A flexible force feedback sensing system and method for a substation inspection robot

Abstract

This invention relates to the field of force feedback technology, and particularly to a flexible force feedback sensing system and method for a substation inspection robot. The system includes a host computer and a slave computer. The host computer is used to complete the entire process of calculation from sensing signal processing to control command generation; the slave computer is used to receive commands from the host computer and execute corresponding actions, while simultaneously acquiring contact force signals. In this solution, a flexible tactile sensing unit with fiber optic gratings is closely attached to the surface of equipment with different curvatures within the substation, avoiding measurement errors caused by the gaps in the contact of rigid sensing units. Simultaneously, the fiber optic grating array within the unit is arranged in a specific matrix, with three orthogonal fiber gratings of different center wavelengths embedded at each sensing point, enabling simultaneous acquisition of multi-dimensional force information. A special silicone rubber coating layer ensures strain transmission efficiency, and an integrated temperature compensation component eliminates the influence of environmental temperature differences on the sensing signal, ensuring stable and accurate acquisition of force signals even in complex environments.

Description

Technical Field

[0001] This invention relates to the field of force feedback technology, and in particular to a flexible force feedback sensing system and method for a substation inspection robot. Background Technology

[0002] With the continuous advancement of smart grid construction, the number of devices in substations is constantly increasing, and the operating environment is becoming more complex and diverse. Traditional manual inspection methods face problems such as low efficiency, high safety risks, and susceptibility to human factors affecting detection accuracy. Substation inspection robots are gradually becoming key equipment to replace manual labor in completing equipment inspection, fault diagnosis, and other maintenance tasks. During inspection operations, robots need to use the end effector of their robotic arms to perform delicate operations such as insulator gripping and joint tightening checks. This places clear demands on the system's force feedback capability, ensuring that the gripping force meets the operational requirements while avoiding excessive force that could damage the outer insulation layer or precision components of the equipment. Currently, most commonly used force feedback solutions in the industry are based on single-dimensional force sensors or strain gauge sensors, which achieve basic force control by collecting the single-point force value at the end of the robotic arm, and can meet simple gripping needs in normal environments.

[0003] However, existing force feedback solutions are difficult to adapt to the complex scenarios of substation inspection. First, their sensing dimensions are relatively limited; traditional sensors can only acquire force values ​​in the normal or a specific direction, failing to capture the pressure distribution characteristics of the contact area. This makes it difficult for the robot to accurately determine whether the contact posture is stable, easily leading to equipment slippage or excessive local stress. Second, their environmental adaptability is poor; existing sensing units are mostly rigid structures with low adhesion to equipment surfaces and lack effective temperature compensation mechanisms. The day-night temperature difference within the substation causes sensor signal drift, affecting the accuracy of the measurement. Third, the control strategy is relatively fixed; impedance control parameters are mostly preset fixed values ​​and cannot be dynamically adjusted according to the contact state. When the contact pressure distribution is uneven, it still operates in a rigid point contact mode, easily causing scratches on the equipment surface or grasping failure. Therefore, we propose a flexible force feedback sensing system and method for substation inspection robots. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a flexible force feedback sensing system and method for substation inspection robots, thereby solving the technical problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A flexible force feedback sensing method based on a flexible force feedback sensing system for a substation inspection robot, wherein the system includes a host computer and a slave computer, which establish a real-time data interaction link through an industrial Ethernet.

[0007] The host computer is used to complete the entire process of computation, from sensor signal processing to control command generation.

[0008] 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:

[0009] The substation inspection robot body is used to perform inspection operations;

[0010] A fiber optic flexible tactile sensing unit is integrated into the end effector gripping surface of the substation inspection robot body to collect force signals during the contact process and transmit them to the host computer.

[0011] The fiber Bragg grating flexible tactile sensing unit comprises a flexible substrate, an embedded fiber Bragg grating array, and a platinum resistance temperature sensor integrated within the substrate. The fiber Bragg grating array consists of 24 sensing points, evenly arranged in a 6x4 matrix within the flexible substrate. Each sensing point embeds three fiber Bragg gratings with center wavelengths of 1520±2 nm, 1540±2 nm, and 1560±2 nm, respectively. The three fiber Bragg gratings are orthogonal to each other and correspond to the X, Y, and Z axes of the Cartesian coordinate system.

[0012] The method includes the following steps:

[0013] S1: Complete the zero-load calibration of the fiber Bragg grating channel and store the initial center wavelength reference value. Force-wavelength sensitivity matrix Set a safe collision force threshold Expected grasping power The contact state identification threshold and initial impedance parameters; the contact state identification threshold includes the asymmetry index threshold. and pressure concentration coefficient threshold The initial impedance parameters include virtual mass. Virtual damping and virtual stiffness ;

[0014] S2: Acquire raw wavelength data of fiber Bragg gratings. After outlier removal, low-pass filtering, and temperature compensation, the purified wavelength value is obtained. ;

[0015] S3: Calculate wavelength shift 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;

[0016] S4: Extract the magnitude of the total resultant force Coordinates of the centroid of pressure distribution, and the asymmetry index of pressure distribution. and pressure concentration coefficient And identify the status identifier according to the following rules :

[0017] like > This is determined to be an abnormal collision state. ;

[0018] If 0.1N≤ ≤ and ≤ , ≤ It is determined to be a stable contact state. ;

[0019] If 0.1N≤ ≤ but > or > It is determined to be an unstable contact state. ;

[0020] like <0.1N, considering measurement error, it is determined to be a non-contact state. ;

[0021] S5: Based on the status indicator 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;

[0022] S6: After the task is completed, stop data acquisition and control command issuance, and archive and store all process data.

[0023] In one possible implementation, the host computer of the system includes:

[0024] 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.

[0025] 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.

[0026] The compliant control module is used to dynamically adjust the impedance control parameters based on the contact state recognition results and generate robot motion commands.

[0027] In one possible implementation, in step S3, the calculation of the three-dimensional force vector for each sensing point... The formula:

[0028]

[0029] in, , , , These represent the wavelength shifts in the X, Y, and Z directions at the sensing point, respectively. for The inverse matrix.

[0030] In one possible implementation, in step S4, the pressure distribution asymmetry index... The calculation formula is:

[0031]

[0032] 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.

[0033] In one possible implementation, in step S5, the basis and Dynamically adjust virtual stiffness and virtual damping The adjustment formulas are as follows:

[0034]

[0035]

[0036] in, For the adjusted virtual stiffness, For the adjusted virtual damping, , This is the adjustment coefficient.

[0037] Beneficial effects compared to existing technologies:

[0038] 1. In this solution, a customized fiber optic flexible tactile sensing unit effectively improves the accuracy and environmental adaptability of force signal acquisition. This sensing unit uses a specially formulated flexible substrate that can closely conform to the surfaces of equipment with different curvatures within the substation, avoiding measurement errors caused by the gaps in the fit of rigid sensing units. Simultaneously, the fiber optic array within the unit is arranged in a specific matrix, with three orthogonal fiber optic gratings of different center wavelengths embedded at each sensing point. This allows for the simultaneous acquisition of multi-dimensional force information. A special silicone rubber coating layer ensures strain transmission efficiency, and integrated temperature compensation components eliminate the influence of environmental temperature differences on the sensing signal, ensuring stable and accurate force signal acquisition even in the complex operating environment of a substation. This solves the problems of insufficient accuracy and poor environmental adaptability in existing sensing solutions.

[0039] 2. In this solution, a two-dimensional pressure distribution reconstruction and multi-feature contact state recognition mechanism is used to accurately determine the contact state and reduce operational risks. 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, using an interpolation algorithm to intuitively reflect the uniformity and concentration of pressure distribution. Simultaneously, it extracts key feature parameters such as the magnitude of the total resultant force, the centroid of the pressure distribution, the asymmetry index, and the concentration coefficient. Through multi-dimensional threshold judgment, it accurately identifies four states: no contact, stable contact, unstable contact, and abnormal collision. This mechanism can identify unstable contact states with uneven pressure distribution in advance, triggering timely attitude adjustments to avoid equipment slippage or collisions due to misjudgment of the state, ensuring the stability and safety of the operation process.

[0040] 3. In this solution, an adaptive impedance parameter control strategy driven by pressure distribution is used to dynamically switch from rigid point contact to flexible surface bonding, thereby improving the success rate of operations. 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 asymmetrical, the virtual stiffness is reduced using a specific formula to avoid excessive local stress; when the pressure is too concentrated, the virtual damping is increased using a corresponding formula to slow down the movement speed and adjust the bonding posture. This adaptive mechanism allows the robot to flexibly switch control modes according to the contact state. When there is no contact, it moves along a preset trajectory; when there is stable contact, it maintains force balance; and when there is an abnormal collision, it quickly retracts to avoid scratching the equipment surface or failure to grasp, improving the adaptability to grasping different types of substation equipment and ensuring the smooth completion of precision operations. Attached Figure Description

[0041] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0042] Figure 1This is a schematic diagram of the flexible force feedback sensing system framework of the present invention;

[0043] Figure 2 This is a schematic diagram of the flexible force feedback sensing method of the present invention;

[0044] Figure 3 This is a schematic diagram of the preparation method of the flexible substrate of the present invention. Detailed Implementation

[0045] Preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention can also be implemented in various different forms, and therefore the present invention is not limited to the embodiments described below. In addition, for the purpose of more clearly describing the present invention, parts not connected to the invention will be omitted from the drawings.

[0046] The technical solutions in this application are designed to address the problems described in the background, and are generally as follows:

[0047] Example:

[0048] Please refer to Figures 1 to 3 This embodiment introduces a flexible force feedback sensing system for a substation inspection robot. The system includes a host computer and a slave computer, which establish a real-time data interaction link through an industrial Ethernet to ensure the synchronization of sensing signal transmission and control command issuance.

[0049] The host computer serves as the computing center for flexible force feedback sensing and control, and is used to complete the entire process of computing from sensing signal processing to control command generation, including a signal processing module, a force mapping decision module, and a compliant control module.

[0050] The signal processing module is used to receive the raw signal of the reflected wavelength of the fiber Bragg grating, remove outliers, perform low-pass filtering, temperature compensation, and output purified wavelength data. Its hardware carrier includes a fiber optic signal demodulator, a high-speed data acquisition card, and a digital signal processor (DSP).

[0051] 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.

[0052] The compliance control module is used to dynamically adjust the impedance control parameters based on the contact state recognition results, generate robot motion commands, and send them to the lower-level machine.

[0053] The lower-level machine acts as the execution unit, receiving instructions from the upper-level machine and executing corresponding actions, while simultaneously collecting contact force signals, including the substation inspection robot body and the fiber optic flexible tactile sensing unit.

[0054] The substation inspection robot body is used to perform inspection operations, including a mobile chassis (maximum moving speed 0.8m / s, positioning accuracy ±5mm), a 6-DOF robotic arm (repeat positioning accuracy ±0.1mm, maximum load 5kg) and an end effector (gripping range 5-150mm). Its robotic arm controller can receive position or speed commands and drive the joint motors to move.

[0055] The fiber optic flexible tactile sensing unit is integrated into the clamping surface of the end effector to collect force signals during the contact process and transmit them to the host computer. It consists of a flexible substrate, an embedded fiber optic array, and a platinum resistance temperature sensor (measurement accuracy ±0.1℃) integrated inside the substrate.

[0056] Among them, the platinum resistance temperature sensor is used to collect ambient temperature to achieve temperature compensation;

[0057] The flexible matrix is ​​made of highly elastic transparent silicone rubber material, and its formula by weight is as follows: 82-88 parts of hydroxyl-terminated polydimethylsiloxane rubber (CAS No.: 70131-67-8), 12-18 parts of fumed silica treated with hexamethyldisilazane (CAS No.: 68611-44-9), 0.8-1.2 parts of 1,3-divinyl-1,1,3,3-tetramethyldisilazane platinum (CAS No.: 68478-92-2), and 4.5-5.5 parts of polymethylhydrosiloxane (CAS No.: 63148-57-2).

[0058] In preparation, hydroxyl-terminated polydimethylsiloxane adhesive and hexamethyldisilazane-treated fumed silica are first added to a planetary mixer and mixed at 45 rpm for 35 minutes to ensure uniform dispersion of the reinforcing filler. Then, 1,3-divinyl-1,1,3,3-tetramethyldisilazane platinum and polymethylhydrosiloxane are added, and the mixing speed is adjusted to 30 rpm for 20 minutes to form a uniform mixture. After degassing under vacuum at -0.095 MPa for 15 minutes, the mixture is injected into a special mold of 80 mm × 60 mm × 5 mm and thermocured at 125 °C for 55 minutes to form a transparent elastomer matrix with a Shore A hardness of 15-25.

[0059] Specifically, the matrix consists of 82 parts of hydroxyl-terminated polydimethylsiloxane resin, 12 parts of fumed silica treated with hexamethyldisilazane, 0.8 parts of 1,3-divinyl-1,1,3,3-tetramethyldisilazane platinum, and 4.5 parts of polymethylhydrosiloxane. The resulting matrix has a Shore A hardness of 15, a tensile strength of 5.8 MPa, an elongation at break of 620%, a visible light transmittance of 92%, and a volume resistivity of 3.5 × 10⁻⁶. 15 Ω·cm, and its mechanical and optical properties decay rate is less than 5% under long-term conditions ranging from -50℃ to 180℃;

[0060] Specifically, the matrix consists of 88 parts of hydroxyl-terminated polydimethylsiloxane resin, 18 parts of fumed silica treated with hexamethyldisilazane, 1.2 parts of 1,3-divinyl-1,1,3,3-tetramethyldisilazane platinum, and 5.5 parts of polymethylhydrosiloxane. The resulting matrix has a Shore A hardness of 25, a tensile strength increased to 8.5 MPa, an elongation at break of 480%, a visible light transmittance of 90%, and a volume resistivity of 2.8 × 10⁻⁶. 15 Ω·cm;

[0061] The fiber Bragg grating array consists of 24 sensing points, evenly arranged in a 6x4 matrix within the flexible substrate, with a center-to-center spacing of 10 mm between adjacent sensing points. Each sensing point embeds three fiber Bragg gratings with center wavelengths of 1520±2 nm, 1540±2 nm, and 1560±2 nm, respectively, orthogonally corresponding to the X, Y, and Z axes of a Cartesian coordinate system (lateral, longitudinal, and normal directions, respectively). The embedding depth is half the substrate thickness (2.5 mm). The fiber Bragg gratings are externally covered with a 0.5-0.8 mm thick layer of methyl vinyl ester silicone rubber to ensure a strain transfer efficiency ≥96%. All fiber Bragg gratings are connected to the fiber optic signal demodulator of the host computer via 3-5 m long stainless steel armored optical cables.

[0062] Based on the above-described host computer and slave computer architecture, this embodiment also introduces a flexible force feedback sensing method for a substation inspection robot, the specific steps of which are as follows:

[0063] S1: System Initialization and Parameter Preset

[0064] This step is achieved through the collaborative operation of the host computer's signal processing module, force mapping decision module, and compliance control module, completing the initialization and preset of system parameters.

[0065] After the signal processing module starts, it first establishes an RS485 communication connection (baud rate 115200bps) with the fiber optic signal demodulator in its own hardware carrier, and sets the sampling frequency to 1000Hz. Then, it controls the demodulator to perform zero-load calibration on 72 fiber optic grating channels (24 sensing points × 3 directions), and reads and stores the initial center wavelength reference value of each channel. ( (The initial wavelength is general; specific wavelengths for a particular channel are distinguished by context.) The force mapping decision module loads the force-wavelength sensitivity matrix pre-obtained through calibration experiments. The matrix is ​​a 3×3 real matrix, where the elements are... 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.

[0066] S2: Synchronous acquisition and digital filtering of multi-channel wavelength signals

[0067] 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.

[0068] 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' threshold at a significance level of 0.05 and a sample size of 10. Therefore, this point is considered an outlier and is replaced with the linear interpolation result of two adjacent normal sampling points. Next, noise suppression is performed on the wavelength data after outlier removal using an eighth-order Chebyshev Type I low-pass digital filter (passband ripple ≤1dB, stopband attenuation ≥40dB) with a cutoff frequency of 100Hz, resulting in filtered data. Temperature compensation is then performed: real-time temperature is acquired using a platinum resistance temperature sensor integrated into the fiber optic flexible tactile sensing unit. Calculate the temperature change ,in, The ambient temperature at which S1 is initialized; the temperature compensation formula is used. The filtered data is corrected to obtain the purified wavelength value. ,in, The temperature sensitivity coefficient of the fiber Bragg grating, provided by the fiber Bragg grating manufacturer and confirmed through experimental calibration, is 10 pm / ℃ in this embodiment. Finally, the signal processing module processes the 72-channel purified wavelength data from each sampling cycle. The data is packaged and transmitted to the force mapping decision module via the internal bus, with the transmission period matching the sampling period.

[0069] S3: Reconstruction of Multidimensional Force and Pressure Distribution Based on Wavelength Drift Calculation

[0070] This step is implemented through the force mapping decision module of the host computer. Based on the purification wavelength data output by S2, it calculates the multidimensional force information of each sensing point, reconstructs the two-dimensional pressure distribution of the contact area, and calculates the total resultant force and total torque at the end of the robotic arm. The force mapping decision module receives... Then, the real-time wavelength shift of each fiber grating channel is calculated first. ,in, The initial center wavelength reference value stored in S1; if If the wavelength shift is less than 1 pm, i.e., less than the minimum resolution of the fiber optic demodulator, then the channel is determined to have no effective signal change, and the drift is set to 0 to avoid noise interference. For each sensing point, the wavelength drift corresponding to its X, Y, and Z directions is extracted to construct a wavelength drift vector. ,in, , , These represent the wavelength shifts (in pm) in the X, Y, and Z directions at the sensing point, respectively. Based on the force-wavelength sensitivity matrix... Establish a linear relationship between wavelength drift and three-dimensional force vector: ,in, This is the three-dimensional force vector (unit: N) at the sensing point. , , These represent the force components in the X, Y, and Z directions, respectively. Solving the above matrix equation yields the formula for calculating the three-dimensional force vector: ,in, for The inverse matrix is ​​calculated and stored in advance using LU decomposition to avoid redundant calculations in real-time solution. After obtaining the three-dimensional force vectors of 24 sensing points, the Z-axis force components of each point are extracted. (Normal force, directly related to contact pressure); Mapping the sensing area of ​​the flexible substrate to a normalized coordinate plane. ,in, , Corresponding to the transverse and longitudinal directions of the substrate, a bicubic interpolation algorithm (with the boundary condition of continuous first derivative) was used for 24 sensing points. Interpolation calculations are performed to reconstruct the two-dimensional pressure distribution across the entire contact area. (Unit: kPa), its value is determined by The ratio to the equivalent contact area of ​​the sensing point (set to 100 mm²) is used for calculation. Simultaneously, the origin of the force coordinate system is defined as the center of the robotic arm's end effector (coinciding with the center of the flexible substrate), and the pre-measured and stored position vector of each sensing point relative to the origin is retrieved. (Unit: m), calculate the total resultant force on the end effector of the robotic arm. Total torque ,in, For the first Three-dimensional force vector of each sensing point For the first The position vector of each sensing point This represents the vector cross product operation.

[0071] Finally, the force mapping decision module calculates the three-dimensional force vectors and two-dimensional pressure distributions of the 24 sensing points. Total synergy and total torque It is temporarily stored in the cache for subsequent contact status recognition.

[0072] S4: Contact State Identification and Decision-Making Based on Pressure Distribution Characteristics

[0073] This step is implemented through the force mapping decision module of the host computer. Based on the mechanical information calculated by S3, the contact state feature parameters are extracted, the contact state is identified through preset rules, and the decision results are output.

[0074] The force mapping decision module first extracts four feature parameters from the mechanical information temporarily stored in S3: the magnitude of the total resultant force. The Euclidean norm is used for calculation, and the formula is as follows: ,in, , , The total resultant force Components along the X, Y, and Z axes; coordinates of the centroid of pressure distribution. The weighted average method is used for calculation, and the formula is as follows: , The summation range is defined by dividing the normalized plane into a discrete grid of 100×100. , For grid point coordinates, This represents the two-dimensional pressure distribution value at that point; the pressure distribution asymmetry index. This is used to characterize the uniformity of pressure distribution. The calculation method is as follows: divide the pressure distribution into a left half-region. right half of the region Calculate the integral value of pressure in the two regions. and ,but Pressure Concentration Coefficient It is used to characterize the degree of pressure concentration, and the calculation method is to find the maximum pressure value in the two-dimensional pressure distribution. Calculate the average pressure value for the entire region. ,in, =1 represents the area of ​​the normalized region, then Then, based on the aforementioned characteristic parameters and the preset threshold of S1, the contact state is identified according to the following rules:

[0075] like > This is determined to be an abnormal collision state. ;

[0076] If 0.1N≤ ≤ and ≤ , ≤ It is determined to be a stable contact state. ;

[0077] If 0.1N≤ ≤ but > or > It is determined to be an unstable contact state. ;

[0078] like <0.1N, considering measurement error, it is determined to be a non-contact state. ;

[0079] 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.

[0080] S5: Compliant control execution based on impedance parameter adaptation

[0081] 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:

[0082] 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.

[0083] when = In the (non-contact state), continue executing the initial motion trajectory planned in S1. The trajectory is decomposed into discrete position commands with a 1ms sampling period, which are then sent to the robot controller to drive the robotic arm to move towards the target position. Simultaneously, it continuously receives status flags transmitted via S4. Once... If the state changes to another state, the control strategy will be switched immediately.

[0084] when = (Stable contact state) or = In the case of unstable contact, the adaptive impedance control loop is initiated, which specifically includes: firstly, based on the pressure distribution asymmetry index extracted from S4... With pressure concentration coefficient Dynamically adjust virtual stiffness and virtual damping (virtual mass) (Keep it unchanged), adjust the formula as follows , ,in, For the adjusted virtual stiffness, For the adjusted virtual damping, , The adjustment coefficients, determined through experimental calibration, are 2 and 0.5 respectively; the physical meaning of this mechanism is: when the pressure distribution is asymmetrical ( When (increase), Reduce the stiffness of the robotic arm to avoid excessive local stress; when the pressure is too concentrated ( When (increase), Increasing the damping of the robotic arm and slowing down the movement speed facilitates adjustment of the contact posture, thereby achieving "flexible surface contact." Next, based on the adjusted impedance parameters... , , Combined with expected grasping power With the actual total force The position correction of the robot end effector is calculated using the impedance control equation. The impedance control equation is ,in, , , These are the acceleration, velocity, and displacement components of the position correction (units are m / s²). 2 (m / s, m); In actual calculations, the Euler method is used to discretize and solve the above differential equations (sampling period 1ms) to obtain discrete position corrections. , For discrete moments. Then, the position correction amount... Superimposed on the current expected trajectory The corrected expected trajectory is obtained above. ,in, For a stable contact state, the trajectory maintains the current position; for an unstable contact state, the trajectory is based on the centroid of the pressure distribution. Adjust the attitude trajectory (e.g., generate a fine-tuned trajectory towards the center of mass when the center of mass shifts). Finally, the corrected desired trajectory is... The kinematics inverse kinematics algorithm is used to convert the commands into angle commands for each joint of the robotic arm. , , , , , (Corresponding to the 6 joints of a 6-DOF robotic arm), and adding motion constraints (joint angle range, upper limit of angular velocity, upper limit of angular acceleration); the joint angle commands are packaged and sent to the robot controller via the Profinet protocol to drive the robotic arm to perform actions, while continuously receiving mechanical parameters transmitted from S4 to update impedance parameters and position corrections in real time, forming a control closed loop. During the control process, the compliant control module monitors the following indicators in real time: total resultant force With expected grasping power Deviation (ensure ≤±10%), pressure distribution index under stable contact conditions ( ≤0.2, ≤0.5), trajectory tracking error at the end of the robotic arm (ensure ≤0.1mm); if the indicators exceed the preset range, adjust the adjustment coefficient. , Expected grasping power Until the indicators return to normal.

[0085] S6: Task Termination and Data Archiving

[0086] This step is achieved through the collaboration of various modules in the host computer and the robot controller in the lower computer, completing the task termination operation and archiving and storing all process data.

[0087] After the substation inspection robot completes its preset tasks (such as insulator grabbing and equipment inspection), the lower-level robot controller sends a task completion signal to the compliant control module. Upon receiving the signal, the compliant control module immediately sends a stop command to the robot controller, driving the robotic arm back to its initial posture (joint angles zeroed) and disengaging the end effector's gripping mechanism. Simultaneously, the compliant control module sends a data acquisition termination signal to the signal processing module, which then stops receiving wavelength data from the fiber optic demodulator in its hardware and closes the communication connection between the high-speed data acquisition card and the demodulator. Subsequently, the upper-level modules begin data archiving: the signal processing module stores the raw wavelength data for all sampling periods. Filtered data Data after purification Each data file contains a timestamp (accurate to milliseconds), channel number, and wavelength value (unit: nm); the force mapping decision module stores the wavelength drift for each sampling period. Three-dimensional force vector of 24 sensing points Two-dimensional pressure distribution (Stored as discrete values ​​in a 100×100 grid), total resultant force Total torque Contact status indicator and characteristic parameters ( , , , The compliant control module stores the impedance parameters for each sampling period. , , , , Position correction amount Expected trajectory Corrected trajectory And the joint angle commands issued.

[0088] All archived data is stored in structured binary files (with the .dat extension). Each file header contains metadata such as task number, start time, end time, and sampling frequency. A corresponding data summary file (with the .txt extension) is also generated, containing the data file's storage path, data volume, and key statistical values ​​(such as maximum resultant force, average pressure, and duration of each contact state) to facilitate subsequent data retrieval and analysis. Archived data is stored on the host computer's solid-state drive (SSD), with a storage capacity supporting at least 100 tasks. Data can also be exported via USB.

[0089] Finally, it should be noted that the above embodiments are merely examples for clearly illustrating the present invention and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A flexible force feedback sensing method based on a flexible force feedback sensing system of a substation inspection robot, characterized by, The system includes a host computer and a slave computer, which establish a real-time data interaction link through an industrial Ethernet. The host computer is used to complete the entire process of computation, from sensor signal processing to control command generation. 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 to collect force signals during the contact process and transmit them to the host computer. The fiber Bragg grating flexible tactile sensing unit comprises a flexible substrate, an embedded fiber Bragg grating array, and a platinum resistance temperature sensor integrated within the substrate. The fiber Bragg grating array consists of 24 sensing points, evenly arranged in a 6x4 matrix within the flexible substrate. Each sensing point embeds three fiber Bragg gratings with center wavelengths of 1520±2 nm, 1540±2 nm, and 1560±2 nm, respectively. The three fiber Bragg gratings are orthogonal to each other and correspond to the X, Y, and Z axes of the Cartesian coordinate system. The method includes the following steps: S1: Complete the zero-load calibration of the fiber Bragg grating channel and store the initial center wavelength reference value. Force-wavelength sensitivity matrix Set a safe collision force threshold Expected grasping power The contact state identification threshold and initial impedance parameters; the contact state identification threshold includes the asymmetry index threshold. and pressure concentration coefficient threshold The initial impedance parameters include virtual mass. Virtual damping and virtual stiffness ; S2: Acquire raw wavelength data of fiber Bragg gratings. After outlier removal, low-pass filtering, and temperature compensation, the purified wavelength value is obtained. ; S3: Calculate wavelength shift 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: Extract the magnitude of the total resultant force Coordinates of the centroid of pressure distribution, and the asymmetry index of pressure distribution. and pressure concentration coefficient And identify the status identifier according to the following rules : like > This is determined to be an abnormal collision state. ; If 0.1N≤ ≤ and ≤ , ≤ It is determined to be a stable contact state. ; If 0.1N≤ ≤ but > or > It is determined to be an unstable contact state. ; like <0.1N, considering measurement error, it is determined to be a non-contact state. ; S5: Based on the status indicator 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: After the task is completed, stop data acquisition and control command issuance, and archive and store all process data.

2. The flexible force feedback sensing method based on a flexible force feedback sensing system for a substation inspection robot as described in claim 1, characterized in that, The host computer of the system includes: 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 compliant control module is used to dynamically adjust impedance control parameters based on the contact state recognition results and generate robot motion commands.

3. The flexible force feedback sensing method based on a flexible force feedback sensing system for a substation inspection robot as described in claim 1, 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.

4. The flexible force feedback sensing method based on a flexible force feedback sensing system for a substation inspection robot as described in claim 1, 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.

5. The flexible force feedback sensing method based on a flexible force feedback sensing system for a substation inspection robot as described in claim 1, characterized in that, In step S5, the basis and Dynamically adjust virtual stiffness and virtual damping The adjustment formulas are as follows: ； ； in, For the adjusted virtual stiffness, For the adjusted virtual damping, , This is the adjustment coefficient.

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