Bridge pushing continuous pose perception and redundant navigation positioning system and method

Abstract

The application discloses a bridge incremental launching continuous pose perception and redundant navigation positioning system and method, comprising: a laser emission module for emitting a reference laser beam to the bridge incremental launching direction; a light spot sensing positioning module for acquiring light spot position information of the reference laser beam on each photosensitive sensing target surface; a multi-source data fusion module for performing space-time alignment, state estimation and pose fusion on sensing data to obtain real-time pose state information in the bridge incremental launching process; an intelligent control module for generating a deviation correction control instruction according to the deviation between the real-time pose state information and a target trajectory; and a walking incremental launching execution module for executing bridge jacking, incremental launching and transverse deviation correction actions according to the deviation correction control instruction. The application can realize continuous pose perception, multi-source information fusion and redundant navigation positioning in a complex construction environment, so as to improve the safety, precision and reliability of incremental launching construction.

Description

Technical Field

[0001] This invention relates to the field of bridge construction, specifically to a continuous pose perception and redundant navigation and positioning system and method for bridge jacking. Background Technology

[0002] Steel truss bridges are widely used in long-span bridge projects due to their light weight, high span capacity, and high construction efficiency. The incremental launching method, a typical erection method, involves assembling the bridge beams behind abutments or piers and applying incremental thrust segment by segment along the bridge axis, causing the bridge structure to continuously slide to its design position on sliding tracks or temporary supports. However, due to the long span, high flexibility, and multi-support characteristics of steel truss beams, they are susceptible to factors such as wind loads, temperature gradient changes, uneven settlement of temporary piers, and synchronization errors of multiple launching devices during the incremental launching process. This can lead to problems such as lateral displacement, longitudinal cumulative errors, and vertical instability of the beams, which in severe cases may cause slippage, local structural instability, or even construction safety accidents.

[0003] Existing methods for monitoring and locating jacking systems mainly include total station / GPS discrete measurement, wire-type displacement sensors, laser ranging and tracking equipment, tilt sensor combinations, and machine vision methods. However, these technical solutions still have significant shortcomings in practical engineering applications:

[0004] 1. Discrete measurement methods (such as total stations and GPS) rely on manual periodic sampling, which cannot achieve continuous pose acquisition throughout the construction process, resulting in significant "blind spots" during the jacking process and making it difficult to reflect instantaneous offsets and dynamic disturbances in a timely manner. 2. Single-type sensors cannot simultaneously acquire spatial position and attitude information, and pose calculations based on tilt angle or strain inversion rely on ideal mechanical models, leading to significant increases in error under complex conditions such as track friction changes, temperature effects, and support settlement. 3. Laser ranging and machine vision methods are easily affected by environmental factors such as dust, rain, fog, and strong light, leading to signal quality degradation or even failure. GNSS also suffers from multipath effects and signal interruption problems in environments obstructed by bridge structures. 4. Fixed laser tracking equipment is limited by its field of view, making it difficult to cover the entire long-distance jacking process. Frequent station changes also introduce new reference errors. 5. Data from various sensors are usually collected by independent systems, with inconsistent sampling frequencies and time bases. The lack of an effective data fusion mechanism makes it difficult to achieve continuous high-precision pose estimation and intelligent decision support under multi-source information collaboration, thus restricting the automation and refined control level of the jacking construction.

[0005] Therefore, to solve the above problems, a continuous pose perception and redundant navigation and positioning system and method for bridge jacking is needed, which can realize continuous pose perception, multi-source information fusion and redundant navigation and positioning in complex construction environments, so as to improve the safety and accuracy of jacking construction. Summary of the Invention

[0006] In view of this, the purpose of this invention is to overcome the defects in the prior art and provide a continuous pose perception and redundant navigation and positioning system and method for bridge jacking, which can realize continuous pose perception, multi-source information fusion and redundant navigation and positioning in complex construction environments, so as to improve the safety and accuracy of jacking construction.

[0007] The bridge jacking continuous pose sensing and redundant navigation and positioning system of the present invention includes:

[0008] The laser emitting module is used to emit a reference laser beam in the direction of bridge jacking;

[0009] The spot sensing and positioning module includes several non-collinearly distributed photosensitive target surfaces; the spot sensing and positioning module is located at the front end of the bridge and is used to obtain the spot position information of the reference laser beam on each photosensitive target surface;

[0010] The multi-source data fusion module is used to perform spatiotemporal alignment, state estimation, and pose fusion on sensor data to obtain real-time pose and state information during the bridge jacking process.

[0011] The intelligent control module is used to generate correction control commands based on the deviation between the real-time pose status information and the target trajectory;

[0012] The walking-type jacking execution module is used to perform bridge jacking, jacking, and lateral correction actions according to the correction control command.

[0013] Furthermore, the photosensitive target surface comprises three surfaces, which are arranged in an isosceles triangle.

[0014] The light spot sensing positioning module calculates the lateral displacement deviation, longitudinal displacement deviation, and yaw angle deviation of the bridge front center point in the global coordinate system using a three-point positioning geometric model.

[0015] Furthermore, the multi-source data fusion module includes a data spatiotemporal alignment unit, a state estimation unit, and a three-dimensional pose reconstruction unit;

[0016] The data spatiotemporal alignment unit is used to resample sensor data with different sampling frequencies to a unified time reference and complete the coordinate system transformation.

[0017] The state estimation unit is used to fuse and estimate multi-source data based on the bridge kinematics model;

[0018] The three-dimensional pose reconstruction unit is used to construct a real-time three-dimensional pose model of the bridge based on the fusion results.

[0019] Furthermore, the intelligent control module includes a cluster synchronization control layer, a single-machine pose control layer, and a servo drive control layer;

[0020] The cluster synchronization control layer is used to calculate the target displacement distribution value of each jacking point based on the overall posture deviation of the bridge.

[0021] The single-machine pose control layer is used to control the lifting degree of freedom, the pushing degree of freedom, and the lateral correction degree of freedom, respectively.

[0022] The servo drive control layer is used to complete the closed-loop speed control of the actuator.

[0023] Furthermore, the intelligent control module employs a fuzzy PID controller, using the pose deviation e and the rate of change of deviation ec as input variables, and a proportional coefficient for correction. Integral coefficient correction amount and the correction amount of differential coefficients As an output variable, the PID parameters are adjusted online according to preset fuzzy rules to achieve adaptive optimization of control parameters.

[0024] Furthermore, the intelligent control module identifies the type of bridge deviation based on real-time pose information and selects the corresponding correction strategy according to different deviation types; wherein, the deviation types include axis offset, same-side offset, and spatial torsional offset.

[0025] Furthermore, when an axis offset is identified, the lateral correction device is controlled to perform lateral displacement correction. When the lateral deviation meets the preset threshold and the yaw angle deviation exceeds the preset threshold, the yaw angle is corrected by a zoned differential jacking method. The zoned differential jacking method involves the jacking equipment on both sides of the bridge performing different jacking speeds.

[0026] Furthermore, the navigation and positioning system performs multi-mode redundancy navigation according to the following method:

[0027] Based on the quality of the laser positioning signal, it switches between the following operating modes: laser positioning mode, laser and satellite fusion positioning mode, satellite-assisted positioning mode, and inertial recursive positioning mode;

[0028] The operating mode automatically switches according to the quality level of the positioning signal, and the pose continuity during the mode switching process is achieved through a state estimation algorithm.

[0029] A positioning method based on the bridge jacking continuous pose perception and redundant navigation and positioning system includes:

[0030] S1. Establish a bridge design trajectory database and collect the initial pose information of the bridge;

[0031] S2. Use the laser emission module and the spot sensing positioning module to obtain laser positioning data at the front end of the bridge, and at the same time obtain monitoring data collected by the tilt sensor, displacement sensor and satellite positioning module;

[0032] S3. Perform time synchronization processing and unified spatial coordinate transformation on the monitoring data, and perform multi-source data fusion based on the state estimation algorithm to obtain the real-time pose and state information of the bridge;

[0033] S4. Compare the real-time pose status information with the target pose information in the design trajectory database to obtain the spatial position deviation and attitude deviation;

[0034] S5. Generate correction control commands based on spatial position deviation and attitude deviation, and control the walking-type jacking execution module to perform jacking, lifting and lateral correction actions;

[0035] S6. Obtain feedback information from the walking-type jacking execution module, verify the correction results, and continue the jacking operation when the deviation meets the preset threshold; otherwise, return to step S2 to execute the next round.

[0036] Furthermore, select the corresponding correction strategy based on the type of deviation, including:

[0037] When the lateral displacement deviation exceeds the first threshold, the lateral correction device is controlled to perform lateral correction.

[0038] When the lateral displacement deviation is less than the first threshold and the yaw angle deviation exceeds the second threshold, the jacking equipment on both sides of the bridge is controlled to use differential jacking to correct the attitude.

[0039] When the quality of the laser positioning signal is lower than a preset threshold, switch to satellite positioning assisted mode;

[0040] When both satellite positioning signal and laser positioning signal are below the preset threshold, the system switches to an inertial recursive positioning mode based on displacement and attitude data, and generates push control commands based on the recursive results.

[0041] The beneficial effects of this invention are as follows: This invention discloses a continuous pose perception and redundant navigation positioning system and method for bridge jacking. By setting a multi-target laser spot sensing positioning module at the front end of the bridge beam, combined with tilt sensors, displacement sensors, and a satellite positioning module, it achieves synchronous acquisition and spatiotemporal alignment of multi-source heterogeneous data during the jacking process. A state estimation algorithm is used to process the fused data, constructing a six-degree-of-freedom real-time pose model of the bridge. Deviations are compared with the designed trajectory to generate correction control commands, driving the walking-type jacking actuator to achieve synchronous jacking and attitude adjustment. Simultaneously, a multi-mode redundant navigation system is designed, which can smoothly switch between laser, satellite, and inertial recursive modes when the laser is blocked or the signal degrades, ensuring positioning continuity and stability. This invention effectively solves the problems of poor continuity, weak anti-interference ability, and difficulty in fusing multi-source data in traditional jacking monitoring, improving the positioning accuracy, real-time performance, and safety reliability of jacking construction. Attached Figure Description

[0042] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0043] Figure 1 This is a schematic diagram of the overall framework of the navigation and positioning system of the present invention;

[0044] Figure 2 This is a schematic diagram showing the connection relationship and data flow of the various modules in the system of the present invention;

[0045] Figure 3 This is a schematic diagram of the installation arrangement of the light spot sensing positioning module of the present invention at the front end of the beam.

[0046] Figure 4 This is a block diagram of the three-layer closed-loop control architecture of the intelligent control module of the present invention;

[0047] Figure 5 This is a schematic diagram of the improved fuzzy PID control principle of the present invention;

[0048] Figure 6 This is a topology diagram of the automatic switching of BeiDou positioning assistance mode under laser optical path obstruction conditions according to the present invention. Detailed Implementation

[0049] The present invention will be further described below with reference to the accompanying drawings, as shown in the figures:

[0050] This embodiment discloses a bridge jacking continuous posture perception and redundant navigation and positioning system, including a laser emission module, a spot sensing and positioning module, a multi-source data fusion module, an intelligent control module, and a walking-type jacking execution module;

[0051] The laser emitting module is used to emit a reference laser beam in the direction of bridge jacking;

[0052] The spot sensing and positioning module includes several non-collinearly distributed photosensitive target surfaces; the spot sensing and positioning module is located at the front end of the bridge and is used to obtain the spot position information of the reference laser beam on each photosensitive target surface;

[0053] The multi-source data fusion module is communicatively connected to the spot sensing and positioning module, tilt sensor, displacement sensor, and satellite positioning module, respectively, and is used to perform spatiotemporal alignment, state estimation, and pose fusion on the sensing data to obtain real-time pose state information during the bridge jacking process. Specifically, the tilt sensor is used to acquire the attitude angle information of the beam during the bridge jacking process; the displacement sensor is used to acquire the local displacement execution amount of each jacking execution unit; and the satellite positioning module is used to acquire the absolute spatial position of the beam in the global coordinate system, thereby providing complementary observations for multi-source fusion pose estimation. The sensing data includes spot position information, attitude angle information, local displacement execution amount, and absolute spatial position information.

[0054] The intelligent control module is communicatively connected to the multi-source data fusion module and is used to generate correction control commands based on the deviation between the real-time pose status information and the target trajectory.

[0055] The walking-type jacking execution module is communicatively connected to the intelligent control module and is used to execute bridge jacking, jacking, and lateral correction actions according to the correction control command.

[0056] In this embodiment, the laser emitting module is fixedly installed at an appropriate position on the top or side of the rear end of the beam to be pushed, emitting a collimated reference laser beam. The laser is a narrow-pulse laser emitter with a beam divergence angle ≤0.5mrad to ensure that the spot size is controllable and the energy is concentrated within the pushing distance range.

[0057] The laser emitting module uses a 635nm red semiconductor laser with an output power not exceeding 5mW, meeting human eye safety standards. A collimating lens compresses the beam divergence angle to 0.3mrad, and a beam shaping system modulates the Gaussian beam at the emitting end into a flat-top beam with uniform energy distribution, ensuring that the beam's dispersion diameter does not exceed 100mm over a 300m transmission distance.

[0058] The light spot sensing and positioning module is located at a predetermined key node at the bottom of the front end of the beam. There are three photosensitive targets, employing a CMOS area array. These three targets are arranged in an isosceles triangle on the front end plane of the beam. The effective sensing area of ​​each target is 120mm × 120mm, with a pixel resolution of 1024 × 1024, a maximum sampling frequency of 200Hz, and a center positioning accuracy controllable within ±0.1 pixels, equivalent to approximately 0.05mm in actual space. The three targets are arranged in an isosceles triangle, with two front targets located approximately 500mm inside the left and right edges of the front end of the guide beam, spaced 6m apart. A rear target is located on the perpendicular bisector of the two front targets, 6m from the top of the guide beam. The centroid coordinates of the light spots output from each target are converted into RS-422 differential digital signals by an integrated signal conditioning module and transmitted via fiber optic cable to the industrial control computer in the central control room.

[0059] The light spot sensing positioning module calculates the lateral displacement deviation of the bridge's front end center point in the global coordinate system using a three-point positioning geometric model. Longitudinal displacement deviation and yaw angle deviation Among them, the three-point positioning geometric model belongs to the spatial geometric positioning method based on multi-target imaging. Its essence is to construct spatial geometric constraint relationships based on multiple non-collinear observation points, and solve the pose information of the target in the local coordinate system through the principle of triangulation. It will not be elaborated here.

[0060] In this embodiment, the multi-source data fusion module includes a data spatiotemporal alignment unit, a state estimation unit, and a three-dimensional pose reconstruction unit;

[0061] The data spatiotemporal alignment unit is used to resample sensor data with different sampling frequencies (100Hz for laser positioning, 50Hz for inclinometer, and 20Hz for displacement sensor) to a unified time reference and complete the coordinate system transformation.

[0062] The state estimation unit is used to fuse and estimate multi-source data based on the bridge kinematic model, effectively eliminating sensor noise and measurement anomalies. The bridge kinematic model is a multi-degree-of-freedom motion description method based on rigid body kinematics and structural mechanics theory, used to characterize the spatial displacement and attitude changes of the bridge under external loads and support constraints, which will not be elaborated here. The state estimation unit adopts the extended Kalman filter algorithm; it constructs a state vector including spatial position, velocity, roll angle, pitch angle, and yaw angle; and obtains the real-time attitude estimation result of the bridge through state prediction, covariance prediction, Kalman gain update, and state correction processes.

[0063] The three-dimensional pose reconstruction unit is used to construct a real-time three-dimensional pose model of the bridge based on the fusion results. The real-time three-dimensional pose model of the bridge is formed by structural three-dimensional reconstruction based on the fusion of state space representation and multi-source observation. It realizes the dynamic reconstruction of the structural spatial pose by mapping the measurement data to a unified coordinate system and combining it with the state estimation method.

[0064] In this embodiment, the intelligent control module includes a cluster synchronization control layer, a single-machine pose control layer, and a servo drive control layer; such as Figure 4 As shown, the servo drive control layer is a servo drive speed loop.

[0065] The cluster synchronization control layer (outer ring) is used to calculate the target displacement allocation value of each jacking point based on the overall bridge pose deviation. Specifically, using the coordinates of the beam front center point generated by the multi-source data fusion module as the tracking target, a deviation coupling control strategy is adopted to synchronously allocate displacement to each group of jacking devices. This layer calculates the target displacement value of each point based on the number of jacking points n, and uses a correction term... Achieve load balancing.

[0066] The single-machine posture control layer (middle loop) is used to control the lifting degree of freedom, pushing degree of freedom, and lateral correction degree of freedom respectively; that is, the vertical lifting, horizontal pushing and lateral correction of a single walking-type jacking device are independently controlled by PID, forming a middle closed loop of position and pressure.

[0067] The servo drive control layer (inner loop) is used to complete the closed-loop speed control of the actuator, that is, to achieve closed-loop adjustment of the motor speed through feedback from the servo motor encoder, so as to ensure that the actuator responds quickly and accurately.

[0068] The intelligent control module employs a fuzzy PID controller, using the pose deviation e and the rate of change of deviation ec as input variables, and a proportional coefficient as the correction factor. Integral coefficient correction amount and the correction amount of differential coefficients As an output variable, the scaling factor is adjusted according to the preset fuzzy rules. Integral coefficient and differential coefficients Online adjustments are made to achieve adaptive optimization of control parameters.

[0069] The formula for calculating the control quantity is: ;

[0070] in, , , The system optimizes in real time based on the current operating conditions (percentage of jacking stroke, cantilever length at the beam end, and load size). Each correction is sent to the controller of the corresponding walking jacking device via industrial Ethernet.

[0071] The intelligent control module identifies the type of bridge deviation based on real-time pose information and selects a corresponding correction strategy for each deviation type. The deviation types include axis offset, same-side offset, and spatial torsional offset. When axis offset is identified, the lateral correction device is controlled to perform lateral displacement correction. When the lateral deviation meets a preset threshold and the yaw angle deviation exceeds a preset threshold, a zoned differential jacking method is used to correct the yaw angle. This zoned differential jacking method involves the jacking devices on both sides of the bridge executing different jacking speeds.

[0072] In this embodiment, the walking-type jacking execution module includes multiple sets of walking-type jacking devices arranged at equal intervals along the jacking direction. Each set of devices includes a vertical lifting device, a horizontal jacking device, and a lateral correction device. The lateral correction device achieves the lateral movement of the slide box (to the left or right) through correction jacks, with a maximum correction stroke of ±150mm and a correction accuracy of ≤1mm.

[0073] In addition, the navigation and positioning system also includes a temperature drift compensation device, which collects ambient temperature in real time and compensates for laser ranging values ​​and spot positioning offsets; that is, PT100 platinum resistance temperature sensors are installed on the laser emission module shell and each sensing target base, with a measurement accuracy of ± The ranging and positioning values ​​are corrected in real time using a compensation algorithm. This compensation method specifically includes: pre-testing in a laboratory environment (temperature range of...). to , with each The measurement offset curves at each temperature point are calibrated (with a calibration step size), and a temperature-offset mapping table is established accordingly. During on-site construction, the temperature sensor performs real-time sampling, and the coordinates of the light spot centroid are dynamically corrected through table lookup and interpolation.

[0074] The navigation and positioning system also includes a BeiDou positioning auxiliary module, which automatically switches to the primary positioning reference when the laser beam path is obstructed by construction work, ensuring the continuity of positioning and navigation. Specifically, three BeiDou-3 high-precision positioning terminals are installed at the front and rear ends of the beam and at key nodes in the middle. Each terminal supports B1C / B2a dual-frequency signals; RTK differential positioning is implemented in conjunction with a ground reference station, achieving a static positioning accuracy of ±2mm and a dynamic positioning accuracy of ±2cm. When the laser measurement beam path is obstructed by a crane, tower, or rain curtain, the system automatically detects the beam signal quality based on the signal-to-noise ratio and beam energy integrity. If the effective area of ​​the beam is determined to be less than 70%, it enters an "obstruction" state and automatically switches to a working mode that uses BeiDou positioning data as the primary source and laser positioning as a secondary source. Furthermore, extended Kalman filtering is used to achieve smooth switching between the two modes to avoid abrupt changes in positioning data.

[0075] The navigation and positioning system has multi-mode redundancy navigation capabilities; it switches between the following operating modes based on the quality of the laser positioning signal: laser positioning mode, laser and satellite fusion positioning mode, satellite-assisted positioning mode, and inertial recursive positioning mode. The operating modes automatically switch according to the positioning signal quality level, and pose continuity during mode switching is achieved through a state estimation algorithm.

[0076] The present invention also relates to a positioning method based on the bridge jacking continuous pose perception and redundant navigation and positioning system described in the above embodiments, comprising the following steps:

[0077] S1. After the beam is precisely positioned, control all walking-type jacking equipment to perform a zero-reset operation, synchronously calibrate and store the initial centroid coordinates of the laser spot on the three sets of induction target surfaces, such as Figure 3 As shown, C1 is (0mm, 0mm), C2 is (6000mm, 0mm), and C3 is (3000mm, 6000mm). Initial values ​​of the jacking height were collected from 28 jacking points, all uniformly set to the design beam elevation. The initial horizontal position of each point was uniformly set to 0mm to determine the standard jacking starting position.

[0078] A target trajectory database was built based on the design centerline of the beam. One mileage control node was set up for every 1 meter of jacking, for a total of 540 mileage nodes. Each node pre-stores the three-dimensional coordinates of the target beam front end at the corresponding mileage position. , , and target yaw angle This serves as the basis for real-time push attitude correction and trajectory following.

[0079] S2. The beam jacking operation was officially launched, and a multi-frequency synchronous acquisition mode was adopted to complete the acquisition of on-site operation data: the real-time coordinates of three sets of target surface light spots were acquired at a high frequency of 100Hz; the roll angle and pitch angle measured by each tilt sensor on site were acquired at a frequency of 50Hz; and the real-time displacement value and hydraulic jacking pressure value of each jacking operation point were acquired at a low frequency of 20Hz, so as to comprehensively obtain the spatial attitude of the beam and the operating status data of the equipment.

[0080] S3. Resample various sensor data collected at different frequencies to 100Hz, strictly controlling the timestamp alignment error of multi-source data to ≤1ms, and achieving time-series data synchronization matching. Construct the system state vector. A state transition matrix F is established based on the rigid body spatial motion model of the beam. An observation matrix H is constructed by integrating the laser positioning equation, the tilt angle attitude observation equation, and the displacement sensing observation equation. The extended Kalman filter algorithm is then used to complete the optimal attitude estimation.

[0081] The extended Kalman filter operates according to the following iterative process:

[0082] 1. State prediction: ;

[0083] 2. Covariance prediction: ;

[0084] 3. Kalman gain calculation: ;

[0085] 4. Optimal state update: .

[0086] In the formula, The process noise covariance matrix is... To observe the noise covariance matrix, parameter tuning was performed based on the sensor's factory-specified accuracy and on-site test data.

[0087] S4. Extract the optimal state variables after filtering and fusion. Inner spatial position components Match the target attitude parameters in the database corresponding to the current pushing mileage. Solve for the three-dimensional positional and angular deviations:

[0088] ; ; ; ;

[0089] Introducing the width of the front end of the beam Offset type determination completed:

[0090] ;

[0091] The deviation of the beam is mainly determined by the lateral offset of the axis; conversely, the deviation is mainly determined by the spatial torsional offset.

[0092] S5. Input the deviation component into the fuzzy PID controller. Taking 8mm (deviation e) and the rate of change of e ec=1.2mm / s as an example, after the input quantity is converted to the domain of discourse [-6,6] by the quantization factor, the corresponding fuzzy subsets (such as "negative large (NB)", "negative medium (NM)", "negative small (NS)", "zero (ZO)", "positive small (PS)", "positive medium (PM)", "positive large (PB)" are activated by the triangular membership function.

[0093] like Figure 5 As shown, based on several fuzzy control rules (such as " After performing fuzzy reasoning and resolving the fuzziness using the area-centroid method, we obtain:

[0094] .

[0095] The final control parameters are: (Kp0, Ki0, and Kd0 are the reference values ​​after parameter self-tuning).

[0096] Control quantity: After solving, it is converted into a hydraulic valve opening control signal (4~20mA current signal).

[0097] This control quantity is allocated to the push-execution module, according to... With an axial offset of 8mm, the intelligent control module primarily issues commands to the lateral correction device, prioritizing its use. The lateral correction jacks of the equipment within the control zone gradually correct the deviation at a speed of 0.5 mm / s and a control accuracy of 0.1 mm; when Reduced to within 2mm and If the yaw angle is still significant, switch to a zone differential speed push strategy (left zone speed > right zone speed) to correct the yaw angle.

[0098] S6. Receive the displacement sensor feedback value y_feedback and calculate the control error. If the horizontal, lateral, and vertical errors are all less than the preset thresholds (e.g., 2mm horizontally / 1mm laterally / 1mm vertically), the current cycle continues; if any error exceeds the limit, the control command for the next cycle is adjusted in real time, and the process returns to step S2 for the next round of fine-tuning and correction.

[0099] In this embodiment, in some construction scenarios, the laser beam path may be blocked by temporary facilities such as beam transport vehicles and tower cranes. In such cases, seamless fault switching capability is required, such as... Figure 6 As shown, the specific strategy is as follows:

[0100] (1) Laser positioning mode (normal mode): The laser emitting module and the three light spot sensing target surfaces constitute the main positioning link. The control cabinet monitors the light spot signal quality indicators of each target surface in real time, including the signal-to-noise ratio (SNR) and the light spot energy integrity. When at least two of the three target surfaces have an SNR > 20dB and an effective light spot area > 70%, the system determines that the laser positioning is normal and uses the laser data as the main positioning source.

[0101] (2) Semi-laser positioning mode (partial occlusion mode): When one target surface is occluded (SNR < 15dB or effective spot area < 50%), but the other two target surfaces are still working normally, the system enters semi-laser mode. At this time, the three-dimensional positioning is downgraded from three-point positioning to two-point positioning, and only the linear displacement deviation (x, y) can be calculated, but the angular deflection cannot be calculated. The insufficient angular deflection information was supplemented by data fusion from the Beidou positioning module and the inclinometer, reducing the positioning accuracy from ±0.5mm to approximately ±5mm, but it still meets the requirements for normal construction.

[0102] (3) Beidou-assisted positioning mode (full occlusion mode): When at least two target surfaces fail due to severe occlusion (the spot energy is below the threshold for more than 5 seconds), the system automatically switches to Beidou positioning assisted mode. The Beidou positioning terminal operates in RTK differential mode, receiving differential correction data from the base station (located at a known coordinate point near the bridge site), with a dynamic positioning accuracy of ±2cm. At the same time, the system fuses Beidou positioning data with inclinometer and displacement sensor data through an extended Kalman filter to smooth and predict the positioning trajectory, ensuring continuous output of control commands. When the laser beam path is restored (SNR>20dB and lasts for 3 seconds), the system uses a slowly varying coefficient. The asymptotic weighting method (0.1→1) gradually returns to the laser main positioning mode, avoiding sudden changes in control caused by mode switching.

[0103] (4) Full redundancy degradation strategy: If the Beidou signal is also blocked (GNSS signal blind zone under the bridge), the system enters the final degradation mode - pure inertial recursive positioning. Based on the last effective positioning data combined with displacement sensor and inclinometer data, dead reckoning is performed. The accuracy of the reckoning deteriorates linearly with the increase of the recursive time (about 1 mm / s of drift accumulation). At this time, the system issues an alarm to prompt the construction personnel to stop pushing or temporarily adjust the position of the obstruction.

[0104] In this embodiment, the bridge construction site environment is harsh; dust, rain, temperature variations, and vibration can all adversely affect the accuracy of laser positioning. This invention employs the following comprehensive measures to ensure the long-term accuracy of laser positioning:

[0105] (1) Optical protection: Dustproof and rainproof window glass (high transmittance coating) is installed on both the laser emission window and the sensing target surface, and an automatic cleaning device (micro fan and dustproof brush) is used to reduce the scattering and absorption effect of dust on the light path at the construction site.

[0106] (2) Background light suppression: The CMOS target surface uses a narrow-band filter (center wavelength 635nm±5nm, out-of-band cutoff depth OD4 or above) in conjunction with a background light difference algorithm. The specific method is as follows: first, a frame of background light image (ambient light distribution) is acquired in the absence of laser emission, and then a target image containing light spot signal is acquired in the laser emission state. The two frames are differentially extracted pixel by pixel to extract the light spot, effectively eliminating the interference of sunlight and construction lighting.

[0107] (3) Vibration isolation: Rubber vibration damping pads are installed between the light spot sensing target surface and the beam body, and the base adopts a tripod-type buffer structure to attenuate the disturbance of construction vibration (frequency mainly concentrated in 5~30Hz) on the target surface imaging by more than 80%. At the algorithm level, robust state estimation based on H infinite filtering is adopted to reduce the impact of vibration noise on the positioning value.

[0108] (4) Long-term stability calibration: The system automatically executes a self-calibration program after each power-on, controlling the laser calibration target (the coordinates of the calibration point are known absolute values) at the front end of the beam to move sequentially to the center of the effective field of view of each sensing target surface. The system's comprehensive error is calculated by comparing the deviation between the actual coordinates and the theoretical coordinates, and the error is stored in the calibration parameter table. Self-calibration is automatically performed before each jacking and after every 2 hours of continuous jacking to ensure that the long-term cumulative deviation does not exceed the preset limit.

[0109] (5) Data validity check: Each frame of spot coordinate data undergoes preprocessing to remove invalid values ​​that exceed the effective boundary of the target surface (|x|>50mm or |y|>50mm), and median filtering (window width 3 frames) is used to remove instantaneous outliers caused by sudden dust particles. Only data that has passed the preprocessing and validity check can be sent to the extended Kalman filter for fusion.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A continuous pose sensing and redundant navigation and positioning system for bridge jacking, characterized in that: include: The laser emitting module is used to emit a reference laser beam in the direction of bridge jacking; The light spot sensing and positioning module includes several light-sensitive target surfaces that are non-collinearly distributed; The spot sensing and positioning module is located at the front end of the bridge and is used to obtain the spot position information of the reference laser beam on each photosensitive target surface; The multi-source data fusion module is used to perform spatiotemporal alignment, state estimation, and pose fusion on sensor data to obtain real-time pose and state information during the bridge jacking process. The intelligent control module is used to generate correction control commands based on the deviation between the real-time pose status information and the target trajectory; The walking-type jacking execution module is used to perform bridge jacking, jacking, and lateral correction actions according to the correction control command.

2. The bridge jacking continuous pose perception and redundant navigation and positioning system according to claim 1, characterized in that: The photosensitive target surface is three, and the three photosensitive target surfaces are arranged in an isosceles triangle. The light spot sensing positioning module calculates the lateral displacement deviation, longitudinal displacement deviation, and yaw angle deviation of the bridge front center point in the global coordinate system using a three-point positioning geometric model.

3. The bridge jacking continuous pose perception and redundant navigation and positioning system according to claim 1, characterized in that: The multi-source data fusion module includes a data spatiotemporal alignment unit, a state estimation unit, and a three-dimensional pose reconstruction unit. The data spatiotemporal alignment unit is used to resample sensor data with different sampling frequencies to a unified time reference and complete the coordinate system transformation. The state estimation unit is used to fuse and estimate multi-source data based on the bridge kinematics model; The three-dimensional pose reconstruction unit is used to construct a real-time three-dimensional pose model of the bridge based on the fusion results.

4. The bridge jacking continuous pose perception and redundant navigation and positioning system according to claim 1, characterized in that: The intelligent control module includes a cluster synchronization control layer, a single-machine pose control layer, and a servo drive control layer. The cluster synchronization control layer is used to calculate the target displacement distribution value of each jacking point based on the overall posture deviation of the bridge. The single-machine pose control layer is used to control the lifting degree of freedom, the pushing degree of freedom, and the lateral correction degree of freedom, respectively. The servo drive control layer is used to complete the closed-loop speed control of the actuator.

5. The bridge jacking continuous pose perception and redundant navigation and positioning system according to claim 4, characterized in that: The intelligent control module employs a fuzzy PID controller, using the pose deviation e and the rate of change of deviation ec as input variables, and a proportional coefficient as the correction factor. Integral coefficient correction amount and the correction amount of differential coefficients As an output variable, the PID parameters are adjusted online according to preset fuzzy rules to achieve adaptive optimization of control parameters.

6. The bridge jacking continuous pose perception and redundant navigation and positioning system according to claim 1, characterized in that: The intelligent control module identifies the type of bridge deviation based on real-time pose information and selects the corresponding correction strategy according to different deviation types; wherein, the deviation types include axis offset, same-side offset, and spatial torsional offset.

7. The bridge jacking continuous pose perception and redundant navigation and positioning system according to claim 6, characterized in that: When an axis offset is detected, the lateral correction device is controlled to perform lateral displacement correction. When the lateral deviation meets the preset threshold and the yaw angle deviation exceeds the preset threshold, the yaw angle is corrected by a zoned differential speed jacking method. The zoned differential speed jacking method is that the jacking equipment on both sides of the bridge performs different jacking speeds.

8. The bridge jacking continuous pose perception and redundant navigation and positioning system according to claim 1, characterized in that: The navigation and positioning system performs multi-mode redundancy navigation according to the following method: Based on the quality of the laser positioning signal, it switches between the following operating modes: laser positioning mode, laser and satellite fusion positioning mode, satellite-assisted positioning mode, and inertial recursive positioning mode; The operating mode automatically switches according to the quality level of the positioning signal, and the pose continuity during the mode switching process is achieved through a state estimation algorithm.

9. A positioning method based on the bridge jacking continuous pose perception and redundant navigation and positioning system according to any one of claims 1-8, characterized in that: include: S1. Establish a bridge design trajectory database and collect the initial pose information of the bridge; S2. Use the laser emission module and the spot sensing positioning module to obtain laser positioning data at the front end of the bridge, and at the same time obtain monitoring data collected by the tilt sensor, displacement sensor and satellite positioning module; S3. Perform time synchronization processing and unified spatial coordinate transformation on the monitoring data, and perform multi-source data fusion based on the state estimation algorithm to obtain the real-time pose and state information of the bridge; S4. Compare the real-time pose status information with the target pose information in the design trajectory database to obtain the spatial position deviation and attitude deviation; S5. Generate correction control commands based on spatial position deviation and attitude deviation, and control the walking-type jacking execution module to perform jacking, lifting and lateral correction actions; S6. Obtain feedback information from the walking-type jacking execution module, verify the correction results, and continue the jacking operation when the deviation meets the preset threshold; otherwise, return to step S2 to execute the next round.

10. The bridge jacking continuous pose perception and redundant navigation and positioning method according to claim 9, characterized in that: Select the corresponding correction strategy based on the type of deviation, including: When the lateral displacement deviation exceeds the first threshold, the lateral correction device is controlled to perform lateral correction. When the lateral displacement deviation is less than the first threshold and the yaw angle deviation exceeds the second threshold, the jacking equipment on both sides of the bridge is controlled to use differential jacking to correct the attitude. When the quality of the laser positioning signal is lower than a preset threshold, switch to satellite positioning assisted mode; When both satellite positioning signal and laser positioning signal are below the preset threshold, the system switches to an inertial recursive positioning mode based on displacement and attitude data, and generates push control commands based on the recursive results.

Images