arms is defined. .

[0036] The control system adjusts the proportional factor in real time based on a pre-set operation type dictionary (abstracting tasks into basic actions such as insertion / removal, tightening, and pressing). For example, during the insertion / removal operation, the system dynamically increases the force distribution ratio k, i.e., increases the output ratio of the main operating arm to overcome the high interface static friction; during the tightening operation, the system correspondingly decreases the value of k, i.e., increases the reaction force closing distribution ratio of the auxiliary stabilizing arm to absorb and counteract the reverse deflection torque induced by the tightening operation; during the pressing stage, the lateral stabilization component is correspondingly increased (…). and The decomposition weights of ).

[0037] Meanwhile, this ratio is not constant, but is continuously and smoothly corrected based on the real-time extracted tool posture offset and the force changes on the object interface. After completing the mathematical decomposition, the underlying control system controls the two arms to perform the cooperative operation based on the main operation output component and the auxiliary support component. That is, the main operation output component is converted into the torque feedforward command of the corresponding servo motor, and the auxiliary support component is converted into the spatial admittance limit boundary, thereby establishing a stable dynamic operation cooperative flow.

[0038] In this embodiment, by implementing the aforementioned technical steps of acquiring multimodal data, establishing a contact semantic model, and performing total workload decomposition, the dual-arm robot, when encountering unknown power tools, possesses a human-like "vision-touch-force" fusion perception capability and an automatic cognitive level for defining master and slave action roles, thus eliminating its heavy reliance on pre-programmed trajectories. Simultaneously, by introducing a comprehensive mechanical equilibrium model encompassing the main operational component, auxiliary closure component, and lateral stabilization component, and dynamically adjusting the force flow distribution ratio coefficient based on specific physical rules at different operational stages, dynamic closed-loop collaboration at the force flow level is achieved from the fundamental level of spatial mechanics. This process not only eliminates single-arm overload lock-up or severe tool swaying at the operating interface caused by uneven torque distribution, but also establishes a stable, robust, and clearly defined initial state for collaborative execution of the entire dual-arm operating platform. This lays the foundation for capturing microscopic anomalies and implementing a more precise real-time impedance adaptive adjustment mechanism in subsequent stages, improving the robot's automated task execution efficiency and safety reliability in highly unstructured and hazardous power operation environments.

[0039] Step S2: During the execution of the cooperative operation, continuously acquire real-time multimodal state data, and extract state change parameters from the real-time multimodal state data; In a preferred embodiment, the step of continuously acquiring real-time multimodal state data and extracting state change parameters from the real-time multimodal state data during the execution of the cooperative operation specifically includes: After the dual-arm control system issues torque feedforward commands and drives the robot's dual arms to move, the system enters the dynamic collaborative operation execution phase. During this phase, the control system continuously reads the underlying data stream of the sensor network through high-frequency sampling (e.g., with a control cycle of 1000Hz) to construct a real-time multimodal state data matrix reflecting the microscopic evolution of physical interactions. To accurately capture key mechanical features that may induce operational instability from massive amounts of multi-source redundant signals, the control system is configured to use specific signal processing algorithms to analyze and extract four core state change parameters from the real-time multimodal state data. These state change parameters specifically include: the rate of change of tactile pressure distribution, the contact center offset, the torque change rate, and the tool propulsion displacement stagnation.

[0040] Specifically, the extraction process for the above-mentioned state change parameters is as follows: First, the rate of change of the tactile pressure distribution is extracted; the control system reads the data from the high-resolution tactile sensor array arranged on the interface of the robot end effector or power operation tool, obtains the pressure scalar value of each sensing unit at the current moment, and constructs a real-time pressure distribution matrix.

[0041] The rate of change of tactile pressure distribution per unit time is calculated by subtracting and differentiating the pressure distribution matrix at the current sampling time with the historical pressure distribution matrix at the previous adjacent sampling time or within a preset time window.

[0042] In order to eliminate the absolute magnitude difference caused by different tool materials or initial grip strength, this embodiment preferably divides the calculated rate of change of tactile pressure distribution by a preset reference rate of change benchmark value to obtain the rate of change of tactile pressure distribution per unit time after reference rate of change normalization. This parameter is used to characterize the degree of transient drastic fluctuation of contact pressure between the tool and the object.

[0043] Secondly, the contact center offset is extracted. Based on the aforementioned real-time pressure distribution matrix, the control system uses a centroid calculation algorithm (i.e., weighted averaging of the position coordinates of each tactile sensing unit with the pressure amplitude it experiences) to calculate the equivalent contact center coordinates of the current physical contact surface in real time. Subsequently, the Euclidean distance or the projected offset distance along a specific force plane in three-dimensional space relative to the reference contact center coordinates established at the initial stage of the operation is calculated. Similarly, to ensure the scale uniformity of parameter flow, this offset distance is divided by the characteristic physical dimensions of the current contact area (such as the equivalent radius or diagonal length of the contact surface) to obtain the contact center offset after contact area scale normalization. This parameter directly maps whether the tool has experienced local sliding or rotational loosening tendencies on the working interface.

[0044] Next, the torque change rate is extracted. The control system synchronously acquires the six-dimensional force vector flow from the wrist force / torque sensor during the current control cycle. Through coordinate transformation, the torque data measured by the wrist is projected onto the main working axis of the power tool (e.g., the rotation axis in the twisting direction) to obtain the main working torque scalar. A first-order backward differential or higher-order sliding window filtering derivative algorithm is used to calculate the gradient of this main working torque scalar within a unit sampling time. After normalization processing using the reference torque change rate, the torque change rate per unit time is output. This parameter is used to sensitively reflect the step phenomenon of resistance torque caused by sudden changes in friction or physical interference during operation.

[0045] Finally, the tool's displacement stall amount is extracted. The control system uses real-time angle data from the joint position encoders of the robot's two arms to calculate the actual spatial coordinates of the tool's end effector using the robot's forward kinematics equations. Simultaneously, it retrieves the planned desired end effector position coordinates in the task space for the current operation phase. The positional deviation between the actual spatial coordinates and the desired end effector position coordinates along the main tool propulsion direction (e.g., the normal axis for insertion / removal operations) is calculated. This positional deviation is accumulated or the maximum difference is calculated within a continuous time window (e.g., a preset 0.1-second observation window), and then normalized by dividing by the total desired propulsion displacement within that observation window. This parameter accurately quantifies the degree of jamming or stalling when the robot's displacement does not change correspondingly under applied propulsion or twisting forces.

[0046] In this embodiment, through the spatiotemporal sequence processing and multi-dimensional physical feature calculation of high-frequency dynamic data described above, the present invention integrates the key tactile distribution matrix and high-frequency six-dimensional torque vector within the visual blind zone in real time. By performing time-division differentiation, spatial-division difference, and dimensional normalization on the pressure distribution, contact centroid, working torque, and execution displacement, the originally chaotic underlying sensor signals are transformed into state change parameters with clear physical representation and unified data scale. The extracted state change parameters can filter out the smooth fluctuation noise brought by normal operation and amplify the transient high-frequency change signals caused by frictional force jumps, local relative sliding, or mechanical interference. This enables the control system to overcome the lag of traditional methods that "only stop and report errors when the position error is large enough to trigger the macroscopic protection threshold," providing a comprehensive and highly confident data source for subsequent millisecond-level identification of abnormal precursors (such as micro-slippage and local jamming).

[0047] Step S3: Based on the state change parameters, if abnormal precursor features are identified in the cooperative operation, the abnormal precursor features are analyzed, and the corresponding current precursor type and force level are extracted. In a preferred embodiment, when identifying abnormal precursor features in the cooperative operation based on the state change parameters, analyzing the abnormal precursor features includes: extracting the tactile pressure distribution change rate and the contact center offset from the state change parameters, and performing a weighted calculation on the tactile pressure distribution change rate and the contact center offset to obtain real-time slip features; extracting the torque change rate and the tool propulsion displacement stagnation from the state change parameters, and performing a weighted calculation on the torque change rate and the tool propulsion displacement stagnation to obtain real-time jamming features; comparing the real-time slip features with a preset slip precursor threshold, and determining a micro-slip precursor feature corresponding to the cooperative operation when the real-time slip feature is greater than the slip precursor threshold; comparing the real-time jamming features with a preset jamming precursor threshold, and determining a local jamming precursor feature corresponding to the cooperative operation when the real-time jamming feature is greater than the jamming precursor threshold; and generating the abnormal precursor feature based on at least one of the determined micro-slip precursor features or local jamming precursor features.

[0048] Preferably, the step of performing a weighted calculation on the rate of change of the tactile pressure distribution and the offset of the contact center to obtain the real-time slip characteristics specifically includes: constructing a joint calculation equation for the real-time slip characteristics: S=α·(ΔP / Δt)+β·ΔC; In the formula, S represents the real-time slippage characteristic, ΔP / Δt represents the rate of change of tactile pressure distribution per unit time, ΔC represents the contact center offset, and α and β are weighting coefficients pre-calibrated based on the contact interface type between the power operation tool and the work object, satisfying α+β=1. Specifically, the preset weighting coefficients reflect the contribution ratio of pressure change and position offset to the slippage phenomenon under different contact types: when the contact interface type is a rigid plug interface (friction is mainly provided by structural interference), pressure abrupt changes can better reflect the destruction of the contact state, so the weight of pressure distribution is set higher (for example, α=0.7, β=0.3); when the contact interface type is a flexible pressing interface (friction mainly depends on surface deformation adhesion), center offset can more directly expose the slippage trend, so the weight of offset is set higher (for example, α=0.3, β=0.7).

[0049] Furthermore, the preset slip precursor threshold (denoted as...) This is a pre-calibrated critical reference value based on the material properties of the power operation tool. For example, a higher judgment threshold is calibrated for metal tools with a low surface friction coefficient (such as taking...). For insulating plastic tools with high surface friction coefficients, a lower judgment threshold is set (e.g., taking...). This allows the sensitivity of the judgment to be adapted to the physical characteristics of the tool itself.

[0050] Preferably, the weighted calculation of the torque change rate and the tool advance displacement stagnation to obtain the real-time jamming characteristics specifically includes: ; In the formula, J represents the real-time jamming characteristic, and ΔM / Δt represents the rate of change of torque per unit time. The tool's displacement stagnation amount is defined as γ, where γ and δ are pre-calibrated weighting coefficients based on the current collaborative operation's task type, satisfying γ + δ = 1. Specifically, when the task type is a twisting operation, the torque change gradient most drastically represents local mechanical jamming, thus assigning a higher weight to torque abrupt changes (e.g., γ = 0.8, δ = 0.2); when the task type is a plugging / unplugging operation, the displacement stagnation in the propulsion direction more directly reflects spatial dimensional jamming, thus assigning a higher weight to displacement stagnation (e.g., γ = 0.2, δ = 0.8). Further, the preset jamming precursor threshold (denoted as...) This is an empirical threshold determined based on the mechanical tolerance level of the workpiece interface. For precision interfaces (with low tolerance and highly susceptible to severe mechanical deformation), a stricter trigger threshold is set (e.g., taking...). For regular interfaces (with a certain tolerance margin), set a more lenient trigger threshold (e.g., take...). This effectively avoids system misjudgment and feature omission caused by tolerance disturbances.

[0051] Preferably, after generating the abnormal precursor features based on at least one of the determined micro-slip precursor features or local jamming precursor features, the step of parsing the abnormal precursor features and extracting the corresponding current precursor type and stress level specifically includes: the control system decoding the data packets of the abnormal precursor features to extract the initial precursor type set and the corresponding underlying state change parameter amplitude. First, extract the current precursor type: determine the number of precursor features contained in the initial precursor type set. When the initial precursor type is a single precursor feature (i.e., only micro-slip or only local jamming exists), the single precursor feature is directly used as the extracted current precursor type; when the initial precursor type contains multiple precursor features (i.e., a high-risk complex working condition where micro-slip and local jamming are detected simultaneously), the system selects the target precursor type from the multiple precursor features according to a preset response priority rule.

[0052] The preset response priority rules are formulated based on the severity of the destructive consequences. For example, given that local jamming can easily induce rigid collisions of the robotic arm and overload lock-up of the servo motor in a very short time, its threat level is usually higher than that of micro-slippage, which has a certain buffer margin. Therefore, the response priority of the precursor features of local jamming is set to be higher than that of the precursor features of micro-slippage. Through this screening mechanism, the target precursor type with the highest priority is selected as the final extracted current precursor type to ensure that the system always converges along the safest boundary under concurrent conflicts. Secondly, the stress level is extracted: the control system maps the peak value of the output torque change trend, the concentration of pressure redistribution in the contact area, and the position deviation in the aforementioned underlying multimodal state data to a preset discrete or continuous stress evaluation space, and outputs a quantified stress level index. This stress level objectively describes the margin space between the current abnormal precursor and the complete physical instability, providing a scaling benchmark for the subsequent adjustment of the step size of the control parameters.

[0053] In this embodiment, by implementing the aforementioned technical steps of weighted calculation of multi-source state parameters, threshold comparison, and priority analysis, the present invention constructs a high-precision precursor diagnosis mechanism based on multi-dimensional information fusion. On the one hand, the traditional single-sensor threshold determination (such as relying solely on force or vision) is upgraded to a tactile-kinematic bidirectional weighted determination algorithm based on dynamic weight coefficients. Both the weighting coefficients and the determination thresholds are strictly customized according to prior physical constraints such as tool material, job type, and interface tolerances, filtering out noise interference from a single signal source in complex electromagnetic interference or vibration environments, thus eliminating misjudgments and missed judgments. On the other hand, when multiple abnormal features are detected concurrently in extreme working conditions, a priority analysis rule based on safety boundaries is introduced. This accurately condenses multi-dimensional abnormal precursor features into a current precursor type with a unique execution direction and quantifies its corresponding force level.

[0054] The above processing effectively solves the problem that existing robots are prone to logic deadlock or unclear feature reference when facing complex working conditions with multiple coupled factors. It enables the control system to accurately identify the most fundamental cause of instability before macro-level task failure (such as tool severe swaying and falling or mechanical arm rigidity damage) occurs. This provides highly confident feedback control input for subsequent accurate and customized calculation of impedance adjustment and force distribution ratio.

[0055] Step S4: Based on the current precursor type and the force level, determine the impedance adjustment amount and force distribution ratio for the main operating arm and the auxiliary stabilizing arm; In a preferred embodiment, determining the impedance adjustment amount and force distribution ratio for the main operating arm and the auxiliary stabilizing arm based on the current precursor type and the force level includes: when the current precursor type includes micro-slip precursor characteristics, determining a first impedance parameter adjustment range based on the force level, and using the first impedance parameter adjustment range as the impedance adjustment amount for the main operating arm and the auxiliary stabilizing arm; when the current precursor type includes local jamming precursor characteristics, determining a second impedance parameter adjustment range based on the force level, and using the second impedance parameter adjustment range as the impedance adjustment amount for the main operating arm and the auxiliary stabilizing arm; simultaneously, extracting the current output torque from the multimodal state data, calculating a proportional adjustment coefficient based on the current output torque, and determining the force distribution ratio based on the proportional adjustment coefficient.

[0056] Preferably, in order to achieve smooth force control and dynamic parameter adjustment of the two arms at the physical execution level, the underlying force control execution logic of both the main operating arm and the auxiliary stabilizing arm in this embodiment satisfies a preset second-order impedance control model. The dynamic equilibrium equation of the second-order impedance control model in the task space is specifically expressed as: In the formula, X represents the actual spatial position of the robot's dual-arm end effector. and These are the actual speed and actual acceleration of the end effector, respectively. The desired end position of the task planning path is sent to the end effector. The external working force vector sensed by the end effector when interacting with the external working environment or power work tools; The target inertial parameter matrix of the system is used to characterize the equivalent mass characteristics of the arm; The target damping parameter matrix is ​​used to characterize the arm's ability to absorb system energy and suppress vibration; This is the target stiffness parameter matrix, used to characterize the arm's ability to resist deformation from the external environment. The set of target inertia, damping, and stiffness matrices constitutes the impedance parameters, which are adjusted by... and These elements can alter the dynamic smoothness response characteristics of the robot's arms to external forces.

[0057] Preferably, when the current precursor type includes micro-slip precursor characteristics, the adjustment range of the first impedance parameter is determined based on the force level, and the adjustment range of the first impedance parameter is used as the impedance adjustment amount for the main operating arm and the auxiliary stabilizing arm. Specifically, this includes: The appearance of micro-slip precursors indicates insufficient friction at the contact interface between the power operation tool and the work object, showing a physical tendency to deviate from the constraint. To prevent the tool from falling due to worsening slip, it is necessary to enhance the gripping rigidity and disturbance damping of the robotic arm. The control system pre-establishes a monotonically increasing mapping function between the force level and the adjustment range of the first impedance parameter. Specifically, the adjustment range of the first impedance parameter includes the adjustment range for the stiffness matrix. The increase ratio and the damping matrix The system calculates the corresponding specific gain percentage by interpolation based on the extracted stress level index within the preset adjustment boundaries (e.g., the calibrated stiffness increase range is 20%–50%, and the damping increase range is 10%–30%).

[0058] The higher the stress level, the stronger the instantaneous impact tendency caused by slippage, and the greater the adjustment range of the stiffness increase percentage and damping increase percentage determined by the system. Finally, the calculated set of increase ratio vectors is used as the impedance adjustment amount, so that the arms can quickly increase stiffness at the moment of sensing slippage and actively suppress the spread of the slippage tendency.

[0059] Preferably, when the current precursor type includes the characteristic of local jamming precursor, the adjustment range of the second impedance parameter is determined according to the force level, and the adjustment range of the second impedance parameter is used as the impedance adjustment amount for the main operating arm and the auxiliary stabilizing arm. Specifically, the occurrence of local jamming precursor indicates that the tool's advancement or turning is obstructed, and rigid interference occurs between mechanical structures. If the original stiffness is maintained and forced output is continued at this time, it is very easy to cause interface damage or servo joint overload. Therefore, it is necessary to make the robotic arm smooth in the advancement direction to actively absorb the reverse impact energy caused by jamming. Similarly, the control system pre-establishes a mapping relationship between the force level and the adjustment range of the second impedance parameter. The adjustment range of the second impedance parameter includes the main diagonal stiffness matrix corresponding to the advancement direction. The reduction ratio of the elements, and the effect on the damping matrix. The proportion of elements decreased.

[0060] Based on the current stress level, the system calculates the attenuation percentage within a preset attenuation boundary (e.g., a stiffness reduction range of 30%–60% and a damping reduction range of 20%–40%). The higher the stress level, the greater the calculated reduction in stiffness and damping. The calculated reduction ratio vector is used as the impedance adjustment amount, enabling the robotic arm to exhibit extremely high compliance in the interference axis, achieving the physical characteristic of softening upon encountering resistance.

[0061] Preferably, in the event of a preliminary sign of localized jamming, simultaneously, the current output torque is extracted from the multimodal state data, a proportional adjustment coefficient is calculated based on the current output torque, and the force distribution ratio is determined based on the proportional adjustment coefficient. Specifically, this includes: When jamming causes difficulty in propulsion, forcibly outputting torque by a single arm (main operating arm) can easily lead to torque overload and tool sway. The control system extracts the current output torque borne solely by the main operating arm from the wrist torque sensor data. The ratio of the current output torque to a preset single-arm rated torque safety threshold is calculated to generate the proportional adjustment coefficient. Based on the proportional adjustment coefficient, the system dynamically corrects the initial force distribution ratio coefficient between the two arms (i.e., ...). Specifically, when the current output torque increases sharply, the output torque weight of the main operating arm is reduced, while the closed support torque weight of the auxiliary stabilizing arm is increased simultaneously. Based on this, the system updates and outputs the force distribution ratio by recalculating the proportions of each component that satisfy mechanical balance. This operation aims to transmit and distribute the excess jamming reaction torque through the mechanical closed-loop structure formed by the two arms.

[0062] In this embodiment, by implementing the aforementioned mechanism for determining the impedance adjustment amount and force distribution ratio based on the current precursor type and force level, the present invention transforms abstract abnormal precursor characteristics into specific physical parameter boundaries that can be directly executed by the underlying motor servo control. For micro-slip characteristics, the system adopts a stiffness and resistance increase strategy amplified proportionally according to the force level, simulating the instinctive increase in grip strength and arm stiffness when a human grasps an object that is about to slip. For local jamming characteristics, the system adopts a stiffness and resistance reduction strategy combined with a dynamic force diversion mechanism, mimicking the instinctive relaxation of the arm and the use of both hands to exert force to avoid sprain when a human gets stuck while tightening a screw. By mapping macroscopic semantic diagnosis to the design of stiffness / damping matrix linkage adjustment in the second-order impedance control equation, not only is the accuracy and smoothness of the adjustment action ensured, but also the destructive energy brought by abnormal working conditions is absorbed to the maximum extent before instability occurs, improving the adaptability of the dual-arm robot to unknown resistance in complex interactive environments, equipment safety, and task execution robustness.

[0063] Step S5: Update the corresponding control command based on the impedance adjustment amount and the force distribution ratio, and perform real-time force control correction on the main operating arm and the auxiliary stabilizing arm according to the control command.

[0064] In a preferred embodiment, the control command updated based on the impedance adjustment amount and the force distribution ratio includes: when the current precursor type is the micro-slip precursor characteristic, determining the normal clamping force increment according to the force level, and the first impedance parameter adjustment amplitude as the impedance adjustment amount, and increasing the damping parameter and stiffness parameter based on the first impedance parameter adjustment amplitude. Extract the contact center offset from the real-time multimodal state data, determine the direction correction compensation amount based on the contact center offset, and perform correction processing on the tangential driving force direction based on the direction correction compensation amount; The increased damping parameter, the stiffness parameter, the normal clamping force increment, and the corrected tangential driving force direction are combined and updated in the control command.

[0065] Preferably, based on the current precursor type and the force level, the impedance adjustment amount and force distribution ratio for the main operating arm and the auxiliary stabilizing arm are determined, and the corresponding control command is updated based on the impedance adjustment amount and the force distribution ratio, including: when the current precursor type is the local jamming precursor feature, the upper limit constraint value of the torque is determined based on the force level, and the adjustment range of the second impedance parameter as the impedance adjustment amount is determined, and the damping parameter and stiffness parameter corresponding to the propulsion direction are reduced based on the adjustment range of the second impedance parameter, while the upper limit of the output torque is updated to the upper limit constraint value of the torque; Extract the current output torque from the real-time multimodal state data, calculate the proportional adjustment coefficient based on the current output torque, and update the force distribution ratio based on the proportional adjustment coefficient; The damping parameter and stiffness parameter after reduction, the updated upper limit of output torque, and the updated force distribution ratio are combined into the control command.

[0066] Preferably, when the current precursor type is the micro-slip precursor characteristic, the normal clamping force increment is determined according to the force level, and the adjustment range of the first impedance parameter as the impedance adjustment amount is determined, and the damping parameter and stiffness parameter are increased based on the adjustment range of the first impedance parameter. Specifically, the appearance of micro-slip precursor indicates that the contact interface friction between the power operation tool and the operation object is insufficient, and there is a risk of deviating from physical constraints.

[0067] The normal clamping force refers to the normal pressure perpendicular to the normal direction of the contact surface between the tool and the object, and its magnitude directly determines the maximum static friction limit that the interface can provide. The control system internally stores preset clamping force compensation mapping rules, specifically multiplying the analyzed force level by a gain coefficient pre-calibrated based on the surface roughness of the tool material to calculate the increment of the normal clamping force; the system then adds this increment of the normal clamping force to the basic clamping command to suppress the slippage tendency through physical pressure. Simultaneously, the control system's underlying layer follows a second-order impedance control model, which describes the dynamic balance between the equivalent inertial force, damping force, stiffness restoring force of the robotic arm, and the forces exerted by the external environment.

[0068] The adjustment range of the first impedance parameter includes an increase factor for the target damping matrix and an increase factor for the target stiffness matrix.

[0069] Depending on the stress level, the system performs linear interpolation calculations within preset adjustment boundaries: for example, under micro-slip precursor conditions, the system sets the stiffness increase range to 20%–50% and the damping increase range to 10%–30%; the higher the stress level, the closer the increase ratio factor calculated by interpolation is to the upper limit value.

[0070] During the amplification process, the control system multiplies the original target stiffness matrix by an amplification factor consisting of a constant and the stiffness amplification factor, to obtain the updated stiffness parameters. Similarly, the original target damping matrix is ​​multiplied by an amplification factor consisting of a constant and the damping amplification factor, to obtain the updated damping parameters.

[0071] This adjustment enables the robot to exhibit stronger anti-disturbance rigidity and energy dissipation capabilities in space, mimicking the human instinct to increase arm muscle tension when an object is about to slip.

[0072] Preferably, the step of extracting the contact center offset from the real-time multimodal state data, determining the direction correction compensation amount based on the contact center offset, and performing correction processing on the tangential driving force direction based on the direction correction compensation amount specifically includes: The contact center offset reflects the geometric direction and amplitude of the tool's sliding on the contact surface. The control system introduces a preset spatial correction proportional-differential control law to calculate the direction correction compensation amount: First, the extracted contact center offset is multiplied by a preset position proportional coefficient to obtain a proportional control vector; second, the first derivative of the contact center offset in the time dimension is calculated, and the derivative result is multiplied by a preset differential damping coefficient to obtain a differential control vector; subsequently, the proportional control vector and the differential control vector are vector-added, and the reverse spatial vector of the addition result is taken as the direction correction compensation amount. This compensation amount is essentially a correction spatial vector that is completely opposite to the actual sliding trend direction. After determining the correction compensation amount, the control system extracts the original planned tangential driving force direction vector, performs spatial vector superposition with the direction correction compensation amount, and performs length normalization processing on the superimposed composite vector to output the corrected tangential driving force direction. Through this correction processing, the robot's actual force direction converges towards the inner side that maintains contact stability, preventing the robot from continuing to output work along the deteriorated path that would lead to slippage.

[0073] Preferably, when the current precursor type is the local jamming precursor characteristic, the upper limit constraint value of the torque is determined based on the force level, and the adjustment range of the second impedance parameter as the impedance adjustment amount is determined. Based on the adjustment range of the second impedance parameter, the damping parameter and stiffness parameter corresponding to the propulsion direction are reduced, and the upper limit of the output torque is updated to the upper limit constraint value of the torque. Specifically, this includes: Local jamming precursors characterize unexpected rigid interference between mechanical structures. The upper limit constraint value of the torque refers to a hardware-level physical cutoff threshold for output torque set to prevent the servo motor from overloading and burning out due to stalling, or to prevent precision interfaces from yielding due to excessive force.

[0074] The preset attenuation mapping table is a multi-level data correspondence structure that pre-stores the mapping logic between multiple continuously increasing force level intervals and corresponding torque attenuation coefficients. Specifically, taking a force level index with a maximum score of 10 as an example, the mapping table clearly divides the jamming threat level from low to high into multiple specific physical state intervals such as mild interference, moderate obstruction, and severe jamming, and configures a progressively decreasing torque attenuation coefficient for each interval. For example, when the analyzed force level is in the interval of 1 to 3, the characterization tool experiences mild interference, and the mapping table configures the corresponding torque attenuation coefficient as 80%; when the force level is in the interval greater than 3 and less than or equal to 7, the characterization tool experiences moderate obstruction, and the mapping table configures the corresponding torque attenuation coefficient as 50%; when the force level is in the interval greater than 7 and less than or equal to 10, the characterization tool experiences severe jamming, and the mapping table configures the corresponding torque attenuation coefficient as 20%. When determining constraint values, the control system first retrieves the state interval to which the currently analyzed force level belongs, then extracts the torque attenuation coefficient matching that interval, and then multiplies the nominal rated output peak value of the robot servo motor by the torque attenuation coefficient to calculate a torque upper limit constraint value lower than the normal rated output peak value. Subsequently, the control system forcibly overwrites and updates the current output torque upper limit parameter of each joint to this constraint value, thereby implementing safety fuse protection. Simultaneously, the adjustment range of the second impedance parameter, which is the impedance adjustment amount, is determined. This includes a stiffness attenuation ratio factor (e.g., a preset range of 30%–60%) and a damping attenuation ratio factor (e.g., a preset range of 20%–40%) for the propulsion direction (i.e., the spatial main axis where jamming occurs). When performing the reduction process, the control system multiplies the main diagonal element in the original target stiffness matrix corresponding to the propulsion direction by an attenuation coefficient consisting of a constant minus the stiffness attenuation ratio factor to obtain the updated stiffness parameter; the damping parameter is also calculated using the same differential ratio attenuation. This step enables the robotic arm to exhibit extremely high mechanical compliance, similar to a soft spring, in the obstructed direction, absorbing the recoil energy generated by local jamming in a proactive yielding manner, and avoiding destructive rigid collisions.

[0075] Preferably, the step of extracting the current output torque from the real-time multimodal state data, calculating the proportional adjustment coefficient based on the current output torque, and updating the force distribution ratio based on the proportional adjustment coefficient specifically includes: the control system defining a force distribution ratio factor between the main operating arm and the auxiliary stabilizing arm, which mathematically represents the ratio of the main operating output component to the auxiliary support component. The control system extracts the current output torque fed back by the wrist sensor and determines whether it approaches the updated torque upper limit constraint value. To achieve smooth yielding, the system constructs a nonlinear proportional adjustment coefficient calculation model based on torque margin: First, the division ratio of the current output torque to the torque upper limit constraint value is calculated, and the difference between this ratio and the preset safety margin benchmark value is obtained; then, the difference is multiplied by a preset sensitivity attenuation constant, and the product result is negative, which is used as the exponent of the natural logarithm base (i.e., constant e) to calculate the proportional adjustment coefficient. Based on this exponential function characteristic, when the output torque increases sharply and approaches the safety margin, the proportional adjustment coefficient will exhibit an exponentially rapid decrease. The system updates the force distribution ratio using a calculated proportional adjustment coefficient. Specifically, it multiplies the force distribution ratio factor from the previous control cycle by the proportional adjustment coefficient to obtain the updated force distribution ratio factor. By lowering this ratio factor, the system significantly reduces the torque output weight of the main operating arm without changing the total workload requirement. Excess jamming reaction force is transferred and diverted to the auxiliary stabilizing arm performing the support task through the dual-arm kinematic closed-loop structure, thereby completely eliminating the risk of single-arm overload and tool position sway.

[0076] Preferably, the combined update is incorporated into the control commands, and real-time force control corrections are performed on the main manipulator and the auxiliary stabilizer based on the control commands. Specifically, this includes: the trajectory planning layer and dynamics calculation layer of the control system acquiring the damping and stiffness parameters after the addition and subtraction processing, the updated upper limit constraint value of the torque, the correction direction vector, and the reconstructed force distribution ratio, and encapsulating the above-mentioned multi-dimensional heterogeneous parameters into a single frame of comprehensive state correction matrix at the data structure level. Subsequently, the control system introduces the Jacobian matrix transpose operation model to perform real-time inverse kinematics calculation of the comprehensive state correction matrix in the Cartesian task space into the target desired current and position feedforward signals in the space of each servo joint of the robot's two arms, thereby generating the control commands suitable for the underlying hardware. Finally, the control system synchronously sends the control commands to the drivers of each joint according to the set millisecond-level industrial Ethernet communication cycle, performing high-frequency current loop servo adjustment to close the force control correction loop.

[0077] In this embodiment, through the refined calculation and reconfiguration allocation of the underlying physical parameters of the control commands, a complete dynamic control closed loop from precursor semantic diagnosis to motor hardware execution is achieved. When facing uncertain working conditions with micro-slippage, a linkage intervention strategy of increasing stiffness and resistance, increasing normal pressure, and correcting tangential deviation in proportion to the force level is adopted, which effectively converges the slippage and divergence trend of the tool in complex interfaces. When facing uncertain working conditions with local jamming, a yielding and cooperative strategy of reducing stiffness and resistance and increasing flexibility, hardware current limiting and cutoff, and exponential dynamic current diversion of the dual-arm load is adopted, which avoids unexpected rigid damage to the mechanical structure and single-arm overload. This enables the robot to not only perceive abnormalities when facing highly complex power operation and maintenance environments, but also to instinctively and quickly adjust the compliance and force structure of the dual arms based on the real-time physical interaction state, just like a human operator. This effectively overcomes the defects of traditional control methods that cause frequent instability in operation due to passive lag, and improves the operational adaptability, equipment safety, and success rate and reliability of the dual-arm robot in unstructured environments.

[0078] like Figure 2 As shown, another embodiment of the present invention also provides an adaptive robot dual-arm force control operation device for power operation tools, including: a cooperative control module, a parameter extraction module, a precursor recognition module, an adjustment amount determination module, and a force control correction module; The collaborative control module is used to acquire multimodal state data of the power operation tool and the work object, and control the robot's two arms, which are assigned dual-arm roles, to perform collaborative operations on the power operation tool based on the multimodal state data. The dual-arm roles include a main operating arm and an auxiliary stabilizing arm. The parameter extraction module is used to continuously acquire real-time multimodal state data during the execution of the collaborative operation, and extract state change parameters from the real-time multimodal state data. The precursor identification module is used to analyze the abnormal precursor features and extract the corresponding current precursor type and force level when the abnormal precursor features are identified in the cooperative operation based on the state change parameters. The adjustment amount determination module is used to determine the impedance adjustment amount and force distribution ratio for the main operating arm and the auxiliary stabilizing arm based on the current precursor type and the force level. The force control correction module is used to update the corresponding control command based on the impedance adjustment amount and the force distribution ratio, and to perform real-time force control correction on the main operating arm and the auxiliary stabilizing arm according to the control command.

[0079] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can realize the adaptive robot dual-arm force control operation method for power operation tools provided by any of the above-described method embodiments of the present invention.

[0080] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0081] Based on the above embodiment of the adaptive robot dual-arm force control operation method for power operation tools, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements any of the adaptive robot dual-arm force control operation methods for power operation tools of the present invention.

[0082] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0083] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0084] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0085] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the adaptive robot dual-arm force control operation method for power operation tools described in any of the above-described method embodiments of the present invention.

[0086] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0087] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for adaptive force control operation of a dual-arm robot for power work tools, characterized in that, include: The system acquires multimodal state data of the power operation tool and the work object, and controls the robot's two arms, which are assigned dual-arm roles, to perform cooperative operations on the power operation tool based on the multimodal state data. The dual-arm roles include a main operating arm and an auxiliary stabilizing arm. During the execution of the collaborative operation, real-time multimodal state data is continuously acquired, and state change parameters are extracted from the real-time multimodal state data; Based on the state change parameters, if abnormal precursor features are identified in the cooperative operation, the abnormal precursor features are analyzed to extract the corresponding current precursor type and force level. Based on the current precursor type and the force level, determine the impedance adjustment amount and force distribution ratio for the main operating arm and the auxiliary stabilizing arm; Based on the updated control command corresponding to the impedance adjustment amount and the force distribution ratio, the main operating arm and the auxiliary stabilizing arm are subjected to real-time force control correction according to the control command.

2. The adaptive robot dual-arm force control operation method for power operation tools as described in claim 1, characterized in that, Controlling the robot's two arms, each assigned a dual-arm role, to perform a collaborative operation on the power work tool based on the multimodal state data includes: assigning dual-arm roles to the robot's two arms based on the multimodal state data; decomposing the total workload into a main operation output component corresponding to the main operating arm and an auxiliary support component corresponding to the auxiliary stabilizing arm based on the assigned dual-arm roles; and controlling the main operating arm and the auxiliary stabilizing arm to perform the collaborative operation on the power work tool based on the main operation output component and the auxiliary support component.

3. The adaptive robot dual-arm force control operation method for power operation tools as described in claim 2, characterized in that, Assigning roles to the robot's two arms based on the multimodal state data includes: extracting candidate contact relationships between the power operation tool and the work object based on the multimodal state data; constructing a contact semantic model based on the candidate contact relationships; and assigning the main operating arm and the auxiliary stabilizing arm to the robot's two arms based on the contact semantic model.

4. The adaptive robot dual-arm force control operation method for power operation tools as described in claim 3, characterized in that, Based on the state change parameters, when abnormal precursor features are identified in the cooperative operation, the abnormal precursor features are analyzed, including: extracting the tactile pressure distribution change rate and the contact center offset from the state change parameters, and performing a weighted calculation on the tactile pressure distribution change rate and the contact center offset to obtain real-time slip features; extracting the torque change rate and the tool propulsion displacement stagnation from the state change parameters, and performing a weighted calculation on the torque change rate and the tool propulsion displacement stagnation to obtain real-time jamming features; comparing the real-time slip features with a preset slip precursor threshold, and when the real-time slip features are greater than the slip precursor threshold, determining the micro-slip precursor features corresponding to the cooperative operation; comparing the real-time jamming features with a preset jamming precursor threshold, and when the real-time jamming features are greater than the jamming precursor threshold, determining the local jamming precursor features corresponding to the cooperative operation; generating the abnormal precursor features based on at least one of the determined micro-slip precursor features or local jamming precursor features.

5. The adaptive robot dual-arm force control operation method for power operation tools as described in claim 4, characterized in that, Based on the current precursor type and the force level, the impedance adjustment amount and force distribution ratio for the main operating arm and the auxiliary stabilizing arm are determined, including: when the current precursor type includes micro-slip precursor characteristics, a first impedance parameter adjustment range is determined based on the force level, and the first impedance parameter adjustment range is used as the impedance adjustment amount for the main operating arm and the auxiliary stabilizing arm; when the current precursor type includes local jamming precursor characteristics, a second impedance parameter adjustment range is determined based on the force level, and the second impedance parameter adjustment range is used as the impedance adjustment amount for the main operating arm and the auxiliary stabilizing arm; simultaneously, the current output torque is extracted from the multimodal state data, a proportional adjustment coefficient is calculated based on the current output torque, and the force distribution ratio is determined based on the proportional adjustment coefficient.

6. The adaptive robot dual-arm force control operation method for power operation tools as described in claim 5, characterized in that, The control command corresponding to the impedance adjustment amount and the force distribution ratio is updated based on the following: When the current precursor type is the micro-slip precursor feature, the normal clamping force increment is determined according to the force level, and the first impedance parameter adjustment range is used as the impedance adjustment amount, and the damping parameter and stiffness parameter are increased based on the first impedance parameter adjustment range. Extract the contact center offset from the real-time multimodal state data, determine the direction correction compensation amount based on the contact center offset, and perform correction processing on the tangential driving force direction based on the direction correction compensation amount; The increased damping parameter, the stiffness parameter, the normal clamping force increment, and the corrected tangential driving force direction are combined and updated in the control command.

7. The adaptive robot dual-arm force control operation method for power operation tools as described in claim 5, characterized in that, Based on the current precursor type and the force level, determine the impedance adjustment amount and force distribution ratio for the main operating arm and the auxiliary stabilizing arm, and update the corresponding control command based on the impedance adjustment amount and the force distribution ratio, including: when the current precursor type is the local jamming precursor feature, determine the upper limit constraint value of the torque based on the force level, and the adjustment range of the second impedance parameter as the impedance adjustment amount, and reduce the damping parameter and stiffness parameter corresponding to the propulsion direction based on the adjustment range of the second impedance parameter, while updating the upper limit of the output torque to the upper limit constraint value of the torque; Extract the current output torque from the real-time multimodal state data, calculate the proportional adjustment coefficient based on the current output torque, and update the force distribution ratio based on the proportional adjustment coefficient; The damping parameter and stiffness parameter after reduction, the updated upper limit of output torque, and the updated force distribution ratio are combined into the control command.

8. An adaptive robotic dual-arm force control operating device for power operation tools, characterized in that, include: The module includes a collaborative control module, a parameter extraction module, a precursor identification module, an adjustment amount determination module, and a force control correction module. The collaborative control module is used to acquire multimodal state data of the power operation tool and the work object, and control the robot's two arms, which are assigned dual-arm roles, to perform collaborative operations on the power operation tool based on the multimodal state data. The dual-arm roles include a main operating arm and an auxiliary stabilizing arm. The parameter extraction module is used to continuously acquire real-time multimodal state data during the execution of the collaborative operation, and extract state change parameters from the real-time multimodal state data. The precursor identification module is used to analyze the abnormal precursor features and extract the corresponding current precursor type and force level when the abnormal precursor features are identified in the cooperative operation based on the state change parameters. The adjustment amount determination module is used to determine the impedance adjustment amount and force distribution ratio for the main operating arm and the auxiliary stabilizing arm based on the current precursor type and the force level. The force control correction module is used to update the corresponding control command based on the impedance adjustment amount and the force distribution ratio, and to perform real-time force control correction on the main operating arm and the auxiliary stabilizing arm according to the control command.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the adaptive robot dual-arm force control operation method for power operation tools as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, the device containing the computer-readable storage medium is controlled to perform the adaptive robot dual-arm force control operation method for power work tools as described in any one of claims 1-7.

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