JP2008033760A - Mobile robot - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To realize a mobile robot capable of highly accurately calculating a present position, by reducing an error in calculating its own position caused by a difference in a friction coefficient of a road surface. <P>SOLUTION: This mobile robot 1 moves on a travel passage composed of a plurality of sections using a wheel 6, and has a guide detecting part 10 detecting an indication marker 4 of the travel passage, a control part 21 identifying the sections by detection of the indication marker 4, a road surface state determining part 23, a position calculating part 15 calculating the present position, and a storage part having a dry-wet state of the road surface and position correction information every section. The position calculating part corrects and calculates the present position by the dry-wert state of the road surface and the position correction information corresponding to the section. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a mobile robot that travels outdoors while detecting obstacles on a traveling road surface, and more particularly to a technique for ensuring self-position detection accuracy when the traveling road surface is wet.

  In recent years, mobile robots have been provided that move along a predetermined route to carry articles and monitor abnormalities. In such a mobile robot, the rotation amount of the wheel is measured, and the current position is measured by calculating the movement distance and the movement route based on the wheel radius and the left-right wheel interval (tread) inputted in advance. Are known.

In Patent Document 1, in particular, it travels along a magnetic tape as a guide means, reads an address designation transmitter provided on the road surface, performs a turn control such as a right turn and a left turn, and measures the amount of wheel rotation with an encoder. Thus, an automatic guided vehicle that calculates a current position from a travel distance is described.
Japanese Patent Laid-Open No. 9-6433 (FIG. 2)

  The conventional automatic guided vehicle described above calculates a moving distance from the rotation amount of the wheel measured by the encoder, and a wheel radius stored in advance is generally used for calculating the moving distance. The automatic guided vehicle controls turning by controlling motors for left and right wheels. In order to calculate the amount of turning, a left and right wheel interval inputted in advance is generally used.

  However, when the current position is detected using the wheel radius or the distance between the wheels, even in the same travel section, the degree of wheel slip differs due to the difference in the friction coefficient of the road surface due to dry and wet conditions. There is a problem that an error may occur in the self-position calculated when the road surface is wet.

  In addition, the moving means of this type of mobile robot is not limited to wheels, for example, a leg-walking type having two or more walking legs, a crawler type having an endless track, etc. There is also. Also in these moving means other than the wheel traveling type, similar to the above-described problem, slip occurs between the moving means and the traveling surface due to the difference in the friction coefficient of the road surface due to dry and wet, and the calculated self-position There was a problem that an error might occur.

  Therefore, the present invention has been made in view of the above problems, and reduces errors in self-position calculation due to differences in road surface conditions such as differences in the friction coefficient of the road surface due to differences in conditions such as dry and wet conditions on the road surface. An object of the present invention is to realize a mobile robot capable of calculating the current position with high accuracy.

The mobile robot according to claim 1 is:
A mobile robot that moves a travel route by a moving means,
A road surface state determination unit for determining the wet and dry state of the road surface;
A position calculation unit for calculating the current position;
A storage unit that stores in advance position correction information for correcting the position of the mobile robot corresponding to the wet and dry state of the road surface;
The position calculating unit is configured to call the position correction information corresponding to the wet and dry condition of the road surface determined by the road surface state determining unit from the storage unit and calculate a current position based on the position correction information.

The mobile robot according to claim 2 is the mobile robot according to claim 1,
Further, an instruction means detection unit that is arranged on the travel route and detects a point instruction means that serves as a section of the section in the travel route;
A section discriminator for discriminating the section by detecting the start point and end point of the section based on the output of the instruction means detector;
The storage unit stores the position correction information in advance corresponding to the wet and dry state of the road surface for each section,
The position calculation unit calls the position correction information corresponding to the road surface dry / wet state determined by the road surface state determination unit and the section determined by the section determination unit from the storage unit, and uses the position correction information as a current position It is characterized by calculating.

The mobile robot according to claim 3 is the mobile robot according to claim 1 or 2,
A wheel as the moving means, and a rotation amount detection unit for detecting the rotation amount of the wheel,
The position calculation unit is characterized in that a calculation formula for calculating a travel distance from the amount of rotation of the wheel is corrected by the position correction information, and the current position is calculated from the calculated travel distance.

The mobile robot according to claim 4 is the mobile robot according to claim 3,
As the wheels, at least a pair of drive wheels,
The position correction information includes a diameter correction coefficient for correcting the diameter data of each driving wheel and a distance correction coefficient for correcting wheel distance data between the driving wheels for each section corresponding to the wet and dry conditions of the road surface. Remember,
The position calculation unit calculates a travel distance for each drive wheel from the rotation amount for each drive wheel and the diameter data corrected by the diameter correction coefficient, and is corrected by the travel distance and the interval correction coefficient. The present invention is characterized in that the current position is obtained by calculating the turning amount from the wheel interval.

The mobile robot according to claim 5 is the mobile robot according to any one of claims 2 to 4,
Further, the position correction information of the section in which the travel has been completed is determined using the driving amount of the moving unit in the section and the actual distance of the section according to the wet and dry state of the road surface determined by the road surface state determination unit in the section. And a position correction information calculation unit for updating the information.

The mobile robot according to claim 6 is:
A mobile robot that moves a travel route composed of a plurality of sections by a moving means,
An instruction means detection unit for detecting a point instruction means arranged on the travel route;
A section discriminator for discriminating each section by detecting the start point and end point of each section based on the output of the instruction means detector;
A storage unit that stores in advance position correction information for correcting the position of the mobile robot for each section;
A position calculated by calling the position correction information corresponding to the section identified by the section determination section from the storage section and correcting the current position of the mobile robot in the section currently moving with the position correction information. A calculation unit;
A road surface state determination unit for determining the wet and dry state of the road surface;
New position correction information is calculated using the driving amount of the moving means in the section where the travel is completed and the actual distance of the section, and the new position correction information and the road surface humidity determined by the road surface state determination unit are calculated. A position correction information calculation unit that updates the position correction information of the section stored in the storage unit with a weighting factor that is determined in advance according to the degree,
It is characterized by having.

  According to the mobile robot of the present invention, it is possible to cope with the situation where the friction coefficient of the road surface is different and to improve the accuracy of the current position calculation.

  Further, when the moving means is a wheel, the current position of the mobile robot can be corrected by correcting the calculation formula for calculating the current position from the rotation amount of the wheel.

  Furthermore, when the moving means has a pair of driving wheels, the reference diameter of each wheel, the diameter correction coefficient of each reference diameter of each wheel, the reference interval of both wheels, and the reference interval of both wheels The current position obtained from the rotation amount of the wheel can be corrected by the interval correction coefficient.

  Further, after the travel of a certain section is completed, if the position correction information of the section is updated with the wet and dry conditions of the road surface of the section and the travel performance in the section, the information with the highest accuracy is always used as the basis. The current position can be corrected, and the current position can be calculated with higher accuracy.

  Further, according to the mobile robot of the present invention, if a single type of information is prepared for each section as the position correction information for correcting the position of the mobile robot, the current position is corrected by the position correction information. After the travel of the section is completed, new position correction information is calculated using the travel performance in the section and the actual distance of the section, and the new position correction information and the previous position correction information are The latest position correction information can be blended at an appropriate ratio using a weighting factor according to the degree of correction, and this can be used for correcting the current position as highly accurate position correction information. Can be performed with high accuracy.

1. Overall configuration (Figure 1)
FIG. 1 is a plan view of a monitoring area, which is an environment in which a mobile robot 1 according to an embodiment of the present invention is used, and a perspective view showing an appearance of the mobile robot 1.
The mobile robot 1 is an autonomous mobile robot that is used for security purposes, for example, and uses an abnormality determination sensor (not shown), an imaging unit 7, and a laser sensor 2 while traveling around a predetermined route in the monitoring area. It detects abnormalities in the monitoring area.

  In FIG. 1A, the periphery of the building is a moving route that the mobile robot 1 circulates in advance, and a white line tape 3 as a guide means is fixedly provided over the entire length of the moving route. . The monitoring area is a range of a predetermined area including the building and the white line tape 3 set to go around the building.

  In addition, at a predetermined point in the route, an indication marker 4 as a point indication means is fixedly arranged in the vicinity of the white line tape 3. The instruction marker 4 is provided as a white rectangular mark at the boundary of the travel section set on the travel route, and the travel section set on the travel route and the point where the instruction marker 4 is set are indicated to the mobile robot 1. It is an instruction.

  In the middle of the white line tape 3, there are a starting point (starting point) and an ending point (arrival point) in one cycle of the monitoring patrol action by the mobile robot 1, which is a hangar when the robot is not used, and further the mobile robot 1 A robot box 5 having a charging device for charging the battery is arranged.

  Although not shown in FIG. 1, for example, a monitoring center is installed in a security-related department in the building. In this monitoring center, the mobile robot 1 of this example and a control device for operating the system related thereto are arranged, and a staff member is in charge of the operation.

  As shown in FIG. 1 (b), the mobile robot 1 is an obstacle detection means equipped on the front side of the main body while traveling along the white line tape 3 with the left and right wheels 6 as the movement means. The laser sensor 2 scans the monitoring area ahead of the moving direction as shown by the dashed line in the figure. The upper part of the main body is equipped with an imaging unit 7 that can image the entire periphery, and when an abnormality is found by the laser sensor 2 or the like or when necessary, an image of a necessary position in the periphery can be captured. Yes.

  When the mobile robot 1 detects an abnormality in the monitoring area during the monitoring patrol action, the mobile robot 1 sends an abnormal signal together with the captured image to the remote monitoring center. When the monitoring center receives the abnormality signal, the received captured image is displayed to check the abnormality, and the mobile robot 1 is remotely operated to deal with the abnormality.

2. Configuration of mobile robot 1 (FIGS. 1 and 2)
FIG. 2 is a functional block diagram showing a specific configuration of the mobile robot 1 of this example.
This mobile robot 1 includes moving means 6 and 8, a guide detection unit 10, a movement control unit 9, a self-position detection unit 13, a distance measuring sensor 16, a storage unit 17, a processing unit 18, an imaging unit 7, a communication unit 20, and these It has the control part 21 which controls each part, and the power supply part 22 which supplies electric power to each part. Each part will be described below.

(1) Moving means The mobile robot 1 has four wheels 6 as shown in FIG. 1B, and two of them, the right front wheel 6 and the left front wheel 6, function as drive wheels. The moving means is composed of two motors 8 for independently driving the right front wheel 6, the left front wheel 6 and the left and right front wheels 6, and the straight traveling speed and the turning traveling speed are controlled by the rotational speed of the left and right front wheels 6, The turning direction is also controlled. The rotational speed of the left and right front wheels 6 is controlled by the movement control unit 9. Instead of controlling the left and right front wheels 6 independently, a method of controlling the turning speed by controlling the rudder angle, a method of independently controlling the left and right crawlers instead of wheel driving, and a left and right leg walking device may be used. A control method may be used.

(2) Guide Detection Unit The guide detection unit 10 is an instruction unit detection unit that detects the above-described white line tape 3 that is the guide unit on the moving path and detects the instruction marker 4.
The guide detection unit 10 includes a white line detection camera 11 and a road surface information extraction unit 12. The white line detection camera 11 is installed on the bottom surface of the mobile robot 1 so that the traveling road surface can be photographed. The road surface information extraction unit 12 detects the white line tape 3 and the pointing marker 4 that should guide the route of the mobile robot 1 from the photographed image of the white line detection camera 11 by processing such as edge extraction and Hough conversion, and sends it to the control unit 21. Output.

  Note that the guide detection unit 10 may be configured by a magnetic sensor, an electromagnetic induction sensor, or the like, and may detect a magnetic guide or an electromagnetic induction guide as guide means installed in the moving path. It is preferable that the guide means and the guide detection unit can be selected depending on the installation environment.

(3) Movement control part The movement control part 9 is a means for controlling the motor 8 which drives the wheel 6 of a moving means. The movement control unit 9 controls the motor 8 so that the mobile robot 1 moves along the white line tape 3 by, for example, well-known PID control according to the detection output of the white line tape 3 by the guide detection unit 10. Further, the movement control unit 9 controls the movement speed based on preset route information according to the detection of the travel section by the position calculation unit 15 of the self-position detection unit 13 described later, and the specific point by the position calculation unit 15 Stops traveling in response to detection of.

(4) Self-position detection unit The self-position detection unit 13 is a resolver 14 as a rotation amount detection unit installed in each motor 8 of the moving means, and a current position of the mobile robot connected to each resolver 14. It comprises a position calculation unit 15 for calculating. The self-position detection unit 13 further includes a wheel information calculation unit 24 as a position correction information calculation unit that calculates position correction information for correcting the position of the mobile robot (wheel information in this example in which the moving unit is a wheel). ing. The resolver 14 detects the absolute position of the rotating shaft of the motor 8. The position calculation unit 15 calculates the rotation amount of each of the left and right front wheels 6 from the rotation amount of the rotating shaft of the motor 8 obtained from the output of the resolver 14, and calculates the rotation amount of each of the left and right front wheels 6 and the wheel radius which is wheel information. The travel distance of the mobile robot 1 is calculated from the calculated average travel distance of the left and right front wheels 6.

  The position calculation unit 15 calculates a change in the posture (orientation) of the mobile robot from the difference between the rotation amounts of the left and right front wheels and the wheel interval, and calculates the self position by dead reckoning from the travel distance and the posture change by the left and right front wheels.

  The self-position calculating unit 13 counts the number of detections of the indication marker 4 according to the detection output of the indication marker 4 by the guide detection unit 10, detects the current travel section based on the route information, and Make corrections.

  Further, each time the mobile robot 1 finishes traveling in a certain section, the wheel information calculation unit 24 uses the rotation amount of each of the left and right wheels in the section and the route information described later indicating the actual distance in the section. The wheel information is calculated, and the wheel information of the section stored in the storage unit 17 to be described later is updated corresponding to the wet and dry state of the road surface of the section determined by the road surface state determination unit 23 described later.

  The wheel information of the present embodiment includes the reference diameter of each driving wheel, which is a reference value unique to the mobile robot 1, the diameter correction coefficient of each reference diameter of each driving wheel, and the reference value unique to the mobile robot 1. The reference interval between the two driving wheels and the interval correction coefficient for the reference interval between the two driving wheels. The correction coefficient is set for each section and for each of the wet and dry conditions on the road surface. The wheel information calculated by the wheel information calculation unit 24 may be a value obtained by multiplying the reference diameter and the diameter correction coefficient and a value obtained by multiplying the reference interval and the distance correction coefficient.

  Then, the position calculation unit 15 calls the road surface dry / wet state determined by the road surface state determination unit 23 described later and wheel information corresponding to the section from the storage unit 17, and uses this to rotate the left and right front wheels 6. The current position is corrected from the amount and calculated. That is, the corrected wheel diameter and the corrected wheel interval are used for calculation of the current position and posture of the travel control in the position calculation unit 15.

  Each time the mobile robot 1 finishes traveling in a certain section, the wheel information of the section updated by the wheel information calculation unit 24 based on the rotation amount of each of the left and right wheels in the section and the actual distance of the section is: , A diameter correction coefficient of each reference diameter of each wheel, and an interval correction coefficient of the reference interval of both wheels. That is, the wheel information calculation unit 24 performs the above calculation processing from the wheel rotation amount measured at the start point and end point of the section in response to the section determination by the control unit which is the section determination unit as will be described later.

(5) Ranging sensor The ranging sensor 16 is a means for scanning the front of the mobile robot 1 to detect the state of the traveling road surface, foreign matter on the traveling road surface, and other detection targets.
As shown in FIG. 3, the distance measuring sensor 16 includes a laser sensor 2 installed on the front side of the main body of the mobile robot 1 toward the front lower side in the traveling direction, and the laser light emitted from the laser oscillator is on the optical path. Receives reflected light when reflected by an object in the area. The distance measuring sensor 16 controls the irradiation direction of the laser light emitted from the laser oscillator by the scanning mirror and means for rotationally driving the scanning mirror, so that a predetermined range including the front of the mobile robot 1 is set to a predetermined scanning cycle. Spatial scanning is performed (for example, 33 ms).

  3A shows the geometric positional relationship between the laser sensor 2 and the measurement point in the mobile robot 1 of this example, and FIG. 3B shows the scanning angle range Φ of the laser sensor 2. An example of the sensing area (hatched area) indicated by and the number of sensor measurement data in the area is shown. As shown in the figure, in this example, 140 ° in front of the mobile robot 1 is set as a sensing area by the laser sensor 2, and the scanning mirror is rotated by 0.5 ° to scan the sensing area. As a result, data of 281 measurement points are obtained in one cycle of scanning by the laser sensor 2.

  Then, the distance measuring sensor 16 calculates the distance between the distance measuring sensor 16 calculated by the time from the irradiation of the laser light to the detection of the reflected light and the object (measurement point) reflecting the laser light, and the scanning mirror driven to rotate. Based on the angle, the relative position of the object (measurement point) reflecting the laser beam is calculated. The relative position is the position (distance value, angle) of the measurement point with reference to the mobile robot 1. The distance measuring sensor 16 outputs a measurement result every time the laser sensor 2 scans for one period.

  The distance measuring sensor 16 may be composed of a sensor other than the laser sensor. For example, the distance measuring sensor 16 may be constituted by an infrared type sensor or a millimeter wave radar type sensor. In addition to a configuration that scans with a scanning mirror, a plurality of light projecting / receiving units are provided for each predetermined angle around the mobile robot, and by operating each of the light projecting / receiving units at a predetermined cycle, the road surface and obstacles below the surroundings can be obtained. The distance value may be obtained. In this case, the distance measuring sensor 16 collectively outputs the measurement results of the light projecting and receiving units.

(6) Storage Unit The storage unit 17 stores information used for various processes of the mobile robot 1. The information stored in the storage unit 17 includes the following items 1) to 3).
1) Route information showing travel route information
2) Position information of the mobile robot 1 calculated by the position calculation unit
3) Wheel information as position correction information

  The route information of 1) corresponds to the number of each section (section from one indication marker 4 to the next indication marker 4) in the traveling route, the traveling speed within the section, the section distance measured in advance, and the starting point of the section A difference in azimuth angle between the end points (angle difference), an operation of the mobile robot 1 on each indication marker 4, and the like are stored. In the route information, the section number is associated with the number of indication markers 4 (number of detections) to be detected by the corresponding section. In the traveling control, the section number and the operation of the mobile robot 1 on the instruction marker 4 are specified from the number of detections of the instruction marker 4.

The position information 2) is the position and posture of the mobile robot 1 calculated by the position calculation unit 15 of the self-position calculation unit 13.
The position and orientation are calculated from the wheel rotation amount using the following wheel information.

  As described above, the wheel information of 3) includes the reference diameter of each driving wheel, the diameter correction coefficient of each reference diameter of each driving wheel, the reference interval of both driving wheels, and the reference of both driving wheels. This is an interval correction coefficient for the interval.

  The reference diameter and the reference interval are fixed values. On the other hand, the diameter correction coefficient and the interval correction coefficient are rewritten as needed to reflect various conditions such as changes due to the air pressure and wear of the wheels and the road surface shape of each section. Accordingly, the correction values of the wheel diameter and the wheel interval calculated by the wheel information calculation unit 24 provide the wheel radius and the wheel interval reflecting the above-mentioned conditions, and whether the road surface is wet or dry. Different values are stored accordingly.

(7) Processing Unit The processing unit 18 includes a road surface state determination unit 23 and an obstacle determination unit 19. The road surface state determination unit 23 determines whether the road surface state is dry or wet based on the output result of the distance measuring sensor 16, and outputs the determination result to the control unit 21. The obstacle determination unit 19 determines whether there is an abnormality based on the output of the distance measuring sensor 16. The obstacle determination unit 19 compares the difference between the distance values included in the data output as the scanning result of one cycle of the distance measuring sensor 16 with a threshold value to determine whether there is an obstacle having a height higher than a predetermined level. Determine whether. When an obstacle having a height that interferes with the traveling of the mobile robot 1 is detected and determined to be abnormal, an abnormal signal is output from the communication unit 20 described later, and the movement control unit 9 stops the mobile robot 1. Or a predetermined process such as deceleration is performed. In this example, the wetness of the road surface is determined from the output result of the distance measuring sensor 16, but means for determining the wetness of the road surface may be provided separately. In this case, the determination is not limited to the method of determining from the reflected wave of the laser sensor 2 as the distance measuring sensor 16, and the determination is made by analyzing the difference between the ultrasonic wave, the reflected wave of infrared rays, the image analysis, and the running sound generated from the wheel. Also good.

(8) Imaging Unit The imaging unit 7 is a unit that is mounted on the upper surface of the main body of the mobile robot 1 and images the surroundings of the mobile robot 1. In this example, a plurality of cameras are arranged outward in the circumferential direction so as to cover the entire circumference.

(9) Communication unit The communication unit 20 is a wireless communication unit that transmits and receives signals to and from a remote monitoring center. When the mobile robot 1 detects an abnormality in the monitoring area, the communication unit 20 outputs an abnormality signal to the remote monitoring center by radio or the like. The communication unit 20 transmits the image captured by the imaging unit 7 and the position of the mobile robot 1 calculated by the self-position detection unit 13 to the remote monitoring center, and receives a control command signal transmitted from the monitoring center. To the control unit 21 described later.

(10) Control Unit The control unit 21 is a unit that comprehensively controls each component of the mobile robot 1 and is configured by a computer including a CPU and the like.

  In addition, in the configuration of each unit described above, the function that can be realized by computer processing may be realized by the same computer. For example, the road surface information extraction unit 12, the position calculation unit 15, the movement control unit 9, the road surface state determination unit 23, the obstacle determination unit 19, the wheel information calculation unit 24, and the like may be realized by the same computer. The storage unit 17 may be realized by a memory of the computer, an external storage device, or the like.

3. Operation of Mobile Robot The operation of the mobile robot according to the present embodiment will be described. Here, first, an overview of the overall operation of the mobile robot 1 will be described, and then an operation for obtaining position correction information will be described.
The mobile robot 1 is set to the patrol mode when it detects the arrival of a preset patrol start time by a timing means (not shown), or receives an input from the operation unit or a command from the monitoring center, and detects the guide. With the signal from the unit 10 as an input, the movement control unit 9 controls the moving means (wheel 6 and motor 8), and starts moving along the white line tape 3 as the guide means. The movement control unit 9 independently controls the rotation of both motors 8 so that the white line tape 3 is detected from the image captured by the white line detection camera 11 and the white line tape 3 always faces a predetermined angle at a predetermined position of the image. . In the present embodiment, the movement control unit 9 operates so that the white line tape 3 is directed vertically in the center of the image. Further, based on the detection of the instruction marker 4, the current moving section and the traveling speed are obtained by referring to the route information in the storage unit 17, and the motor rotation is controlled according to the traveling speed.

  When starting to move, the mobile robot 1 activates the distance measuring sensor 16 to spatially scan the front. When the distance measuring sensor 16 detects the reflected light, it outputs distance measurement data at the measurement point. This distance measurement data is an angle indicating the irradiation direction of the laser beam, and it is assumed that the distance measurement sensor 16 scans a predetermined range of road surface ahead of the mobile robot 1 as shown in FIG. The distance measuring sensor 16 outputs measurement data at predetermined time intervals.

When the obstacle determination unit 19 receives an input from the distance measuring sensor 16, the obstacle determination unit 19 determines whether there is an obstacle.
Further, the road surface state determination unit 23 determines the road surface state upon receiving an input from the distance measuring sensor 16. The mobile robot 1 travels to the end point of the moving route while performing such obstacle determination and road surface state determination.

FIG. 4 is a flowchart for explaining the operation of the first embodiment. In the present embodiment, position correction information calculated when traveling in a section where the distance is known in the past is used for correcting the travel distance, and different position correction information is used depending on whether or not the road surface is wet. To do.
In the following description, the wheel A will be described as the right driving wheel, and the wheel B will be described as the left driving wheel.

In S01, the initial state of the mobile robot 1 (cart) is set. Specifically:
-The position coordinates and posture of the mobile robot 1 are initialized to the following home positions. The home position is a position where the mobile robot 1 is in a standby position (the robot box 5) where the charging stand is located.
Position: (X0, Y0)
Posture: θ0
・ Set the wet road surface counter to zero. The wet road surface counter counts up when it acquires information on whether or not the road surface obtained by scanning the laser sensor 2 is wet from the road surface state determination unit 23 every predetermined period and detects the wet road surface.
・ Initialize the section number to 1. The section number is counted up every time the pointing marker 4 is detected.

Next, in S02, the absolute position of the drive shafts of the left and right wheels is measured by the resolver 14, and the measured value (for example, the value of the rotation angle) is stored as follows.
Wheel A absolute position: La (0)
Wheel B absolute position: Lb (0)

Next, in S03, the route information and wheel information of the first section are read from the storage unit 17.
The route information includes, for example:
-Section number-Section distance D, angle difference between start point and end point of section n (turning angle) φ
-Coordinates of the indicator marker 4-Whether the section is a straight line or a curve-Travel speed (upper speed)
The wheel information includes the following.
・ Reference diameters (specific values) of wheels A and B: Ra, Rb
・ Reference interval (specific value) of both wheels: W
-Wheel A diameter correction factor, Wheel B diameter correction factor, Wheel interval correction factor (Dry) (Cad, Cbd, Cwd)
-Wheel A diameter correction coefficient, Wheel B diameter correction coefficient, Wheel interval correction coefficient (when wet) (Caw, Cbw, Cww)
The wheel information is set for each section in association with the section number.

Next, in S <b> 04, information on whether the road surface ahead of the traveling road is wet is acquired from the road surface state determination unit 23 via the control unit 21. The determination result as to whether the road surface is wet may be acquired for each cycle of the laser sensor 2 or may be acquired for each predetermined interval longer than the cycle of the laser sensor 2. Further, in the present embodiment, as shown in FIG. 3, since the laser sensor 2 scans an oblique front of the mobile robot 1, information on the point is acquired when the driving wheel of the mobile robot 1 passes through the scanned point. It is preferable to provide a time delay so that it is possible.
The road surface state is determined based on the scanning result of the laser sensor 2, and FIG. The description will be described later. The determination of the road surface state is not limited to the one using the scanning result of the laser sensor, and may be determined by ultrasonic waves, infrared reflected waves, image analysis, wheel running sound analysis, or the like.

  Next, when it is determined in S05 that the road surface is wet (YES in S04), the wet road surface counter is incremented.

Next, in S06, values for the wet road surface are selected as the diameter correction coefficient Ca of the wheel A, the diameter correction coefficient Cb of the wheel B, and the wheel interval correction coefficient Cw. In other words, the following values are assigned to the correction coefficients Ca, Cb, and Cw.
Ca = Caw, Cb = Cbw, Cw = Cww

On the other hand, if it is determined in S04 that the road surface is dry (NO in S04), a value for the dry road surface is substituted as a correction coefficient in S07 as follows.
Ca = Cad, Cb = Cbd, Cw = Cwd

In S08, the absolute positions of the left and right wheel shafts are measured by the resolver 14, and the measured values are stored as follows. Here, k is incremented for each period in which measurement is performed by the resolver 14, the current period and the previous value are stored in the storage unit, and the previous values are sequentially deleted. The same applies to the parameters marked with (k).
Wheel A absolute position: La (k)
Wheel B absolute position: Lb (k)

In S09, the amount of rotation of the left and right wheels is calculated. Specifically, it is calculated as follows from the difference between the current absolute position of the left and right wheel axles measured and stored in S08 and the absolute position stored one cycle before.
Wheel A rotation amount: Δθa (k) (= La (k) −La (k−1))
La (k−1): Absolute position of wheel A before one cycle Wheel B rotation amount: Δθb (k) (= Lb (k) −Lb (k−1))
Lb (k−1); Absolute position of wheel B one cycle before

In S10, the location and posture of the transfer robot 1 are calculated. The position and orientation of the mobile robot 1 are generally obtained by using a technique known as dead reckoning, and multiplying the reference diameter and the reference interval by each of the correction factors selected in S06 or S07 depending on the wheel rotation amount and the road surface condition. The corrected wheel radius and wheel interval are calculated by the following equations.
Wheel A radius after applying the correction factor: Ra '= Ra x Ca
Wheel B radius after applying the correction factor: Rb '= Rb x Cb
Wheel spacing after applying the correction factor: W '= W x Cw
Then, based on the calculated wheel radii Ra ′, Rb ′, the wheel interval W ′, and the wheel rotation amount obtained in S09, the travel distances ΔDa (k) ′, ΔDb of each wheel from the previous cycle to the present time. (k) ′, the turning angle θ (k) ′ of the mobile robot 1 and the translation distance Ds (k) ′ are calculated by the following equations, respectively.
Wheel A travel distance for one cycle: ΔDa (k) '(= Ra' x Δθa (k))
Wheel B travel distance for one cycle: ΔDb (k) '(= Rb' x Δθb (k))
Car turning angle for one cycle: Δθ (k) '(= (ΔDb (k)'-ΔDa (k) ') / W')
Translation distance for one period: ΔDs (k) ′ (= (ΔDa (k) ′ + ΔDb (k) ′) / 2)
Then, the current position and orientation are calculated from the stored position (X (k−1) ′, Y (k−1) ′) and the orientation θ (k) ′ one cycle before by the following formula.
Current position: (X (k) ′, Y (k) ′) = (X (k−1) ′ + ΔDs (k) ′ × cos (Δθ (k) ′), Y (k−1) ′ + ΔDs (k ) '× sin (Δθ (k)'))
Posture: θ (k) '= θ (k−1)' + Δθ (k) '
In addition, since it is used for calculation of update of the correction coefficient, which will be described later, the total travel distance in the current travel section at the current position and the total turning angle of the mobile robot 1 are calculated by the following formula from the wheel radius and the wheel interval not multiplied by the correction coefficient. The
Wheel A travel distance: Da (k) = Da (k-1) + ΔDa (k)
(ΔDa (k) = Ra x Δθa (k))
Wheel B travel distance: Db (k) = Db (k-1) + ΔDb (k)
(ΔDb (k) = Rb x Δθb (k))
Mobile robot 1 turning angle: θ (k) = (ΔDb (k) −ΔDa (k)) / W

  In S11, it is determined whether or not the pointing marker 4 is detected by the guide detection unit. If not detected, the process returns to S04.

When the instruction marker is detected in S11 (YES in S11), the correction coefficient relating to the wheel information of the travel section that has been traveled so far is updated in S12.
This correction coefficient update processing is made into a subroutine, and will be specifically described with reference to the flowchart of FIG.

  The reason why the correction coefficient needs to be updated is first the slope of the road surface (traverse). This is a case in which the rotation amount of the wheel on the mountain side in the traveling direction of the mobile robot 1 is reduced due to the inclination of the road surface, and thus the correction coefficient is updated so that the radius of the wheel on the valley side is corrected to be smaller.

  Another reason that the correction coefficient needs to be updated is that the design length of the section and the actual length are different depending on construction reasons. In that case, it is necessary to update the correction coefficient so that the radius of the wheel is corrected in accordance with the actual distance. For example, when the section is a slope, the actual travel distance is longer than the distance on the design plan view, so it is necessary to update the correction coefficient so that the wheel diameter is corrected to be small. is there.

  In S13, the current position (coordinates) of the mobile robot 1 is corrected to the marker position (coordinates) stored in the storage unit 17 corresponding to the marker detected in S11.

  Next, the wet road surface counter is set to 0 in S14, the section number is incremented in S15, and the route information and wheel information of the next travel section are read out in S16.

  In S17, it is determined whether or not the section that has been traveled so far is the final section. If there is still a next travel section (NO in S17), the process returns to S04, and there is no next travel section, that is, the mobile robot 1 If it is determined that the end point has been reached (YES in S17), a standby operation is entered in S18.

Next, a subroutine for updating the correction coefficient in S12 will be described with reference to the flowchart of FIG.
S51: A correction degree (correction amount) for correcting the wheel information in the section is calculated from the total travel distances Da and Db, the total turning angle θ, and the route information obtained by traveling in the section.
Specifically, the section distance D in the section obtained from the route information, the angle difference between the start point and the end point (turning angle φ), and the total travel distance Da in the section of the left and right wheels calculated without using the correction coefficient in S10. , Db, and the total turning angle θ of the mobile robot 1, the correction amount in the section is calculated by the following equation. Da, Db, and θ are values of Da (k), Db (k), and θ (k), respectively, at the marker detection point.
Correction amount of wheel A radius in the section = D / Da
Correction amount of wheel B radius in this soft section = D / Db
Wheel interval correction amount in the section = θ / φ

  S52: In the update processing in FIG. 5, either the wet or dry correction coefficient is updated according to the degree of wetness of the road surface in the section. In S52, using the wet road surface counter value, which is the accumulation of the determination results determined as wet road surfaces in each cycle in S04, the degree of wetness (dry / wet state) for the entire section is obtained by “counter value / number of scans”. Then, if the “counter value / number of scans” is 50% or more, it is determined to be wet (YES in S52), and if it is less than 50%, it is determined to be dry (NO in S52). In addition, the trial threshold value used by this process is not limited to 50%, You may set suitably as needed.

  S53: When it is determined in S52 that the road surface is wet, it is determined whether or not the wet road surface correction coefficient has been acquired in the past, that is, whether or not the wet road surface correction coefficient has been updated in the past. At the time of start-up, there is no record of the correction coefficient, and in this case, 1 is stored in advance as the correction coefficient.

S54: If it is determined in S53 that the wet road surface correction coefficient has been captured in the past (YES in S53), the wet road surface correction fraud is updated by the following equation.
Diameter correction factor for wheel A: Caw = (1−α) × Caw + α × D / Da
Diameter correction factor for wheel B: Cbw = (1−α) × Cbw + α × D / Db
Wheel spacing correction coefficient: Cww = (1-α) x Cww + α x θ / φ
Here, Caw, Cbw, and Cww on the right side are values before update. Although not shown in the flowchart, when the section is a straight line, the diameter correction coefficient Caw of the wheel A and the diameter correction coefficient Cbw of the wheel B are updated. When the section is a curve, the wheels A and B are updated. Only the interval correction coefficient Cww may be updated.
In order to update the correction coefficient, α uses the stored correction coefficient (before update) and the degree to be corrected (correction maximum) calculated as a result of traveling this section this time, in any distribution It is a weighting coefficient that determines whether or not to be performed, and the value thereof may be fixed or may be changed according to the degree of wetness.

  S55: When there is no past acquisition record (NO in S53), the correction amount calculated in S51 is newly set as a wet road surface correction coefficient.

  S56: On the other hand, if it is determined in S52 that the road surface is not wet (NO in S52), it is determined whether there is a past acquisition record of the correction coefficient for dry road surface. At the time of start-up, there is no record of the correction coefficient, and in this case, 1 is stored in advance as the correction coefficient.

S57: If it is determined in S56 that there is a past record of the correction coefficient for dry road surface (YES in S56), the correction coefficient for dry road surface is updated by the following equation.
Diameter correction factor for wheel A: Cad = (1−α) × Cad + α × D / Dd
Diameter correction factor for wheel B: Cbd = (1−α) × Cbd + α × D / Dd
Wheel spacing correction coefficient: Cwd = (1-α) × Cwd + α × θ / φ
Here, Cad, Cbd, and Cwd on the right side are values before update. Although not shown in the flowchart, when the section is a straight line, the diameter correction coefficient Cad of the wheel A and the diameter correction coefficient Cbd of the wheel B are updated. When the section is a curve, the wheels A and B are updated. Only the interval correction coefficient Cwd is updated.
The value of α is the same as the correction coefficient for wet road surface in S54, and the degree to be corrected calculated as a result of traveling this time in the stored correction coefficient (before update) and the section in order to update the correction coefficient This is a weighting factor that determines how the (correction amount) is used, and may be fixed or may be changed according to the degree of stagnation.

S58: If there is no past acquisition record (NO in S56), the correction amount calculated in S51 is newly set as a coefficient for the dry road surface.
S59: The updated or newly set correction coefficient is stored together with the section number.

  Next, a method of determining the wetness of the road surface state from the measurement result of the cycle of the laser sensor 2 will be described using the flowchart of FIG. The determination result in this process is used in S04 described above.

  S71: The road surface state determination unit 23 acquires measurement data (sensor data) up to the measurement point where the laser beam reflected by the output signal from the laser sensor 2 is reflected. The laser sensor irradiates the traveling road surface about 3 m ahead in the range of the central angle close to 180 degrees to obtain reflected light (see FIG. 3).

  S72: The road surface state determination unit 23 selects N pieces of data corresponding to the front in the traveling direction of the mobile robot 1 (for example, 121 pieces of data corresponding to 60 degrees in front) from the sensor data.

  S73: The road surface state determination unit 23 counts the number of sensor data in which the value of each sensor data (distance value y) is equal to or less than a predetermined value among the N data corresponding to the front in the traveling direction.

  Here, the constant value is a numerical value slightly higher than the distance value obtained when the light is reflected on the road surface, and the obtained value of each sensor data (distance value y) is equal to or less than the constant value is a laser beam. Is reflected on the traveling road surface and returned to the laser sensor 2, which means that the reflected light is obtained from the portion of the traveling road surface where the laser light hits. In other words, the count number here means the number of laser beams reflected from the traveling road surface and returned to the laser sensor 2 out of N pieces of sensor data.

  When the traveling road surface is wet, the laser light may be totally reflected on the traveling road surface and may not return to the laser sensor 2. In this case, the sensor data in the laser sensor 2 is evaluated as infinity, and the sensor The data value (distance value y) is determined to be a certain value or more. Alternatively, when the traveling road surface is wet, the laser light irregularly reflected on the traveling road surface may be output as a large distance value that is significantly different from the distance value from the road surface in the laser sensor. Therefore, in this example, the sensor data indicating a significantly large distance value as compared with the case where the light is reflected on the road surface is processed on the assumption that it is based on the laser light reflected on the wet road surface.

  S74: The road surface state determination unit 23 determines whether the ratio of the count number to the number N of the sensor data is equal to or greater than a predetermined value (for example, 70%) set in advance.

  S75: When the ratio of the count number to the number N of the sensor data is equal to or greater than the predetermined value (YES in S74), it is determined that the road surface is dry.

  S76: If the ratio of the count to the total number N of sensor data is not equal to or greater than the predetermined value (NO in S74), it is determined that the road surface is wet.

<< Second Embodiment >>
FIG. 6 is a flowchart for explaining the operation of the second embodiment.
In the present embodiment, a plurality of correction coefficients obtained in a plurality of past runs are weighted and averaged using a weighting factor α, and the weight of the correction coefficient obtained in running when the road surface is dry is high, and the road surface is wet. The weight of the correction coefficient obtained at the time is reduced. (The correction coefficient when the road surface gets wet is low in reliability, so the weight when updating the correction coefficient is lowered.)

  In the flowchart of FIG. 6, when it is determined in S205 that the road surface is wet, the wet road surface counter is incremented, and when it is not wet, the flow proceeds to S206 without doing anything. That is, in the present embodiment, the correction coefficient used for calculating the current position is the same coefficient regardless of the wet / dry road surface unlike the first embodiment. As will be described later, this correction coefficient is a value obtained by allocating weights to the running results during drying and wetting, and weighted averages, thereby absorbing errors caused by differences in road surface conditions and improving reliability. It is improving. In addition, since it is the same as that of the first embodiment except for the correction coefficient updating process of S210, the description thereof is omitted.

The process of updating the correction coefficient in S210 of FIG. 6 will be described using the flowchart of FIG.
S301: The correction amount of the wheel radius in the section is calculated from the total travel distances Da and Db in the section, the total turning angle θ, and the route information. The calculation method for calculating the correction amount is the same as S51 described above.

  S302: It is determined whether there is a past acquisition record. At the complete start-up, there is no record of the correction coefficient, and in this case, 1 is stored in advance as the correction coefficient.

  S303: Based on the value of the wet road surface counter, the degree of wetness (dry / wet state) of the traveled section is obtained and determined by “counter value / number of scans”. If the “counter value / number of scans” is 50% or more, it is determined to be wet, and if it is less than 50%, it is determined to be dry. The threshold value is not limited to 50% and may be set as appropriate.

S304: If the road surface is determined to be wet in S303 (YES in S303), the correction coefficient is updated using the following equation. The correction coefficients are a wheel A diameter correction coefficient, a wheel B diameter correction coefficient, and a wheel interval correction coefficient, as in the first embodiment.
New correction coefficient = (1−β) × stored correction coefficient + β × correction amount β calculated this time is a weighting coefficient. In S304, this β is smaller than when the road surface condition is dry. Set to a value. This is because the reliability is low when wet, and the contribution to the new correction coefficient is reduced and updated. The value of the weighting factor β may be fixed, or may be further reduced when the degree of wetness is large according to the degree of wet road surface.

S305: If it is determined in S303 that the road surface is not wet (NO in S303), a correction coefficient is calculated with a weight coefficient β that is larger than that when wet (S304). The calculation formula is the same as S304.
S306: The correction coefficient is stored together with the section number.

  As described above, in any of the embodiments, it is possible to deal with a situation in which the friction coefficient varies depending on the wet and dry conditions of the road surface, and the accuracy of the current position calculation is improved. In particular, after the travel of a certain section is completed, the position correction information of the section can be updated with the wet and dry conditions of the road surface of the section and the travel performance in the section, so always use the most accurate information as a basis. The current position can be corrected, and the current position can be calculated with higher accuracy.

  The mobile robot 1 of each embodiment described above has a moving means using wheels, and various travel controls and position corrections are performed based on the number of rotations of the wheels, but the wheels are an example of moving means. However, even in the case of other moving means, if the accuracy of position calculation is affected because the friction coefficient varies depending on the wet and dry road surface, the position calculation accuracy is improved by using the method of the present invention. Needless to say, it can be achieved. For example, even with a leg-walking or crawler-type moving means, the position calculation accuracy decreases due to a slip between the leg and the road surface or between the endless track and the road surface due to a change in the friction coefficient with the road surface. In this case, the same effect can be obtained by correcting the appropriate control parameters of these moving means with the appropriate position correction information corresponding thereto.

FIG. 1A is a plan view of a monitoring area, which is an environment in which the mobile robot 1 according to an embodiment of the present invention is used, and FIG. 1B is a perspective view showing the appearance of the mobile robot 1. It is. FIG. 2 is a functional block diagram showing a specific configuration of the mobile robot 1 of this example. FIG. 3A is a side view showing a geometric positional relationship between the laser sensor 2 and the measurement point in the mobile robot 1 of this example, and FIG. 3B is a scanning angle range Φ of the laser sensor 2. It is a top view which shows the sensing area (shaded area) etc. which are shown by. FIG. 4 is a flowchart for explaining the operation of the first embodiment. FIG. 5 is a flowchart showing a procedure for updating the correction coefficient in the first embodiment. FIG. 6 is a flowchart for explaining the operation of the second embodiment. FIG. 7 is a flowchart showing a procedure for updating the correction coefficient in the second embodiment. FIG. 8 shows a specific flow of wet road surface detection processing by the laser sensor used in the determination of S04 of FIG. 4 and S204 of FIG.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Mobile robot 4 ... Instruction marker as point instruction means 6 ... Wheel as movement means 8 ... Motor as movement means 10 ... Guide detection part as instruction means detection part 15 ... Position calculation part 17 ... Storage part 21 ... Section Control unit 23 that also functions as a determination unit 23 ... road surface state determination unit 24 ... wheel information calculation unit as a position correction information calculation unit

Claims (6)

A mobile robot that moves a travel route by a moving means,
A road surface state determination unit for determining the wet and dry state of the road surface;
A position calculation unit for calculating the current position;
A storage unit that stores in advance position correction information for correcting the position of the mobile robot corresponding to the wet and dry state of the road surface;
The position calculation unit calls up the position correction information corresponding to the wet and dry state of the road surface determined by the road surface state determination unit from the storage unit, and calculates a current position based on the position correction information. . Further, an instruction means detection unit that is arranged on the travel route and detects a point instruction means that serves as a section of the section in the travel route;
A section discriminator for discriminating the section by detecting the start point and end point of the section based on the output of the instruction means detector;
The storage unit stores the position correction information in advance corresponding to the wet and dry state of the road surface for each section,
The position calculation unit calls the position correction information corresponding to the road surface dry / wet state determined by the road surface state determination unit and the section determined by the section determination unit from the storage unit, and uses the position correction information as a current position The mobile robot according to claim 1, wherein the mobile robot is calculated. A wheel as the moving means, and a rotation amount detection unit for detecting the rotation amount of the wheel,
The position calculation unit corrects a calculation formula for calculating a travel distance from the rotation amount of the wheel by the position correction information, and calculates the current position from the calculated travel distance. The mobile robot described in 1. As the wheels, at least a pair of drive wheels,
The position correction information includes a diameter correction coefficient for correcting the diameter data of each driving wheel and a distance correction coefficient for correcting wheel distance data between the driving wheels for each section corresponding to the wet and dry conditions of the road surface. Remember,
The position calculation unit calculates a travel distance for each drive wheel from the rotation amount for each drive wheel and the diameter data corrected by the diameter correction coefficient, and is corrected by the travel distance and the interval correction coefficient. The mobile robot according to claim 3, wherein a current position is obtained by calculating a turning amount from a wheel interval. Further, the position correction information of the section in which the travel has been completed is determined using the driving amount of the moving unit in the section and the actual distance of the section according to the wet and dry state of the road surface determined by the road surface state determination unit in the section. 5. The mobile robot according to claim 2, further comprising a position correction information calculation unit that updates the position information. A mobile robot that moves a travel route composed of a plurality of sections by a moving means,
An instruction means detection unit for detecting a point instruction means arranged on the travel route;
A section discriminator for discriminating each section by detecting the start point and end point of each section based on the output of the instruction means detector;
A storage unit that stores in advance position correction information for correcting the position of the mobile robot for each section;
A position calculated by calling the position correction information corresponding to the section identified by the section determination section from the storage section and correcting the current position of the mobile robot in the section currently moving with the position correction information. A calculation unit;
A road surface state determination unit for determining the wet and dry state of the road surface;
New position correction information is calculated using the driving amount of the moving means in the section where the travel is completed and the actual distance of the section, and the new position correction information and the road surface humidity determined by the road surface state determination unit are calculated. A position correction information calculation unit that updates the position correction information of the section stored in the storage unit with a weighting factor that is determined in advance according to the degree,
A mobile robot characterized by comprising:

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