Control method for excavator capable of automatically detecting geological hardness

Abstract

The present invention provides a control method for an excavator capable of automatically detecting geological hardness, comprising: rotating the excavator with its rotation axis as the center of a circle, acquiring a depth image of a construction range with a radius R through a depth image acquisition unit; constructing a three-dimensional terrain model of the construction range using the depth image; generating an initial nonlinear excavation trajectory based on a surface undulation dataset and an obstacle distribution map in the three-dimensional terrain model, wherein the initial nonlinear excavation trajectory includes a plurality of sampling points set at intervals; controlling the excavator capable of automatically detecting geological hardness to traverse the sampling points along the initial nonlinear excavation trajectory for sampling, acquiring depth images of the sampling points in real time during the sampling process through the depth image acquisition unit, and judging whether the hardness of the sampling points exceeds a set value based on the depth images of the sampling points and marking the values; and finally generating a three-dimensional terrain-geological hardness model of the construction range.

Description

Technical Field

[0001] The present 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 existing excavator construction technology, manual or traditional equipment is usually required to conduct preliminary detection and assessment 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, making it difficult to fully understand key parameters such as the surface undulations, obstacle distribution, and soil hardness of 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 unsafe: 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 propose 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:

[0005] The present invention provides a control method for an excavator capable of automatically detecting geological hardness. 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;

[0006] The control method comprises the following steps:

[0007] S1. Rotate the excavator capable of automatically detecting geological hardness with its rotation axis as the center of a circle, and obtain a depth image of the area to be constructed with a radius of R through the depth image acquisition unit;

[0008] S2. constructing a three-dimensional terrain model of the area to be constructed using the depth image;

[0009] S3. generating an initial nonlinear excavation trajectory based on a surface relief dataset and an obstacle distribution map in the three-dimensional terrain model, wherein the initial nonlinear excavation trajectory includes a plurality of sampling points set at intervals;

[0010] S4, controlling the excavator capable of automatically detecting geological hardness to traverse the sampling points along 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 determining whether the hardness of the sampling points exceeds a set value based on the depth images of the sampling points and marking the hardness of the sampling points;

[0011] S5. Finally, a three-dimensional terrain-geology hardness model within the scope to be constructed is generated.

[0012] The beneficial effects of the present invention are as follows: the present invention provides a control method for an excavator that can automatically detect geological hardness, which has the following beneficial effects:

[0013] 1. Automated detection and modeling: Through the depth image acquisition unit and controller, it can automatically obtain depth images of the construction area and construct a three-dimensional terrain model including surface undulation datasets and obstacle distribution maps, significantly improving the efficiency of terrain assessment before construction.

[0014] 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 a real-time basis for adjusting the construction plan.

[0015] 3. Efficient sampling and coverage: By generating the initial nonlinear excavation trajectory and evenly distributing sampling points, the entire construction area can be covered with the least number of sampling points, thereby improving sampling efficiency.

[0016] 4. Comprehensive model generation: The final generated 3D terrain-geology hardness model includes surface undulations, obstacle distribution, and soil hardness information, providing comprehensive data support for construction planning, equipment scheduling, and safety management.

[0017] 5. Adaptability to complex environments: Ability to operate safely in complex terrain and dangerous environments, reduce human intervention and lower construction risks.

[0018] Through the above 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

[0019] The accompanying drawings illustrate exemplary embodiments of the invention and together with the description serve to explain the principles of the invention. These drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification.

[0020] Figure 1 A schematic diagram of path planning for a control method for an excavator capable of automatically detecting geological hardness provided by an embodiment of the present invention;

[0021] Figure 2 This is a schematic structural diagram of an excavator provided in Example 1 of the present invention;

[0022] Figure 3 is a schematic diagram of a swing structure of an excavating arm in an excavator provided in Example 1 of the present invention;

[0023] Figure 4 This is an exploded schematic diagram of a digging arm swing structure in an excavator provided in Example 1 of the present invention;

[0024] Figure 5 This is a schematic structural diagram of another excavator swing structure provided in Example 2 of the present invention, in which the excavator arm swing structure is installed on the excavator;

[0025] Figure 6 It is a structural schematic diagram of the swinging of the digging arm in another excavator provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. It should be noted that the description of these embodiments is intended to help understand the present invention, but does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0027] Reference Figure 1 As shown, an embodiment of the present invention provides a control method for an excavator capable of automatically detecting geological hardness. The excavator capable of automatically detecting geological hardness includes: a vehicle frame 6, a rotary excavation mechanism 1 rotatably disposed on the vehicle frame 6, a depth image acquisition unit 7 disposed around the vehicle frame 6, and a controller (not shown in the figure), wherein the controller is respectively connected to the rotary excavation mechanism 1, the vehicle frame 6, and the depth image acquisition unit 7. The depth image acquisition unit 7 can be disposed on top of the rotary excavation mechanism 1 so as to obtain a global field of view, and only one set is required to scan all areas within the construction range. The vehicle frame 6 is not limited to a tracked vehicle, but may also be a wheeled vehicle, and this is not limited here.

[0028] The control method comprises the following steps:

[0029] 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 of the area to be constructed with a radius of R through the depth image acquisition unit 7;

[0030] S2. constructing a three-dimensional terrain model of the area to be constructed using the depth image;

[0031] S3, generating an initial nonlinear excavation trajectory based on the surface relief dataset 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;

[0032] S4, controlling the excavator capable of automatically detecting geological hardness to traverse the sampling points along the initial nonlinear excavation trajectory to perform sampling, collecting depth images of the sampling points in real time through the depth image acquisition unit 7 during the sampling process, and determining whether the hardness of the sampling points exceeds a set value based on the depth images of the sampling points and marking the hardness of the sampling points;

[0033] S5. Finally, a three-dimensional terrain-geology hardness model within the scope to be constructed is generated.

[0034] In step S1, after the depth image acquisition unit 7 acquires the depth image within the scope to be constructed with a radius of R, the method may further include: stitching the depth image within the scope to be constructed to form a complete construction site image.

[0035] 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:

[0036] Processing the depth image to remove noise;

[0037] Converting the depth image into a three-dimensional point cloud;

[0038] Interpolating the three-dimensional point cloud to obtain a surface height function h(x, y);

[0039] Detect the obstacle 10 according to the height threshold τ and represent the obstacle 10 information as a binary matrix O;

[0040] Output 3D terrain model: .

[0041] The converting of the depth image into a three-dimensional point cloud specifically includes:

[0042] The resolution of the depth image is defined 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.

[0043] The pixel coordinates (u, v) are converted into three-dimensional world coordinates (x, y, z) using the intrinsic parameter matrix K in the depth image acquisition unit 7, wherein the intrinsic parameter matrix K is:

[0044] ; Among them: fx , f y is the focal length of the camera, c x , c y are the pixel coordinates of the camera principal point respectively.

[0045] Therefore, for each pixel (u, v) in the depth image, its corresponding three-dimensional coordinates (x, y, z) can be calculated using the following formula:

[0046] .

[0047] 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:

[0048] .

[0049] As a further improvement, in order to generate a continuous relief dataset of the surface, the point cloud P can be interpolated to generate a continuous three-dimensional terrain model, in which the height of the surface at the position (x, y) can be represented by the surface height function h(x, y).

[0050] For the detection of obstacle 10:

[0051] 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):

[0052] ; where τ is a height threshold used to distinguish between the ground and obstacles10, and its specific value can be defined according to actual needs.

[0053] Furthermore, the distribution map of the obstacles 10 can be represented by a binary matrix O:

[0054] ; 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.

[0055] The final three-dimensional terrain model including the surface relief dataset and obstacle distribution map is as follows:

[0056] .

[0057] As a further improvement, in other embodiments, in step S3, the following steps are further included:

[0058] The plurality of sampling points arranged at intervals are evenly distributed along the arm span length of the excavator. The advantage of such arrangement is that the area to be constructed can be covered to the maximum extent with the least number of sampling points.

[0059] 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 along the initial nonlinear excavation trajectory to perform sampling specifically includes:

[0060] At each sampling point, the excavator capable of automatically detecting geological hardness rotates around its axis of rotation, with its arm span as the radius, and sampling and excavation are performed at intervals of a predetermined arc α, where 90° ≥ α ≥ 30°. In other embodiments, the arc α can be 30°, 40°, 45°, 50°, 55°, 60°, 75°, or 90°. It will be appreciated that a smaller arc α results in a denser number of sampling points, providing more accurate feedback on the geological conditions of the area to be constructed. In this embodiment, the arc α is 90°, meaning that three sampling and excavation runs are performed at each sampling point.

[0061] As a further improvement, in other embodiments, in step S4, the step of acquiring a depth image of the sampling point in real time by the depth image acquisition unit 7 during the sampling process, and determining whether the hardness of the sampling point exceeds a set value and marking it according to the depth image of the sampling point specifically includes:

[0062] Obtaining three-dimensional point cloud data of two depth images of the sampling point before and after sampling;

[0063] It is determined whether the difference in the three-dimensional point cloud data exceeds a set value. If so, 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.

[0064] 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 .

[0065] 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:

[0066] ; where σ is the standard deviation of the Gaussian filter.

[0067] Furthermore, the depth value is normalized to the range [0, 1]:

[0068] ; 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 the denoised image after sampling.

[0069] 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:

[0070] ; Among them, d norm is the normalized depth value.

[0071] Furthermore, the difference between the 3D point cloud before and after sampling is calculated as follows:

[0072] .

[0073] Furthermore, the difference between the three-dimensional point clouds before and after sampling is normalized:

[0074] .

[0075] Then, the soil hardness is determined by the formula as follows:

[0076] ; Wherein, σ is the set threshold value, 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 significantly 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 differences 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.

[0077] The final three-dimensional terrain-geology hardness model including surface relief dataset, obstacle distribution, and soil hardness information is as follows:

[0078] .

[0079] In other embodiments, the method may further include:

[0080] The differences between the 3D point clouds before and after sampling are divided into multiple levels of variation, thereby providing a preliminary classification of soil hardness. This has the advantage of providing a preliminary assessment of the overall soil environment in the construction area, providing guidance for subsequent construction safety.

[0081] In other embodiments, a vibration sensor can be further added to the excavation arm to detect abnormal vibration signals during excavation. It is understood that when the sampling point is in a hard rock area, the vibration feedback received by the excavation arm is obviously different from that of other soil types. Based on the vibration feedback and point cloud data, it can be roughly determined that the area is hard rock, thereby avoiding mechanical damage to the excavation arm and drive cylinder caused by secondary excavation.

[0082] like Figures 2-4 As shown, the excavator also includes an excavating arm swing structure, which has a fixed arm seat 2 and a boom seat 3 arranged on the rotary excavating mechanism 1 and matched therewith. 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 on slopes, ditches or uneven ground are required, 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.

[0083] 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, the connecting plate 4 and the bottom of the boom seat 3 are provided with matching fixing holes 42, and the connecting plate 4 and the boom seat 3 are bolted; a connecting ear 41 is fixedly provided on the side of the connecting plate 4, and the bottom of the rotary excavating mechanism 1 is also provided with a telescopic mechanism 5 that cooperates with the connecting ear 41. 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 that cooperate 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 to 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 surface of the connecting ear 41 is in contact with the side surface of the boom seat 3; at the same time, a locking piece 411 is provided on the connecting ear 41, and a lubrication port 412 is 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 excavating 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 along the fixed arm seat 2 at a small angle, so that the excavating arm can be driven to rotate 360 degrees by the excavator rotary excavating mechanism 1. It can also be further offset to the left and right by a small angle when the telescopic mechanism 5 is extended and retracted; in this way, a relatively simple structure can provide the excavating arm with the function of swinging left and right, and it can also be conveniently installed on the existing excavator arm seat.

[0084] like Figures 5 and 6 As 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, a corresponding telescopic mechanism 5 is also required to be added 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.

[0085] In the description of the present invention, it should 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" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0086] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include one or more of the features.

[0087] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection 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 vehicle frame, a rotary excavation mechanism rotatably arranged on the vehicle frame, a depth image acquisition unit arranged around the vehicle 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. Rotate the excavator capable of automatically detecting geological hardness with its rotation axis as the center of a circle, and obtain a depth image of the area to be constructed 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 excavation trajectory based on a surface relief dataset and an obstacle 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 along 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 determining whether the hardness of the sampling points exceeds a set value based on the depth images of the sampling points and marking the hardness of the sampling points; The step of acquiring a depth image of the sampling point in real time by the depth image acquisition unit during the sampling process, and determining whether the hardness of the sampling point exceeds a set value and marking the hardness 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; Determine whether the difference in the three-dimensional point cloud data exceeds a set value, if yes, determine that the hardness of the sampling point does not exceed the set value; otherwise, determine that the hardness of the sampling point exceeds the set value; S5. Finally, a three-dimensional terrain-geology hardness model within the scope to be constructed is generated.

2. The control method for 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 surface 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 for 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 are arranged at intervals and are evenly distributed along the arm span length of the excavator's excavating arm.

4. The control method for 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 axis of rotation as the center of the circle and its arm span as the radius, and sampling and excavation are performed at intervals of a predetermined arc α, wherein 90°≥α≥30°.

5. The control method for 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.

6. The control method for 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: The resolution of the depth image is defined 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 to three-dimensional world coordinates (x, y, z) using the intrinsic parameter matrix K in the depth image acquisition unit, where the intrinsic parameter matrix K is: ; Among them: f x , f y is the focal length of the camera, c x , c y are the pixel coordinates of the camera principal point; 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 an interpolation operation on the sparse 3D point cloud to generate a continuous 3D terrain model, in which the height of the ground at the position (x, y) is represented by a ground 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: ; where O i，j =1 indicates position (x i ,y j ) There is an obstacle at O i，j =0 means no obstacle; The final three-dimensional terrain model including the surface relief dataset and obstacle distribution map is as follows: 。

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