Self-propelled robot

Abstract

To provide a nondestructive component estimation device and a self-propelled robot that can estimate the state of inner components in the growth step of fruits relatively accurately.SOLUTION: A detected scattering light is divided and a spectrum showing the light intensity distribution for each wavelength is measured. On the measured spectrum, multivariate analysis is conducted on the correlation between the measured spectrum and a plurality of spectrums stored in a library so that an estimation model that has learned to estimate the state of an inner component of a fruit is formed. On the formed estimation model, the state of the inner component in the growth step of the fruit is estimated.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a self-propelled robot, and is suitable for use in, for example, a self-propelled robot equipped with a non-destructive component estimation device that non-destructively measures the sugar content of fruit without plucking the fruit from a fruit tree. [Background technology]

[0002] Conventionally, in order to measure the sugar content of fruits such as tomatoes, a non-destructive measurement method using near-infrared spectroscopy has been put into practical use, in which the fruit is irradiated with near-infrared light, the absorbance is measured, and the sugar content is estimated from the absorbance.

[0003] However, typical non-destructive measurement methods are often carried out after the fruit has been picked from the trees and collected, and fruit that is determined to have a sugar content that does not meet the required level is likely to be discarded as having no commercial value.

[0004] To solve this problem, a handheld saccharometer has been developed that directly measures the sugar content of fruit while it is still on the tree (see Patent Document 1). This handheld saccharometer is designed to measure light that is irradiated from a light source and reflected by the surface of fruit or vegetable using a light receiving sensor, and calculates the sugar content based on the reflected light. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2016-114544 A Summary of the Invention [Problem to be solved by the invention]

[0006] However, the handheld sugar content meter shown in Patent Document 1 has a problem in that the operator must measure a large number of fruits on the trees while holding them one by one with his / her hands, which is very time-consuming for the operator. In addition, since fruit trees are greatly affected by sunlight in the field, a variable neutral density filter is provided in the light amount adjustment means, but the configuration becomes complicated.

[0007] In fruit trees that are actually exposed to sunlight, it is desirable to be able to keep track of the growth process of the large number of fruits that grow on the trees, while measuring and managing the internal components, including sugar content, of each fruit with high accuracy; however, the above-mentioned Patent Document 1 still has problems that make it insufficient for practical use.

[0008] The present invention has been made in consideration of the above points, and aims to propose a self-propelled robot equipped with a non-destructive component estimation device that can estimate the state of internal components during the growth process of fruit with high accuracy. [Means for solving the problem]

[0009] In order to solve such problems, the present invention provides a self-propelled robot that is equipped with a non-destructive component estimation device that estimates sugar content by irradiating light on fruit and that travels on the ground autonomously or in response to external operation, the robot comprising: a travel drive unit that drives the robot body to travel on the ground in a desired direction at a desired speed; an arm support unit that supports the base of an arm unit having a multi-joint mechanism with multiple degrees of freedom and is connected to the upper part of the travel drive unit integrally with the arm unit so as to be freely rotatable relative to the travel drive unit; and a travel control unit that drives and controls the travel drive unit and the arm support unit. The non-destructive component estimation device includes a light detection unit that detects scattered light generated from inside the fruit through the skin surface of the fruit when light is irradiated onto the fruit, a contact detection unit that is provided adjacent to the light detection unit and detects a state of contact with the fruit, an adsorption light shielding unit whose tip is made of an elastic material that can be adsorbed to the skin surface of the fruit and that covers the periphery of the light detection unit and the contact detection unit when adsorbed onto the fruit to block external light, a light source that irradiates light so as to irradiate the vicinity of the adsorption site of the fruit that is adsorbed onto the adsorption light shielding unit, and an image capturing unit that captures an image of the adsorption target by the adsorption light shielding unit and simultaneously captures the adsorption target. the optical axis of the fruit is detected by the optical axis measuring unit and the optical axis of the fruit is detected by the optical axis measuring unit, the optical axis measuring unit is detected by the optical axis measuring unit, and the optical axis of the fruit is detected by the optical axis measuring unit. The optical axis measuring unit is configured to measure the distance to the fruit to be detected, the optical axis measuring unit is configured to measure the distance to the fruit to be detected, the optical axis measuring unit is configured to measure the distance to the fruit to be detected, the optical axis measuring unit is configured to measure the distance to the fruit to be detected, the optical axis measuring unit is configured to measure the distance to the fruit to be detected, and the optical axis measuring unit is configured to measure the distance to the fruit to be detected,

[0010] In this way, the self-propelled robot can move to the location of a desired fruit tree by itself, and then position the non-destructive component estimation device mounted on the arm for each of the multiple fruits on that tree, and estimate the state of the internal components of each fruit during its growth with relatively high accuracy. When there are many fruits on a tree, this is extremely effective because it improves work efficiency significantly compared to individual manual work.

[0011] In this case, the non-destructive component estimation device can estimate the state of the internal components during the growth process of the fruit with a relatively high degree of accuracy. In particular, by recording the changes in the internal components during the growth process of the fruit in chronological order, it becomes possible to contribute to research on improving the varieties of each fruit on the fruit tree to make them more valuable varieties based on the records. Furthermore, the non-destructive component estimation device can estimate the state of the internal components during the growth process of the fruit with a relatively high degree of accuracy without being affected by external light.

[0012] In addition, the present invention comprises an environmental imaging unit which images the environment in which the robot body is traveling, an object detection unit which detects specific fruit trees in the traveling environment based on images captured by the environmental imaging unit, and an environmental map creation unit which estimates the robot's position relative to the traveling environment and at the same time creates a two-dimensional or three-dimensional environmental map including the ground, and the traveling control unit controls the traveling drive unit to move to the specific fruit tree detected by the object detection unit in the environmental map created by the environmental map creation unit, and then drives the arm unit to abut the suction shading unit against each of the multiple fruits on the tree, and assigns addresses sequentially, and manages data representing the state of the internal components of the fruit during its growth process estimated by the internal state estimation unit of the non-destructive component estimation device on an address-by-address basis.

[0013] As a result, the self-propelled robot can automatically move to the planting position of the desired fruit tree within the cultivation area, and by individually managing the addresses of multiple fruits growing on a specific fruit tree, it is possible to chronologically store the state of the internal components of each fruit as it grows.

[0014] Furthermore, in the present invention, the estimation model construction unit uses a supervised machine learning method as the estimation model, and the internal state estimation unit evaluates the accuracy of the learning result by the supervised machine learning method by cross-validation. As a result, in the nondestructive component estimation device, the accuracy of the accuracy evaluation by the internal state estimation unit improves with an increase in the number of learning rounds, making it possible to estimate the state of the internal components during the growth process of the fruit with even higher accuracy.

[0015] Furthermore, in the present invention, the fruit is left on the tree and the internal components of the fruit consist of at least one of sugar content, acidity, γ-aminobutyric acid, hardness, density, and color. As a result, the non-destructive component estimation device can specifically confirm the changes in the internal components during the development of the fruit, individually or in combination, and also the correlation between them. Effect of the Invention

[0021] According to the present invention, it is possible to realize a self-propelled robot equipped with a non-destructive component estimation device that can estimate the state of internal components during the growth process of a fruit with relatively high accuracy. [Brief description of the drawings]

[0022] [Figure 1] 1 is an external view showing the overall configuration of a nondestructive component estimation device according to an embodiment of the present invention. [Diagram 2] 2 is a block diagram showing the internal configuration of the nondestructive component estimation device shown in FIG. 1. [Diagram 3] 1 is a graph for searching for an optimum wavelength region for estimating sugar content. [Figure 4] 13 is a graph showing the results of sugar content estimation. [Diagram 5] 1 is a graph showing the relationship between the average sugar content and the error. [Figure 6] 1 is an external view showing the overall configuration of a self-propelled robot according to an embodiment of the present invention; [Figure 7] 7 is a schematic diagram showing the configuration of the bottom side of the self-propelled robot shown in FIG. 6. [Figure 8] FIG. 2 is a block diagram showing the configuration of a control system of the self-propelled robot. [Figure 9] FIG. 2 is a schematic diagram illustrating the autonomous movement of a self-propelled robot. [Figure 10] 11 is a schematic diagram illustrating the sugar content estimation operation of a tomato by the nondestructive component estimating device; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] An embodiment of the present invention will now be described in detail with reference to the drawings.

[0024] (1) Configuration of the Nondestructive Component Estimation Device According to the Present Embodiment 1(A) and (B) show a nondestructive component estimation device 1 according to this embodiment, which has a main body 2 containing a control board and an electrical system for controlling the entire device, an optical detection unit 3 provided at the front end of the main body 2, and an imaging unit 4 provided on the upper side of the main body.

[0025] The optical detection unit 3 is composed of a pair of light sources 5A, 5B that irradiate the fruit with light, and a light detection section 6 that detects the scattered light generated from inside the fruit through the skin surface of the fruit. Each of the light sources 5A, 5B is a halogen lamp with a wavelength range of 350 [nm] to 3,500 [nm]. The light detection section 6 has a light detection capability of 598 [nm] to 1,047 [nm], and detects the scattered light obtained from inside the fruit.

[0026] The optical detection unit 3 has a contact detection section 10 next to the light detection section 6 that detects the contact state with the fruit, and an adhesive light-shielding section 11 made of an elastic material is provided to surround the light detection section 6 and the contact detection section 10.

[0027] The contact detection unit 10 is composed of a photoconductive element such as a cadmium sulfide (CdS) cell, and detects the contact state with the fruit. The adsorption / light shielding unit 11 has a structure (such as a suction cup structure) at the tip that allows it to be adsorbed to the skin surface of the fruit, and when it is adsorbed to the fruit, it covers the periphery of the light detection unit 6 and the contact detection unit 10 to shield them from external light.

[0028] In the optical detection unit 3, a pair of light sources 5A, 5B are arranged at positions sandwiching the adsorption light-shielding portion 11 (including the light detection portion 6 and the contact detection portion 10) from above and below, so as to be able to irradiate light onto the vicinity of the adsorption portion of the fruit that is adsorbed onto the adsorption light-shielding portion 11.

[0029] In addition, the pair of light sources 5A, 5B can irradiate a collective light by combining the light reflected by an internal mirror provided at the irradiation port of the housing of the optical detection unit 3, and can make the optical detection unit 3 itself as small as possible.

[0030] The imaging unit 4 is also composed of a target imaging section 20 that images the target to be attracted by the attraction and light shielding section 11, and a distance measuring section 21 that measures the distance to the target imaged by the target imaging section 20. The target imaging section 20 is made up of an imaging element such as a CMOS image sensor, and the distance measuring section 21 is made up of an optical displacement sensor. Note that the distance measuring section 21 may be of various types, such as an eddy current type, an ultrasonic type, or a laser focus type, other than an optical type, as long as it is a non-contact type distance measuring sensor.

[0031] 2, in the nondestructive component estimation device 1, a device control unit 30 and a storage unit 31 are provided as a control board for controlling the entire device in the main body unit 2. The device control unit 30 has functions as a spectroscopic measurement unit 32, an estimated model construction unit 33, and an internal state estimation unit .

[0032] In this embodiment, a tomato was used as the fruit, and an experiment was conducted on the case of estimating the sugar content as an internal component of the fruit.

[0033] The spectroscopic measurement unit 32 separates the scattered light detected by the light detection unit 6 and measures a spectrum that represents the light intensity distribution for each wavelength in the wavelength band of 598 [nm] to 1,047 [nm]. The memory unit 31 stores scattered light spectra corresponding to a plurality of fruits of the same kind but different from the fruit as a library.

[0034] The estimation model construction unit 33 constructs an estimation model that is trained to estimate the state of the internal components of the fruit by performing multivariate analysis of the correlation between the spectrum measured by the spectroscopic measurement unit 32 and multiple spectra stored in the library of the storage unit 31. This estimation model is formed by a supervised machine learning method, and various methods such as a neural network, a support vector machine, or a hidden Markovnikov may be applied.

[0035] For example, as shown in Figure 3, experiments showed that among the combinations of scattered light in the wavelength range of 598 nm to 1,047 nm, the wavelength band with the highest accuracy in estimating sugar content is 794 nm to 961 nm (the range included in frame F in the figure).

[0036] The internal state estimation unit 34 estimates the state of the internal components during the growth process of the fruit, based on the estimation model constructed by the estimation model construction unit 33. That is, the internal state estimation unit 34 evaluates the accuracy of the learning result by the supervised machine learning method by cross-validation.

[0037] Specifically, the internal state estimation unit 34 calculates a calibration curve by multiple regression analysis among multivariate analyses using the wavelength band 794 [nm] to 961 [nm], which has the highest estimation accuracy obtained in the above-mentioned Fig. 3. Note that, in addition to multiple regression analysis, various multivariate analysis methods such as principal component analysis, factor analysis, and cluster analysis may be applied.

[0038] Figure 4(A) shows the results of estimating the sugar content of 100 tomatoes of the same variety using an estimation model (using a wavelength range of 794 nm to 961 nm) created based on the scattered light spectra of 336 tomatoes (Frutica: product name). Figure 4(B) shows the relationship between the average sugar content and the error based on the results of Figure 4(A). As shown in Figure 4(B), the sugar content estimation accuracy is ±0.60 degrees.

[0039] Inside the main body 2, there is provided an internal battery 35 consisting of a secondary battery, and at the rear end of the main body 2, there is provided an arm mount (not shown) for detachably attaching the tip of the arm portion 44 of the self-propelled robot 40 (Figure 5).

[0040] This arm mount is provided with a power supply terminal that is conductive with built-in battery 35, and as necessary, power can be supplied from driving battery 76 (FIG. 7) on the self-propelled robot 40 side via this power supply terminal to charge built-in battery 35. Meanwhile, when nondestructive component estimation device 1 is attached to self-propelled robot 40, it is also possible to supply power directly from driving battery 76 on the self-propelled robot 40 side as operating power for the entire nondestructive component estimation device 1.

[0041] (2) Configuration of the Self-Propelled Robot According to the Present Embodiment 5 shows a self-propelled robot 40 according to this embodiment as a whole. Self-propelled robot 40 is a two-wheel drive mobile body that travels on a floor surface autonomously or in response to external operation, and includes a traveling base unit (traveling drive unit) 43 that drives and rotates a pair of drive wheels 41A, 41B simultaneously or independently to travel robot body 42 in a desired direction, and an arm support unit 45 that is connected to the upper part of traveling base unit 43 and supports the end of an arm unit 44 that has at least one joint mechanism.

[0042] 6, traveling base unit 43 is provided with a pair of omni-wheels 46A, 46B as front wheels, and an auxiliary wheel 47 is provided at the center of the rear end between a pair of drive wheels 41A, 41B, which are the rear wheels. This allows self-propelled robot 40 to travel in the front-to-rear direction by traveling base unit 43 simultaneously driving and rotating the pair of drive wheels 41A, 41B, but also to travel in either the left or right direction by independently rotating and moving the pair of drive wheels 41A, 41B, with each omni-wheel 46A, 46B following suit.

[0043] That is, a pair of drive wheels 41A, 41B are independently rotated by drive motors (e.g., in-wheel motors) 48A, 48B, respectively, and the self-propelled robot 40 moves forward and backward by the forward or backward rotation of drive wheels 41A, 41B, and moves right or left while moving forward by giving a difference in the forward rotation angle of drive wheels 41A, 41B. Also, by rotating drive wheels 41A, 41B in opposite directions, self-propelled robot 40 spins, i.e., changes direction at that position.

[0044] The arm support part 45 is supported by an end of the arm part 45 having at least one joint mechanism, which is rotatable about the vertical direction, with a base body part 50 connected to the upper part of the traveling base part 43 as a base. A tray 51 is fixed to the upper part of the traveling base part 43 at a position close to the connecting part of the arm support part 45.

[0045] A laser range sensor 52 and an RGB-D sensor 53 capable of three-dimensional scanning are provided on the front side (direction of travel) of the running base section 43, and multiple 3D distance image sensors 54 are arranged at predetermined intervals around the upper side in all directions, so as to detect obstacles diagonally forward and to the left and right.

[0046] Specifically, the laser range sensor 52 shines light on an object (obstacle) as seen from the installation position, receives the reflected light, and calculates the distance. By measuring the distance at regular angular intervals, it is possible to obtain fan-shaped distance information on a plane with a maximum range of 30 m and an angle of 240 degrees.

[0047] In addition to the RGB color camera function, the RGB-D sensor 53 also has a depth sensor that can measure the distance to an object (obstacle) as seen from the camera, and can perform 3D scanning of the object. This depth sensor is made of an infrared sensor, and captures an image of the object while projecting a single pattern of structured light onto the object, and calculates the depth of each point on the image by triangulation using the parameters.

[0048] For example, when a Kinect (registered trademark of Microsoft Corporation) is used as the RGB-D sensor 53, it is possible to capture an image with a horizontal field of view of 57 degrees, a vertical field of view of 43 degrees, and a sensor range of 1.2 m to 3.5 m, and the RGB image is 640 x 480 and the depth image is 320 x 240 pixels, both of which can be acquired at 30 frames per second.

[0049] The RGB-D sensor 53 is installed in the center of the top of the traveling base unit 43 because a height of 0.6 m to 1.8 m from the floor surface is required to ensure a vertical field of view.

[0050] The 3D distance image sensor 54 emits an LED pulse, measures the arrival time of the reflected light from the object in pixel units, and simultaneously calculates distance information to the object in pixel units by superimposing the acquired image information.

[0051] This 3D distance image sensor 54 is necessary as a complementary sensor for outdoor use because it has a more accurate detection capability than the above-mentioned RGB-D sensor 53 and a wider viewing angle than the laser range sensor 52. When Pixel Soleil (a product name of Nippon Signal Co., Ltd.), for example, is used as the 3D distance image sensor 54, it is possible to capture an image with a horizontal field of view of 72 degrees, a vertical field of view of 72 degrees, and a sensor range of 0.3 m to 4.0 m.

[0052] In the self-propelled robot 40 of the present invention, a laser range sensor 52, an RGB-D sensor 53, and a 3D distance image sensor 54 are used to realize SLAM (Simultaneous Localization and Mapping) technology, which estimates the robot's own position relative to the external environment and simultaneously creates an environmental map.

[0053] A self-propelled robot 40 using this SLAM technology can determine its own movement path and move autonomously within the environment by dynamically generating an environmental map that represents the three-dimensional positions of objects in the real space while estimating its own position with high accuracy.

[0054] An imaging camera 55 equipped with multiple super-wide-angle lenses is provided on the base main body 50 of the arm support part 45, so that images of the space in all directions (360 degrees) of the self-propelled robot 40 can be captured. A sound-collecting microphone and speaker (neither shown) are also mounted on the base main body 50 of the arm support part 45, so that sounds from the surrounding environment can be collected and speech or warning sounds can be emitted as necessary.

[0055] (3) Configuration of the Arm Section According to the Present Embodiment In the arm support portion 45, the arm portion 44 is constituted by a root main body portion 50 connected to the running base portion 43 as a base, a pivot support portion 60 which is rotatably connected to the root main body portion 50, a first upper arm portion 61 which is rotatably connected to the pivot support portion 60 in a direction perpendicular to the pivot support portion 60, a second upper arm portion 62 which is rotatably connected in the same direction as the first upper arm portion 61, a forearm portion 63 which is rotatably connected in a direction perpendicular to the rotation direction of the second upper arm portion 62, and a wrist portion 64 which is rotatably connected in a direction perpendicular to the rotation direction of the forearm portion 63.

[0056] That is, the arm support section 45 is configured such that an arm section 44 (rotating support section 60, first upper arm section 61, second upper arm section 62, forearm section 63, wrist section 64) having a multi-joint structure with five degrees of freedom is connected to a base main body section 50 so as to be rotatable about each axis (Axis A to Axis E) as a rotation center. The nondestructive component estimation device 1 (FIG. 1) is removably attached to the tip of the wrist section 64 constituting the arm section 44 as an end effector.

[0057] Specifically, the base main body portion 50 and the rotating support portion 60 are rotatably connected around the A axis, the rotating support portion 60 and the first upper arm portion 61 are rotatably connected around the B axis, the first upper arm portion 61 and the second upper arm portion 62 are rotatably connected around the C axis, the second upper arm portion 62 and the forearm portion 63 are rotatably connected around the D axis, and the forearm portion 63 and the wrist portion 64 are rotatably connected around the E axis.

[0058] The joint portions between the base main body portion 50 and the pivoting support portion 60, the joint portion between the pivoting support portion 60 and the first upper arm portion 61, the joint portion between the first upper arm portion 61 and the second joint portion 62, the joint portion between the second joint portion 62 and the forearm portion 63, and the joint portion between the forearm portion 63 and the wrist portion 64 are each provided with an actuator, for example a DC servo motor, and are rotated via a transmission mechanism not shown.

[0059] A connector (not shown) is formed at the tip of wrist 64 constituting arm 44, so that it can be electrically connected to an arm mount (not shown) formed on main body 2 of nondestructive component estimation device 1. When nondestructive component estimation device 1 is connected to arm 44, self-propelled robot 40 is configured so that nondestructive component estimation device 1 can be driven and controlled in conjunction with the operation of arm 44 as an end effector.

[0060] (4) Internal structure of the self-propelled robot 7 shows the internal circuit configuration of self-propelled robot 40. In self-propelled robot 40, which travels on a floor surface autonomously or in response to external operations, travel control unit 70, which is responsible for controlling the entire robot, is mainly composed of a microcomputer.

[0061] Under the control of the travel control unit 70, the travel drive unit 71 controls the left and right motor drivers 65A, 65B to control the rotation of the drive motors 48A, 48B, thereby driving the robot body 42 to travel at a desired speed in a desired direction on the floor surface.

[0062] The environmental map creation unit 72 estimates its own position relative to the driving environment based on driving route information from a target driving route memory unit 73, which stores preset driving route information, and the detection signals from the laser range sensor 52, the RGB-D sensor 53, and the 3D distance image sensor 54, and at the same time creates a two-dimensional or three-dimensional environmental map including the floor surface, while determining whether the driving route is appropriate or needs to be changed, and whether there are any driving obstacles.

[0063] For example, when a self-propelled robot 40 in a greenhouse (cultivation area) travels along a taught travel path, it determines whether or not it will come into contact with an obstacle such as a wall or stone steps, and stops just before coming into contact and changes its travel path in a direction along the travel path.

[0064] In fact, self-propelled robot 40 uses the above-mentioned SLAM technology to automatically create an environmental map of the target area within a vinyl greenhouse (cultivation area) in which robot body 42 can move. Specifically, self-propelled robot 40 creates an environmental map that represents the entire desired target area while setting a local map on a two-dimensional grid as an area that indicates the movement environment, based on distance information and angle information from the target object obtained from laser range sensor 52 and 3D distance image sensor 54.

[0065] At the same time, the distance traveled by the self-propelled robot 40 is calculated based on the rotation angle read from an encoder (not shown) corresponding to a pair of drive wheels 41A, 41B of the self-propelled robot 40, and the self-position is estimated from the distance traveled by the self-propelled robot 40 and the next location map, which is matched with the environmental map created up to that point. An actual environmental map M1 showing the entire target area is shown in Figure 8(A).

[0066] Furthermore, in self-propelled robot 40, the traveling environment of robot main body 42 is imaged by imaging camera (environment detection unit) 55, and object detection unit 74 detects a specific object in the traveling environment based on the image captured by imaging camera 55. Fig. 8(B) shows a state in which a specific object S1 in the traveling environment is superimposed on the environmental map M1 shown in Fig. 8(A) described above.

[0067] The travel control unit 70 also drives and controls the travel base unit 43 and the arm support unit 45, and controls the nondestructive component estimation device 1 attached as an end effector to the arm unit 44. When the nondestructive component estimation device 1 is connected to the arm unit 44 in this self-propelled robot 40, the nondestructive component estimation device 1 is controlled by the travel control unit 70 to fulfill its functions.

[0068] The self-propelled robot 40 also has a communication unit 75 that wirelessly communicates with an external information input device (not shown), and transmits environmental map data and receives data indicating the content of operational instructions from the information input device according to the control of the driving control unit 70.

[0069] Furthermore, the self-propelled robot 40 has a relatively large capacity built-in driving battery 76 consisting of a secondary battery or a capacitor, and by conductively connecting the charging terminals of the driving battery 76 to a power supply terminal provided on an external power supply stand (not shown), it is possible to supply power from a commercial power source via the power supply terminal to the driving battery 76 to charge it.

[0070] (5) Operation of the Self-Propelled Robot According to the Present Invention In this embodiment, the travel control unit 70 of the self-propelled robot 40 drives and controls the travel base unit 43 and the arm support unit 45 so that the self-propelled robot 40 moves to a position in front of a specific tomato tree that is to be measured among multiple tomato trees planted in a vinyl greenhouse (cultivation area), for example.

[0071] That is, the travel control unit 70 detects a specific fruit tree in the travel environment based on an image captured of the travel environment of the robot body 42, while estimating its own position with respect to the travel environment, and at the same time creates a two-dimensional or three-dimensional environmental map including the ground, and controls the travel drive unit 71 to move to the specific fruit tree on the environmental map. As a result, the self-propelled robot 40 can automatically move to the planting position of the desired tomato tree in the cultivation area.

[0072] Next, the travel control unit 70 controls the imaging unit 4 in the nondestructive component estimation device 1 and at the same time drives and controls the arm unit 44 to abut the adsorption and shading portion 11 of the optical detection unit 3 against each of the multiple fruits growing on a specific tomato tree (Figure 9).

[0073] That is, the travel control unit 70 images the fruit using the target imaging section 20 in the imaging unit 4 of the nondestructive component estimation device 1, and at the same time measures the distance to the fruit using the distance measuring section 21, while driving and controlling the arm section 44 to align the adsorbing and shading section 11 of the optical detection unit 3 with the fruit, and then adsorbs the adsorbing and shading section 11 to the surface of the fruit while detecting the contact state with the fruit using the contact detection section 10 of the optical detection unit 3 (Figure 10).

[0074] In this way, the self-propelled robot 40 is extremely effective when there are a large number of fruits on the fruit trees, as it improves work efficiency significantly compared to individual manual work.

[0075] Next, the travel control unit 70 controls the device control unit 30 of the nondestructive component estimation device 1 to irradiate light from a pair of light sources 5A, 5B onto the fruit and cause the light detection unit 6 to detect scattered light generated from inside the fruit through the surface of the fruit's skin.

[0076] The device control unit 30 of the nondestructive component estimation device 1 disperses the scattered light detected by the light detection unit 6, measures a spectrum representing the light intensity distribution for each wavelength, and constructs an estimation model trained to estimate the state of the internal components of the fruit by performing multivariate analysis of the correlation between the measured spectrum and multiple spectra stored in a library (memory unit 31), and estimates the state of the internal components during the fruit's growth process based on the constructed estimation model.

[0077] In this way, the non-destructive component estimation device 1 can estimate the state of the internal components during the development of the fruit with a relatively high degree of accuracy. In particular, by recording the changes in the internal components during the development of the fruit in chronological order, it becomes possible to contribute to research into improving the varieties of each fruit on the fruit tree so that they become even more valuable varieties based on the records.

[0078] Furthermore, for a specific fruit tree detected by the target detection section 74, the self-propelled robot 40 drives the arm section 44 to abut each of the multiple fruits growing on the tree with the suction light-shielding section 11 of the optical detection unit 3 in the non-destructive component estimation device 1, and sequentially assigns addresses to each of the fruits.The self-propelled robot 40 manages data representing the state of the internal components during the growth process of the fruit, estimated by the internal state estimation section 34 (device control section 30) of the non-destructive component estimation device 1, on an address-by-address basis.

[0079] As a result, the self-propelled robot 40 can individually manage the addresses of multiple fruits growing on a specific fruit tree, thereby chronologically storing the state of the internal components of each fruit as it grows.

[0080] (6) Other embodiments As described above, in this embodiment, the self-propelled robot 40 is a two-wheel drive type mobile body that can run autonomously or in response to external operation, but the present invention is not limited to this, and a four-wheel drive type may be used, and a wide variety of drive methods and numbers of wheels may be used as long as the robot can move while supporting the arm unit 44. Also, although the pair of front wheels are each omni-wheels, various other running mechanisms such as casters or caterpillar tracks may also be used.

[0081] In addition, in the above embodiment, a case has been described in which a multi-joint structure having five degrees of freedom is applied as the arm unit 44, but the present invention is not limited to this, and various arm units may be applied as long as they are made of a multi-joint mechanism having multiple degrees of freedom.

[0082] Furthermore, in the above embodiment, a tomato is used as the fruit, and sugar content is estimated as the internal component of the fruit, but the present invention is not limited to this, and various fruits other than tomatoes (apples, mandarins, etc.) may be used, and further, at least one of sourness, γ-aminobutyric acid, hardness, density, and color may be applied as the internal component of the fruit other than sugar content. As a result, the nondestructive component estimation device can confirm the correlation between the changes in the internal components during the growth process of the fruit, either individually or in combination. [Explanation of symbols]

[0083] 1...Non-destructive component estimation device, 2...Main body, 3...Optical detection unit, 4...Imaging unit, 5A, 5B...Light source, 6...Light detection unit, 10...Contact detection unit, 11...Absorption and light shielding unit, 20...Target imaging unit, 21...Distance measurement unit, 30...Device control unit, 31...Memory unit, 32...Spectroscopic spectrum measurement unit, 33...Estimation model construction unit, 34...Internal state estimation unit, 35...Built-in battery, 40...Self-propelled robot, 41A, 41B...Driving wheels, 42...Robot body, 43...Running base unit, 44...Arm unit, 45...Arm support unit, 46A, 46 B...Omni wheel, 47...Auxiliary wheels, 48A, 48B...Drive motor, 50...Base main body portion, 51...Tray, 52...Laser range sensor, 53...RGB-D sensor, 54...3D distance image sensor, 55...Imaging camera, 60...Rotation support portion, 61...First upper arm portion, 62...Second upper arm portion, 63...Forearm portion, 64...Wrist portion, 65A, 65B...Motor driver, 70...Driving control portion, 71...Driving drive portion, 72...Environmental map creation portion, 73...Target driving path memory portion, 74...Object detection portion, 75...Communication portion, 76...Driving battery.

Claims

1. A self-propelled robot equipped with a non-destructive component estimation device that estimates sugar content by shining light on fruit, and that moves on the ground autonomously or in response to external control, a travel drive unit that drives the robot body to travel in a desired direction on the ground at a desired speed; an arm support section that supports a base section of an arm section having a multi-joint mechanism with multiple degrees of freedom, and is connected to an upper section of the traveling drive section integrally with the arm section so as to be rotatable relative to the traveling drive section; a travel control unit that controls the driving of the travel drive unit and the arm support unit; Equipped with The non-destructive component estimation device comprises: a light detection unit that detects scattered light generated from inside the fruit through a skin surface of the fruit when light is irradiated onto the fruit; a contact detection unit provided adjacent to the light detection unit and configured to detect a contact state with the fruit; an adsorption / light shielding portion having a tip made of an elastic material capable of being adsorbed to a surface of the skin of the fruit, the adsorption / light shielding portion covering the periphery of the light detection portion and the contact detection portion when the adsorption / light shielding portion is adsorbed to the fruit, thereby shielding the light detection portion from external light; a light source that irradiates light so as to irradiate a vicinity of an adsorption portion of the fruit that is adsorbed to the adsorption light-shielding portion; an image pickup distance measurement unit that captures an image of an object to be attracted by the attraction and light blocking unit and simultaneously measures a distance to the object; a spectroscopic measurement unit that separates the scattered light detected by the light detection unit and measures a spectrum that represents a light intensity distribution for each wavelength; a library in which spectra of the scattered light corresponding to a plurality of fruits of the same kind but different from the fruit are stored; an estimation model constructing unit that constructs an estimation model that is trained to estimate a state of an internal component of the fruit by performing a multivariate analysis of correlation between the spectrum measured by the spectroscopic measurement unit and a plurality of the spectra stored in the library; an internal state estimation unit that estimates a state of an internal component during a growth process of the fruit based on the estimation model constructed by the estimation model construction unit; Equipped with The traveling control unit is Based on the image of the object to be attracted by the adsorbing and light-shielding part captured by the imaging distance measuring part and the measured distance to the object, the arm support part is rotated and each joint mechanism of the arm part is bent or extended to align the adsorbing and light-shielding part with the fruit to be attracted, and then the adsorbing and light-shielding part is caused to adhere to the outer surface of the fruit while detecting the contact state of the fruit by the contact detection part. A self-propelled robot characterized by the above.

2. An environment imaging unit that images an environment in which the robot body is traveling; an object detection unit that detects a specific fruit tree in the driving environment based on an image captured by the environmental imaging unit; an environmental map creation unit that estimates the vehicle's position relative to the driving environment and creates a two-dimensional or three-dimensional environmental map including the ground; the travel control unit controls the travel drive unit to move to a specific fruit tree detected by the target detection unit in the environmental map created by the environmental map creation unit, and then drives the arm unit to bring the adsorbing and shading units into contact with the plurality of fruits growing on the specific fruit tree, and sequentially assigns addresses to the fruits; The data representing the state of the internal components during the growth process of the fruit estimated by the internal state estimation unit is managed in units of the addresses.

2. The self-propelled robot according to claim 1 .

3. the estimation model construction unit uses a supervised machine learning method as the estimation model, The internal state estimation unit evaluates the accuracy of the learning result based on the supervised machine learning method by cross-validation.

3. The self-propelled robot according to claim 1 or 2.

4. The fruit is in a state where it is still attached to the fruit tree, The internal components of the fruit include at least one of sugar content, sourness, γ-aminobutyric acid, hardness, density, and color.

3. The self-propelled robot according to claim 1 or 2.

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