JP6642319B2 - Autonomous mobile control device - Google Patents

Description

  The present invention relates to an autonomous mobile body control device.

  2. Description of the Related Art Conventionally, various techniques have been proposed in which a specific moving path is formed by laying a magnetic tape, a rail, or the like in a moving area, and a moving body is automatically moved along the moving path. On the other hand, in recent years, an autonomous mobile body that autonomously moves in a moving area without forming a specific moving route in the moving area has been proposed. Such an autonomous mobile body is disclosed in Patent Document 1, for example.

  This autonomous mobile body includes a drive device, a storage device, an environment information detection device, a self-movement amount detection device, and an autonomous mobile body control device. The driving device is configured by a motor or the like. The storage device stores map information of the moving area. The environment information detection device detects environment information around an autonomous mobile body in a moving area. The self-moving amount detection device detects a moving amount of the autonomous moving body in the moving area. The autonomous mobile body control device controls a drive device, a storage device, an environment information detection device, and a self-movement amount detection device.

  In such an autonomous mobile body control device, when the autonomous mobile body travels autonomously in the moving area, the environment information detection device is controlled to detect the environment information. Here, the moving area includes a geometric feature portion and a non-geometric feature portion. For this reason, the environment information detected by the environment information detecting device includes geometric environment information based on the geometric feature portion and non-geometric environment information based on the non-geometric feature portion. Then, based on the geometric environment information and the non-geometric environment information and the map information stored in the storage device, a plurality of coordinates and postures of the autonomous moving body in the moving area are calculated by a stochastic method. Is calculated. Then, a virtual value of a portion that is most overlapped with the environment information from the map information is determined as a definite value. In this way, the autonomous moving body control device recognizes the determined value as the current coordinates and posture of the autonomous moving body in the moving area, and controls the driving device and the self-movement amount detecting device to move the autonomous moving body within the moving area. Move. By repeating these processes, the autonomous moving body control device autonomously moves the autonomous moving body within the moving area while correcting the current coordinates and posture of the autonomous moving body within the moving area.

  By the way, in the moving area, an environment in which many geometrical features exist and an environment in which many non-geometrical features exist, in other words, an environment with few geometrical features can coexist. In an environment where a large number of non-geometric feature parts are present, the non-geometric environment information detected by the environment information detection device is large, and therefore the autonomous mobile body control device uses the stochastic method to determine the current coordinates of the autonomous mobile body and It is difficult to obtain the posture with high accuracy. When the amount of non-geometric environment information increases, the processing load when calculating each virtual value by the probabilistic method increases, and not only the time required for the calculation becomes longer, but also the variation of the calculated virtual values increases. This is because the autonomous mobile body control device cannot appropriately determine the final value from each virtual value.

  Therefore, in the above-mentioned autonomous mobile body control device, a plurality of optical action members are provided in the moving area. Thereby, even in an environment where the environment information detection device originally detects a large number of non-geometric environment information, it is possible to detect the optical action member as the geometric environment information. That is, in the autonomous mobile body control device, the non-geometric environment information detected by the environment information detection device is reduced, while the detected geometric environment information is increased. It is possible to obtain the current coordinates and attitude of the autonomous mobile body with high accuracy by using a dynamic method. Thus, in the autonomous mobile body control device, it is possible to autonomously move the autonomous mobile body within the moving area with high accuracy.

JP 2013-25351 A

  However, in the above-mentioned autonomous mobile body control device, in order to obtain the current coordinates and posture of the autonomous mobile body with high accuracy, not only a large number of optical working members are required, but also a position where each optical working member is arranged. It needs to be measured accurately. Further, each time the layout of the moving area is changed, it is necessary to change the arrangement of each optically acting member. For these reasons, in the autonomous mobile body control device, the cost required for operation increases.

  The present invention has been made in view of the above-described conventional circumstances, and enables an autonomous mobile object to autonomously move within a moving area with high accuracy, and reduces the cost required for operation. An object of the present invention is to provide a body control device.

An autonomous mobile body control device according to a first aspect of the present invention is used for an autonomous mobile body that autonomously moves within a preset moving area,
The autonomous moving body includes a driving device, a storage device that stores map information of the moving area, a self-moving amount detecting device that detects a moving amount of the self in the moving area, and a surrounding of the self in the moving area. Environment information detection device for detecting the environment information of the
An autonomous moving body control device that controls the driving device, the storage device, the self-moving amount detecting device, and the environment information detecting device while recognizing the x coordinate, the y coordinate, and the posture of the autonomous moving object in the moving area. And
The moving area includes a geometric feature and a non-geometric feature,
The map information is obtained by excluding the non-geometric feature portion in advance,
An extraction unit that refers to the map information and the environment information and extracts only specific environment information that matches the map information from the environment information;
A first calculation unit configured to calculate a first virtual value including a first estimated x coordinate, a first estimated y coordinate, and a first estimated attitude of the autonomous moving body based on a movement amount of the autonomous moving body;
A second calculating unit configured to calculate a plurality of second virtual values including a second estimated x coordinate, a second estimated y coordinate, and a second estimated attitude of the autonomous moving object by a probabilistic method based on the specific environment information;
The first virtual value and said performing a first output mode to determine a definite value from the second virtual value, have a first output mode unit for outputting the determined value to the drive device,
In the first output mode, the final value is determined by weighting each of the second virtual values and a value obtained by adding a matching ratio of the second virtual value to the first virtual value. It is characterized by the following.

  In the autonomous mobile body control device according to the first aspect of the invention, the specific environment information extracted by the extraction unit includes only environment information based on a geometric feature in the moving area, that is, only the geometric environment information. For this reason, based on this specific environment information, the processing load when the second calculation unit calculates a plurality of second virtual values by the stochastic method can be reduced, and each second virtual value is calculated quickly. be able to. Further, the dispersion of the calculated second virtual values is also reduced. For this reason, in the first output mode, when determining the current x- and y-coordinates and the final value as the attitude of the autonomous mobile body in the moving area, the accuracy of the final value can be increased. Therefore, the autonomous mobile body control device can accurately recognize the x coordinate, the y coordinate, and the posture of the autonomous mobile body in the moving area.

  As described above, in the autonomous mobile body control device, if the map information in which the non-geometric feature portion in the moving area is excluded is prepared and the specific environment information is extracted from the map information and the environment information, the accuracy is high. In obtaining the fixed value, it is not necessary to provide the above-mentioned optical action member in the moving area. Further, in the autonomous mobile body control device, even when the layout of the moving area is changed, it is sufficient to correct the map information stored in the storage device, so that it is possible to relatively easily cope with the change.

  Therefore, according to the autonomous mobile body control device of the first invention, it is possible to autonomously move the autonomous mobile body within the moving area with high accuracy, and it is possible to reduce the cost required for operation.

The autonomous mobile body control device of the second invention is used for an autonomous mobile body that autonomously moves within a preset moving area,
The autonomous moving body includes a driving device, a storage device that stores map information of the moving area, a self-moving amount detecting device that detects a moving amount of the self in the moving area, and a surrounding of the self in the moving area. Environment information detection device for detecting the environment information of the
An autonomous moving body control device that controls the driving device, the storage device, the self-moving amount detecting device, and the environment information detecting device while recognizing the x coordinate, the y coordinate, and the posture of the autonomous moving object in the moving area. And
The moving area includes a geometric feature and a non-geometric feature,
The environment information includes geometric environment information based on the geometric feature portion, and non-geometric environment information based on the non-geometric feature portion,
A moving speed of the autonomous moving body, a reference value calculating unit that calculates a reference value based on a traveling direction of the autonomous moving body in the moving area,
A generation unit that generates edited environment information obtained by deleting the non-geometric environment information from the environment information based on the reference value;
A first calculation unit configured to calculate a first virtual value including a first estimated x coordinate, a first estimated y coordinate, and a first estimated attitude of the autonomous moving body based on a movement amount of the autonomous moving body;
A second calculating unit configured to calculate a plurality of second virtual values including a second estimated x coordinate, a second estimated y coordinate, and a second estimated attitude of the autonomous mobile body based on the edited environment information by a probabilistic method;
The first virtual value and said performing a first output mode to determine a definite value from the second virtual value, have a first output mode unit for outputting the determined value to the drive device,
In the first output mode, the final value is determined by weighting each of the second virtual values and a value obtained by adding a matching ratio of the second virtual value to the first virtual value. It is characterized by the following.

  In the autonomous mobile body control device according to the second aspect, the edited environment information generated by the generation unit does not include non-geometric environment information. For this reason, based on this edited environment information, even in the autonomous mobile body control device, it is possible to reduce the processing load when the second calculation unit calculates a plurality of second virtual values by a stochastic method, Each second virtual value can be calculated quickly. Further, the dispersion of the calculated second virtual values is also reduced. As a result, even in the autonomous mobile body control device, the accuracy of the determined value is increased, and the x coordinate, the y coordinate, and the attitude of the autonomous mobile body in the moving area can be recognized with high accuracy.

  As described above, in the autonomous mobile body control device, the generation unit generates the edited environment information from the environment information detected by the environment information detection device. There is no need to provide an optical action member for the camera. Further, in the autonomous mobile body control device, even when the layout of the moving area is changed, it can be handled more easily than the autonomous mobile body control device of the first invention.

  Therefore, the autonomous mobile body control device according to the second aspect of the present invention can also autonomously move the autonomous mobile body within the moving area with high accuracy, and can reduce the cost required for operation.

  It is preferable that the autonomous mobile body control device of the second invention has a determination unit that determines a range of the environment information detected by the environment information detection device based on the edited environment information. In this case, it is possible to reduce the need for the environment information detection device to detect unnecessary environment information, so that it is possible to reduce processing when updating the edited environment information.

  An autonomous mobile body control device according to a first invention and an autonomous mobile body control device according to a second invention are configured to calculate a displacement amount between a fixed value and a first virtual value based on a fixed value; If the value does not exceed the threshold value, a second output mode for executing the second output mode for determining the sub-determined value from the first virtual value and outputting the sub-determined value to the driving device may be provided. Then, the first output mode section preferably executes the first output mode when the displacement amount exceeds the threshold.

  When the amount of displacement of the first virtual value does not exceed the threshold value with respect to the determined value obtained in the first output mode, that is, the x-coordinate, y-coordinate, and posture of the autonomous mobile body in the moving area based on the determined value On the other hand, when the accuracy of the first estimated x coordinate, the first estimated y coordinate, and the first estimated attitude is high, it is less necessary to further calculate the second virtual value. Therefore, if the amount of displacement of the first virtual value with respect to the determined value does not exceed the threshold value, the first virtual value is set as a sub-determined value, and this sub-determined value is used as the current x-coordinate, y By setting the coordinates and the posture, the autonomous mobile body control device can reduce the processing load.

In the first output mode, the final value is determined by weighting each second virtual value and a value obtained by adding a matching ratio of each second virtual value to the first virtual value . For this reason , in the first output mode, a more accurate determined value can be determined.

  Preferably, the probabilistic approach is a particle filter based on the SLAM algorithm. In this case, a plurality of highly accurate second virtual values can be calculated relatively easily.

  The autonomous mobile body is preferably an autonomously traveling industrial vehicle. In a moving area such as a factory or a warehouse where an industrial vehicle autonomously travels, there are many cases where a large number of non-geometric features exist around the industrial vehicle, and the layout is frequently changed. For this reason, if the autonomous mobile object control device of the present invention is used for an industrial vehicle, it is possible to accurately obtain the current coordinates and attitude of the industrial vehicle within the moving area. As a result, the industrial vehicle can autonomously travel in the moving area with high accuracy, and the work efficiency can be improved.

  According to the autonomous mobile body control device of the first invention and the autonomous mobile body control device of the second invention, it is possible to autonomously move the autonomous mobile body within the moving area with high accuracy and to reduce the cost required for operation. Can be reduced.

FIG. 1 is a schematic diagram illustrating an industrial vehicle in which the autonomous mobile body control device according to the first embodiment is employed. FIG. 2 is a schematic diagram illustrating a state in which an industrial vehicle autonomously travels in a moving area. FIG. 3 is a schematic diagram illustrating map information stored in a storage device according to the autonomous mobile body control device of the first embodiment. FIG. 4 is a control flow when the autonomous mobile body control device of the first embodiment causes an industrial vehicle to autonomously travel in a moving area. FIG. 5 is an enlarged schematic diagram illustrating a portion corresponding to the area X shown in FIG. 2 in the environment information detected by the environment information detection device according to the autonomous mobile body control device of the first embodiment. FIG. 6 is an enlarged schematic diagram illustrating a portion of the map information corresponding to the area X illustrated in FIG. 2 according to the autonomous mobile body control device of the first embodiment. FIG. 7 is an enlarged schematic diagram illustrating a state in which only specific environment information is extracted from environment information according to the autonomous mobile body control device of the first embodiment. FIG. 8 is a schematic diagram illustrating a dispersion state of specific particles at a point A in FIG. 2 according to the autonomous mobile body control device of the first embodiment. FIG. 9 is a schematic diagram illustrating a dispersion state of specific particles at a point B in FIG. 2 according to the autonomous mobile body control device of the first embodiment. FIG. 10 is a schematic diagram illustrating a dispersion state of specific particles at a point B in FIG. 2 according to the autonomous mobile body control device of the comparative example. FIG. 11 is a schematic diagram illustrating an industrial vehicle in which the autonomous mobile body control device according to the second embodiment is employed. FIG. 12 is a control flow when the autonomous mobile body control device of the second embodiment causes an industrial vehicle to autonomously travel in a moving area. FIG. 13 is an enlarged schematic diagram illustrating a part of the environment information detected by the environment information detection device according to the autonomous mobile body control device of the second embodiment. FIG. 7A shows the environment information detected by the environment information detecting device when the industrial vehicle is located most east of the moving area in FIGS. FIG. 2B shows the environment information detected by the environment information detecting device when the industrial vehicle moves to the west of the moving area as compared with FIG. FIG. (C) shows the environment information detected by the environment information detecting device when the industrial vehicle has moved to the west of the moving area as compared to FIG. (B). FIG. 14 is an enlarged schematic diagram illustrating a part of the edited environment information according to the autonomous mobile body control device of the second embodiment. FIG. 15 is an enlarged schematic diagram illustrating a portion corresponding to FIG. 14 in the map information according to the autonomous mobile body control device of the second embodiment.

  Hereinafter, Embodiments 1 and 2 that embody the present invention will be described with reference to the drawings.

(Example 1)
As shown in FIG. 1, the CPU 1 according to the first embodiment is mounted on an automatic guided vehicle 3. The CPU 1 is an example of the autonomous mobile body control device of the present invention. The automatic transport vehicle 3 is an industrial vehicle that can autonomously travel in the warehouse 5 shown in FIG. 2 without a worker driving, and is an example of the autonomous mobile body of the present invention. The warehouse 5 is an example of a moving area according to the present invention.

  In the warehouse 5, the first to fourth chambers 51 to 54 having different sizes from each other are defined by a plurality of flat wall surfaces 50, and a corridor 55 connected to the first to fourth chambers 51 to 54 is provided. It is partitioned. In the first to fourth rooms 51 to 54, a work desk 57 and the like are arranged in addition to the various luggage shelves 56.

  The corridor 55 is constituted by first to fourth corridors 55a to 55d. The first corridor 55a is connected to the first room 51 and extends in the north-south direction. The second corridor 55b connects to the third room 53, and also connects to the first corridor 55a, the third corridor 55c, and the fourth corridor 55d, and extends in the east-west direction. The third corridor 55c is connected to the fourth room 54 and to the second corridor 55b, and extends in the north-south direction. The fourth corridor 55d is connected to the second room 52 and to the second corridor 55b, and extends in the north-south direction.

  Further, a plurality of columns 58 are provided in the warehouse 5. The pillars are arranged in the first to fourth rooms 51 to 54 and the first to fourth corridors 55a to 55d, respectively. Each wall 50 is provided along each column 58. In FIG. 2 and the like, the arrangement of each pillar 58 in the warehouse 5 is simplified for ease of explanation.

  Here, the shapes of the luggage shelf 56, the work desk 57, and the pillar 58 include a relatively large number of geometric shapes. On the other hand, since the wall surface 50 is formed flat, the shape includes few geometric shapes. For this reason, in the warehouse 5, the luggage shelf 56, the work desk 57, and the pillar 58 are regarded as geometric features in the present invention, and the wall surfaces 50 are regarded as non-geometric features in the present invention. I have. Therefore, the first to fourth rooms 51 to 54 in which a number of luggage shelves 56 and work desks 57 are arranged in addition to the pillars 58 and the wall surfaces 50, provide an environment rich in geometric features in the warehouse 5. On the other hand, the first to fourth corridors 55 a to 55 d are constituted by the pillars 58 and the wall surfaces 50. As described above, although the pillars 58 are present, most of them are constituted by the wall surfaces 50, so that the first to fourth corridors 55a to 55d have an environment in which the geometric features are scarce in the warehouse 5.

  As shown in FIG. 1, the automatic guided vehicle 3 includes a plurality of driving wheels 7, a ROM 9, a RAM 11, an encoder 13, an external sensor 15, and an information input unit 17 in addition to the CPU 1. Although not shown, the automatic transport vehicle 3 includes a known steering device, an electric motor, and the like.

  Each drive wheel 7 constitutes a drive device 19 together with a steering device, an electric motor, and the like, and allows the automatic transport vehicle 3 to travel in the warehouse 5.

  The ROM 9 stores a control program for causing the automatic guided vehicle 3 to travel autonomously. The control program is specifically a SLAM (Simultaneous Localization and Mapping) algorithm, and executes a particle filter as a stochastic method.

  The RAM 11 is an example of a storage device according to the present invention. The RAM 11 stores the map information 500 of the warehouse 5 shown in FIG. The map information 500 is created as coordinate information in the warehouse 5 when the east-west direction of the warehouse 5 is set as the x-axis and the north-south direction of the warehouse 5 is set as the y-axis. Although the map information 500 includes information on the luggage shelf 56, the work desk 57, and the pillar 58, which are geometric features, information on each wall 50, which is a non-geometric feature, is created. It has been excluded in advance by editing the time. That is, unlike the actual warehouse 5 shown in FIG. 2, each wall surface 50 does not exist in the map information 500 shown in FIG.

  The RAM 11 shown in FIG. 1 can temporarily store not only the route information input to the information input unit 17 but also first and second virtual values and fixed values, threshold values, and the like. The details of the first and second virtual values will be described later.

  The encoder 13 is an example of the self-moving amount detecting device according to the present invention, and can detect the moving amount of the automatic transport vehicle 3 in the warehouse 5 by measuring the rotation speed of each drive wheel 7 and the steering angle of the steering device. Has become. Note that the encoder 13 may be configured to detect the moving amount of the automatic transport vehicle 3 in the warehouse 5 by measuring the number of rotations of the electric motor. Further, instead of the encoder 13, a GPS device or the like may be employed as the self-movement amount detecting device.

  The external sensor 15 is an example of the environment information detecting device according to the present invention, and is attached to a lower part of the automatic guided vehicle 3. The external sensor 15 irradiates a certain area around the automatic guided vehicle 3 with a laser as shown by, for example, hatching in FIG. Then, the laser beam reflected by the wall surface 50, the pillar 58, and the like and returned to the external sensor 15 is received. Thus, as shown in FIG. 5, the environment information 150 around the automatic guided vehicle 3 in the warehouse 5 can be detected. The external sensor 15 detects the environment information 150 and, at the same time, measures the distance from the wall surface 50, the pillar 58, and the like to the automatic transport vehicle 3.

  Here, as described above, in the warehouse 5, there are columns 58 and the like that are geometric features and the wall surface 50 that is a non-geometric features. Therefore, the environment information 150 detected by the external sensor 15 includes the geometric environment information 150a based on the pillar 58 and the like and the non-geometric environment information 150b based on the wall surface 50.

  The information input unit 17 shown in FIG. 1 includes an operation display, a keyboard, and the like (not shown), and enables an operator to input route information such as the current position and the destination of the automatic guided vehicle 3.

  The CPU 1 controls the driving device 19, the ROM 9, the RAM 11, the encoder 13, the external sensor 15, and the information input unit 17 to cause the automatic guided vehicle 3 to travel autonomously. Specifically, the CPU 1 performs each operation processing as an extraction unit, a first operation unit, a second operation unit, a third operation unit, a first output mode unit, and a second output mode unit in the present invention. Thereby, the CPU 1 causes the automatic guided vehicle 3 to autonomously travel in the warehouse 5 while executing the first output mode or the second output mode described later.

  Next, an example in which the automatic transport vehicle 3 transports the luggage (not shown) in the luggage rack 56 in the first room 51 to the luggage rack 56 in the fourth room 54 will be described. Each calculation process of the CPU 1 when the vehicle is caused to autonomously travel will be specifically described.

  As shown in FIG. 4, in causing the automatic guided vehicle 3 to travel autonomously, first, the CPU 1 performs a process based on the map information 500 of the warehouse 5 stored in the RAM 11 and the route information input from the information input unit 17. The coordinates on the map information 500 of the current position of the automatic guided vehicle 3 and the fourth room 54 as the destination are calculated. Then, the CPU 1 starts traveling of the automatic guided vehicle 3 toward the fourth chamber 54 by controlling the driving device 19 (step S101).

  As the automatic guided vehicle 3 travels, the encoder 13 measures the rotation speed of each drive wheel 7 and the steering angle of the steering device to detect the moving amount of the automatic guided vehicle 3. Then, based on the movement amount, the CPU 1 calculates a first virtual value including the first estimated x coordinate, the first estimated y coordinate, and the first estimated attitude θ of the automatic guided vehicle 3 (step S102). Is the inclination in the y-axis direction with respect to the x-axis direction in the traveling direction of the automatic guided vehicle 3. Note that the first virtual value calculated by the CPU 1 is stored in the RAM 11.

  Further, when the automatic guided vehicle 3 travels, the external sensor 15 detects environmental information 150 (see FIG. 5) around the automatic guided vehicle 3 as needed (step S103). This environment information 150 is stored in the RAM 11. Therefore, the environment information 150 is gradually accumulated in the RAM 11 as the automatic guided vehicle 3 travels. Then, the CPU 1 extracts the specific environment information 200 (see FIG. 7) from the environment information 150 (Step S104). The extracted specific environment information 200 is stored in the RAM 11.

  The extraction of the specific environment information 200 will be specifically described with an example in which the automatic guided vehicle 3 travels in the area X shown in FIG. The area X is a part of the second corridor 55b and includes a point B described later. Then, as the automatic guided vehicle 3 travels in the area X, the external sensor 15 detects the environment information 150 shown in FIG. Here, since the region X is a part of the second corridor 55b, the external sensor 15 detects the pillar 58 and the wall surface 50. Therefore, the environment information 150 includes the geometric environment information 150a based on each column 58 and the non-geometric environment information 150b based on each wall surface 50 existing between the columns 58. As described above, since the external sensor 15 is irradiated in the traveling direction of the automatic guided vehicle 3, a part of the pillar 58 is not irradiated with the laser of the external sensor 15. Therefore, the actual shape of the pillar 58 shown in FIG. 2 is partially different from the shape of the geometric environment information 150a shown in FIG.

  Further, the CPU 1 refers to the map information 500 stored in the RAM 11 and the environment information 150 detected as described above. Here, since the wall surface 50 is excluded from the map information 500, a portion corresponding to the region X in the map information 500 is as shown in FIG. Then, the CPU 1 extracts, from the environment information 150 shown in FIG. 5, a part matching the map information 500 shown in FIGS. 3 and 6 as the specific environment information 200 shown in FIG. That is, in the area X, the location where the environment information 150 matches the map information 500 is the geometric environment information 150a based on each pillar 58. Therefore, the CPU 1 extracts the geometric environment information 150a based on each pillar 58 as the specific environment information 200. In other words, the specific environment information 200 includes only the geometric environment information 150a. In the present embodiment, the first to fourth corridors 55a to 55d shown in FIG. For this reason, the CPU 1 stores the geometric environment information 150a based on each pillar 58 in the specific environment regardless of the position of the area X and the automatic transport vehicle 3 traveling in any of the first to fourth corridors 55a to 55d. It will be extracted as information 200. On the other hand, for example, when the automatic guided vehicle 3 is traveling in the first room 51, the CPU 1 determines not only the geometrical environment information 150a based on each pillar 58 but also the geometrical environment information 150a based on each luggage shelf 56. The environmental information 150a is also extracted as the specific environment information 200.

  Next, based on the specific environment information 200, the CPU 1 calculates a plurality of second virtual values including a second estimated x coordinate, a second estimated y coordinate, and a second estimated attitude θ of the automatic guided vehicle 3 by a particle filter ( Step S105 shown in FIG. 4). Specifically, the CPU 1 disperses a number of particles that are virtual bodies of the automatic transport vehicle 3 around the automatic transport vehicle 3. Then, based on the specific environment information 200, a plurality of specific particles that match the current coordinates and posture of the automatic guided vehicle 3 in the warehouse 5 with a likelihood of a predetermined size are extracted from each particle. Then, the above-described second virtual values, that is, each second estimated x coordinate, each second estimated y coordinate, and each second estimated posture θ are calculated from the x coordinate, y coordinate, and posture θ of each specific particle.

  Next, the CPU 1 determines whether or not the first output mode has been already executed based on the information stored in the RAM 11 (step S106). Then, when the first output mode has been executed (step S106: YES), the CPU 1 determines whether the first virtual value has been changed based on the determined value already obtained in the first output mode. The amount is calculated (step S107). Specifically, the calculation of the displacement amount is performed based on the first estimated x coordinate, the first estimated y coordinate, the first estimated attitude θ, and the current x coordinate, y, of the automatic guided vehicle 3 in the warehouse 5 based on the determined value. This is performed by comparing the coordinates and the posture θ. The details of the first output mode and the fixed value will be described later.

  The CPU 1 determines whether the calculated amount of displacement of the first virtual value exceeds a threshold value stored in the RAM 11 (step S108). Here, if the displacement amount does not exceed the threshold (step S108: NO), the first estimated x coordinate, the first estimated x coordinate, This means that the accuracy of the estimated y coordinate and the first estimated attitude θ is high. For this reason, the CPU 1 executes the second output mode (step S109). In the second output mode, the CPU 1 determines the first estimated x coordinate, the first estimated y coordinate, and the first estimated attitude θ of the first virtual value with the current x coordinate, y coordinate, and attitude θ of the warehouse 5 automatic transport vehicle 3. Let it be a certain sub-determined value. Thus, the CPU 1 recognizes the current x-coordinate, y-coordinate, and posture θ of the automatic guided vehicle 3 in the warehouse 5. Then, the CPU 1 causes the automatic guided vehicle 3 to autonomously travel by storing the sub-determined value in the RAM 11 and outputting the value to the driving device 19. In the second output mode, the previously determined value obtained in the first output mode is not erased from the RAM 11.

  On the other hand, when the displacement amount exceeds the threshold value (step S108: YES), the first estimated x coordinate, the first estimated y coordinate, and the first estimated This means that the deviation of the estimated attitude θ is large and the accuracy of the first virtual value is low. If the first virtual value is used as a sub-determined value until such a case, the x-coordinate, y-coordinate and attitude θ of the automatic transport vehicle 3 in the warehouse 5 recognized by the CPU 1 and the automatic transport vehicle 3 in the warehouse 5 The error between the current x coordinate, y coordinate, and posture θ increases. Therefore, when the displacement exceeds the threshold, the CPU 1 executes the first output mode (step S110). In addition, even when the first output mode has never been executed (step S106: NO), such as immediately after the automatic guided vehicle 3 is made to autonomously travel, the CPU 1 executes the processing of the above steps S107 to S109. Instead, the first output mode is executed.

In the first output mode, a final value is determined from the first virtual value calculated in step S102 and each second virtual value calculated in step S105. The final value is determined by the CPU 1 weighting each second virtual value and a value obtained by adding a matching ratio of each second virtual value to the first virtual value. As a result, the CPU 1 recognizes the second estimated x coordinate, the second estimated y coordinate, and the second estimated attitude θ of the determined value as the current x coordinate, y coordinate, and attitude θ of the warehouse 5 automatic transport vehicle 3. Then, the CPU 1 causes the automatic guided vehicle 3 to autonomously travel by storing the determined value in the RAM 11 and outputting the determined value to the driving device 19. Here, if the previous fixed value is already stored in the RAM 11, the newly obtained fixed value is overwritten. The CPU 1 also causes the RAM 11 to store the fact that the first output mode has been executed.

  In this way, if the automatic guided vehicle 3 has not reached the fourth chamber 54, which is the destination (step S111: NO), the CPU 1 repeats steps S102 to S110 to execute the first output mode or the second output mode. Execute output mode. As a result, the CPU 1 moves the automatic transport vehicle 3 from the first room 51 to the second corridor 55b through the first corridor 55a, as shown by the arrow in FIG. 2, and moves from the second corridor 55b to the third corridor 55c. It is possible to autonomously travel to the fourth room 54 through a path leading to the fourth room 54. Then, when the automatic transport vehicle 3 reaches the luggage rack 56 in the fourth chamber 54 (step S114: YES), the CPU 1 ends the control of the automatic transport vehicle 3.

  In this way, the CPU 1 calculates each second virtual value based on the specific environment information 200, so that each second virtual value can be calculated quickly. Further, in the first output mode, the CPU 1 can determine a highly accurate fixed value as the current x-coordinate, y-coordinate, and attitude of the automatic transport vehicle 3 in the warehouse 5. Therefore, the CPU 1 can accurately recognize the x-coordinate, the y-coordinate, and the posture of the automatic transport vehicle 3 in the warehouse 5.

  The operation and effect of the CPU 1 according to the first embodiment will be described in more detail in comparison with a comparative example. In the comparative example, information on the wall surface 50 is included in the map information stored in the RAM 11. Thereby, in the comparative example, the environment information 150 detected by the external sensor matches the map information. In the comparative example, the CPU 1 does not extract the specific environment information 200 from the environment information 150. For this reason, in the comparative example, the CPU 1 calculates each second virtual value based on the environment information 150 including the geometric environment information 150a and the non-geometric environment information 150b.

  By the way, generally, when a probabilistic method such as a particle filter is used, each second virtual value is calculated with a certain variation. The variation of each second virtual value changes depending on the ratio of the geometric environment information 150a included in the environment information 150, and the more the geometric environment information 150a is, the smaller the variation of each second virtual value is.

  Here, in the first embodiment, the specific environment information 200 extracted by the CPU 1 includes only the geometric environment information 150a. Thereby, the CPU 1 of the first embodiment executes the particle filter at the point A (see FIG. 2) of the first room 51 where the x coordinate, the y coordinate, and the attitude θ on the map information 500 are “xA: yA: θA”. In this case, as shown in FIG. 8, the specific particles P1 to P10 extracted based on the specific environment information 200 are distributed in a range near the point A. That is, variation between the specific particles P1 to P10 is small.

  Similarly, the CPU 1 of the first embodiment executes the particle filter at the point B (see FIG. 2) in the second corridor 55b where the x coordinate, the y coordinate, and the attitude θ on the map information 500 are “xB: yB: θB”. Also in this case, as shown in FIG. 9, the specific particles P1 to P10 extracted based on the specific environment information 200 are distributed in a range close to the point B. That is, also in this case, the variation between the specific particles P1 to P10 is small. The distribution of the specific particles P1 to P10 at the point A and the point B is an example.

  On the other hand, in the comparative example, the CPU 1 calculates each second virtual value based on the environment information 150. Here, in addition to the first room 51 where the point A exists, in the second to fourth rooms 52 to 54, in addition to the pillars 58 and the wall surfaces 50, many luggage shelves 56 and work desks 57 are arranged in the room. I have. For this reason, in the comparative example, when the external sensor 15 detects the environment information 150 in the first to fourth rooms 51 to 54, the environment information 150 includes a lot of geometric environment information 150a, In addition, the non-geometric environment information 150b is reduced. For this reason, when the CPU 1 of the comparative example executes the particle filter at the point A, the specific particles P1 to P10 extracted based on the environment information 150 are in a range close to the point A as in the case of the CPU 1 of the first embodiment. Distribute.

  However, in the first, third, and fourth corridors 55a, 55c, and 55d, in addition to the second corridor 55b where the point B exists, the pillars 58 exist, but most of them become the wall surfaces 50. That is, the environment information 150 at the point B includes less geometric environment information 150a and more non-geometric environment information 150b. Therefore, when the CPU 1 of the comparative example executes the particle filter at the point B, as shown in FIG. 10, the variation of the specific particles P1 to P10 extracted based on the environment information 150 increases.

  As described above, in the CPU 1 of the comparative example, when the non-geometric environment information 150b included in the environment information 150 increases, the processing load at the time of extracting each second virtual value, that is, the specific particles P1 to P10 increases. In addition to the increase in the time required for the calculation, the variation in each of the specific particles P1 to P10 increases, so that it is not possible to appropriately determine the final value from each of the second virtual values. As a result, the CPU 1 of the comparative example cannot accurately recognize the current x coordinate, y coordinate, and attitude of the automatic guided vehicle 3 when traveling in the first to fourth corridors 55a to 55d.

  In this regard, based on the specific environment information 200, the CPU 1 of the first embodiment can reduce the processing load when calculating each second virtual value, and can quickly calculate each second virtual value. . In addition, not only in the environment where the geometrical features are rich in the warehouse 5 like the first room 51, but also in the environment where the geometrical features are poor in the warehouse 5 like the second corridor 55b and the fourth corridor 55d. Also, the variation of the specific particles P1 to P10 extracted in calculating each second virtual value is reduced. Therefore, the CPU 1 can increase the accuracy of the final value determined in the first output mode. For this reason, in the CPU 1, regardless of the first to fourth rooms 51 to 54 and the first to fourth corridors 55a to 55d, that is, the CPU 1 is related to an environment rich in geometric features or an environment poor in geometric features. In addition, the present x-coordinate, y-coordinate, and attitude of the automatic guided vehicle 3 in the warehouse 5 can be recognized with high accuracy. As a result, the CPU 1 allows the automatic guided vehicle 3 to autonomously travel in the warehouse 5 with high accuracy, and the work efficiency of the automatic guided vehicle 3 can be improved.

  Then, the CPU 1 prepares the map information 500 from which the wall surface 50 in the warehouse 5 is excluded, and extracts the specific environment information 200 from the map information 500 and the environment information 150. There is no need to separately provide members such as optical working members in the warehouse 5. In the warehouse 5, the layout may be changed frequently, but even in such a case, the CPU 1 only needs to correct the map information 500 stored in the RAM 11, so that the change is relatively easy. It is possible to respond.

  Therefore, according to the CPU 1 of the first embodiment, it is possible to autonomously move the automatic transport vehicle 3 within the warehouse 5 with high accuracy, and it is possible to reduce the cost required for operation.

  In particular, when the CPU 1 has already executed the first output mode and the determined value obtained at that time is stored in the RAM 11, the CPU 1 calculates the displacement of the first virtual value based on the determined value. If the displacement amount does not exceed the threshold value, the second output mode in which the first virtual value is set as the sub-determined value is executed. Accordingly, when the accuracy of the first virtual value is high and it is not necessary to calculate each second virtual value to determine the final value, the CPU 1 controls the automatic transport vehicle 3 autonomously while reducing the processing load. It is possible to run.

(Example 2)
As shown in FIG. 11, the CPU 10 according to the second embodiment is mounted on an automatic guided vehicle 30. The CPU 10 is also an example of the autonomous mobile body control device of the present invention. Further, like the automatic transport vehicle 3 described above, the automatic transport vehicle 30 is an industrial vehicle capable of autonomous traveling in the warehouse 5 and is an example of the autonomous mobile body of the present invention.

  Since the configuration of the automatic guided vehicle 30 is the same as that of the automatic guided vehicle 3 except for the CPU 10, the same components are denoted by the same reference numerals and detailed description of the configuration will be omitted. Here, in the automatic guided vehicle 30, the map information 600 (see FIG. 15) stored in the RAM 11 is different from the above-described map information 500, and does not exclude information on each wall surface 50 that is a non-geometric feature portion. . That is, the map information 600 is coordinate information in the warehouse 5 including each wall surface 50, each luggage shelf 56, each column 58, and the like.

  The CPU 10 controls the driving device 19, the ROM 9, the RAM 11, the encoder 13, the external sensor 15, and the information input unit 17 to cause the automatic guided vehicle 30 to travel autonomously. Specifically, the CPU 10 includes a reference value calculation unit, a generation unit, a determination unit, a first calculation unit, a second calculation unit, a third calculation unit, a first output mode unit, and a second output mode unit in the present invention. Perform arithmetic processing. Thereby, the CPU 10 also causes the automatic guided vehicle 30 to autonomously travel in the warehouse 5 while executing the first output mode or the second output mode.

  Hereinafter, as in the case of the CPU 1 of the first embodiment, the automatic transport vehicle 30 is autonomously controlled in the warehouse 5 by taking as an example a case in which the luggage in the luggage shelf 56 in the first room 51 is transported to the luggage shelf 56 in the fourth room 54. Each calculation process of the CPU 10 at the time of running will be specifically described.

  As shown in FIG. 12, even when the automatic transport vehicle 30 is caused to autonomously travel, first, the CPU 10 determines the automatic transport vehicle based on the map information 600 stored in the RAM 11 and the route information input from the information input unit 17. The coordinates on the map information 600 of the current position 30 and the fourth room 54 as the destination are calculated. Then, the CPU 10 starts traveling of the automatic guided vehicle 30 toward the fourth chamber 54 by controlling the driving device 19 (step S201).

  As the automatic transport vehicle 30 travels, the encoder 13 detects the amount of movement of the automatic transport vehicle 30. Then, based on this movement amount, the CPU 10 calculates a first virtual value including the first estimated x coordinate, the first estimated y coordinate, and the first estimated attitude θ of the automatic guided vehicle 30 (step S202). . The first virtual value is also stored in the RAM 11.

As shown in FIG. 13, as the automatic guided vehicle 30 travels, the external sensor 15 detects environmental information 150 around the automatic guided vehicle 30 (step S203 in FIG. 12). Here, FIG. 13 shows the environment information 150 detected when the automatic guided vehicle 30 travels from the east side to the west side in the second corridor 55b shown in FIG. As shown in the figure, by the automatic guided vehicle 30 travels toward the west side of the second corridor 55b from the east, the environmental information 150 to be detected is sequentially changed in the order of (A) ~ (C) of FIG. I do. The environment information 150 is stored in the RAM 11. As described above, in addition to the second corridor 55b, the first, third, and fourth corridors 55a, 55c, and 55d include the columns 58 and the wall surfaces 50. For this reason, the environment information 150 includes the geometric environment information 150a based on the pillar 58, but most of the environment information 150 is occupied by the non-geometric environment information 150b based on the wall surface 50.

  As shown in FIG. 12, when the automatic transport vehicle 30 travels, the CPU 10 calculates a reference value based on the moving speed of the automatic transport vehicle 30 and the traveling direction of the automatic transport vehicle 30 in the warehouse 5. (Step S204). This reference value is stored in the RAM 11.

  Next, the CPU 10 determines whether it is necessary to delete the non-geometric environment information 150b from the environment information 150 (Step S205). This determination is made based on, for example, whether or not a condition such as the ratio of the non-geometric environment information 150b exceeding a certain value is satisfied in the environment information 150 stored in the RAM 11.

  When it is determined that the non-geometric environment information 150b needs to be deleted from the environment information 150 (step S205: YES), the CPU 10 edits the environment information 150 by deleting the non-geometric environment information 150b. Generated environment information 300 (see FIG. 14) (step S206). The edited environment information 300 is stored in the RAM 11.

  In generating the edited environment information 300, the CPU 10 determines, based on the reference value calculated in step S204, that the degree of change in the environment information 150 accumulated as the automatic guided vehicle 30 travels satisfies the reference value. The non-geometric environment information is specified as non-geometric environment information 150b, and is deleted from the environment information 150. This point will be specifically described based on FIGS. 2 and 13 by taking an example in which the automatic guided vehicle 30 travels from the east side to the west side in the second corridor 55b. Around the automatic guided vehicle 30 traveling from the east side to the west side in the second corridor 55b, a state in which only the wall surface 50 is present continues for a certain extent, and occasionally a state in which the pillar 58 is present. Then, the state where only the wall surface 50 is present continues to a certain extent is repeated. For this reason, when the automatic guided vehicle 30 travels in a place where only the wall surface 50 exists, the environment sensor 150 detects only the wall surface 50, and the environment information 150 almost changes while only the wall surface 50 is detected. Disappears. On the other hand, when the automatic guided vehicle 30 travels in a place where the pillar 58 exists, the environment information 150 changes because the external sensor 15 detects the pillar 58 in addition to the wall surface 50.

  Thus, as shown in FIG. 14, the CPU 10 determines that the environmental sensor 150 detects the wall surface 50 of the stored environmental information 150 whose degree of change does not satisfy the reference value, that is, the non-geometric It is determined that the information is the environmental information 150b, and the edited environment information 300b is deleted to generate the edited environment information 300. As a result, as shown in FIG. 15, although the map information 600 includes the wall surface 50 and the columns 58, the edited environment information 300 shown in FIG. 14 includes the geometric environment information 150a based on the columns 58. Although present, the non-geometric environment information 150b based on the wall surface 50 no longer exists.

  Then, the CPU 10 updates the edited environment information 300 accordingly, when the automatic guided vehicle 30 further travels in the warehouse 5 and the environment sensor 15 further detects the environment information 150. As a result, when the external sensor 15 detects the luggage shelf 56 or the work desk 57, the edited environment information 300 includes the geometric information based on the luggage shelf 56 and the work desk 57 in the same manner as the geometric environment information 150a based on the pillar 58. The environmental information 150a also exists.

  After generating the edited environment information 300 in this way, the CPU 10 determines the range of the environment information 150 detected by the external sensor 15 based on the edited environment information 300 (step S207 shown in FIG. 12). Specifically, the CPU 10 determines the direction and range of the laser beam emitted by the external sensor 15 so that the external sensor 15 can detect the luggage shelf 56, the work desk 57, and the pillar 58 while preventing the wall surface 50 from being detected as much as possible. adjust.

  Then, based on the edited environment information 300, the CPU 10 calculates a plurality of second virtual values including the second estimated x coordinate, the second estimated y coordinate, and the second estimated posture θ of the automatic guided vehicle 30 by the particle filter ( Step S208). The calculation of each second virtual value is performed in the same manner as in the CPU 1 of the first embodiment, based on the x coordinate, y coordinate, and attitude θ of each specific particle, each second estimated x coordinate, each second estimated y coordinate, and each second estimated attitude. is calculated.

  On the other hand, if it is determined that it is not necessary to delete the non-geometric environment information 150b from the environment information 150 (step S205: NO), the CPU 10 uses the particle filter to perform the A plurality of second virtual values composed of 2 estimated x coordinates, second estimated y coordinates, and second estimated attitude θ are calculated (step S209).

  Next, the CPU 10 determines whether or not the first output mode has already been executed based on the information stored in the RAM 11 (step S210). If the first output mode has been executed (step S210: YES), the CPU 10 calculates the displacement amount of the first virtual value based on the determined value (step S211), similarly to the CPU 1 of the first embodiment. Then, it is determined whether or not the amount of displacement of the first virtual value exceeds a threshold (step S212). If the displacement does not exceed the threshold (step S212: NO), the CPU 10 executes the second output mode, and performs the first estimated x coordinate, first estimated y coordinate, and first estimated value of the first virtual value. The attitude θ is set as a sub-determined value that is the current x coordinate, y coordinate, and attitude θ of the warehouse 5 automatic transport vehicle 30 (step S213). Thus, the CPU 10 recognizes the current x-coordinate, y-coordinate, and posture θ of the automatic guided vehicle 30 in the warehouse 5. Then, the CPU 1 causes the automatic guided vehicle 30 to autonomously travel by storing the sub-determined value in the RAM 11 and outputting the value to the driving device 19.

On the other hand, when the displacement amount exceeds the threshold value (step S212: YES) or when the first output mode has never been executed (step S210: NO), the CPU 10 executes the first output mode. (Step S214).

  In the first output mode, the CPU 10 determines a final value from the first virtual value calculated in step S202 and the respective second virtual values calculated in step S208 or S209. The determination of the final value is performed in the same manner as in the CPU 1 of the first embodiment. As a result, similarly to the CPU 1 of the first embodiment, the CPU 10 determines the second estimated x coordinate, the second estimated y coordinate, and the second estimated attitude θ of the final value by the current x coordinate, y coordinate, and Recognize as posture θ. Then, the CPU 10 causes the automatic guided vehicle 30 to autonomously travel by storing the determined value in the RAM 11 and outputting the determined value to the driving device 19. If the previous fixed value is already stored in the RAM 11, the newly obtained fixed value is overwritten. Further, the CPU 10 causes the RAM 11 to store the fact that the first output mode has been executed.

  In this way, if the automatic guided vehicle 30 has not reached the fourth chamber 54 as the destination (Step S215: NO), the CPU 10 repeats the above-described Steps S202 to S214 to execute the first output mode or the second output mode. Execute output mode. Thereby, the CPU 10 can also make the automatic guided vehicle 30 travel autonomously to the fourth chamber 54. When the automatic transport vehicle 30 reaches the fourth chamber 54 (step S215: YES), the CPU 10 ends the control of the automatic transport vehicle 30.

  Thus, the edited environment information 300 generated by the CPU 10 does not include the non-geometric environment information 150b. For this reason, in step S208, by calculating each second virtual value based on the edited environment information 300, similarly to the CPU 1 in the first embodiment, the CPU 10 also has a processing load when calculating each second virtual value. Can be reduced, and each second virtual value can be calculated quickly. Further, based on the edited environment information 300, even in an environment where the geometric feature portion in the warehouse 5 is scarce such as the first to fourth corridors 55a to 55d, it is extracted in calculating each second virtual value. Variation of specific particles is reduced.

  Here, the CPU 10 may calculate the second virtual value based on the environment information 150 (step S209). However, in the environment information 150 at this time, since the ratio of the non-geometric environment information 150b is small, even if it is based on the environment information 150, the variation of the specific particles is small. For this reason, the CPU 10 can also increase the accuracy of the determined value determined in the first output mode, and can autonomously travel the automatic guided vehicle 30 in the warehouse 5 with high accuracy.

  Unlike the CPU 1 of the first embodiment, the CPU 10 does not need to edit the map information 600 in advance. Therefore, when the layout of the warehouse 5 is changed, the CPU 10 can more easily cope with the change than the CPU 1 of the first embodiment.

  Further, since the CPU 10 determines the range of the environment information 150 detected by the external environment sensor 15 based on the edited environment information 300, the detection of the wall surface 50 by the external sensor 15 can be reduced. Therefore, when the CPU 10 updates the edited environment information 300 in accordance with the travel of the automatic guided vehicle 30, the processing can be reduced. Other operations of the CPU 10 are the same as those of the CPU 1 of the first embodiment.

  In the above, the present invention has been described with reference to the first and second embodiments. However, the present invention is not limited to the first and second embodiments, and may be appropriately modified and applied without departing from the spirit thereof. Needless to say.

  For example, the CPU 1 may always execute the first output mode and cause the automatic guided vehicle 3 to autonomously travel without executing the second output mode. The same applies to the CPU 10.

  Further, the CPU 1 may calculate a plurality of second virtual values by a stochastic method other than the particle filter, or may calculate each second virtual value by combining a plurality of stochastic methods. The same applies to the CPU 10.

  Further, in the first embodiment, one CPU 1 performs the first operation unit, the second operation unit, the third operation unit, the determination unit, the main determination unit, the sub-determination unit, the first output mode unit, the second output mode unit, Each calculation process is performed as a three-output mode unit and a correction mode unit. However, the present invention is not limited to this, and a plurality of CPUs 1 may be used, and each of the CPUs 1 may share the above arithmetic processing. The same applies to the CPU 10.

  Further, the CPU 1 and the CPU 10 are provided outside the automatic guided vehicles 3 and 30, and the CPU 1 and the CPU 10 remotely control the driving device 19, the ROM 9, the RAM 11, the encoder 13, the external sensor 15, and the information input unit 17 by wireless communication. It is good.

  In addition, the automatic transport vehicles 3 and 30 may be configured to be able to switch between the case of performing the above-described autonomous traveling and the case of traveling by a worker's operation.

  INDUSTRIAL APPLICABILITY The present invention is applicable to autonomously movable robots, automobiles, industrial vehicles, and the like.

1. CPU (autonomous mobile control device, extraction unit, first operation unit, second operation unit, third operation unit, first output mode unit, and second output mode unit)
3. Automatic carrier (autonomous mobile, industrial vehicle)
5… Warehouse (moving area)
10 CPU (autonomous mobile control device, reference value calculation unit, generation unit, determination unit, first calculation unit, second calculation unit, third calculation unit, first output mode unit, and second output mode unit)
11 RAM (storage device)
13 ... Encoder (self-moving amount detection device)
15 ... External sensor (environmental information detection device)
19: drive device 50: wall surface (non-geometric features)
56 ... Luggage shelf (geometric features)
57 ... Work desk (geometric features)
58 ... pillar (geometric feature)
150 ... Environment information 150a ... Geometric environment information 150b ... Non-geometric environment information 200 ... Specific environment information 300 ... Edited environment information 500,600 ... Map information

Claims (6)

Used for an autonomous mobile body that autonomously moves within a preset moving area,
The autonomous moving body includes a driving device, a storage device that stores map information of the moving area, a self-moving amount detecting device that detects a moving amount of the self in the moving area, and a surrounding area of the self in the moving area. Environment information detection device for detecting the environment information of the
An autonomous moving body control device that controls the driving device, the storage device, the self-moving amount detecting device, and the environment information detecting device while recognizing the x coordinate, the y coordinate, and the posture of the autonomous moving object in the moving area. And
The moving area includes a geometric feature and a non-geometric feature,
The map information is obtained by excluding the non-geometric feature portion in advance,
An extraction unit that refers to the map information and the environment information and extracts only specific environment information that matches the map information from the environment information;
A first calculation unit configured to calculate a first virtual value including a first estimated x coordinate, a first estimated y coordinate, and a first estimated attitude of the autonomous moving body based on a movement amount of the autonomous moving body;
A second calculating unit configured to calculate a plurality of second virtual values including a second estimated x coordinate, a second estimated y coordinate, and a second estimated attitude of the autonomous moving object by a probabilistic method based on the specific environment information;
The first virtual value and said performing a first output mode to determine a definite value from the second virtual value, have a first output mode unit for outputting the determined value to the drive device,
In the first output mode, the final value is determined by weighting each of the second virtual values and a value obtained by adding a matching rate of the second virtual value to the first virtual value. An autonomous mobile body control device, characterized in that: Used for an autonomous mobile body that autonomously moves within a preset moving area,
The autonomous moving body includes a driving device, a storage device that stores map information of the moving area, a self-moving amount detecting device that detects a moving amount of the self in the moving area, and a surrounding area of the self in the moving area. Environment information detection device for detecting the environment information of the
An autonomous moving body control device that controls the driving device, the storage device, the self-moving amount detecting device, and the environment information detecting device while recognizing the x coordinate, the y coordinate, and the posture of the autonomous moving object in the moving area. And
The moving area includes a geometric feature and a non-geometric feature,
The environment information includes geometric environment information based on the geometric feature portion, and non-geometric environment information based on the non-geometric feature portion,
A moving speed of the autonomous moving body, a reference value calculating unit that calculates a reference value based on a traveling direction of the autonomous moving body in the moving area,
A generation unit that generates edited environment information obtained by deleting the non-geometric environment information from the environment information based on the reference value;
A first calculation unit configured to calculate a first virtual value including a first estimated x coordinate, a first estimated y coordinate, and a first estimated attitude of the autonomous moving body based on a movement amount of the autonomous moving body;
A second calculating unit configured to calculate a plurality of second virtual values including a second estimated x coordinate, a second estimated y coordinate, and a second estimated attitude of the autonomous mobile body based on the edited environment information by a probabilistic method;
The first virtual value and said performing a first output mode to determine a definite value from the second virtual value, have a first output mode unit for outputting the determined value to the drive device,
In the first output mode, the final value is determined by weighting each of the second virtual values and a value obtained by adding a matching rate of the second virtual value to the first virtual value. An autonomous mobile body control device, characterized in that:   The autonomous mobile body control device according to claim 2, further comprising a determination unit that determines a range of the environment information detected by the environment information detection device based on the edited environment information. A third calculating unit that calculates a displacement amount between the determined value and the first virtual value based on the determined value;
A second output mode unit that executes a second output mode for determining a sub-determined value from the first virtual value if the displacement amount does not exceed a threshold, and outputs the sub-determined value to the driving device. ,
The autonomous mobile body control device according to claim 1, wherein the first output mode unit executes the first output mode when the displacement amount exceeds the threshold. The probabilistic approach, the autonomous moving body control device according to any one of claims 1 to 4, which is a particle filter-based SLAM algorithm. The autonomous mobile body control device according to any one of claims 1 to 5 , wherein the autonomous mobile body is an industrial vehicle that runs autonomously.

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