KR101888170B1 - Method and device for deleting noise in detecting obstacle by unmanned surface vessel - Google Patents

Abstract

The present invention provides a method for eliminating noise in detecting an obstacle by an unmanned surface vehicle and a device thereof which eliminate noise data generated in a water surface while an unmanned surface vehicle driven on the water surface detects an obstacle by using a three-dimensional lidar sensor. The method for eliminating noise in detecting an obstacle by an unmanned surface vehicle comprises the following steps of: obtaining three-dimensional lidar data for detecting the obstacle in the unmanned surface vehicle; generating a virtual trajectory passing over the water surface by the unmanned surface vehicle; extracting the noise data generated from the water surface of a rear surface of the unmanned surface vehicle in the virtual trajectory; and deleting the noise data among the three-dimensional lidar data.

Description

Technical Field [0001] The present invention relates to a method and apparatus for detecting an obstacle in an unmanned aerial vehicle,

The present invention relates to a noise canceling method and apparatus for detecting unmanned aerial vehicles. More particularly, the present invention relates to an apparatus and a method for detecting an obstacle by using a three-dimensional Lydia sensor alone and removing noise data generated from the water surface.

Recently, marine robots have been used in various forms according to their purpose in order to carry out marine exploration and specific missions according to the research. Among marine robots, the unmanned surface vehicle (USV) has been researched and developed for various purposes such as military, environmental protection, and exploration in aquatic environment.

In order to carry out the mission according to each purpose, common factors such as self-location, path planning, path follow-up, and obstacle avoidance are required for autonomous navigation.

Among them, obstacle avoidance is an important part for safe autonomous navigation. Various researches have been conducted from a simple algorithm that recognizes and stops obstacles to a method that avoids obstacles by loading a very sophisticated algorithm.

The obstacle avoidance algorithm can be classified into two kinds. Reflective control that avoids using only the data that is currently input depending on past sensor data usage and reactive control that uses past data together can be divided into.

Reflective control has a small amount of computation, it can easily apply fast motion to external stimuli, but it is difficult to apply it to path planning or tracking. On the other hand, there are disadvantages in that the reaction control has a large amount of computation and the environmental map must be stored, but it has an advantage that it can be utilized not only for obstacle avoidance but also for path generation and follow-up. In order to apply such an obstacle avoidance algorithm, environment recognition should be basically possible.

Most of the location and environment recognition algorithms use GPS (Global Positioning System) as main system and they are fused with INS (Inertial Navigation System) to develop continuous position recognition even when GPS is impossible.

In particular, in the case of unmanned vehicles, it is not easy to recognize the position using GPS. In the environment where the change of the roll, pitch, and yaw of the vehicle is severe, the cumulative error of the INS may occur continuously. Of DGPS and INS. In addition, laser-based LIDAR and camera are used as main sensors. Especially, LIDAR has strong reliability distance information and fast data processing. However, resolution and sensor specifications are improved from 2D to 3D, There was a lot of data problems.

Korean Patent No. 10-1514407 refers to a real-time sea-area observation system, which is a method for acquiring various information obtained from aquatic or aquatic waters beyond the observation information in the form of a point by the observation equipment located in the water of the observation area Is analyzed and analyzed as regional type observation information. However, it is still not expected to reduce the processing speed of the unmanned system platform.

SUMMARY OF THE INVENTION The present invention has been conceived to solve the above-described problems, and it is an object of the present invention to provide a noise reduction apparatus and a noise reduction method, It is intended to remove noise data.

However, the objects of the present invention are not limited to those mentioned above, and other objects not mentioned can be clearly understood by those skilled in the art from the following description.

According to an aspect of the present invention, there is provided a method of detecting obstacles, the method comprising: acquiring three-dimensional Lattice data for obstacle detection in an unmanned water estimation; Extracting noise data generated from the water surface in the trajectory, and deleting the noise data among the three-dimensional Lattice data.

Preferably, the step of generating a virtual trajectory includes the steps of: acquiring the unmanned awards defined azimuth and speed through a GPS module; obtaining the unmanned airstop turn radius using the obtained azimuth and speed; And generating the virtual trajectory on which the unattended spin table moves based on the radius.

Preferably, the step of generating a virtual trajectory generates a circle having a radius of the unmanned awards positive definition radius r using the unattended augmented positive azimuth.

Preferably, the step of extracting the noise data further comprises the steps of: determining a backside length of the unmanned image based on the unmanned image forming speed in the virtual trajectory; and determining the three-dimensional image data obtained in the area corresponding to the back side length And extracting the data with the noise data, wherein the rear length is a length directed outward from an outer wall of the rear face of the unmanned water cannon.

Preferably, the region corresponding to the rear length is defined by a first boundary parallel to the inside of the imaginary locus corresponding to the rear length of the virtual locus, and a second boundary parallel to the outside of the imaginary locus, (M is a positive integer) of the positive width.

According to another aspect of the present invention, there is provided an obstacle detection system including an obstacle detection unit for obtaining three-dimensional Lattice data for obstacle detection in an unmanned water reception, a virtual locus generation unit for generating a virtual locus passing over the water surface, And a noise data removing unit for removing the noise data among the three-dimensional Lattice data. The apparatus for removing noise in an unmanned aerial vehicle obstacle detecting apparatus according to the present invention includes:

Preferably, the virtual locus generator obtains the unmanned awards defined azimuth and speed using the GPS module, obtains the unmanned awards defined turning radius using the obtained azimuth and speed, Thereby generating the virtual trajectory to which the target object moves.

Preferably, the virtual locus generator generates a circle having a radius of the unmanned awards defined turning radius r by using the unmanned awards defined azimuth.

Preferably, the noise data extracting unit may determine the rear length of the unmanned image data according to the unmanned image forming speed in the virtual trajectory, calculate the three-dimensional image data obtained in the area corresponding to the back length, And the rear length is a length directed outward from the outer wall of the rear surface of the unmanned water supply system.

Preferably, the region corresponding to the rear length is defined by a first boundary parallel to the inside of the virtual trajectory corresponding to the rear length of the virtual trajectory and a second boundary parallel to the outside of the virtual trajectory, (M is a positive integer) of the width.

Since the present invention utilizes a 3D Lidar sensor alone for detecting an obstacle in the unmanned water guidance, the data throughput can be relatively reduced as compared with a system using multiple sensors, and the movement control process The load can be reduced, and the unmanned vehicle can be quickly and quickly settled.

In addition, the present invention can improve the reliability of the obstacle detection data by deleting the noise data generated by the wave waves due to the driving of the unmanned vehicle, in addition to the data for detecting and acquiring obstacles.

1 is a block diagram illustrating a schematic configuration of an unmanned aerial vehicle according to an embodiment of the present invention.
2 is a block diagram showing a specific configuration of a control unit of an apparatus for removing noise in the detection of an unmanned aerial vehicle according to an embodiment of the present invention.
FIG. 3 is a reference diagram for obtaining the rear length according to the unmanned image forming speed according to an embodiment of the present invention.
FIG. 4 is an exemplary diagram illustrating an example of a virtual trajectory generated according to an azimuth angle and a speed at which an unmanned parking lot is driven according to an embodiment of the present invention.
5 is an exemplary view illustrating an example of forming a first boundary and a second boundary to set a region of interest according to an embodiment of the present invention.
FIG. 6 is a flowchart for explaining a method of removing noise in the detection of an unmanned aerial vehicle according to an embodiment of the present invention. Referring to FIG.
7 is a flowchart for explaining a specific method of extracting noise data according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, the same reference numerals are used to designate the same or similar components throughout the drawings. In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear. In addition, the preferred embodiments of the present invention will be described below, but it is needless to say that the technical idea of the present invention is not limited thereto and can be variously modified by those skilled in the art.

The present invention relates to a technique for eliminating noise when detecting unmanned aerial vehicles. The present invention efficiently removes noise data generated from a water surface generated by driving an unmanned water well on a water surface when acquiring data using a three-dimensional water sensor for detecting obstacles. Hereinafter, the present invention will be described in detail.

FIG. 1 is a block diagram illustrating a schematic configuration of an unmanned aerial vehicle according to an embodiment of the present invention. FIG. 2 is a block diagram of a control unit of an apparatus for removing noise in detecting an unmanned aerial vehicle according to an embodiment of the present invention. FIG. 3 is a reference diagram for obtaining the rear length according to the unmanned image forming speed according to an embodiment of the present invention, and FIG. 4 is a schematic view showing a state where the unmanned image forming apparatus according to the embodiment of the present invention FIG. 5 is a view illustrating an example of forming a first boundary and a second boundary in order to set a region of interest according to an embodiment of the present invention. Fig.

The unmanned aerial view (100) is put into an environment where it is difficult to directly input manpower, and it is used for a variety of purposes such as generating marine and marine environmental models as well as military purposes such as mine removal, submarine search, security and reconnaissance Purpose.

In this unmanned water elevation 100, a three dimensional (LIDAR) light detection and ranging (110) method is applied for the generation of a digital elevation model for a feature or obstacle detection.

The three-dimensional ladder 110 is composed of a transmission optical system for emitting a light source such as a pulsed laser and a reception optical system for receiving a light source which is reflected and returned to the object. The three-dimensional laser 110 can measure the atmospheric temperature, humidity, and corrective distance by scanning and injecting a laser.

The M8 sensor, a 3-dimensional Lidar sensor, acquires environmental data at 8-channel and 20-degree vertical angles, and can detect obstacles that are 1 to 200 m apart under optimal conditions. For this purpose, the three-dimensional array 110 includes eight modules corresponding to eight channels. Each of the eight modules includes a transmitting optical system and a receiving optical system. Each of the eight modules rotates at the same cycle, irradiates the light source, and receives a light source that reflects and returns to the object.

In particular, although a gap between layers corresponding to each module is close to 3 degrees, a large number of blind spots between layers occur at a relatively long distance. In the present invention, however, two three- So that the effect of 16 channels is generated. Environmental data acquired from 16 channels is stored in a point cloud system. Here, in the present invention, the three-dimensional array 110 is provided with eight modules corresponding to eight channels. However, the present invention is not limited to this, and the number of modules As shown in Fig.

In the present invention, in the case where the unmanned image sensor is used for detecting obstacles around the object through the three-dimensional image sensor 110, the three-dimensional image sensor 110 acquires noise data on the surface in addition to the data for detecting and acquiring an obstacle Therefore, we want to remove such noise data.

Although the three-dimensional ladder 110 is basically based on the principle of the laser distance measuring sensor for detecting an obstacle, in the present invention, in the present invention, the distance sensor rotates by 360 degrees for each layer of the hierarchical structure, Measure the distance. The present invention performs a mission by using a 3D Lidar sensor alone for obstacle detection.

On the other hand, the environment recognition technology of the unmanned system platform of the prior art relies on an image recognition sensor including image information and a radar sensor capable of acquiring remote obstacle data to detect an obstacle and to converge with two-dimensional or three-dimensional information Through the recognition of obstacles and the avoidance maneuvering in the movement of the unmanned system platform. Due to the system designed by fusing a large number of such sensors, expensive equipment is required, and the processing speed of the lower / upper controller is limited, so that there is a problem that the unmanned system platform can not be driven at a high speed or at high speed.

On the other hand, the present invention can reduce the data throughput because it uses the 3D Lidar sensor alone for obstacle detection, and can reduce the load on the behavior control process such as unmanned a priori avoidance start, .

Distance values and measurement angle data included in the eight layers corresponding to the three-dimensional grid 110 of the present invention are acquired as a point cloud and stored in the memory 130. In this case, the three-dimensional Lada sensor has the effect of 16 channels using two units, and the gap between the respective layers can be reduced. That is, in the case of detecting a distant obstacle, a gap on a vertical line between neighboring layers is narrow at a distance adjacent to the three-dimensional array, but an obstacle between the gaps can not be detected as the gap between the layers gradually increases . In order to solve this problem, it is possible to increase the probability of detecting an obstacle by reducing gap between layers by using two 3-dimensional Lidar sensors so that each one of 8 layers is located between 8 layers gaps of one other .

The waterless assumption 100 basically includes an inertial measurement unit (IMU) (not shown) and a GPS module 120 that can acquire the aquifer definition position information and attitude information. The GPS module 120 measures the azimuth angle and the speed at which the water-free water can 100 is driven.

The control unit 140 controls the overall operation of the respective constituent elements of the unattended water control 110.

The control unit 140 includes an obstacle detecting unit 141, a virtual locus generating unit 142, a noise data extracting unit 143, and a noise data removing unit 144, as shown in FIG.

The obstacle detection unit 141 analyzes the distance value and the measurement angle data obtained by the point cloud for each of the eight layers through the three-dimensional line 110 mounted on the unmanned water canal 100 as three-dimensional LIDAR data, Lt; / RTI > Here, three-dimensional (110) layers are formed by rotating the 360 degree angle of the vertical angle of each layer at an interval of 2.5 degrees in accordance with the same period, and the distance data extracted from the positions corresponding to the respective angles of rotation And measurement data are acquired in a point cloud. Here, the point cloud is information that can be used to estimate the detected obstacle. As described above, the three-dimensional Lattice sensors have the effect of 16 channels using two units, and the gap between the respective layers can be reduced. In this case as well, the acquired environmental data is stored in the point cloud system.

)과 속도(v)를 획득하고, 획득된 방위각(

)과 속도(v)를 이용하여 무인 수상정(100)의 선회반경(r)을 구하며, 상기 선회반경(r)에 기반하여 무인 수상정(100)이 이동하는 가상 궤적을 생성한다.The virtual locus generator 142 generates a virtual locus 200 in which the unmanned water wheel 100 passes over the water surface in a circular form as shown in FIGS. Specifically, the virtual locus generating unit 142 generates a virtual locus based on the azimuth angle ("

) And velocity (v) are obtained, and the obtained azimuth angle

) And the velocity v to obtain a turning radius r of the unmanned water control 100 and generates a virtual trajectory in which the unmanned water wheel 100 moves based on the turning radius r.

)을 이용해서 방위각(

)에 따른 무인 수상정의 선회반경(r)의 반지름을 갖는 원 형태를 생성한다. 생성된 원에서 일정영역을 관심영역으로 설정하기 위해서 GPS 모듈(120)로부터 취득하는 무인 수상정(100)의 속도(v)를 파라미터로 사용한다. First, as shown in Equation 3, the azimuth angle

) And the azimuth angle

) To generate a circle shape with radius of the uninitialized augmented turning radius r. The velocity v of the unmanned water intake 100 acquired from the GPS module 120 is used as a parameter in order to set a certain region as a region of interest in the generated circle.

[Equation 1]

Here, L is an actual aberration length, r is determined by Equation (1), and the imaginary circle generated by using the determined value is expressed by Equation (2) Region of interest.

As shown in FIG. 3, the ROI for erasing the unmanned image rear face wave is determined by the width and the length depending on the image definition speed and the heading value. Thus, the 3-dimensional RI data included in the determined virtual area is processed by filtering.

Also, the ratio to the speed is knotted at m / s, which is the current unmanned augmentation speed value, as shown in Equation (2), for example, and the back surface wave is deleted while considering the length of the unmanned aberration correction as 8m.

&Quot; (2) "

In particular, the ratio calculated by considering the speed of the unmanned aerial image in equation (2) depends on the speed at which the actual unmanned aerial image is generated during operation. Considering the maximum length of the rear surface wave, n _max is defined as 2, The virtual area is set by adding the number of times.

That is, the noise data extracting unit 143 extracts noise data due to a wave that passes through the unmanned water estimation 100 in the virtual trajectory. More specifically, the noise data extracting unit 143 determines a back side length at which a wave wave is generated after passing the unmanned water based correction 100 according to the velocity v of the unmanned water based correction system 100 in the virtual trajectory, Dimensional lidar data obtained in a region corresponding to the backside ratio is extracted as noise data.

To this end, as shown in Fig. 5, a first boundary 1 parallel to the inside of the imaginary locus corresponding to the rear length of the imaginary locus, and a second boundary (e.g., 2 may be formed to be spaced apart from each other by the width of the unmanned water preserver 100 so that an area between the first boundary 1 and the second boundary 2 can be set as a region of interest.

Here, the rear length is a length corresponding to a distance at which noise data is detected by three-dimensional data on the water surface of the rear surface of the unmanned water tank, and is a length from the outer wall of the unmanned water tank, Can be calculated by a mathematical expression used as a parameter.

FIG. 6 and FIG. 7 are flowcharts for explaining a noise removal method in the detection of an unmanned aerial vehicle according to an embodiment of the present invention.

Referring to FIG. 6, a method for removing noise in detecting an unmanned aerial vehicle according to an exemplary embodiment of the present invention first acquires 3D ray data for obstacle detection in the unmanned aerial photographing system 100 (S110).

Specifically, the distance values and the measurement data included in the eight layers of three-dimensional ladder mounted on the unmanned water assumption are acquired in a point cloud and stored in the memory 130.

In the present invention, the three-dimensional ray is basically based on the principle of the laser distance measuring sensor for obstacle detection. However, in the present invention, the distance sensor rotates 360 degrees for each layer of the layer structure, .

Specifically, the three-dimensional image is rotated by 360 degrees at intervals of 2.5 degrees with respect to each layer, and the distance data and the distance data are extracted from the positions corresponding to the respective angles of rotation according to the rotation angles. Each data is acquired in a point cloud. Here, the point cloud is information that can be used to estimate the detected obstacle.

 In this case, the three-dimensional Lada sensor has the effect of 16 channels using two units, and the gap between the respective layers can be reduced. That is, in the case of detecting a distant obstacle, a gap on a vertical line between neighboring layers is narrow at a distance adjacent to the three-dimensional array, but an obstacle between the gaps can not be detected as the gap between the layers gradually increases . In order to solve this problem, it is possible to increase the probability of detecting an obstacle by reducing gap between layers by using two 3-dimensional Lidar sensors so that each one of 8 layers is located between 8 layers gaps of one other .

The present invention performs a mission by using a 3D Lidar sensor alone for obstacle detection. Since the present invention utilizes the 3D Lidar sensor alone for obstacle detection, it can reduce the data throughput and reduce the load on the behavior control process such as unmanned augmentation avoidance start. .

Next, an imaginary locus driving on the water surface is generated by the unmanned aerial platform (S120). Specifically, in the step of generating the virtual trajectory, the azimuth angle and speed at which the unmanned parking space control is driven are obtained through the GPS module, the unmanned parking space defined by the azimuth angle and the velocity is obtained, Thereby generating a virtual trajectory in which the unmanned aerial platform moves. Herein, the virtual trajectory can be generated as a circle having the radius of the unmanned aerial image defining radius of gyration using the unmanned aerial image definition azimuth angle, but it is possible to generate in other forms within a range not departing from the gist of the present invention .

Next, the noise data generated at the water surface passing through the unmanned parking space is extracted from the virtual trajectory (S130). Specifically, in order to extract noise data, a back length corresponding to a region where a wave wave is generated by the unmanned aberration-correcting driving is determined according to the unmanned image forming speed in the virtual trajectory, And extracts the dimensionality data as noise data.

Here, the rear length is a length corresponding to the distance at which noise data is detected by the three-dimensional image data on the rear surface of the unmanned image forming apparatus, and is a length from the outer surface of the unmanned image forming apparatus to the outer surface thereof. Can be calculated by using the following equation.

The area corresponding to the rear length corresponds to the rear length of the virtual trajectory as shown in FIG. 5, and the first boundary parallel to the inside of the virtual trajectory and the second boundary parallel to the outside of the virtual trajectory are defined as the unmanned averaging And the area between the first boundary and the second boundary is set as a region of interest.

Here, the distance between the first boundary (or the second boundary) and the virtual trajectory can be calculated as m times (m is a positive number) times the width of the unmanned image. However, the present invention does not depart from the gist of the present invention that the noise region generated by the water surface generated by passing the unmanned water jetting through the ROI is a region detected by three-dimensionally.

Next, the noise data among the three-dimensional Raidata is deleted (S140).

Thus, the reliability of the obstacle detection data can be improved by deleting the noise data generated from the water surface due to the driving of the unmanned aerial vehicle in addition to the data obtained by detecting and acquiring obstacles.

It is to be understood that the present invention is not limited to these embodiments, and all elements constituting the embodiment of the present invention described above are described as being combined or operated in one operation. That is, within the scope of the present invention, all of the components may be selectively coupled to one or more of them. In addition, although all of the components may be implemented as one independent hardware, some or all of the components may be selectively combined to perform a part or all of the functions in one or a plurality of hardware. As shown in FIG. In addition, such a computer program may be stored in a computer readable medium such as a USB memory, a CD disk, a flash memory, etc., and read and executed by a computer to implement an embodiment of the present invention. As the recording medium of the computer program, a magnetic recording medium, an optical recording medium, or the like can be included.

Furthermore, all terms including technical or scientific terms have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, unless otherwise defined in the Detailed Description. Commonly used terms, such as predefined terms, should be interpreted to be consistent with the contextual meanings of the related art, and are not to be construed as ideal or overly formal, unless expressly defined to the contrary.

It will be apparent to those skilled in the art that various modifications, substitutions and substitutions are possible, without departing from the scope and spirit of the invention as disclosed in the accompanying claims. will be. Therefore, the embodiments disclosed in the present invention and the accompanying drawings are intended to illustrate and not to limit the technical spirit of the present invention, and the scope of the technical idea of the present invention is not limited by these embodiments and the accompanying drawings . The scope of protection of the present invention should be construed according to the following claims, and all technical ideas within the scope of equivalents should be construed as falling within the scope of the present invention.

Claims (11)

Obtaining three-dimensional Lidar data for obstacle detection in the unmanned water forecasting;
Generating a virtual trajectory passing over the water surface of the unmanned water well;
Extracting noise data generated from the water surface of the rear surface of the unmanned waterproofing system in the virtual trajectory; And
And deleting the noise data among the three-dimensional Lattice data,
Wherein the step of generating the virtual trajectory includes:
Acquiring the azimuth anomaly azimuth and velocity;
Obtaining the unmanned augmented turning radius using the obtained azimuth angle and velocity; And
And generating the virtual trajectory to which the unmanned parking fixture moves based on the turning radius. The method according to claim 1,
Wherein the step of acquiring the azimuth augmented azimuth and the velocity acquires the azimuth augmented azimuth and velocity using a GPS module. The method according to claim 1,
Wherein the step of generating the virtual trajectory includes:
Wherein a circle having a radius of the unmanned water well defined turning radius (r) is generated by using a triangle relation between the unmanned water surface defining length (L), the azimuth angle (?) And the velocity (v) A method for removing noise. The method of claim 3,
The triangular relationship between the length L, azimuth angle?

Lt; / RTI >

Is the azimuth-free azimuth angle, and r is the turning radius. The method according to claim 1,
The step of extracting the noise data includes:
Determining a backside length of the unmanned image on the virtual trajectory according to the unmanned image forming speed;
Extracting the 3D Lidar data obtained in the ROI corresponding to the back length with the noise data,
Wherein the back surface length corresponds to a distance at which noise data is detected due to a three-dimensional surface on the water surface of the rear surface of the unmanned water tank, and is a length directed outward from the outer wall of the water tank. / RTI > 6. The method of claim 5,
The area corresponding to the rear length is,
The first boundary parallel to the inside of the imaginary locus and the second boundary parallel to the outside of the imaginary locus corresponding to the back length of the imaginary locus are m times (m is a positive integer) Wherein the first and second sensors are spaced apart from each other. An obstacle detection unit for obtaining three-dimensional Lada data for obstacle detection in the unmanned water reception;
A virtual locus generating unit for generating a virtual locus passing over the water surface of the unmanned water well;
A noise data extracting unit for extracting noise data generated from the water surface in the virtual trajectory; And
And noise data removing means for deleting the noise data from the three-dimensional linear data,
Wherein the virtual locus generator comprises:
Acquiring the azimuth augmentation azimuth and velocity, obtaining the unmanned augmentation turning radius using the azimuth and velocity, and generating the virtual trajectory based on the turning radius, A device that removes noise during detection of unmanned aerial vehicles. 8. The method of claim 7,
Wherein the virtual locus generator comprises:
Wherein the azimuth angle and the azimuth angle are obtained through a GPS module. 8. The method of claim 7,
Wherein the virtual locus generator comprises:
Wherein a circle having a radius of the unmanned water well defined turning radius (r) is generated by using a triangle relation between the unmanned water surface defining length (L), the azimuth angle (?) And the velocity (v) A device for removing noise. 8. The method of claim 7,
The noise data extracting unit extracts,
Dimensional lidar data obtained in a region corresponding to the rear length is extracted as the noise data, and the three-dimensional LIDAR data is extracted from the virtual trajectory,
Wherein the rear length is a length from the outer wall to the outer side at the rear surface of the unmanned water supply. 11. The method of claim 10,
The area corresponding to the rear length is,
The first boundary parallel to the inside of the imaginary locus and the second boundary parallel to the outside of the imaginary locus corresponding to the rear length of the imaginary locus are set to m times (m is a positive integer) Wherein the noise eliminating unit is configured to be spaced apart from the noise eliminating unit.

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