WO2023007878A1 - Cargo vehicle system and in-vehicle controller - Google Patents

Abstract

The present invention addresses the problem of generating a traveling route that ensures high safety and conveyance efficiency while the weight of cargos and the gravity center position thereof are taken into account. The present invention comprises: an operation management unit that transmits conveyance destinations of cargos; a cargo shape calculation unit that calculates the shapes of the cargos; a cargo gravity center calculation unit that calculates the gravity center position of the cargos; a cargo weight calculation unit that calculates the weight of the cargos; and an environment map storage unit that transmits a two-dimensional map in a warehouse. The present invention further comprises: a traveling speed upper limit calculation unit for calculating a speed upper limit value at which a vehicle can travel while maintaining traveling stability, on the basis of a traveling route candidate, the gravity center position of the cargos, and a cargo bed movement amount candidate; an obstacle avoidance determination unit for executing determination of interference between a cargo vehicle at the time of traveling and a fixed obstacle, on the basis of the traveling route candidate, the shapes of the cargos, the two-dimensional map, and the cargo bed movement amount candidate; and a traveling command decision unit for deciding, on the basis of the calculation result of the traveling speed upper limit calculation unit and the determination result of the obstacle avoidance determination unit, a cargo bed movement amount and a traveling route on which the cargo vehicle can avoid the fixed obstacle and in which the time of arrival at the conveyance destination is short.

Description

Cargo handling vehicle system and on-board controller

The present invention relates to technology for controlling the operation of a cargo handling vehicle that performs cargo handling.

Cargo handling vehicles are currently in use. As an example, unmanned guided vehicles, forklifts, and other cargo handling vehicles are used in cargo transportation operations in distribution warehouses. On the other hand, in recent years, with the labor shortage due to the declining birthrate and aging population and the increase in the number of logistics due to the expansion of the e-commerce market, labor saving and improvement of work efficiency in distribution warehouses have become issues. In order to solve this problem, the introduction of unmanned industrial vehicles that can operate unmanned is underway.

From the perspective of safety, cargo handling vehicles are required to travel in such a way that they do not overturn or collide with surrounding obstacles. In addition, from the viewpoint of transportation efficiency, it is necessary to shorten the travel time required for a given load to reach its destination. Cargo handling vehicles change their travelable route and speed upper limit depending on the shape, weight, and center of gravity of the cargo to be conveyed, so a flexible travel control method according to the condition of the cargo is required. Desired.

For example, Patent Document 1 discloses a technique for avoiding obstacles by changing the traveling route based on the shape of the load. More specifically, we acquire the shape of the cargo handling vehicle during loading, in which the transport vehicle and cargo are integrated, and generate a travel route that improves transportation efficiency within the range where the cargo handling vehicle can avoid obstacles during loading. Is going. By realizing the configuration of Patent Document 1, it is possible to travel on a travel route with high transportation efficiency without interference between the cargo and surrounding obstacles, and safety and productivity during travel of the cargo handling vehicle are improved.

JP 2020-77295 A

However, in Patent Document 1, route generation is not performed in consideration of the vehicle speed upper limit due to the position of the center of gravity of the load and the weight. As a result, when traveling along the determined route, it is necessary to decelerate more than necessary, and there is a possibility that the transport efficiency will be lowered. SUMMARY OF THE INVENTION Accordingly, it is an object of the present invention to generate a travel route that is safe and highly efficient in transportation considering the weight and the position of the center of gravity of the load.

In order to solve the above problems, in the present invention, the movement of the cargo handling vehicle is analyzed using physical characteristics that indicate the dynamic characteristics of the cargo handling vehicle and the cargo (automated guided vehicle for loading, which is the cargo handling vehicle loaded with the cargo). create a plan;

More specifically, in a cargo handling vehicle system for supporting the cargo handling operation of a cargo handling vehicle, it shows the dynamic characteristics of an automated guided vehicle for loading, which is the cargo handling vehicle loaded with cargo to be handled. An operation plan candidate generating unit for generating a plurality of operation plan candidates, which are operation plan candidates for the cargo handling of the cargo handling vehicle, based on the physical characteristics, and a travel speed upper limit calculator for calculating an upper speed limit of the cargo handling vehicle. an obstacle avoidance judging unit for judging the possibility of avoiding obstacles in the operation of the cargo handling vehicle; and an operation plan is determined from the plurality of operation plan candidates according to the speed upper limit value and the result of the judgment. It is a cargo handling vehicle system having an operation plan determination unit that

The present invention also includes an in-vehicle controller and a traffic control unit that constitute a cargo handling vehicle system, or a method using these.

According to the present invention, it is possible to create a more appropriate operation plan and operate cargo handling vehicles more safely and with high transportation efficiency.

1 is a side view of an automatic guided vehicle according to a first embodiment; FIG. 1 is a top view of an automatic guided vehicle according to Example 1. FIG. 1 is a block diagram showing a functional configuration example of a cargo handling vehicle system according to a first embodiment; FIG. 4 is a diagram showing a travel route and obstacles according to the first embodiment; FIG. 4 is a diagram illustrating the coordinate system and size of the automatic guided vehicle according to the first embodiment; FIG. FIG. 2 is a functional block diagram showing a functional configuration example of a travel command determination unit according to the first embodiment; FIG. 4 is a flow chart showing a processing flow of the cargo handling vehicle system according to the first embodiment; 7 is a flow chart showing a processing flow of step S721 according to the first embodiment; 7 is a flow chart showing the processing flow of step S722 according to the first embodiment; FIG. 4 is a diagram for explaining a load travel area in the first embodiment; FIG. 5 is a diagram showing an example of travel route/cargo bed movement amount information used in the first embodiment; FIG. 11 is a functional block diagram showing an example of the functional configuration of a travel command determination unit according to the second embodiment; FIG. FIG. 10 is a diagram showing a travel route according to the second embodiment; FIG. FIG. 11 is a block diagram showing a functional configuration example of a cargo handling vehicle system according to a third embodiment; 10 is a flow chart showing a processing flow of the cargo handling vehicle system according to the third embodiment;

An embodiment of the present invention will be described below. In this embodiment, an operation plan for cargo handling in a cargo handling vehicle capable of transporting cargo is created. At this time, based on the physical characteristics of the cargo handling vehicle on which the cargo is loaded, interference with obstacles and the like and running stability are determined, and an operation plan is determined based on the results of this determination. It should be noted that the movement includes traveling and loading of goods on a loading platform and the like.

Next, Examples 1 to 3, which more specifically show the embodiment, will be described. In each embodiment, an automatic guided vehicle will be described as an example of a cargo handling vehicle.

First, Example 1 will be described with reference to FIGS. 1 to 11. FIG. In this embodiment, an automatic guided vehicle, which is an example of a cargo handling vehicle, is taken as an example. In this embodiment, as an operation plan, based on the shape of the load, the weight of the load, and the position of the center of gravity of the load, the traveling route from the vehicle position when the load is loaded to the destination of the load and the movement amount of the loading platform are determined. decide. The details are described below.

<Automated guided vehicle 100>
First, an unmanned guided vehicle 100 capable of unmanned operation used in this embodiment will be described.
FIG. 1 is a side view of an automatic guided vehicle 100 according to this embodiment. FIG. 2 is a top view of the automatic guided vehicle 100 according to this embodiment. As shown in FIGS. 1 and 2, the automatic guided vehicle 100 includes a vehicle frame 101 having a bumper 115, a transfer device 102 capable of freely moving a loaded load vertically and horizontally, and a loaded load. A cargo handling member 103 is provided to support the . A transfer actuator 104 is provided inside the automatic guided vehicle 100 , and the transfer device 102 is driven by the transfer actuator 104 .

Next, the drive-related configuration of the automatic guided vehicle 100 will be described. The automatic guided vehicle 100 is provided with driving wheels 105, driven wheels 106, and caster wheels 107 for supporting itself. A travel motor 108 is provided inside the automatic guided vehicle 100 , and the drive wheels 105 are driven by the travel motor 108 . A steering motor 109 is provided inside the automatic guided vehicle 100 , and the driving wheels 105 are driven by the steering motor 109 . The configuration of the driving relationship is not limited to this configuration. In particular, when a so-called carrier robot is used as the unmanned carrier 100, it is desirable that the driving wheels 105 are four-wheel drive.

Furthermore, the automatic guided vehicle 100 includes a plurality of load sensors 110 (for example, pressure sensors) that acquire the load of the load to be loaded and the position of the center of gravity in the left-right direction on the cargo handling member 103 in the front-rear direction. Moreover, the automatic guided vehicle 100 equips the cargo handling member 103 with an inclination sensor 111 that acquires the inclination of the cargo handling member 103 .

Also, the automatic guided vehicle 100 has a LiDAR 112 for load shape detection. For example, one left and one right are installed on the vehicle frame 101 . LiDAR (Light Detection and Ranging) refers to a sensor that measures the distance to an object existing in the irradiation range while changing the irradiation angle of laser light. It is possible to acquire point group information of feature points indicating the shape of an object from information on the optical axis angle at the time of laser irradiation and information on the distance to the object. A sensor other than LiDAR may be used as long as it can recognize an object.

In addition, the upper surface of the vehicle frame 101 is equipped with a LiDAR 113 for self-position estimation. The self-position estimation LiDAR 113 measures the distance to surrounding obstacles during travel. The LiDAR 113 for self-position estimation may also use other sensors.

An in-vehicle controller 114 is mounted on the upper surface of the vehicle frame 101 . The in-vehicle controller 114 calculates an operation command value for realizing the motion control of the automatic guided vehicle 100 based on the self-position estimation of the vehicle and the self-position based on the values obtained from the sensors described above.

<Cargo handling vehicle system 300>
Next, the cargo handling vehicle system 300 that determines the travel route will be described. FIG. 3 is a block diagram showing a functional configuration example of the cargo handling vehicle system 300 according to this embodiment. In FIG. 3, solid lines with arrows represent data flow.

A cargo handling vehicle system 300 according to this embodiment includes a traffic control unit 201 and an in-vehicle controller 114 . Then, on the on-vehicle controller 114 mounted on the automatic guided vehicle 100, a travel command calculation unit 310 that executes the main processing of this embodiment is mounted. Hereinafter, functions of the traffic control unit 201 and the in-vehicle controller 114 will be described. Note that only the outline of each function will be described below, and the specific processing flow of each function will be described later.
Each device and each part constituting the cargo handling vehicle system 300 will be described below.

<Traffic control unit 201>
The traffic control unit 201 manages operation of the automatic guided vehicle 100 . In the present invention, the traffic control unit 201 is composed of an environment map storage unit 202 , an operation management unit 203 , a travel route candidate generation unit 204 and a communication device 205 . Each part of the traffic control unit 201 will be described below.

<Environment map storage unit 202>
The environment map storage unit 202 stores a two-dimensional map provided with position information of obstacles in the warehouse, which is the travel area (operating environment) of the automatic guided vehicle 100 . Here, FIG. 4 is a diagram showing a travel route and obstacles based on the two-dimensional map stored in the environmental map storage unit 202. As shown in FIG.

The two-dimensional map has the environment map position reference coordinate system 400 as the position reference coordinates, and two orthogonal axes represented by the _XG axis and the _YG axis as reference axes. The environment map storage unit 202 stores the position and shape information of the shelf 421 and the wall 422 in the environment map position reference coordinate system 400, which is the reference coordinate system shown in FIG. The two-dimensional map creation method is, for example, a method of acquiring point cloud data of surrounding obstacles using the LiDAR 113 for self-position estimation while manned driving the unmanned guided vehicle 100, and creating it by SLAM (Simultaneous Localization and Mapping) method. There is

<Operation management unit 203>
Next, the operation management unit 203 determines the destination of the load loaded by the automatic guided vehicle 100 . For the determination of the destination, for example, input from the user or the schedule for transporting the cargo is used.

<Traveling route candidate generation unit 204>
The travel route candidate generation unit 204 generates travel route candidates 401 that allow the automatic guided vehicle 100 to reach the cargo destination specified by the operation management unit 203 and avoid fixed obstacles stored on the two-dimensional map. Generate multiple. Here, the details of the plurality of travel route candidates 401 to be created will be described with reference to FIG. Each route of the travel route candidate 401 is composed of a node 403 that serves as a passage point for the cargo handling vehicle and an edge 404 that connects adjacent nodes. Each node is provided with two-dimensional position coordinates with respect to the environmental map position reference coordinate system 400, upper travel speed limit, and traveling direction. For example, the standard for the interval between nodes is about 2.0 (m) to 5.0 (m).

In addition, the following three examples are given as setting examples of the travel route candidates 401 .
(1) The shortest route 405 that the automatic guided vehicle 100 passes when traveling without cargo,
(2) A travel route 406 on which the unmanned guided vehicle 100 can travel while maintaining the high speed upper limit when traveling without cargo;
(3) A route 407 in which the unmanned guided vehicle 100 is the farthest from fixed obstacles when traveling without cargo.

Alternatively, it is possible to calculate an area in which the unmanned guided vehicle 100 can travel without interfering with fixed obstacles when it is empty, and then set a curve that equally divides that area as the travel route candidate 401 . As information necessary for generating the travel route candidates 401, the travel route candidate generation unit 204 includes information on the vehicle frame 101 and the cargo handling member 103 in the vehicle body width direction and the vehicle body length direction, which characterize the shape of the automatic guided vehicle 100. It has 2D shape information.

The automatic guided vehicle 100 realizes travel by following the travel route using the forward gaze model. It is assumed that positional and attitude errors of the vehicle with respect to the route occur during route tracking using the forward gaze model. Therefore, the travel route candidate generation unit 204 generates the travel route candidates 401 based on these errors. Rough guides for the route following error are a position error of 0.10 (m) and an attitude error of 0.05 (rad).

As described above, the travel route candidate generation unit 204 is a type of motion plan candidate generation unit that generates the travel route candidate 401 that is a type of motion plan for the automatic guided vehicle 100 and the platform movement amount candidate storage unit 308 . Note that the operation plan candidate generation unit may generate at least one of the travel route candidate 401 and the bed movement amount candidate as the operation plan candidate. Therefore, in this embodiment, it is desirable to have a platform movement amount candidate generator (not shown). In addition, the travel route candidate generation unit 204 and the motion plan candidate generation unit may be provided in the in-vehicle controller 114 .

<Communication device 205>
The communication device 205 transmits the information stored in the traffic control unit 201 to the in-vehicle controller 114 . The above is the internal configuration of the traffic control unit 201 .

It should be noted that the traffic control unit 201 can also be realized by a computer connected to the in-vehicle controller 114 via a network. In this case, the traffic control unit 201 can be implemented as a so-called server as a traffic management device.

<In-vehicle controller 114>
Next, the internal configuration of the in-vehicle controller 114 will be described. Hereinafter, each function implemented inside the in-vehicle controller 114 will be described. The in-vehicle controller 114 can be realized by a so-called ECU (Electronic Control Unit). Each part of the in-vehicle controller 114 will be described below.

<Self-position estimation unit 309>
Self-position estimation unit 309 estimates self-position 412 and self-orientation 413 of automatic guided vehicle 100 with respect to environment map position reference coordinate system 400 shown in FIG. FIG. 5 is a diagram for explaining the coordinate system and size of the automatic guided vehicle 100 according to this embodiment. FIG. 5 shows the cargo handling vehicle reference coordinate system 411 used in estimating the self-position 412 and the self-orientation 413 . The position reference point of the automatic guided vehicle 100 is the center position of the vehicle (the position of the black circle in the figure).

An axis extending from the position reference point of the automatic guided vehicle 100 toward the leading end of the cargo handling member 103 is the Yv axis. Also, the axis extending from the position reference point of the automatic guided vehicle 100 toward the center of the tire of the driven wheel 106 on the right side with respect to the positive direction of the Yv axis is defined as the Xv axis. Also, the axis extending vertically in the vehicle height direction from the position reference point of the automatic guided vehicle 100 is defined as the Zv axis. The self position 412 corresponds to the position coordinates of the position reference point of the automatic guided vehicle 100 in the environment map position reference coordinate system 400, and the self attitude 413 corresponds to the _YG axis of the environment map position reference coordinate system 400 and the cargo handling vehicle reference coordinate system. 411 corresponds to the angle formed by the Yv axis. The above situation is estimated by the self-position estimation unit 309 .

<Load amount calculation unit 301>
The load amount calculation unit 301 calculates the weight m (kg) of the load loaded on the automatic guided vehicle 100 based on the detection result of the load sensor 110 . For this purpose, it is desirable to provide the automatic guided vehicle 100 with a weight scale.

<Center of load calculation unit 302>
The center-of-load calculation unit 302 calculates the three-dimensional center-of-gravity position (x1, y1, z1) (m) of the load in the cargo handling vehicle reference coordinate system 411 based on the detection result of the load sensor 110 .

<Package shape calculator 303>
The load shape calculator 303 calculates the width w1 (m) and the depth l1 (m) of the load 500 (that is, the load to be transported) loaded on the automatic guided vehicle 100 shown in FIG. is calculated based on the detection result of the package shape LiDAR 112 .

<Information storage unit 304>
The information storage unit stores information about the vehicle body characteristics of the automatic guided vehicle 100 . The information storage unit 304 includes a vehicle shape storage unit 305 , a vehicle weight storage unit 306 , a vehicle center of gravity position storage unit 307 , and a platform movement amount candidate storage unit 308 .

The vehicle shape storage unit 305 stores information indicating shape characteristics of the automatic guided vehicle 100 . FIG. 5 shows an outline of values stored in the vehicle shape storage unit 305. As shown in FIG. The vehicle shape storage unit 305 stores the tread length W1 (m) as length information regarding the width direction of the automatic guided vehicle 100 . The vehicle weight storage unit 306 stores the weight M (kg) of the automatic guided vehicle 100 . The vehicle center-of-gravity position storage unit 307 stores the center-of-gravity position (x2, y2, z2) (m) of the automatic guided vehicle 100 in the cargo handling vehicle reference coordinate system 411 . The platform movement amount candidate storage unit 308 stores a platform movement amount candidate value when the automatic guided vehicle 100 is traveling.

FIG. 5 shows a state in which the unmanned guided vehicle 100 performs s (m) bed movement (side shift) in the positive direction of the Xv axis. A platform movement amount candidate storage unit 308 stores a plurality of platform movement amount candidates corresponding to s(m) in FIG. An example of the candidate values for the amount of movement of the platform includes ten candidate values obtained by dividing the maximum range of motion of the side shift of the automatic guided vehicle 100 into ten.

Here, the movement of the cargo bed refers to the operation of moving the position of the cargo while the vehicle is supporting the cargo, such as the side shift operation of moving a pair of cargo handling members in the same direction on the left and right. The amount of bed movement refers to the difference between the position of the bed at the reference position and the position of the bed after the bed is moved. Note that this movement of the loading platform also occurs in the forks of a forklift, which is another example of the cargo handling vehicle.

<Running command calculation unit 310>
The travel command calculation unit 310 selects a travel route along which the automatic guided vehicle 100 travels from among the travel route candidates 401 generated by the travel route candidate generation unit 204 and the platform movement amount candidates stored in the carrier movement amount candidate storage unit 308. , the amount of movement of the loading platform during travel along the travel route is determined. In other words, the travel route candidate generation unit 204 is a kind of operation plan determination unit, and may determine at least one of the travel route and the bed movement amount.

It should be noted that the unmanned guided vehicle 100, while traveling, changes the amount of bed movement triggered by reaching each node on the traveling route. Therefore, the travel command calculation unit 310 determines the travel route and the bed movement amount at each node of the travel route.

Here, FIG. 6 is a functional block diagram showing a functional configuration example of the travel command calculation unit 310 according to this embodiment. The travel command calculation unit 310 includes a travel speed upper limit calculation unit 501 , an obstacle avoidance determination unit 502 , and a travel command determination unit 503 . Hereinafter, an outline of the functions of the travel command calculation unit 310 will be described.

<Travel speed upper limit calculator 501>
The travel speed upper limit calculator 501 calculates the upper limit of travel speed when traveling with the cargo bed moved based on each cargo bed movement amount on each route and each node position stored in the travel route candidate 401 . The upper speed limit mentioned here refers to the maximum value within the range in which the overturning moment of the vehicle does not exceed the restoring moment of the vehicle. be done. In addition, the unmanned guided vehicle 100 at the time of loading is hereinafter referred to as the unmanned guided vehicle 600 at the time of loading as one configuration together with the loaded cargo.

<Obstacle Avoidance Determination Unit 502>
The obstacle avoidance determination unit 502 determines the position of each node stored in the travel route candidate 401 by moving the cargo bed based on the amount of movement of the cargo bed. Determine whether or not to interfere with obstacles.

<Run command determination unit 503>
The travel command determination unit 503 determines the travel route and the amount of bed movement at each node in the travel route information based on the calculation result of the travel speed upper limit calculation unit 501 and the determination result of the obstacle avoidance determination unit 502 . The traveling route and the movement amount of the loading platform reach the final node 402 when the automatic guided vehicle for loading 600 can avoid interference with fixed obstacles and travels within a range in which the traveling stability of the automatic guided vehicle for loading 600 is ensured. The one with the shortest time is selected. Note that, as described above, the travel route and the bed movement amount are a kind of operation plan. An example of the functional block configuration of the travel command calculation unit 310 has been described above. This is merely an example, and other configurations may be added or some configurations may be omitted. This completes the description of the travel command calculation unit 310 .

<Operation command generator 311>
The operation command generation unit 311 is configured to perform cargo handling operations in accordance with the travel route and the bed movement amount calculated by the travel command calculation unit 310 based on the inputs from the travel command calculation unit 310 and the self-position estimation unit 309. Calculates motion commands. Then, the motion command generator 311 outputs motion commands to the traveling motor 108 , the steering motor 109 , and the transfer actuator 104 . As a result, the automatic guided vehicle 100 performs an operation according to the traveling route and the amount of bed movement. The above is an example of the functional block configuration for realizing this embodiment.

<Processing flow>
Next, the processing flow of Example 1 will be described in detail with reference to FIGS. 7 to 11. FIG. FIG. 7 is a flow chart showing the processing flow of the cargo handling vehicle system 300 in this embodiment.

When the loading of the automatic guided vehicle 100 is completed, the travel route candidate generation unit 204 acquires the two-dimensional environment map information from the environment map storage unit 202 in step S701. Also, travel command calculation unit 310 acquires two-dimensional environment map information from environment map storage unit 202 via communication device 350 .

Next, in step S702, the operation management unit 203 transmits the position coordinates of the transportation destination of the cargo to the travel route candidate generation unit 204. Next, in step S<b>703 , the travel route candidate generation unit 204 calculates an area in which the unmanned guided vehicle 100 can travel without interfering with fixed obstacles until it reaches the final node 402 . For this purpose, the travel route candidate generation unit 204 generates the two-dimensional environment map acquired in step S701, the location information of the transport destination acquired in step S702, the shape of the vehicle frame 101 and the cargo handling member 103, the position error when following the route. and attitude error information. Based on the calculation results, the operation management unit 203 sets a plurality of travel route candidates 401 that pass through areas in which the automatic guided vehicle 100 can travel. Then, the operation management unit 203 transmits the set travel route candidate 401 to the travel command calculation unit 310 .

Next, in step S<b>704 , the LiDAR 112 for load shape detection transmits the optical axis angle information and the corresponding distance information to the peripheral object to the load shape calculator 303 . Next, in step S705, the load shape calculator 303 calculates the shape of the load from the distance information between the optical axis angle information received in step S704 and the corresponding obstacle. An example of the load shape includes a load width w1 (m) and a depth l1 (m) shown in FIG. As an example of the calculation method in this case, the distance between each piece of point cloud data acquired in step S704 is measured, and linear approximation is performed for the point cloud within a threshold value. Then, there is a method of calculating the lengths of the load in the width direction and the depth direction from the lengths of the approximated straight lines.

Note that the load shape calculation method may be other than the above. For example, the load point cloud data may be acquired with the automatic guided vehicle 100 facing the load before the load handling operation, and the shape of the load may be detected. . In step S<b>705 , the load shape calculation unit 303 transmits the calculation result to the travel command calculation unit 310 .

Next, in step S<b>706 , the self-position estimation LiDAR 113 transmits the obtained distance information to the surrounding obstacles to the self-position estimation unit 309 . Next, in step S<b>707 , the self-position estimation unit 309 acquires two-dimensional map information from the communication device 350 . Then, self-position estimation section 309 calculates self-position 412 and self-orientation 413 using the two-dimensional map information and the distance information to surrounding obstacles received in step S706. In addition, self-position estimation section 309 transmits this calculation result to travel command calculation section 310 .

Also, in step S708, the load sensor 110 measures the load applied to the sensor.
The load sensor 110 then transmits the acquired value to the load amount calculation unit 301 and the load center calculation unit 302 . Next, in step S709, the load amount calculation unit 301 calculates the load m (kg) of the cargo loaded on the cargo handling member 103 from the sum of the measurement values of the load sensor 110 acquired in step S708. The load amount calculation unit 301 then transmits the calculated value to the travel command calculation unit 310 .

Also, in step S710, the tilt sensor 111 acquires the tilt angle θ (rad) of the cargo handling member 103 . Then, the tilt sensor 111 transmits the acquired value to the center-of-load calculation unit 302 .

Also, in step S711, the load center computing unit 302 computes the center of gravity position (x1, y1, z1) (m) of the load in the cargo handling vehicle position reference system. For this calculation, the center-of-load calculation unit 302 calculates the center-of-gravity position x1 (m) in the horizontal direction and the center-of-gravity position y1 (m) in the front-rear direction based on the measurement value of the load sensor 110 acquired in step S708. do. In addition, in order to specify these center-of-gravity positions, a plurality of load sensors 110 may be prepared and the load ratio of these sensors may be used. Further, the center-of-load calculation unit 302 calculates the center-of-gravity position z1(m) in the height direction of the load according to (Equation 1) below.

Here, in (Formula 1), the following information is used.
Installation distance D1 (m) in the front-rear direction of the load sensor 110
Load weight m (kg) measured in step S708
Gravitational acceleration g (m/s2 ⁾
the inclination angle θ (rad) of the cargo handling member 103 measured in step S710,
A detection value F1 (kg) of the load sensor 110 when the cargo handling member 103 is horizontal to the ground, and an inclination angle θ (rad) of the cargo handling member to the ground
Detected value F2 (kg) of the load sensor provided at the tip of the cargo handling member 103 when tilted
z1=D1/(mg·tan θ)·(F1−F2/cos θ) (Equation 1)
Then, the load center calculation unit 302 transmits the calculated load center-of-gravity position (x1, y1, z1) (m) to the travel command calculation unit 310 . Note that the processing order of steps S706 to S711 is not particularly limited to the described order.

Next, in step S712, travel command calculation unit 310 acquires the following information.
vehicle shape information stored in the vehicle shape storage unit 305;
vehicle weight stored in the vehicle weight storage unit 306;
the vehicle center-of-gravity position stored in the vehicle center-of-gravity position storage unit 307;
Platform movement amount candidates stored in the platform movement amount candidate storage unit 308 .

Thus, in step S712, the travel command calculation unit 310 identifies the shape, weight, and center of gravity position of the automatic guided vehicle for loading 600. These are one type of physical characteristics of the automatic guided vehicle 600 for loading. Therefore, part of the shape, weight, and center of gravity position, or other information may be used.

Next, in step S713, the travel command determination unit 503 determines the travel route and the bed movement amount, which are a type of operation plan. Therefore, in step S713, the travel speed upper limit value is calculated (step S721), the interference between the load 500 and the fixed obstacle is determined (step S722), and the travel route and the bed movement amount are determined based on these. Here, in step S721, the travel speed upper limit calculator 501 calculates the travel speed upper limit value for the combination of each node position in the travel route and each loading platform movement amount. Further, in step S722, the obstacle avoidance determination unit 502 performs interference determination between the load 500 and the fixed obstacle with respect to the combination of each node position in the travel route and each loading platform movement amount. Then, the travel command determination unit 503 determines the travel route and the bed movement amount based on the result of the travel speed upper limit calculation in step S721 and the result of the obstacle interference determination.

Details of these steps S721 and S722 will be described below. FIG. 8 is a flow chart showing the processing flow of step S721 for calculating the traveling speed upper limit according to this embodiment. FIG. 9 is a flow chart showing the processing flow of the load 500 and fixed obstacle collision determination step S722 according to the present embodiment.

First, the details of step S721 will be described using FIG. In step S741, the travel speed upper limit calculation unit 501 acquires one travel route from the travel route candidates 401 generated in step S703.

Next, in step S742, the travel speed upper limit calculator 501 calculates the node that the automatic guided vehicle 100 should reach first after it starts traveling. For this purpose, the traveling speed upper limit calculation unit 501 uses the self position 412 on the environment map position reference coordinate system 400 received in step S707 and the node information of the traveling route acquired in step S721. Here, the node with the smallest distance between the self-position 412 and the position coordinates of each node included in the travel route candidate information is set as the closest node in each travel route. That is, in this step, the nearest neighbor node is calculated.

If the closest node exists in the traveling direction of the automatic guided vehicle 100, the closest node is the node that should be reached first when the automatic guided vehicle 100 starts running. Also, if the closest node exists in the direction opposite to the traveling direction of the automatic guided vehicle 100, the node specified next to the closest node becomes the relevant node.

Next, in step S743, the travel speed upper limit calculator 501 acquires one node that constitutes the travel route acquired in step S741. Further, the traveling speed upper limit calculation unit 501 calculates the turning radius at the node position from the angle of the edge connecting the obtained node and the nodes before and after it. Further, in step S744, the travel speed upper limit calculation unit 501 acquires one bed movement amount from the bed movement amount candidates acquired in step S712.

Further, in step S745, the travel speed upper limit calculation unit 501 calculates the center of gravity position (xg, yg, zg) of the automatic guided vehicle for loading 600 in the cargo handling vehicle reference coordinate system 411 with respect to the loading platform movement amount acquired in step S712. do.

Here, assuming that the bed movement amount is s (m), the following information is used to calculate the center of gravity position (xg, yg, zg) according to the following (Equation 2) to (Equation 4).
Position of center of gravity of load 500 after platform movement (x1+s, y1, z1) (m)
The center of gravity position (x2, y2, z2) (m) of the automatic guided vehicle 100 acquired in step S712
Weight m (kg) of load 500 acquired in step S711
Weight M (kg) of the automatic guided vehicle 100 acquired in step S712
xg=(m.(x1+s)+M.x2)/(M+m) (Equation 2)
yg=(m·(y1+s)+M·y2)/(M+m) (Equation 3)
zg=(m.(z1+s)+M.z2)/(M+m) (Equation 4)
Next, in step S746, the running speed upper limit calculation unit 501 calculates a speed upper limit at which the vehicle can stably run. For this reason, the travel speed upper limit calculation unit 501 calculates the center of gravity position (xg, yg, zg) (m) of the loading automatic guided vehicle 600 after the loading platform is moved calculated in step S745 and the node point obtained in step S743. Use the curvature of the travel path. The upper limit of speed is the upper limit of speed for preventing the vehicle from overturning, and information indicating the running stability of the automatic guided vehicle 600 for loading may be used.

Here, assuming a travel route where centrifugal force is applied in the Xv-axis direction when the automatic guided vehicle for loading 600 travels, d is the distance between the center of gravity of the loading vehicle and the tires in the Xv-axis direction, and g is the gravitational acceleration. (m/s ² ), r (m) is the turning radius, and h (m) is the height of the center of gravity. can be done.
v=√(rgd/h) (Equation 5)
However, d=(W1/2)-xg(m) and h=zg(m). Thus, in step S746, the traveling speed upper limit calculation unit 501 calculates the traveling speed upper limit value for the traveling node acquired in step S743 and the bed movement amount acquired in step S744.

Next, in step S747, the travel speed upper limit calculation unit 501 determines whether steps S744 to S746 have been executed for each of the bed movement amount candidates acquired in step S712. In other words, it is determined whether or not the processing for each platform movement amount candidate has been completed. As a result, if the process is being executed (Yes), the process proceeds to step S748. If not executed (No), the process returns to step S744 to execute processing for the remaining platform movement amount candidates.

Next, in step S748, the traveling speed upper limit calculation unit 501 determines whether steps S742 to S747 have been executed for each node connecting the target arrival positions from the nearest neighbor nodes acquired in step S742. As a result, if it is executed (Yes), the process proceeds to step S748. If not (No), the process returns to step S743 to execute processing for the remaining nodes.

Next, in step S749, if the traveling speed upper limit calculation unit 501 has executed steps S742 to S748 for each traveling route candidate 401 generated in step S703 (Yes), the process proceeds to step S750. If not (No), the process returns to step S741 and the remaining travel route candidates 401 are processed.

Next, in step S<b>750 , the travel speed upper limit calculation unit 501 transmits to the travel command determination unit 503 the calculation result of the travel speed upper limit for each combination of the travel node and the bed movement amount.
The above is the details of the processing in step S721.

Next, using FIG. 9, details of the step S722 for determining interference between the load 500 and the fixed obstacle will be described.

First, in step S761, the obstacle avoidance determination unit 502 acquires one travel route from the travel route candidates 401 generated in step S703, as in step S741. Next, in step S762, the obstacle avoidance determination unit 502 calculates a node that the automatic guided vehicle 100 should reach first after starting traveling, as in step S742.

Next, in step S763, similarly to step S744, the obstacle avoidance determination unit 502 acquires one carrier movement amount from the carrier movement amount candidates acquired in step S712. Note that steps S761 to S763, steps S741, steps S742, and steps S744 may be executed together.

Next, in step S764, the obstacle avoidance determination unit 502 calculates the traveling area of the cargo in the cargo handling vehicle reference coordinate system 411. Here, with reference to FIG. 10, the cargo travel area will be described. Areas surrounded by A, B, C, D, E, and F in FIG. 10 are cargo travel areas. This area has a position error of 0.10 (m) and an attitude error of 0.05 (rad) as margins for the shape of the load 500, which is assumed as a route following error when the automatic guided vehicle 600 for loading is traveling. It is a thing. In the obstacle determination described later, the travel area of the cargo calculated here is mapped to an arbitrary travel route, and the collision determination with fixed obstacles on the two-dimensional map is performed, taking into consideration the route following error. Obstacle interference determination can be performed.

Further, the coordinate positions of characteristic points A, B, C, D, E, and F of the cargo in the cargo handling vehicle reference coordinate system 411 when the cargo bed is moved in the Xv direction by s (m) as shown in FIG. (Equation 6) to (Equation 11).
A(xa, ya)=(w1/2+s+0.10+l1/2tan(0.05), l1/2) (Equation 6)
B(xb, yb)=(-w1/2+s-0.10-l1/2tan(0.05), l1/2) (Equation 7)
C(xc, yc)=(-w1/2+s-0.10-l1/2tan(0.05),-l1/2) (Equation 8)
D(xd, yd)=(w1/2+s-0.10-l1/2tan(0.05),-l1/2) (Equation 9)
E(xe,ye)=(w1/2+s+0.10,0) (Equation 10)
F(xf, yf)=(-w1/2+s+0.10,0) (Equation 11)
As described above, in step S764, the areas connected by A, B, C, D, E, and F are calculated as the cargo travel area in the cargo handling vehicle reference coordinate system 411 .

Next, in step S765, the obstacle avoidance determination unit 502 determines whether the travel area and fixed obstacles are interfering with each other. For this purpose, the obstacle avoidance determination unit 502 uses the cargo travel area in the cargo handling vehicle position reference coordinate system calculated in step S, the travel route acquired in step S761, and the environment map acquired in step S701. In other words, the obstacle avoidance determination unit 502 uses these to map the traveling area of the cargo 500 after the cargo bed is moved to the node position stored in each traveling route candidate on the environment map, and makes a judgment. conduct.

Next, in step S766, the obstacle avoidance determination unit 502 determines whether steps S763 to S765 have been performed for each bed movement amount acquired in step S712. As a result, when it is executed (Yes), the process proceeds to step S767. If not (No), the process returns to step S763, and the remaining platform movement amount candidates are processed.

Next, in step S767, the obstacle avoidance determination unit 502 determines whether steps S762 to S766 have been performed for each travel route candidate acquired in step S703. As a result, if the process has been executed (Yes), the process proceeds to step S768. If not (No), the process returns to step S761 and the remaining travel route candidates are processed.

Next, in step S768, the obstacle avoidance determination unit 502 transmits to the travel command determination unit 503 the calculation result, which is the determination result of interference with respect to the combination of each traveling node and the bed movement amount. This completes the detailed processing of step S722.

Then, return to the description of the flowchart in FIG. In step S713, the travel command determination unit 503 determines the travel route and the bed movement amount using the results of steps S721 and S722. For this purpose, the determination result of interference transmitted in step S768 at the upper travel speed limit transmitted in step S750 is merged. FIG. 11 shows an example of travel route/carrying bed movement amount information created by this merging.

In the example shown in FIG. 11, there are travel route candidates A, B, and C composed of three nodes. exists. For the cells in the table corresponding to each route, node, and bed movement amount, the travel speed upper limit value (m/s) calculated in step S721 in the upper row, and the result of collision determination with a fixed obstacle in step S722 in the lower row (interference with obstacle , or can avoid obstacles).

Also, in step S713, the travel command determining unit 503 extracts the node position and the amount of bed movement that allow obstacle avoidance, using the travel route/bed travel amount information. Specifically, for each node and bed movement amount on the travel route in FIG. 11, the travel node and bed movement amount described as avoidance in the lower row are extracted. For example, for node 2 on route A, interference is determined for all bed movement amounts. In this case, the travel route candidate A is excluded from the travel route options.

In addition, based on the travel route/bed movement amount information, the travel command determination unit 503 selects the bed movement amount with a large travel speed upper limit and the travel speed upper limit value from among the deck movement amount candidates assigned to each node. Extract. Here, the upper limit of the traveling speed includes a predetermined value or more or a predetermined number of cases (for example, the largest).

For example, for the route B, the table movement amount c (m) is selected for node 1, the bed movement amount b (m) is selected for node 2, and the bed movement amount a (m) is selected for node 3. By this processing, it is possible to calculate an appropriate loading platform movement amount at the node position of each route.

Next, the travel command determination unit 503 calculates the travel time for each route from the travel speed upper limit and the edge length between nodes at the acquired node positions. For example, for route B, the traveling speed of the edge next to node 1 is 0.8 [m/s], and the edge distance is the distance between node 1 and node 2, so the traveling time is (the distance between node 1 and node 2 )/0.8(s). This calculation is performed between each node to calculate the travel time of each route.

In addition, the travel command determination unit 503 extracts travel routes with shorter travel times from the calculation results of the travel times for each travel route acquired in the preprocessing. Then, the travel command determination unit 503 transmits the extracted travel route and the bed movement amount at each node to the operation command generation unit 311 . As described above, in step S713 of this embodiment, the traveling route and the amount of bed movement are determined. This is a kind of action plan determination in the action plan determination unit. For this reason, at least one of the travel route and the bed movement amount, or other information may be used as the operation plan for the automatic guided vehicle 100 . When this step is executed, the automatic guided vehicle 100 travels by the following processing.

Next, in step S714, the self-position estimation unit 309 calculates the self-position 412 and self-orientation 413 in the same manner as in step S707. In addition, self-position estimation section 309 transmits the self-position estimation result to action command generation section 311 .

Next, in step S715, the operation command generation unit 311 generates a command value for the traveling motor 108 to run at the upper limit of traveling speed calculated in step S713, based on the self-position information estimated in step S714. do.

Also, the operation command generator 311 generates a command value for the steering motor 109 to follow the travel route determined in step S713. Further, the operation command generation unit 311 generates a transfer actuator command value for realizing the loading platform movement amount at each node position determined in step S713. Since the target platform movement amount is set for each node, the transfer actuator changes the platform movement amount when the distance of the closest node with respect to the self-position and the traveling direction becomes equal to or less than a threshold value.

Next, in step S716, the operation command generator 311 drives the traveling motor 108, steering motor 109, and transfer actuator 104 based on the command values calculated in step S715. As a result, the traveling motor 108, the steering motor 109, and the transfer actuator 104 are driven, and the automatic guided vehicle 100 travels and carries the load.

Next, in step S717, the action command generator 311 determines whether the automatic guided vehicle 100 has arrived at the destination of the load. For this reason, when the distance between the self-position of the automatic guided vehicle 100 and the final node 402, which is the destination of the load, becomes equal to or less than a threshold value, the motion command generation unit 311 causes the automatic guided vehicle 100 to move to the destination of the load. Determine that you have arrived. Then, when the transport destination is reached, the motion command generator 311 outputs a stop command to the travel motor 108 , the steering motor 109 , and the transfer actuator 104 . As a result, the automatic guided vehicle 100 stops.

Also, if the unmanned guided vehicle 100 has not arrived at the cargo destination, the process returns to step S714 to continue traveling. This completes the description of the first embodiment.

Next, Example 2 will be described with reference to FIGS. 12 and 13. FIG. In the present embodiment and a third embodiment to be described later, the same reference numerals are assigned to the configurations common to the first embodiment, and detailed description thereof will be omitted.

In comparison with the first embodiment, this embodiment adds on-the-spot turning route candidates to the traveling route candidates. Here, the on-the-spot turning means that the unmanned guided vehicle 100 uses the middle point of the driven wheels 106, which is the vehicle position reference point, as the turning center position, and changes only the attitude of the vehicle while the turning center position is fixed, and changes direction. It refers to the action to be performed. FIG. 12 is a functional block diagram showing a functional configuration example of the travel command calculation unit 310 according to this embodiment. Hereinafter, functional blocks added or changed to the travel command calculation unit 310 (FIG. 6) of the first embodiment will be mainly described. Note that the cargo handling vehicle system in this embodiment has the same configuration as that of 2 except for the travel command calculation unit 310 .

<Travel route candidate generation unit 204>
In this embodiment, the travel route candidate generating unit 204 generates a travel route including an on-the-spot turning route. This travel route will be described with reference to FIG. 13 . FIG. 13 is a diagram showing a travel route according to this embodiment. A travel route that is the shortest route 405 and a travel route 406 shown in FIG. 13 are travel turning routes, and a travel route 1110 is a travel route for carrying out spot turns.
Here, the traveling route 1110 is composed of a spot turning node 1111, an edge 1112 traveling before spot turning, and an edge 1113 traveling after spot turning. The on-the-spot turning node 1111 is set to a position coordinate that enables on-the-spot turning when the cargo is empty. However, the position coordinates of the on-the-spot turning node to be set may be other than this, and multiple candidates may be owned.

Each node has two-dimensional position coordinates with respect to the environmental map position reference coordinate system 400, the upper limit of running speed, and the direction of travel, as well as information indicating whether or not to perform spot turns on that node. In the following description, a node that performs on-the-spot turning is referred to as an on-the-spot turning node.

<Traveling Turning Route Extraction Unit 2002>
A traveling turning route extraction unit 2002 extracts a traveling route that does not include a spot turning node from among the traveling route candidates generated by the traveling route candidate generating unit 204 .

<On-the-Spot Turning Route Extraction Unit 2003>
The on-the-spot turning route extracting unit 2003 extracts a traveling route including the on-the-spot turning node from the traveling route candidates generated by the traveling route candidate generating unit 204.

<Travel turning travel command calculation unit 2004>
Based on the travel and turn route information acquired from the travel and turn route extraction unit 2002, the travel and turn travel command calculation unit 2004 selects a route candidate and a loading platform movement amount candidate that have been acquired so that obstacles can be avoided and travel time is shortened. Calculates the combination of travel route and bed movement amount.

<On-the-Spot Turning Command Calculation Unit 2005>
The on-the-spot turning travel command calculation unit 2005 performs obstacle avoidance determination and travel time calculation during travel for the on-spot turning route stored in the on-spot turning route extraction unit 2003, thereby enabling obstacle avoidance and shortening the travel time. Calculate a driving route such as Note that specific processing contents will be described later.

<Run command determination unit 503>
The travel command determination unit 503 determines the travel route to be transmitted to the action command generation unit 311 from the travel route acquired from the travel turning travel command calculation unit 2004 and the travel route acquired from the spot turning travel command calculation unit 2005 . Note that specific processing contents will be described later.

The above is the configuration of the travel command calculation unit 310 in the second embodiment. In addition, in the present embodiment, the traveling speed upper limit calculating section 501, the obstacle avoidance determining section 502 and the traveling command determining section 503 of the traveling command calculating section 310 of the first embodiment may also be provided.

<Processing flow>
The processing flow of the second embodiment will be described below. Note that the difference between the processes of the first embodiment and the second embodiment is from the state where step S712 is completed to the state where step S713 is completed.
Therefore, only this difference will be described below.

First, the travel turning route extraction unit 2002 transmits the node group information of the travel turning route created by the travel route candidate generation unit 204 to the travel turning travel command calculation unit 2004 . After that, the traveling/turning command calculation unit 2004 performs the same processing as step S713 described in the first embodiment using the acquired traveling/turning route information. As a result, the travel/turn travel command calculation unit 2004 determines a combination of the travel route and the amount of movement of the bed so that the obstacle can be avoided and the travel time is the shortest. After that, the travel/turn travel command calculation unit 2004 transmits the determined travel route and the bed movement amount to the travel command determination unit.

Next, the spot turning route extraction unit 2003 transmits the node group information of the spot turning route extracted by the travel route candidate generation unit 204 to the spot turning travel command calculation unit 2005 . After that, the on-the-spot turning travel command calculation unit 2005 performs obstacle avoidance determination for the cargo traveling area shown in FIG.
It should be noted that the cargo travel area described here is calculated by the same processing as in step S764 of the first embodiment.

In the obstacle avoidance judgment during on-the-spot turning, the environment map position reference coordinate system from the state where the unmanned guided vehicle 600 for loading reaches the on-spot turning node to the state where the on-the-spot turning ends and faces the direction of the edge 1113 The position and orientation of the automatic guided vehicle 600 for loading on the 400 are calculated. Next, the on-the-spot turning travel command calculation unit 2005 maps the travel area of the load calculated based on the self-position 412 and the self-orientation 413 of the automatic guided vehicle 600 for loading during the on-spot turning on a two-dimensional map. and determine if it interferes with an obstacle. After this determination is performed for each spot turning route, the process proceeds to the next step.

Next, the on-the-spot turning travel command calculation unit 2005 calculates the time required to travel the traveling route for the on-spot turning route, the time required for passing the edge 1112, the time required for the spot turning, and the time required to pass the edge 1113. It is calculated by the sum of the time to The time to pass the edge 1112 and the time to pass the edge 1113 are calculated from the running speed upper limit stored in the preceding node and the length of the edge. Also, the time required for spot turning is calculated from the upper limit of speed during spot turning determined by the vehicle body performance and the angle formed by edge 1112 and edge 1113 . The time required for a spot turn varies depending on the aircraft, but generally it takes about 15.0 (s) for a 90 (degree) turn. After calculating the travel time for each spot turning route, the process proceeds to the next step.

Next, the on-the-spot turning travel command calculation unit 2005 calculates travel route information that enables avoidance of obstacles and shortens travel time based on the obstacle avoidance determination result and travel time calculation result for each spot turn travel route. , and transmits information on the running time to the running command determination unit 503 . After that, the processing proceeds to the next step.

Next, the travel command determination unit 503 compares the time required for traveling and turning with the time required for on-the-spot turning travel. As a result, when the time required for traveling and turning is shorter, the traveling command determination unit 503 transmits the traveling route and the bed movement amount transmitted from the traveling and turning travel command calculation unit 2004 to the operation command generation unit 311 . Also, if the time required for spot turning is shorter, the travel route sent from the travel/turn travel command calculation unit 2004 is sent to the motion command generation unit 311 .
The above is the processing flow of the travel command calculation unit 310 in the second embodiment.

Hereinafter, Example 3 will be described using FIGS. 14 and 15. FIG. In this embodiment, the motion plan is determined in consideration of the positional deviation of the load and the deviation of the center of gravity of the load. Problems related to the present embodiment will be described below.

When the automatic guided vehicle 100 has a high calculation load during travel, the calculation cycle of the onboard controller 114 becomes longer. Therefore, the transmission cycle of the operation command from the operation command generation unit 311 to the traveling motor 108, the steering motor 109, and the transfer actuator 104 becomes longer. As a result, the displacement of the position and attitude during the transmission of the command values to the traveling motor 108 and the steering motor 109 becomes large, causing meandering operation and the like, and the tracking accuracy with respect to the target route decreases.

Therefore, in Embodiments 1 and 2, in order to reduce the calculation load during traveling and eliminate the decrease in tracking accuracy with respect to the target route, first, the shape of the load, the position of the center of gravity of the load, and the weight of the load are calculated before the start of traveling. obtained. Then, using the obtained results, the destination, travel route, and bed movement amount are determined, and then travel is started.

On the other hand, in Embodiments 1 and 2, the unmanned guided vehicle 100 travels at a low speed during traveling and turning in consideration of traveling safety. At this time, since the displacement of the position and orientation with respect to time is small, even when the computational load increases, the degree of deterioration in the route following accuracy is small.

A problem when the unmanned guided vehicle 100 travels is that the position of the load and the position of the center of gravity deviate after performing a traveling turn or a spot turn. In Examples 1 and 2, the traveling route and the amount of bed movement were determined based on the shape of the load and the position of the center of gravity of the load obtained before the start of travel. For this reason, it is not possible to consider the positional deviation of the load and the positional deviation of the center of gravity that occurs during traveling and turning.

Based on the above, in this embodiment, considering the positional deviation of the load and the deviation of the center of gravity of the load after traveling and turning on the spot, the traveling route and the loading platform movement are performed based on the position of the load and the position of the center of load after the end of turning. Determine quantity.

Hereinafter, differences in processing between the first and second embodiments and the third embodiment will be described in detail. FIG. 14 is a block diagram showing a functional configuration example of the cargo handling vehicle system 300 according to this embodiment.
Hereinafter, an outline of functional blocks newly added or changed with respect to the first and second embodiments will be described.

<Turn end determination unit 3001>
The turning end determination unit 3001 determines whether or not the unmanned guided vehicle 100 that is traveling has reached the traveling turning end node. Here, as features of nodes, three nodes are defined: a straight line node, a turn node, and a travel turn end node. A straight line node refers to a node having an angle of π (rad) formed by an edge connecting the nodes, and a turn node refers to other nodes. Based on this, the above-mentioned travel turning end node refers to the case where the node itself is a turning node and the node one ahead in the traveling direction is a straight line node.

<Load amount calculation unit 301>
The load amount calculation unit 301 recalculates the load amount M (kg) by the same calculation method as the load amount calculation unit 301 of the first embodiment when the automatic guided vehicle 100 reaches the turning end node.

<Center of load calculation unit 302>
When the automatic guided vehicle 100 reaches the turning end node, the center-of-load computing unit 302 calculates the center-of-gravity position (x1, y1, z1) (m) of the load by the same computing method as the center-of-load computing unit 302 of the first embodiment. Recalculate.

<Package shape calculator 303>
When the automatic guided vehicle 100 reaches the turning end node, the load shape calculator 303 recalculates the shape of the load by the same calculation method as the load shape calculator 303 of the first embodiment.

<Running command calculation unit 310>
The travel command calculation unit 310 determines the travel route and the cargo bed movement amount for the automatic guided vehicle 100 based on the information received from the load amount calculation unit 301 , the load center calculation unit 302 , and the load shape calculation unit 303 . For this reason, the travel command calculation unit 310 selects the travel route candidates 401 generated by the travel route candidate creation unit 204 and the platform movement amount candidates stored in the platform movement amount candidate storage unit 308, and selects the travel distance of the first embodiment. A calculation method similar to that of the command calculation unit 310 is used.

The above is the characteristic configuration of the third embodiment.

<Processing flow>
Hereinafter, the processing flow of the third embodiment will be described in detail with reference to FIG. 15 . FIG. 15 is a flowchart showing the processing flow of the cargo handling vehicle system 300 according to this embodiment. In the third embodiment, step S3101 is added after step S717 described in the first and second embodiments. Step S717 is a process of determining whether the automatic guided vehicle 100 has reached the destination of the load. In the third embodiment, if the automatic guided vehicle 100 has reached the final node 402 to which the goods are to be conveyed, all the processes are completed, and if the automatic guided vehicle 100 has not yet reached the final node 402, The process moves to step S3101.

In step S3101, the turning end determination unit 3001 determines whether the unmanned guided vehicle 100 has reached the turning end node on the target route based on the self-position estimation result and the travel route information calculated by the travel route calculation unit. do. For this purpose, first, the position of the closest node of the cargo handling vehicle is determined using the self-position estimation result and the position coordinate data of the node. Next, it is determined whether the nearest node is the turn end node. If the nearest node is a turn node and the node next to the nearest node is a straight line node, the nearest node is a turn end node.

Next, the distance between the position of the automatic guided vehicle 100 and the position of the nearest node is measured, and if the distance is equal to or less than the threshold, it is determined that the travel end node has been reached. If the determination result indicates that the travel turning end node has been reached (Yes), the process proceeds to step S704. If not reached (No), the process proceeds to step S714. This completes the description of the third embodiment.

This completes the description of each embodiment, but the present invention is not limited to these embodiments. For example, a forklift can be used as a cargo handling vehicle. Also, the traffic control unit 201 can be realized by a traffic management device, which is a cloud-type server. Also, as an operating environment, it can be applied to factories other than warehouses. Furthermore, the operating environment may be an open environment such as a general road. Furthermore, the functions of the traffic management device and the in-vehicle controller 114 may be executed according to a program. In this case, this program is preferably stored in a storage medium.

201... Traffic control unit, 202... Environment map storage unit, 203... Operation management unit, 204... Travel route candidate generation unit, 205... Communication device, 300... Cargo handling vehicle system, 301 Load amount calculation unit 302 Load center calculation unit 303 Load shape calculation unit 304 Information storage unit 309 Self-position estimation unit 310 Travel command Operation unit 311 Operation command generation unit 501 Running speed upper limit calculation unit 502 Obstacle avoidance determination unit 503 Travel command determination unit 2002 Travel turning route extraction unit , 2003... on-the-spot turning route extraction unit, 2004... traveling/turning travel command calculation unit, 2005... on-the-spot turning travel command calculation unit, 3001... turning end determination unit

Claims (8)

1. In a cargo handling vehicle system for supporting the cargo handling operation of a cargo handling vehicle,
   A plurality of operation plan candidates for the cargo handling of the cargo handling vehicle based on physical characteristics indicating dynamic characteristics of the automatic guided vehicle for loading, which is the cargo handling vehicle loaded with the cargo to be handled. an operation plan candidate generation unit that generates an operation plan candidate;
   a travel speed upper limit calculation unit that calculates the speed upper limit of the cargo handling vehicle;
   an obstacle avoidance determination unit that determines the possibility of avoiding obstacles in the operation of the cargo handling vehicle;
   A cargo handling vehicle system comprising an operation plan determination unit that determines an operation plan from the plurality of operation plan candidates according to the speed upper limit value and the result of the determination.
2. In the cargo handling vehicle system according to claim 1,
   The physical characteristics are information indicating the shape, center of gravity and weight of the automatic guided vehicle for loading,
   The running speed upper limit calculation unit calculates the speed upper limit based on the center of gravity and the weight,
   The cargo handling vehicle system, wherein the traveling speed upper limit calculation unit uses the shape to determine the possibility of avoiding the obstacle.
3. In the cargo handling vehicle system according to claim 2,
   The cargo handling vehicle system, wherein the motion plan candidate generation unit generates at least one of a travel route candidate and a cargo bed movement amount candidate of the cargo handling vehicle as the motion plan candidate.
4. In the cargo handling vehicle system according to claim 2,
   The cargo handling vehicle system, wherein the operation plan candidate generation unit generates a traveling route candidate of the cargo handling vehicle including spot turning.
5. In an in-vehicle controller that is provided in a cargo handling vehicle and controls the operation of the cargo handling vehicle for cargo handling,
   An operation plan candidate for the cargo handling of the cargo handling vehicle, which is created based on physical characteristics indicating dynamic characteristics of the automatic guided vehicle for loading, which is the cargo handling vehicle loaded with the cargo to be handled. a communication unit that receives a plurality of operation plan candidates;
   a travel speed upper limit calculation unit that calculates the speed upper limit of the cargo handling vehicle;
   an obstacle avoidance determination unit that determines obstacle avoidance in the operation of the cargo handling vehicle;
   An in-vehicle controller comprising an operation plan determination unit that determines an operation plan from the plurality of operation plan candidates according to the speed upper limit value and the result of the determination.
6. The in-vehicle controller according to claim 5,
   The physical characteristics are information indicating the shape, center of gravity and weight of the automatic guided vehicle for loading,
   The running speed upper limit calculation unit calculates the speed upper limit based on the center of gravity and the weight,
   The traveling speed upper limit calculation unit is an in-vehicle controller that uses the shape to determine the possibility of avoiding the obstacle.
7. The in-vehicle controller according to claim 6,
   The communication unit is an in-vehicle controller that receives at least one of a travel route candidate and a loading platform movement amount candidate of the cargo handling vehicle as the operation plan candidate.
8. The in-vehicle controller according to claim 6,
   The communication unit is an in-vehicle controller that receives travel route candidates for the cargo handling vehicle, including on-the-spot turning.

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