A patrol robot and a traffic control method and system for electric buses
By installing laser sensors on electric buses and combining them with sliding median filtering and abrupt change detection algorithms, the problem of inaccurate communication of electric bus location information was solved, realizing unmanned traffic control between robots and electric buses, and improving operational safety and efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHUZHOU CSR TIMES ELECTRIC CO LTD
- Filing Date
- 2025-10-16
- Publication Date
- 2026-07-03
AI Technical Summary
In existing technologies, the real-time location of electric buses cannot be monitored and controlled by the dispatching system, resulting in inaccurate communication of location information between the robot and the electric bus, which poses a safety risk. Furthermore, laser data detection is affected by the shape and height differences of the undercarriage components, leading to inaccurate detection of vehicle position and speed information.
Laser ranging data of electric bus wheelset axle sections are collected by laser sensors installed at fixed positions on the track. Outliers are filtered out using large-step sliding median filtering and small-step sliding median filtering, and valid data segments are selected. The mode of the axle profile is calculated and the profile is identified using a mutation point detection algorithm. Traffic control is achieved in conjunction with a robot scheduling system.
It has achieved unmanned traffic control of robots and electric buses, improved operational safety and efficiency, ensured the simultaneous and efficient operation of electric buses and robots, with high real-time signal performance and accurate positioning, and improved overall dispatch efficiency by 50%.
Smart Images
Figure CN121341249B_ABST
Abstract
Description
Technical Field
[0001] This invention mainly relates to the field of electric bus inspection technology, specifically to an inspection robot and an electric bus traffic control method and system. Background Technology
[0002] The electric tram and the inspection robot are two independent systems. To ensure safety during inspection operations, traffic control is required when the robot and the electric tram are in motion. Currently, there is no fully independent safety assurance system solution in China. Daily inspection personnel manually confirm the status of the electric tram before carrying out inspection operations, which poses certain safety risks.
[0003] Compared to the internal workings of the robot, traffic control between the robot and the electric train presents the following challenges:
[0004] 1. The real-time location of the electric train is not within the monitoring and control range of the dispatching system, making it impossible to exchange information on the location of the robot and the electric train;
[0005] 2. The laser data used to monitor the status of electric buses is affected by factors such as the different shapes and sizes of the undercarriage components, surface conditions, clearance height of different vehicles, and differences in the wheels, which leads to inaccurate detection of vehicle position and speed information and causes safety risks. Summary of the Invention
[0006] To address the technical problems existing in the prior art, this invention provides an inspection robot and a method and system for controlling electric bus traffic, which improves operational safety and efficiency.
[0007] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:
[0008] A method for traffic control using an inspection robot and electric buses, comprising the following steps:
[0009] S1. Laser ranging data of the electric bus wheelset axle section is collected by a laser sensor installed at a fixed position on the track. The ranging data includes amplitude and time information.
[0010] S2. Perform large-step sliding median filtering on the laser ranging data to filter out abnormal measurement values; the large step size is greater than the width of the data segment;
[0011] S3. Filter valid data segments based on negative pulse width. Identify negative pulses and their starting points by setting an amplitude threshold, and determine valid data segments containing axle data based on the pulse width threshold.
[0012] S4. Extract the effective data segment from the original data and perform small-step sliding median filtering on it to suppress high-frequency interference and preserve the abrupt changes in the axle profile; the small step size is less than the width of the groove between the axle and the wheelset floor.
[0013] S5. Calculate the mode of the amplitude in the valid data segment, and use the mode to replace the abnormal amplitude that exceeds the preset threshold, so as to eliminate the distortion caused by sensor abnormality;
[0014] S6. Using a mutation point detection algorithm, identify mutation points in the wheelset profile within the valid data segment;
[0015] S7. Screen the mutation points and remove step-like mutation points caused by trend terms;
[0016] S8. Based on the mutation point and its time information, fit the axle profile and calculate the position and forward speed of the electric bus;
[0017] S9. Transmit the electric train's location and speed information to the robot scheduling system;
[0018] S10. The robot scheduling system makes traffic control decisions based on the real-time location and speed prediction results of the electric train and the real-time task path of the robot to ensure safe operation.
[0019] Preferably, in step S5, the process of calculating the mode of the amplitude in the valid data segment is as follows:
[0020] Calculate a histogram of the laser data amplitude for the effective data segment, sort the intervals of the histogram by the number of data points, select the interval with the most data points, and use the mean of the start and end points of the interval as the mode.
[0021] Preferably, the specific process of step S6 is as follows:
[0022] The data is segmented by randomly selecting cutoff points;
[0023] Calculate the statistical characteristics of each data segment;
[0024] Sum the residuals of the data points and statistical characteristics within each segment;
[0025] Choose the cutoff point that minimizes the total residual as the mutation point.
[0026] Preferably, the specific process of step S6 is as follows: find the point in the signal that crosses the set amplitude as the change point, specifically: set a threshold, calculate the point in the data that crosses the threshold, and determine whether the point is a rising edge or a falling edge based on the value before and after the crossing point, thereby finding the change point in the data.
[0027] Preferably, in step S7, the mutation points are screened according to the principle of "positive positive take the larger one, negative negative take the smaller one", that is, if positive mutation points appear consecutively, the larger mutation point is selected; if negative mutation points appear consecutively, the smaller mutation point is selected.
[0028] Preferably, in step S10, the traffic control decision is as follows: if there is a task involving electric buses entering and leaving the warehouse in the task path, the robot operation task is suspended, and the robot operation is resumed after the electric buses complete the task of entering and leaving the warehouse; if there is no task involving electric buses entering and leaving the warehouse in the robot task path, the robot directly performs the inspection task.
[0029] Preferably, in step S1, the laser line of the laser sensor is perpendicular to the cross section of the wheelset axle.
[0030] The present invention also discloses a computer program product, comprising a computer program that, when executed by a processor, performs the steps of the method described above.
[0031] The present invention further discloses a computer-readable storage medium having a computer program stored thereon, the computer program executing the steps of the method described above when run by a processor.
[0032] The present invention also discloses an inspection robot and electric bus traffic control system, including a memory and a processor connected to each other, wherein the memory stores a computer program, and the computer program executes the steps of the method described above when run by the processor.
[0033] Compared with the prior art, the advantages of the present invention are as follows:
[0034] This invention relates to the safety of inspection robots and electric train traffic control when operating in urban rail daily inspection depots. It utilizes automatic robot navigation, real-time location recording and monitoring, and multi-sensor fusion monitoring of electric train entry and exit status. Through comprehensive management by a dispatching system, it achieves fully unmanned traffic control for both robots and electric trains, ensuring operational safety. This invention belongs to the field of intelligent inspection robots for rail vehicles.
[0035] This invention uses the principle of "taking the larger positive and the smaller negative" to screen mutation points, eliminating the interference of step-type mutation points caused by factors such as the trend term of the rotation pair data segment.
[0036] This invention converts laser measurement information into the position and speed information of electric trains, and then integrates the data into a robot dispatching system. The dispatching system uses robot path assignment and electric train position prediction within the same time dimension to achieve intelligent control in level crossing areas. Compared with manual confirmation of track shunting information and judgment by the traffic light system, the overall dispatching efficiency is improved by 50%.
[0037] This invention utilizes large-step median filtering to first filter out outliers in segments. After determining the starting point of the valid data segment for the wheelset, this data segment is extracted from the original data. Then, small-step median filtering is used to remove high-frequency noise, and the mode is used to replace amplitudes exceeding a threshold. Abrupt changes are found through iteration. Compared to existing technologies, this method is unaffected by high-frequency noise in the data, and the setting of values requires less experience.
[0038] In summary, compared to existing manual methods, sensor-based traffic control maximizes the safety and efficiency of system operation, aligning with the concept of unmanned robotics. This invention uses laser as the location signal medium for electric buses, maximizing the role of the central brain of the dispatching system. It not only utilizes the principle of laser ranging and signal fusion and filtering to obtain the real-time location information of the electric buses, but also achieves collaborative control between different systems, fully leveraging the function of the dispatching system. In terms of application results, it offers high real-time signal performance, accurate electric bus positioning, efficient simultaneous operation of electric buses and robots, and ensures high safety in traffic control. Attached Figure Description
[0039] Figure 1 This is a structural diagram of the inspection robot and electric bus traffic control system of the present invention in an embodiment.
[0040] Figure 2 This is a schematic diagram illustrating the traffic control principle of the inspection robot and electric bus of the present invention.
[0041] Figure 3 This is a schematic diagram illustrating the principle of electric bus position and speed measurement according to the present invention.
[0042] Figure 4 This is a flowchart of an embodiment of the inspection robot and electric bus traffic control method of the present invention. Detailed Implementation
[0043] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.
[0044] like Figure 1 As shown, this invention converts multi-range laser data into electric bus position and speed information, and realizes robot traffic control through a robot scheduling system.
[0045] like Figure 2 As shown, three sets of laser sensors, one in front, one in the middle, and one in the rear, are installed in the robot's inspection lane. When the electric train enters or exits the lane, the robot calculates the position and status of the electric train through the combined and fused perception of the laser sensor sets, and transmits the data to the robot's scheduling system. The scheduling system combines the robot's real-time position and task plan, prioritizes the operation of the electric train, and provides corresponding traffic control strategies to achieve autonomous and safe robot inspection.
[0046] like Figure 3 As shown, the laser sensor is installed at a fixed position on the track. When the electric bus passes on the track, the laser line of the laser sensor scans through the wheel set axle profile perpendicularly. The laser data is transmitted to the calculation software and processed by the time series signal processing module. The continuous height values are fitted to the axle profile position and speed values, representing the position and speed of the vehicle.
[0047] like Figure 4 As shown, the inspection robot and electric bus traffic control method provided in this embodiment of the invention utilizes the ranging principle of multiple sets of laser sensors to monitor the electric bus's entry and exit positions and speed in real time. The monitoring signals are transmitted to the robot scheduling system, which, combined with the robot's real-time position signal, coordinates control to achieve highly secure autonomous inspection. The specific steps are as follows:
[0048] S1. Data Acquisition. The laser sensor is installed at a fixed position on the track, so that the laser beam is basically perpendicular to the wheel axle section. When the electric train passes through the track, the laser sensor measures the distance of the axle section. The changes in distance in the laser data can reflect the position of the train axle along the direction of the electric train's movement. The laser data points contain amplitude and time information.
[0049] This invention utilizes laser sensors to measure the arcuate features of standard components on the undercarriage of an electric multiple unit (EMU) during operation. The measurement frequency is converted to a time period, yielding precise position and speed information for the train. The laser-measured vehicle feature position data is converted into EMU position information with an error controlled within ±5mm. Compared to manual verification, this significantly improves operational efficiency, and the redundancy of multiple sensors enhances overall safety.
[0050] S2. Perform large-step sliding median filtering on the laser data. Laser sensors are frequently affected by the environment, often triggering abnormal measurements. This manifests as data segments containing the maximum measurement value with a width (referring to the laser data duration, which can be converted to distance) reaching the centimeter level. Negative pulse data generated by the axle will be split into two segments, leading to the failure to extract valid data segments. Selecting a large step size greater than the width of this data segment for sliding median filtering can avoid the impact of abnormally missing laser data.
[0051] S3. Filtering valid data segments by negative pulse width. By processing the laser data with a specific amplitude threshold, points in the laser data whose amplitude crosses that value can be obtained. By identifying the points that cross the threshold in the positive direction (from small to large) and the negative direction (from large to small), the negative pulses present in the data and their starting points can be obtained. Then, based on whether the width of the negative pulse is greater than a specific width threshold, the valid data segments containing axle data under the vehicle are filtered, and the starting points of the valid data segments are obtained.
[0052] S4. Extract and filter the valid data segments. Based on the starting point position of the valid data segments calculated in the previous step, the valid data segments can be extracted from the original data. Since the data processed by large-step sliding median filtering eliminates outliers, it also averages out useful groove information (grooves between the axle and the wheelset floor, hereinafter referred to as grooves). The extracted valid data segments are filtered by small-step sliding median filtering to suppress high-frequency interference. The step size must not be longer than the groove width. Median filtering can better protect the abrupt changes in the data. Conversely, mean filtering can reduce the abruptness of abrupt changes in the data.
[0053] S5. Mode Replacement of Outliers. A histogram of the laser data amplitude for the valid data segments is calculated. The intervals of the histogram are sorted by the number of data points, and the interval with the most data points is selected. The mean of the start and end points of this interval is used as the mode. Since the laser scan passes through the axle cross-section, there should be no data segment with a maximum value (i.e., the maximum range value of the laser sensor). If such a segment exists, it is considered an outlier. The system checks if any amplitude exceeds a certain threshold. If so, the mode is used to replace the outlier, eliminating data distortion caused by sensor anomalies.
[0054] S6. Using a mutation point detection algorithm, identify mutation points in the wheelset profile within the valid data segment.
[0055] Specifically, the mutation point algorithm is used to find the pulse start point in the wheelset data segment. The steps are as follows:
[0056] Randomly select a point to truncate the signal into two segments;
[0057] Calculate the selected statistical characteristics for each signal segment;
[0058] For each signal segment, accumulate the residual between each sample point and the statistical feature;
[0059] Then, the cumulative residuals of each signal segment are summed to calculate the total residual;
[0060] Calculate the total residuals at different cutoff points; the cutoff point with the smallest total residual is the abrupt change point.
[0061]
[0062] Where k is the selected signal cutoff point, Let χ be the objective function, and let χ be the empirical estimate of the characteristics of the piecewise signal. This represents the calculated residual.
[0063] This invention uses an iterative approach to find mutation points. It selects data breakpoints and calculates the statistical characteristics (such as mean and standard deviation) of each truncated data segment. The sum of the residuals between each data point and the statistical characteristic value of that segment constitutes the objective function. The point with the smallest objective function is the mutation point of the data, accurately extracting mutation points in the data whose mutability meets a certain threshold.
[0064] In other embodiments, points in the signal that cross a specific amplitude can be identified as abrupt change points. Specifically, a threshold is set, points in the data that cross the threshold are calculated, and the magnitude of the values before and after the crossing point determines whether the point is a rising edge or a falling edge, thereby identifying abrupt change points in the data.
[0065] S7. Use the principle of "positive positives take the larger value, negative negatives take the smaller value" to screen mutation points and remove step-like mutation points caused by trend terms.
[0066] Specifically, due to the presence of trend terms in the rotation pair data segments, the calculated abrupt change points may exhibit a step-like pattern under special circumstances, leading to calculation errors. The abrupt change points are filtered using the principle of "taking the larger positive abrupt change point and the smaller negative abrupt change point." That is, if consecutive positive abrupt change points appear, the larger abrupt change point is selected; if consecutive negative abrupt change points appear, the smaller abrupt change point is selected.
[0067] S8. Calculate the axle position and forward speed. By repeating steps S1-S7, the continuous data is fitted to the axle profile. Combined with time information, the change of the profile over time is calculated to obtain the speed value.
[0068] S9. Electric Bus Position Data Transmission. After the sensor combination signals obtain the electric bus's position status signal, the signal is transmitted to the robot scheduling system. The scheduling system monitors the electric bus's position in real time and makes collaborative judgments with the robot's real-time position.
[0069] S10. Traffic Control Judgment. The robot scheduling system makes judgments based on the robot's real-time tasks and issued path points, combined with the real-time location and speed prediction of the electric bus and the robot's synchronous position. If the task path includes electric bus entry / exit tasks, the robot's operation is paused, and it resumes operation after the electric bus completes its entry / exit task. If the robot's task path does not include electric bus entry / exit tasks, the robot directly performs the inspection task.
[0070] This invention relates to the safety of inspection robots and electric train traffic control when operating in urban rail daily inspection depots. It utilizes automatic robot navigation, real-time location recording and monitoring, and multi-sensor fusion monitoring of electric train entry and exit status. Through comprehensive management by a dispatching system, it achieves fully unmanned traffic control for both robots and electric trains, ensuring operational safety. This invention belongs to the field of intelligent inspection robots for rail vehicles.
[0071] This invention converts laser measurement information into the position and speed information of electric trains, and then integrates the data into a robot scheduling system. The scheduling system achieves intelligent control of level crossing areas by issuing robot paths and predicting the position of electric trains in the same time dimension. Compared with manual confirmation of track shunting information and judgment by the traffic light system, the overall scheduling efficiency is improved by 50%. This invention uses large-step median filtering to first filter out outliers in segments, determines the starting point of the effective data segment for wheelsets, extracts the data segment from the original data, and then uses small-step median filtering to remove high-frequency noise, using the mode to replace amplitudes exceeding the threshold; it uses an iterative approach to find abrupt change points. Compared with existing technologies, this method is not affected by high-frequency noise in the data, and the value setting does not require much experience. This invention uses the principle of "taking the larger positive value and the smaller negative value" to screen abrupt change points, eliminating the interference of step-type abrupt change points caused by factors such as the trend term of the wheelset data segment.
[0072] In summary, compared to existing manual methods, sensor-based traffic control maximizes the safety and efficiency of system operation, aligning with the concept of unmanned robotics. This invention uses laser as the location signal medium for electric buses, maximizing the role of the central brain of the dispatching system. It not only utilizes the principle of laser ranging and signal fusion and filtering to obtain the real-time location information of the electric buses, but also achieves collaborative control between different systems, fully leveraging the function of the dispatching system. In terms of application results, it offers high real-time signal performance, accurate electric bus positioning, efficient simultaneous operation of electric buses and robots, and ensures high safety in traffic control.
[0073] The present invention also discloses a computer program product, comprising a computer program that, when executed by a processor, performs the steps of the method described above.
[0074] The present invention further discloses a computer-readable storage medium having a computer program stored thereon, the computer program executing the steps of the method described above when run by a processor.
[0075] The present invention also discloses an inspection robot and electric bus traffic control system, including a memory and a processor connected to each other, wherein the memory stores a computer program, and the computer program executes the steps of the method described above when run by the processor.
[0076] The products, media, and systems of the present invention, corresponding to the methods described above, also possess the advantages described above.
[0077] The present invention can implement all or part of the processes in the methods of the above embodiments, or it can be implemented by hardware related to computer program instructions. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above method embodiments. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium includes: any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. The memory is used to store computer programs and / or modules. The processor implements various functions by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may include high-speed random access memory, as well as non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital (SD) cards, flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.
[0078] Definitions:
[0079] Electric train: A rail train that carries passengers in a subway rail transit system;
[0080] Scheduling system: A unified control center for the robot's operating area;
[0081] Track: A trench with rails where electric trains are parked;
[0082] Level crossing: The area between the two lanes used for pedestrians or robots;
[0083] Sliding median filtering: The median of the data points within a step size centered on this point is used as the smoothed value of this point;
[0084] Mode: The value that appears most frequently within a data segment;
[0085] Statistical characteristics: such as mean, standard deviation, etc.
[0086] The above are merely preferred embodiments of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should be considered within the scope of protection of the present invention.
Claims
1. A method for traffic control of electric buses using an inspection robot, characterized in that, Including the following steps: S1. Laser ranging data of the electric bus wheelset axle section is collected by a laser sensor installed at a fixed position on the track. The ranging data includes amplitude and time information. S2. Perform large-step sliding median filtering on the laser ranging data to filter out abnormal measurement values; the large step size is greater than the width of the data segment; S3. Filter valid data segments based on negative pulse width. Identify negative pulses and their starting points by setting an amplitude threshold, and determine valid data segments containing axle data based on the pulse width threshold. S4. Extract the effective data segment from the original data and perform small-step sliding median filtering on it to suppress high-frequency interference and preserve the abrupt changes in the axle profile; the small step size is less than the width of the groove between the axle and the wheelset floor. S5. Calculate the mode of the amplitude in the valid data segment, and use the mode to replace the abnormal amplitude that exceeds the preset threshold, so as to eliminate the distortion caused by sensor abnormality; S6. Using a mutation point detection algorithm, identify mutation points in the wheelset profile within the valid data segment; S7. Screen the mutation points and remove step-like mutation points caused by trend terms; S8. Based on the mutation point and its time information, fit the axle profile and calculate the position and forward speed of the electric bus; S9. Transmit the electric train's location and speed information to the robot scheduling system; S10. The robot scheduling system makes traffic control decisions based on the real-time location and speed prediction results of the electric train and the real-time task path of the robot to ensure safe operation.
2. The inspection robot and electric bus traffic control method according to claim 1, characterized in that, In step S5, the process of calculating the mode of the amplitude in the valid data segment is as follows: Calculate a histogram of the laser data amplitude for the effective data segment, sort the intervals of the histogram by the number of data points, select the interval with the most data points, and use the mean of the start and end points of the interval as the mode.
3. The inspection robot and electric bus traffic control method according to claim 1, characterized in that, The specific process of step S6 is as follows: The data is segmented by randomly selecting cutoff points; Calculate the statistical characteristics of each data segment; Sum the residuals of the data points and statistical characteristics within each segment; Choose the cutoff point that minimizes the total residual as the mutation point.
4. The inspection robot and electric bus traffic control method according to claim 1, characterized in that, The specific process of step S6 is as follows: find the point in the signal that crosses the set amplitude as the change point. Specifically, set a threshold, calculate the point in the data that crosses the threshold, and determine whether the point is a rising edge or a falling edge based on the value before and after the crossing point, thereby finding the change point in the data.
5. The inspection robot and electric bus traffic control method according to any one of claims 1-4, characterized in that, In step S7, the mutation points are screened according to the principle of "positive positive take the larger one, negative negative take the smaller one". That is, if positive mutation points appear consecutively, the larger mutation point is selected; if negative mutation points appear consecutively, the smaller mutation point is selected.
6. The inspection robot and electric bus traffic control method according to any one of claims 1-4, characterized in that, In step S10, the traffic control decision is as follows: if there is a train entering or leaving the depot in the task path, the robot operation task is suspended and the robot operation is resumed after the train enters or leaves the depot; if there is no train entering or leaving the depot in the robot task path, the robot directly performs the inspection task.
7. The inspection robot and electric bus traffic control method according to any one of claims 1-4, characterized in that, In step S1, the laser line of the laser sensor is perpendicular to the cross section of the wheelset axle.
8. A computer program product, comprising a computer program, characterized in that, The computer program is executed by the processor to perform the steps of the method as described in any one of claims 1-7.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program, when run by a processor, performs the steps of the method as described in any one of claims 1-7.
10. A patrol robot and electric bus traffic control system, comprising a memory and a processor interconnected, wherein the memory stores a computer program, characterized in that, The computer program, when run by a processor, performs the steps of the method as described in any one of claims 1-7.