A rail car positioning solution

Abstract

The present application relates to a kind of rail trolley positioning solutions, comprising the following steps: step one: equipment installation, laser radar is vertically installed in the top of RGV trolley head by connecting component;Step two: establish measurement model, through the RGV trolley in step one and corresponding software establish motor train unit head template and train bottom template;Step three: real-time positioning of detection robot, compared with prior art, the present application is mainly laser radar, laser ranging is assisted to carry out the high-precision automatic positioning of fault detection robot, and the measurement template is constructed by software and compared with the information collected to compensate for the difference, reduce the existence of error, so that its positioning is more accurate.

Description

Technical Field

[0001] This invention relates to the field of train parking and positioning technology, and in particular to a positioning solution for a track-mounted trolley. Background Technology

[0002] Currently, the application of track-mounted robots in the logistics automation industry is relatively mature, with various positioning methods that basically meet the needs of existing application scenarios. However, since the position is fixed and does not change during use, it is only necessary to solve the problem of precise positioning at a single location.

[0003] This invention addresses the application scenario of a first-level maintenance and inspection robot for high-speed trains. When the track-mounted robot is operating, it needs to stop and inspect the train at the corresponding location. Currently, when the high-speed train enters the depot, it is driven manually, and the stopping position is affected by human factors, making it impossible to determine the stopping position each time. This necessitates solving the positioning accuracy problem caused by the different stopping positions each time.

[0004] To address the aforementioned issues, many documents have cited technical methods for calculating the travel distance and determining the vehicle position by using the pulse output of an optical encoder mounted on the rotating wheel axle. However, in the application environment of the EMU undercarriage fault inspection robot, the position deviation problem caused by the inconsistent stopping position of the EMU needs to be specially considered and solved.

[0005] Therefore, existing technologies propose:

[0006] Option 1: Use a wheeled odometer and a strapdown inertial navigation system to calculate the distance the robot moves to detect under-vehicle faults, and manually create parking position data for each stop;

[0007] Option 2: Use a high-precision laser rangefinder to directly calculate the distance and use photoelectric detection to detect the position of the train that needs to stop. Two solutions are available.

[0008] While the two solutions described above address the problem to some extent, they also present the following issues in practical applications:

[0009] Option 1: Wheel-mounted odometers and strapdown inertial navigation systems can achieve relatively accurate values ​​over short distances, but this method suffers from cumulative error, which increases gradually with the distance traveled, ultimately rendering the results unreliable. Manually creating the data involves a significant amount of engineering work.

[0010] Option 2: High-precision laser rangefinders can directly measure distances, but train position detection has very high requirements for photoelectric properties, making it difficult to meet the needs of scenarios with high precision requirements;

[0011] Therefore, a reliable solution is urgently needed to address the positional deviation problem caused by inconsistent stopping positions of high-speed trains. Summary of the Invention

[0012] To address this issue, the present invention provides a track-based trolley positioning solution that uses lidar as the primary method and laser ranging as a secondary method for high-precision automatic positioning of the fault detection robot, thereby solving the problem of the robot's stopping position changing due to the different stopping positions of the high-speed trains.

[0013] A positioning solution for a track-mounted vehicle includes the following steps:

[0014] Step 1: Equipment installation. Using a lidar, the lidar is vertically mounted on the top of the RGV vehicle's front end via connecting components.

[0015] Step Two: Establish a measurement model. Using the RGV trolley and corresponding software from Step One, establish the head template and undercarriage template of the EMU train. This includes the following steps:

[0016] 1) Constructing a train head point cloud model: After the EMU train comes to a complete stop, the RGV trolley is stopped at the starting position, the train head point cloud information is obtained through lidar, and a train head point cloud model is constructed based on the obtained EMU train head point cloud information.

[0017] 2) Create a 2D model of the train's front end and establish a template for the train's front end:

[0018] ①The point cloud data model in 1) is used to draw a 2D model of the car front by calling the Graphics.DrawLine function in the software, and the 2D model is similar to the car front;

[0019] The function is called as follows:

[0020] Graphics.DrawLine(new Pen(Color.Black),new point(x1,y1),new Point(x2,y2));

[0021] Graphics.DrawLine(new Pen(Color.Black),new point(x2,y2),new Point(x3,y3));

[0022] ...

[0023] Graphics.DrawLine(new Pen(Color.Black),new point(xn-1,yn-1),new Point(xn-1,yn-1));

[0024] ② Use the function min(x1, x2, x3...) in the outlined 2D model to find the minimum point in the x-coordinate, record the current point coordinate as Y1, and take the cusp point Y2;

[0025] ③ Measure the difference in x-coordinates Δx between points Y1 and Y2. Let L0 be the actual horizontal distance between the corresponding positions of points Y1 and Y2 on the actual vehicle front. Δx should be approximately equal to L0, i.e., |Δx − L0| ≤ m, where m is a pre-set allowable deviation threshold. If |Δx − L0| > m, the above steps need to be repeated.

[0026] ④ Record the x-coordinate of point Y1 in the relative position data table as the front data of the vehicle;

[0027] ⑤ Store the 2D model of the train head obtained at this time as a measurement template in the template database;

[0028] Step 3: Detect the robot's real-time positioning, which includes the following steps:

[0029] 1) Obtain the data Y1 of the train head at this time according to the method of obtaining the train head template. Then the actual position of the train stopping is s=l+x1, where l represents the length of the robot car body.

[0030] 2) Based on the head train data of this EMU train, add the relative coordinate data of the template to obtain the rough absolute position coordinate data of the robot to stop and inspect;

[0031] 3) Turn on the lidar and laser rangefinder, start the robot, and make the robot move at a constant speed V under the train. During the movement, the lidar acquires the map of the train under the train. Since the length L of the train is fixed, the robot will stop when the laser rangefinder detects that it has run to the set length (set length S>=l+x1+L).

[0032] 4) The robot control system matches the 2D model acquired in this study with the template 2D model, and finds the corresponding position on the current 2D model by using the same features of the calibrated position;

[0033] 5) Calculate the x-axis coordinates by comparing the location coordinates found this time with the calibration coordinates of the template, calculate the position difference between the same position of the vehicle bottom map collected this time and the template bottom map, and automatically update the absolute position data table of the robot to be located. The robot is then positioned according to the position data in the position data table.

[0034] Furthermore, the establishment of the EMU train head template mentioned in step two...

[0035] Furthermore, the establishment of the EMU train chassis template in step two includes the following steps:

[0036] 1) Constructing a point cloud model of the train car bottom: Start the lidar on the RGV trolley and start the RGV trolley to move at a constant speed under the train car, so that the lidar can acquire point cloud information of the complete bottom of the train car, and use this information to construct a 2D model of the EMU train car bottom.

[0037] 2) Establish detection positions and construct train undercarriage template: The robot stops and detects points on the 2D model of the train undercarriage obtained in 1) through the control program. After the calibration is completed, the calibration coordinate data is stored in the relative position data table by calling the loop function. The 2D model of the undercarriage constructed at this time is stored in the template database as a measurement template to complete the construction of the train undercarriage template.

[0038] Compared with existing technologies, this invention uses lidar as the main technology and laser ranging as a supplement to achieve high-precision automatic positioning of fault detection robots. By constructing measurement templates through software and comparing them with the collected information to compensate for differences, the existence of errors is reduced, making the positioning more accurate. Attached Figure Description

[0039] Figure 1 This is a point cloud diagram of the train head of the EMU (Electric Multiple Unit) according to the present invention;

[0040] Figure 2 for Figure 1 The 2D model drawn by the Graphics.DrawLine function;

[0041] Figure 3 This is a diagram of the detection robot's operation in step three of this invention;

[0042] Figure 4 This is a comparison diagram of the positional deviation between the vehicle undercarriage map and the template undercarriage map in the embodiment. Detailed Implementation

[0043] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0044] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0045] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0046] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0047] The present invention will be further described in conjunction with the following embodiments.

[0048] refer to Figures 1 to 3 As shown, a track-based vehicle positioning solution includes the following steps:

[0049] Step 1: Equipment installation. Using a lidar, the lidar is vertically mounted on the top of the RGV vehicle's front end via connecting components.

[0050] Step Two: Establish a measurement model. Using the RGV trolley and corresponding software from Step One, create templates for the train's head and undercarriage.

[0051] in, Figure 3 Point A is the robot's fixed starting position, and point B is the robot's real-time working position.

[0052] The establishment of the train head template includes the following steps:

[0053] 1) Constructing a train head point cloud model: After the EMU train comes to a complete stop, the RGV trolley is stopped at the starting position, the train head point cloud information is obtained through lidar, and a train head point cloud model is constructed based on the obtained EMU train head point cloud information.

[0054] 2) Create a 2D model of the train's front end and establish a template for the train's front end:

[0055] ①The point cloud data model in 1) is used to draw a 2D model of the car front by calling the Graphics.DrawLine function in the software, and the 2D model is similar to the car front;

[0056] The function is called as follows:

[0057] Graphics.DrawLine(new Pen(Color.Black),new point(x1,y1),new Point(x2,y2));

[0058] Graphics.DrawLine(new Pen(Color.Black),new point(x2,y2),new Point(x3,y3));

[0059] ...

[0060] Graphics.DrawLine(new Pen(Color.Black),new point(x n-1 ,y n-1 ),new Point(x n-1 ,y n-1 ));

[0061] ② Use the function min(x1, x2, x3...) in the outlined 2D model to find the minimum point in the x-coordinate, record the current point coordinate as Y1, and take the cusp point Y2;

[0062] ③ Measure the difference in x-coordinates between Y1 and Y2. △x should be approximately equal to the actual distance, i.e., |△x|≤m (m is the error range). If |△x|>m, the above steps need to be repeated.

[0063] ④ Record the x-coordinate of point Y1 in the relative position data table as the front data of the vehicle;

[0064] ⑤ Store the 2D model of the train head obtained at this time as a measurement template in the template database;

[0065] The establishment of the EMU train car body template includes the following steps:

[0066] 1) Constructing a point cloud model of the train car bottom: Start the lidar on the RGV trolley and start the RGV trolley to move at a constant speed under the train car, so that the lidar can acquire point cloud information of the complete bottom of the train car, and use this information to construct a 2D model of the EMU train car bottom.

[0067] 2) Establish detection positions and construct train undercarriage template: The robot stops and detects points on the 2D model of the train undercarriage obtained in 1) through the control program. After the calibration is completed, the calibration coordinate data is stored in the relative position data table by calling the loop function. The 2D model of the undercarriage constructed at this time is stored in the template database as a measurement template to complete the construction of the train undercarriage template.

[0068] Step 3: Detect the robot's real-time positioning, which includes the following steps:

[0069] 1) Obtain the data Y1 of the train head at this time according to the method of obtaining the train head template. Then the actual position of the train stopping this time is s=l+x1 (l is the length of the robot body).

[0070] 2) Based on the head train data of this EMU train, add the relative coordinate data of the template to obtain the rough absolute position coordinate data of the robot to stop and inspect;

[0071] 3) Turn on the lidar and laser rangefinder, start the robot, and make the robot move at a constant speed V under the train. During the movement, the lidar acquires the map of the train under the train. Since the length L of the train is fixed, the robot will stop when the laser rangefinder detects that it has run to the set length (set length S>=l+x1+L).

[0072] 4) The robot control system matches the 2D model acquired in this study with the template 2D model, and finds the corresponding position on the current 2D model by using the same features of the calibrated position;

[0073] 5) Calculate the x-axis coordinates by comparing the location coordinates found this time with the calibration coordinates of the template, calculate the position difference between the same position of the vehicle bottom map collected this time and the template bottom map, and automatically update the absolute position data table of the robot to be located. The robot is then positioned according to the position data in the position data table.

[0074] In this embodiment, as Figure 4 As shown in the figure, △l1 is the positional deviation of the first position, which needs to be increased based on the original data. △l2 is the positional deviation of the second position, which needs to be decreased based on the original data. Other data can be obtained by analogy.

[0075] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0076] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A positioning solution for a track-type trolley, characterized in that, Includes the following steps: Step 1: Equipment installation. Using a lidar, the lidar is vertically mounted on the top of the RGV vehicle's front end via connecting components. Step Two: Establish a measurement model. Using the RGV trolley and corresponding software from Step One, establish the head template and undercarriage template of the EMU train. This includes the following steps: 1) Constructing a train head point cloud model: After the EMU train comes to a complete stop, the RGV trolley is stopped at the starting position, the train head point cloud information is obtained through lidar, and a train head point cloud model is constructed based on the obtained EMU train head point cloud information. 2) Create a 2D model of the train's front end and establish a template for the train's front end: ①The point cloud data model in 1) is used to draw a 2D model of the car front by calling the Graphics.DrawLine function in the software, and the 2D model is similar to the car front; The function is called as follows: Graphics.DrawLine(new Pen(Color.Black),new point(x1,y1),new Point(x2,y2)); Graphics.DrawLine(new Pen(Color.Black),new point(x2,y2),new Point(x3,y3)); …… Graphics.DrawLine(new Pen(Color.Black),new point(x n-1 ,y n-1 ),new Point(x n-1 ,y n-1 )); ② Use the function min(x1, x2, x3...) in the outlined 2D model to find the minimum point in the x-coordinate, record the current point coordinate as Y1, and take the cusp point Y2; ③ Measure the difference Δx between the x-coordinates of points Y1 and Y2, and denote the actual horizontal distance between the corresponding positions of points Y1 and Y2 on the actual vehicle front as Δx. Δx should be related to Approximately equal, that is , where m is a pre-set allowable deviation threshold; if If so, the above steps need to be repeated; ④ Record the x-coordinate of point Y1 in the relative position data table as the front data of the vehicle; ⑤ Store the 2D model of the train head obtained at this time as a measurement template in the template database; Step 3: Detect the robot's real-time positioning, which includes the following steps: 1) Obtain the data Y1 of the train head at this time according to the method of obtaining the train head template. Then the actual position of the train stopping is s=l+x1, where l represents the length of the robot car body. 2) Based on the head train data of this EMU train, add the relative coordinate data of the template to obtain the rough absolute position coordinate data of the robot to stop and inspect; 3) Turn on the lidar and laser rangefinder, start the robot, and make the robot move at a constant speed V under the train. During the movement, the lidar acquires the map of the train under the train. Since the length L of the train is fixed, the robot will stop when the laser rangefinder detects that it has run to the set length S, where S>=l+x1+L. 4) The robot control system matches the 2D model acquired in this study with the template 2D model, and finds the corresponding position on the current 2D model by using the same features of the calibrated position; 5) Calculate the x-axis coordinates by comparing the location coordinates found this time with the calibration coordinates of the template, calculate the position difference between the same position of the vehicle bottom map collected this time and the template bottom map, and automatically update the absolute position data table of the robot to be located. The robot is then positioned according to the position data in the position data table.

2. The track-type trolley positioning solution according to claim 1, characterized in that, The establishment of the train undercarriage template in step two includes the following steps: 1) Constructing a point cloud model of the train car bottom: Start the lidar on the RGV trolley and start the RGV trolley to move at a constant speed under the train car, so that the lidar can acquire point cloud information of the complete bottom of the train car, and use this information to construct a 2D model of the EMU train car bottom. 2) Establish detection positions and construct train undercarriage template: The robot stops and detects points on the 2D model of the train undercarriage obtained in 1) through the control program. After the calibration is completed, the calibration coordinate data is stored in the relative position data table by calling the loop function. The 2D model of the undercarriage constructed at this time is stored in the template database as a measurement template to complete the construction of the train undercarriage template.

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