Rail-mounted robot running speed dynamic control method and device, medium and equipment

Abstract

The invention provides an on-rail robot running speed dynamic control method and device, a medium and equipment, and the method comprises the steps: obtaining the position of an on-rail robot in real time, and obtaining the speed of the on-rail robot based on a differential mode; deploying a visual identification tag at a task point of the robot, and judging whether the rail-mounted robot enters a motion control interval before a target point; if the on-rail robot enters the motion control interval, the MPC module carries out target speed planning, and a three-section speed regulation algorithm is used for converting the target speed into acceleration change and executing the acceleration change; a preset identification pattern is recognized through a camera, the actual distance deviation is calculated in real time, whether the deviation exceeds a preset threshold value or not is judged, and if the deviation exceeds the threshold value, a control system sends out a fine adjustment instruction, and position calibration is achieved in a low-speed crawling mode; and after the test is finished, the speed is recovered to the preset cruise speed. According to the method, high-precision positioning and stable speed control of the robot are realized, and the test stability and accuracy are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of robotics technology, and in particular to a rail-mounted robot running speed dynamic control device, medium and equipment. Background Art

[0002] With the development of intelligent technology, automated inspection of substation equipment has become a key means of improving power system operational safety and management efficiency. Traditional substation inspections rely primarily on manual inspections, a method that is not only inefficient and labor-intensive, but also difficult to ensure the accuracy of inspection results. During manual inspections, workers need to carry various testing tools and inspect numerous devices within the substation one by one. The entire process is time-consuming and labor-intensive, and is prone to omissions and data recording errors due to human error.

[0003] To address the shortcomings of traditional inspection methods, intelligent inspection robots are becoming an increasingly important technology for substation inspections. A key component of this technology is the substation track-mounted robot, which patrols from a fixed track, enabling efficient equipment inspections in complex environments. The track on which these robots operate is typically designed based on the substation's layout, ensuring coverage of all critical equipment areas. During the inspection process, the robot moves along the track, monitoring the equipment using its various sensors.

[0004] However, in the complex environment of substations, equipment layouts are intricate and workstations are widely distributed. Inspection robots must be able to move accurately and smoothly to each test point and precisely decelerate and stop during testing to ensure stable data acquisition by the detection equipment. After completing the test, the robot must also promptly resume normal speed and continue to the next inspection point. Existing robot speed control methods often struggle to meet this requirement. Improper speed control can cause equipment damage or inaccurate test data, thus affecting the reliability and effectiveness of the entire substation inspection work. Summary of the Invention

[0005] Based on the above background, the present invention proposes a method for dynamically controlling the motor speed and acceleration / deceleration based on the target position, targeting the application scenario where a robot in a substation needs to perform a short stop test at multiple key equipment points.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides a method for dynamically controlling the running speed of a rail-mounted robot, comprising:

[0008] Obtain the position of the rail-hanging robot in real time, and obtain the speed value of the rail-hanging robot based on the differential method;

[0009] Deploy visual recognition tags at the robot's task point and determine whether the track-mounted robot has entered the motion control range corresponding to the target point;

[0010] If the rail-mounted robot enters the motion control range, the MPC objective function is designed to construct the MPC model for target speed planning. The objective function is:

[0011]

[0012] Among them, x(t) is the current location of the robot, x target is the position of the task point in the orbital coordinate system, v(t) is the current velocity, a(t) is the acceleration, j(t) is the jerk, ɑ, β, γ, and λ are weight coefficients;

[0013] By minimizing the objective function, the optimal speed curve is obtained in real time, and the target speed is converted into specific acceleration change control instructions and executed through a three-stage speed regulation algorithm;

[0014] The camera recognizes the pre-set identification pattern, calculates the actual distance deviation between the robot and the target point in real time, and determines whether the actual deviation exceeds the pre-set deviation threshold. If it exceeds, the robot position is calibrated in a low-speed crawling mode. After the position calibration, the robot performs the inspection task;

[0015] After the detection task is completed, the target speed is planned according to the objective function minimized by the MPC model, and the speed is restored to the preset cruising speed in combination with the three-stage speed regulation algorithm.

[0016] In a second aspect, an embodiment of the present invention provides a device for dynamically controlling the running speed of a rail-mounted robot, the device comprising:

[0017] The state acquisition module is used to determine the inspection path of the rail-hanging robot and obtain the position of the rail-hanging robot in real time, and obtain the speed value of the rail-hanging robot based on the differential method;

[0018] The interval determination module is used to deploy visual recognition tags at the robot's task points and determine whether the track-mounted robot has entered the motion control interval corresponding to the target point;

[0019] The speed control module is used to optimize the objective function according to the MPC model to perform optimal speed planning when the rail-mounted robot enters the motion control range. It also converts the target speed into specific acceleration change control instructions through a three-stage speed regulation algorithm and executes them, and introduces acceleration constraints for speed control;

[0020] The deviation calibration module is used to identify the marking pattern on the track or equipment through the camera, calculate the actual distance deviation between the robot and the target point in real time, and determine whether the actual deviation exceeds the preset deviation threshold. If it exceeds, the robot position is calibrated in a low-speed crawling mode;

[0021] The speed recovery module is used to accelerate and restore the speed to the preset cruising speed according to the three-stage speed regulation algorithm after the test.

[0022] In a third aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, in which a computer program is stored. The computer program is loaded and executed by a processor to implement the above-mentioned method for dynamically controlling the running speed of the rail-hanging robot.

[0023] In a fourth aspect, an embodiment of the present invention provides an electronic device comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for dynamically controlling the running speed of the rail-hanging robot when executing the computer program.

[0024] The beneficial effects of the present invention are as follows:

[0025] For substation robots that need to perform brief stop tests at multiple key equipment points, such as infrared temperature measurement and visual recognition, this method dynamically adjusts the motor speed and acceleration by real-time monitoring of the robot's current position relative to the target test point. This allows for smooth deceleration and precise parking when approaching the test point, as well as rapid recovery after the test, ensuring efficient, stable, and accurate inspections. This method can flexibly adapt to various complex environments based on the specific substation layout and equipment distribution characteristics, achieving high-precision positioning and smooth speed control. This provides a reliable speed control solution for intelligent substation inspections, effectively improving test stability and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0027] Figure 1 A flow chart of a method for dynamically controlling the running speed of a rail-hanging robot provided in an embodiment of the present invention;

[0028] Figure 2 Schematic diagram of the dynamic control device for the running speed of a rail-hanging robot provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to further understand the present invention, preferred embodiments of the present invention are described below in conjunction with examples. However, it should be understood that these descriptions are only for further illustrating the features and advantages of the present invention, rather than limiting the claims of the present invention.

[0030] Explanation of terms:

[0031] MPC (Model Predictive Control) is a model-based, closed-loop optimization control strategy widely used in fields such as autonomous driving and path planning. MPC predicts the system's behavior over a period of time and optimizes control inputs to keep the system close to the target state while satisfying various constraints. Its core concept is to use the system's state transition model to predict the system's state over a period of time based on the current state and control inputs. Then, using an optimization algorithm, it finds the control input sequence that minimizes the objective function.

[0032] Example 1

[0033] See also Figure 1 The present invention provides a method for dynamically controlling the running speed of a rail-mounted robot, comprising:

[0034] S1. Obtain the position of the rail-hanging robot in real time, and obtain the speed value of the rail-hanging robot based on the differential method;

[0035] S2. Deploy visual recognition tags at the robot's task point and determine whether the track-mounted robot has entered the motion control range before the target point;

[0036] S3. If the rail-mounted robot enters the motion control range, the MPC objective function is designed to build an MPC model for target speed planning, and the three-stage speed regulation algorithm is used to convert the planned target speed into a specific acceleration change control instruction and execute it;

[0037] S4. Use the camera to identify the marking pattern on the track or equipment, calculate the actual distance deviation between the robot and the target point in real time, and determine whether the actual deviation exceeds the preset deviation threshold. If it exceeds, the robot will be calibrated in a low-speed crawling manner to achieve the final position;

[0038] S5. After the test, target speed planning and three-stage speed regulation algorithm are performed according to the model predictive control MPC control objective function to restore the speed to the preset cruising speed.

[0039] Specifically:

[0040] In step S1, the inspection path of the rail-hanging robot is determined and a high-resolution photoelectric encoder is used to obtain the travel displacement of the rail-hanging robot in real time. Among them, the high-resolution photoelectric encoder is suitable for speed and displacement measurement of the rail-hanging robot in the substation due to its advantages of high precision, real-time performance, reliability, anti-interference ability and easy integration, and provides strong technical support for the realization of dynamic speed control based on the target position. Specifically, the high-resolution photoelectric encoder is installed at both ends of the robot's wheel axle, close to the position of the wheel's rotation axis, so as to monitor the angular displacement and speed of the wheel in real time. The photoelectric encoder converts the rotation angle of the wheel into a pulse signal by emitting and receiving light. When the wheel rotates, the encoder's grating disk rotates accordingly, blocking and exposing the photoelectric detection device, generating a series of pulse signals. The number of pulse signals is proportional to the angle of rotation of the wheel, thereby reflecting the robot's travel displacement. Specifically:

[0041]

[0042] Where x(t) is the robot's displacement, r is the robot's wheel radius, Δθi is the pulse encoder angle increment, Δt is the time interval, and v(t) represents the robot's current velocity.

[0043] This method can achieve high-precision real-time monitoring of the robot's speed, thereby providing accurate and reliable speed feedback information for subsequent dynamic control of speed and acceleration and deceleration, ensuring the robot's precise and stable operation in the substation.

[0044] The method for determining whether the rail-hanging robot enters the motion control area corresponding to the target point in step S2 is to deploy a visual recognition tag at the robot's task point to assist in positioning and identifying the target position.

[0045] At the same time, set the position of the task point in the orbital coordinate system to x target , the current position of the robot is x(t), the current speed is v(t), and the maximum allowable deceleration after considering mechanical safety factors is a max (The engineering experience value is 0.4~0.8m / s 2 ), then according to the classic uniform deceleration kinematic model, the shortest distance D required for the robot to stop completely can be obtained min :

[0046]

[0047] In order to avoid premature stopping or insufficient deceleration, a reserved redundant distance Δd is set. The redundant distance Δd is generally set to: Δd = k1·d 误差 +k2·v·t 延迟 +d 安全边界 ,

[0048] In the formula, d 误差is the maximum deviation caused by position measurement error, recommended value is 0.01~0.03m; t 延迟 is the control system response delay time, the recommended value is 0.1~0.3s; d 安全边界 To ensure the accuracy of the detection task, the recommended value of the additional redundant space is 0.02 to 0.05 m; k is the empirical coefficient, generally 1.5 to 2; v is the current speed, m / s.

[0049] The final threshold for determining whether to enter the deceleration zone is:

[0050] D threshold =D min +Δd

[0051] If the conditions are met:

[0052] x(t)-x target ≤D threshold

[0053] It is considered that the robot enters the pre-control area before the target point, and the MPC model is started to dynamically adjust the speed of the robot to ensure that it can decelerate to the target point smoothly and accurately and stop.

[0054] The speed control strategy in step S3 aims to ensure smooth speed changes and avoid shocks as the robot approaches the target point. This method uses a three-stage control algorithm to precisely execute the deceleration process based on the optimal target speed curve obtained through MPC model optimization. A "jerk" constraint is also introduced to smoothen acceleration changes, avoid shocks caused by sudden speed changes, ensure stable operation of the robot during deceleration, and improve equipment safety.

[0055] Specifically include:

[0056] S301: In each control cycle, the MPC module predicts the state of the robot in the future. Based on the robot dynamics model and preset constraints, which include maximum speed, maximum acceleration, and maximum deceleration, the module generates an optimal speed curve by solving the optimization problem. The MPC module outputs the desired target speed. The speed curve covers the acceleration zone, uniform speed zone, and deceleration zone, providing a theoretical basis for subsequent precise speed regulation. The control cycle refers to the process in which the MPC controller updates the control input every fixed sampling time (such as 0.1 seconds), that is, the time interval between each time the MPC controller re-acquires the current state, re-solves the optimization problem, and updates the control instructions.

[0057] Specifically:

[0058] S3011: Obtain the current state information of the robot, including real-time acquisition of the robot's current position x(t), current velocity v(t), acceleration a(t), and jerk j(t);

[0059] S3012: Define the MPC control objective function as:

[0060]

[0061] Among them, ɑ, β, γ, and λ are weight coefficients, indicating the relative importance of each target. They are adjusted according to actual vehicle tests. In this embodiment, the initial values ​​are set to 100, 10, 1-5, and 0.1-1, respectively. During actual vehicle tests, ɑ is adjusted based on whether the vehicle can be accurately stopped, β is adjusted based on whether there is impact or sliding when parking, and γ or λ is adjusted based on whether the acceleration or deceleration is severe.

[0062] S3013: Determine the constraints of the objective function as follows:

[0063] Speed ​​limit: |v(t)|≤v max , ensure that the robot speed is within a safe and reasonable range;

[0064] Acceleration limit: |a(t)|≤a max , limit the magnitude of acceleration to prevent damage to the mechanical structure due to excessive acceleration;

[0065] Jerk limit: |j(t)|≤j max , constrain the rate of change of acceleration, realize the natural decrease of speed to zero, and avoid the impact caused by sudden change of speed;

[0066] End position and speed limit: x(N)=x target , v(N) = 0, where N represents the number of steps in the prediction time domain, and the recommended value for the rail-mounted robot is 20 to 50.

[0067] Each control cycle minimizes the above objective function and optimizes the optimal speed curve, thereby reducing speed fluctuations and making the deceleration process smoother, ensuring that the robot's speed changes smoothly when approaching the target point, avoiding impact and achieving precise parking.

[0068] S302: Based on the target speed curve generated by the MPC, a three-stage speed control algorithm is used to convert the target speed curve into specific acceleration changes and execute them. The acceleration and duration of each stage are dynamically adjusted according to the target speed curve to ensure the smoothness and accuracy of the entire deceleration process.

[0069] The three-stage speed regulation algorithm is a trajectory planning method that considers the maximum acceleration and maximum speed constraints and divides the speed change process into three stages: "acceleration-constant speed-deceleration". It is widely used in motion control systems to achieve smooth, controllable and efficient speed regulation.

[0070] Assume that the current speed of the robot is known to be v0, and the distance to the target point is d. According to the MPC output, the maximum speed of the speed control process is v perk .Set the maximum acceleration to a max (Determined by the maximum output torque and transmission radius of the robot's drive motor, the engineering experience value is 0.4~0.8m / s 2 ) and the maximum deceleration is a min (Determined by the effective detection range of the robot, the engineering experience value is 0.5~1.0m / s 2 ), the following is the specific process of three-stage speed regulation:

[0071] (1) Phase 1 (acceleration phase)

[0072] The robot speed increases from v0 to v perk The time required is

[0073] The displacement in the acceleration section is

[0074] (2) The second stage (constant speed stage)

[0075] The robot speed remains at v prek The displacement is s c =ds a -s d ,

[0076] The time required is

[0077] (3) The third stage (deceleration stage)

[0078] The robot speed is given by v prek The time required to decelerate to 0 is

[0079] The displacement of the deceleration section is

[0080] The three-stage speed regulation algorithm precisely controls the robot's speed during acceleration, constant speed, and deceleration, ensuring smoothness and accuracy during deceleration. Furthermore, the entire process takes into account the limits of maximum acceleration and deceleration, ensuring safe and stable operation.

[0081] S303: After combining the MPC and three-stage speed control algorithms, the microcontroller (MCU) or programmable logic controller (PLC) executes the robot's operating speed control. The MPC is responsible for initial path planning and target speed curve generation, providing a theoretically optimal speed reference curve. Based on this curve, the three-stage speed control algorithm approximates and tracks the MPC speed curve, breaking down the complex speed control task into three phases: acceleration, constant speed, and deceleration, simplifying the execution burden on the robot motion controller.

[0082] This speed control method effectively avoids the sudden speed changes found in traditional uniform acceleration models. These models can experience sudden acceleration changes during speed changes, leading to shock and sliding errors in the robot. However, by using an optimal target speed curve optimized by MPC, combined with three-stage control and jerk constraints, the robot can achieve smooth speed changes and precise deceleration as it approaches the target point, bringing it to a steady stop. This effectively avoids speed shocks and sliding errors, improving the robot's control performance and operational efficiency.

[0083] This control strategy ensures the stable operation of the robot in the complex environment of the substation, reduces mechanical stress and equipment wear caused by sudden speed changes, extends the service life of the robot, and improves the accuracy and safety of inspections.

[0084] Step S4 is to further improve positioning accuracy by introducing a visual feedback mechanism to assist in correcting deviations caused by friction, inertia, encoder errors, etc. The specific operations are as follows:

[0085] The robot completes deceleration and thinks it has reached the target point x target After that, the visual SLAM (Simultaneous Localization and Mapping) positioning and recognition module is triggered to monitor the position of the robot. The visual SLAM module uses the camera to recognize the identification pattern pre-set on the track or equipment, and calculates the actual distance deviation between the robot and the target point in real time:

[0086] ε=x vision -x target

[0087] If the deviation |ε|≤ε threshold , it is considered to have been accurately positioned; ε threshold It is the allowable deviation threshold, generally set to ±0.01m.

[0088] If the deviation exceeds the threshold, the control system issues a fine-tuning command to achieve final position calibration by slow forward or backward crawling. After position calibration, the robot executes the test program. The specific fine-tuning calibration process is as follows:

[0089] Calculate the required fine-tuning distance: Determine the distance the robot needs to move forward or backward based on the actual deviation;

[0090] Low speed fine-tuning: The control system uses low speed v low (Usually 0.05m / s to 0.1m / s) controls the robot to crawl forward or backward. This low-speed fine-tuning strategy significantly improves positioning accuracy, avoiding position overshoot or repeated adjustments caused by excessive speed, ensuring that the robot accurately stays at the target detection point.

[0091] Real-time monitoring and feedback: During the fine-tuning process, the visual SLAM module continues to monitor the robot's position in real time to ensure that it reaches the target point accurately.

[0092] Reaching the target position: After the robot has moved the required distance, position verification is performed again to ensure that the deviation is within the allowable range.

[0093] Through the above visual feedback assisted correction mechanism, the positioning accuracy of the robot can be effectively improved, which not only improves the quality and efficiency of inspection, but also reduces the duplication of work and potential risks caused by positioning deviation. Step S5 is that after the inspection task is completed, the robot needs to return to the original cruising speed v nominal To ensure a smooth acceleration process, the MPC+ and three-stage speed regulation control method is also used. The rail-mounted robot accelerates smoothly from a stationary state to avoid sudden changes in motor torque and mechanical shock. The following is the specific implementation process:

[0094] Set the robot's target speed to the original cruising speed v nominal ,This speed is the standard speed of the robot during routine inspections on the substation track.

[0095] The MPC module predicts the robot state for a period of time in each control cycle and generates an optimal velocity curve based on the robot dynamics model and preset constraints (maximum acceleration, maximum velocity, etc.). This curve defines the optimal velocity curve from static to v nominal Acceleration path to ensure a smooth and stable acceleration process.

[0096] Based on the speed curve generated by MPC, the three-stage speed regulation algorithm is used to smoothly achieve the speed v nominal , and maintain stability to ensure smooth operation after reaching the target speed.

[0097] The acceleration process introduces a jerk constraint to limit the rate of acceleration change, ensuring a natural speed change. Simultaneously, the MCU / PLC controller monitors the robot's speed and position in real time and adjusts control commands based on feedback to ensure accurate tracking of the MPC speed curve and achieve smooth acceleration.

[0098] Through the above control strategy, the robot accelerates smoothly from standstill to the original cruising speed, effectively avoiding sudden changes in motor torque and mechanical shock, extending its service life and ensuring inspection efficiency.

[0099] Furthermore, a specific application case of the whole process control of deceleration, detection, and re-acceleration based on visual tag inspection points is as follows:

[0100] A robot was used for a track-mounted inspection at a 110kV substation, inspecting multiple high-voltage switchgear, transformers, and disconnect switches. The inspection task required the robot to precisely reach the inspection point, stop, perform infrared and visible light inspections, and then automatically accelerate to resume normal speed.

[0101] (1) The control process is determined as follows:

[0102] 1) Inspection Path Planning: The operator or control system generates the robot's inspection path in advance based on the substation site map. As the robot performs its inspection, it uses real-time positioning technology to determine its current position along the path, allowing for precise planning of subsequent motion control positions.

[0103] 2) MPC Velocity Planning: When the robot is 30 meters from the detection point (entering the motion control zone), the MPC module is triggered. During each control cycle, the MPC module generates a velocity trajectory v(t) based on dynamic constraints such as the robot's current velocity and position and the distance to the detection point, and plans a smooth deceleration process. This velocity trajectory ensures smooth deceleration as the robot approaches the detection point.

[0104] 3) Three-stage velocity approximation: Converts the velocity results planned by the MPC into a three-stage velocity trajectory. This includes: Acceleration: If the current velocity does not reach the velocity planned by the MPC, the robot will perform uniform acceleration until the velocity matches the planned velocity; Constant Speed: The robot strives to maintain the speed recommended by the MPC, ensuring that it approaches the detection point at a stable speed during this phase; Deceleration: The robot begins to decelerate in advance until the speed drops to 0 m / s, preparing for a precise stop at the detection point;

[0105] 4) Precise parking at the detection point: When the robot approaches the detection point and enters the range of 5 meters from the detection point, the visual recognition system is activated to identify the positioning tag and use the tag information to correct the error of its own position to ensure that the deviation between the parking position and the detection point does not exceed ±0.01m.

[0106] 5) Accelerate after the inspection task is completed: After the inspection task is completed, the MPC model is triggered to re-plan the speed trajectory to the next inspection point. The robot achieves smooth acceleration through three-stage speed control, returns to the original cruising speed, and continues to perform the inspection task.

[0107] (2) Some control parameters determined during the application process are as follows:

[0108] 1) Initial speed (cruising speed): 0.6 m / s, targeting the inspection point. This is the standard speed for the robot during routine inspections on the substation track, ensuring that the robot can move efficiently between inspection points.

[0109] 2) Acceleration limit: a max =0.5m / s 2 , a min =0.6m / s 2 ; It aims to ensure that the robot will not produce mechanical shock or instability due to excessive acceleration during the acceleration process; ensure that the robot can accurately and smoothly decelerate and stop before the detection point, avoiding shock or overshoot due to excessive deceleration;

[0110] 3) Maximum jerk: j max =1.0m / s 3 ; Limiting the rate of change of acceleration makes the robot's speed changes more smooth and natural during acceleration and deceleration, further improving the smoothness and comfort of operation;

[0111] 4) Distance from detection point: x target = 6 meters; indicates the distance from the detection point to the starting position where the robot begins to perform deceleration and precise parking operations, ensuring that the robot has enough time and space to adjust speed and stop.

[0112] (3) Compared with the traditional five-stage speed regulation system, the application effect is as follows:

[0113]

[0114]

[0115] The traditional five-stage speed planning method divides the motion process into five stages: acceleration, uniform acceleration, uniform speed, uniform deceleration, and deceleration. Compared to the three-stage speed planning method, the five-stage speed control logic is cumbersome. Due to the complexity of the five-stage speed regulation system, its hardware requirements are relatively high, requiring more complex control circuits and higher-performance motor drivers to achieve precise control in each stage. Furthermore, due to the complexity of the five-stage speed regulation system and the switching between multiple stages, its overall efficiency may be affected, resulting in high energy loss and low efficiency.

[0116] The present invention has been put into practical use in a certain 110kV substation and has shown significant advantages over the traditional five-stage speed regulation system. See the table above, specifically:

[0117] 1) The average braking distance error is smaller and the braking distance control is more precise;

[0118] 2) The probability of overshoot at the detection point is reduced, effectively avoiding the situation where the robot needs to readjust its position due to overshoot, thereby improving detection efficiency and accuracy;

[0119] 3) After the inspection is completed, the recovery time is shorter, which reduces the robot's residence time at the inspection point and improves the overall efficiency of the inspection;

[0120] 4) The track operation time is shortened, which significantly improves the inspection efficiency of the robot and enables it to complete more inspection tasks in the same time;

[0121] 5) The higher success rate indicates that the robot can reach the designated inspection point more reliably, and the reliability of performing inspection tasks is significantly improved.

[0122] In summary, in the application of rail inspection in 110kV substations, the present invention effectively improves the control performance and inspection efficiency of the robot through a control strategy that combines MPC and three-stage speed regulation, providing a more accurate, reliable and efficient solution for the automated inspection of substations.

[0123] Example 2

[0124] See also Figure 2 The embodiment of the present invention provides a device for dynamically controlling the running speed of a rail-mounted robot, the device comprising:

[0125] The state acquisition module is used to determine the inspection path of the rail-hanging robot and obtain the position of the rail-hanging robot in real time, and obtain the speed value of the rail-hanging robot based on the differential method;

[0126] The interval determination module is used to deploy visual recognition tags at the robot's task points and determine whether the track-mounted robot has entered the motion control interval corresponding to the target point;

[0127] The speed control module is used to optimize the objective function of the MPC model in real time when the rail-mounted robot enters the motion control range. It converts the target speed into specific acceleration change control instructions through a three-stage speed regulation algorithm and executes them. It also introduces acceleration constraints for speed control.

[0128] The deviation calibration module is used to identify the marking pattern on the track or equipment through the camera, calculate the actual distance deviation between the robot and the target point in real time, and determine whether the actual deviation exceeds the preset deviation threshold. If it exceeds, the robot will be calibrated in the form of low-speed crawling;

[0129] The speed recovery module is used to accelerate and restore the speed to the preset cruising speed according to the three-stage speed regulation algorithm after the test.

[0130] Example 3

[0131] An embodiment of the present invention provides a non-transitory computer-readable storage medium, in which a computer program is stored. The computer program is loaded and executed by a processor to implement the method for dynamically controlling the running speed of the rail-hanging robot described in the above embodiment 1.

[0132] Example 4

[0133] An embodiment of the present invention provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for dynamically controlling the running speed of the rail-hanging robot described in the above-mentioned embodiment 1 is implemented.

[0134] The above embodiments are only intended to help understand the method and core concept of the present invention. It should be noted that, without departing from the principles of the present invention, a number of improvements and modifications may be made to the present invention by those skilled in the art, and such improvements and modifications also fall within the scope of protection of the claims of the present invention.

[0135] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terms used herein in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0136] It should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of the steps can be performed in parallel, concurrently, or simultaneously. In addition, the order of the steps can be rearranged. A process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. A process can correspond to a method, function, procedure, subroutine, subprogram, etc.

Claims

1. A method for dynamically controlling the running speed of a rail-mounted robot, characterized in that: include, Obtain the position of the rail-hanging robot in real time, and obtain the speed value of the rail-hanging robot based on the differential method; Deploy visual recognition tags at the robot's task point and determine whether the track-mounted robot has entered the motion control range corresponding to the target point; If the rail-mounted robot enters the motion control range, the MPC objective function is designed to construct the MPC model for target speed planning. The objective function is: Among them, x(t) is the current location of the robot, x target is the position of the task point in the orbital coordinate system, v(t) is the current velocity, a(t) is the acceleration, j(t) is the jerk, ɑ, β, γ, and λ are weight coefficients; The optimal speed curve is obtained in real time by minimizing the objective function, and the target speed is converted into a specific acceleration change control instruction and executed through a three-stage speed regulation algorithm; The camera recognizes the pre-set identification pattern, calculates the actual distance deviation between the robot and the target point in real time, and determines whether the actual deviation exceeds the pre-set deviation threshold. If it exceeds, the robot position is calibrated in a low-speed crawling mode. After the position calibration, the robot performs the inspection task; After the detection task is completed, the target speed is planned according to the objective function minimized by the MPC model, and the speed is restored to the preset cruising speed in combination with the three-stage speed regulation algorithm.

2. The method for dynamically controlling the running speed of a rail-mounted robot according to claim 1, characterized in that: The real-time acquisition of the position of the rail-hanging robot and the acquisition of the speed value of the rail-hanging robot based on the differential method include: The photoelectric encoder is used to obtain the travel displacement of the rail-hanging robot in real time, and the current speed value of the rail-hanging robot is obtained based on the differential method. Specifically: Where x(t) is the robot’s displacement, r is the wheel radius, Δθi is the pulse encoder angle increment, Δt is the time interval, and v(t) represents the robot’s current velocity.

3. The method for dynamically controlling the running speed of a rail-mounted robot according to claim 1, characterized in that: The process of deploying visual recognition tags at the robot's task point and determining whether the track-mounted robot has entered the motion control interval corresponding to the target point includes: Get the coordinate position of the task point visual recognition label in the orbit coordinate system as x target , the robot's current position is x(t), its speed is v(t), and its maximum allowable deceleration is a max , then the shortest distance D required for the robot to stop completely min for Set the reserved redundant distance Δd, then set D threshold =D min +Δd is the threshold for determining whether the rail-mounted robot enters the motion control range; If x(t)-x target ≤D threshold , it is considered that the robot enters the motion control interval before the target point.

4. The method for dynamically controlling the running speed of a rail-mounted robot according to claim 3, characterized in that: The reserved redundant distance Δd=k1·d 误差 +k2·v·t 延迟 +d 安全边界 , where d 误差 is the maximum deviation caused by position measurement error; t 延迟 is the control system response delay time; d 安全边界 The extra redundant space required to ensure the accuracy of the detection task; k is the empirical coefficient; v is the current speed.

5. The method for dynamically controlling the running speed of a rail-mounted robot according to claim 1, characterized in that: The constraints of the MPC objective function are: Speed ​​limit: |v(t)|≤v max ; Acceleration limit: |a(t)|≤a max ; Jerk limit: |j(t)|≤j max ; End position and speed limit: x(N)=x target , v(N)=0, where N represents the number of steps in the prediction time domain.

6. The method for dynamically controlling the running speed of a rail-mounted robot according to claim 1, characterized in that: The determining whether the actual deviation exceeds a preset deviation threshold, and if so, implementing the robot position calibration in a low-speed crawling manner includes: Calculate the required fine-tuning distance to determine the distance the robot needs to move forward or backward; Control robot movement at low speed and provide real-time monitoring and feedback through visual recognition; After reaching the target position, perform position verification again to ensure that the deviation is within the allowable range.

7. The method for dynamically controlling the running speed of a rail-mounted robot according to claim 1, characterized in that: The converting the target speed into a specific acceleration change control instruction and executing it through the three-stage speed regulation algorithm also includes introducing a jerk constraint to perform speed control.

8. A dynamic control device for the running speed of a rail-mounted robot, characterized in that: The device comprises, The state acquisition module is used to determine the inspection path of the rail-hanging robot and obtain the position of the rail-hanging robot in real time, and obtain the speed value of the rail-hanging robot based on the differential method; The interval determination module is used to deploy visual recognition tags at the robot's task points and determine whether the track-mounted robot has entered the motion control interval corresponding to the target point; The speed control module is used to optimize the objective function of the MPC model in real time when the rail-mounted robot enters the motion control range. It converts the target speed into specific acceleration change control instructions through a three-stage speed regulation algorithm and executes them. It also introduces acceleration constraints for speed control. The deviation calibration module is used to identify the marking pattern on the track or equipment through the camera, calculate the actual distance deviation between the robot and the target point in real time, and determine whether the actual deviation exceeds the preset deviation threshold. If it exceeds, the robot position is calibrated in a low-speed crawling mode; The speed recovery module is used to restore the speed to the preset cruising speed according to the three-stage speed regulation algorithm after the test.

9. A non-transitory computer-readable storage medium, characterized in that The storage medium stores a computer program, which is loaded and executed by a processor to implement the method for dynamically controlling the running speed of a rail-hanging robot as described in any one of claims 1 to 7.

10. An electronic device comprising: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for dynamically controlling the running speed of a rail-hanging robot as described in any one of claims 1 to 7 when executing the computer program.

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