An adaptive compensation method for drilling errors of cable trench robots

Abstract

The embodiment of the present invention discloses an adaptive compensation method for drilling errors of a cable trench robot, which is applied to the robot. The method comprises: determining a zero reference point, and based on the zero reference point, calculating the three-dimensional coordinates of each drilling point according to the design hole spacing requirements and the measurement results of the laser sensing technology, and determining the target drilling path and the posture of the drilling mechanism; performing a drilling operation based on the target drilling path and the posture of the drilling mechanism, and in the process of the drilling operation, timely starting an adaptive compensation mechanism for adjustment by real-time monitoring of the drilling error data; the drilling error data includes but is not limited to drilling depth, angle deviation and position offset, and the adaptive compensation mechanism includes but is not limited to adjusting the posture of the drilling mechanism, adjusting the drilling parameters and using the fine-tuning function of the walking wheel to adjust the position; precise control and real-time optimization of the drilling error can be achieved, and the accuracy and efficiency of the drilling operation can be significantly improved.

Description

Technical Field

[0001] The invention relates to the technical field of cable trench construction robots, and in particular to a self-adaptive compensation method for drilling errors of cable trench robots. Background Art

[0002] As one of the important components of substation construction, cable trench is an indispensable facility in power grid construction. Traditional cable trench bracket fixing hole punching operations usually rely on manual operation or simple mechanized equipment. These methods are not only inefficient, but also difficult to ensure the accuracy and consistency of punching. Especially in complex construction environments, such as encountering irregular trench walls, slope changes or obstacles, traditional punching methods are often difficult to adapt, resulting in increased errors and even affecting the safe laying of cables. Therefore, there is an urgent need for a robot technology that can adaptively adjust and accurately punch holes to solve this problem. Summary of the invention

[0003] The main purpose of the present invention is to provide a cable trench robot drilling error adaptive compensation method to improve the accuracy and efficiency of drilling.

[0004] To achieve the above objectives, the present application provides a cable trench robot drilling error adaptive compensation method, which is applied to the robot, and the method comprises:

[0005] Determine a zero reference point, which is a starting reference point for the robot's drilling operation and a reference point for measurement, calibration and error compensation;

[0006] Based on the zero reference point, the three-dimensional coordinates of each punching point are calculated according to the design hole spacing requirements and the measurement results of the laser sensing technology, and the target punching path and the posture of the punching mechanism are determined;

[0007] The punching operation is performed based on the target punching path and the posture of the punching mechanism. During the punching operation, the punching error data is monitored in real time and an adaptive compensation mechanism is activated in time to make adjustments. The punching error data includes but is not limited to punching depth, angle deviation and position offset, and the adaptive compensation mechanism includes but is not limited to adjusting the posture of the punching mechanism, adjusting the punching parameters and using the fine-tuning function of the walking wheel to make position adjustments.

[0008] Optionally, the real-time monitoring of the punching error data and timely activation of the adaptive compensation mechanism for adjustment include:

[0009] When the error is less than a preset error threshold, rapid compensation is performed by adjusting the posture of the punching mechanism and the punching parameters, wherein adjusting the punching parameters includes reducing the rotation speed or increasing the feed speed;

[0010] When the error is greater than the preset error threshold, the fine-tuning function of the walking wheel and the path planning algorithm are used to replan the punching path and the posture of the punching mechanism.

[0011] Optionally, the quick compensation by adjusting the posture of the punching mechanism and the punching parameters includes:

[0012] Calculating the compensation amount that needs to be adjusted according to the punching error data and the mechanism kinematic model;

[0013] Converting the compensation amount into a control instruction and sending it to the driver of the punching mechanism so that the driver is adjusted in a specified direction and amplitude;

[0014] After the adjustment, the robot detects through sensors whether the current error is reduced to within a preset error range;

[0015] If not, the puncturing parameters are adjusted according to the current error.

[0016] Optionally, the adjustment of the punching parameters includes but is not limited to one or more of the following:

[0017] A fuzzy controller is used, the punching error data is used as an input variable, and the punching parameters are used as output variables, wherein the punching parameters include rotation speed, feed speed, and punching pressure;

[0018] According to the fuzzy rule base, determining the adjustment strategy of the punching parameters under different error conditions and making adjustments;

[0019] According to the monitored drilling error data, the target drilling parameter adjustment value is calculated by the fuzzy inference engine and the adjustment is performed;

[0020] Use the trained neural network model to predict and adjust the punching parameters under different error conditions.

[0021] Optionally, the position adjustment using the fine-tuning function of the walking wheel includes:

[0022] Adopting an adaptive control algorithm to control the speed and direction of the travel wheel, wherein an integral term is introduced to eliminate static errors, and a differential term is introduced to predict future error trends;

[0023] According to the real-time position deviation and speed feedback, the parameters of the PID controller are dynamically adjusted to achieve position adjustment. The parameters of the PID controller include a proportional coefficient, an integral time constant, and a differential time constant.

[0024] Optionally, the position adjustment using the fine-tuning function of the walking wheel includes:

[0025] Adopt multi-wheel cooperative control algorithm to optimize the driving force distribution strategy of the running wheels;

[0026] Use force feedback sensors or torque sensors to monitor the force conditions of each wheel in real time, and dynamically adjust the driving force distribution ratio according to the force conditions;

[0027] A distributed control architecture is adopted to realize information interaction and coordinated control among the wheels.

[0028] Optionally, the zero reference point is a zero reference point set at the starting end of the cable trench and is marked with a marker;

[0029] Determining the zero reference point includes:

[0030] Recognize the marker by the robot's visual recognition system to locate the zero reference point; or,

[0031] The zero reference points are multiple zero reference points set in the cable trench, and the multiple zero reference points form a reference network;

[0032] The method further comprises:

[0033] Optimizing the reference network to determine a target measurement path;

[0034] Based on the target measurement path, the trench wall of the cable trench is scanned and measured using laser sensing technology to obtain shape and size information.

[0035] On the other hand, the present application also provides a cable trench robot drilling error adaptive compensation device, comprising:

[0036] A determination module, used to determine a zero reference point, which is a starting reference point for the robot's drilling operation and a reference point for measurement, calibration and error compensation;

[0037] A calculation module is used to calculate the three-dimensional coordinates of each punching point based on the zero reference point, according to the design hole spacing requirements and the measurement results of the laser sensing technology, and determine the target punching path and the posture of the punching mechanism;

[0038] A control module is used to perform a punching operation based on the target punching path and the posture of the punching mechanism. During the punching operation, the punching error data is monitored in real time and an adaptive compensation mechanism is activated in time for adjustment; the punching error data includes but is not limited to punching depth, angle deviation and position offset, and the adaptive compensation mechanism includes but is not limited to adjusting the posture of the punching mechanism, adjusting the punching parameters and using the fine-tuning function of the walking wheel to adjust the position.

[0039] The present application provides an adaptive compensation method for drilling errors of a cable trench robot, which is applied to the robot, by determining a zero reference point, which is the starting reference point of the robot's drilling operation and the reference point for measurement, calibration and error compensation; based on the zero reference point, according to the design hole spacing requirements and the measurement results of the laser sensing technology, the three-dimensional coordinates of each drilling point are calculated, and the target drilling path and the posture of the drilling mechanism are determined; the drilling operation is performed based on the target drilling path and the posture of the drilling mechanism, and during the drilling operation, the drilling error data is monitored in real time, and the adaptive compensation mechanism is timely started for adjustment; the drilling error data includes but is not limited to drilling depth, angle deviation and position offset, and the adaptive compensation mechanism includes but is not limited to adjusting the posture of the drilling mechanism, adjusting the drilling parameters and using the fine-tuning function of the walking wheel for position adjustment; by comprehensively using technical means such as zero reference point establishment, laser sensing technology measurement and calibration and adaptive error compensation, accurate control and optimization of the drilling error of the cable trench robot are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] in:

[0042] Figure 1 A schematic flow chart of a method for adaptively compensating a drilling error of a cable trench robot provided in an embodiment of the present application;

[0043] Figure 2 A schematic diagram of the structure of a cable trench robot drilling error adaptive compensation device provided in an embodiment of the present application;

[0044] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0046] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0047] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0048] The proportional-integral-derivative control (PID) mentioned in the embodiments of the present application constitutes a control deviation based on a given value and an actual output value, and controls the controlled object by linearly combining the deviation in proportion, integration and differentiation.

[0049] The present application aims to provide a method for adaptive compensation of drilling errors of cable trench robots. This method realizes precise control and real-time optimization of drilling errors by comprehensively using technical means such as zero reference point establishment, laser sensing technology measurement and calibration, and adaptive error compensation, thereby significantly improving the accuracy and efficiency of the drilling operation.

[0050] The embodiments of the present application are described below in conjunction with the drawings in the embodiments of the present application.

[0051] See also Figure 1 , is a flow chart of a method for adaptively compensating a cable trench robot drilling error provided in an embodiment of the present application. Figure 1 As shown, the method includes:

[0052] 101. Determine a zero reference point, which is a starting reference point for the drilling operation of the robot and a reference point for measurement, calibration and error compensation;

[0053] 102. Based on the above zero reference point, according to the design hole spacing requirements and the measurement results of the laser sensing technology, the three-dimensional coordinates of each punching point are calculated, and the target punching path and the posture of the punching mechanism are determined;

[0054] 103. Perform a punching operation based on the target punching path and the posture of the punching mechanism. During the punching operation, monitor the punching error data in real time and start the adaptive compensation mechanism to make adjustments in time. The punching error data includes but is not limited to the punching depth, angle deviation and position offset. The adaptive compensation mechanism includes but is not limited to adjusting the posture of the punching mechanism, adjusting the punching parameters and using the fine-tuning function of the walking wheel to make position adjustments.

[0055] For the above step 101, specifically, the embodiment of the present application can select a stable and easily identifiable point at the starting end or fixed position of the cable trench as the zero reference point. This point is not only the starting reference point for the robot's drilling operation, but also the reference for subsequent measurement, calibration and error compensation. To ensure the accuracy and stability of the zero reference point, a special marker can be used for identification, and the robot's visual recognition system can be used for automatic identification and positioning.

[0056] In an optional embodiment, the zero reference point is a zero reference point set at the starting end of the cable trench and is marked with a marker;

[0057] The above step 101 includes:

[0058] Recognize the marker through the robot's visual recognition system to locate the zero reference point; or,

[0059] The above-mentioned zero reference points are multiple zero reference points set in the cable trench, and the above-mentioned multiple zero reference points form a reference network;

[0060] The above method further includes:

[0061] Optimize the above-mentioned benchmark network and determine the target measurement path;

[0062] Based on the above target measurement path, laser sensing technology is used to scan and measure the trench wall of the above cable trench to obtain shape and size information.

[0063] The specific description of the reference point setting is as follows:

[0064] (1) Selection and arrangement of reference points

[0065] Location selection: The reference point should be selected in a relatively fixed position in the cable trench, not easily disturbed by the outside world, and easy for the robot to access. For example, it can be arranged at key locations such as the corners of the cable trench, intersections, and near important equipment.

[0066] Quantity determination: According to the length, complexity and accuracy requirements of the cable trench, the number of benchmark points should be reasonably determined. Generally speaking, the benchmark points should be evenly distributed to ensure that the entire cable trench area can be effectively covered.

[0067] Identification and protection: Set up obvious identification for each reference point, such as QR code, RFID tag, etc., to facilitate the robot to quickly identify and locate. At the same time, the reference point should be properly protected to prevent damage or movement.

[0068] (2) Reference point calibration

[0069] High-precision calibration equipment: Use high-precision measuring equipment (such as laser rangefinder, total station, etc.) to calibrate the reference points to ensure the accuracy and stability of their position coordinates.

[0070] Periodic recalibration: Recalibrate the benchmark points regularly to eliminate position deviations caused by environmental factors (such as ground subsidence, temperature changes, etc.).

[0071] If a benchmark network is constructed, the specific implementation method is described as follows:

[0072] (1) Network topology design

[0073] Mesh layout: A mesh layout is used to build a reference network to ensure that multiple connection paths are formed between each reference point, thereby improving the redundancy and reliability of the network.

[0074] Optimization algorithm: Use graph theory and optimization algorithms (such as Dijkstra algorithm, A* algorithm, etc.) to optimize the benchmark network, find the optimal or suboptimal measurement path, and reduce measurement errors and calculations.

[0075] (2) Data communication and sharing

[0076] Wireless communication technology: Wireless communication technology (such as Wi-Fi, Zigbee, LoRa, etc.) is used to achieve data communication and sharing between reference points. This ensures that the robot can obtain the position information and calibration data of each reference point in real time during inspection or drilling.

[0077] Cloud platform support: Connect the data of the benchmark network to the cloud platform to achieve remote monitoring and data management. Through the cloud platform, the benchmark network can be centrally managed and maintained to improve data security and reliability.

[0078] Optionally, the method further includes dynamic reference point adjustment. The details are as follows:

[0079] Real-time monitoring: Sensors (such as vibration sensors, displacement sensors, etc.) are set around the reference points to monitor the stability and position changes of the reference points in real time.

[0080] Automatic adjustment: When the robot detects that the reference point position is offset, it can automatically calibrate and adjust to ensure the accuracy and stability of the reference point.

[0081] (2) Robot autonomous calibration

[0082] Built-in calibration module: The calibration module is integrated inside the robot, including high-precision sensors and calibration algorithms. The robot can autonomously calibrate and correct its own position to improve the accuracy of measurement and drilling.

[0083] Intelligent algorithm support: Use intelligent algorithms such as machine learning and deep learning to optimize and iterate the robot's calibration process to improve the accuracy and efficiency of calibration.

[0084] (3) Multi-source data fusion and verification

[0085] Multi-sensor fusion: Combine data from multiple sensors (such as laser ranging, visual recognition, inertial navigation, etc.) to perform multi-source data fusion and verification to improve the accuracy and reliability of measurement results.

[0086] Cross-validation: Use multiple reference points in the reference network for cross-validation to ensure the accuracy and consistency of the measurement results. When inconsistent or abnormal data is found, the robot can automatically check and correct it.

[0087] In one implementation, the visual recognition system involved in the embodiments of the present application is introduced as follows:

[0088] (1) Camera selection and configuration: Select high-resolution, low-distortion industrial cameras and configure appropriate light sources and lenses to ensure stable imaging under different lighting and working environments.

[0089] (2) Image processing algorithm: Develop or use a mature image processing library (such as OpenCV) to implement preprocessing steps such as image denoising, edge detection, and feature extraction. Subsequently, the deviation between the actual position of the punch point and the theoretical position is calculated through algorithms such as template matching and feature point tracking.

[0090] For the above step 102, specifically, the robot in the embodiment of the present application can be equipped with a high-precision laser sensing device, which can accurately measure the shape, size and position of the trench wall by emitting a laser beam to the inner wall of the cable trench and receiving the reflected signal, and obtain the above measurement results. The high precision and non-contact characteristics of laser sensing technology ensure the accuracy and reliability of the measurement results. The robot can calculate the three-dimensional coordinates of each punching point according to the design hole spacing requirements and the measurement results of the laser sensing technology, and determine the optimal punching path (target punching path) and posture, which is mainly the posture of its punching mechanism.

[0091] In one implementation, the high-precision sensor integration involved in the embodiments of the present application is introduced as follows:

[0092] Laser distance sensor: An industrial-grade high-precision laser distance sensor can be used, with a measurement accuracy of up to micron level. Through programming, the sensor can send the current drilling depth data to the control system in real time, and compare it with the preset drilling depth value to quickly calculate the depth error.

[0093] Angle sensor: A high-precision encoder or gyroscope is used as an angle sensor and is directly installed on the rotating shaft of the punching mechanism. These sensors can measure and output the current angle of the punching mechanism in real time, compare it with the theoretical angle value, and accurately identify the angle deviation.

[0094] For the above step 103, specifically, during the drilling operation, the robot can monitor the drilling error data such as drilling depth, angle deviation and position offset in real time, and promptly start the adaptive compensation mechanism for adjustment. The adaptive compensation mechanism may include but is not limited to adjusting the posture of the drilling mechanism, optimizing the drilling parameters, and using the fine-tuning function of the walking wheel to adjust the position. These compensation measures are intended to reduce errors and ensure high precision and high efficiency of the drilling operation.

[0095] In addition, the above-mentioned adaptive compensation mechanism can also include real-time analysis and processing of error data. By counting and analyzing the source, type and size of error data, the robot can identify the main error factors and influencing rules in the drilling operation, and provide data support for subsequent optimization strategies. At the same time, according to the changing trend of error data, predictions and adjustments are made to eliminate potential error risks in advance, further improving the stability and reliability of the drilling operation.

[0096] In an optional implementation, the method can achieve cyclic execution and optimization. Specifically, the robot can continuously cycle through the above steps 101 to 103 during the entire drilling process, that is, execute steps such as setting up a zero reference point, laser sensing technology measurement and calibration, and adaptive error compensation, to achieve continuous control and optimization of the drilling error. As the drilling operation proceeds and data accumulates, the robot can gradually improve and optimize the drilling strategy, and improve its adaptive ability and intelligence level. This cyclic optimization mechanism enables the robot to maintain stable drilling performance when facing a complex and changing construction environment.

[0097] In an optional implementation, in the above step 103, by real-time monitoring of the perforation error data, the adaptive compensation mechanism is activated in time for adjustment, including:

[0098] 31. When the error is less than a preset error threshold, rapid compensation is performed by adjusting the posture of the punching mechanism and the punching parameters, wherein the adjustment of the punching parameters includes reducing the rotation speed or increasing the feed speed;

[0099] 32. When the error is greater than the preset error threshold, the punching path and the posture of the punching mechanism are replanned using the fine-tuning function of the walking wheel and the path planning algorithm.

[0100] Further optionally, the above step 31 includes:

[0101] According to the above punching error data and the kinematic model of the mechanism, the compensation amount that needs to be adjusted is calculated;

[0102] Convert the compensation amount into a control instruction and send it to the driver of the punching mechanism so that the driver can adjust in a specified direction and amplitude;

[0103] After adjustment, the robot detects through sensors whether the current error is reduced to within a preset error range;

[0104] If not, adjust the punching parameters according to the current error.

[0105] The preset error threshold can be set and adjusted as needed for error classification and identification. Optionally, the robot monitors various error data in the drilling process in real time through integrated sensors (such as laser rangefinders, angle encoders, visual systems, etc.), such as depth error, angle deviation, position offset, etc. According to the nature and size of the error, it is classified into "small error" and "large error";

[0106] Furthermore, a reasonable error threshold can be set for each type of error based on historical data and experience. When the error value is less than the preset error threshold, it is considered a "small error"; otherwise, it is considered a "large error".

[0107] For small errors, the robot can compensate by adjusting the posture of the punching mechanism, which includes adjusting the inclination angle and rotation direction of the punching head. The specific algorithm is as follows:

[0108] Calculate compensation amount: Calculate the compensation amount that needs to be adjusted (such as angle increment, displacement increment) based on the error data and the mechanism kinematic model.

[0109] Posture adjustment instruction: The compensation amount is converted into a control instruction and sent to the driver of the punching mechanism. The driver adjusts according to the direction and amplitude specified by the control instruction.

[0110] Feedback verification: After adjustment, the robot can use sensors to detect whether the error is reduced to an acceptable range (preset error range) to ensure the compensation effect. If the detection error is not reduced to the preset error range, compensation adjustment can be made again, for example, the punching parameters can be adjusted according to the current error.

[0111] Parameter optimization: At the same time, the robot can also dynamically adjust the punching parameters (such as feed speed, rotation speed, punching pressure, etc.) according to the error data to further optimize the punching effect.

[0112] In an optional implementation, the adjustment of the punching parameters in the above step 31 includes but is not limited to one or more of the following:

[0113] A fuzzy controller is used, the punching error data is used as an input variable, and the punching parameters are used as output variables, wherein the punching parameters include rotation speed, feed speed, and punching pressure;

[0114] According to the fuzzy rule base, the adjustment strategy of the above punching parameters under different error conditions is determined and adjusted;

[0115] According to the monitored drilling error data, the target drilling parameter adjustment value is calculated by the fuzzy inference engine and the adjustment is performed;

[0116] Use the trained neural network model to predict and adjust the punching parameters under different error conditions.

[0117] Specifically, the above-mentioned method for automatically adjusting the punching parameters is introduced as follows:

[0118] Fuzzy logic control:

[0119] (1) Design a fuzzy controller that takes error data (such as depth error, angle deviation, and position offset) as input variables and drilling parameters (such as rotation speed, feed speed, and drilling pressure) as output variables.

[0120] (2) Based on expert experience and experimental data, a fuzzy rule base is developed to describe the adjustment strategy of punching parameters under different error conditions.

[0121] (3) In real-time operation, the optimal drilling parameter adjustment value is calculated based on the monitored error data through the fuzzy inference engine.

[0122] Neural Network Optimization:

[0123] (1) Construct a neural network model with error data as input and punching parameters as output, and learn the complex mapping relationship between error and parameters through training data.

[0124] (2) Gradient descent, back propagation and other algorithms are used to train the neural network so that it can accurately predict and adjust the punching parameters to reduce errors.

[0125] In an optional implementation, the position adjustment using the fine-tuning function of the walking wheel includes:

[0126] Adopting an adaptive control algorithm to control the speed and direction of the above-mentioned running wheel, an integral term is introduced to eliminate static errors, and a differential term is introduced to predict future error trends;

[0127] According to the real-time position deviation and speed feedback, the parameters of the PID controller are dynamically adjusted to achieve position adjustment. The parameters of the PID controller include a proportional coefficient, an integral time constant, and a differential time constant.

[0128] Specifically, the optimization of the closed-loop control algorithm mentioned above is introduced as follows:

[0129] (1) Use PID control or more advanced adaptive control algorithms to accurately control the speed and direction of the running wheels.

[0130] (2) Integral terms are introduced to eliminate static errors, and differential terms are introduced to predict future error trends, thereby improving the stability and response speed of the system.

[0131] (3) According to the real-time position deviation and speed feedback, the parameters of the PID controller (such as proportional coefficient, integral time constant, and differential time constant) are dynamically adjusted to achieve more precise position adjustment.

[0132] Optionally, the position adjustment using the fine-tuning function of the travel wheel includes:

[0133] Adopt multi-wheel cooperative control algorithm to optimize the driving force distribution strategy of the running wheels;

[0134] Use force feedback sensors or torque sensors to monitor the force conditions of each wheel in real time, and dynamically adjust the driving force distribution ratio according to the above force conditions;

[0135] A distributed control architecture is adopted to realize information interaction and coordinated control among the wheels.

[0136] Specifically, the multi-round collaborative control strategy mentioned above is introduced as follows:

[0137] (1) Design a multi-wheel collaborative control algorithm, consider factors such as the load distribution of each wheel and the ground friction coefficient, and optimize the driving force distribution strategy of the walking wheel.

[0138] (2) Introduce force feedback sensors or torque sensors to monitor the force conditions of each wheel in real time and dynamically adjust the driving force distribution ratio according to the force conditions.

[0139] (3) A distributed control architecture is adopted to realize information interaction and coordinated control among the wheels, thereby improving the overall position adjustment accuracy and stability.

[0140] The following is an example of a specific algorithm for fine-tuning the walking wheel provided in this application:

[0141] 1. Input: Deviation between current position and target position (Δx, Δy, Δθ);

[0142] 2. Control parameter initialization: set the proportional coefficient Kp, integral time constant Ti, and differential time constant Td of the PID controller;

[0143] 3. Calculate the control quantity:

[0144] Deviation calculation: e(t) = Δx^2 + Δy^2 + (Δθ*conversion coefficient)^2

[0145] Proportional term: P = Kp*e(t)

[0146] Integral term: I = Kp*Ti*∫e(t)dt (calculated using numerical integration method)

[0147] Differential term: D = Kp*Td*(e(t)-e(t-1)) / Δt (Δt is the sampling time)

[0148] Total control quantity: u(t)=P+I+D;

[0149] 4. Output: convert u(t) into the speed and steering control instructions of the travel wheel and send them to the travel control system;

[0150] 5. Feedback and iteration: Based on the feedback from the walking control system, the position deviation is updated in real time, and steps 3-4 are repeated until the deviation is reduced to within the set range.

[0151] Optionally, the dynamic path planning algorithm in the embodiment of the present application can be set and implemented according to the following rules:

[0152] (1) When adjusting the position, the robot’s kinematic constraints (such as joint angle limits, speed limits, etc.) and dynamic characteristics (such as inertia, friction, etc.) should be considered.

[0153] (2) Generate an optimal or suboptimal path from the current location to the target location using algorithms such as graph search, sample-based programming, or optimization control.

[0154] (3) During the path execution process, the path is dynamically adjusted and optimized based on real-time error data and robot status information to ensure that the robot can reach the target position accurately and quickly.

[0155] The cable trench robot drilling error adaptive compensation method in the embodiment of the present application realizes precise control and optimization of the cable trench robot drilling error by comprehensively using technical means such as zero reference point establishment, laser sensing technology measurement and calibration, and adaptive error compensation. Compared with the traditional drilling method, it has the following advantages:

[0156] Improves the accuracy and consistency of drilling, and reduces the potential hidden dangers of errors to the safe laying of cables;

[0157] Improves the efficiency of drilling operations, reduces construction costs and dependence on labor;

[0158] It adapts to the complex and changing construction environment and has strong adaptive ability and intelligent level;

[0159] It provides useful reference and lessons for the construction of similar projects and promotes the application and development of robot technology in the field of cable trench construction.

[0160] In order to better illustrate the method in the embodiment of the present application, examples are given below in combination with specific application scenarios.

[0161] Scenario 1:

[0162] During the construction of a cable trench, the robot needs to punch holes on the trench wall according to the design hole spacing requirements. First, a stable zero reference point is set at the starting end of the cable trench, marked with a special marker, and automatically identified and located by the robot's visual recognition system. Then, the robot uses laser sensing technology to measure and calibrate the cable trench, obtain the accurate size and position information of the trench wall, and calculate the three-dimensional coordinates of each punching point.

[0163] According to the measurement and calibration results, the robot adjusts its position and posture to ensure the accuracy of the drilling operation. During the adjustment process, the back-and-forth adjustment function of the walking wheel plays an important role in achieving fine-tuning of the robot's position. Then, the robot starts the punching mechanism to perform the drilling operation. During the drilling process, the robot monitors the error data in real time, including the drilling depth, angle deviation, and position offset. When the error data is detected, the robot immediately starts the adaptive compensation mechanism to compensate for the error. The compensation mechanism includes adjusting the drilling parameters, using the walking wheel for position adjustment, and other means.

[0164] For example, when the robot detects that the position of a punching point is greatly offset, it will adjust its own position through the fine-tuning function of the walking wheel to realign the punching mechanism with the punching point. At the same time, the robot will also adjust the punching parameters according to the changing trend of the error data, such as reducing the rotation speed or increasing the feed speed, to reduce the error and improve the punching quality.

[0165] By cyclically executing steps such as setting the zero reference point, measuring and calibrating with laser sensing technology, and adaptive error compensation, the robot successfully completed the drilling operation and achieved continuous control and optimization of the drilling error. This method not only improves the accuracy and efficiency of drilling, but also reduces construction costs and dependence on labor.

[0166] Scenario 2:

[0167] In another cable trench construction project, the construction environment was more complex, with irregular shapes and slope changes on the trench walls. To meet these challenges, the robot used more advanced laser sensing equipment and more sophisticated error compensation strategies.

[0168] First, the robot sets multiple zero reference points in the cable trench to form a reference network to improve the accuracy and stability of measurement and calibration. Then, a high-precision laser sensor device is used to fully scan and measure the trench wall to obtain detailed shape and size information.

[0169] During the drilling process, the robot monitors the error data in real time and adopts different compensation measures according to the size and type of the error. For example, for smaller errors, the robot quickly compensates by adjusting the posture and parameters of the drilling mechanism; for larger errors, the robot uses the fine-tuning function of the walking wheels and the path planning algorithm to replan the drilling path and posture.

[0170] The specific processing steps involved in the above process can refer to the specific description in the above embodiments.

[0171] By combining advanced laser sensing technology and sophisticated error compensation strategies, the robot achieves high-precision and high-efficiency drilling operations in complex construction environments. This not only improves construction quality and safety, but also provides a useful reference for the construction of similar projects.

[0172] Based on the description of the aforementioned method embodiment, the embodiment of the present application also provides a cable trench robot drilling error adaptive compensation device.

[0173] Figure 2 This is a schematic diagram of the structure of a cable trench robot drilling error adaptive compensation device provided in an embodiment of the present application. Figure 2 As shown, the device 200 includes:

[0174] A determination module 210 is used to determine a zero reference point, which is a starting reference point for the drilling operation of the robot, as well as a reference point for measurement, calibration and error compensation;

[0175] The calculation module 220 is used to calculate the three-dimensional coordinates of each punching point based on the above-mentioned zero reference point, according to the design hole spacing requirements and the measurement results of the laser sensing technology, and determine the target punching path and the posture of the punching mechanism;

[0176] The control module 230 is used to perform a punching operation based on the target punching path and the posture of the punching mechanism. During the punching operation, the punching error data is monitored in real time and the adaptive compensation mechanism is activated in time for adjustment. The punching error data includes but is not limited to the punching depth, angle deviation and position offset. The adaptive compensation mechanism includes but is not limited to adjusting the posture of the punching mechanism, adjusting the punching parameters and using the fine-tuning function of the walking wheel for position adjustment.

[0177] It can be understood that the relevant contents of each module in the above device have been described in detail in the aforementioned method embodiment, and the details can be referred to the contents in the method embodiment; that is, the cable trench robot drilling error adaptive compensation device provided in the present application can be executed as follows Figure 1 Any steps in the illustrated embodiment will not be described in detail here.

[0178] In one embodiment of the present application, an electronic device is also provided. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 300 may include a processor 301 and a memory 302. The memory 302 stores a computer program. When the computer program is executed by the processor 301, the following operations will be performed: Figure 1 Any step in the method embodiment shown. The electronic device 300 may also include an input / output device, etc. In a specific implementation, the electronic device 300 may be a robot, such as a cable trench robot, and the embodiment of the present application does not limit the appearance of the robot.

[0179] In one embodiment, a computer-readable storage medium is further provided, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor executes any step in the method embodiment.

[0180] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0181] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0182] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A cable trench robot drilling error adaptive compensation method, characterized in that: Applied to a robot, the method comprises: Determine a zero reference point, which is a starting reference point for the robot's drilling operation and a reference point for measurement, calibration and error compensation; Based on the zero reference point, the three-dimensional coordinates of each punching point are calculated according to the design hole spacing requirements and the measurement results of the laser sensing technology, and the target punching path and the posture of the punching mechanism are determined; The punching operation is performed based on the target punching path and the posture of the punching mechanism. During the punching operation, the punching error data is monitored in real time and an adaptive compensation mechanism is activated in time to make adjustments. The punching error data includes but is not limited to punching depth, angle deviation and position offset. The adaptive compensation mechanism includes but is not limited to adjusting the posture of the punching mechanism, adjusting the punching parameters and using the fine-tuning function of the walking wheel to make position adjustments.

2. The cable trench robot drilling error adaptive compensation method according to claim 1 is characterized in that: The real-time monitoring of the punching error data and timely activation of the adaptive compensation mechanism for adjustment include: When the error is less than a preset error threshold, rapid compensation is performed by adjusting the posture of the punching mechanism and the punching parameters, wherein adjusting the punching parameters includes reducing the rotation speed or increasing the feed speed; When the error is greater than the preset error threshold, the fine-tuning function of the walking wheel and the path planning algorithm are used to replan the punching path and the posture of the punching mechanism.

3. The cable trench robot drilling error adaptive compensation method according to claim 2 is characterized in that: The method of performing rapid compensation by adjusting the posture of the punching mechanism and the punching parameters includes: Calculating the compensation amount that needs to be adjusted according to the punching error data and the mechanism kinematic model; Converting the compensation amount into a control instruction and sending it to the driver of the punching mechanism so that the driver is adjusted in a specified direction and amplitude; After the adjustment, the robot detects through sensors whether the current error is reduced to within a preset error range; If not, the puncturing parameters are adjusted according to the current error.

4. The cable trench robot drilling error adaptive compensation method according to claim 1 is characterized in that: The adjustment of the punching parameters includes but is not limited to one or more of the following: A fuzzy controller is used, the punching error data is used as an input variable, and the punching parameters are used as output variables, wherein the punching parameters include rotation speed, feed speed, and punching pressure; According to the fuzzy rule base, determining the adjustment strategy of the punching parameters under different error conditions and making adjustments; According to the monitored drilling error data, the target drilling parameter adjustment value is calculated by the fuzzy inference engine and the adjustment is performed; Use the trained neural network model to predict and adjust the punching parameters under different error conditions.

5. The cable trench robot drilling error adaptive compensation method according to claim 1 is characterized in that: The position adjustment using the fine-tuning function of the walking wheel includes: Adopting an adaptive control algorithm to control the speed and direction of the travel wheel, wherein an integral term is introduced to eliminate static errors, and a differential term is introduced to predict future error trends; According to the real-time position deviation and speed feedback, the parameters of the PID controller are dynamically adjusted to achieve position adjustment. The parameters of the PID controller include a proportional coefficient, an integral time constant, and a differential time constant.

6. The cable trench robot drilling error adaptive compensation method according to claim 1 is characterized in that: The position adjustment using the fine-tuning function of the walking wheel includes: Adopt multi-wheel cooperative control algorithm to optimize the driving force distribution strategy of the running wheels; Use force feedback sensors or torque sensors to monitor the force conditions of each wheel in real time, and dynamically adjust the driving force distribution ratio according to the force conditions; A distributed control architecture is adopted to realize information interaction and coordinated control among the wheels.

7. The cable trench robot drilling error adaptive compensation method according to claim 1 is characterized in that: The zero reference point is a zero reference point set at the starting end of the cable trench and is marked with a marker; Determining the zero reference point includes: Recognize the marker by the robot's visual recognition system to locate the zero reference point; or, The zero reference points are multiple zero reference points set in the cable trench, and the multiple zero reference points form a reference network; The method further comprises: Optimizing the reference network to determine a target measurement path; Based on the target measurement path, the trench wall of the cable trench is scanned and measured using laser sensing technology to obtain shape and size information.

8. An adaptive compensation device for drilling errors of a cable trench robot, characterized in that: include: A determination module, used to determine a zero reference point, which is a starting reference point for the robot's drilling operation and a reference point for measurement, calibration and error compensation; A calculation module is used to calculate the three-dimensional coordinates of each punching point based on the zero reference point, according to the design hole spacing requirements and the measurement results of the laser sensing technology, and determine the target punching path and the posture of the punching mechanism; A control module is used to perform a punching operation based on the target punching path and the posture of the punching mechanism. During the punching operation, the punching error data is monitored in real time and an adaptive compensation mechanism is activated in time to make adjustments. The punching error data includes but is not limited to punching depth, angle deviation and position offset. The adaptive compensation mechanism includes but is not limited to adjusting the posture of the punching mechanism, adjusting the punching parameters and using the fine-tuning function of the walking wheel to make position adjustments.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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