A system for estimating the position of a moving object during the manufacturing process of a moving object.

Abstract

This system provides a mechanism to suppress the decrease in accuracy of position estimation. [Solution] The system for estimating the position of a moving object in the manufacturing process of a moving object comprises: an acquisition unit that acquires three-dimensional point cloud data of the moving object measured using a distance measuring device; an area estimation unit that estimates the liquid-wetting region, which is the region containing liquid droplets, from the region that combines the surface region of the moving object and the region surrounding the moving object; and a position estimation unit that estimates the position of the moving object using the three-dimensional point cloud data from which the three-dimensional point cloud data of the liquid-wetting region has been excluded.

Description

Technical Field

[0001] The present disclosure relates to a system for estimating the position of a moving object in the manufacturing process of the moving object.

Background Art

[0002] Patent Document 1 discloses a technique for driving a vehicle autonomously or by remote control in the manufacturing process of the vehicle. In the control of such a vehicle that does not require an occupant, the position of the vehicle is estimated. The position estimation is performed by matching three-dimensional point cloud data related to the vehicle with previously prepared map information. The three-dimensional point cloud data is acquired using a distance measuring device to which a technique such as LiDAR (Light Detection And Ranging) provided in the vehicle manufacturing factory is applied.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] As one step of the vehicle manufacturing process, there is a step using water such as a water leakage inspection step of the vehicle. In such a step, water is sprayed onto the vehicle by a water spraying device such as a shower tester. The water droplets and water splashes generated at this time scatter the laser light emitted by the distance measuring device. As a result, errors are included in the three-dimensional point cloud data used for matching, and the accuracy of vehicle position estimation may decrease. Such a problem is common not only in water but also in steps where an arbitrary liquid such as a cleaning liquid is used. Further, it is common in the manufacturing process of an arbitrary moving object not limited to a vehicle.

Means for Solving the Problems

[0005] This disclosure can be implemented in the following forms:

[0006] (1) According to one embodiment of the present disclosure, a system is provided for estimating the position of a moving body in the manufacturing process of the moving body. The system comprises: an acquisition unit that acquires three-dimensional point cloud data of the moving body measured using a distance measuring device; an area estimation unit that estimates a liquid-wetting area which is an area containing liquid droplets from the area combining the surface area of ​​the moving body and the area surrounding the moving body; and a position estimation unit that estimates the position of the moving body using three-dimensional point cloud data from which the three-dimensional point cloud data of the liquid-wetting area has been excluded from the acquired three-dimensional point cloud data. In this system configuration, the estimation unit estimates the position of the moving object using three-dimensional point cloud data from which three-dimensional point cloud data of liquid-wetting regions have been excluded. Compared to a configuration in which the position is estimated without excluding three-dimensional point cloud data of liquid-wetting regions, this configuration can suppress the occurrence of errors in the three-dimensional point cloud data due to liquid droplets and prevent a decrease in the accuracy of position estimation. (2) The system of the above form may further include a water spraying device position acquisition unit that acquires positional information relating to the position of a water spraying device that sprays water on the moving body, and the area estimation unit may estimate the liquid wetting area using the acquired positional information. In this type of system, the area estimation unit estimates the wetted area using positional information regarding the position of the water spraying device, so the wetted area can be estimated even if the position of the water spraying device fluctuates. (3) The system of the above form may further include a process information acquisition unit that acquires process information relating to a process performed on the moving body, wherein the process information includes information relating to a region in which liquid is sprayed during a water leak inspection process or a cleaning process performed on the moving body, and the region estimation unit may estimate the liquid wetting region using the acquired process information. In this type of system, the region estimation unit estimates the liquid-wetting region using process information that includes information about the region where the liquid is sprayed. Therefore, by preparing appropriate process information in advance, the liquid-wetting region can be estimated with high accuracy. (4) The system of the above form may further include an imaging data acquisition unit that acquires imaging data of the moving body and the area around the moving body, which is imaging data captured by an imaging device, and the area estimation unit may estimate the liquid wetting area using the acquired imaging data. In this type of system, the region estimation unit estimates the liquid-wetting region using imaging data captured by the imaging device. For example, the liquid-wetting region can be estimated using a camera that films the inside of a factory.

[0007] This disclosure can be implemented in forms other than the system described above, such as a vehicle position estimation device, a position estimation method, a program for implementing the method, a non-temporary recording medium on which the program is recorded, or a program product. The program product may be provided, for example, as a recording medium on which the program is recorded, or as a program product that can be distributed via a network. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram illustrating the system in the first embodiment. [Figure 2] This is a block diagram showing the system configuration. [Figure 3] This is a flowchart showing the processing procedure for vehicle driving control in the first embodiment. [Figure 4] This flowchart shows the procedure for estimating the position of a vehicle that includes a liquid-wetting area. [Figure 5] This is a diagram illustrating the water leak inspection process. [Figure 6] This is an explanatory diagram showing the schematic configuration of the system in the fourth embodiment. [Figure 7] This is a flowchart showing the processing procedure for vehicle driving control in the fourth embodiment. [Modes for carrying out the invention]

[0009] A. First Embodiment: <Overview of System 50> Figure 1 is a conceptual diagram illustrating the system 50 in the first embodiment. The system 50 is used to operate the vehicle 100 as a mobile object in an unmanned manner. The system 50 is also used to estimate the position of the vehicle 100 in the manufacturing process.

[0010] In this disclosure, “mobile object” means an object that can move, such as a vehicle or an electric vertical take-off and landing aircraft (so-called flying car). A vehicle may be a wheeled vehicle or a tracked vehicle, such as a passenger car, truck, bus, motorcycle, car, or construction vehicle. Vehicles include electric vehicles (BEVs: Battery Electric Vehicles), gasoline vehicles, hybrid vehicles, and fuel cell vehicles. If the mobile object is not a vehicle, the terms “vehicle” and “car” in this disclosure may be replaced with “mobile object” as appropriate, and the term “driving” may be replaced with “moving” as appropriate.

[0011] Vehicle 100 is configured to operate autonomously. "Autonomous operation" means operation without the operation of a passenger. Operation refers to operations related to at least one of the following: "going," "turning," or "stopping" of vehicle 100. Autonomous operation is achieved by automatic or manual remote control using a device located outside vehicle 100, or by autonomous control of vehicle 100. Vehicle 100 operating autonomously may have passengers on board who do not perform operation. Passengers who do not perform operation include, for example, people simply sitting in the seats of vehicle 100, or people performing tasks other than operation, such as assembly, inspection, or operating switches, while on board vehicle 100. Operation by a passenger is sometimes called "manned operation."

[0012] As used herein, "remote control" includes "complete remote control" in which all operations of the vehicle 100 are completely determined from outside the vehicle 100, and "partial remote control" in which some operations of the vehicle 100 are determined from outside the vehicle 100. Further, "autonomous control" includes "complete autonomous control" in which the vehicle 100 autonomously controls its own operations without receiving any information from a device outside the vehicle 100, and "partial autonomous control" in which the vehicle 100 autonomously controls its own operations using information received from a device outside the vehicle 100.

[0013] In the present embodiment, the system 50 is used in a factory FC that manufactures the vehicle 100. The reference coordinate system of the factory FC is the global coordinate system GC, and any position within the factory FC can be expressed by the coordinates of X, Y, and Z in the global coordinate system GC. The factory FC includes a first location PL1 and a second location PL2. The first location PL1 and the second location PL2 are connected by a road TR on which the vehicle 100 can travel. In the first location PL1 and the second location PL2, various processes for manufacturing the vehicle 100 are executed. Such processes include, for example, a member assembly process, a painting process, a cleaning process, an inspection process, and the like. In the present embodiment, a water leakage inspection process is executed in the second location PL2. The "water leakage inspection process" will be described later. The vehicle 100 moves from the first location PL1 to the second location PL2 through the road TR by autonomous driving.

[0014] In the factory FC, a plurality of external sensors 300 are installed along the road TR. The positions of each external sensor 300 in the factory FC are adjusted in advance.

[0015] <Configuration of System 50> FIG. 2 is a block diagram showing the configuration of the system 50. The system 50 includes a vehicle 100, a server 2 hundred, and one or more external sensors 300\.

[0016] Vehicle 100 includes a vehicle control device 110 for controlling each part of the vehicle 100, an actuator group 120 including one or more actuators that are driven under the control of the vehicle control device 110, and a communication device 130 for communicating with an external device such as a server 200 through wireless communication. The actuator group 120 includes an actuator of a driving device for accelerating the vehicle 100, an actuator of a steering device for changing the traveling direction of the vehicle 100, and an actuator of a braking device for decelerating the vehicle 100.

[0017] The vehicle control device 110 is constituted by a computer including a processor 111, a memory 112, an input / output interface 113, and an internal bus 114. The processor 111, the memory 112, and the input / output interface 113 are connected so as to be communicable bidirectionally via the internal bus 114. The actuator group 120 and the communication device 130 are connected to the input / output interface 113. The processor 111 realizes various functions including the function as a vehicle control unit 115 by executing a program PG1 stored in the memory 112.

[0018] The vehicle control unit 115 causes the vehicle 100 to travel by controlling the actuator group 120. The vehicle control unit 115 can cause the vehicle 100 to travel by controlling the actuator group 120 using a travel control signal received from the server 200. The travel control signal is a control signal for causing the vehicle 100 to travel. In the present embodiment, the travel control signal includes the acceleration and the steering angle of the vehicle 100 as parameters. In other embodiments, the travel control signal may include the speed of the vehicle 100 as a parameter instead of or in addition to the acceleration of the vehicle 100.

[0019] The external sensor 300 is a sensor located outside the vehicle 100. In this embodiment, the external sensor 300 is a distance measuring device to which LiDAR (Light Detection And Ranging) is applied. The external sensor 300 outputs three-dimensional point cloud data of the vehicle 100. The external sensor 300 is equipped with a communication device (not shown) and can communicate with other devices such as the server 200 via wired or wireless communication.

[0020] The server 200 is composed of a computer comprising a processor 201, memory 202, an input / output interface 203, and an internal bus 204. The processor 201, memory 202, and input / output interface 203 are connected via the internal bus 204 to enable bidirectional communication. A communication device 205 for communicating with various external devices of the server 200 is connected to the input / output interface 203. The communication device 205 can communicate with the vehicle 100 via wireless communication and can communicate with each external sensor 300 via wired or wireless communication. The processor 201 executes a program PG2 stored in memory 202 to realize various functions, including those of an acquisition unit 210, a remote control unit 211, an area estimation unit 212, a vehicle position estimation unit 213, and a water spraying device position acquisition unit 214.

[0021] The acquisition unit 210 acquires three-dimensional point cloud data of the vehicle 100 measured using the external sensor 300. The three-dimensional point cloud data measured by the external sensor 300 is transmitted to the server 200 via a communication device (not shown) of the external sensor 300.

[0022] The remote control unit 211 generates a driving control signal to control the actuator group 120 of the vehicle 100 using detection results from various sensors, including the external sensor 300, and transmits the driving control signal to the vehicle 100, thereby driving the vehicle 100 by remote control. In addition to the driving control signal, the remote control unit 211 may also generate and output control signals to control various auxiliary equipment and actuators that operate various devices such as wipers, power windows, and lamps, which are provided on the vehicle 100. In other words, the remote control unit 211 may operate these various devices and auxiliary equipment by remote control.

[0023] The region estimation unit 212 estimates the liquid-wetting region, which is the region containing liquid droplets, from the region that combines the region of the vehicle's surface and the region surrounding the vehicle. The "liquid-wetting region" and "estimation of the liquid-wetting region" will be described later.

[0024] The vehicle position estimation unit 213 estimates the position of the vehicle 100 using the three-dimensional point cloud data of the vehicle 100 acquired by the acquisition unit 210. In this disclosure, the vehicle position estimation unit 213 also estimates the position of the vehicle 100 using three-dimensional point cloud data from which the three-dimensional point cloud data of the liquid-wetting region has been excluded. The "estimation of the position of the vehicle 100" will be described later. The vehicle position estimation unit 213 corresponds to the "position estimation unit" in this disclosure.

[0025] The water spraying device position acquisition unit 214 acquires positional information regarding the position of the water spraying device. The water spraying device is used to spray water on the vehicle 100 in processes that use water, such as water leak inspection processes and cleaning processes. In this embodiment, the positional information of the water spraying device is acquired using three-dimensional point cloud data measured by an external sensor 300. Details of the water spraying device position acquisition unit 214 will be described later.

[0026] <Vehicle 100 driving control> Figure 3 is a flowchart showing the processing procedure for vehicle 100 driving control in the first embodiment. Driving control is the process for driving the vehicle unmanned within the factory fuel cell. Driving control is executed when the vehicle 100 is switched on within the factory fuel cell. In the processing procedure shown in Figure 3, the processor 201 of the server 200 functions as a remote control unit 211 by executing program PG2. The processor 111 of the vehicle 100 functions as a vehicle control unit 115 by executing program PG1.

[0027] In step S1, the processor 201 of the server 200 acquires vehicle position information using the measurement results of the external sensor 300. The vehicle position information is the position information that forms the basis for generating the driving control signal. In this embodiment, the vehicle position information includes the position and orientation of the vehicle 100 in the global coordinate system GC of the factory FC.

[0028] In detail, in step S1, the vehicle position estimation unit 213 of the processor 201 estimates the position of the vehicle 100 by, for example, detecting the outline of the vehicle 100 using three-dimensional point cloud data, calculating the coordinates of the positioning point of the vehicle 100 in the local coordinate system, and converting the calculated coordinates to coordinates in the global coordinate system GC. The outline of the vehicle 100 can be detected, for example, by inputting the three-dimensional point cloud data into a detection model DM that utilizes artificial intelligence. The detection model DM is prepared, for example, within or outside the system 50 and stored in the memory 202 of the server 200. Examples of the detection model DM include a trained machine learning model that has been trained to implement either semantic segmentation or instance segmentation. As this machine learning model, for example, a convolutional neural network (hereinafter referred to as CNN) trained by supervised learning using a training dataset can be used. The training dataset includes, for example, multiple training images containing vehicle 100, and labels indicating whether each region in the training images represents vehicle 100 or something other than vehicle 100. During CNN training, it is preferable to update the CNN parameters using backpropagation to reduce the error between the output result of the detection model DM and the labels. The processor 201 can also obtain the orientation of vehicle 100 by estimating it based on the direction of the vehicle 100's movement vector calculated from the positional changes of the vehicle 100's feature points between frames of the three-dimensional point cloud data, for example, using the optical flow method. Vehicle position information may also be obtained by template matching using reference point cloud data. The reference point cloud data is stored in memory 202 beforehand. Vehicle position information is obtained using the position and orientation of vehicle 100 estimated in this way.

[0029] In step S2, the processor 201 of the server 200 determines the next target location that the vehicle 100 should head to. In this embodiment, the target location is represented by X, Y, Z coordinates in the global coordinate system GC. The memory 202 of the server 200 pre-stores a reference route RR, which is the path that the vehicle 100 should travel. The route is represented by a node indicating the starting point, nodes indicating waypoints, a node indicating the destination, and links connecting each node. The processor 201 uses the vehicle position information and the reference route RR to determine the next target location that the vehicle 100 should head to. The processor 201 determines the target location on the reference route RR beyond the vehicle 100's current location.

[0030] In step S3, the processor 201 of the server 200 generates a driving control signal to drive the vehicle 100 toward the determined target position. The processor 201 calculates the vehicle's speed from the change in the vehicle's position and compares the calculated speed with the target speed. Overall, the processor 201 determines the acceleration so that the vehicle 100 accelerates if the speed is lower than the target speed, and determines the acceleration so that the vehicle 100 decelerates if the speed is higher than the target speed. Furthermore, if the vehicle 100 is located on the reference path RR, the processor 201 determines the steering angle and acceleration so that the vehicle 100 does not deviate from the reference path RR, and if the vehicle 100 is not located on the reference path RR, in other words, if the vehicle 100 has deviated from the reference path RR, the processor 201 determines the steering angle and acceleration so that the vehicle 100 returns to the reference path RR.

[0031] In step S4, the processor 201 of the server 200 transmits the generated driving control signal to the vehicle 100. The processor 201 repeats the acquisition of vehicle position information, determination of target position, generation of driving control signal, and transmission of driving control signal at predetermined intervals.

[0032] In step S5, the processor 111 of the vehicle 100 receives a driving control signal transmitted from the server 200. In step S6, the processor 111 of the vehicle 100 controls the actuator group 120 using the received driving control signal, thereby driving the vehicle 100 at the acceleration and steering angle indicated in the driving control signal. The processor 111 repeats the reception of the driving control signal and the control of the actuator group 120 at predetermined intervals. According to the system 50 in this embodiment, the vehicle 100 can be driven by remote control, and the vehicle 100 can be moved without using transport equipment such as cranes or conveyors.

[0033] <Estimation of the position of vehicle 100 including the liquid-wetting area> Figure 4 is a flowchart showing the procedure for estimating the position of vehicle 100, including the liquid-wetting area. The procedure shown in Figure 4 is performed when liquid-based processes such as a water leak inspection process or a cleaning process are carried out. In this embodiment, an example in which a water leak inspection process is performed on vehicle 100 will be described.

[0034] Figure 5 is a diagram illustrating the water leak inspection process. The water leak inspection process is performed to check for water intrusion into the vehicle interior by spraying water on the vehicle 100. The water is sprayed by a water spraying device 500. The water spraying device 500 is also called a shower tester. The water spraying device 500 moves around the vehicle 100 while spraying water in a predetermined direction. Alternatively, the position of the water spraying device 500 may be fixed, and the vehicle 100 may move relative to the water spraying device. In the water leak inspection process, there is a region AR1 on the surface of the vehicle 100 where droplets exist, and a region AR2 around the vehicle 100 where scattered droplets 510 exist. In this embodiment, regions AR1 and AR2 together are called the wetted region.

[0035] Liquid in a wetted region can scatter light. Therefore, errors may be present in the three-dimensional data measured by the external sensor 300. Specifically, in a wetted region, even though no object actually exists, the scattering of light by the liquid may result in three-dimensional data that appears as if an object exists. Such errors may lead to an incorrect estimation of the vehicle 100's position. To suppress position estimation errors caused by the wetted region, the procedure for estimating the vehicle 100's position, including the wetted region shown in Figure 4, is performed.

[0036] In step S10 of Figure 4, the acquisition unit 210 acquires three-dimensional point cloud data of the vehicle 100 measured using the external sensor 300. This three-dimensional point cloud data includes three-dimensional point cloud data relating to the surface area of ​​the vehicle 100 and the area surrounding the vehicle 100. In addition, this three-dimensional point cloud data also includes three-dimensional point cloud data relating to the liquid-wetting area.

[0037] In step S20, the region estimation unit 212 estimates the wetted region. In this embodiment, the region estimation unit 212 estimates the wetted region using positional information regarding the position of the water spraying device 500 acquired by the water spraying device position acquisition unit 214. The positional information is acquired using three-dimensional point cloud data measured by the external sensor 300, using the same method as described in step S1 of Figure 3. Since the water spraying device 500 discharges water in a predetermined direction, the region AR2 in which scattered droplets 510, as shown in Figure 5, exist can be estimated within the wetted region by using the positional information of the water spraying device 500.

[0038] Furthermore, the region estimation unit 212 estimates the wetted area using information about the shape of the vehicle 100 in addition to the position information of the water spraying device 500. The information about the shape of the vehicle 100 is stored in memory 202 in advance. By using the position information of the water spraying device 500 and the information about the shape of the vehicle 100, the region AR1 on the surface of the vehicle 100 shown in Figure 5 can be estimated within the wetted area.

[0039] In step S30 shown in Figure 4, the vehicle position estimation unit 213 estimates the position of the vehicle 100 using the three-dimensional point cloud data acquired by the acquisition unit 210, from which the three-dimensional point cloud data of the liquid-wetting region has been excluded. The position estimation using the three-dimensional point cloud data is performed using the same method as described in step S1 shown in Figure 3.

[0040] The position of vehicle 100 estimated by the method described above is used to obtain the vehicle position information in step S1 shown in Figure 3.

[0041] According to the system 50 of the first embodiment described above, the vehicle position estimation unit 213 estimates the position of the moving object using three-dimensional point cloud data from which three-dimensional point cloud data of liquid-wetting regions have been excluded. Compared to a configuration in which the position is estimated without excluding three-dimensional point cloud data of liquid-wetting regions, this suppresses the occurrence of errors in the three-dimensional point cloud data due to liquid droplets and prevents a decrease in the accuracy of position estimation.

[0042] Furthermore, according to the system 50 of the first embodiment, the region estimation unit 212 estimates the wetted region using positional information regarding the position of the water spraying device 500, so that the wetted region can be estimated even if the position of the water spraying device 500 changes from a predetermined position.

[0043] B. Second Embodiment: The system of the second embodiment differs from the system 50 of the first embodiment in that the processor 201 further functions as a process information acquisition unit, and in the method of estimating the liquid-wetting region by the region estimation unit 212. The other configurations of the system of the second embodiment are the same as those of the system 50 of the first embodiment, so their description will be omitted.

[0044] The process information acquisition unit functions when the processor 201 shown in Figure 2 executes the program PG2 stored in memory 202. The process information acquisition unit is not shown in the diagram. The process information acquisition unit acquires process information related to the processes being performed on the vehicle 100. As described above, various processes are performed on the vehicle 100 within the factory FC shown in Figure 1. The order in which the processes are executed is predetermined and stored as sequence information in memory 202. When the execution of a process on the vehicle 100 is completed, completion information indicating that the process has been completed is written to memory 202. The process information acquisition unit uses the sequence information and the completion information to acquire the process currently being performed on the vehicle 100 as process information. In this embodiment, the process information includes information related to the water leak inspection process and the cleaning process. The process information also includes information related to the area where liquid is sprayed in the water leak inspection process or the cleaning process. That is, the process information includes information related to the area that may become a liquid-wetting area in the water leak inspection process or the cleaning process.

[0045] The region estimation unit 212 shown in Figure 2 estimates the wetted region using process information acquired by the process information acquisition unit. In the first embodiment, the region estimation unit 212 estimated the wetted region using position information regarding the position of the water spraying device 500 acquired by the water spraying device position acquisition unit 214. In contrast, the region estimation unit 212 in the second embodiment estimates the wetted region using process information instead of the position information of the water spraying device 500.

[0046] The system of the second embodiment described above can also suppress the occurrence of errors in three-dimensional point cloud data due to droplets, thereby preventing a decrease in the accuracy of position estimation.

[0047] Furthermore, according to the system 50 of the second embodiment, the region estimation unit 212 estimates the liquid-wetting region using process information that includes information about the region where the liquid is sprayed. Therefore, by preparing appropriate process information in advance, the liquid-wetting region can be estimated with high accuracy.

[0048] C. Third Embodiment: The system of the third embodiment differs from the system 50 of the first embodiment in that it includes an additional imaging device separate from the external sensor 300, the processor 201 further functions as an imaging data acquisition unit, and the method for estimating the liquid-wetting region by the region estimation unit 212. The other configurations of the system of the second embodiment are the same as those of the system 50 of the first embodiment, so their description is omitted.

[0049] The imaging device photographs the vehicle 100 and the area surrounding the vehicle 100, and outputs the image data. The imaging device is installed within the factory FC, such as the track TR, first location PL1, and second location PL2 shown in Figure 1. The imaging device is, for example, a camera. Note that the image device is not shown in Figure 1. The image data is transmitted to the server 200 by wired or wireless communication.

[0050] The imaging data acquisition unit functions when the processor 201 shown in Figure 2 executes the program PG2 stored in memory 202. Note that the imaging data acquisition unit is not shown in the diagram. The imaging data acquisition unit acquires imaging data output by the imaging device.

[0051] The region estimation unit 212 estimates the wetted area using the acquired imaging data. In the first embodiment, the region estimation unit 212 estimated the wetted area using positional information regarding the position of the water spraying device 500 acquired by the water spraying device position acquisition unit 214. In contrast, the region estimation unit 212 in the third embodiment estimates the wetted area using imaging data instead of the positional information of the water spraying device 500. The region estimation unit 212 estimates the wetted area using known image detection techniques, such as a convolutional neural network. Alternatively, the region estimation unit 212 may estimate the wetted area from the position of the device 500 included in the imaging data.

[0052] The system of the third embodiment described above can also suppress the occurrence of errors in three-dimensional point cloud data due to droplets, thereby preventing a decrease in the accuracy of position estimation.

[0053] Furthermore, according to the system of the third embodiment, the region estimation unit 212 estimates the liquid-wetting region using imaging data captured by the imaging device, so for example, the liquid-wetting region can be estimated using a camera that takes pictures inside a factory fuel cell.

[0054] D. Fourth Embodiment: <Outline configuration of the 50V system> Figure 6 is an explanatory diagram showing the schematic configuration of system 50v in the fourth embodiment. In this embodiment, system 50v differs from the first embodiment in that it does not have a server 200. Also, in this embodiment, vehicle 100v can be driven by autonomous control of vehicle 100v. The other configurations are the same as in the first embodiment unless otherwise specified.

[0055] In this embodiment, the processor 111v of the vehicle control device 110v functions as a vehicle control unit 115v by executing the program PG1 stored in memory 112v. The vehicle control unit 115v acquires the output results from the sensors, generates a driving control signal using the output results, and outputs the generated driving control signal to operate the actuator group 120, thereby enabling the vehicle 100v to be driven autonomously. In this embodiment, in addition to the program PG1, the detection model DM and the reference path RR are pre-stored in memory 112v.

[0056] <Vehicle 100V driving control> Figure 7 is a flowchart showing the processing procedure for controlling the driving of vehicle 100v in the fourth embodiment. The processing procedure shown in Figure 7 is executed by vehicle 100v alone to drive vehicle 100 in unmanned operation. In other words, it differs from the driving processing procedure shown in Figure 3 in that it does not use server 200. In the processing procedure in Figure 7, the processor 111v of vehicle 100v functions as a vehicle control unit 115v by executing program PG1.

[0057] In step S901, the processor 111v of the vehicle control device 110v acquires vehicle position information using the measurement results output from the external sensor 300. In step S902, the processor 111v determines the target position to which the vehicle 100v should next go. In step S903, the processor 111v generates a driving control signal to drive the vehicle 100v toward the determined target position. In step S904, the processor 111v controls the actuator group 120 using the generated driving control signal to drive the vehicle 100v according to the parameters expressed in the driving control signal. The processor 111v repeats the acquisition of vehicle position information, determination of the target position, generation of the driving control signal, and control of the actuators at a predetermined cycle. According to the system 50v in this embodiment, the vehicle 100v can be driven by autonomous control of the vehicle 100v without remote control of the vehicle 100v by the server 200.

[0058] <Other configurations for System 50V> In this embodiment, the processor 111v also functions as an acquisition unit 155v, a region estimation unit 165v, a vehicle position estimation unit 175v, and a water spraying device position acquisition unit 185v by executing the program PG1 stored in memory 112. Each of the acquisition unit 155v, region estimation unit 165v, vehicle position estimation unit 175v, and water spraying device position acquisition unit 185v has the same functions as the acquisition unit 210, region estimation unit 212, vehicle position estimation unit 213, and water spraying device position acquisition unit 214 of the first embodiment. Therefore, in this embodiment, the same processing as the position estimation of the vehicle 100 including the liquid-wetting region shown in Figure 4 is performed by the processor 111v.

[0059] Furthermore, the processor 111v may function as the process information acquisition unit described in the second embodiment and the imaging data acquisition unit described in the third embodiment by executing the program PG1. In other words, the system of the second embodiment and the system of the third embodiment may be realized by the vehicle 100 alone without using the server 200.

[0060] The system 50v of the fourth embodiment described above can also perform vehicle control processing and position estimation processing of the vehicle 100 including the wetted area.

[0061] E. Other Embodiments 1: (E1) In each of the above embodiments, the position estimation process of the vehicle 100 including the liquid-wetting region may be performed in any step using a liquid. The liquid may be any liquid other than water.

[0062] (E2) In each of the above embodiments, the server 200 may be an information processing device with any configuration.

[0063] (E3) In each of the above embodiments, the memories 112, 112v, and 202 may be any storage device. Such storage devices include, for example, an HDD (Hard Disk Drive), an SSD (Solid State Drive), and a DRAM (Dynamic Random Access Memory).

[0064] (E4) In each of the above embodiments, the external sensor 300 may be any distance measuring device that employs technology other than LiDAR.

[0065] (E5) In each of the above embodiments, the water spraying device 500 may spray any liquid other than water.

[0066] F. Other Embodiments 2: (F1) In the first embodiment described above, the server 200 performs the processing from acquiring vehicle position information to generating a driving control signal. In contrast, the vehicle 100 may perform at least a part of the processing from acquiring vehicle position information to generating a driving control signal. For example, the following forms (1) to (3) may be used.

[0067] (1) The server 200 may acquire vehicle location information, determine the next target location that vehicle 100 should head to, and generate a route from the vehicle 100's current location, as shown in the acquired vehicle location information, to the target location. The server 200 may generate a route to the target location between the current location and the destination, or it may generate a route to the destination. The server 200 may transmit the generated route to vehicle 100. Vehicle 100 may generate a driving control signal so that vehicle 100 travels along the route received from the server 200, and may use the generated driving control signal to control the actuator group 120.

[0068] (2) The server 200 may acquire vehicle location information and transmit the acquired vehicle location information to the vehicle 100. The vehicle 100 may determine the next target location to which the vehicle 100 should go, generate a route from the vehicle 100's current location shown in the received vehicle location information to the target location, generate a driving control signal so that the vehicle 100 travels along the generated route, and control the actuator group 120 using the generated driving control signal.

[0069] (3) In the embodiments of (1) and (2) above, the vehicle 100 is equipped with internal sensors, and detection results output from the internal sensors may be used for at least one of the generation of the path and the generation of the driving control signal. The internal sensors are sensors mounted on the vehicle 100. The internal sensors may include, for example, sensors that detect the motion state of the vehicle 100, sensors that detect the operating state of each part of the vehicle 100, and sensors that detect the environment around the vehicle 100.

[0070] Specifically, internal sensors may include, for example, cameras, LiDAR, millimeter-wave radar, ultrasonic sensors, GPS sensors, acceleration sensors, gyroscope sensors, etc. For example, in the embodiment of (1) above, the server 200 may acquire the detection results of the internal sensors and reflect the detection results of the internal sensors in the route when generating a route. In the embodiment of (1) above, the vehicle 100 may acquire the detection results of the internal sensors and reflect the detection results of the internal sensors in the driving control signal when generating a driving control signal. In the embodiment of (2) above, the vehicle 100 may acquire the detection results of the internal sensors and reflect the detection results of the internal sensors in the route when generating a route. In the embodiment of (2) above, the vehicle 100 may acquire the detection results of the internal sensors and reflect the detection results of the internal sensors in the driving control signal when generating a driving control signal.

[0071] (F2) In the fourth embodiment described above, the vehicle 100v is equipped with an internal sensor, and the detection result output from the internal sensor may be used in at least one of the generation of the route and the generation of the driving control signal. For example, the vehicle 100v may acquire the detection result from the internal sensor and reflect the detection result from the internal sensor in the route when generating the route. The vehicle 100v may acquire the detection result from the internal sensor and reflect the detection result from the internal sensor in the driving control signal when generating the driving control signal.

[0072] (F3) In the fourth embodiment described above, the vehicle 100v acquires vehicle position information using the detection results of the external sensor 300. Alternatively, the vehicle 100v may be equipped with an internal sensor, which may acquire vehicle position information using the detection results of the internal sensor, determine the next target location to which the vehicle 100v should go, generate a route from the vehicle 100v's current location to the target location as shown in the acquired vehicle position information, generate a driving control signal for traveling along the generated route, and control the actuator group 120 using the generated driving control signal. In this case, the vehicle 100v can travel without using the detection results of the external sensor 300 at all. The vehicle 100v may also acquire the target arrival time and congestion information from outside the vehicle 100v and reflect the target arrival time and congestion information in at least one of the route and the driving control signal.

[0073] (F4) In the first embodiment described above, the server 200 automatically generates a driving control signal to be transmitted to the vehicle 100. Alternatively, the server 200 may generate a driving control signal to be transmitted to the vehicle 100 in accordance with the operation of an external operator located outside the vehicle 100. For example, the external operator may operate a control device that includes a display for displaying captured images output from the external sensor 300, a steering wheel for remotely controlling the vehicle 100, an accelerator pedal, a brake pedal, and a communication device for communicating with the server 200 via wired or wireless communication, and the server 200 may generate a driving control signal in accordance with the operation applied to the control device.

[0074] (F5) In each of the above embodiments, the vehicle 100 only needs to have a configuration that allows it to move by unmanned operation, and may, for example, be in the form of a platform having the configuration described below. Specifically, in order for the vehicle 100 to perform the three functions of "driving," "turning," and "stopping" by unmanned operation, it is sufficient to have at least a vehicle control device 110 and an actuator group 120. When the vehicle 100 acquires information from the outside for unmanned operation, the vehicle 100 may further have a communication device 130. That is, the vehicle 100 that can move by unmanned operation does not need to have at least some of the interior parts such as the driver's seat and dashboard attached, it does not need to have at least some of the exterior parts such as the bumper and fender attached, and it does not need to have a body shell attached. In this case, the remaining parts such as the body shell may be attached to the vehicle 100 before the vehicle 100 is shipped from the factory FC, or the remaining parts such as the body shell may be attached to the vehicle 100 after the vehicle 100 has been shipped from the factory FC without the remaining parts such as the body shell attached to the vehicle 100. Each component may be attached to the vehicle 100 from any direction, such as the top, bottom, front, rear, right, or left side, and may be attached from the same direction or from different directions. The positioning of the platform can also be determined in the same way as for the vehicle 100 in the first embodiment.

[0075] (F6) Vehicle 100 may be manufactured by combining multiple modules. A module means a unit composed of one or more parts grouped together according to the configuration and function of vehicle 100. For example, the platform of vehicle 100 may be manufactured by combining a front module that constitutes the front part of the platform, a central module that constitutes the central part of the platform, and a rear module that constitutes the rear part of the platform. The number of modules that constitute the platform is not limited to three, but may be two or fewer, or four or more. In addition to the platform, or in place of the platform, parts of vehicle 100 other than the platform may be modularized. Various modules may also include any exterior parts such as bumpers and grilles, or any interior parts such as seats and consoles. Furthermore, not limited to vehicle 100, any type of mobile body may be manufactured by combining multiple modules. Such modules may be manufactured, for example, by joining multiple parts by welding or fasteners, or by integrally molding at least a part of the module as a single part by casting. The molding method of integrally molding at least a part of the module as a single part is also called gigacast or megacast. By using Gigacast, parts of a mobile body that were conventionally formed by joining multiple components can be formed as single components. For example, the front module, central module, and rear module mentioned above may be manufactured using Gigacast.

[0076] (F7) Transporting vehicle 100 using the unmanned operation of vehicle 100 is also called "autonomous transport." The configuration for realizing autonomous transport is also called a "vehicle remote control autonomous driving transport system." Furthermore, a production method that uses autonomous transport to produce vehicle 100 is also called "autonomous production." In autonomous production, for example, at a factory FC that manufactures vehicle 100, at least a portion of the transport of vehicle 100 is realized by autonomous transport.

[0077] (F8) In each of the above embodiments, some or all of the functions and processes implemented in software may be implemented in hardware. Also, some or all of the functions and processes implemented in hardware may be implemented in software. As hardware for implementing the various functions in each of the above embodiments, various circuits such as integrated circuits and discrete circuits may be used.

[0078] This disclosure is not limited to the embodiments described above, and can be implemented in various configurations without departing from its spirit. For example, the technical features in the embodiments corresponding to the technical features in each form described in the summary of the invention can be replaced or combined as appropriate in order to solve some or all of the above-described problems, or to achieve some or all of the above-described effects. Furthermore, if a technical feature is not described as essential in this specification, it can be deleted as appropriate. [Explanation of symbols]

[0079] 50, 50V... System, 100, 100V... Vehicle, 110, 110V... Vehicle control device, 111, 111V, 201... Processor, 112, 112V, 202... Memory, 113, 203... Input / Output interface, 114, 204... Internal bus, 115, 115V... Vehicle control unit, 120... Actuator group, 130, 205... Communication device, 155V, 210... Acquisition unit, 165V, 212... Area Estimation unit, 175v, 213… Vehicle position estimation unit, 185v, 214… Water spraying device position acquisition unit, 200… Server, 211… Remote control unit, 300… External sensor, 500… Water spraying device, 510… Scattered droplets, AR1, AR2… Area, DM… Detection model, FC… Factory, GC… Global coordinate system, PG1, PG2… Program, PL1… First location, PL2… Second location, RR… Reference path, TR… Track

Claims

1. A system for estimating the position of a moving object in the manufacturing process of the moving object, An acquisition unit that acquires three-dimensional point cloud data of the moving object measured using a distance measuring device, A region estimation unit estimates a liquid-wetting region, which is a region containing a liquid droplet, from the region that is the sum of the region on the surface of the moving body and the region surrounding the moving body. A position estimation unit estimates the position of the moving object using the three-dimensional point cloud data from which the three-dimensional point cloud data of the liquid-wetting region has been excluded from the acquired three-dimensional point cloud data, A system that includes these features.

2. The system according to claim 1, The unit further includes a water spraying device position acquisition unit that acquires positional information regarding the position of a water spraying device that sprays water on the moving body, The region estimation unit estimates the liquid-wetting region using the acquired position information. system.

3. The system according to claim 1, The system further includes a process information acquisition unit that acquires process information relating to the processes performed on the moving body, The process information includes information regarding the area in which liquid is sprayed during a water leak inspection or cleaning process performed on the moving body. The region estimation unit estimates the liquid wetting region using the acquired process information. system.

4. The system according to claim 1, The imaging device captures imaging data, and the device further comprises an imaging data acquisition unit that acquires imaging data of the moving object and the area around the moving object. The region estimation unit estimates the liquid-wetting region using the acquired imaging data. system.

Images