JP4745151B2 - Mobile robot - Google Patents

Description

  The present invention relates to a mobile robot that travels outdoors while detecting an obstacle on a traveling road surface, and more particularly to a technique for ensuring the accuracy of detecting an obstacle even when the traveling road surface is wet.

  2. Description of the Related Art Conventionally, a mobile robot that travels while detecting an obstacle on a traveling road surface uses a distance detection sensor to emit laser light for distance measurement in the left-right direction toward the floor (traveling road surface) as disclosed in Patent Document 1. Light was projected and received at predetermined sampling intervals, and obstacles were detected based on the obtained distance data.

  FIG. 14 is a diagram for explaining a state of obstacle detection by a conventional mobile robot R, and shows a side view of the positional relationship among the mobile robot R, laser light L, traveling road surface S, and obstacle H on the right side. The left side shows a plan view of the distance data measured by the distance detection sensor. The distance data on the left is schematically shown in an orthogonal coordinate system viewed from the left and right (X axis direction) and front and rear (Y axis direction) with the distance detection sensor as the center. Further, in order to see the movement history of the mobile robot R, the mobile robot R is shown in a line up and down in order of time t, t + 1, t + 2, t + 3, and t + 4.

  At time t, the laser beam L of the mobile robot R is projected onto the traveling road surface S, and the reflected light from the traveling road surface S is received. For this reason, as shown in the left column, the distance to the place where the laser light L of the traveling road surface S hits is measured.

At time t + 1, the mobile robot R is slightly closer to the obstacle H, and the laser beam L is in a state where it hits a height close to the traveling road surface S of the obstacle H. In this case, as shown in the left column, the measured distance data has a difference between the distance from the reflected light from the traveling road surface S and the distance from the reflected light from the obstacle H.
At t + 2, the mobile robot R is closer to the time point t + 1, and the laser beam L has hit an intermediate height of the obstacle H. In this case, as shown in the left column, the measured distance data has a difference between the distance due to the reflected light from the traveling road surface S and the distance due to the reflected light from the obstacle H greater than the time point t + 1.
At t + 3, the mobile robot R is closer to the time point t + 2 and the laser beam L hits the upper part of the obstacle H. In this case, as shown in the left column, in the measured distance data, the difference between the distance from the reflected light from the traveling road surface S and the distance from the reflected light from the obstacle H becomes larger than the time point t + 2.

At t + 4, the laser beam L of the mobile robot R passes over the obstacle H and is projected onto the traveling road surface S, and the reflected light from the traveling road surface S is received. For this reason, as shown in the left column, the distance to the place where the laser light L of the traveling road surface S hits is measured.
Normally, in order to prevent the mobile robot R from colliding with an obstacle, a stop or avoidance operation is performed when the obstacle H is detected, so that control is performed so as not to move to t + 4. It is described for the purpose.

In the mobile robot R, the obstacle H is detected based on the distance data as shown in the left column of FIG. That is, as is apparent from the figure, the distance data based on the reflected light from the traveling road surface S is measured as substantially equidistant data, and the obstacle H is closer to the distance data than the distance data based on the reflected light from the traveling road surface S. It becomes data. Therefore, in the mobile robot R, if there is a distance difference of a predetermined threshold or more from the distance data by the traveling road surface S, it is detected as an obstacle. Note that the predetermined threshold value is used to determine whether or not the obstacle is an obstacle in consideration of the fact that the distance difference corresponds to the height of the obstacle from the road surface.
Japanese Unexamined Patent Publication No. 1-293410

  However, when it rains and the road surface gets wet, it is difficult to obtain a correct distance value by the laser sensor. In particular, when the laser sensor is installed at a shallow angle with respect to the road surface, depending on the road surface condition, the laser beam is specularly reflected and the distance value cannot be obtained at all, or the waveform of the signal from the received reflected light The error of the distance value may become large because it is difficult to determine the rising position. If the distance value error results in a shorter value (closer distance) than the actual road surface, it may be mistaken for an obstacle. May be judged.

  For example, if the robot travels at a speed of 10 km / h (277.7 cm / s) and the sampling period of the laser sensor is 33 ms, sensor data is obtained every time the robot moves 9.2 cm. Here, when laser light hits the obstacle, if the sensor mounting angle is about 10 degrees, the position where the laser light hits the obstacle is 1.6 cm (= tan10 ° in the height direction of the obstacle as the robot moves) * 9.2) It will be shifted one by one.

Here, when the robot can get over a step of up to 5 cm, it is necessary to detect an object having a height of 5 cm or more as an obstacle that hinders running.
Therefore, in order to find an obstacle 5 cm high, the sensing result must be within the range of 3.4 cm <h ≤ 5 cm, that is, the threshold must be set to 3.4 cm to reliably find 5 cm. .

  However, as described above, the error increases when the road surface gets wet, and if a value shorter than the actual distance is obtained, an object with a height of 3.4 cm or more is erroneously detected even if the object is low in height. There was a problem that there is a possibility.

  As described above, when the road surface is wet, there is a possibility that the accuracy of the obstacle determination may be lowered in the normal obstacle determination method as described above. In addition, when water accumulates on most of the road surface due to heavy rain or the like, the laser beam projected on the road surface is specularly reflected by the surface of the water accumulated on the road surface, and distance values are often not obtained. On the other hand, since the laser beam hits the vertical plane from the obstacle and is reflected and returns to the laser sensor, a distance value can be obtained for the obstacle. In such a case, it is difficult to obtain a value corresponding to the height of the obstacle from the comparison of the distance values between the road surface and the obstacle, and as a result, there is a high possibility that the obstacle cannot be detected. It was.

  The present invention has been made in view of the above-mentioned problems. Of course, when the road surface is dry, the road surface is wet due to rain or the like, so that the detection accuracy of an obstacle is reduced, especially heavy rain. Even if water is accumulated on the road surface and it becomes difficult to detect obstacles, it is always possible to detect obstacles regardless of the road surface condition by using complementary detection logic of different types of obstacles. The object is to provide a mobile robot that can detect with high accuracy.

The mobile robot according to claim 1 is a mobile robot that travels on a road surface,
A distance measuring sensor that periodically transmits a detection wave for distance measurement to the road surface and receives a reflected wave to measure a distance to the mobile robot;
A storage unit that stores distance data measured by the distance measuring sensor for a plurality of cycles;
First determination means for detecting obstacles on the road surface based on distance data for the latest one cycle measured by the distance measuring sensor;
Second determination means for detecting an obstacle on the road surface based on distance data for a predetermined period stored in the storage unit;
In the first and second moving robot determine if at least one the obstacle is detected there obstacle and each determining means,
Furthermore, it has a road surface state determination means for determining whether the road surface is wet,
When the road surface state determining means determines that the road surface is not wet, only the first determining means determines the presence or absence of an obstacle,
When the road surface state determining means determines that the road surface is wet, it is determined that there is an obstacle if an obstacle is detected by at least one of the first determining means and the second determining means. It is characterized by that.

The mobile robot according to claim 2 is a mobile robot that travels on a road surface,
A distance measuring sensor that periodically transmits a detection wave for distance measurement to the road surface and receives a reflected wave to measure a distance to the mobile robot;
A storage unit that stores distance data measured by the distance measuring sensor for a plurality of cycles;
First determination means for detecting obstacles on the road surface based on distance data for the latest one cycle measured by the distance measuring sensor;
Second determination means for detecting an obstacle on the road surface based on distance data for a predetermined period stored in the storage unit;
In the mobile robot that determines that there is an obstacle when the obstacle is detected in at least one of the first and second determination means,
Furthermore, the road surface state determination means for determining the degree of wetness of the road surface,
A controller that controls the traveling speed of the mobile robot when it is determined that the road surface is at least wet;
When it is determined that the road surface is not wet by the road surface state determination means, and when it is determined that the road surface is less than a predetermined degree, the presence or absence of an obstacle is determined only by the first determination means,
When the road surface condition determining means determines that the road surface is wet above the predetermined level, an obstacle is detected when at least one of the first determining means and the second determining means is detected. It is characterized by determining that there is.

According to the mobile robot described in claim 1, the first determination means determines whether there is an obstacle based on data obtained by one measurement by the distance measuring sensor. The accuracy may be reduced, but in other cases (especially when the road surface is dry), it can be determined instantaneously with high detection accuracy. On the other hand, the second determination means cannot make an instantaneous determination, but heavy rain Insensitive to cases. For this reason, by using the 1st and 2nd judging means complementarily, not only when the road surface is dry, but also because the road surface is wet due to rain or the like, the detection accuracy of the obstacle is reduced. In particular, even when water is accumulated on the road surface due to heavy rain and it becomes difficult to detect the obstacle, an effect of always detecting the obstacle with high accuracy can be obtained regardless of the road surface condition. Then, it is further determined whether or not the road surface is wet by the road surface state determination means, and based on the result, the first determination means and the second determination means are used in a complementary manner. Effectively, it is possible to obtain an effect that an obstacle can always be detected with high accuracy without depending on a situation such as a wet road surface.

According to the mobile robot described in claim 2, since the first determination means determines whether there is an obstacle based on the data obtained by one measurement by the distance measuring sensor, it is detected in case of heavy rain. The accuracy may be reduced, but in other cases (especially when the road surface is dry), it can be determined instantaneously with high detection accuracy. On the other hand, the second determination means cannot make an instantaneous determination, but heavy rain Insensitive to cases. For this reason, by using the 1st and 2nd judging means complementarily, not only when the road surface is dry, but also because the road surface is wet due to rain or the like, the detection accuracy of the obstacle is reduced. In particular, even when water is accumulated on the road surface due to heavy rain and it becomes difficult to detect the obstacle, an effect of always detecting the obstacle with high accuracy can be obtained regardless of the road surface condition. In particular, even in a situation where a large amount of water is accumulated in a wide area of the road due to heavy rain, an effect of detecting an obstacle with high accuracy can be obtained.

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, for example, an autonomous mobile robot used for security purposes, 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 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 travels along the white line tape 3 with the left and right wheels 6 serving as moving means, and the laser sensor 2 provided on the front side of the main body, The monitoring area ahead of the moving direction is scanned as indicated by a 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 steering angle may be used, or a method of independently controlling the left and right crawlers instead of driving the wheels.

(2) Guide Detection Unit The guide detection unit 10 detects the above-described white line tape 3 which is guide means on the moving path.
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 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. Further, each point on the patrol route may be recognized based on position information calculated by dead reckoning or GPS without providing a guide unit or a point instruction unit on the patrol route.

(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 includes a resolver 14 as a rotation amount detection unit installed in each motor 8 of the moving means, and a position calculation unit 15 connected to each resolver 14. . 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 rotation shaft of the motor 8 obtained from the output of the resolver 14, and calculates the left and right calculated from the rotation amount and wheel radius of each of the left and right front wheels 6. The travel distance of the mobile robot 1 is calculated from the average travel distance of each front wheel 6.

  In this example, since the traveling route of the mobile robot 1 is fixed, the traveling distance can be set as the self-position. The travel distance may be recorded as the total distance from the start point or as the distance from the position of each indication marker 4. The position calculation unit 15 calculates the change in the posture (orientation) of the mobile robot from the difference in rotation amount between the left and right front wheels and the wheel interval, and calculates the self position by dead reckoning from the travel distance and posture change by the left and right front wheels. May be.

  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.

(5) Ranging Sensor The ranging sensor 16 is a means for scanning the front of the mobile robot 1 to detect the road surface condition, foreign objects on the road surface, and other detection targets.
As shown in FIG. 3, the distance measuring sensor 16 includes a laser sensor 2 serving as an obstacle detection sensor installed on the front side of the main body of the mobile robot 1 toward the front lower side in the traveling direction. The laser sensor 2 includes a projecting and receiving wave unit that emits laser light from a laser oscillator and receives reflected light when reflected by an object on the optical path of the laser light and outputs a detection signal. The laser sensor 2 has a scanning mirror that is a scanning means for laser light and means for rotationally driving the scanning mirror, and controls the irradiation direction (irradiation angle) of the laser light emitted from the laser oscillator. Thus, a predetermined range including the front of the mobile robot 1 is spatially scanned at a predetermined cycle (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 reflecting the laser beam, that is, the 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 may be constituted by a millimeter wave radar type sensor or an ultrasonic 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 4).
1) Route information showing travel route information
2) Obstacle map
3) Position information of the mobile robot 1 calculated by the position calculation unit
4) Threshold for obstacle judgment

  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, and the traveling speeds maxH and maxL (maxH> maxL) within the section, The section distance measured, the difference in azimuth between the start point and end point of the section (angle difference), the wheel diameter when traveling in each section, and the operation of the mobile robot 1 on each indication marker 4 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, and the wheel diameter in each section is specified from the section number.

  The obstacle map 2) is a map showing a predetermined range around the mobile robot 1 for temporarily storing information detected by the distance measuring sensor 16 and the processing unit 18. As shown in FIG. 4, a stop area and a deceleration area are set and detected in the map area with the position of the laser sensor 2 installed in the mobile robot 1 as the origin (0, 0). Obstacles and road wetting information are registered on the map. Each time the mobile robot 1 travels, coordinate conversion of obstacle data registered on the map is performed in accordance with the movement. That is, data for the past predetermined period measured by the distance measuring sensor is registered in the obstacle map, and processing by the second determination unit described later is performed using the data for the plurality of periods.

The position information 3) is self-position information of the mobile robot 1 calculated by the position calculation unit 15 of the self-position detection unit 13.
The threshold for obstacle determination in 4) is a threshold that is compared with a value corresponding to the height of the object in order to detect an object having a height that interferes with traveling that the mobile robot 1 cannot get over as an obstacle. It is. As a value set as a threshold, a relatively large value thH (a strict value as a threshold for determining that there is an obstacle) and a relatively low value thL (a loose value as a threshold for determining that there is an obstacle) Two values are stored in advance, and any one of them is set as an obstacle determination threshold value in the control related to obstacle detection described later.

(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 laser sensor 2, and outputs the determination result to the control unit 21. As a result, the control unit 21 outputs a signal to the movement control unit 9 so as to travel at the upper limit speed (maxH or maxL) according to the road surface condition. The obstacle determination unit 19 includes a first determination unit 19a and a second determination unit 19b, and determines whether there is an abnormality based on the output of the distance measuring sensor 16. The first determination means 19a compares the distance value included in the data output as the scanning result of one cycle of the distance measuring sensor 16 with a threshold value (thH or thL) and has a height higher than a predetermined level. It is determined whether or not an object exists. As described above, the distance value difference (the distance value difference for each angle of the scanning mirror) included in the data output as a result of the distance measurement sensor 16 scanning the road surface is a value corresponding to the height of the obstacle. Become. By performing threshold processing on a value corresponding to this obstacle height, it is determined whether or not there is an obstacle having a height that interferes with the traveling of the mobile robot 1. The second determination unit 19b observes data output from the distance measuring sensor 16 over a plurality of periods when the mobile robot 1 is moving. When the mobile robot 1 is moving, a measurement point (a point where the laser beam is reflected) obtained by scanning a flat road surface by the distance measuring sensor 16 is always at a position away from the mobile robot 1 by a predetermined distance. At this time, in the scanning results over a plurality of cycles stored in the obstacle map of the storage unit, the position of the measurement point in the environment also moves for each scanning cycle of the distance measuring sensor in accordance with the movement of the mobile robot in the environment. It will be. On the other hand, as shown in FIG. 14, when there is an obstacle within the scanning range of the distance measuring sensor 16, even if the mobile robot 1 moves in the environment, as long as the laser light strikes the obstacle, The position of the measurement point obtained by the reflected light indicates the position of the obstacle without changing every period, and the measurement point is obtained at substantially the same place over a plurality of periods. Therefore, the second determination unit 19b registers the scanning result output by the distance measuring sensor 16 in the obstacle map, and the obstacle is detected when the measurement points are obtained at substantially the same place over a plurality of periods. Judge that it exists. When an obstacle is detected and determined to be abnormal, an abnormality signal is output from the communication unit 20 described later, and the movement control unit 9 performs predetermined processing such as stopping or deceleration of the mobile robot 1. A specific example of processing by the obstacle determination unit 19 will be described later. In this example, the wetness of the road surface is determined from the output result of the laser sensor 2, but means for determining the wetness of the road surface may be separately provided. In this case, the determination is not limited to the method of determining from the reflected wave of the laser sensor 2, and may be determined by analyzing the difference between the ultrasonic wave, the reflected wave of infrared rays, the image analysis, or the running sound generated from the wheel.

(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, 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. Procedures for Information Processing in the Control System of the Mobile Robot and Operations Based on the Procedures The control operation during travel of the mobile robot 1 of the present example will be described below. First, the overall obstacle detection during travel of the mobile robot 1 of the present example will be described. An explanation will be given of the control procedure (FIG. 5), and the travel control of the mobile robot 1 performed by using the information acquired in the procedure of FIG. 5 will be explained (FIG. 6). Next, the wet road surface detection control (FIG. 7) and the obstacle detection control (FIGS. 8, 10, and 12) in the control procedure at the time of obstacle detection will be described in detail. In the following description using each flowchart, each step of the control procedure is indicated by a serial number starting with S.

(1) Overall control for obstacle detection (Figure 5)
FIG. 5 is a flowchart showing the entire obstacle detection procedure during travel of the mobile robot 1 of this example. In this procedure, when a wet road surface is detected in the wet road surface detection process (S01), a speed setting request for controlling the upper limit of the traveling speed of the mobile robot 1 is made and a threshold for determining that there is an obstacle is raised. In addition, the first determination means 19a performs two-stage processing from the result of one scanning by the laser sensor, and performs two types of obstacle detection processing (S07, S08) using the threshold value. These two types of obstacle detection processes are an obstacle detection process with a certain width on a flat road surface (FIGS. 8 and 9), and a gap detection process such as a step or a pole (FIG. 8). 10 and FIG. 11). Further, a third obstacle detection process (S09) is then performed by the second determination means 19b. This third obstacle detection process is an obstacle detection process based on time-series data (FIGS. 12 and 13). The above-described first and second obstacle detection processes are particularly performed when there is a puddle on the road surface due to heavy rain or the like. Obstacles that cannot be detected by this obstacle detection process can be detected with high accuracy. When the presence of an obstacle is detected in any of the above three types of obstacle detection processing, the mobile robot 1 is requested to stop or decelerate according to the detection result. In this example, an example in which two types of processing are performed as the obstacle detection processing by the first determination unit has been described. However, the present invention is not limited to this and may be configured to perform any one processing. .

S01: In the wet road surface detection process, it is determined whether or not the road surface is wet. Details of this subroutine are shown in FIG.
S02: If the road surface is wet, proceed to S05. If there is no wetting, the process proceeds to S03.

S03: When the road surface is not wet (NO in S02), the controller 21 is commanded to set the upper limit of the traveling speed to maxH. The upper limit maxH of the traveling speed indicates the upper limit of the normal speed when the road surface is not wet. In addition, wetting is detected because it is not preferable to perform speed control frequently when there are water puddles due to the unevenness of the road surface, and the wet and dry surface changes in the result of wet road surface judgment processing at short intervals. After that, the upper limit speed is not changed even if it is determined that the vehicle is dry until it has traveled a predetermined distance, or the position determined to be wet is stored on the obstacle map, and the mobile robot 1 travels to make it move on the map. You may make it restart the determination of a dry road surface after the position determined to be wet from a predetermined area disappears.
S04: As a threshold value (a value corresponding to the obstacle height) for determining that there is an obstacle according to the traveling road surface condition (no wetting), a threshold value thL that is relatively lower than that when the road surface is wet is set. Set. That is, since the error of the distance detected by the laser sensor 2 is relatively smaller on a dry traveling road surface than on a wet road surface, the upper limit of the traveling speed is relatively high (maxH) and it is determined that there is an obstacle. The threshold value at that time is relatively low (thL). The set threshold value thL is stored in the storage unit 17.

S05: When the road surface is wet (YES in S02), the controller 21 is commanded to set the upper limit of the traveling speed to maxL (<maxH). The upper limit maxL of the traveling speed indicates the upper limit of the speed when the road surface is wet. That is, when the road surface is wet, the speed of the mobile robot 1 is lowered as compared with the case where the road surface is not wet.
S06: A relatively high threshold value thH is set as a threshold value (a value corresponding to the obstacle height) for determining that there is an obstacle according to the traveling road surface condition (with wetness) as compared with the case where the road surface is not wet. To do. That is, since the error of the distance detected by the laser sensor 2 is relatively larger on the wet traveling road surface than the case where the distance is dry, the upper limit of the traveling speed is relatively lowered (maxL) and it is determined that there is an obstacle. The threshold value at that time is relatively high (thH). The set threshold value thH is stored in the storage unit 17.

  S07: A first obstacle detection process (distance difference detection method with respect to the road surface) is executed. This subroutine is shown in FIG. 8, and will be described in detail later with reference to FIG.

  S08: The second obstacle detection process (distance gap detection method) is executed. This subroutine is shown in FIG. 10 and will be described in detail later with reference to FIG.

  S09: A third obstacle detection process (obstacle detection based on time-series data) is executed. This subroutine is shown in FIG. 12, and will be described in detail later with reference to FIG.

  S10: It is determined whether or not it is determined that there is an obstacle in any of the obstacle detection processes of S07, S08, and S09. If it is determined that there is an obstacle in any of S07 to S09, the process proceeds to S11, and if it is not determined that there is an obstacle in any process, the process proceeds to S15.

  S11: If it is determined that there is an obstacle (YES in S10), it is determined whether this obstacle is in the stop area (see FIG. 4) in the obstacle map (S11), and if it is in the stop area ( If YES in S11, the control unit 21 is instructed to stop the mobile robot 1 in S12, and if not (NO in S11), the process proceeds to S13.

  S13: It is determined whether or not the detected obstacle is in the deceleration area (see FIG. 4) in the obstacle map. If it is in the deceleration area (YES in S13), the controller 21 is instructed to decelerate the mobile robot 1 in S14, and if not (NO in S13), the process proceeds to S15.

  S15: It is determined whether or not an instruction to stop the obstacle detection process is input from the control unit 21, and if not, the process returns to S01 and the above processing procedure is repeated, and if there is an instruction to stop, the process ends.

(2) Overall travel control (Fig. 6)
FIG. 6 shows a flowchart in the case where the travel control of the mobile robot 1 of this example is performed with one travel section between the instruction marker 4 and the instruction marker 4, while traveling on a prescribed travel route according to the initial setting. FIG. 5 shows an information processing procedure performed based on a travel speed setting request, a stop request, a deceleration request, etc., output in the above-described obstacle detection process (FIG. 5).

S51: The control unit 21 acquires the speed designated as maxH as the speed information of the current travel section from the route information stored in the storage unit 17. In the route information, two speeds maxH and maxL are stored as travel speeds for each travel section. The mobile robot 1 normally travels at a speed specified by maxH, and travels with a travel speed of maxL when a wet road surface is detected.
S52: The control unit 21 causes the mobile robot 1 to travel at the travel speed specified as the acquired maxH.

S53: The control unit 21 obtains information on the traveling speed (maxH set speed, maxL set speed, stop request, or deceleration request) output to the control unit 21 in the flow of FIG. 5 described above by the processing unit 18. To do.
S54: The control unit 21 determines whether or not there is a stop request in the information on the traveling speed from the processing unit 18. If there is a stop request (YES in S54), the traveling of the mobile robot 1 is stopped in S55. When the mobile robot 1 stops, the control unit 21 maintains the mobile robot 1 in a stopped state until a command to start traveling is received again.

  S56: If there is no stop request (NO in S54), it is determined whether or not a speed change is necessary based on information on the running speed from the processing unit 18 (hereinafter referred to as “designated speed”). The control unit 21 compares the designated speed with the current running speed, and decelerates to the designated speed if the current running speed is faster than the designated speed, and accelerates to the designated speed if the current running speed is slower than the designated speed. Run.

In other words, when the upper limit of the specified speed is increased or decreased (for example, when the road surface is changed from maxL when the road surface is wet to maxH when the road surface is dry or when it is changed from maxH when the road surface is dry to maxL when the road surface is wet) ) If the current travel speed is the same as the upper limit of the specified speed, the vehicle will continue to travel at the current travel speed. If the current travel speed is slower than the upper limit of the specified speed, the vehicle will accelerate to the upper limit of the specified speed. If it is faster than the upper limit of, it will decelerate to the upper limit of the specified speed. The upper limit of the speed specified as maxH is the value of the traveling speed stored in advance in the route information of the storage unit 17 for each section, for example, 10 km / h, 6 km / h, etc., and specified as maxL The upper limit of the speed to be set is, for example, 3 km / h or less which is less than maxH.
In addition, when a deceleration request is made because the obstacle is in the deceleration area in the obstacle map, the vehicle is decelerated regardless of the travel speed request by maxH or maxL.

  S57: If the control unit 21 determines that the speed change is necessary by the process as described above (YES in S56), the speed is changed to the designated travel speed.

  S58: The control unit 21 determines whether or not the travel section in which the mobile robot 1 is traveling has ended. If not completed, the process returns to S53 and the above procedure is repeated. If the travel section has ended, the process returns to S51 to start travel control in the next travel section. Further, when the end point of the route is reached, the above control is terminated.

(3) Wet road surface detection control (Fig. 7)
FIG. 7 shows a specific flow of wet road surface detection processing by the laser sensor that is made into a subroutine in S01 of FIG.
S71: The road surface state determination unit 23 acquires data (sensor data) of the distance value y to the measurement point that reflects the laser beam by the output signal from the laser sensor 2. 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 compared to the case where the light is reflected on the road surface is processed by assuming that the sensor data 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.

  In the wet road surface detection control in this example, in the processing of a plurality of sensor data obtained by the laser sensor 2, sensor data having a distance value equal to or smaller than a predetermined value is regarded as being from a dry road surface, and the count number is totally set. When the ratio is more than a certain value, it is judged that the road surface is dry, and when the ratio is less than a certain value, the road surface is judged to be wet. Sensor data is regarded as being from a wet road surface.If the percentage of the total count is above a certain value, the road surface is judged to be wet.If the ratio is less than a certain value, the road surface It is good also as a structure which judges that is dry.

(4) Obstacle detection control (first obstacle detection process, FIGS. 8 and 9)
This obstacle detection control is a method for detecting any obstacle present on a flat road surface.

S81: In the sensor data for one cycle, those whose difference in distance value is equal to or less than a predetermined value between adjacent data are grouped and labeled.
FIG. 9 is a plot of sensor data of distance value y indicating each position detected by the laser sensor in one cycle scanning on the xy coordinate plane, as in FIG. And the Y axis indicate distance values from the mobile robot 1. Although it is a continuous straight line in the figure, it is actually a set of measurement points, and the horizontal straight lines on both sides in the figure are data on a flat road surface, and the central convex part is placed on the road surface. This is data indicating the distance to the obstacle, and the continuous part of both indicates data by reflection from the side of the obstacle. Although not shown, the position of the laser sensor is the origin of the XY coordinates and is below in the figure.

  Here, labeling means classifying a plurality of data that are close in the y direction and arranged in the x direction in a permutation of sensor data as one set. In the example of FIG. Two horizontal straight lines and a central convex part are labeled.

  S82: Focusing on one piece of labeled data (labeling data), is there labeling data comprising K (for example, 10) or more measurement points on both sides in the X direction of the labeling data? Judge whether or not. The value of K is appropriately set in consideration of the total number of sensor data, the size of the obstacle to be detected, and the like.

  S83: If there are K or more data on both sides (YES in S82), it is determined whether or not the labeling data on both sides have substantially the same y coordinate value. Thus, it is determined whether the labeling data on both sides is based on the traveling road surface.

  S84: The difference between the average value of the y coordinate values of the focused labeling data and the average value of the y coordinate values of the labeling data on both sides is calculated. Thereby, a difference in distance value between the target detection object and the detection objects on both sides thereof is obtained. This distance difference corresponds to the height of the target detection object.

  S85: The difference calculated in S84 is greater than or equal to the obstacle determination threshold value (threshold value corresponding to the height of the obstacle: thL or thH) stored in the storage unit 17 in the control related to detection shown in FIG. It is determined whether or not. As shown in FIG. 5, the storage unit 17 stores a relatively high value thH as a value corresponding to the height if the road surface is wet, and the height if the road surface is not wet. Any of relatively low values thL is set as a threshold for obstacle determination as a corresponding value.

  S86: If the difference calculated in S84 is greater than or equal to the obstacle determination threshold (YES in S85), the noted labeling data is recognized as an obstacle and it is determined that there is an obstacle.

  If it is determined that there is an obstacle and if NO in S82, S83, and S85, respectively, it is determined whether or not the above processing has been performed for all labeling data, and if all are processed (YES in S87), this flow When the processing procedure is completed and the above processing is not performed for all the labeling data (NO in S87), the process returns to S82 and the above procedure is repeated focusing on the next labeling data.

(5) Obstacle detection control (second obstacle detection processing, FIG. 10 and FIG. 11)
This obstacle detection control is a method for detecting an object having a narrow width in the x direction, which is difficult to detect by the method described in the above (4), by detecting a difference in y-coordinate (distance difference). Small objects such as poles placed on the road surface can also be detected as obstacles.

  FIG. 11 is a graph in which sensor data of distance value y indicating each position detected by the laser sensor by scanning in one cycle is plotted on the xy coordinate plane, the X axis is an angle from the front of the mobile robot 1, and the Y axis is The distance value from the mobile robot 1 is shown. In the figure, it is shown that there are gaps (distance differences) in the y direction in a plurality of data arranged between the upper and lower horizontal straight lines.

  S101: It is determined whether or not there is an unprocessed portion where the fluctuation amount of the y coordinate value of M (for example, five) consecutive sensor data is less than or equal to the threshold value. In FIG. 11, since each y-coordinate value of the M pieces of data on the right side is a fluctuation amount equal to or less than the threshold value, the relevant part is specified in the determination of this step.

S102: The end point of the location (M data) is a comparison point 1, and its adjacent point (a point not included in the M data) is a comparison point 2.
S103: The difference between the y coordinate values of the comparison point 1 and the comparison point 2 is calculated.
S104: It is determined whether or not the difference between the y-coordinate values of the comparison point 1 and the comparison point 2 is greater than or equal to a predetermined threshold value.

  S105: When the difference between the y-coordinate values of comparison point 1 and comparison point 2 is equal to or greater than the threshold value (YES in S104), each of comparison points 1 and 2 is on the opposite side from the side where M data exists. Shift. That is, the new comparison point 1 becomes the previous comparison point 2, and the new comparison point 2 becomes the adjacent point of the new comparison point 1. Returning to step S103, the comparison of the y coordinates of the new comparison points 1 and 2 is repeated.

  S106: If the difference between the y coordinate values of the comparison points 1 and 2 is not greater than or equal to the threshold (NO in S104), the difference between the y coordinate values of the first comparison point 1 and the current comparison point 1 is calculated. This difference is a gap (distance difference in the y direction) corresponding to the height of the obstacle.

  S107: The difference between the y-coordinate values of the first comparison point 1 and the current comparison point 1 is greater than or equal to the obstacle determination threshold (threshold thL or thH corresponding to the height of the obstacle) stored in the storage unit 17. It is determined whether or not there is. This threshold value is the same as the threshold value (see S85) in the obstacle detection (distance difference method from the road surface) described with reference to FIG.

  S108: If the difference calculated in S106 is greater than or equal to the obstacle determination threshold (YES in S107), the gap portion is recognized as an obstacle and it is determined that there is an obstacle.

  S109: When it is determined that there is an obstacle and the difference calculated in S106 is not greater than or equal to the obstacle determination threshold (NO in S107), it is determined whether or not the above processing has been performed for all data, and all processing is performed. If so (YES in S109), the processing procedure according to this flow is terminated. If all the data has not been processed (NO in S109), the process returns to S101 and the above procedure is repeated for each data.

(6) Obstacle detection control (third obstacle detection processing, FIG. 12 and FIG. 13)
In each of the methods described in (4) and (5) above, the first determination means 19a determines the presence or absence of an obstacle from sensor data obtained by one scanning of the laser sensor. When the laser beam is specularly reflected by the pool, the obstacle detection accuracy may be lowered. On the other hand, the third obstacle detection control described in this section is based on the correlation of sensor data obtained by scanning a plurality of cycles of the laser sensor while the mobile robot moves while the second determination unit 19b moves. It is used to determine the presence or absence of an object, and has a feature that the detection accuracy of an obstacle is hardly affected even in the case of heavy rain in which the laser is specularly reflected by a puddle on the road surface.

  FIG. 13 shows that each time (that is, different time) when the mobile robot 1 moves at a predetermined speed and the laser sensor 2 scans the front road surface with a predetermined period (that is, a plurality of periods) at a predetermined period. The obtained data indicating the distance to the road surface and the obstacle is taken as sensor data of the distance value y, and an example in which the angle from the front of the mobile robot is plotted on the xy coordinate plane with respect to the x axis is shown. In this figure, the position of the mobile robot is at the origin of the xy coordinate axis below, and in a plurality of lines representing the sensor data of each cycle, the horizontal portion represents the road surface, and the downwardly convex portion detected in part is projected. This is a portion that is a target for determining whether or not the portion is an obstacle. Therefore, the interval adjacent to the y direction of the horizontal line representing the road surface corresponds to the moving distance of the mobile robot 1 in one scanning cycle by the laser sensor 2. This example is an example of a dry road surface, but when a puddle or the like is generated on the road surface due to heavy rain or the like, the horizontal portion representing the above road surface cannot obtain a distance value due to the mirror reflection of the laser, and the obstacle portion (convex In some cases, a distance value can be obtained only for (part). This is based on the knowledge that the point where the laser beam is reflected on the obstacle as shown in FIG. 14 is a surface rising from the road surface and is not easily affected by a puddle or the like.

Hereinafter, with reference to FIG. 12 and FIG. 13, an obstacle detection procedure based on time-series data by the processing unit 18 will be described.
S200: The obstacle determination unit 19 acquires current travel distance information from the route information stored in the storage unit 17.
S201: It is determined whether or not the moving distance of the mobile robot 1 from the previous detection process is a certain distance or more. If the movement distance has not reached a certain value, the process is terminated. If the movement distance has been reached (S201, YES), the process proceeds to S202.

  S202: In accordance with the movement of the mobile robot 1, the past P-1 period sensing data registered in the obstacle map of the storage unit 17 and the detected obstacle registration data are matched with the movement distance of the mobile robot 1. Convert the coordinates and re-register the obstacle map. Also, the current cycle data is registered in the obstacle map.

  S203: At the time of the current cycle, labeling is performed in the same procedure as that shown in FIG. 9 (see S81 in FIG. 8), and the data of the portion to be determined whether or not it is a central obstacle, and the road surfaces on both sides thereof According to, labeling data.

  S204: Correlating each labeling data of the current period with each data of a plurality of periods, which is a period before this registered in the obstacle map. In this example, P data from the current cycle to the P-1 cycle before (for example, P = 3 in FIG. 13, data from the current cycle to the current cycle-2) are compared. P is an appropriate value based on the number of distance values obtained by the laser sensor with respect to the lower limit (for example, 5 cm) of the height of the obstacle to be detected. The number of distance values that can be obtained is determined by the traveling speed of the robot and the scanning interval of the laser sensor.

  S205: It is determined whether or not the coordinates registered in the obstacle map are the same in all the compared cycles, that is, whether or not the positions of the detected objects are substantially overlapped. If there is no overlap in one cycle (S205, NO), the process proceeds to S207 assuming that it is not an obstacle, and if it overlaps in all cycles (S205, YES), the process proceeds to S206.

  S206: If there is an overlap among the labeling data (time-series data) of a plurality of periods as a result of the comparison, this portion is due to the reflected light from the same object as the mobile robot 1 moves. Is determined as an obstacle, and is registered in the obstacle map.

  S207: Next, when there is an unprocessed labeling data in the current cycle (NO), the process returns to S204 to perform the above-described comparison process (S204 to S206), and when all are processed (NO) End.

  According to the obstacle detection based on the time series data described above, the distance value of the obtained data itself is not compared with a threshold corresponding to the height to determine whether the obstacle is an obstacle, but the laser sensor 2 continues. If data is obtained from reflected light from the same location more than a predetermined number of times in multiple scans, the logic is used to determine that there is an obstacle at that location. It is less affected and is advantageous for detecting obstacles in such cases.

  Further, as the mobile robot 1 moves, mechanical vibration is generated in the mobile robot 1 and vibration due to unevenness of the traveling road surface is unavoidable. However, according to the obstacle detection based on the time series data described above, the laser is detected. Since it is determined whether or not the data overlaps at the same position over a certain distance from the position several cycles before the scanning by the sensor 2 to the present position, the above-mentioned detection is avoided while avoiding erroneous detection due to a cause other than the obstacle. Reliable obstacle detection can be performed.

  As described above, according to the present example, the first determination means (distance difference detection method with respect to the road surface (FIG. 8)) for determining the presence or absence of an obstacle based on the data obtained by one scan by the laser sensor 2 and Distance gap detection method (FIG. 10)) and second determination means for determining the presence or absence of an obstacle based on data obtained by scanning a plurality of times by the laser sensor 2 (obstacle detection method based on time-series data (FIG. 12) )), And both of the judging means are used in a complementary manner, and the presence of an obstacle can be judged based on the judgment result of at least one of the judging means. Of course, when the road surface is wet due to rain, etc., it may be difficult to detect obstacles, especially when it is difficult to detect obstacles due to heavy rain. Even if Effect that can be detected always accurately obstacles without due status of such road is obtained.

  Further, in the embodiment described above, the three obstacle determination means function in sequence as shown in S07 to S09 in the overall obstacle detection flow (FIG. 5). When the road surface state determining means determines that the road surface is dry, only the first determining means determines whether there is an obstacle, and the road surface state determining means determines that the road surface is wet Alternatively, the presence / absence of an obstacle may be determined using the second determination unit together. In that case, it is only necessary to determine whether or not the road surface is wet before S09 in the flowchart of FIG. Further, the second determination means may be used only when the laser beam is specularly reflected by a puddle on the road surface as in the case of heavy rain on the road surface. In this case as well, it is only necessary to change the flowchart of FIG. 5 so that the wet state is determined before S09 as described above. For example, the wet state is determined by using the counter value used in the determination of S74 in FIG. 7 to determine that the smaller the counter value, the higher the degree of wetness, and to distinguish between normal wet and heavy rain. Good.

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. 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 diagram showing a map area registered as a map in which the position of the laser sensor installed in the mobile robot 1 is the origin (0, 0) in the storage unit 17 of the mobile robot 1 of this example. FIG. 5 is a flowchart showing the entire obstacle detection procedure in the travel control of the mobile robot 1 of this example. FIG. 6 shows a flowchart in the case where the travel between the instruction marker 4 and the instruction marker 4 is one travel section in the travel control of the mobile robot 1 of this example. FIG. 7 shows a specific flow of wet road surface detection processing by the laser sensor that is made into a subroutine in S01 of FIG. FIG. 8 is a flowchart showing the obstacle detection control based on the distance difference detection method from the road surface suitable for detecting any obstacle present on the flat road surface. FIG. 9 is a graph in which sensor data of a distance difference y indicating each position detected by a laser sensor in one cycle is plotted on an xy coordinate plane. FIG. 10 is a flowchart showing obstacle detection control by a distance gap detection method for detecting an object having a narrow width in the x direction based on a difference in y coordinates (distance difference). FIG. 11 is a plot of sensor data of a distance difference y indicating each position detected by the laser sensor in one cycle of scanning on an xy coordinate plane. FIG. 12 is a flowchart showing obstacle detection based on time-series data. FIG. 13 shows the road surface and obstacles obtained in each cycle when the mobile robot 1 moves at a predetermined speed and the laser sensor 2 scans the front road surface a plurality of times with a predetermined cycle. Data indicating the distance is plotted on the xy coordinate plane as sensor data of the distance difference y. FIG. 14 is a diagram showing, in a time series, states of obstacle detection during traveling in the mobile robot 1 having a basic structure that detects an obstacle while scanning a laser obliquely downward and forward while moving. At each time t to t + 4, the laser beam, the side view of the mobile robot 1 and the obstacle, and the distance difference between the detection target and the mobile robot 1 in the plane xy coordinate system screen with the position of the laser sensor 2 as the origin (see FIG. (Distance in the middle y direction) is expressed by a laser trajectory.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Mobile robot 2 ... Laser sensor as a ranging sensor 16 ... Ranging sensor 19 ... Obstacle determination part 21 ... Control part

Claims (2)

A mobile robot traveling on a road surface,
A distance measuring sensor that periodically transmits a detection wave for distance measurement to the road surface and receives a reflected wave to measure a distance to the mobile robot;
A storage unit that stores distance data measured by the distance measuring sensor for a plurality of cycles;
First determination means for detecting obstacles on the road surface based on distance data for the latest one cycle measured by the distance measuring sensor;
Second determination means for detecting an obstacle on the road surface based on distance data for a predetermined period stored in the storage unit;
In the first and second moving robot determine if at least one the obstacle is detected there obstacle and each determining means,
Furthermore, it has a road surface state determination means for determining whether the road surface is wet,
When the road surface state determining means determines that the road surface is not wet, only the first determining means determines the presence or absence of an obstacle,
When the road surface state determining means determines that the road surface is wet, it is determined that there is an obstacle if an obstacle is detected by at least one of the first determining means and the second determining means. A mobile robot characterized by that. A mobile robot traveling on a road surface,
A distance measuring sensor that periodically transmits a detection wave for distance measurement to the road surface and receives a reflected wave to measure a distance to the mobile robot;
A storage unit that stores distance data measured by the distance measuring sensor for a plurality of cycles;
First determination means for detecting obstacles on the road surface based on distance data for the latest one cycle measured by the distance measuring sensor;
Second determination means for detecting an obstacle on the road surface based on distance data for a predetermined period stored in the storage unit;
In the first and second moving robot determine if at least one the obstacle is detected there obstacle and each determining means,
Furthermore, the road surface state determination means for determining the degree of wetness of the road surface,
A controller that controls the traveling speed of the mobile robot when it is determined that the road surface is at least wet;
When it is determined by the road surface state determination means that the road surface is not wet, and when it is determined that the road surface is less than a predetermined degree, the presence or absence of an obstacle is determined only by the first determination means,
When the road surface condition determining means determines that the road surface is wet above the predetermined level, an obstacle is detected when at least one of the first determining means and the second determining means is detected. A mobile robot characterized by being determined to be present.

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