JP2018206004A - Cruise control device of autonomous traveling carriage, and autonomous travelling carriage - Google Patents

Abstract

To enable an autonomous traveling carriage to travel seamlessly indoors and outdoors.SOLUTION: A control unit 7 comprises a distance sensor 5, a GNSS receiver 11, and a traveling part 3, and is used for an autonomous traveling carriage 1 moving autonomously in a mobile environment in a planned or unplanned manner. A storage unit 27 stores a map of the mobile environment. Via an I/O port 31, data is input to the distance sensor 5 and the GNSS receiver 11, and a traveling command is output to the traveling part 3. The control unit 7 acquires a self position of the autonomous traveling carriage 1 on the map of the mobile environment as SLAM probability distribution by using the data of the distance sensor 5 and the map of the mobile environment, acquires the self position of the autonomous traveling carriage 1 as GNSS probability distribution by using the data of the GNSS receiver 11, synthesizes the SLAM probability distribution and the GNSS probability distribution to create SLAM/GNSS probability distribution, determines a peak position of the SLAM/GNSS probability distribution as the self position of the autonomous traveling carriage 1 in the mobile environment, and creates the traveling command on the basis of the determined self position.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a traveling control device for an autonomous traveling vehicle and an autonomous traveling vehicle.

  Conventionally, an autonomous traveling vehicle that autonomously moves in the surrounding environment is known. In order to move autonomously, an environment map that represents an area where an object (hereinafter referred to as an obstacle) in the moving space exists and an area where the object does not exist is required. While various environmental map acquisition methods have been devised, SLAM (Simultaneous Localization and Mapping) has attracted attention as a technology for performing self-location estimation and environmental map creation in real time while moving. . An autonomous traveling vehicle using SLAM creates an environmental map in advance using topographic data obtained as a result of distance measurement by a distance sensor, and maps the environmental map with distance data at each time point during autonomous movement. Thus, self-position estimation is performed (see, for example, Patent Document 1).

JP 2014-219721 A JP 2008-83777 A

In the conventional autonomous traveling vehicle, it is required to travel the autonomous traveling vehicle not only indoors but also outdoors. However, in this case, the following problems can be considered.
First, in a wide environment such as the ground, there is a high possibility that no object exists at the sensing effective distance of the distance sensor. Therefore, the environment map cannot be created normally, and it becomes difficult to estimate the self-location of the autonomous traveling vehicle.
Second, even if a surrounding object exists at the sensing effective distance, there are cases where accurate distance measurement cannot be performed depending on the situation. For example, when the LRF is used for the distance sensor, it is affected by disturbance due to strong sunlight.

Therefore, in order to expand the travelable range of the autonomous traveling vehicle from indoors to outdoors, self-position estimation using some sensor other than the distance sensor is necessary.
In addition, the automatic guided vehicle which can switch a GPS receiver and a laser radar outdoors and indoors is known (refer patent document 2). However, this automatic guided vehicle cannot solve the problem that GPS does not function sufficiently even when traveling outdoors, for example, in the vicinity of a building.

  An object of the present invention is to enable an autonomous traveling vehicle to travel seamlessly indoors and outdoors.

  Hereinafter, a plurality of modes will be described as means for solving the problems. These aspects can be arbitrarily combined as necessary.

A travel control device according to an aspect of the present invention includes a first sensor that two-dimensionally or three-dimensionally acquires a distance from an autonomous traveling vehicle to an obstacle around it, and an absolute position of the autonomous traveling vehicle on the ground. This is used for an autonomous traveling vehicle that includes a second sensor for acquiring the vehicle and a traveling unit and moves autonomously in a moving environment in a planned or unplanned manner.
The travel control device includes a storage, an input / output device, and an arithmetic device.
The storage stores a map of the mobile environment.
The input / output device receives data from the first sensor and the second sensor, and outputs a travel command to the travel unit.
The arithmetic device performs the following operations.
Using the data of the first sensor and the map of the moving environment, the self-location of the autonomous traveling vehicle on the moving environment map is obtained as a first probability distribution.
Using the data of the second sensor, the self position of the autonomous traveling vehicle is obtained as the second probability distribution.
A third probability distribution is created by combining the first probability distribution and the second probability distribution.
The peak position of the third probability distribution is determined as the self position of the autonomous traveling vehicle in the moving environment.
◎ Create a travel command based on the determined self-position.
In this travel control device, the autonomous traveling vehicle travels seamlessly indoors and outdoors. In particular, the first probability distribution obtained using the data of the first sensor and the map of the moving environment and the second probability distribution obtained using the data of the second sensor are combined to generate the third probability distribution. Is created, and the peak position is determined as the self-position, so that the most appropriate self-position estimation can be performed according to the driving environment. For example, indoors can be accurately grasped by placing importance on the first probability distribution. Further, in an area where there are no obstacles such as buildings near the outdoors, the self-position can be accurately grasped by placing importance on the second probability distribution. Furthermore, in an area where there are obstacles such as buildings near the outdoors, the self-position can be accurately grasped by placing importance on the first probability distribution compared to the case where there are no obstacles.

The arithmetic unit may change a coefficient for combining the first probability distribution and the second probability distribution according to the detection amount of the first sensor.
In this travel control device, the coefficient is changed according to the detection amount of the first sensor. Therefore, when the detection amount is large, the first probability distribution is strengthened and the second probability distribution is weakened. Conversely, when the detected amount is small, the first probability distribution can be weakened and the second probability distribution can be reflected strongly in the third probability distribution.

The computing device may change a coefficient for combining the first probability distribution and the second probability distribution according to the estimated self-location of the autonomous traveling vehicle on the map of the moving environment.
In this traveling control device, the coefficient is changed according to the estimated position of the autonomous traveling vehicle, so that the first probability distribution and the second probability distribution are changed to the third probability distribution according to the exact position and the surrounding situation. Can be reflected.

Autonomous traveling carts run seamlessly in indoor and outdoor environments,
A coefficient that reflects the second probability distribution more strongly when the autonomous traveling vehicle is in an outdoor environment may be set.
In this travel control device, the second probability distribution is more strongly reflected outdoors than in the indoor environment, so that self-position estimation while traveling outdoors is more accurate.

The first sensor is a laser range sensor that two-dimensionally acquires the distance from the autonomous traveling vehicle to the obstacle, and a map of the moving environment and the first probability distribution are created by SLAM (Simultaneous Localization and Mapping) It may be.
In this travel control apparatus, since SLAM is employed, self-position estimation based on the first probability distribution becomes more accurate.

The second sensor may be a receiver of a satellite positioning system (Navigation Satellite System), and an absolute position obtained by RTK (Real Time Kinetic) positioning may be input to the input / output device.
In this travel control apparatus, since the satellite positioning system is adopted, self-position estimation based on the second probability distribution becomes more accurate.

The autonomous traveling vehicle according to another aspect of the present invention autonomously moves in a moving environment systematically or unplanned. The autonomous traveling vehicle has a first sensor, a second sensor, a traveling unit, and the traveling control device.
An autonomous traveling vehicle travels seamlessly indoors and outdoors. In particular, the first probability distribution obtained using the data of the first sensor and the map of the moving environment and the second probability distribution obtained using the data of the second sensor are combined to generate the third probability distribution. Is created, and the peak position is determined as the self-position, so that the most appropriate self-position estimation can be performed according to the driving environment. For example, indoors can be accurately grasped by placing importance on the first probability distribution. Further, in an area where there are no obstacles such as buildings near the outdoors, the self-position can be accurately grasped by placing importance on the second probability distribution. Furthermore, in an area where there are obstacles such as buildings near the outdoors, the self-position can be accurately grasped by placing importance on the first probability distribution as compared with the case where there are no obstacles.

  In the present invention, the autonomous traveling vehicle can travel seamlessly indoors and outdoors.

The schematic block diagram which shows the structure of the autonomous running vehicle of 1st Embodiment. The flowchart which shows the outline of operation | movement of an autonomous traveling vehicle. The flowchart which shows operation | movement of the autonomous running vehicle in execution in reproduction driving mode. The flowchart of self-position estimation operation | movement. The typical top view which shows the driving | running | working condition of an autonomous traveling trolley. The graph which shows SLAM probability distribution. The graph which shows GNSS probability distribution. The graph which shows SLAM / GNSS probability distribution. The graph which shows SLAM probability distribution. The graph which shows GNSS probability distribution. The graph which shows SLAM / GNSS probability distribution.

1. First Embodiment (1) Overall Configuration of Autonomous Traveling Car The autonomous traveling car 1 of this embodiment includes self-position estimation by SLAM using a distance sensor and map matching, and a GNSS (Global Navigation Satellite System / Global Positioning Satellite System). In combination with the positioning results, the technology for extending the travelable range of the autonomous traveling vehicle 1 from indoor to outdoor is realized. Thereby, the autonomous traveling vehicle 1 can travel seamlessly in an indoor environment and an outdoor environment.

Hereinafter, the overall configuration of the autonomous traveling vehicle 1 according to the first embodiment of the present invention will be described with reference to FIG. FIG. 1 is a schematic block diagram showing the configuration of the autonomous traveling vehicle of the first embodiment.
The autonomous traveling vehicle 1 has a traveling unit 3. The traveling unit 3 is provided in the main body of the autonomous traveling vehicle 1 and drives the autonomous traveling vehicle 1 to travel.

The autonomous traveling vehicle 1 has a distance sensor 5. The distance sensor 5 is a sensor for acquiring the distance from the autonomous traveling vehicle 1 to an obstacle around it in a two-dimensional or three-dimensional manner. Specifically, the distance sensor 5 is a front laser range sensor and a rear laser range sensor that are provided before and after the traveling direction of the autonomous traveling carriage 1 as a two-dimensional distance acquisition.
The laser range sensor, for example, irradiates a target such as an obstacle with laser light pulsed by a laser oscillator, and receives reflected light reflected from the target with a laser receiver, thereby determining the distance to the target. It is a laser range finder (LRF) to be calculated. These can scan the laser beam in a fan shape at a predetermined angle using a rotating mirror.
The distance sensor 5 can employ a stereo camera or a TOF (Time Of Flight) camera as a three-dimensional distance acquisition.

The autonomous traveling vehicle 1 includes a control unit 7 (an example of a computing device). The control unit 7 includes a CPU (Central Processing Unit), a storage device as a storage (RAM (Random Access Memory), ROM (Read Only Memory), HDD (Hard Disk Drive), or SSD (Solid State Drive device). Configured) and various interfaces. Part or all of the functions of each component of the control unit 7 to be described later may be realized by executing a predetermined program stored in the storage device.
Alternatively, some or all of the functions of the constituent elements of the control unit 7 may be realized by hardware such as a custom IC.

The autonomous traveling vehicle 1 includes an operation unit 9. The operation unit 9 receives an instruction input by an operator's manual operation. The operation unit 9 is a user interface that is operated by an operator when the autonomous traveling vehicle 1 travels by manual operation, and includes a throttle that receives a traveling speed instruction, a handle that receives a traveling direction instruction, and the like.
The autonomous traveling vehicle 1 has a display unit 10. The display unit 10 displays information related to the current driving situation and various other information, and includes a liquid crystal display, an LED lamp, and the like.

  The autonomous traveling vehicle 1 has a GNSS (Global Navigation Satellite System) receiver 11. The absolute coordinates (latitude / longitude) of the current location obtained by RTK (Real Time Kinetic) positioning are input from the GNSS receiver 11 to the control unit 7. The absolute coordinates of the current location are stored in the storage unit 27 (described later).

(2) Detailed Configuration of Traveling Unit The traveling unit 3 will be described in detail with reference to FIG. The traveling unit 3 has two traveling wheels 13a and 13b.
The traveling unit 3 further includes a pair of motors 15a and 15b, encoders 17a and 17b, and motor driving units 19a and 19b corresponding to the traveling wheels 13a and 13b, respectively. Traveling wheels 13a and 13b are connected to output rotation shafts of the pair of motors 15a and 15b.

The encoders 17a and 17b are connected to the output rotation shaft of the motor 15a and the output rotation shaft of the motor 15b, respectively, and detect the rotation positions of the motors 15a and 15b.
The motor drive units 19a and 19b respectively correspond to the corresponding motors based on the control amount input from the travel control unit 29 (described later) and the rotational positions of the motors 15a and 15b detected by the corresponding encoders 17a and 17b. 15a and 15b are feedback controlled.

(3) Detailed Configuration of Control Unit The control unit 7 will be described in detail with reference to FIG. The control unit 7 includes an obstacle information conversion unit 21, a teaching data creation unit 23, a SLAM processing unit 25, a storage unit 27, a travel control unit 29, and an I / O port 31 as an input / output device. .
The obstacle information conversion unit 21 converts the detection signal of the distance sensor 5 into position information of an obstacle existing around the autonomous traveling vehicle 1. For example, the obstacle information conversion unit 21 converts the detection signal into obstacle position information that is a coordinate value on a predetermined coordinate.

The teaching data creation unit 23 creates a travel schedule that is a set of passage time data corresponding to the passage time and the passage time in the teaching travel mode.
The SLAM processing unit 25 executes SLAM processing that simultaneously performs position estimation and environmental map creation (described later).

The storage unit 27 (an example of storage) is a storage area formed in at least a part of the storage area of the storage device of the control unit 7. The storage unit 27 stores environment map restoration data and an environment map.
The environmental map restoration data is obstacle position information acquired in advance by the distance sensor 5 at a predetermined position in a moving region where the autonomous traveling vehicle 1 moves. The environment map is an environment map created by the map creation unit 33 at the time of the previous position estimation.

The storage unit 27 further stores a travel schedule. The travel schedule indicates a travel route along which the autonomous traveling vehicle 1 autonomously moves when the reproduction travel mode is executed. When the reproduction travel mode is executed, the autonomous traveling vehicle 1 refers to the target position indicated in the travel schedule and controls the traveling unit 3 so as to reach the target position.
The travel control unit 29 generates control amounts for the motors 15 a and 15 b based on the input travel command and outputs the control amounts to the travel unit 3. The travel control unit 29 is, for example, a motion controller.

The travel command is an instruction input from an operator that is input via the operation unit 9 in the teaching travel mode. On the other hand, in the reproduction travel mode, the travel command is generated based on a comparison between the travel map and the position on the environment map estimated by the SLAM processing unit 25.
Data of the distance sensor 5 and the GNSS receiver 11 are input to the I / O port 31. Further, the I / O port 31 outputs a control amount to the traveling unit 3.

(4) Detailed Configuration of SLAM Processing Unit The SLAM processing unit 25 will be described in detail with reference to FIG.
The SLAM processing unit 25 includes a map creation unit 33 and a self-position estimation unit 35.

The map creation unit 33 creates an environmental map including the travel route of the autonomous traveling vehicle 1 as a global map based on the position information of the obstacle acquired by the distance sensor 5.
The map creation unit 33 associates the position information of the obstacle located around the autonomous traveling vehicle 1 with the acquired time in the teaching travel mode, and stores it in the storage unit 27 as environmental map restoration data.
In the reproduction travel mode, the map creation unit 33 reads environment map restoration data corresponding to a time earlier than the current passing point from the storage unit 27, and updates the global map based on the read data.

The self-position estimation unit 35 has a function of obtaining a SLAM probability distribution by the following operation. In the following descriptions (I) to (V), it is assumed that the autonomous traveling vehicle 1 has moved from the first position P1 to the second position P2. The probability finally obtained at each position is the “posterior probability”, which is used as the “prior probability” at the next position.
(I) When the autonomous traveling vehicle 1 exists at the second position P2, the self-position estimation unit 35 creates a local map (position information of the obstacle) using the distance sensor 5 at the position.
(II) The self-position estimating unit 35 is based on dead recognition (the amount of movement by the traveling unit 3 (in this embodiment, calculated from the rotational speeds of the traveling wheels 13a and 13b obtained from the encoders 17a and 17b)). The posterior probability of the first position P1 (= prior probability of the second position P2) is arranged at the estimated position P2 ′.
(III) The self-position estimation unit 35 uses a plurality of temporary local maps and a global map as a plurality of temporary local maps at the temporary self-position obtained by moving the local map created in (I) around the estimated position P2 ′. Each matching degree is calculated by matching with a map (map matching). Thereby, the likelihood of each temporary local map is obtained.
(IV) By multiplying the posterior probability of the first position P1 (= the prior probability of the second position P2) in (II) and each likelihood of (III), the posterior probability at each temporary self-position is calculated. The
(V) Of the plurality of calculated posterior probabilities, for example, the one having the highest peak is selected as the SLAM probability distribution.

The self-position estimation unit 35 further has a function of obtaining the self-position of the autonomous traveling vehicle 1 as a GNSS probability distribution using the data of the GNSS receiver 11.
Furthermore, the self-position estimation unit 35 creates a SLAM / GNSS probability distribution by synthesizing the SLAM probability distribution and the GNSS probability distribution, and sets the coordinates at which the probability is a peak as the self-position of the autonomous traveling vehicle 1. (Described later).

(5) Schematic operation of autonomous traveling cart The schematic operation of the autonomous traveling cart 1 will be described with reference to FIG. FIG. 2 is a flowchart showing an outline of the operation of the autonomous traveling vehicle.
In step S1, the control unit 7 determines whether or not a mode selection has been performed by the operator. Specifically, it is determined that the mode has been selected when an instruction input is received by an operation of the operation unit 9 by the operator.
In step S2, the control unit 7 determines whether the selected mode is the teaching travel mode or the reproduction travel mode.

In step S3, the control unit 7 executes the reproduction travel mode. The reproduction travel mode is a mode in which the vehicle travels autonomously on a predetermined travel route, and the control unit 7 estimates the position of the autonomous travel vehicle 1 in the movement area and the estimated position of the autonomous travel vehicle 1. Based on the comparison with the position information indicated in the travel schedule, the travel unit 3 is controlled (described later).
In step S4, the control unit 7 executes the teaching travel mode. The teaching travel mode is executed to acquire environmental map restoration data and a travel schedule. The control unit 7 controls the traveling unit 3 based on an operation from the operation unit 9 described later while estimating the position of the autonomous traveling vehicle 1 in the moving region.

In step S5, the control unit 7 determines whether or not to end the operation of the autonomous traveling vehicle 1. Specifically, the control unit 7 receives an instruction input to end the process by an operation of the operation unit 9 by the operator, receives an instruction input signal to end the process from the remote control, or teach travel For example, when it is determined that the travel schedule created by the mode has been completed, it is determined that the operation of the autonomous traveling vehicle 1 is to be terminated.
If it is determined that the operation of the autonomous traveling vehicle 1 is to be terminated, the process ends. On the other hand, if it is determined that the operation of the autonomous traveling vehicle 1 is to be continued, the process returns to step S1.

(6) Reproduction driving mode of autonomous traveling vehicle The reproduction traveling mode of the autonomous traveling vehicle will be described with reference to FIG. FIG. 3 is a flowchart showing the operation of the autonomous traveling vehicle that is executing the reproduction traveling mode.
In step S31, position information of obstacles existing around the autonomous traveling vehicle 1 is acquired. Specifically, the distance sensor 5 receives the reflected light that is irradiated with the laser light and further reflected from the obstacle. The obstacle information conversion unit 21 converts the detection signal into obstacle position information (for example, coordinate values on predetermined coordinates). The position information of the obstacle is a local map acquired at the current position.

  In step S32, the self-position estimating unit 35 estimates the current position of the autonomous traveling vehicle 1 (described later).

  In step S33, the map creation unit 33 creates an environment map (at the current estimated position) for the next self-position estimation with the next target arrival point indicated in the travel schedule as the future position. Store in the storage unit 27.

  In step S34, the traveling unit 3 is controlled based on the determined self-position. Specifically, the travel command created based on the comparison between the current estimated position acquired from the SLAM processing unit 25 and the next target arrival point acquired from the travel schedule indicating the travel route currently being reproduced. Accordingly, the traveling control unit 29 calculates control amounts of the motors 15a and 15b for moving from the current estimated position to the next target arrival point, and outputs the calculated control amounts to the motor driving units 19a and 19b. Therefore, based on the received control amount, the motor drive units 19a and 19b calculate drive signals for driving the motors 15a and 15b, respectively, and output them to the motors 15a and 15b. As a result, the autonomous traveling vehicle 1 travels from the current estimated position toward the next target arrival point.

In step S35, the control unit 7 determines whether or not the reproduction travel mode has ended. For example, when it is determined that the autonomous traveling vehicle 1 has passed all of the target arrival points indicated in the travel schedule, it is determined that the reproduction travel mode has ended.
When it is determined that the reproduction travel mode is to be ended, the control unit 7 stops the control of the travel unit 3 and ends the reproduction travel mode. On the other hand, if it is determined that the reproduction travel mode is to be continued, the process returns to step S31.
By executing the reproduction travel mode described above, the autonomous traveling vehicle 1 can move autonomously while reproducing a predetermined traveling schedule stored in the storage unit 27.

The self-position estimation operation (step S32) will be specifically described with reference to FIG. FIG. 4 is a flowchart of the self-position estimation operation.
In step S41, the self-position estimating unit 35 obtains a SLAM probability distribution using the data of the distance sensor 5 and the map of the moving environment.
In step S42, the self-position estimation unit 35 obtains a GNSS probability distribution using the data of the GNSS receiver 11.
In step S43, the self-position estimation unit 35 determines a ratio (hereinafter referred to as “GNSS probability distribution mix coefficient”) that mixes the GNSS probability distribution with the SLAM probability distribution (described later).

In step S44, the self-position estimation unit 35 creates a SLAM / GNSS probability distribution by combining the SLAM probability distribution and the GNSS probability distribution using the GNSS probability distribution mix coefficient. The obtained SLAM / GNSS probability distribution is used as a prior probability when self-position estimation is performed by SLAM next time.
In step S45, the self-position estimating unit 35 determines the peak position of the SLAM / GNSS probability distribution as the self-position of the autonomous traveling vehicle 1 in the moving environment.

(7) Synthesis of SLAM probability distribution and GNSS probability distribution using GNSS probability distribution coefficient The calculation formula of SLAM / GNSS probability distribution is as follows.
{SLAM probability distribution × (1.0−GNSS probability distribution mix coefficient)} + (GNSS probability distribution × GNSS probability distribution mix coefficient) = SLAM / GNSS probability distribution The GNSS probability distribution mix coefficient is 0.0 to 1.0. Changed in range.

The GNSS probability distribution mix coefficient is set to be large when it is desired to strongly reflect the GNSS probability distribution, and is set to be small when it is desired to strongly reflect the SLAM probability distribution.
In the former case, the accuracy of GNSS positioning is high. For example, the autonomous traveling vehicle 1 is an open sky (a region that is outdoor and away from the building, and has nothing to block the sky such as a roof or a tree) ).
The latter case is a case where the accuracy of the GNSS positioning is low, for example, a case where the autonomous traveling vehicle 1 travels in the building (including the underground) and around the building. In particular, GNSS positioning is impossible in a building, under a roof or tree, or between buildings.

  Note that, as data for determining the GNSS probability distribution mix coefficient, for example, a detection amount (that is, the number of detection data) that is a sensing state of the distance sensor 5 is used. The reason is that the number of detected data decreases, for example, in the order of indoor, outdoor building periphery, and open sky.

Hereinafter, a method for determining the GNSS probability distribution mix coefficient and the peak coordinates of the SLAM / GNSS probability distribution in three types of situations will be described.
First, since the sensing state of the distance sensor 5 is good indoors, that is, the number of detection data of the distance sensor 5 is large, the GNSS probability distribution mix coefficient is set to zero or extremely small. This is because, in an indoor environment, an object exists within the distance measurement range of the distance sensor 5, so that the reliability of position determination by SLAM is high, and conversely, the reliability of measurement by GNSS is low or positioning is impossible. In this case, the peak coordinate of the SLAM probability distribution becomes the peak coordinate of the SLAM / GNSS probability distribution as it is.
Secondly, in the case of an open sky environment, since the number of detection data of the distance sensor 5 is eliminated, the GNSS probability distribution mix coefficient is set large (for example, 1). This is because the measurement reliability by GNSS is high in an open sky environment. As a result, in this case, the peak coordinates of the GNSS probability distribution are directly used as the peak coordinates of the SLAM / GNSS probability distribution.

Thirdly, even in the vicinity of obstacles such as buildings, the number of data detected by the distance sensor 5 is larger in the vicinity of obstacles such as buildings, so the GNSS probability distribution mix coefficient is set smaller. This is because the reliability of position determination by SLAM is higher than that of the open sky environment, and conversely, the reliability of measurement by GNSS is low (for example, the position from the satellite cannot be detected and the positioning becomes unstable, Because it becomes impossible).
As a result of the above, if the GNSS positioning does not work well in an environment that is not only indoors but is outdoors but close to an obstacle such as a building, self-position estimation by SLAM can compensate for that. As a result, the autonomous traveling vehicle 1 can recognize its own position in various environments regardless of whether it is indoors or outdoors, thereby expanding the traveling range.

(8) Example Using FIG. 5, a situation where the autonomous traveling vehicle 1 actually travels outdoors will be described. FIG. 5 is a schematic plan view showing a traveling state of the autonomous traveling vehicle.
As shown in FIG. 5, the autonomous traveling vehicle 1 travels from the first area A, which is an open sky environment with no buildings around, toward the second area B, which is a building peripheral area in the vicinity of the buildings C and D. To do. In the first area A, there are a flower bed E and a tennis court F.

The SLAM / GNSS probability distribution and self-position estimation operation in the first region A will be described with reference to FIGS. 6A to 6C. FIG. 6A is a graph showing a SLAM probability distribution. FIG. 6B is a graph showing a GNSS probability distribution. FIG. 6C is a graph showing a SLAM / GNSS probability distribution.
As shown in FIG. 6A, in the SLAM probability distribution, the peak is in the vicinity of the coordinate “8”.
As shown in FIG. 6B, in the GNSS probability distribution, the peak is near the coordinate “5”.

  As shown in FIG. 6C, in the SLAM / GNSS probability distribution, the peak is in the vicinity of “5” of the coordinate similarly to the GNSS probability distribution. The reason is that, in the first region A, the GNSS probability distribution mix coefficient is set to be large, so that the GNSS probability distribution is reflected more strongly in the SLAM / GNSS probability distribution.

The SLAM / GNSS probability distribution and self-position estimation operation in the second region B will be described with reference to FIGS. 7A to 7C. FIG. 7A is a graph of SLAM probability distribution. FIG. 7B is a graph of a GNSS probability distribution. FIG. 7C is a graph of SLAM / GNSS probability distribution.
As shown in FIG. 7A, in the SLAM probability distribution, the peak is in the vicinity of the coordinate “8”.
As shown in FIG. 7B, in the GNSS probability distribution, the peak is near the coordinate “5”.

As shown in FIG. 7C, in the SLAM / GNSS probability distribution, the peak is in the vicinity of the coordinate “8” similarly to the SLAM probability distribution. The reason is that in the second region B, the GNSS probability distribution mix coefficient is set to be small, so that the GNSS probability distribution is not reflected weakly or not in the SLAM / GNSS probability distribution.
As a result, the peak of the SLAM / GNSS probability distribution is estimated to be the current position of the autonomous traveling vehicle 1.

2. Description of Embodiments The above embodiments can also be described as follows.
The control unit 7 (an example of a travel control device) is a distance sensor 5 (a first sensor) that acquires a distance from an autonomous traveling vehicle 1 (an example of an autonomous traveling vehicle) to an obstacle around it in a two-dimensional or three-dimensional manner. An GNSS receiver 11 (an example of a second sensor) that acquires the absolute position of the autonomous traveling vehicle 1 on the ground, and a traveling unit 3 (an example of a traveling unit), and plans a moving environment. It is used for the autonomous traveling vehicle 1 that moves autonomously or unplanned.
The control unit 7 includes a storage unit 27, an I / O port 31, and a self-position estimation unit 35.
The storage unit 27 (an example of storage) stores a map of the mobile environment.
The I / O port 31 (an example of an input / output device) receives data from the distance sensor 5 and the GNSS receiver 11 and outputs a travel command to the travel unit 3.

The self-position estimating unit 35 (an example of a computing device) performs the following operation.
Using the data of the distance sensor 5 and the map of the moving environment, the self position of the autonomous traveling vehicle 1 on the moving environment map is obtained as a SLAM probability distribution (an example of a first probability distribution).
Using the data of the GNSS receiver 11, the self position of the autonomous traveling vehicle 1 is obtained as a GNSS probability distribution (an example of a second probability distribution).
Synthesize SLAM probability distribution and GNSS probability distribution to create SLAM / GNSS probability distribution (an example of a third probability distribution).
The SLAM / GNSS probability part distribution peak position is determined as the self position of the autonomous traveling vehicle 1 in the moving environment.
◎ Create a travel command based on the determined self-position.

  In this case, the autonomous traveling vehicle 1 travels seamlessly indoors and outdoors. In particular, since a SLAM / GNSS probability distribution is created and its peak position is determined as a self-position, the most appropriate self-position estimation is possible according to the driving environment. Therefore, for example, indoors, the self-position can be accurately grasped by placing importance on the SLAM probability distribution. In the first region A (an example of a region where there are no obstacles such as buildings nearby), the self-position can be accurately grasped by placing importance on the GNSS probability distribution. Furthermore, in the second region B (an example of a region where there is an obstacle such as a building outdoors), the self-position can be accurately grasped by placing importance on the SLAM probability distribution.

3. Other Embodiments Although one embodiment of the present invention has been described above, the present invention is not limited to the above embodiment, and various modifications can be made without departing from the scope of the invention. In particular, a plurality of embodiments and modifications described in this specification can be arbitrarily combined as necessary.
In the above-described embodiment, the description has been focused on the reproduction travel mode, but the present invention is also applicable to the teaching travel mode.
In the above-described embodiment, the LRF has been described as an example of the distance sensor, but any sensor that can obtain distance data may be used. For example, a TOF camera or an ultrasonic sensor may be used.
In the above embodiment, the absolute position of the autonomous traveling vehicle on the ground is acquired by the GNSS method. However, another method may be used for acquiring the absolute position of the autonomous traveling vehicle.

In the above embodiment, RTK-GPS (Real Time Kinetics Global Positioning System) is used as a positioning method by GNSS, but GPS (Global Positioning System), QZSS (Quasi-Zenith Satellite System) May be.
In the above embodiment, the detection amount of the distance sensor 5 is used as the reference data for changing the GNSS probability distribution mix coefficient. However, the estimated self-location of the autonomous traveling vehicle 1 on the map of the moving environment or the GNSS positioning result is used. It may be. In this case, the SLAM probability distribution and the GNSS probability distribution can be reflected in the SLAM / GNSS probability distribution according to an accurate position and surrounding conditions.

  The present invention can be widely applied to a traveling control device for an autonomous traveling vehicle and an autonomous traveling vehicle.

1: Autonomous traveling trolley 3: Traveling unit 5: Distance sensor 7: Control unit 8: Coordinate 9: Operation unit 10: Display unit 11: GNSS receiver 13a: Traveling wheel 13b: Traveling wheel 15a: Motor 15b: Motor 17a: Encoder 17b: Encoder 19a: Motor drive unit 19b: Motor drive unit 21: Obstacle information conversion unit 23: Teaching data creation unit 25: SLAM processing unit 27: Storage unit 29: Travel control unit 31: I / O port 33: Map creation Part 35: Self-position estimation part

Claims (7)

A first sensor that two-dimensionally or three-dimensionally obtains a distance from an autonomous traveling vehicle to an obstacle around it, a second sensor that acquires an absolute position of the autonomous traveling vehicle on the ground, and a traveling unit In the traveling control device of the autonomous traveling vehicle that autonomously moves the moving environment in a planned or unplanned manner,
Storage for storing a map of the mobile environment;
An input / output device that receives data of the first sensor and the second sensor and outputs a travel command to the travel unit;
Using the data of the first sensor and the map of the moving environment, the self-location of the autonomous traveling vehicle on the map of the moving environment is obtained as a first probability distribution, and the data of the second sensor is used to A self-location of the autonomous traveling vehicle is obtained as a second probability distribution, the first probability distribution and the second probability distribution are combined to create a third probability distribution, and a peak position of the third probability distribution Calculating as the self-position of the autonomous traveling vehicle in the moving environment, and creating the travel command based on the determined self-position,
A traveling control device for an autonomous traveling vehicle comprising:   2. The travel control of the autonomous traveling vehicle according to claim 1, wherein the arithmetic device changes a coefficient for combining the first probability distribution and the second probability distribution according to a detection amount of the first sensor. apparatus.   The arithmetic unit changes a coefficient for combining the first probability distribution and the second probability distribution according to an estimated self-location of the autonomous traveling vehicle on a map of the moving environment. Travel control device for autonomous traveling carts. The autonomous traveling vehicle travels seamlessly between an indoor environment and an outdoor environment,
The travel control device for an autonomous traveling vehicle according to claim 2 or 3, wherein a coefficient that reflects the second probability distribution more strongly is set when the autonomous traveling vehicle is in an outdoor environment.   The first sensor is a laser range sensor that two-dimensionally acquires a distance from the autonomous traveling vehicle to the obstacle, and a map of the moving environment and the first probability distribution are SLAM (Simultaneous Localization and Mapping). The travel control device for an autonomous traveling vehicle according to any one of claims 1 to 4, wherein the travel control device is created by the following.   The second sensor is a receiver of a satellite positioning system (Navigation Satellite System), and an absolute position obtained by RTK (Real Time Kinetic) positioning is input to the I / O port. 5. The traveling control device for an autonomous traveling vehicle according to any one of 4 above. An autonomous traveling vehicle that autonomously moves in a moving environment in a planned or unplanned manner,
A first sensor for acquiring two-dimensionally or three-dimensionally the distance from the autonomous traveling vehicle to an obstacle around the vehicle;
A second sensor for obtaining an absolute position of the autonomous traveling vehicle on the ground;
A traveling section;
The travel control device according to any one of claims 1 to 6,
Autonomous traveling trolley equipped with.

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