Forklift, Estimation System and Estimation Program

Abstract

To provide a forklift capable of adjusting the pedal weight to an appropriate pedal weight for each driver.SOLUTION: A forklift 1 includes at least one pedal selected from a brake pedal, an accelerator pedal, a clutch pedal, and an inching pedal, a weight estimating unit, and a weight changing unit. The weight estimating unit estimates an appropriate pedal weight for a driver O based on the physical characteristics of the driver O. The weight changing unit changes the pedal weight based on the pedal weight estimated by the weight estimating unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a forklift, and an estimation system and an estimation program related to the forklift. [Background technology]

[0002] There are two types of forklifts: reach forklifts and countertop forklifts. The number and functions of pedals on a forklift vary depending on the type of forklift.

[0003] As disclosed in Patent Document 1, a reach forklift is equipped with a brake pedal and a presence pedal. The presence pedal is configured to be able to stop the vehicle from traveling and loading and unloading, and the driver cannot travel or unload unless he or she depresses the presence pedal.

[0004] As disclosed in Patent Document 2, countertop forklifts are equipped with an accelerator pedal and a brake pedal, and engine-powered countertop forklifts are further equipped with a clutch pedal. Among battery-powered forklifts, automatic forklifts are further equipped with an inching pedal instead of a clutch pedal. By stepping on the inching pedal, the driver can put the forklift into a half-clutch state and cut off the driving power.

[0005] However, unlike a typical passenger vehicle, a forklift requires the driver to step on each pedal frequently, and so it is preferable that the weight of the pedals be appropriate for the driver. On the other hand, unlike a passenger vehicle, a single forklift may be ridden by multiple drivers in turn, and the appropriate weight of each pedal differs for each driver, due to differences in gender, weight, muscle strength, etc. However, conventional forklifts have been unable to adjust the pedal weight to an appropriate level for each driver. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2016-047759 A [Patent Document 2] JP 2017-166663 A Summary of the Invention [Problem to be solved by the invention]

[0007] Therefore, an object of the present invention is to provide a forklift truck that can adjust the pedal weight to an appropriate pedal weight for each operator. [Means for solving the problem]

[0008] In order to solve the above problems, the forklift according to the present invention comprises: At least one pedal selected from a brake pedal, an accelerator pedal, a clutch pedal, and an inching pedal; a weight estimation unit that estimates an appropriate pedal weight for a driver based on the physical characteristics of the driver; and a weight changing section for changing the weight of the pedal based on the weight of the pedal estimated by the weight estimating section.

[0009] The forklift preferably comprises: A camera that photographs the driver, A first trained model that uses images of a plurality of people and weights of people corresponding to the images of the people as training data and that has been trained to output the weight of a driver when an image of a driver generated by a camera is input, The weight estimation unit estimates an appropriate pedal weight based on the driver's weight output by the first trained model.

[0010] The forklift preferably comprises: A camera that photographs the driver, A second trained model that uses images of a plurality of people and muscle mass corresponding to each person's image as training data and is trained to output the muscle mass of a driver when an image of the driver generated by a camera is input, The weight estimation unit estimates an appropriate pedal weight based on the driver's muscle mass output by the second trained model.

[0011] The forklift preferably comprises: A camera that captures the driver's face, A third trained model that uses a plurality of face images and the ages of people corresponding to each face image as training data and is trained to output the age of the driver when a face image of the driver generated by a camera is input, The weight estimation unit estimates an appropriate pedal weight based on the driver's age output by the third trained model.

[0012] The forklift preferably comprises: The camera is configured to capture an image of a driver wearing a predetermined outfit; The training data for images of people is assumed to be images of people wearing specific clothes.

[0013] The forklift preferably comprises: The camera is configured to capture an image of a driver wearing a helmet; The training data for facial images is assumed to be facial images of people wearing helmets.

[0014] The forklift preferably comprises: The camera is configured to detect people wearing specific clothing as drivers and automatically capture their images.

[0015] The forklift truck may, for example, A characteristic storage unit that stores physical characteristics of the driver in advance is further provided, The weight estimation unit estimates an appropriate pedal weight for the driver based on the physical characteristics of the driver stored in the characteristics storage unit.

[0016] The forklift truck may, for example, A feature storage unit that stores physical features of a plurality of drivers together with corresponding facial images in advance; A camera that captures the driver's face, A driver identification unit that identifies a driver stored in the characteristic storage unit based on a face image generated by the camera, The weight estimation unit estimates an appropriate pedal weight for the driver based on physical characteristics corresponding to the identified driver.

[0017] In order to solve the above problem, the estimation system according to the present invention comprises: At least one pedal selected from a brake pedal, an accelerator pedal, and an inching pedal provided on the forklift; a weight estimation unit that estimates an appropriate pedal weight for a driver based on the physical characteristics of the driver; and a weight changing section for changing the weight of the pedal based on the estimated appropriate pedal weight.

[0018] In order to solve the above problem, the estimation program according to the present invention comprises: At least one pedal selected from a brake pedal, an accelerator pedal, and an inching pedal; a weight changing unit that changes the weight of the pedal based on the estimated appropriate pedal weight; A program for a forklift having a computer, The computer is caused to function as a weight estimation unit that estimates an appropriate pedal weight for a driver based on the driver's physical characteristics. Effect of the Invention

[0019] The forklift according to the present invention can adjust the pedal weight to an appropriate pedal weight for each driver. As a result, the forklift according to the present invention can control the pedal weight to an appropriate level, thereby reducing the burden on the driver. In addition, the accelerator pedal can appropriately assist in acceleration and deceleration, the brake pedal can appropriately assist in stopping, and the inching pedal can appropriately assist in cutting off the driving power. [Brief description of the drawings]

[0020] [Figure 1] 1 is a front view of a forklift according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram showing a driver photographed by a camera. [Diagram 3] FIG. 2 is a functional block diagram of a control unit. [Figure 4] FIG. 13 is a functional block diagram showing a modified example of the control unit. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a forklift, an estimation system, and an estimation program according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0022] Fig. 1 is a front view of a forklift 1 according to this embodiment. The forklift 1 is a battery-powered countertop forklift. As shown in Fig. 1, the forklift 1 includes a plurality of wheels 10, a vehicle body 11, a driver's seat 12, a head guard 13, pedals 14 (see Fig. 3), left and right masts 15, left and right forks 16, a camera 17, a weight changing unit 18 (see Fig. 3), and a control unit 20 (see Fig. 3).

[0023] A plurality of wheels 10 are provided on all four sides of a vehicle body 11. A driver's seat 12 is provided on the vehicle body 11, and a head guard 13 is provided above the driver's seat 12.

[0024] The pedals 14 include an accelerator pedal, a brake pedal, and an inching pedal, and are provided below the driver's seat 12.

[0025] The left and right forks 16 are configured to be capable of being raised and lowered via the left and right masts 15, and the driver O raises and lowers the forks 16 to perform loading and unloading work.

[0026] The camera 17 is fixed to the head guard 13 and configured to capture an image of the entire body of the driver O standing to the side of the vehicle body 11. As shown in Figs. 1 and 2, the driver O is photographed at least from the front and side at the side of the forklift 1 while wearing a helmet H and a uniform U. The uniform U corresponds to the "predetermined clothing" of the present invention. The camera 17 may be a video camera. The camera 17 may be configured to detect, for example, a person wearing the uniform U (i.e., the driver O) as the driver O, and automatically capture an image of the person when the driver O approaches the forklift 1. The camera 17 may be configured to include multiple cameras.

[0027] The camera 17 also captures the face of the driver O wearing the helmet H to generate a facial image. The whole-body image and facial image generated by the camera 17 are transmitted to the control unit 20.

[0028] When capturing an image with the camera 17, the forklift 1 may issue a voice message to the driver O, such as "Please look forward" or "Please look to the side."

[0029] The weight change unit 18 changes the weight of the pedal 14 when the driver O presses it down, based on the weight of the pedal 14 estimated by a weight estimation unit 24 described later. The weight change unit 18 is configured by, for example, a device that can adjust the weight by hydraulic pressure or a device that can adjust the weight by a spring.

[0030] Also, for example, the weight change unit 18 may be an assist device that assists the depression force of the pedal 14. In this case, the weight change unit 18 may adjust the assist force based on the weight of the pedal 14 estimated by the weight estimation unit 24, and may substantially change the weight of the pedal 14 when the driver O depresses it.

[0031] Alternatively, the weight change unit 18 may be configured to change the magnitude of the power or braking force of each device responsive to the amount of depression force of the pedal 14 based on the weight of the pedal 14 estimated by the weight estimation unit 24. In this way, the weight change unit 18 effectively changes the weight of the pedal 14.

[0032] The control unit 20 is configured by a computer arranged in the vehicle body 11, and has an arithmetic unit, a storage device, and a memory. The storage device stores an estimation program that causes the computer to function as a weight estimation unit 24, which will be described later.

[0033] Fig. 3 is a functional block diagram of the forklift 1. As shown in Fig. 3, the control unit 20 has a body weight estimation unit 21, a muscle mass estimation unit 22, an age estimation unit 23, and a weight estimation unit 24.

[0034] The weight estimation unit 21 corresponds to the "first trained model" of the present invention. The weight estimation unit 21 uses images of a plurality of people and the weights of the people corresponding to each image as teacher data, and has learned in advance by a neural network using deep learning to output the weight of the driver O when an image of the driver O generated by the camera 17 is input. Thus, when an image generated by the camera 17 is input, the weight estimation unit 21 estimates the weight of the driver O and transmits the estimated weight to the weight estimation unit 24.

[0035] The muscle mass estimation unit 22 corresponds to the "second trained model" of the present invention. The muscle mass estimation unit 22 learns in advance, by a neural network using deep learning, to output the muscle mass of the driver O when an image of the driver O generated by the camera 17 is input, using images of a plurality of people and muscle mass corresponding to each image as teacher data. Thus, when an image generated by the camera 17 is input, the muscle mass estimation unit 22 estimates the muscle mass of the driver O and transmits the estimated muscle mass to the weight estimation unit 24.

[0036] The images of people in the teacher data used by the weight estimation unit 21 and the muscle mass estimation unit 22 include facial images of both men and women. This allows the weight estimation unit 21 and the muscle mass estimation unit 22 to more appropriately estimate the weight and muscle mass of the driver O, respectively, taking into account the gender.

[0037] The image of a person among the teacher data used for learning by the body weight estimation unit 21 and the muscle mass estimation unit 22 may be, for example, an image of a person wearing a uniform U. The uniform U may also be processed to make it easier to estimate the body weight and muscle mass. This allows the body weight estimation unit 21 and the muscle mass estimation unit 22 to more appropriately estimate the body weight and muscle mass of the driver O, respectively.

[0038] The age estimation unit 23 corresponds to the "third trained model" of the present invention. The age estimation unit 23 uses a neural network using deep learning to learn in advance to output the age of the driver O when a facial image of the driver O generated by the camera 17 is input, using multiple facial images and the age of the person corresponding to each facial image as teacher data. As a result, when a facial image generated by the camera 17 is input, the age estimation unit 23 estimates the age of the driver O and transmits the estimated age to the weight estimation unit 24.

[0039] The facial images of both men and women are included in the training data used by the age estimation unit 23. This allows the age estimation unit 23 to more appropriately estimate the age of the driver O by taking the gender into account.

[0040] The face image among the teacher data used for learning by the age estimation unit 23 may be, for example, a face image of a person wearing a helmet H. This allows the age estimation unit 23 to estimate the age of the driver O more appropriately.

[0041] The weight estimation unit 24 estimates an appropriate weight of the pedal 14 based on the weight, muscle mass, and age of the driver O estimated by the weight estimation unit 21, muscle mass estimation unit 22, and age estimation unit 23.

[0042] For example, the weight estimation unit 24 may estimate the weight of the pedal 14 heavier the heavier the weight estimated by the weight estimation unit 21. Also, the weight estimation unit 24 may estimate the weight of the pedal 14 heavier the greater the muscle mass estimated by the muscle mass estimation unit 22. Furthermore, the weight estimation unit 24 may estimate the weight of the pedal 14 heavier the younger the age estimated by the age estimation unit 23.

[0043] In addition, the weight estimation unit 24 may reduce the estimated weight of the pedal 14 if the estimated body weight is heavy but the estimated muscle mass is less than a certain amount, or may reduce the estimated weight of the pedal 14 depending on the estimated age even if the estimated muscle mass is high.

[0044] In this way, the weighting of the weight of the pedal 14 estimated by the weight estimation unit 24 based on the body weight, muscle mass, and age is not limited. Also, the weight of the pedal 14 appropriate for each driver O estimated by the weight estimation unit 24 does not have to be an estimate of the weight of all pedals. In other words, the weight estimation unit 24 only needs to estimate the weight of at least one of the brake pedal, accelerator pedal, and inching pedal.

[0045] In addition, in the case of an inching pedal, the weight estimation unit 24 may estimate only the weight of the inching pedal until the inching pedal is put into a half-clutch state, or may estimate the weight of the pedal 14 including the weight of the inching pedal until it is fully depressed.

[0046] The weight changing unit 18 changes the weight of each pedal 14 based on the appropriate pedal 14 weight estimated by the weight estimating unit 24 .

[0047] The forklift 1 according to this embodiment is configured as described above, and therefore can provide the driver O with an appropriate weight of the pedal 14.

[0048] Although an embodiment of the forklift 1, the estimation system, and the estimation program according to the present invention has been described above, the present invention is not limited to the above embodiment. The forklift, the estimation system, and the estimation program according to the present invention may be implemented, for example, in each of the following modified examples or in a combination of the modified examples.

[0049] <Modification> The forklift 1 may be a reach forklift. In this case, the pedal 14 may include a presence pedal. The forklift 1 may be an engine-powered forklift. In this case, the pedal 14 may include a clutch pedal.

[0050] For example, if the weight estimation unit 21 and the muscle mass estimation unit 22 can estimate the weight and muscle mass of the driver O from an image of a part of the driver O's body, the camera 17 may be configured to capture only a part of the driver O's body.

[0051] In the above embodiment, the weight estimation unit 24 estimated the appropriate weight of the pedal 14 for the driver O by referring to the driver's body weight, muscle mass, and age. However, the weight estimation unit 24 may estimate the appropriate weight of the pedal 14 by referring to, for example, any one of the driver's body weight, muscle mass, and age.

[0052] The control unit 20 may have a characteristic storage unit 25 that stores the physical characteristics (weight, muscle mass, age) of each driver O in advance, instead of the weight estimation unit 21, the muscle mass estimation unit 22, and the age estimation unit .

[0053] In this case, the forklift 1 may further include an input unit that accepts input of a name, an identifier, etc. corresponding to each driver, or input of a tool for identifying each driver, such as an ID card, and the weight estimation unit 24 may identify the driver O based on the input name, identifier, etc., or input of the above-mentioned tool, and estimate an appropriate weight of the pedal 14 by referring to the physical characteristics of the driver O.

[0054] Furthermore, when the control unit 20 has the characteristic memory unit 25, the forklift 1 is provided with a face camera (camera 17) for photographing the face of the driver O, for example, in the driver's seat 12, and as shown in FIG. 4, the control unit 20 further has a driver identification unit 26 for identifying the driver O stored in the characteristic memory unit 25 based on the face image generated by the face camera, and the weight estimation unit 24 may estimate an appropriate weight of the pedal 14 based on the physical characteristics of the identified driver O.

[0055] Furthermore, in addition to the case where the control unit 20 has the characteristic memory unit 25, if the helmet H has a tag, a QR code (registered trademark), etc. that identifies each driver, each driver may be identified by a tool that detects them, and the weight estimation unit 24 may estimate an appropriate weight of the pedal 14 based on the physical characteristics of the identified driver O.

[0056] The control unit 20 may be configured, for example, by a server computer provided on the cloud, and the forklift 1 may estimate an appropriate weight of the pedal 14 for the driver O by communicating with this server computer. [Explanation of symbols]

[0057] O Driver H Helmet U uniform 1. Forklift 10 wheels 11 Body 12 Driver's seat 13 Head Guard 14 Pedals 15 Mast 16 Fork 17 Camera 18 Weight change section 20 Control section 21 Weight Estimation Section 22 Muscle mass estimation section 23 Age Estimation Department 24 Weight Estimation Section 25 Feature Memory Unit 26 Driver Identification Department

Claims

1. At least one pedal selected from a brake pedal, an accelerator pedal, a clutch pedal, and an inching pedal; a weight estimation unit that estimates an appropriate pedal weight for the driver based on a physical characteristic of the driver; a weight change unit that changes the weight of the pedal based on the weight of the pedal estimated by the weight estimation unit; A camera for photographing the driver; a second trained model that uses images of a plurality of people and muscle masses corresponding to the images of the people as training data, and that has been trained to output the muscle mass of the driver when an image of the driver generated by the camera is input; The weight estimation unit estimates an appropriate weight of the pedal based on the muscle mass of the driver output by the second trained model.

2. At least one pedal selected from a brake pedal, an accelerator pedal, a clutch pedal, and an inching pedal; a weight estimation unit that estimates an appropriate pedal weight for the driver based on a physical characteristic of the driver; a weight change unit that changes the weight of the pedal based on the weight of the pedal estimated by the weight estimation unit; A camera for photographing the face of the driver; a third trained model that uses a plurality of face images and the ages of people corresponding to each of the face images as training data, and that has been trained to output the age of the driver when a face image of the driver generated by the camera is input; The weight estimation unit estimates an appropriate weight of the pedal based on the age of the driver output by the third trained model.

3. The camera is configured to capture an image of the driver wearing a predetermined outfit; The forklift according to claim 1 , wherein the training data of the image of a person is an image of a person wearing the predetermined clothing.

4. The camera is configured to capture an image of the driver wearing a helmet; 3. The forklift according to claim 2, wherein the training data of the face image is a face image of a person wearing the helmet.

5. The forklift according to claim 3 , wherein the camera is configured to detect a person wearing the specific clothing as the driver and automatically photograph the person.

6. At least one pedal selected from a brake pedal, an accelerator pedal, a clutch pedal, and an inching pedal; a weight estimation unit that estimates an appropriate pedal weight for a driver based on all or any of a driver's weight, muscle mass, and age; a weight change unit that changes the weight of the pedal based on the weight of the pedal estimated by the weight estimation unit; a characteristic storage unit that stores in advance all or any of the weight, the muscle mass, and the age of the driver, The weight estimation unit estimates an appropriate pedal weight for the driver based on all or any of the weight, the muscle mass, and the age of the driver stored in the characteristic storage unit.

7. At least one pedal selected from a brake pedal, an accelerator pedal, a clutch pedal, and an inching pedal; a weight estimation unit that estimates an appropriate pedal weight for a driver based on all or any of a driver's weight, muscle mass, and age; a weight change unit that changes the weight of the pedal based on the weight of the pedal estimated by the weight estimation unit; a feature storage unit that stores in advance all or any of the weights, muscle masses, and ages of a plurality of the drivers together with corresponding face images; A camera for photographing the face of the driver; a driver identification unit that identifies the driver stored in the characteristic storage unit based on a face image generated by the camera, The weight estimation unit estimates an appropriate pedal weight for the driver based on all or any of the body weight, the muscle mass, and the age corresponding to the identified driver.

8. At least one of a brake pedal, an accelerator pedal, and an inching pedal provided on the forklift; a weight estimation unit that estimates an appropriate pedal weight for the driver based on the muscle mass or age of the driver; and a weight change unit that changes the weight of the pedal based on the estimated appropriate weight of the pedal.

9. At least one of a brake pedal, an accelerator pedal, and an inching pedal provided on the forklift; a characteristic storage unit that stores in advance all or any of the weight, muscle mass, and age of the driver; a weight estimation unit that estimates an appropriate pedal weight for the driver based on all or any of the weight, the muscle mass, and the age of the driver stored in the characteristic storage unit; and a weight change unit that changes the weight of the pedal based on the estimated appropriate weight of the pedal.

10. At least one pedal selected from a brake pedal, an accelerator pedal, and an inching pedal; a weight changing unit that changes the weight of the pedal based on the estimated appropriate weight of the pedal; A program for a forklift having a computer, The computer, and a weight estimation unit that estimates an appropriate pedal weight for a driver based on the driver's muscle mass or age.

11. At least one pedal selected from a brake pedal, an accelerator pedal, and an inching pedal; a weight changing unit that changes the weight of the pedal based on the estimated appropriate weight of the pedal; A forklift estimating program comprising: The computer stores in advance all or any of the driver's weight, muscle mass, and age, The estimation program includes: The computer, and an estimation program for causing the computer to execute a weight estimation unit that estimates an appropriate pedal weight for the driver based on all or any of the stored weight, muscle mass, and age of the driver.

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