Dexterous hand operating force dynamic detection system and method

Abstract

The invention discloses a dexterous hand operating force dynamic detection system and method, and belongs to the technical field of robot testing. Wherein the mechanical arm positioning unit comprises a mechanical arm base and a six-axis cooperative mechanical arm arranged on the mechanical arm base, and is used for fixing and moving a to-be-tested dexterous hand; the operating force detection unit comprises a plurality of modularized detection modules and is used for detecting various operating force parameters of the dexterous hand; a central processing control unit is arranged in the distributed control cabinet, is in communication connection with the mechanical positioning unit and the operating force detection unit, and is configured to calculate the motion trail of the six-axis cooperative mechanical arm through a path planning algorithm based on the pose of the target detection module, generate a control instruction and send the control instruction to the central processing control unit; the six-axis cooperative mechanical arm is driven to move to a target detection position along the movement track; synchronously acquiring and processing data from a force sensor in the operating force detection unit; the detection efficiency and the consistency are obviously improved, and errors caused by manual operation are avoided.

Description

Technical Field

[0001] This application belongs to the field of robot testing technology, specifically relating to a dynamic detection system and method for dexterous hand manipulation force. Background Technology

[0002] Humanoid robots represent the cutting edge of robotics technology. Their dexterous hands, as key actuators for precise physical interaction with the external environment, directly determine the breadth and depth of tasks the robot can perform. The various manipulative capabilities of the dexterous hand, including fingertip force, fingertip force, grasping force, pinching force, and passive load-bearing capacity, are crucial indicators for evaluating its performance. Therefore, accurate and efficient testing of these manipulative forces is essential for its research and development, quality control, and industrial application.

[0003] However, there are many types of dexterous hands on the market, which vary significantly in size, number of knuckles, and joint structure.

[0004] Most existing testing solutions employ specialized fixtures customized for specific dexterity hand models. While these solutions provide relatively stable testing conditions for the target model, their fundamental flaw lies in their severe lack of versatility. Specifically, when faced with the testing requirements of new models or dexterity hands with different configurations, it is necessary to redesign, manufacture, and replace the corresponding specialized fixtures and sensor mounting mechanisms. This process not only significantly increases equipment investment and maintenance costs but also leads to cumbersome testing procedures, long preparation cycles, and low overall testing efficiency, making it difficult to adapt to the industry trend of rapid iteration in dexterity hand technology and diversified product development. Summary of the Invention

[0005] In order to address the problems of low automation, poor compatibility and insufficient measurement accuracy in the existing technology of dexterity hand operation force detection, this application provides a dynamic detection system and method for dexterity hand operation force.

[0006] The technical solution is as follows: Firstly, a system for dynamically detecting the manipulative force of a dexterous hand is provided, comprising: The robotic arm positioning unit includes a robotic arm base and a six-axis collaborative robotic arm mounted on the robotic arm base, used to fix and move the dexterous hand to be tested; The operational force detection unit includes multiple modular detection modules, each of which integrates different types of force sensors to detect various operational force parameters of the dexterous hand; The distributed control cabinet contains a central processing and control unit, which is communicatively connected to the robotic arm positioning unit and the operating force detection unit, and is configured as follows: Based on the pose of the target detection module, the motion trajectory of the six-axis collaborative robotic arm is calculated through a path planning algorithm, and control commands are generated. Through the integrated multi-protocol I / O interface, control commands are sent to the robotic arm positioning unit, driving the six-axis collaborative robotic arm to move along the motion trajectory to the target detection position; Data from the force sensor in the operating force detection unit is collected and processed synchronously.

[0007] Preferably, the detection module is subdivided according to function, including: Tension and compression sensors are used to detect maximum fingertip force, rated fingertip force, rated fingertip force, and overall lifting force. Three-dimensional force sensor used to detect passive load-bearing capacity; A grip force sensor is used to detect maximum fingertip force, five-finger grip force, and two-finger / multi-finger pinch force.

[0008] Furthermore, to improve system fault tolerance and equipment safety, the force sensor in the detection module is connected to the detection module base via a flexible connector, which is a rubber pad. The force sensor and its bracket are fixedly connected to the detection module base by bolts, thereby ensuring force transmission efficiency while effectively absorbing positioning errors and impacts during the docking process.

[0009] Preferably, it also includes a human-computer interaction display terminal.

[0010] Secondly, a method for dynamically detecting the manipulative force of a dexterous hand is provided, employing the aforementioned dynamic detection system for the manipulative force of a dexterous hand, comprising: Path planning steps: Based on the pose of the target detection module, a path planning algorithm that integrates inverse kinematics solution and time parameter optimization is used to calculate the motion trajectory of the six-axis collaborative robot arm to safely and efficiently transfer the dexterous hand to the target detection module from the current position; Trajectory tracking steps: Control the six-axis collaborative robotic arm to move along the motion trajectory, and introduce an adaptive PID controller for closed-loop control. Dynamically fine-tune the motion trajectory according to the real-time status to cope with load changes and external interference. Data analysis steps: The synchronously collected and processed operational force parameter data is associated and stored with the corresponding test items, dexterity hand model and other information, and a multi-curve linkage and interactive data analysis interface is provided to support in-depth performance evaluation.

[0011] Preferably, the path planning algorithm specifically includes: Inverse kinematics solution steps: Based on the pose of the target detection module, a numerical optimization method combined with the pseudo-inverse of the Jacobian matrix is ​​used to calculate the joint angle sequence of the six-axis collaborative robot arm from the initial position to the target position; preferably, the numerical optimization method adopts the LM optimization algorithm, and the core iterative calculation formula is as follows: ; in, The Jacobian matrix for a six-axis collaborative robotic arm. The damping factor, It is the identity matrix. This is the error vector between the current pose and the target pose. This represents the increment of the joint angle that needs to be solved.

[0012] Path trajectory smoothing step: Perform joint space interpolation (such as cubic spline interpolation) on the joint angle sequence to generate continuous and smooth path points; Time optimization steps: Perform time parameterization optimization on the smooth path by minimizing the total path time and constraining velocity and acceleration, dynamically adjust path parameters, and generate the time-optimal trajectory.

[0013] Preferably, the proportional gain of the adaptive PID controller ,integral ,differential The gain parameter is not a fixed value, but is dynamically adjusted online according to a set of adaptive rules based on fuzzy inference, based on the real-time control error and its rate of change, in order to maintain optimal control performance. Preferably, it also includes real-time monitoring and online local replanning, which triggers online local replanning when the error between the desired pose and the actual pose exceeds a preset threshold.

[0014] Preferably, before the path planning step, a positioning step is further included, comprising: Based on the mechanical structure characteristics of different dexterous hands, the optimal positioning point and the relative positional relationship between the hand and the grasping test module when interacting with each test module are pre-calibrated, and the corresponding offset is calculated accordingly. During actual positioning, based on the offset, the final target pose of the six-axis collaborative robotic arm is finely adjusted through an automated compensation mechanism, thereby achieving high-precision and repeatable automatic positioning of the dexterous hand relative to the detection module.

[0015] The technical solution includes at least the following technical effects: 1. By integrating a six-axis collaborative robotic arm and modular inspection modules, a highly automated inspection platform has been constructed. The system can automatically move the dexterous arm to different inspection stations to complete the inspection of various static and dynamic manipulator forces, significantly improving inspection efficiency and consistency, and avoiding errors caused by manual operation.

[0016] 2. The modular detection module design, combined with preset positioning points and offset compensation mechanisms based on the structural characteristics of the dexterous hand, enables the system to quickly adapt to different types and sizes of dexterous hands. The flexible connector design between the detection module and the base effectively absorbs minor errors during robotic arm positioning and dexterous hand grasping, protecting the expensive dexterous hand and force sensor from rigid impact damage, while also ensuring effective force transmission and accurate measurement, greatly enhancing the system's practicality and durability.

[0017] 3. A path planning algorithm based on inverse kinematics and time parameterization optimization, combined with an adaptive PID controller, constitutes a high-performance closed-loop control system. This system not only calculates smooth, time-optimal motion trajectories but also adjusts control parameters in real time through online fuzzy inference, effectively addressing load variations and system nonlinearities. This ensures the positioning accuracy and motion stability of the dexterous hand in complex detection paths. An online local replanning mechanism further guarantees system safety and task completion capabilities in the event of unexpected deviations.

[0018] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] Figure 1 A structural block diagram of a dexterous hand manipulator force dynamic detection system provided in a preferred embodiment of this application; Figure 2 A flowchart of a method for dynamically detecting the manipulative force of a dexterous hand, provided as a preferred embodiment of this application; Figure 3 A flowchart of a method for dynamically detecting the manipulative force of a dexterous hand, provided as an optional embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[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] As attached Figure 1 As shown: Example 1 like Figure 1 As shown, this embodiment provides a dexterous hand dynamic force detection system. The system adopts a modular integrated architecture and mainly includes a robotic arm positioning unit, a force detection unit, and a distributed control cabinet.

[0024] The robotic arm positioning unit includes a robotic arm base and a six-axis collaborative robotic arm mounted on the base. The end of the six-axis collaborative robotic arm is equipped with an adapter interface for quickly mounting and securing the dexterous hand to be tested. Under the precise three-dimensional spatial positioning and attitude closed-loop control of the central processing unit, the six-axis collaborative robotic arm is responsible for accurately moving the dexterous hand to each testing station.

[0025] The manipulative force detection unit adopts a modular design, including multiple quickly replaceable detection modules. Each detection module integrates a specific type and range of force sensor for different manipulative force detection items. The replaceable test modules are designed according to the eight static / dynamic manipulative forces to be tested in actual needs, and are compatible with different types of dexterous hands.

[0026] The distributed control cabinet houses a central processing and control unit, which is communicatively connected to the robotic arm positioning unit and the manipulating force detection unit. This central processing and control unit executes control commands and synchronously processes data from the manipulating force detection unit. As the system's real-time control and signal scheduling hub, the distributed control cabinet integrates the central processing and control unit, multi-protocol I / O interface modules, and an operating interface.

[0027] The central processing unit (CPU) is the control core of the test system. It is responsible for the deterministic execution of most components in the test system, such as hardware I / O communication, data logging, stimulus generation, and model execution.

[0028] The I / O interface module supports industrial bus protocols such as EtherCAT, CAN, and EtherNet, enabling high-speed and stable communication with peripherals such as six-axis robotic arms, various force sensors, and optical inertial measurement systems. The I / O interface provides analog, digital, and bus signals for interaction with the device under test (DUT), and can be used to generate excitation signals and acquire data for recording and analysis.

[0029] User Interface: Communicates with the real-time processor to provide test commands and implement visualizations. Typically, this component also handles configuration management, test automation, analysis, and task report generation.

[0030] The central processing unit is configured as follows: Based on the pose of the target detection module, the motion trajectory of the six-axis collaborative robotic arm is calculated through a path planning algorithm, and control commands are generated. Through the integrated multi-protocol I / O interface, control commands are sent to the robotic arm positioning unit, driving the six-axis collaborative robotic arm to move along the motion trajectory to the target detection position; Data from the force sensor in the operating force detection unit is collected and processed synchronously.

[0031] The path planning algorithm can autonomously locate the dexterous hand and finely adjust the relative position of the dexterous hand and the sensor on the test module to improve the accuracy of detection.

[0032] The dexterity hand operation force dynamic detection system in this embodiment also includes a human-computer interaction display terminal.

[0033] The human-machine interface display terminal serves as a unified operating interface for users to interact with the testing system. It provides users with functions such as equipment status monitoring, testing process configuration (e.g., selecting testing items and setting parameters), manual / automatic control mode switching, data visualization (real-time display of force and motion data in the form of curves, charts, etc.), and test report generation. Through an intuitive graphical interface, users can easily manage testing tasks and conduct in-depth analysis of testing results.

[0034] This system combines hardware and software to achieve data acquisition, real-time curve recording, routine data recording, equipment monitoring and control system parameter recording, communication data acquisition, manual data storage, and a data black box. Real-time curve recording can record more than 20 channels, and channels and coordinate values ​​can be changed online. Manual storage allows for the manual saving of steady-state data, with adjustable storage time and the ability to add and delete channel parameters. The data black box automatically displays key parameters before and after shutdown when the equipment experiences an alarm or shutdown due to an anomaly; the time and data storage range are user-configurable, and the maximum data storage frequency is greater than 1kHz.

[0035] The dexterity hand dynamic force detection system in this embodiment supports online data analysis. In the curve data analysis function, points at different locations on the curve can be selected as needed, and their coordinate values ​​will be dynamically displayed on the interface in real time based on the specific location of the selected point. One can choose to fix a point on the curve and then continue to select other points for analysis; simultaneously, points can be selected on multiple different curves within the same coordinate system, based on the same coordinate axis, to achieve linked analysis between multiple curves, thereby more comprehensively comparing and studying the relationships and trends between the curves.

[0036] Preferably, the detection module is subdivided according to function, including: A grip force sensor is used to detect maximum fingertip force, five-finger grip force, and two-finger / multi-finger pinch force. The grip force sensor consists of a pressure-sensitive element and two metal plates arranged in a cylindrical structure. The sensor detects the applied force when these two metal plates are subjected to pressure.

[0037] Tension / compression sensors are used to detect maximum fingertip force, rated fingertip force, rated fingertip force, and overall lifting force. These sensors are employed to detect the dexterity hand's ability to lift heavy objects with its entire hand. The sensors contain an elastic body; when an external force is applied, the elastic body deforms, causing a change in the resistance of a strain gauge. This change converts the force signal into an electrical signal, thus detecting the tension / compression force. Measuring the overall lifting force requires the dexterity hand to grip the support mounted on the sensor with all five fingers in a lifting gesture. The dexterity hand then lifts the support, and the sensor acquires the overall lifting force of the dexterity hand.

[0038] A three-dimensional force sensor is used to detect passive load-bearing capacity. This sensor detects the static force that the distal phalanx of each finger of a dexterous hand can withstand on its pad, back, and side. Strain gauges are installed in different directions inside the three-dimensional force sensor, allowing it to detect force in the x, y, and z directions when applied. To measure passive load-bearing capacity, the dexterous hand extends and holds its fingers, while the sensor slowly applies static force along the fingertip, back, and side, respectively, thus acquiring the dexterous hand's passive load-bearing capacity.

[0039] Specifically: The tension / compression sensor is a DYLF-102 spoke-type pressure sensor with a range of 500N; it is used for maximum fingertip force detection, overall lifting force detection, rated fingertip force detection, and rated fingertip force detection. The collected parameter is the force applied to the sensor.

[0040] The three-dimensional force sensor, model LH-SZ-02, is used for passive load-bearing capacity detection. The acquired parameter is the force applied to the sensor.

[0041] The grip force sensor, model DYWL-001D, has a measurement range of 100N. It is a custom-designed sensor available in four different sizes: Ф22mm x 115mm, Ф60mm x 190mm, Ф100mm x 190mm, and 40mm x 40mm x 190mm. The Ф22mm x 115mm grip force sensor is used for detecting maximum fingertip force in dexterous hands; the Ф60mm x 190mm and Ф100mm x 190mm grip force sensors are used for detecting five-finger grip force in two-knuckle and three-knuckle dexterous hands, respectively; and the 40mm x 40mm x 190mm grip force sensor is used for detecting two-finger and multi-finger pinching force in dexterous hands. The collected parameter is the force applied to the sensor.

[0042] The key improvement lies in the fact that in all detection modules, the force sensor is connected to the detection module base via a flexible connector. In this embodiment, the flexible connector is a rubber pad of specific hardness, which secures the force sensor and sensor bracket to the detection module base using bolts. This allows for buffering and adaptive adjustment through the slight deformation of the rubber pad when there are minor errors in the robotic arm's positioning. This protects the dexterous hand and sensor from rigid impact damage while ensuring effective force transmission, thus improving detection accuracy and equipment durability. Specifically: Regarding the structure of the five-finger average grip strength detection module, after determining the sensor type and size, the sensor bracket was designed. Since the dexterous hand moves to the test position via a collaborative robotic arm, there is a small positioning error. To maximize the protection of both the dexterous hand and the sensor, a flexible connection is used between the sensor and its bracket. Specifically, the test module uses two ball screw slides to form the bracket, and one end of the sensor is connected to the bracket via a rubber pad. This allows the sensor to move in both vertical and horizontal directions, and the rubber pad provides a flexible connection between the sensor and the bracket.

[0043] The design of the maximum fingertip force detection module or the two-finger / multi-finger pinch force detection module is similar to that of the five-finger average grip force detection module. The connection between the sensor and the test module bracket is achieved through a flexible connection using rubber pads. Two ball screw slides are used to form a bracket to enable the sensor to move in both the up-down and left-right directions.

[0044] Regarding the structure of the passive load-bearing capacity testing module, after determining the sensor type and size, the sensor bracket was designed. Since the passive load-bearing capacity testing of a dexterous hand requires fixing the dexterous hand and then using a force gauge to slowly apply static force in each direction to the fingertip of a single finger until a preset load is reached, the testing module was designed to be three-axis movable. Movement along the three axes is achieved through ball screw slides in the x, y, and z directions. The sensor is installed at the end and, in conjunction with the sensor indenter, detects the passive load-bearing capacity of the dexterous hand.

[0045] Regarding the structure of the overall lifting force detection module, the maximum fingertip force detection module, and the rated fingertip / finger pad force detection module, the tension and compression sensors are mounted on the table using fixing blocks. A bracket and a rubber grip are installed above the sensors. The rubber grip provides a certain amount of flexible contact space to minimize damage to the equipment and dexterous hands caused by positioning errors.

[0046] Example 2 like Figure 2 and Figure 3 As shown, this embodiment provides a method for dynamically detecting the manipulative force of a dexterous hand, using the dynamic detection system for manipulative force of a dexterous hand described in Embodiment 1, and mainly includes the following steps: Step S1, Positioning Step: Before path planning, high-precision positioning is performed first. Based on the mechanical structural characteristics of different types of dexterous hands, the optimal positioning point and corresponding position and posture offsets for best interaction with each test module are pre-set in the software. When the six-axis collaborative robot arm carrying the dexterous hand moves to the vicinity of the detection module, the system uses this offset and an automatic compensation mechanism to fine-tune the final pose of the end effector of the six-axis collaborative robot arm, achieving high-precision automatic positioning of the dexterous hand.

[0047] Position and orientation of the end-effector of the collaborative arm ; Module positioning point coordinates ; After the dexterous hand is installed, the three-axis offsets of its positioning point from the end of the collaborating arm are x, y, z, and the target pose of the dexterous hand is... Calculation of position and attitude offsets: Step S2, Path Planning Step: Based on the pose of the target detection module, a path planning algorithm that integrates inverse kinematics solution and time parameter optimization is used to calculate the motion trajectory of the six-axis collaborative robotic arm to safely and efficiently transfer the dexterous hand to the target detection module from the current position.

[0048] The path planning algorithm specifically includes the following steps: Step S201, Inverse Kinematics Solution: Based on the pose of the target detection module, a numerical optimization method combined with the pseudo-inverse of the Jacobian matrix is ​​used to calculate the joint angle sequence of the six-axis collaborative robot arm from the initial position to the target position; preferably, the numerical optimization method adopts the LM (Levenberg-Marquardt) optimization algorithm, and the core iterative calculation formula is as follows: ; in, Let Jacobian matrix be the value of the robotic arm. This is the damping factor, used to ensure numerical stability. It is the identity matrix. This is the error vector between the current pose and the target pose. This represents the increment of the joint angles that need to be solved. Through iterative calculation, the final sequence of joint angles of the robotic arm from the initial position to the target position is obtained.

[0049] Step S202, Path Trajectory Smoothing Step: Perform joint space interpolation (such as cubic spline interpolation) on the joint angle sequence to generate a joint space path that is continuous and smooth in terms of position, velocity, and acceleration.

[0050] Step S203, Time Optimization Step: The Time Optimal Path Parameterization (TOPP) algorithm is used to optimize the smooth path in terms of time parameters. A suitable time law is assigned to the smooth path, and the shortest motion trajectory is generated under the premise of satisfying the speed, acceleration and torque constraints of each joint.

[0051] Step S3, trajectory tracking step: Control the six-axis collaborative robotic arm to move along the motion trajectory, and introduce an adaptive PID controller for closed-loop control. The motion trajectory is dynamically fine-tuned based on real-time status to ensure the robotic arm can accurately track the preset path. Optionally, the adaptive PID controller is a position-velocity dual-loop PID controller.

[0052] Among them, the gain parameter of the adaptive PID controller, the proportional gain parameter... ,integral ,differential It is not a fixed value, but rather it is dynamically adjusted online based on a set of adaptive rules based on fuzzy inference, according to the real-time control error and its rate of change, in order to maintain optimal control performance.

[0053] The specific tuning method includes an offline phase and an online phase. The offline phase is used to acquire the initial gain and safety range of each joint control loop in one go, while the online phase is used to adjust the gain in real time during equipment operation. The tuning method involves the initial adjustment and setting of the corresponding gain parameters.

[0054] 1. Offline parameter tuning Relay feedback steps: The first... The joint's servo driver is set to position mode, and the host computer sends an amplitude of ±d and a hysteresis of... to the joint. The relay signal causes the joint to generate a stable limit cycle oscillation; the oscillation amplitude is recorded. With period The critical gain is calculated using the following formula: .

[0055] Initial gain calculation steps: Obtain the initial value according to the modified Ziegler-Nichols formula: ; Steps for establishing a load-gain interpolation table: Divide the load range from no-load to 100% rated load into equal portions. For each load point, a step response test is performed, and fine-tuning is carried out with the criteria of "steady-state error ≤ 0.02° and overshoot ≤ 5%". ,integral ,differential This forms a two-dimensional lookup table for use in the online phase.

[0056] 2. Online adaptive gain scheduling Error normalization: , in, , .

[0057] Fuzzy reasoning: Will and Input a 7×7 fuzzy rule table, and obtain the increment by defuzzifying using the centroid method. , ;、 The rule table states that "the larger the error, the larger the proportion". A large rate of change of error increases the derivative. "This principle is to pre-define it in the FPGA."

[0058] Limiting and updating: ; Among them, integral ,differential Same method for amplitude limitation.

[0059] Step S4, Real-time Monitoring and Online Replanning: When the error between the desired pose and the actual pose exceeds a preset safety threshold, the system immediately triggers online local replanning to recalculate the local trajectory from the current point to the target point, ensuring the accuracy and safety of the motion. Online local replanning employs the online local TOPP-RA recalculation method.

[0060] Step S5, Data Analysis: The synchronously collected and processed operational force parameter data is associated and stored with the corresponding testing items, dexterity hand model, and other information. The software provides powerful data analysis functions, supporting the linked analysis of multiple data curves within the same coordinate system. Users can select any point on the curve to read the coordinates and compare multiple curves, thereby comprehensively evaluating the performance of the dexterity hand.

[0061] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0063] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0065] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

Claims

1. A dexterous hand manipulator force dynamic detection system, characterized in that, include: The robotic arm positioning unit includes a robotic arm base and a six-axis collaborative robotic arm mounted on the robotic arm base, used to fix and move the dexterous hand to be tested; The operational force detection unit includes multiple modular detection modules, each of which integrates different types of force sensors to detect various operational force parameters of the dexterous hand; The distributed control cabinet contains a central processing and control unit, which is communicatively connected to the robotic arm positioning unit and the operating force detection unit, and is configured as follows: Based on the pose of the target detection module, the motion trajectory of the six-axis collaborative robotic arm is calculated through a path planning algorithm, and control commands are generated. The control command is sent to the robotic arm positioning unit to drive the six-axis cooperative robotic arm to move along the motion trajectory to the target detection position; Data from the force sensor in the operating force detection unit is collected and processed synchronously.

2. The dexterity hand operation force dynamic detection system according to claim 1, characterized in that, The detection module includes: Tension and compression sensors are used to detect maximum fingertip force, rated fingertip force, rated fingertip force, and overall lifting force. Three-dimensional force sensor used to detect passive load-bearing capacity; A grip force sensor is used to detect maximum fingertip force, five-finger grip force, and two-finger / multi-finger pinch force.

3. The dexterity hand operation force dynamic detection system according to claim 1, characterized in that, The force sensor in the detection module is connected to the base of the detection module via a flexible connector, which is a rubber pad.

4. The dexterity hand operation force dynamic detection system according to claim 3, characterized in that, It also includes human-computer interaction display terminals.

5. A method for dynamically detecting the manipulative force of a dexterous hand, employing the dynamic manipulative force detection system for a dexterous hand as described in any one of claims 1 to 4, characterized in that, include: Path planning steps: Based on the pose of the target detection module, a path planning algorithm that integrates inverse kinematics solution and time parameter optimization is used to calculate the motion trajectory of the six-axis collaborative robot arm to move the dexterous hand from the current position to the target detection module; Trajectory tracking steps: Control the six-axis collaborative robotic arm to move along the motion trajectory, and use an adaptive PID controller for closed-loop control to dynamically adjust the motion trajectory; Data analysis steps: The collected and processed operational force parameter data are associated and stored with the corresponding test items and dexterity hand model information, and multi-curve linkage data analysis is provided.

6. The method for dynamic detection of dexterous hand manipulation force according to claim 5, characterized in that, The path planning algorithm includes: Inverse kinematics solution steps: Based on the pose of the target detection module, the joint angle sequence of the six-axis collaborative robot arm from the initial position to the target position is calculated by using numerical optimization methods combined with the pseudo-inverse of the Jacobian matrix. Path trajectory smoothing step: Perform joint space interpolation on the joint angle sequence to generate a smooth path; Time optimization steps: Perform time parameterization optimization on the smooth path, and generate the time-optimal trajectory by minimizing the total path time and constraining the speed and acceleration of joint motion.

7. The method for dynamic detection of dexterous hand manipulation force according to claim 6, characterized in that, The numerical optimization method used in the inverse kinematics solution step is the LM optimization algorithm, and its iterative calculation formula is as follows: ； in, The Jacobian matrix for a six-axis collaborative robotic arm. The damping factor, It is the identity matrix. This is the error vector between the current pose and the target pose. This represents the increment of the joint angle that needs to be solved.

8. The method for dynamic detection of dexterous hand operating force according to claim 5, characterized in that, The proportional, integral, and derivative gain parameters of the adaptive PID controller are adjusted online in real time according to adaptive rules based on fuzzy inference.

9. The method for dynamic detection of dexterous hand operating force according to claim 5, characterized in that, It also includes real-time monitoring and online local replanning, which triggers online local replanning when the error between the desired pose and the actual pose exceeds a preset threshold.

10. The method for dynamic detection of dexterous hand operating force according to claim 5, characterized in that, Prior to the path planning step, a positioning step is also included, comprising: Based on the mechanical structure characteristics of the dexterous hand, the relative positional relationship between the optimal positioning point and the gripping test module is pre-set, and the corresponding offset is calculated accordingly. Based on the offset, the final target pose of the six-axis collaborative robot arm is fine-tuned through an automated compensation mechanism.

Images