Flexible force feedback sensing system and method for substation inspection robot

Abstract

The invention relates to the technical field of force feedback, in particular to a transformer substation inspection robot flexible force feedback sensing system and method.The system comprises an upper computer and a lower computer, and the upper computer is used for completing whole-process operation from sensing signal processing to control instruction generation; the lower computer is used for receiving instructions of the upper computer, executing corresponding actions and collecting contact force signals at the same time. In the scheme, the fiber bragg grating flexible touch sensing unit is tightly attached to the surfaces of equipment with different curvatures in the transformer substation, so that measurement errors caused by attaching gaps of the rigid sensing unit are avoided; meanwhile, the fiber bragg grating arrays in the units are arranged according to a specific matrix, three orthogonal fiber bragg gratings with different central wavelengths are embedded in each sensing point, multi-dimensional force information can be synchronously collected, the strain transmission efficiency is guaranteed in cooperation with a special silicone rubber coating layer, and the influence of environment temperature difference on sensing signals is eliminated in combination with an integrated temperature compensation assembly. And an accurate force signal can be stably obtained in a complex environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of force feedback, in particular to a flexible force feedback sensing system and method for substation inspection robots. BACKGROUND

[0002] With the continuous advancement of smart grid construction, the number of devices in substations continues to increase and the operating environment is complex and diverse. The traditional manual inspection method faces problems such as low efficiency, high safety risk, and detection accuracy easily affected by human factors. Substation inspection robots have gradually become key equipment for replacing manual operation to complete device detection, fault diagnosis and other operation and maintenance work. During the inspection operation, the robot needs to use the end effector of the mechanical arm to complete fine operations such as insulator grabbing and joint tightening inspection, which puts clear requirements on the force feedback capability of the system, both to ensure that the grabbing force meets the operation requirements and to avoid excessive force that damages the outer insulation layer or precision components of the device. Currently, the commonly used force feedback schemes in the industry are mostly based on single-dimensional force sensors or strain gauge sensors, which realize basic force control by collecting single-point force values at the end of the mechanical arm, and can meet the simple grabbing requirements in the conventional environment.

[0003] However, the existing force feedback scheme is difficult to adapt to the complex scenarios of substation inspection. First, the sensing dimension is relatively single. Traditional sensors can only obtain the force value in the normal direction or a specific direction, and cannot capture the pressure distribution characteristics of the contact area, making it difficult for the robot to accurately determine whether the contact posture is stable, and prone to device slipping or excessive local stress. Second, the environmental adaptability is poor. The existing sensing units are mostly rigid structures, with low adhesion to the surface of the device, and lack effective temperature compensation mechanisms. The day-night temperature difference in the substation will cause the sensing signal to drift, affecting the accuracy of force measurement. Third, the control strategy is relatively rigid. The impedance control parameters are mostly fixed values set in advance, and cannot be dynamically adjusted according to the contact state. When the contact pressure distribution is uneven, the robot still operates in a rigid point contact mode, which can easily cause scratches on the surface of the device or failure to grab. Therefore, a flexible force feedback sensing system and method for substation inspection robots are needed. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a flexible force feedback sensing system and method for substation inspection robots, thereby solving the technical problems mentioned in the background art.

[0005] To achieve the above purpose, the present application is implemented by the following technical solutions:

[0006] A flexible force feedback sensing system for substation inspection robots, comprising an upper computer and a lower computer, which establish a real-time data interaction link through industrial Ethernet;

[0007] The upper computer is used for completing the whole process operation from the sensing signal processing to the control instruction generation, and comprises the following modules:

[0008] The signal processing module is used for realizing the reception, abnormal value elimination, low-pass filtering, temperature compensation and output of the purified wavelength data of the fiber grating reflection wavelength original signal.

[0009] The force mapping decision module is used for converting the purified wavelength data into multi-dimensional force information, reconstructing the two-dimensional pressure distribution of the contact area, extracting the contact state characteristic parameters and completing the contact state recognition.

[0010] The compliance control module is used for dynamically adjusting the impedance control parameters according to the contact state recognition result and generating the robot motion instruction.

[0011] The lower computer is used for receiving the instruction of the upper computer and executing the corresponding action, and simultaneously collecting the contact force signal, and comprises the following modules:

[0012] The transformer station inspection robot body is used for executing the inspection operation action.

[0013] The fiber grating flexible tactile sensing unit is integrated in the end effector clamping surface of the transformer station inspection robot body, and is used for collecting the force signal in the contact process and transmitting the force signal to the upper computer.

[0014] In a possible implementation manner, the fiber grating flexible tactile sensing unit is composed of a flexible matrix, a fiber grating array embedded in the flexible matrix and a platinum resistance temperature sensor integrated in the flexible matrix.

[0015] The flexible matrix adopts a high-elastic transparent silicone rubber material, and the formula of the high-elastic transparent silicone rubber material includes, in terms of mass parts, 82-88 parts of hydroxyl-terminated polydimethylsiloxane base glue, 12-18 parts of fumed silica reinforcing filler, 0.8-1.2 parts of platinum gold catalyst and 4.5-5.5 parts of hydrogen-containing silicone oil crosslinking agent.

[0016] In a possible implementation manner, the fiber grating array is composed of 24 sensing points, which are uniformly arranged in the flexible matrix in the form of a 6-row 4-column matrix; each sensing point embeds three fiber gratings with central wavelengths of 1520±2 nm, 1540±2 nm and 1560±2 nm respectively, and the three fiber gratings correspond to the X, Y and Z axes of the Cartesian coordinate system in mutually orthogonal directions.

[0017] In a possible implementation manner, a transformer station inspection robot flexible force feedback sensing method comprises the following steps:

[0018] S1: system initialization and parameter presetting, completing zero load calibration of the fiber grating channel and storing the initial central wavelength reference value , loading the force-wavelength sensitivity matrix Set the safety collision force threshold, the desired gripping force, the contact state recognition threshold, and the initial impedance parameters;

[0019] S2: Synchronous acquisition and digital filtering of multi-channel wavelength signals, acquiring raw wavelength data of fiber Bragg gratings. After outlier removal, low-pass filtering, and temperature compensation, the purified wavelength value is obtained. ;

[0020] S3: Reconstruction of multidimensional force and pressure distribution based on wavelength drift calculation, and calculation of wavelength drift. According to the force-wavelength sensitivity matrix Solve the three-dimensional force vector of each sensing point And reconstruct the two-dimensional pressure distribution in the contact area;

[0021] S4: Contact state identification and decision-making based on pressure distribution characteristics, extracting the total resultant force magnitude, pressure distribution centroid coordinates, and pressure distribution asymmetry index. and pressure concentration coefficient Identify status indicators ;

[0022] S5: Compliant control execution based on impedance parameter adaptation, according to status indicators. Select control strategies and based on and Dynamically adjust virtual stiffness and virtual damping The position correction of the robot end effector is calculated using the impedance control equation. Generate and issue robot movement commands;

[0023] S6: Task Termination and Data Archiving. After the job is completed, data acquisition and control command issuance are stopped, and all data from the entire process is archived and stored.

[0024] In one possible implementation, in step S1, the contact state recognition threshold includes an asymmetry index threshold. and pressure concentration coefficient threshold The initial impedance parameters include virtual mass. Virtual damping and virtual stiffness .

[0025] In one possible implementation, in step S3, the calculation of the three-dimensional force vector for each sensing point... The formula:

[0026]

[0027] wherein, ΔΛ = [ Δ λ x , Δ λ y , Δ λ z ] T , , , respectively the wavelength shift of the sensing point in X, Y, Z direction, is the inverse matrix of

[0028] In a possible implementation, in step S4, the formula for calculating the pressure distribution asymmetry index

[0029] wherein, and respectively the pressure integral value of the pressure distribution in the left half region u ∈ [ 0 , 0 . 5 ] and the right half region u ∈ [ 0 . 5 , 1 ]

[0030] In a possible implementation, in step S5, the formula for adjusting the virtual stiffness and the virtual damping respectively are:

[0031]

[0032]

[0033] wherein, , are adjustment coefficients.

[0034] Compared with the prior art, the beneficial effects are:

[0035] 1. In the present scheme, the customized fiber Bragg grating flexible tactile sensing unit effectively improves the precision and environmental adaptability of force signal acquisition. The sensing unit adopts a flexible substrate with a specific formula, which can closely fit the surface of equipment with different curvatures in the substation, avoiding measurement errors caused by the fitting gap of rigid sensing units. At the same time, the fiber Bragg grating array in the unit is arranged in a specific matrix, and each sensing point is embedded with three orthogonal fiber Bragg gratings with different center wavelengths, which can synchronously collect multi-dimensional force information. Combined with the special silicone rubber coating layer to ensure strain transmission efficiency, and the integrated temperature compensation component to eliminate the influence of environmental temperature difference on the sensing signal, accurate force signals can be stably obtained in the complex operating environment of the substation, thereby solving the problems of insufficient precision and poor environmental adaptability of existing sensing schemes.

[0036] ​​​2. In this scheme, the contact state is accurately judged by two-dimensional pressure distribution reconstruction and multi-feature contact state recognition mechanism, reducing the operation risk. The force mapping decision module can reconstruct the two-dimensional pressure distribution of the contact area based on the multi-dimensional force data collected by the sensing unit, intuitively reflect the uniformity and concentration position of the pressure distribution, and extract key feature parameters such as total force, pressure distribution centroid, asymmetry index, and concentration coefficient. Through multi-dimensional threshold judgment, accurate recognition of four states of no contact, stable contact, unstable contact, and abnormal collision is realized. This mechanism can identify unstable contact state due to uneven pressure distribution in advance, trigger posture adjustment in time, avoid equipment sliding or collision caused by state misjudgment, and ensure the stability and safety of the operation process;

[0037] 3. In this scheme, the impedance parameter adaptive control strategy driven by pressure distribution is used to realize dynamic switching from rigid point contact to flexible surface fitting, thereby improving the operation success rate. The compliant control module dynamically adjusts the impedance control parameters according to the pressure distribution characteristics of the contact area. When the pressure distribution is asymmetric, the virtual stiffness is reduced through a specific formula to avoid excessive local stress. When the pressure is too concentrated, the virtual damping is increased through a corresponding formula to slow down the movement speed to adjust the fitting posture. This adaptive mechanism enables the robot to flexibly switch control modes according to the contact state. When there is no contact, it moves according to the preset trajectory. When there is stable contact, it maintains force balance. When there is abnormal collision, it quickly retreats to avoid scratching the device surface or failing to grasp, thereby improving the grasping adaptability of different types of transformer station equipment and ensuring the smooth completion of fine operation. BRIEF DESCRIPTION OF DRAWINGS

[0038] The above description is only a summary of the technical scheme of the present application. In order to more clearly understand the technical means of the present application and can be implemented according to the content of the description, the following will be described in detail with the preferred embodiments of the present application and with the help of the accompanying drawings.

[0039] Figure 1 The above description is only a summary of the technical scheme of the present application. In order to more clearly understand the technical means of the present application and can be implemented according to the content of the description, the following will be described in detail with the preferred embodiments of the present application and with the help of the accompanying drawings.

[0040] Figure 2 The above description is only a summary of the technical scheme of the present application. In order to more clearly understand the technical means of the present application and can be implemented according to the content of the description, the following will be described in detail with the preferred embodiments of the present application and with the help of the accompanying drawings.

[0041] Figure 3 The above description is only a summary of the technical scheme of the present application. In order to more clearly understand the technical means of the present application and can be implemented according to the content of the description, the following will be described in detail with the preferred embodiments of the present application and with the help of the accompanying drawings. DETAILED DESCRIPTION

[0042] The preferred embodiments of the present application will be described in detail with reference to the accompanying drawings, but the present application can be implemented in various forms, and therefore the present application is not limited to the embodiments described below, and components not connected to the invention will be omitted from the drawings for a clearer description of the present application.

[0043] The technical solutions in the embodiments of the present application are to solve the problems in the background art, and are generally as follows:

[0044] Embodiments:

[0045] Please refer to Figures 1 to 3 The embodiment introduces a flexible force feedback sensing system of a substation inspection robot, which comprises an upper computer and a lower computer. The two computers establish a real-time data interaction link through an industrial Ethernet to ensure the synchronization of sensing signal transmission and control instruction issuing.

[0046] The upper computer serves as an operation center of flexible force feedback sensing and control, and is used for completing the whole-process operation from sensing signal processing to control instruction generation, including a signal processing module, a force mapping decision module and a compliant control module.

[0047] The signal processing module is used for realizing the reception, abnormal value elimination, low-pass filtering, temperature compensation and output of purified wavelength data of the original reflection wavelength signal of the fiber grating, and the hardware carrier comprises a fiber signal demodulator, a high-speed data acquisition card and a digital signal processor (DSP);

[0048] The force mapping decision module is used for converting the purified wavelength data into multi-dimensional force information, reconstructing the two-dimensional pressure distribution of the contact area, extracting the contact state characteristic parameters and completing the contact state recognition.

[0049] The compliant control module is used for dynamically adjusting the impedance control parameters according to the contact state recognition result, generating the robot motion instruction and issuing the same to the lower computer.

[0050] The lower computer serves as an execution unit, and is used for receiving the instruction of the upper computer and executing the corresponding action, and collecting the contact force signal at the same time, including a substation inspection robot body and a fiber grating flexible tactile sensing unit.

[0051] The substation inspection robot body is used for executing the inspection operation action, including a mobile chassis (maximum moving speed 0.8 m / s, positioning accuracy ±5 mm), a 6-degree-of-freedom mechanical arm (repeated positioning accuracy ±0.1 mm, maximum load 5 kg) and an end effector (clamping range 5-150 mm), and the mechanical arm controller can receive the position or speed instruction and drive the joint motor to act.

[0052] The fiber grating flexible tactile sensing unit is integrated in the clamping surface of the end effector, and is used for collecting the force signal in the contact process and transmitting the same to the upper computer, and is composed of a flexible matrix, a fiber grating array embedded therein and a platinum resistance temperature sensor (measurement accuracy ±0.1℃) integrated in the matrix.

[0053] The platinum resistance temperature sensor is used for collecting the ambient temperature to realize temperature compensation.

[0054] The flexible base is made of high-elastic transparent silicone rubber material, and the formula is as follows in terms of mass fraction: 82-88 parts of hydroxyl-terminated polydimethylsiloxane base, 12-18 parts of fumed silica reinforcing filler, 0.8-1.2 parts of platinum catalyst, and 4.5-5.5 parts of hydrogen-containing silicone oil crosslinking agent. In the preparation, the hydroxyl-terminated polydimethylsiloxane base and the fumed silica are first added to a planetary mixer and mixed at a speed of 45 revolutions per minute for 35 minutes to ensure uniform dispersion of the reinforcing filler; then the platinum catalyst and the hydrogen-containing silicone oil crosslinking agent are added, and the speed is adjusted to 30 revolutions per minute for continued stirring for 20 minutes to form a uniform mixture; the mixture is vacuum degassed at-0.095 MPa for 15 minutes, and then injected into a specially designed mold of 80 mm x 60 mm x 5 mm, and heat-cured at 125℃ for 55 minutes to form a transparent elastomer base with a Shore A hardness of 15-25.

[0055] The fiber grating array is composed of 24 sensing points, which are uniformly arranged in a 6-row and 4-column matrix inside the flexible base, and the center-to-center distance between adjacent sensing points is 10 mm. Each sensing point is embedded with three fiber gratings with center wavelengths of 1520±2 nm, 1540±2 nm and 1560±2 nm, respectively, corresponding to the X, Y and Z axes (horizontal, vertical and normal) of the Cartesian coordinate system in mutually orthogonal directions, and the embedding depth is 1 / 2 of the base thickness (2.5 mm). The fiber gratings are coated with a 0.5-0.8 mm thick layer of methyl vinyl silicone rubber to ensure a strain transmission efficiency of ≥96%; all fiber gratings are connected to the fiber signal demodulator of the upper computer through a 3-5 m long stainless steel armored cable.

[0056] Based on the above-mentioned upper computer and lower computer architecture system, the embodiment also introduces a flexible force feedback sensing method for a substation inspection robot, and the specific steps are as follows:

[0057] S1: System initialization and parameter presetting

[0058] This step is realized by the signal processing module, force mapping decision module and compliant control module of the upper computer, and the initialization and presetting of the system parameters are completed.

[0059] After the signal processing module is started, it first establishes an RS485 communication connection (baud rate 115200 bps) with the fiber signal demodulator in its own hardware carrier, and sets the sampling frequency to 1000 Hz; then controls the demodulator to calibrate the 72 fiber grating channels (24 sensing points x 3 directions) under zero load, reads and stores the initial center wavelength reference value of each channel which is a general initial wavelength, and is distinguished by context when corresponding to a specific channel). The force mapping decision module loads the force-wavelength sensitivity matrix obtained through the calibration experiment in advance , which is a 3x3 real matrix, and the elements​ Indicates the first Force changes in the direction of the first force (X, Y, Z axis) cause the first force to change. Wavelength drift coefficient for each wavelength channel (unit: pm / N); Simultaneously set operating parameters: safety collision force threshold. (Value range 15-25N, set to 20N in this embodiment), desired gripping force (Value range 5-8N, set to 6N in this embodiment), contact state recognition threshold (asymmetry index threshold) =0.3, pressure concentration coefficient threshold =0.6. Compliance control module sets initial impedance parameters: virtual mass =0.5kg, virtual damping =20 N·s / m, virtual stiffness =500N / m; simultaneously plan the initial motion trajectory of the robotic arm. The trajectory uses linear interpolation, starting at the current position of the robotic arm and ending at the target grasping position, with a movement speed of 50 mm / s and an acceleration of 20 mm / s². 2 After all parameters are set, each module of the host computer performs a self-check to confirm that the communication link is smooth, the parameters are loaded correctly, and the robot body is in standby mode. Then, the system enters the real-time control loop.

[0060] S2: Synchronous acquisition and digital filtering of multi-channel wavelength signals

[0061] This step is achieved through the collaboration of the signal processing module of the host computer and the fiber optic flexible tactile sensing unit of the slave computer to complete the acquisition and preprocessing of multi-channel wavelength signals and remove noise and temperature interference.

[0062] The hardware carrier of the signal processing module, the fiber optic signal demodulator, synchronously acquires the reflection center wavelengths of 72 fiber optic grating channels (the fiber optic grating array is connected to the demodulator via armored optical cable) at a sampling frequency of 1000Hz set in S1, obtaining the raw wavelength data. ,in, The sampling time is indicated, and the 72 channels of data for each sampling cycle are packaged and transmitted to the digital signal processor of the signal processing module via industrial Ethernet, with a transmission delay of ≤1ms. The signal processing module first uses Grubbs' criterion to... Outlier removal: Calculate the mean of the last 10 consecutive sampling points for each channel. and standard deviation If a certain sampling point satisfies ,in, = 2.176 is the Grubbs critical value for significance level 0.05, sample size 10, so the point is determined to be an outlier, and the linear interpolation result of the adjacent two normal sampling points is used to replace it. Then, the wavelength data after removing the abnormal value is applied to an eighth-order Chebyshev type I low-pass digital filter (passband ripple ≤ 1 dB, stopband attenuation ≥ 40 dB) with a cutoff frequency of 100 Hz to suppress noise, and the filtered data is obtained . Subsequently, temperature compensation is performed: the real-time temperature is collected by the platinum resistance temperature sensor integrated in the fiber Bragg grating flexible tactile sensing unit , and the temperature change amount is calculated , wherein is the ambient temperature at S1 initialization; the temperature compensation formula is used to correct the filtered data, and the purified wavelength value is obtained, wherein is the temperature sensitivity coefficient of the fiber Bragg grating, provided by the fiber Bragg grating manufacturer and verified by experimental calibration, and in this embodiment, it is 10 pm / °C. Finally, the signal processing module packs the 72-channel purified wavelength data of each sampling period

[0063] and transmits it to the force mapping decision module through the internal bus, and the transmission period is consistent with the sampling period.

[0064] This step is realized by the force mapping decision module of the upper computer, based on the purified wavelength data output by S2, the multi-dimensional force information of each sensing point is calculated, the two-dimensional pressure distribution of the contact area is reconstructed, and the total force and total torque of the robot end are calculated. After the force mapping decision module receives , first, the real-time wavelength drift of each fiber Bragg grating channel is calculated , wherein is the initial center wavelength reference value stored by S1; if < 1 pm, i.e. less than the minimum resolution of the fiber signal demodulator, it is determined that there is no effective signal change in the channel, and the drift is set to 0 to avoid noise interference. For each sensing point, the wavelength drift amounts corresponding to the X, Y and Z directions are extracted, and the wavelength drift vector ΔΛ = [ Δ λ x , Δ λ y , Δ λ z ] T , wherein , , are the wavelength drift amounts (unit: pm) in the X, Y and Z directions of the sensing point, respectively. According to the force-wavelength sensitivity matrix , a linear relationship between wavelength drift and three-dimensional force vector is established: , wherein F = [ F x , F y , F z ] T is the three-dimensional force vector (unit: N) of the sensing point, , , are the force components in X, Y, Z direction respectively. Solving the above matrix equation, the solution formula of three-dimensional force vector is obtained: where, is the inverse matrix of (pre-calculated and stored by LU decomposition method to avoid redundant operation of real-time calculation). After obtaining the three-dimensional force vector of 24 sensing points, the Z direction force component (normal force, directly related to contact pressure) of each point is extracted; the sensing area of the flexible substrate is mapped to the normalized coordinate plane where, u ∈ [ 0 , 1 ] , v ∈ [ 0 , 1 ] corresponding to the lateral and longitudinal directions of the substrate respectively, the bicubic interpolation algorithm (boundary condition is first derivative continuity) is used to interpolate of 24 sensing points, reconstructing the two-dimensional pressure distribution (unit: kPa) of the entire contact area, the value of which is converted from the ratio of to the equivalent contact area of the sensing point (set to 100 mm²). At the same time, the origin of the force coordinate system is defined as the center of the end effector of the robot arm (coinciding with the center of the flexible substrate), the position vector r = [ x , y , z ] T (unit: m) of each sensing point relative to the origin is called, and the total force and the total torque experienced by the robot arm are calculated, where, is the three-dimensional force vector of the th sensing point, is the position vector of the th sensing point, represents the vector cross product operation.

[0065] Finally, the force mapping decision module temporarily stores the calculated 24 sensing points three-dimensional force vector, two-dimensional pressure distribution , total force and total torque in the cache area for subsequent contact state recognition.

[0066] S4: Contact state recognition and decision based on pressure distribution characteristics

[0067] This step is realized by the force mapping decision module of the host computer, based on the mechanical information extracted in S3, the contact state characteristic parameters are extracted, the contact state is recognized through the preset rules, and the decision result is output.

[0068] The force mapping decision module first extracts four characteristic parameters from the mechanical information temporarily stored in S3: total force size , calculated using the Euclidean norm, formula , where , , are the components of the total force in the X, Y, Z axes; pressure distribution centroid coordinates , calculated using the weighted average method, formula , , where the summation range is to divide the normalized plane into a 100x100 discrete grid, , are the grid point coordinates, is the two-dimensional pressure distribution value at the point; pressure distribution asymmetry index , used to represent the uniformity of the pressure distribution, the calculation method is: divide the pressure distribution into left half area u ∈ [ 0 , 0 . 5 ] and right half area u ∈ [ 0 . 5 , 1 ] , calculate the pressure integral values and of the two areas, then ; pressure concentration coefficient , used to represent the degree of pressure concentration, the calculation method is: find the maximum pressure value in the two-dimensional pressure distribution, calculate the average pressure value of the entire area, where =1 is the normalized area, then . Then according to the above characteristic parameters and the threshold value preset by S1, the contact state is identified according to the following rules:

[0069] If > , it is determined as abnormal collision state ;

[0070] If 0 ≤ and ≤ , ≤ , it is determined as stable contact state ;

[0071] If 0 ≤ but > or > , it is determined as unstable contact state ;

[0072] like <0.1N, considering measurement error, it is determined to be a non-contact state. ;

[0073] Finally, the force mapping decision module will identify the state flags. (The value is,) , , , ) and extracted feature parameters ( , , , Total synergy and total torque The packaged components are transmitted to the compliant control module of the host computer via the internal bus.

[0074] S5: Compliant control execution based on impedance parameter adaptation

[0075] This step is implemented through the compliant control module of the host computer. Based on the status flags and mechanical parameters output by S4, the corresponding control strategy is selected, the impedance parameters are dynamically adjusted, and robot motion commands are generated. The compliant control module receives the status flags. Then, select the control strategy according to the following logic:

[0076] when = In the event of an abnormal collision, immediately interrupt the current motion command and initiate an emergency retreat procedure; based on the total resultant force... The direction generates along - Trajectory of retreat in the opposite direction (i.e., the opposite direction of the collision force) The retraction distance is set to 80mm (range 50-100mm), and the retraction speed is set to 100mm / s (higher than normal movement speed to ensure rapid collision avoidance). The retraction trajectory is converted into position commands in the joint space of the robotic arm through inverse kinematics, and then sent to the lower-level robot controller (the control core of the substation inspection robot) via the Profinet protocol to drive the robotic arm to perform the retraction action. Continuous monitoring is performed during the retraction process. ,when When the value is less than 0.1N, the robot stops retracting and enters standby mode, while simultaneously sending a collision alarm signal to the host computer.

[0077] when = In the (non-contact state), continue executing the initial motion trajectory planned in S1. ; the trajectory is decomposed into discrete position commands with 1ms sampling period and sent to the robot controller to drive the robot to move towards the target position, while continuously receiving the state identifier transmitted by S4, and once it becomes other states, the control strategy is switched immediately.

[0078] When = (stable contact state) or = (unstable contact state), the adaptive impedance control loop is started, which specifically includes: first, according to the pressure distribution asymmetry index and the pressure concentration coefficient extracted by S4, the virtual stiffness and virtual damping (the virtual mass remains unchanged) are dynamically adjusted, and the adjustment formula is , wherein is the adjusted virtual stiffness, is the adjusted virtual damping, , is the adjustment coefficient, which is determined by experiment calibration and is 2 and 0.5 respectively; the physical meaning of this mechanism is: when the pressure distribution is asymmetric (the pressure distribution asymmetry index increases), the virtual stiffness decreases to reduce the stiffness of the robot arm to avoid excessive local stress; when the pressure is too concentrated (the pressure concentration coefficient increases), the virtual damping increases to increase the damping of the robot arm to slow down the movement speed and facilitate the adjustment of the contact posture, so as to realize the "flexible surface fitting". Then, based on the adjusted impedance parameters , , , combined with the expected grasping force and the actual total force , the position correction amount of the robot end is calculated through the impedance control equation, and the impedance control equation is wherein , , are the acceleration, speed and displacement components of the position correction amount (units are m / s 2 , m / s, m) respectively; in actual calculation, Euler method is used to discretize and solve the above differential equation (sampling period 1ms) to obtain the discrete position correction amount , is the discrete time. Then, the position correction amount is superimposed on the current expected trajectory to obtain the corrected expected trajectory wherein For stable contact state, the trajectory is the current position, and for unstable contact state, the trajectory is based on the pressure distribution centroid The adjusted pose trajectory (e.g., a fine-tuned trajectory generated towards the centroid when the centroid is offset). Finally, the revised desired trajectory is converted into angle instructions for each joint of the robotic arm through a kinematic inverse solution algorithm , , , , , corresponding to the 6 joints of the 6-DOF robotic arm), and motion constraints (joint angle range, angular velocity upper limit, angular acceleration upper limit) are added; the joint angle instructions are packaged and transmitted to the robot controller through the Profinet protocol to drive the robotic arm to perform actions, while continuously receiving the mechanical parameters transmitted by S4 to update the impedance parameters and position correction in real time, forming a control loop. During control, the compliance control module monitors the following indicators in real time: total force deviation from the desired grasping force (ensuring ≤ ± 10%), pressure distribution indicators in stable contact state ≤ 0.2, ≤ 0.5), trajectory tracking error of the robotic arm end (ensuring ≤ 0.1 mm); if the indicators exceed the preset range, adjust the adjustment coefficient , desired grasping force until the indicators return to normal.

[0079] S6: Task termination and data archiving

[0080] This step is achieved through the cooperation of the upper computer modules and the lower computer robot controller, completing the task termination operation and archiving storage of the whole process data.

[0081] When the substation inspection robot completes the preset task (such as insulator grabbing, equipment detection), the robot controller of the lower computer sends a task completion signal to the compliance control module; after receiving the signal, the compliance control module immediately sends a stop command to the robot controller, driving the robotic arm to return to the initial pose (joint angle zero) and closing the clamping mechanism of the end effector. At the same time, the compliance control module sends a data acquisition termination signal to the signal processing module, which stops receiving the wavelength data from the fiber signal demodulator in its hardware carrier and closes the communication connection between the high-speed data acquisition card and the demodulator. Subsequently, the upper computer modules begin data archiving: the signal processing module stores all sampling period wavelength raw data , filtered data , and purified data Each data file contains timestamp (accurate to milliseconds), channel number, wavelength value (unit: nm); the force mapping decision module stores the wavelength drift amount of each sampling period , three-dimensional force vector of 24 sensing points , two-dimensional pressure distribution (stored in the form of discrete values in a 100x100 grid), total force , total moment , contact state identification and characteristic parameters , , , ); the compliance control module stores the impedance parameters of each sampling period , , , , , position correction amount , expected trajectory , corrected trajectory and the issued joint angle command.

[0082] All archived data are stored in structured binary files (suffix.dat), and the header of each file contains task number, start time, end time, sampling frequency and other metadata; at the same time, the corresponding data summary file (suffix.txt) is generated, which contains the storage path of the data file, data volume, key indicator statistical value (such as maximum force, average pressure, duration of each contact state), facilitating subsequent data retrieval and analysis. The archived data are stored in the solid state disk (SSD) of the upper computer, and the storage capacity supports at least 100 times of data storage, and also supports data export through the USB interface.

[0083] Finally, it should be noted that: obviously, the above embodiments are only examples for clearly illustrating the present application, and are not limitations on the embodiments. For ordinary skilled persons in the art, other different forms of changes or variations can be made on the basis of the above description. Here, it is not necessary and impossible to exhaust all the embodiments. The obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. A flexible force feedback sensing system for a substation inspection robot, characterized in that, This includes a host computer and a slave computer, which establish a real-time data interaction link through industrial Ethernet; The host computer is used to complete the entire process of computation from sensor signal processing to control command generation, including: The signal processing module is used to receive the raw signal of the fiber optic grating reflected wavelength, remove outliers, perform low-pass filtering, temperature compensation, and output purified wavelength data. The force mapping decision module is used to convert the purified wavelength data into multi-dimensional force information, reconstruct the two-dimensional pressure distribution of the contact area, extract contact state feature parameters, and complete contact state identification. The compliance control module is used to dynamically adjust the impedance control parameters based on the contact state recognition results and generate robot motion commands. The lower-level machine is used to receive instructions from the upper-level machine and execute corresponding actions, while simultaneously acquiring contact force signals, which include: The substation inspection robot body is used to perform inspection operations; A fiber optic flexible tactile sensing unit is integrated into the end effector gripping surface of the substation inspection robot body, and is used to collect force signals during the contact process and transmit them to the host computer.

2. The flexible force feedback sensing system for a substation inspection robot as described in claim 1, characterized in that, The fiber grating flexible tactile sensing unit consists of a flexible substrate, an embedded fiber grating array, and a platinum resistance temperature sensor integrated inside the substrate. The flexible matrix is ​​made of highly elastic transparent silicone rubber material, and its formula includes, by weight: 82-88 parts of hydroxyl-terminated polydimethylsiloxane rubber, 12-18 parts of fumed silica reinforcing filler, 0.8-1.2 parts of platinum catalyst, and 4.5-5.5 parts of hydrogen-containing silicone oil crosslinking agent.

3. The flexible force feedback sensing system for a substation inspection robot as described in claim 2, characterized in that, The fiber grating array consists of 24 sensing points, which are evenly arranged in a 6x4 matrix inside the flexible substrate. Each sensing point is embedded with three fiber gratings with center wavelengths of 1520±2 nm, 1540±2 nm, and 1560±2 nm, respectively. The three fiber gratings are orthogonal to each other and correspond to the X, Y, and Z axes of the Cartesian coordinate system.

4. A flexible force feedback sensing method for a substation inspection robot using the system described in any one of claims 1 to 3, characterized in that, Includes the following steps: S1: System initialization and parameter preset, complete the zero-load calibration of the fiber Bragg grating channel and store the initial center wavelength reference value. Force-wavelength sensitivity matrix Set the safety collision force threshold, the desired gripping force, the contact state recognition threshold, and the initial impedance parameters; S2: Synchronous acquisition and digital filtering of multi-channel wavelength signals, acquiring raw wavelength data of fiber Bragg gratings. After outlier removal, low-pass filtering, and temperature compensation, the purified wavelength value is obtained. ; S3: Reconstruction of multidimensional force and pressure distribution based on wavelength drift calculation, and calculation of wavelength drift. According to the force-wavelength sensitivity matrix Solve the three-dimensional force vector of each sensing point And reconstruct the two-dimensional pressure distribution in the contact area; S4: Contact state identification and decision-making based on pressure distribution characteristics, extracting the total resultant force magnitude, pressure distribution centroid coordinates, and pressure distribution asymmetry index. and pressure concentration coefficient Identify status indicators ; S5: Compliant control execution based on impedance parameter adaptation, according to status indicators. Select control strategies and based on and Dynamically adjust virtual stiffness and virtual damping The position correction of the robot end effector is calculated using the impedance control equation. Generate and issue robot movement commands; S6: Task Termination and Data Archiving. After the job is completed, data acquisition and control command issuance are stopped, and all data from the entire process is archived and stored.

5. A flexible force feedback sensing method for a substation inspection robot as described in claim 4, characterized in that, In step S1, the contact state recognition threshold includes the asymmetry index threshold. and pressure concentration coefficient threshold ; The initial impedance parameters include virtual mass. Virtual damping and virtual stiffness .

6. The flexible force feedback sensing method for a substation inspection robot as described in claim 4, characterized in that, In step S3, the calculation of the three-dimensional force vector of each sensing point The formula: ； in, , , , These represent the wavelength shifts in the X, Y, and Z directions at the sensing point, respectively. for The inverse matrix.

7. The flexible force feedback sensing method for a substation inspection robot as described in claim 4, characterized in that, In step S4, the pressure distribution asymmetry index The calculation formula is: ; in, and The pressure distribution is in the left half of the region. and the right half of the region The integral value of pressure.

8. A flexible force feedback sensing method for a substation inspection robot as described in claim 4, characterized in that, In step S5, the dynamic adjustment of virtual stiffness and virtual damping The formulas are as follows: ； ； in, , This is the adjustment coefficient.

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