Control method of excavator capable of automatically detecting geological hardness

Abstract

The invention provides a control method of an excavator capable of automatically detecting geological hardness, which comprises the following steps of: rotating by taking a rotating axis of the excavator capable of automatically detecting geological hardness as a circle center, and acquiring a depth picture in a to-be-constructed range with a radius R through a depth image acquisition unit; constructing a three-dimensional terrain model of the to-be-constructed range by using the depth picture; according to the surface fluctuation data set and the obstacle distribution diagram in the three-dimensional terrain model, an initial nonlinear excavation track is generated, and the initial nonlinear excavation track comprises a plurality of sampling points arranged at intervals; an excavator capable of automatically detecting the geological hardness is controlled to carry out sampling along sampling points of the initial nonlinear excavation track clock, depth images of the sampling points are collected in real time through a depth image collection unit in the sampling process, and whether the hardness of the sampling points exceeds a set value or not is judged according to the depth images of the sampling points and marked; and finally, a three-dimensional terrain-geological hardness model in the to-be-constructed range is generated.

Description

Technical Field

[0001] The invention relates to the technical field of excavators, and in particular to a control method for an excavator capable of automatically detecting geological hardness. Background Art

[0002] In the existing excavator construction technology, manual or traditional equipment is usually required to conduct preliminary detection and evaluation of the geological conditions of the construction area. However, these methods have the following shortcomings: Low efficiency of manual detection: Traditional methods rely on manual sampling and testing, which is time-consuming and labor-intensive, and cannot cover large construction areas. Incomplete information: Traditional detection methods can usually only provide limited geological information, and it is difficult to fully understand key parameters such as surface undulations, obstacle distribution, and soil hardness in the construction area. In addition, manual detection has poor real-time performance: existing technologies cannot obtain geological information in real time during the construction process, resulting in the inability to dynamically adjust the construction plan according to actual conditions; in addition, manual detection is also insufficient in safety: in complex terrain or dangerous environments, manual detection has safety hazards and other problems. Summary of the invention

[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a control method for an excavator that can automatically detect geological hardness, so as to solve the problems mentioned in the above background technology section.

[0004] The present invention is achieved through the following technical solutions: The present invention provides a control method for an excavator capable of automatically detecting geological hardness, wherein the excavator capable of automatically detecting geological hardness comprises: a frame, a rotary excavation mechanism rotatably arranged on the frame, a depth image acquisition unit arranged around the frame, and a controller, wherein the controller is respectively connected to the rotary excavation mechanism and the depth image acquisition unit; The control method comprises the following steps: S1, rotating the excavator capable of automatically detecting geological hardness with its rotation axis as the center of a circle, and acquiring a depth image within a to-be-constructed area with a radius of R through the depth image acquisition unit; S2. constructing a three-dimensional terrain model of the area to be constructed using the depth image; S3, generating an initial nonlinear mining trajectory according to a surface undulation data set and an obstacle distribution map in a three-dimensional terrain model, wherein the initial nonlinear mining trajectory includes a plurality of sampling points arranged at intervals; S4, controlling the excavator capable of automatically detecting geological hardness to traverse the sampling points of the initial nonlinear excavation trajectory to perform sampling, collecting depth images of the sampling points in real time through the depth image acquisition unit during the sampling process, and judging whether the hardness of the sampling points exceeds the set value according to the depth images of the sampling points and marking them; S5. Finally, a three-dimensional terrain-geology hardness model within the scope to be constructed is generated.

[0005] The beneficial effects of the present invention are as follows: the present invention provides a control method for an excavator capable of automatically detecting geological hardness, which has the following beneficial effects: 1. Automated detection and modeling: Through the depth image acquisition unit and controller, it is possible to automatically obtain depth images of the construction area and build a three-dimensional terrain model including a surface undulation data set and an obstacle distribution map, significantly improving the efficiency of terrain assessment before construction.

[0006] 2. Real-time hardness detection: During the sampling process, by collecting the depth image of the sampling point in real time, it is possible to quickly determine whether the soil hardness exceeds the set value and mark it, providing real-time basis for adjusting the construction plan.

[0007] 3. Efficient sampling and coverage: By generating the initial nonlinear excavation trajectory and evenly distributing the sampling points, the entire construction area can be covered with the least number of sampling points, thus improving the sampling efficiency.

[0008] 4. Comprehensive model generation: The final three-dimensional terrain-geological hardness model contains surface undulations, obstacle distribution and soil hardness information, providing comprehensive data support for construction planning, equipment scheduling and safety management.

[0009] 5. Adapt to complex environments: Able to operate safely in complex terrain and dangerous environments, reduce human intervention and reduce construction risks.

[0010] Through the above-mentioned beneficial effects, the present invention can significantly improve the intelligence level of the excavator in a complex construction environment and provide technical guarantee for efficient and safe construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings illustrate exemplary embodiments of the present invention and together with the description serve to explain the principles of the present invention. These drawings are included to provide a further understanding of the present invention and are incorporated in and constitute a part of this specification.

[0012] Figure 1 A schematic diagram of path planning for a control method of an excavator capable of automatically detecting geological hardness provided by an embodiment of the present invention; Figure 2 is a structural schematic diagram of an excavator provided in Embodiment 1 of the present invention; Figure 3 is a schematic diagram of a digging arm swing structure in an excavator provided in Example 1 of the present invention; Figure 4 is an exploded schematic diagram of a digging arm swing structure in an excavator provided in Example 1 of the present invention; Figure 5 It is a structural schematic diagram of another excavator arm swing structure provided in Embodiment 2 of the present invention installed on the excavator; Figure 6 It is a schematic structural diagram of the swing of the digging arm in another excavator provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0014] Reference Figure 1 As shown, an embodiment of the present invention provides a control method for an excavator capable of automatically detecting geological hardness, wherein the excavator capable of automatically detecting geological hardness comprises: a frame 6, a rotary excavation mechanism 1 rotatably arranged on the frame 6, a depth image acquisition unit 7 arranged around the frame 6, and a controller (not shown in the figure), wherein the controller is respectively connected to the rotary excavation mechanism 1, the frame 6, and the depth image acquisition unit 7. The depth image acquisition unit 7 can be arranged on the top of the rotary excavation mechanism 1, so that a global field of view can be obtained, and only one group needs to be arranged to scan all areas within the construction range. The frame 6 is not limited to a tracked vehicle, but can also be a wheeled vehicle, which is not limited here.

[0015] The control method comprises the following steps: S1, rotating the excavator capable of automatically detecting geological hardness with its rotation axis as the center of a circle, and obtaining a depth image within a to-be-constructed area with a radius of R through the depth image acquisition unit 7; S2. constructing a three-dimensional terrain model of the area to be constructed using the depth image; S3, generating an initial nonlinear excavation trajectory according to the surface undulation data set and the obstacle 10 distribution map in the three-dimensional terrain model, wherein the initial nonlinear excavation trajectory includes a plurality of sampling points set at intervals; S4, controlling the excavator capable of automatically detecting geological hardness to traverse the sampling points of the initial nonlinear excavation trajectory to perform sampling, during the sampling process, the depth image acquisition unit 7 acquires the depth image of the sampling point in real time, and determines whether the hardness of the sampling point exceeds the set value according to the depth image of the sampling point and marks it; S5. Finally, a three-dimensional terrain-geology hardness model within the scope to be constructed is generated.

[0016] In step S1, after the depth image acquisition unit 7 acquires the depth image within the to-be-constructed area with a radius of R, the method may further include: stitching the depth image within the to-be-constructed area to form a complete construction site image.

[0017] In other embodiments, as a further improvement, in step S2, the step of constructing a three-dimensional terrain model of the area to be constructed using the depth image specifically includes: Processing the depth image to remove noise; Converting the depth image into a three-dimensional point cloud; Interpolating the three-dimensional point cloud to obtain a ground height function h(x, y); Detect the obstacle 10 according to the height threshold τ, and represent the information of the obstacle 10 as a binary matrix O; Output 3D terrain model: .

[0018] The converting the depth image into a three-dimensional point cloud specifically includes: Define the resolution of the depth image as W×H, and each pixel (u, v) corresponds to a depth value d u，v , and the depth value d u，v Indicates the distance from the pixel to the camera.

[0019] The pixel coordinates (u, v) are converted into three-dimensional world coordinates (x, y, z) using the intrinsic matrix K in the depth image acquisition unit 7, wherein the intrinsic matrix K is: ; Among them: f x , f y is the focal length of the camera (in pixels), c x , c y are the pixel coordinates of the camera principal point, respectively.

[0020] Therefore, for each pixel (u, v) in the depth image, its corresponding three-dimensional coordinates (x, y, z) can be calculated by the following formula: .

[0021] Convert each pixel (u, v) in the depth image to a three-dimensional coordinate (x, y, z) to obtain a sparse three-dimensional point cloud: .

[0022] As a further improvement, in order to generate a continuous undulating dataset of the ground surface, the point cloud P may be interpolated to generate a continuous three-dimensional terrain model, in which the height of the ground surface at position (x, y) may be represented by a surface height function h(x, y).

[0023] For the detection of obstacle 10: Each point (x, y, z) in the point cloud P can be marked as an obstacle if its height z is significantly higher than the ground height h(x, y): ; Wherein, τ is a height threshold used to distinguish between the ground surface and obstacles10, and its specific value can be defined according to actual needs.

[0024] Furthermore, the distribution map of the obstacles 10 can be represented by a binary matrix O: ; Among them, O i，j =1 indicates position (x i ,y j ) There is an obstacle at O i，j =0 indicates no obstacle. In one embodiment, three obstacles 10-1, 10-2, and 10-3 are included.

[0025] Finally, the three-dimensional terrain model including the surface undulation data set and obstacle distribution map is obtained as follows: .

[0026] As a further improvement, in other embodiments, in step S3, further comprising: The plurality of sampling points arranged at intervals are evenly distributed according to the arm span length of the excavator arm. The advantage of such arrangement is that the scope to be constructed can be covered to the maximum extent with the least number of sampling points.

[0027] As a further improvement, in other embodiments, in step S4, the step of controlling the excavator capable of automatically detecting geological hardness to traverse the sampling points of the initial nonlinear excavation trajectory to perform sampling specifically includes: At each sampling point, the excavator capable of automatically detecting geological hardness rotates with the rotation axis as the center of the circle, the arm span length as the radius, and sampling and excavation are performed at a predetermined arc α, wherein 90°≥α≥30°. In other embodiments, the arc α may be 30°, 40°, 45°, 50°, 55°, 60°, 75°, or 90°. It can be understood that the smaller the arc, the denser the number of sampling points, and the more accurate the geological conditions of the construction range can be fed back. In this embodiment, the arc α is 90°, that is, three sampling and excavation are performed at one sampling point.

[0028] As a further improvement, in other embodiments, in step S4, the step of acquiring the depth image of the sampling point in real time by the depth image acquisition unit 7 during the sampling process, and judging whether the hardness of the sampling point exceeds the set value and marking it according to the depth image of the sampling point specifically includes: Obtaining three-dimensional point cloud data of two depth images of the sampling point before and after sampling; It is determined whether the difference of the three-dimensional point cloud data exceeds the set value. If yes, it is determined that the hardness of the sampling point does not exceed the set value; otherwise, it is determined that the hardness of the sampling point exceeds the set value.

[0029] Specifically, the depth image before sampling is defined as I before（x，y） , where each pixel (x, y) corresponds to a depth value d before，x，y ; Depth image after sampling: represented as I after（x，y） , where each pixel (x, y) corresponds to a depth value d after，x，y .

[0030] Before acquiring 3D point cloud data, the depth images before and after sampling can be denoised to reduce the impact of noise on subsequent calculations: ; where σ is the standard deviation of the Gaussian filter.

[0031] Furthermore, the depth value is normalized to the range [0, 1]: ; Among them, min (I before，filtered )、max(I before，filtered ) are the minimum and maximum values ​​of denoising before sampling; min(I aftee，filtered )、max(I aftee，filtered ) are the minimum and maximum values ​​of denoising after sampling.

[0032] The three-dimensional point cloud data of the two depth images before and after the sampling point can be obtained by the above method. For each pixel (x, y), the three-dimensional coordinates before and after the sampling are calculated: ; where d norm is the normalized depth value.

[0033] Furthermore, the difference between the 3D point cloud before and after sampling is calculated as follows: .

[0034] Furthermore, the difference between the three-dimensional point clouds before and after the sampling is normalized: .

[0035] Then, the soil hardness is determined by the formula as follows: ; Wherein, σ is the set threshold, Hardness Flag (x, y) = 0, indicating that the hardness does not exceed the set value, and Hardness Flag (x, y) = 1, indicating that the hardness exceeds the set value. It can be understood that when the difference between the three-dimensional point cloud before and after sampling is large and exceeds a certain threshold, it means that the sampling point has changed greatly before and after being excavated by an excavator that can automatically detect geological hardness, resulting in obvious differences in point cloud data; and for hard soil, it means that the sampling point has changed little before and after being excavated by an excavator that can automatically detect geological hardness, resulting in no obvious difference in point cloud data. In other embodiments, if there is almost no change before and after the excavator excavates, resulting in no difference in point cloud data, this may indicate that the sampling point may be a hard rock area.

[0036] Finally, the three-dimensional terrain-geological hardness model including surface undulation data set, obstacle distribution and soil hardness information is obtained as follows: .

[0037] In other embodiments, the method may further include: The difference between the 3D point cloud before and after sampling is divided into multiple change levels, so as to make a preliminary classification of the soil hardness level. The advantage of this is that it can make a preliminary judgment on the overall soil environment of the construction area and provide certain guidance for the safety of subsequent construction.

[0038] In other embodiments, a vibration sensor may be further provided, and the vibration sensor may be provided on the digging arm to detect abnormal vibration signals during the digging process. It is understood that when the sampling point is a hard rock area, the vibration feedback received by the digging arm is obviously completely different from that of other soil types. By judging from the vibration feedback and point cloud data, it can be roughly judged as a hard rock area, thereby avoiding mechanical damage to the digging arm and the driving cylinder caused by secondary excavation.

[0039] like Figures 2~4As shown, the excavator also includes an excavating arm swinging structure, which has a fixed arm seat 2 and a boom seat 3 which are arranged on the rotary excavating mechanism 1 and cooperate with it. The fixed arm seat 2 is fixedly connected to the rotary excavating mechanism 1, and the fixed arm seat 2 and the boom seat 3 are hinged. The excavating arm is installed on the boom seat 3. In this way, when the rotary excavating mechanism 1 rotates, it can rotate with the excavating arm and cooperate with the extension of the excavating arm to perform operations. Furthermore, when operations are required on slopes, ditches or uneven ground, in order to make the bucket effectively fit the working surface and avoid the phenomenon of "empty digging" or "biased digging", the excavating arm can perform more refined operations, such as automatically detecting geological hardness.

[0040] The excavating arm swing structure also includes a connecting plate 4 arranged at the bottom of the boom seat 3, and the connecting plate 4 and the bottom of the boom seat 3 are detachably connected. Specifically, matching fixing holes 42 are opened at the bottom of the connecting plate 4 and the boom seat 3, and the connecting plate 4 and the boom seat 3 are bolted; a connecting ear 41 is fixedly arranged on the side of the connecting plate 4, and a telescopic mechanism 5 cooperating with the connecting ear 41 is also arranged at the bottom of the rotary excavating mechanism 1. Since the telescopic mechanism 5 does not bear the load-bearing function and is only responsible for pushing the excavating arm to rotate left and right, the telescopic mechanism 5 can be one of a hydraulic cylinder, a pneumatic push rod or an electric push rod. The tail of the telescopic mechanism 5 is rotatably connected to the bottom of the rotary excavating mechanism 1, and the telescopic end of the telescopic mechanism 5 is hinged to the connecting ear 41; in this way, when in use, the connecting plate 4, the connecting ear 41 and the telescopic mechanism 5 cooperating with the rotary excavating mechanism 1 and the boom seat 3 are prefabricated first. In order to facilitate the cooperation between the connecting ear 41 and the boom seat 3 and facilitate the pushing of the telescopic mechanism 5, the height of the connecting ear 41 is higher than the connecting plate 4. After the connecting plate 4 and the boom seat 3 are fixed, the side of the connecting ear 41 is in contact with the side of the boom seat 3; at the same time, a locking piece 411 is also provided on the connecting ear 41, and a lubrication port 412 is also provided on the locking piece 41 to lubricate the hinge between the telescopic end of the telescopic mechanism 5 and the connecting ear 41. During installation, the combination of the connecting plate 4 and the connecting ear 41 is fixed to the bottom of the boom seat 3 by bolts, and then the tail of the telescopic mechanism 5 is rotatably connected to the bottom of the rotary excavation mechanism 1, and the telescopic end of the telescopic mechanism 5 is hinged to the connecting ear 41. In this way, when the telescopic end of the telescopic mechanism 5 is extended and retracted, the boom seat 3 can be driven to rotate left and right at a small angle along the fixed arm seat 2, so that the excavating arm can be driven by the excavator rotary excavation mechanism 1 to rotate 360 ​​degrees. When the telescopic mechanism 5 is extended and retracted, it can also be further rotated left and right at a small angle.

[0041] like Figures 5 and 6As shown, in other embodiments, in order to enhance structural stability and reduce the requirements for the telescopic mechanism 5, two connecting ears 41 are provided, and the two connecting ears 41 are respectively fixed on both sides of the connecting plate 4. Correspondingly, it is also necessary to add a corresponding telescopic mechanism 5 to the connecting ear 41 on the other side. In this way, through the symmetrical arrangement, it can be made more stable when swinging left and right.

[0042] In the description of the present invention, it is to be understood that the terms “center”, “longitudinal”, “lateral”, “length”, “width”, “thickness”, “up”, “down”, “front”, “back”, “left”, “right”, “vertical”, “horizontal”, “top”, “bottom”, “inside”, “outside”, “clockwise”, “counterclockwise”, “axial”, “radial”, “circumferential”, etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0043] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0044] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A control method for an excavator capable of automatically detecting geological hardness, characterized in that: The excavator capable of automatically detecting geological hardness comprises: a frame, a rotary excavation mechanism rotatably arranged on the frame, a depth image acquisition unit arranged around the frame, and a controller, wherein the controller is respectively connected to the rotary excavation mechanism and the depth image acquisition unit; The control method comprises the following steps: S1, rotating the excavator capable of automatically detecting geological hardness with its rotation axis as the center of a circle, and acquiring a depth image within a to-be-constructed area with a radius of R through the depth image acquisition unit; S2. constructing a three-dimensional terrain model of the area to be constructed using the depth image; S3, generating an initial nonlinear mining trajectory according to a surface undulation data set and an obstacle distribution map in a three-dimensional terrain model, wherein the initial nonlinear mining trajectory includes a plurality of sampling points arranged at intervals; S4, controlling the excavator capable of automatically detecting geological hardness to traverse the sampling points of the initial nonlinear excavation trajectory to perform sampling, during the sampling process, the depth image acquisition unit acquires the depth image of the sampling point in real time, and determines whether the hardness of the sampling point exceeds the set value according to the depth image of the sampling point and marks it; S5. Finally, a three-dimensional terrain-geology hardness model within the scope to be constructed is generated.

2. The control method of an excavator capable of automatically detecting geological hardness according to claim 1, characterized in that: In step S2, the step of constructing a three-dimensional terrain model of the area to be constructed using the depth image specifically includes: Processing the depth image to remove noise; Converting the depth image into a three-dimensional point cloud; Interpolating the three-dimensional point cloud to obtain a ground height function h(x, y); Detect obstacles according to the height threshold τ and represent the obstacle information as a binary matrix O; Output 3D terrain model: .

3. The control method of an excavator capable of automatically detecting geological hardness according to claim 1, characterized in that: In step S3, it further includes: The plurality of sampling points arranged at intervals are evenly distributed according to the arm span length of the excavating arm of the excavator.

4. The control method of an excavator capable of automatically detecting geological hardness according to claim 3, characterized in that: In step S4, the step of controlling the excavator capable of automatically detecting geological hardness to traverse the sampling points of the initial nonlinear excavation trajectory to perform sampling specifically includes: At each sampling point, the excavator capable of automatically detecting geological hardness rotates with its rotation axis as the center of the circle, with the arm span length as the radius, and sampling and excavation are performed at intervals of a predetermined arc α, wherein 90°≥α≥30°.

5. The control method of an excavator capable of automatically detecting geological hardness according to claim 3, characterized in that: In step S4, the step of acquiring the depth image of the sampling point in real time by the depth image acquisition unit during the sampling process, and judging whether the hardness of the sampling point exceeds the set value and marking it according to the depth image of the sampling point specifically includes: Obtaining three-dimensional point cloud data of two depth images of the sampling point before and after sampling; It is determined whether the difference of the three-dimensional point cloud data exceeds the set value. If yes, it is determined that the hardness of the sampling point does not exceed the set value; otherwise, it is determined that the hardness of the sampling point exceeds the set value.

6. The control method of an excavator capable of automatically detecting geological hardness according to claim 1, characterized in that: It may further include: The differences between the three-dimensional point clouds before and after sampling are divided into multiple change levels, thereby making a preliminary classification of the soil hardness levels.

7. The control method of an excavator capable of automatically detecting geological hardness according to claim 1, characterized in that: The step S2 specifically includes: S21, converting the depth image into a three-dimensional point cloud, which specifically includes: Define the resolution of the depth image as W×H, and each pixel (u, v) corresponds to a depth value d u，v , and the depth value d u，v Indicates the distance from the pixel to the camera; the pixel coordinates (u, v) are converted into three-dimensional world coordinates (x, y, z) using the intrinsic matrix K in the depth image acquisition unit, where the intrinsic matrix K is: ; Among them: f x , f y is the focal length of the camera (in pixels), c x , c y are the pixel coordinates of the camera principal point, respectively; For each pixel (u, v) in the depth image, its corresponding three-dimensional coordinates (x, y, z) are calculated using the following formula: ； Convert each pixel (u, v) in the depth image to a three-dimensional coordinate (x, y, z) to obtain a sparse three-dimensional point cloud: ； Performing interpolation operation on the sparse three-dimensional point cloud to generate a continuous three-dimensional terrain model, in which the height of the ground surface at the position (x, y) is represented by a ground surface height function h(x, y); S22, obstacle detection includes the following steps: Mark each point (x, y, z) in the point cloud P as an obstacle according to the following formula: ; where τ is a height threshold used to distinguish between the ground and obstacles; the distribution map of the obstacles is represented by a binary matrix O: ; Among them O i，j =1 indicates position (x i ,y j ) There is an obstacle at O i，j =0 means no obstacle; Finally, the three-dimensional terrain model including the surface undulation data set and obstacle distribution map is obtained as follows: 。

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