A method, device, bulldozer and readable storage medium for detecting a scarp

Abstract

The application discloses a cliff detection method and device, a bulldozer and a readable storage medium, and is applied to the bulldozer. A scanning unit is arranged on a front cover of the bulldozer. The method comprises the following steps: acquiring point cloud data collected by the scanning unit, and constructing a grid map according to a preset detection range and the point cloud data, wherein at least one grid area is included in the grid map; candidate cliff points of each grid area are determined respectively; a current position of the bulldozer is acquired, and a target cliff point is determined according to the current position and the candidate cliff points; a cliff detection distance is determined according to the target cliff point, and the cliff detection distance is displayed on a center console of the bulldozer. The method provided by the application solves the problems of poor precision and high time delay when the millimeter wave radar is used for cliff detection, uses the scanning unit to realize fast and accurate scanning of the ground in front, and accurately and timely provides the cliff detection distance of the front area to the driver when the bulldozer is working in real time.

Description

Technical Field

[0001] This invention relates to the field of image detection technology, and in particular to a cliff detection method, apparatus, bulldozer, and readable storage medium. Background Technology

[0002] In traditional engineering construction, bulldozer operations often face complex terrain and environmental conditions, such as cliffs and steep slopes. These features can lead to accidents or damage to the bulldozer during operation, and even pose a safety threat to the operator. Therefore, bulldozers need to maintain accurate terrain awareness during rapid operation to avoid accidents caused by failure to detect hazards in time.

[0003] Currently, millimeter-wave radar is used for cliff detection during bulldozing operations. However, millimeter-wave radar has a long wavelength, making it unable to effectively distinguish complex and subtle terrain features. Furthermore, millimeter-wave radar has limitations in real-time rapid response; when bulldozers are operating at high speeds, the terrain data provided by millimeter-wave radar may pose a risk of delayed safety warnings. Summary of the Invention

[0004] This invention provides a cliff detection method, device, bulldozer, and readable storage medium to solve the problems of poor accuracy and high latency when using millimeter-wave radar for cliff detection. It utilizes a scanning unit such as a lidar or depth camera sensor to scan the ground in front, providing a scanning unit with higher accuracy, faster response, and stronger environmental adaptability to achieve rapid and accurate scanning of the ground in front.

[0005] According to one aspect of the present invention, a cliff detection method is provided, applied to a bulldozer, wherein a scanning unit is provided on the front hood of the bulldozer; the method includes:

[0006] The point cloud data collected by the scanning unit is acquired, and a grid map is constructed based on the pre-set detection range and the point cloud data, wherein the grid map includes at least one grid area;

[0007] Determine the candidate cliff points for each grid region;

[0008] Obtain the current position of the bulldozer, and determine the target cliff point based on the current position and the candidate cliff points;

[0009] The cliff detection distance is determined based on the target cliff point and displayed on the bulldozer's control panel.

[0010] According to another aspect of the present invention, a cliff detection device is provided, applied to a bulldozer, wherein a scanning unit is provided on the front hood of the bulldozer; the device includes:

[0011] The module is used to acquire point cloud data collected by the scanning unit and construct a grid map based on the pre-set detection range and point cloud data, wherein the grid map includes at least one grid area.

[0012] The determination module is used to determine candidate cliff points for each grid region;

[0013] The acquisition module is used to acquire the current position of the bulldozer and determine the target cliff point based on the current position and the candidate cliff points;

[0014] The display module is used to determine the cliff detection distance based on the target cliff point and display the cliff detection distance on the bulldozer's central control panel.

[0015] According to another aspect of the present invention, a bulldozer is provided, the bulldozer comprising:

[0016] At least one processor; and

[0017] A memory that is communicatively connected to at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the cliff detection method of any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the cliff detection method according to any embodiment of the present invention.

[0020] The cliff detection method provided in this invention acquires point cloud data collected by a scanning unit and constructs a grid map based on a pre-set detection range and the point cloud data. Candidate cliff points are determined for each grid area. The current position of the bulldozer is acquired, and the target cliff point is determined based on the current position and the candidate cliff points. The cliff detection distance is determined based on the target cliff point and displayed on the bulldozer's control panel. On one hand, the method constructs a grid map based on the detection range and the distribution of the point cloud data. Each grid map includes at least one grid area, achieving a reasonable division of the point cloud data and providing accurate data for subsequent cliff point determination. Simultaneously, it solves the problems of poor precision, high latency, and slow response when using millimeter-wave radar for cliff detection. By utilizing a scanning unit such as a lidar or depth camera sensor to scan the ground ahead, it provides a scanning unit with higher precision, faster response, and stronger environmental adaptability, enabling rapid and accurate scanning of the ground ahead. On the other hand, based on the bulldozer's current position and candidate cliff points, the target cliff point is determined, enabling accurate identification of the cliff point closest to the bulldozer's current location during real-time operation. Finally, the cliff detection distance is determined based on the target cliff point and displayed on the bulldozer's central control panel, providing real-time data on the ground ahead to the bulldozer driver. This allows the driver to be aware of potential hazards and make timely judgments. Ultimately, this improves the bulldozer driver's experience and effectively enhances operational safety and efficiency.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart of a cliff detection method provided in an embodiment of the present invention;

[0024] Figure 2 A flowchart of another cliff detection method provided in an embodiment of the present invention;

[0025] Figure 3 This is a schematic diagram of a bulldozer provided in an embodiment of the present invention;

[0026] Figure 4 Example diagram of a grid map provided in an embodiment of the present invention;

[0027] Figure 5 This is a schematic diagram of the structure of a cliff detection device provided in an embodiment of the present invention;

[0028] Figure 6 This is another structural schematic diagram of a bulldozer provided in an embodiment of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "candidate," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Figure 1 This is a flowchart illustrating a cliff detection method provided by an embodiment of the present invention. This embodiment is applicable to bulldozer operations in complex terrain conditions. The method can be executed by a cliff detection device, which can be implemented in hardware and / or software and can be configured within the bulldozer. In this embodiment, the bulldozer is equipped with electronic equipment such as a data processing unit. A scanning unit is installed on the front hood of the bulldozer. Figure 1 As shown, the method includes:

[0032] S101. Acquire point cloud data collected by the scanning unit, and construct a grid map based on the pre-set detection range and point cloud data.

[0033] The grid map includes at least one grid area. The scanning unit is used to scan data of the ground in front of the bulldozer. In this embodiment, the scanning unit can be an instrument capable of scanning the ground using laser pulses, such as a lidar or depth camera sensor.

[0034] Specifically, the scanning unit is installed above the front hood of the bulldozer. When the bulldozer begins operation, the scanning unit scans the ground in front of it, obtaining the three-dimensional coordinates of various points on the ground, forming point cloud data. At this time, the bulldozer can obtain the point cloud data collected by the scanning unit.

[0035] Since point cloud data is based on a coordinate system established by scanning units—that is, if the scanning unit is a LiDAR, the point cloud data is in the LiDAR coordinate system—it needs to be transformed to a vehicle coordinate system centered on the bulldozer to ensure coordinate system consistency. This can be achieved by determining the rotation and translation matrices and using the coordinate system transformation formula. The vehicle coordinate system has its origin at the vertical projection of the bulldozer's geometric center onto the ground, the positive Y-axis along the bulldozer's forward direction, the positive X-axis along the right side of the bulldozer (and the negative X-axis along the left side), and the positive Z-axis along the direction perpendicular to the bulldozer's track plane.

[0036] After converting the coordinate system of the point cloud data to the vehicle coordinate system, all acquired point cloud data can be segmented according to a pre-set detection range. For example, if the range of point cloud data acquired at one time is 100*100m, the pre-set detection range can be 80*80m. Furthermore, after segmenting the point cloud data according to the detection range, a grid can be constructed for the segmented detection range based on the distribution of the point cloud data. When constructing the grid map, since the Y-axis points in the vehicle's forward direction, the grid is only divided along the X-axis. For example, when the point cloud data density is high, the X-axis grid can be divided at a smaller scale. When the point cloud data density is low, the X-axis grid can be divided at a larger scale.

[0037] After dividing the point cloud data into grids, a grid map is obtained, with the detection range as the whole, including different grid regions x1*y, ..., xn*y. Here, y is the total length of the Y-axis, and x is the total length of the X-axis. Dividing the X-axis yields equally divided x1, ..., xn.

[0038] In this embodiment, the collected point cloud data is used to construct a grid map based on the detection range and the distribution of the point cloud data. Each grid map includes at least one grid region, achieving a reasonable division of the point cloud data and providing accurate data for subsequent determination of cliff points based on the point cloud data. Simultaneously, it solves the problem that millimeter-wave radar cannot effectively distinguish complex and subtle terrain features and has limitations in real-time rapid response when used for cliff detection. By utilizing a scanning unit such as a lidar or depth camera sensor to scan the ground ahead, a scanning unit with higher precision, faster response, and stronger environmental adaptability is provided to achieve rapid and accurate scanning of the ground ahead.

[0039] S102. Determine the candidate cliff points for each grid region.

[0040] Among them, candidate cliff points are cliff points to be selected.

[0041] Specifically, for each grid region, it is necessary to determine the candidate cliff points within that grid region.

[0042] In one implementation, multiple direction lines can be determined according to a preset directional sequence, with the bulldozer's location as the origin. If a ground point or cavity is found on the current direction line, it can be used as a candidate cliff point. The ground point is determined based on its slope.

[0043] In another implementation, each point cloud data point in the grid region is sorted according to its Y-coordinate from smallest to largest, and the difference in Y-coordinate between adjacent points is calculated sequentially. Candidate cliff points are determined based on the Y-coordinate difference and a pre-set threshold. A candidate cliff point is a point cloud data point with a coordinate difference greater than or equal to the pre-set threshold and a smaller Y-coordinate value.

[0044] S103. Obtain the current position of the bulldozer and determine the target cliff point based on the current position and the candidate cliff points.

[0045] Among them, the target cliff point is the cliff point selected as the target from the candidate cliff points.

[0046] Specifically, since the bulldozer is in operation, it is necessary to determine its current position and, based on this position, identify the target cliff point from among the candidate cliff points. For example, based on the bulldozer's current position, first determine which grid area it falls within or is closest to. Then, based on the candidate cliff points within this grid area and the bulldozer's current position, determine the target cliff point.

[0047] In this embodiment, the target cliff point is determined based on the bulldozer's current position and candidate cliff points. This enables the accurate determination of the cliff point closest to the bulldozer's current position while the bulldozer is working in real time, providing a basis for determining the distance between the cliff point and the current position.

[0048] S104. Determine the cliff detection distance based on the target cliff point and display the cliff detection distance on the bulldozer's control panel.

[0049] The central control console is installed in the bulldozer's driver's cab and can display relevant information for controlling the bulldozer to the driver.

[0050] Specifically, after identifying the target cliff point, the distance between the target cliff point and the bulldozer can be determined based on the current position of the target cliff point and the bulldozer, i.e., the cliff detection distance. Furthermore, after determining the cliff detection distance, it can be displayed on the bulldozer's control panel to remind the bulldozer driver to avoid the cliff point ahead in time.

[0051] In this embodiment, the cliff detection distance is determined based on the target cliff point and displayed on the bulldozer's central control panel. This enables real-time display of ground data to the bulldozer driver, allowing the driver to promptly identify hazards ahead and make timely judgments. This improves the bulldozer driver's experience and effectively enhances operational safety and efficiency when using the bulldozer.

[0052] The cliff detection method provided in this invention acquires point cloud data collected by a scanning unit and constructs a grid map based on a pre-set detection range and the point cloud data. Candidate cliff points are determined for each grid area. The current position of the bulldozer is acquired, and the target cliff point is determined based on the current position and the candidate cliff points. The cliff detection distance is determined based on the target cliff point and displayed on the bulldozer's control panel. On one hand, the method constructs a grid map based on the detection range and the distribution of the point cloud data. Each grid map includes at least one grid area, achieving a reasonable division of the point cloud data and providing accurate data for subsequent cliff point determination. Simultaneously, it solves the problems of poor precision, high latency, and slow response when using millimeter-wave radar for cliff detection. By utilizing a scanning unit such as a lidar or depth camera sensor to scan the ground ahead, it provides a scanning unit with higher precision, faster response, and stronger environmental adaptability, enabling rapid and accurate scanning of the ground ahead. On the other hand, based on the bulldozer's current position and candidate cliff points, the target cliff point is determined, enabling accurate identification of the cliff point closest to the bulldozer's current location during real-time operation. Finally, the cliff detection distance is determined based on the target cliff point and displayed on the bulldozer's central control panel, providing real-time data on the ground ahead to the bulldozer driver. This allows the driver to be aware of potential hazards and make timely judgments. Ultimately, this improves the bulldozer driver's experience and effectively enhances operational safety and efficiency.

[0053] Figure 2 This is a flowchart of another cliff detection method provided by an embodiment of the present invention. Based on the above embodiments, this embodiment describes in detail the steps before "constructing a grid map based on a pre-set detection range and the point cloud data", the step of "constructing a grid map based on a pre-set detection range and the point cloud data", the step of "determining candidate cliff points in the grid area", the step of "determining a target cliff point based on the current position and the candidate cliff points", and the step of "determining the cliff detection distance based on the target cliff point".

[0054] Before proceeding, one structure of the bulldozer provided in the embodiments of the present invention will be described. Figure 3 This is a schematic diagram of a bulldozer provided in an embodiment of the present invention. Figure 3As shown, a bulldozer 10 may include a central control console 101, a data processing unit 102, and a scanning unit 20 mounted above the front hood of the bulldozer 10. The central control console 101 can be installed in the cab of the bulldozer 10, providing the driver with control levers or buttons to operate the bulldozer and displaying various operating statuses of the bulldozer to the driver. The data processing unit 102 is the data processing unit in the bulldozer 10 and can be presented in the form of an electronic device. The data processing unit 102 is electrically or communicatively connected to the central control console 101 and communicatively connected to the scanning unit 20. In this embodiment, the bulldozer acts as the main executor of the entire solution, and its specific processing can be performed by the data processing unit within the bulldozer.

[0055] Continue to refer to Figure 2 ,like Figure 2 As shown, the method includes:

[0056] S201. Obtain point cloud data collected by the scanning unit.

[0057] Specifically, the scanning unit is installed above the front hood of the bulldozer. When the bulldozer begins operation, the scanning unit scans the ground in front of it, obtaining the three-dimensional coordinates of various points on the ground, forming point cloud data. At this time, the bulldozer can obtain the point cloud data collected by the scanning unit.

[0058] S202. Determine the current working direction of the bulldozer.

[0059] Specifically, since the coordinate system of the point cloud data is established based on the scanning unit, while the final determined cliff detection distance needs to be determined based on the bulldozer, the vehicle's coordinate system needs to be determined according to the bulldozer's current working status. First, the bulldozer's current working direction can be determined.

[0060] S203. Convert the coordinate system of the point cloud data according to the current working direction.

[0061] The transformed coordinate system is the bulldozer's own coordinate system, which is established with the bulldozer as the origin and the current working direction as the positive Y-axis. In practice, the origin of the coordinate system can be the vertical projection point of the bulldozer's geometric center onto the ground.

[0062] Specifically, after determining the bulldozer's current working direction, a coordinate system can be established with the vertical projection point of the bulldozer's geometric center on the ground as the origin of the coordinate system and the bulldozer's current working direction as the positive Y-axis direction of the vehicle coordinate system.

[0063] For example, the position and orientation of the scanning unit relative to the vehicle coordinate system are determined, i.e., the translation vector and rotation matrix are determined. Then, the point cloud data is transformed using the rotation matrix and translation vector.

[0064] In this embodiment, the point cloud data is integrated by transforming it into the vehicle coordinate system. At the same time, based on the point cloud data in the vehicle coordinate system, real-time detection data can be provided to detect whether there is a cliff point in front of the bulldozer, thus improving the real-time performance of data acquisition and processing.

[0065] S204. Determine the grid map range based on the detection range.

[0066] Specifically, since the range of the acquired point cloud data is determined based on the scanning range of the scanning unit, and the scanning range of the scanning unit is not fixed, the detection range can be preset, that is, the required detection range can be preset, and the scanning range obtained by the scanning unit can be segmented based on the detection range to obtain the point cloud data within the required detection range, that is, to determine the range of the grid map.

[0067] S205. Based on the distribution quantity of point cloud data and the range of the grid map, the range of the grid map is divided along the X-axis of the vehicle coordinate system to obtain the grid map.

[0068] Specifically, since the distribution of point cloud data needs to be determined based on the specific terrain, the partition size needs to be determined based on the quantity of point cloud data before each partitioning. Furthermore, dividing the grid map area along the X-axis of the vehicle coordinate system yields multiple grid regions. All grid regions constitute a single, complete grid map.

[0069] For example, Figure 4 An example diagram of a grid map provided for an embodiment of the present invention. For example... Figure 4 As shown, if the grid map's range is x6*y, then the entire grid map can be divided according to the distribution of point cloud data and the grid map's range. For example, using... Figure 4 The grid is divided in the manner described above, resulting in different grid regions with varying ranges: x1*y, x2*y, x3*y, x4*y, x5*y, and x6*y. Each grid region contains point cloud data that is identical or similar to the others. x1 to x3 represent the x-coordinates in the negative X-axis direction, and x4 to x6 represent the x-coordinates in the positive X-axis direction; these are merely illustrative examples used to illustrate the division of the grid map. 0 represents the position of the bulldozer's geometric center projected vertically onto the ground.

[0070] S206. For any given grid region, determine the order in which each point cloud data is arranged within that grid region.

[0071] The order of arrangement is based on the coordinate values ​​of the point cloud data on the Y-axis of the vehicle coordinate system, arranged from smallest to largest.

[0072] Specifically, for any given grid region, the point cloud data can be sorted from smallest to largest based on its Y-coordinate value.

[0073] S207. Calculate the coordinate difference between two adjacent point cloud data points on the Y-axis of the vehicle coordinate system in the order of arrangement.

[0074] Specifically, according to the arrangement order of the point cloud data, the coordinate difference between two adjacent point cloud data in the Y-axis of the vehicle coordinate system is calculated sequentially.

[0075] For example, suppose there are three point cloud data points A, B, and C in a certain grid region. The Y-coordinate of A is Ay, the Y-coordinate of B is By, and the Y-coordinate of C is Cy. Where Ay < By < Cy. In this case, the coordinate difference between A and B, By-Ay, and the coordinate difference between B and C, Cy-By, can be determined.

[0076] S208. Determine candidate cliff points based on coordinate differences.

[0077] Specifically, candidate cliff points can be determined as follows:

[0078] (1) If at least one coordinate difference in the grid region is greater than or equal to a preset threshold, then the point cloud data with the smaller Y-axis coordinate value corresponding to the coordinate difference greater than or equal to the preset threshold is determined as a candidate cliff point.

[0079] (2) If all coordinate differences in the grid area are less than the preset threshold, the point cloud data with the largest coordinate value on the Y-axis of the vehicle coordinate system will be identified as the candidate cliff point.

[0080] For example, continuing the above example, suppose at least one of the coordinate difference between A and B (By-Ay) and the coordinate difference between B and C (Cy-By) is greater than or equal to a preset threshold, for example, By-Ay is greater than or equal to the preset threshold. In this case, A can be identified as a candidate cliff point. Suppose both By-Ay and Cy-By are greater than or equal to the preset threshold, then A and B can be identified as candidate cliff points. Suppose both the coordinate difference between A and B (By-Ay) and the coordinate difference between B and C (Cy-By) are less than the preset threshold, then C is identified as a candidate cliff point.

[0081] S209. Obtain the current position of the bulldozer.

[0082] Specifically, positioning instruments can be used to obtain the current location of the bulldozer.

[0083] S210. Discretize the candidate cliff points and remove the data to obtain the intermediate cliff points.

[0084] Specifically, all candidate cliff points in the grid area are discretized, and after removing the discrete candidate cliff points, the remaining candidate cliff points are used as intermediate cliff points.

[0085] S211. Determine the intermediate cliff point with the smallest Y-axis coordinate value relative to the current position as the target cliff point.

[0086] Specifically, the Y-coordinates of all intermediate cliff points are compared with the Y-coordinate value of the current position. The intermediate cliff point corresponding to the Y-coordinate closest to the current position is determined as the target cliff point. That is, the intermediate cliff point with the smallest Y-coordinate value is determined as the target cliff point.

[0087] S212. Determine the Y-axis coordinate value of the target cliff point as the cliff detection distance.

[0088] Specifically, after determining the target cliff point, in one implementation method, the Y-axis coordinate value of the target cliff point can be directly determined as the cliff detection distance.

[0089] In this embodiment, the target cliff point is directly based on the intermediate cliff point that is closest to the current position of the bulldozer, thus enabling rapid determination of the cliff detection distance.

[0090] S213. Using the point-to-surface calculation method, calculate the distance from the target cliff point to the bulldozer, and determine the distance from the target cliff point to the bulldozer as the cliff detection distance.

[0091] Specifically, after determining the target cliff point, another approach is to calculate a more accurate distance from the target cliff point to the bulldozer using the coordinates of the target cliff point and the surface coordinates of the bulldozer's current position, and then define this distance as the cliff detection distance.

[0092] In this embodiment, after determining the target cliff point, the cliff detection distance can be calculated more accurately by using a point-to-surface calculation method.

[0093] It is worth noting that S212 and S213 are two different methods for determining the cliff detection distance. In practice, either one can be chosen as the method for determining the cliff detection distance in this case.

[0094] S214. Display the cliff detection distance on the bulldozer's central control panel.

[0095] Specifically, after calculating the cliff detection distance, the cliff detection distance can be sent to the bulldozer's central control console so that the bulldozer driver knows the current distance of the bulldozer to the edge of the cliff, thereby avoiding dangerous areas such as cliffs in a timely manner.

[0096] In this embodiment, the cliff detection distance is displayed on the central control panel of the bulldozer. This not only provides the bulldozer operator with accurate danger warnings, helping him to avoid dangerous areas such as cliffs in time, but also allows the solution to be applied to unmanned and remote-controlled bulldozers, effectively improving the safety and automation level of operations. It is particularly suitable for mining, spoil heaps and complex terrain construction environments, significantly reducing accident risks and improving work efficiency.

[0097] The cliff detection method provided in this invention involves: acquiring point cloud data collected by a scanning unit; determining the current working direction of the bulldozer; transforming the coordinate system of the point cloud data according to the current working direction; determining the grid map range according to the detection range; dividing the grid map range along the X-axis of the vehicle coordinate system according to the distribution quantity of the point cloud data and the grid map range to obtain a grid map; determining the arrangement order of each point cloud data in the grid area for any given grid region; and calculating the Y-axis coordinate system of adjacent two point cloud data in the vehicle coordinate system according to the arrangement order. The process involves: calculating the coordinate difference of the point cloud data; determining candidate cliff points based on the coordinate difference; obtaining the current position of the bulldozer; discretizing and eliminating candidate cliff points to obtain intermediate cliff points; identifying the intermediate cliff point with the smallest Y-axis coordinate value relative to the current position as the target cliff point; determining the Y-axis coordinate value of the target cliff point as the cliff detection distance; or, using a point-to-surface calculation method, calculating the distance from the target cliff point to the bulldozer and determining this distance as the cliff detection distance; and displaying the cliff detection distance on the bulldozer's center console. This technical solution, on the one hand, integrates the point cloud data by transforming it to the vehicle coordinate system; on the other hand, based on the point cloud data in the vehicle coordinate system, it provides real-time detection data for detecting whether a cliff point exists in front of the bulldozer, improving the real-time performance of data acquisition and processing. On the other hand, by directly using the intermediate cliff point with the smallest distance from the bulldozer's current position as the target cliff point, the cliff detection distance can be quickly determined. Alternatively, after determining the target cliff point, a point-to-area calculation method can be used to calculate the cliff detection distance more accurately. Finally, displaying the cliff detection distance on the bulldozer's control panel not only provides the bulldozer operator with accurate hazard warnings, helping them to avoid dangerous areas such as cliffs in a timely manner, but also allows this solution to be applied to unmanned and remote-controlled bulldozers, effectively improving operational safety and automation levels. It is particularly suitable for mining, spoil heaps, and complex terrain construction environments, significantly reducing accident risks and improving work efficiency.

[0098] Figure 5 This is a schematic diagram of a cliff detection device provided in an embodiment of the present invention. It is applied to a bulldozer, and a scanning unit is installed on the front hood of the bulldozer; for example... Figure 5 As shown, the device includes:

[0099] The construction module 501 is used to acquire point cloud data collected by the scanning unit and construct a grid map based on the pre-set detection range and point cloud data, wherein the grid map includes at least one grid area;

[0100] Module 502 is used to determine candidate cliff points for each grid region;

[0101] The acquisition module 503 is used to acquire the current position of the bulldozer and determine the target cliff point based on the current position and the candidate cliff points;

[0102] Display module 504 is used to determine the cliff detection distance based on the target cliff point and display the cliff detection distance on the bulldozer's central control panel.

[0103] Optionally, before constructing the grid map based on the pre-set detection range and point cloud data, the construction module 501 is also used for:

[0104] Determine the current working direction of the bulldozer; transform the coordinate system of the point cloud data according to the current working direction, where the transformed coordinate system is the bulldozer's own coordinate system, which is a coordinate system established with the bulldozer as the origin and the current working direction as the positive Y-axis.

[0105] Optionally, a grid map is constructed based on the pre-set detection range and point cloud data. The construction module 501 is specifically used for:

[0106] Based on the detection range, the grid map range is determined; based on the distribution quantity of point cloud data and the grid map range, the grid map range is divided along the X-axis direction of the vehicle coordinate system to obtain the grid map.

[0107] Optionally, for any given grid region, module 502 is specifically used for:

[0108] Determine the arrangement order of each point cloud data in the grid area, wherein the arrangement order is based on the coordinate values ​​of the point cloud data on the Y-axis of the vehicle coordinate system from smallest to largest; calculate the coordinate difference between two adjacent point cloud data on the Y-axis of the vehicle coordinate system according to the arrangement order; determine candidate cliff points based on the coordinate difference.

[0109] Optionally, candidate cliff points are determined based on coordinate differences. Module 502 is specifically used for:

[0110] If at least one coordinate difference in the grid region is greater than or equal to a preset threshold, the point cloud data with the smaller Y-axis coordinate value corresponding to the coordinate difference greater than or equal to the preset threshold is determined as a candidate cliff point; if all coordinate differences in the grid region are less than the preset threshold, the point cloud data with the largest Y-axis coordinate value in the vehicle coordinate system is determined as a candidate cliff point.

[0111] Optionally, module 503 is specifically used for:

[0112] Discretize the candidate cliff points to remove data and obtain intermediate cliff points; then, select the intermediate cliff point with the smallest Y-axis coordinate value relative to the current position as the target cliff point.

[0113] Optionally, the display module 504 is specifically used for:

[0114] The Y-axis coordinate value of the target cliff point is determined as the cliff detection distance; or, using the point-to-surface calculation method, the distance from the target cliff point to the bulldozer is calculated, and the distance from the target cliff point to the bulldozer is determined as the cliff detection distance.

[0115] The cliff detection device provided in this embodiment of the invention can execute the cliff detection method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0116] Figure 6 This is another structural schematic diagram of a bulldozer provided in an embodiment of the present invention. In this embodiment, the bulldozer can be presented as an electronic device, or as a data processing unit within the bulldozer presented as an electronic device. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0117] like Figure 6As shown, the bulldozer 6 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 and a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer programs stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the bulldozer 6. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0118] Multiple components in bulldozer 6 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows bulldozer 6 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0119] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as cliff detection methods.

[0120] In some embodiments, the cliff detection method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the bulldozer 6 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the cliff detection method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the cliff detection method by any other suitable means (e.g., by means of firmware).

[0121] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0122] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0123] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0124] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0125] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0126] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0127] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and no limitation is imposed herein.

[0128] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for detecting cliff faces, characterized in that, Applied to bulldozers, wherein a scanning unit is provided on the front hood of the bulldozer; the method includes: The point cloud data collected by the scanning unit is acquired to determine the current working direction of the bulldozer; the coordinate system of the point cloud data is transformed according to the current working direction, and a grid map is constructed based on the pre-set detection range and the point cloud data, wherein the grid map includes at least one grid area, and the transformed coordinate system is the vehicle coordinate system of the bulldozer, which is a coordinate system established with the bulldozer as the origin and the current working direction as the positive Y-axis; Candidate cliff points are determined for each of the grid regions. For any given grid region, determining the candidate cliff points includes: determining the arrangement order of each point cloud data in the grid region, wherein the arrangement order is based on the coordinate values ​​of the point cloud data on the Y-axis of the vehicle coordinate system from smallest to largest; calculating the coordinate difference between two adjacent point cloud data on the Y-axis of the vehicle coordinate system according to the arrangement order; and determining the candidate cliff points based on the coordinate differences. Obtain the current position of the bulldozer, and determine the target cliff point based on the current position and the candidate cliff points; The cliff detection distance is determined based on the target cliff point, and the cliff detection distance is displayed on the central control panel of the bulldozer.

2. The cliff detection method according to claim 1, characterized in that, The step of constructing a grid map based on a pre-set detection range and the point cloud data includes: The grid map range is determined based on the detection range; Based on the distribution quantity of the point cloud data and the range of the grid map, the range of the grid map is divided along the X-axis direction of the vehicle coordinate system to obtain the grid map.

3. The cliff detection method according to claim 1, characterized in that, Determining the candidate cliff point based on the coordinate difference includes: If at least one coordinate difference in the grid region is greater than or equal to a preset threshold, then the point cloud data with the smaller Y-axis coordinate value corresponding to the coordinate difference greater than or equal to the preset threshold is determined as the candidate cliff point. If all coordinate differences in the grid area are less than a preset threshold, then the point cloud data with the largest coordinate value on the Y-axis of the vehicle coordinate system is determined as the candidate cliff point.

4. The cliff detection method according to claim 1, characterized in that, The step of determining the target cliff point based on the current location and the candidate cliff points includes: Discretize the candidate cliff points to remove data and obtain intermediate cliff points; The intermediate cliff point with the smallest Y-axis coordinate value relative to the current position is determined as the target cliff point.

5. The cliff detection method according to claim 1, characterized in that, Determining the cliff detection distance based on the target cliff point includes: The Y-axis coordinate value of the target cliff point is determined as the cliff detection distance; or, Using a point-to-surface calculation method, the distance from the target cliff point to the bulldozer is calculated, and the distance from the target cliff point to the bulldozer is determined as the cliff detection distance.

6. A cliff detection device, characterized in that, Applied to bulldozers, the bulldozer's front hood is equipped with a scanning unit; the device includes: A construction module is used to acquire point cloud data collected by the scanning unit, determine the current working direction of the bulldozer, transform the coordinate system of the point cloud data according to the current working direction, and construct a grid map based on the pre-set detection range and the point cloud data. The grid map includes at least one grid area, and the transformed coordinate system is the bulldozer's vehicle coordinate system, which is a coordinate system established with the bulldozer as the origin and the current working direction as the positive Y-axis. A determination module is used to determine candidate cliff points for each of the grid regions. For any given grid region, determining the candidate cliff points for that grid region includes: determining the arrangement order of each point cloud data in the grid region, wherein the arrangement order is based on the coordinate values ​​of the point cloud data along the Y-axis of the vehicle coordinate system from smallest to largest; calculating the coordinate difference between two adjacent point cloud data along the Y-axis of the vehicle coordinate system according to the arrangement order; and determining the candidate cliff points based on the coordinate differences. The acquisition module is used to acquire the current position of the bulldozer and determine the target cliff point based on the current position and the candidate cliff points; The display module is used to determine the cliff detection distance based on the target cliff point and display the cliff detection distance on the central control panel of the bulldozer.

7. A bulldozer, characterized in that, include: One or more processors; Memory, used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the cliff detection method as described in any one of claims 1 to 5.

8. A readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the cliff detection method as described in any one of claims 1 to 5.

Images