Autonomous mobility for detecting buried object and method for detecting buried object using same

Abstract

The present invention relates to an autonomous mobility for detecting a buried object and a method for detecting a buried object using same. The autonomous mobility comprises: a driving control unit for, when a magnetic field signal is detected by a magnetic field sensor while the mobility is traveling in a designated range in an autonomous driving mode, switching the autonomous driving mode to a tracking driving mode, and controlling the mobility to travel along the position in which the magnetic field signal is detected; a data collection unit for collecting reception position data, intensity data, and time data required for signal transmission and reception of a magnetic field signal received while the mobility is traveling in the tracking driving mode; an information analysis unit for analyzing buried object information including a position, a type, and a depth of the buried object by using the collected data; and a map generation unit for storing a driving route on which the mobility travels in the tracking driving mode and generating a buried object map in which the buried object information is reflected in the stored driving route.

Description

Autonomous mobility for detecting buried objects and a method for detecting buried objects using the same

[0001] The present invention relates to an autonomous driving mobility for detecting underground facilities and a method for detecting underground facilities using the same, and more particularly, to an autonomous driving mobility for detecting underground facilities that detects the location of underground facilities using a magnetic field sensor and a method for detecting underground facilities using the same.

[0002] Urban infrastructure, including water supply, sewerage, gas, heating, communications, and power, is typically buried underground. Therefore, accurate information on the location of these underground facilities is crucial when undertaking construction projects to maintain and improve infrastructure. If construction is undertaken without properly identifying these facilities, heavy equipment can damage gas pipes, potentially leading to safety hazards such as explosions.

[0003] In addition, there is a problem that safety accidents frequently occur due to inaccurate location information for old underground facilities that have been buried for a long time, and even if the location of the facility is known accurately, the location may change while it is buried, so a secondary detection process is essential before underground work.

[0004] For this type of secondary detection, previously the primary method used was to use detectors to detect the location of buried facilities above ground and then mark them with paint. However, this method required manual detection with detectors, resulting in high labor costs, time-consuming detection, and low detection accuracy. Furthermore, even after detection, information updates and monitoring processes were inconvenient. Therefore, the development of technologies capable of resolving these issues and accurately detecting and determining the location of underground facilities is essential.

[0005]

[0006] The technology underlying the invention is disclosed in Korean Patent Registration No. 10-1730296 (published on April 25, 2017).

[0007] The present invention has been devised to solve the above-mentioned problems, and the technical task to be achieved by the present invention is to provide an autonomous driving mobility for detecting underground facilities by using a magnetic field sensor provided in the mobility to detect the location of underground facilities, and a method for detecting underground facilities using the same.

[0008] In addition, the present invention provides an autonomous driving mobility for detecting buried facilities and a method for detecting buried facilities using the autonomous driving mobility for creating and providing a map of buried facilities that reflects the location, type, and depth of buried facilities by using the driving path of the mobility and data received through a magnetic field sensor.

[0009] According to an embodiment of the present invention for achieving such a technical task, an autonomous driving mobility for detecting buried objects includes: a driving control unit that, when a magnetic field signal is detected by a magnetic field sensor while driving in a designated range in an autonomous driving mode, switches to a tracking driving mode and controls driving along a location where the magnetic field signal is detected; a data collection unit that collects reception location data, intensity data, and time data required for signal transmission and reception of a magnetic field signal received while driving in the tracking driving mode; an information analysis unit that analyzes buried object information including the location, type, and depth of the buried object using the collected data; and a map generation unit that stores a driving route driven in the tracking driving mode and generates a buried object map in which the buried object information is reflected in the stored driving route.

[0010] At this time, the driving control unit can control the vehicle to drive along the pipeline of the buried object transmitting the magnetic field signal by executing a pre-arranged line tracing algorithm when driving in the tracking driving mode.

[0011] In addition, the information analysis unit analyzes the GNSS sensor provided in the mobility and the reception location data of the magnetic field signal to determine the location of the buried facility, extracts the features of the intensity data and inputs the extracted features into a pattern recognition model prepared in advance to determine the type of the buried facility, analyzes the time data to calculate the time required for signal transmission and reception to determine the burial depth of the buried facility, and the pattern recognition model may be a deep learning-based model prepared by performing learning on feature value data extracted from intensity data received for each type of buried facility as learning data to determine the type of the buried facility.

[0012] In addition, the map generation unit can generate and store the buried facility map by reflecting the determined location of the buried facility, the type of the buried facility, and the buried depth of the buried facility to the driving route.

[0013] In addition, the above mobility may be equipped with magnetic field sensors on both sides of the front for detecting magnetic field signals from underground facilities, a GNSS (Global Navigation Satellite System) sensor for identifying the location where the magnetic field signals are detected, and a camera sensor, a lidar sensor, and an ultrasonic sensor for autonomous driving in an autonomous driving mode or a tracking driving mode according to the control of the driving control unit.

[0014] In addition, a method for detecting a buried facility according to another embodiment of the present invention includes the steps of: driving a mobility vehicle in a designated range in an autonomous driving mode until a magnetic field signal is detected by a magnetic field sensor; switching to a tracking driving mode when a magnetic field signal is detected during the driving and driving along a location where the magnetic field signal is detected; collecting reception location data, intensity data, and time data required for signal transmission and reception of a magnetic field signal received while driving in the tracking driving mode; analyzing buried facility information including a location, type, and depth of the buried facility using the collected data; and storing a driving route driven in the tracking driving mode and generating a buried facility map in which the buried facility information is reflected in the stored driving route.

[0015] At this time, the step of driving along the location where the magnetic field signal is detected may be performed by executing a pre-arranged line tracing algorithm to drive along the pipeline of the buried object transmitting the magnetic field signal.

[0016] In addition, the step of analyzing the buried information may include analyzing the GNSS sensor provided in the mobility and the reception location data of the magnetic field signal to determine the location of the buried facility, extracting the features of the intensity data and inputting the extracted features into a pre-prepared pattern recognition model to determine the type of the buried facility, analyzing the time data to calculate the time required for signal transmission and reception to determine the buried depth of the buried facility, and the pattern recognition model may be a deep learning-based model prepared by performing learning using feature value data extracted from intensity data received for each type of buried facility as learning data to determine the type of the buried facility.

[0017] In addition, the step of generating the above-described buried facility map can generate and store the buried facility map by reflecting the determined location of the buried facility, the type of the buried facility, and the buried depth of the buried facility in the driving route.

[0018] In addition, the mobility may be equipped with magnetic field sensors on both sides of the front for detecting magnetic field signals from underground structures, a GNSS sensor for identifying the location where the magnetic field signals are detected, and a camera sensor, a lidar sensor, and an ultrasonic sensor for autonomous driving in an autonomous driving mode or a tracking driving mode according to the control of the driving control unit.

[0019] In this way, according to the present invention, the location of underground facilities can be detected using a magnetic field sensor provided in mobility, thereby having the effect of preventing safety accidents caused by underground facilities.

[0020] In addition, according to the present invention, a map of underground facilities reflecting the location, type, and depth of underground facilities is generated using data received through the driving path of the mobility and the magnetic field sensor, thereby providing highly reliable data, thereby enabling the location and type of underground facilities to be quickly determined, and thus can be usefully utilized at construction sites.

[0021] In addition, according to the present invention, by automating the process of detecting underground facilities using autonomous driving mobility and establishing a smart infrastructure system to minimize human intervention, safety accidents caused by underground facilities at construction sites can be minimized, and a safe construction environment can be created.

[0022] FIG. 1 is a block diagram of an autonomous driving mobility for detecting buried objects according to an embodiment of the present invention.

[0023] Figure 2 is an exemplary drawing illustrating the autonomous driving mobility of Figure 1.

[0024] FIG. 3 is a flowchart illustrating the operation flow of a method for detecting buried objects using autonomous driving mobility according to an embodiment of the present invention.

[0025] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the attached drawings. In this process, the thickness of lines and the sizes of components depicted in the drawings may be exaggerated for clarity and convenience of explanation.

[0026] Furthermore, the terms described below are defined based on their functions within the present invention, and may vary depending on the intent or custom of the user or operator. Therefore, the definitions of these terms should be based on the overall content of this specification.

[0027] Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the drawings.

[0028]

[0029] First, an autonomous driving mobility for detecting buried objects according to an embodiment of the present invention will be described through FIGS. 1 and 2.

[0030] In an embodiment of the present invention, autonomous driving mobility is provided with magnetic field sensors on both sides of the front for autonomous driving in an autonomous driving mode or a tracking driving mode to detect underground facilities, and for detecting magnetic field signals from underground facilities, a GNSS (Global Navigation Satellite System) sensor for identifying the location where the magnetic field signal is detected, and a camera sensor, a lidar sensor, and an ultrasonic sensor for autonomous driving in an autonomous driving mode or a tracking driving mode according to control.

[0031] FIG. 1 is a block diagram of an autonomous driving mobility for detecting buried objects according to an embodiment of the present invention.

[0032] As shown in FIG. 1, the autonomous driving mobility (100) for detecting buried objects according to an embodiment of the present invention includes a driving control unit (110), a data collection unit (120), an information analysis unit (130), a storage unit (140), and a map generation unit (150).

[0033] First, when a magnetic field signal is detected by a magnetic field sensor while the mobility (100) is driving in a designated range in autonomous driving mode, the driving control unit (110) switches to tracking driving mode and controls the vehicle to drive along the location where the magnetic field signal is detected.

[0034] At this time, the driving control unit (110) may control the vehicle to drive along the pipeline of the buried object transmitting the magnetic field signal by executing a pre-prepared line tracing algorithm when driving in the tracking driving mode.

[0035] Here, the line tracing algorithm is an algorithm that processes information about lines detected through sensors and controls driving along a set line.

[0036] Accordingly, the driving control unit (110) can control the mobility (100) to switch to a tracking driving mode when a magnetic field signal is detected while the mobility (100) is driving in autonomous driving mode, and to trace a line while driving crosswise so that the magnetic field sensors (Hall sensors) provided on each side of the front of the mobility (100) can be located within the pipeline of the buried object.

[0037] And the data collection unit (120) collects the reception location data, intensity data, and time data required for signal transmission and reception of the magnetic field signal received while the mobility (100) is driving in tracking driving mode.

[0038] At this time, the data collection unit (120) can collect location data on the location where the magnetic field signal was received using the GNSS sensor provided in the mobility (100).

[0039] In addition, the intensity data of the magnetic field signal can be extracted and collected from the received magnetic field signal, and the time required for transmitting and receiving the magnetic field signal can be measured to collect time data.

[0040] And the information analysis unit (130) uses the data collected by the data collection unit (120) to analyze information on buried facilities, including the location, type, and depth of the buried facilities.

[0041] In detail, the information analysis unit (130) analyzes the reception location data of the magnetic field signal to determine the location of the buried object.

[0042] Here, GNSS sensors use satellites to measure location. Positioning is accomplished by measuring the time it takes for a signal transmitted from a satellite to reach the receiver, thereby calculating the distance to the satellite. Therefore, by measuring the distance to each received satellite, triangulation can be used to calculate the current location.

[0043] In addition, the information analysis unit (130) analyzes the intensity data of the magnetic field signal to extract features and inputs the extracted features into a pre-prepared pattern recognition model to determine the type of buried object.

[0044] At this time, the pattern recognition model may be a deep learning-based model that is prepared by learning feature value data extracted from the intensity data of the magnetic field signal as learning data to determine the type of buried object.

[0045] In detail, the pattern recognition model is a model that extracts feature data from the intensity data of a magnetic field signal and generates the extracted feature data using at least one machine learning among supervised, unsupervised, and reinforcement learning, and the type of buried object is determined using the pattern recognition model generated in this way. The type of buried object can also be determined using at least one artificial intelligence algorithm among ANN (Artificial Neural Network), RNN (Recurrent Neural Network), LSTM (Long Short Term Memory) network, DNN (Deep Neural Network), CNN (Convolutional Neural Network), and BRDNN (Bidirectional Recurrent Deep Neural Network).

[0046] Therefore, in an embodiment of the present invention, by analyzing the intensity data pattern of a magnetic field signal using a pattern recognition model, the material of the buried facility, such as iron or copper, can be distinguished, and the type of the buried facility (e.g., six types of underground facilities, i.e., water supply and sewage, communication, heating, gas, and electricity) can be determined.

[0047] That is, the reflectivity of the magnetic field signal varies depending on the medium, and by reflecting this characteristic in the pattern recognition model, the intensity data of the received magnetic field signal can be classified.

[0048] In addition, the information analysis unit (130) extracts the characteristic values ​​of the intensity data of the magnetic field signal and clusters the extracted characteristic values, calculates the characteristic values ​​(reference values) for each type of buried object prepared in advance and the Euclidean-based distance value, finds the reference value having a distance value below a preset threshold value, and determines the type of buried object by identifying the type of buried object matching the reference value.

[0049] In addition, the information analysis unit (130) can analyze the time data of the magnetic field signal and calculate the time required for transmitting and receiving the magnetic field signal to determine the burial depth of the buried object.

[0050] Therefore, the information analysis unit (130) can determine the location, type, and burial depth of the buried object using the location, intensity, and time data of the magnetic field signal.

[0051] And the storage unit (140) stores the driving route driven in the tracking driving mode.

[0052] That is, since the tracking driving mode is to drive along the location where the magnetic field signal is received, the driving path can be saved and used as data when generating a map.

[0053] Finally, the map generation unit (150) generates a buried object map by reflecting the buried object information analyzed by the information analysis unit (130) on the driving route stored in the storage unit (140).

[0054] At this time, the map generation unit (150) reflects the location of the buried facility, the type of the buried facility, and the buried depth of the buried facility determined by the information analysis unit (130) to the driving route stored in the storage unit (140) to generate a buried facility map and store it in the storage unit (140).

[0055] Figure 2 is an exemplary drawing illustrating the autonomous driving mobility of Figure 1.

[0056] As shown in Fig. 2, when a mobility (100) that autonomously drives according to a control command receives a magnetic field signal transmitted from a buried facility while autonomously driving in an autonomous driving mode within a predetermined range, it switches to a tracking driving mode immediately upon receiving the magnetic field signal and autonomously drives along the location where the magnetic field signal is detected, collecting the received magnetic field signals. At this time, the collected signals are analyzed to determine the location, type, and burial depth of the buried facility, and the determined result is reflected in the driving route driven while driving in the tracking driving mode to generate a buried facility map. At this time, the generated buried facility map can be processed so that it can be transmitted to an external server to enable linkage with smart glasses and other devices.

[0057]

[0058] Hereinafter, a method for detecting buried objects using autonomous driving mobility according to an embodiment of the present invention will be described with reference to FIG. 3.

[0059] FIG. 3 is a flowchart illustrating the operation flow of a method for detecting buried objects using autonomous driving mobility according to an embodiment of the present invention, and the specific operation of the present invention will be described with reference to this.

[0060] According to an embodiment of the present invention, first, an autonomous driving mobility (100) for detecting buried objects drives in a designated range in an autonomous driving mode until a magnetic field signal is detected by a magnetic field sensor (S10, S11).

[0061] At this time, the mobility (100) may be equipped with magnetic field sensors on both sides of the front for detecting magnetic field signals from underground structures, a GNSS (Global Navigation Satellite System) sensor for identifying the location where the magnetic field signal is detected, and a camera sensor, lidar sensor, and ultrasonic sensor for autonomous driving in an autonomous driving mode or a tracking driving mode according to the control of the driving control unit (110).

[0062] If a magnetic field signal is detected, the vehicle switches to tracking driving mode and drives along the location where the magnetic field signal is detected (S20).

[0063] In detail, the mobility (100) can run along the pipeline of the buried object transmitting the magnetic field signal by executing a pre-arranged line tracing algorithm.

[0064] And, while driving in tracking driving mode, the reception position data, intensity data, and time data required for signal transmission and reception are collected respectively (S30).

[0065] Then, using the data collected in step S30, the buried facility information including the location, type, and depth of the buried facility is analyzed (S40).

[0066] In detail, step S40 analyzes the reception location data of the magnetic field signal to determine the location of the buried object, analyzes the intensity data of the magnetic field signal to extract features, and inputs the extracted features into a pre-prepared pattern recognition model to determine the type of buried object.

[0067] At this time, the pattern recognition model may be a deep learning-based model that is prepared by learning feature value data extracted from the intensity data of the magnetic field signal as learning data to determine the type of buried object.

[0068] And by analyzing the time data of the magnetic field signal, the time required for transmitting and receiving the magnetic field signal is calculated to determine the burial depth of the buried object.

[0069] And, in step S20, the driving route driven in tracking driving mode is saved (S50).

[0070] That is, since the tracking driving mode is to drive along the location where the magnetic field signal is received, the driving path can be saved and used as data when generating a map.

[0071] Finally, a buried object map is created by reflecting the buried object information analyzed in step S40 on the driving route saved in step S50 (S60).

[0072] That is, step S60 generates a map of buried facilities by reflecting the location of the buried facilities, the type of buried facilities, and the buried depth of the buried facilities determined in step S40 to the driving route saved in step S50, and can also save the generated map of buried facilities.

[0073]

[0074] Meanwhile, the present invention can also be implemented as a computer-readable code on a computer-readable recording medium.

[0075] Such computer-readable recording media may contain program commands, data files, data structures, etc. alone or in combination, and include all types of recording devices that store data that can be read by a computer system. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs (Compact Disk Read Only Memory) and DVDs (Digital Video Disks), magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program commands such as ROMs (Read Only Memory), RAMs (Random Access Memory), and flash memory.

[0076] Additionally, computer-readable recording media can be distributed across network-connected computer systems, allowing computer-readable code to be stored and executed in a distributed manner. Furthermore, functional programs, codes, and code segments for implementing the present invention can be readily inferred by programmers skilled in the art to which the present invention pertains.

[0077] According to the present invention as described above, the location of underground facilities can be detected using a magnetic field sensor provided in mobility, thereby preventing safety accidents caused by underground facilities.

[0078] In addition, by using the driving path of the mobility and data received through the magnetic field sensor, a map of underground facilities reflecting the location, type, and depth of underground facilities is created, providing highly reliable data, enabling quick determination of the location and type of underground facilities, making it useful in construction sites.

[0079] Furthermore, by automating the process of detecting underground facilities using autonomous mobility and establishing a smart infrastructure system to minimize human intervention, safety accidents caused by underground facilities at construction sites can be minimized and a safe construction environment can be created.

[0080] While the present invention has been described with reference to the embodiments illustrated in the drawings, these are merely exemplary, and those skilled in the art will understand that various modifications and equivalent embodiments are possible. Therefore, the true technical protection scope of the present invention should be determined by the technical spirit of the following claims.

[0081] [Explanation of symbols]

[0082] 100: Mobility 110: Driving Control Unit

[0083] 120: Data Collection Department 130: Information Analysis Department

[0084] 140: Storage unit 150: Map generation unit

Claims

1. In autonomous mobility driving in autonomous driving mode or tracking driving mode to detect buried objects, A driving control unit that, when a magnetic field signal is detected by a magnetic field sensor while driving within a designated range in the autonomous driving mode, switches to the tracking driving mode and controls driving along a location where the magnetic field signal is detected; A data collection unit that collects reception position data, intensity data, and time data required for signal transmission and reception of a magnetic field signal received while driving in the above tracking driving mode; An information analysis unit that analyzes information on buried structures, including the location, type, and depth of the buried structures, using the collected data; and An autonomous driving mobility comprising a map generation unit that stores a driving route driven in the above tracking driving mode and generates a facility map in which the facility information is reflected in the stored driving route.

2. In paragraph 1, The above driving control unit, An autonomous driving mobility that controls driving along a pipeline of a buried facility transmitting the magnetic field signal by executing a pre-arranged line tracing algorithm when driving in the above tracking driving mode.

3. In paragraph 1, The above information analysis department, The location of the buried object is determined by analyzing the GNSS sensor equipped in the above mobility and the reception location data of the magnetic field signal. The features of the above century data are extracted and the extracted features are input into a pre-prepared pattern recognition model to determine the type of buried object. By analyzing the above time data, the time required for signal transmission and reception is calculated to determine the burial depth of the facility. The above pattern recognition model is, Autonomous driving mobility is a deep learning-based model that uses feature value data extracted from intensity data received by type of buried facility as learning data to determine the type of buried facility.

4. In paragraph 3, The above map generation unit, Autonomous driving mobility that creates and stores a map of the buried facilities by reflecting the location of the determined buried facilities, the type of the buried facilities, and the buried depth of the buried facilities on the driving route.

5. In paragraph 1, The above mobility is, An autonomous driving mobility, wherein the magnetic field sensors for detecting magnetic field signals from underground structures are provided on each side of the front, a GNSS (Global Navigation Satellite System) sensor for identifying the location where the magnetic field signals are detected, and a camera sensor, a lidar sensor, and an ultrasonic sensor for autonomous driving in an autonomous driving mode or a tracking driving mode according to the control of the driving control unit.

6. A method for detecting buried objects using autonomous driving mobility for detecting buried objects, A step of driving the above mobility in an autonomous driving mode within a designated range until a magnetic field signal is detected by a magnetic field sensor; A step of switching to a tracking driving mode when a magnetic field signal is detected during the driving and driving along the location where the magnetic field signal is detected; A step of collecting reception position data, intensity data, and time data required for signal transmission and reception of a magnetic field signal received while driving in the above tracking driving mode; A step of analyzing the buried facility information including the location, type and depth of the buried facility using the collected data; and A method for detecting a buried facility, comprising the steps of: storing a driving route driven in the above tracking driving mode; and generating a buried facility map in which the buried facility information is reflected in the stored driving route.

7. In paragraph 6, The step of driving along the location where the above magnetic field signal is detected is: A method for detecting buried structures by running along a pipeline of buried structures transmitting the magnetic field signal by executing a pre-arranged line tracing algorithm.

8. In paragraph 6, The steps for analyzing the above-mentioned information are: The location of the buried object is determined by analyzing the GNSS sensor equipped in the above mobility and the reception location data of the magnetic field signal, the characteristics of the intensity data are extracted, and the extracted characteristics are input into a pre-prepared pattern recognition model to determine the type of the buried object, and the time data is analyzed to calculate the time required for signal transmission and reception to determine the burial depth of the buried object. The above pattern recognition model is, A method for detecting buried structures, which is a deep learning-based model that is created by performing learning on feature value data extracted from intensity data received for each type of buried structure to determine the type of buried structure.

9. In paragraph 8, The steps for generating the above-mentioned facility map are: A method for detecting buried facilities, which generates and stores a map of the buried facilities by reflecting the location of the determined buried facilities, the type of the buried facilities, and the buried depth of the buried facilities on the driving route.

10. In paragraph 6, The above mobility is, A method for detecting underground facilities, wherein the magnetic field sensors for detecting magnetic field signals caused by underground facilities are provided on each side of the front, a GNSS (Global Navigation Satellite System) sensor for identifying the location where the magnetic field signals are detected, and a camera sensor, a lidar sensor, and an ultrasonic sensor for autonomous driving in an autonomous driving mode or a tracking driving mode according to the control of the driving control unit are respectively provided.

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