Method, system, and storage medium for determining a loading stop for an unmanned mine vehicle

Abstract

The application provides a method, a system and a storage medium for determining a loading stop point of an unmanned mine car. The method comprises: obtaining mine area map information; determining a loading area and a boundary line of the loading area; obtaining a material area; selecting a part of the boundary line of the loading area as a loading line according to the material area; making the unmanned mine car travel to the loading area and stop outside the loading area to obtain a travel trajectory line of the unmanned mine car; recording an end point of the travel trajectory line as a loading guide point, the loading guide point comprising heading information and position information; performing a loading stop point searching process according to the loading line and the loading guide point to obtain a loading stop point set; determining an optimal loading stop point from the loading stop point set according to the material area; and sending position information and heading information of the optimal loading stop point to the unmanned mine car to guide stopping of the unmanned mine car.

Description

Technical Field

[0001] This invention relates to a method, system, and storage medium for determining the loading and stopping points of unmanned mining trucks. Background Technology

[0002] With the continuous development of mining technology, unmanned mining trucks are increasingly widely used in open-pit mines. Unmanned mining truck systems utilize automation technology to transport mineral materials, significantly improving operational efficiency, reducing labor costs, and minimizing safety risks. Loading is a crucial step in the unmanned mining truck operation process, and determining suitable loading and stopping points is of great importance to the efficiency and safety of the entire system.

[0003] Currently, in open-pit mine loading operations, excavator operators typically rely on personal experience and subjective judgment to designate loading and stopping points for driverless mining trucks. This traditional manual point selection method has several technical problems: First, manual point selection depends entirely on the operator's subjective judgment and personal experience, lacking objective and accurate data support. In the complex and ever-changing mining environment, experience-based judgment is easily affected by factors such as light, terrain, and weather, leading to significant errors in guidance information. Second, due to the lack of point selection standards that match the logic of autonomous driving algorithms, manually selected loading and stopping points often do not conform to the vehicle's kinematic characteristics, requiring driverless mining trucks to frequently adjust their driving trajectory, increasing ineffective driving distances and reducing operational efficiency. Third, the inaccuracy of manual point selection can easily lead to driverless mining trucks attempting to enter multiple times and repeatedly adjusting their positions. This not only wastes a lot of time but may also cause safety accidents. For example, collisions with excavators or other equipment, or rollovers due to inaccurate material loading positions causing vehicle instability.

[0004] Furthermore, existing technologies lack a standardized and automated solution for determining loading docking points, thus failing to fully leverage the advantages of autonomous driving technology. Traditional manual point selection methods are susceptible to human factors such as operator fatigue and operating habits, leading to significant fluctuations in operational efficiency and making it difficult to achieve stable and efficient loading operations.

[0005] Therefore, there is an urgent need for a technical solution that can automatically determine the loading and stopping points of unmanned mining trucks to solve the technical problems existing in the current technology, such as low point selection accuracy, poor algorithm adaptability, and unstable operation efficiency. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, and storage medium for determining the loading and stopping points of unmanned mining trucks.

[0007] According to one aspect of the present invention, a method for determining the loading and stopping point of an unmanned mining truck is provided, characterized by comprising: acquiring mining area map information; determining a loading area and the boundary line of the loading area from the mining area map information; acquiring a material area identified by sensors of an excavator; selecting a portion of the boundary line of the loading area as a loading line based on the material area; causing the unmanned mining truck to travel to the loading area and stop outside the loading area to obtain the driving trajectory line of the unmanned mining truck; recording the endpoint of the driving trajectory line as a loading guidance point, the loading guidance point including heading information and location information; performing a loading and stopping point search process based on the loading line and the loading guidance point to obtain a set of loading and stopping points; determining an optimal loading and stopping point from the set of loading and stopping points based on the material area; and sending the location information and heading information of the optimal loading and stopping point to the unmanned mining truck to guide the unmanned mining truck to stop.

[0008] In some embodiments, the loading docking point finding process includes: performing a point acquisition process on the loading line at a first predetermined interval to obtain multiple points on the loading line, wherein each point includes its own location information and heading information; and recording the location information and heading information of each point as information items for each point, and generating a first information item set based on the information items of all points.

[0009] In some embodiments, the loading docking point finding process further includes: performing an expansion process on the first information item set to obtain a second information item set; and the expansion process includes: rotating the heading angle of each point to the right by multiple angles so that each point has multiple heading information, and obtaining multiple information items for each point based on the multiple heading information of each point; and the expansion process further includes: generating the second information item set based on the respective multiple information items of all points.

[0010] In some embodiments, the loading docking point finding process further includes: traversing each information item in the second information item set, and calculating the angle between the rays of the loading guide point and the heading information based on the location information and heading information of the loading guide point; and filtering the angles and corresponding information items that meet the conditions according to the first set conditions, so as to include the filtered information items in the third information item set.

[0011] In some embodiments, the loading docking point finding process further includes: obtaining the point corresponding to each information item in the third information item set; traversing each point in the plurality of points corresponding to the third information item set, calculating the straight-line distance between each point and the loading guide point; and filtering the distances and corresponding points that meet the conditions according to the second set conditions, and generating a set of filtered points based on the filtered points.

[0012] In some embodiments, the loading docking point finding process further includes: traversing each filter point in the filter point set, performing a point selection process at a second predetermined interval on the line connecting the filter point and the loading guide point to generate a candidate point set; calculating the straight-line distance between each candidate point in the candidate point set and its nearest boundary line; and filtering candidate points that meet the conditions according to a third set condition, and using the filtered candidate points as loading docking points that can be used for the unmanned mining truck, and generating the loading docking point set.

[0013] In some embodiments, determining the optimal loading docking point further includes: selecting, based on the material area, the loading docking point closest to the material area from the set of loading docking points as the optimal loading docking point; and the optimal loading docking point includes location information and heading information, and the unmanned mining vehicle docks according to the location information and heading information of the optimal loading docking point.

[0014] In some embodiments, the material area is located outside the loading area; and a portion of the boundary line of the loading area near the material area is selected as the loading line.

[0015] In some embodiments, obtaining the heading information of each of the loading guide point, the point on the loading line, and the optimal loading docking point includes: calculating the heading information based on the position information of each point and its neighboring points, wherein the position information is latitude and longitude information.

[0016] According to another aspect of the present invention, a system for determining the loading and stopping point of an unmanned mining truck is provided. The system performs the method according to the present invention and includes: an information receiving module configured to receive mining area map information, obtain a material area from an excavator, and obtain the driving trajectory line of the unmanned mining truck; an information processing module configured to determine a loading area and its boundary line from the mining area map information, select a portion of the boundary line of the loading area as a loading line based on the material area, and record the endpoint of the driving trajectory line as a loading guidance point, the loading guidance point including heading information and location information; a calculation module configured to perform a loading and stopping point search process based on the loading line and the loading guidance point to obtain a set of loading and stopping points, and determine an optimal loading and stopping point from the set of loading and stopping points based on the material area; and a communication module configured to send the location information and heading information of the optimal loading and stopping point to the unmanned mining truck to guide its stopping.

[0017] According to another aspect of the present invention, a computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to perform the method according to the present invention.

[0018] 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

[0019] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of the invention.

[0020] Figure 1 The steps of a method for determining the loading and stopping point of an unmanned mining truck according to an embodiment of the present invention are shown.

[0021] Figure 2 The specific execution steps of the loading docking point finding process according to an embodiment of the present invention are shown.

[0022] Figure 3 A schematic diagram of the loading area and material area according to an embodiment of the present invention is shown.

[0023] Figure 4 A schematic diagram of a system for determining the loading and stopping point of an unmanned mining truck according to an embodiment of the present invention is shown. Detailed Implementation

[0024] To more clearly illustrate the objectives, technical solutions, and advantages of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the following description of the embodiments is intended to explain and illustrate the overall concept of the present invention and should not be construed as limiting the present invention. In the specification and drawings, the same or similar reference numerals refer to the same or similar parts or components. For clarity, the drawings are not necessarily drawn to scale, and some well-known parts and structures may be omitted from the drawings.

[0025] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. The word “a” or “an” does not exclude a plurality. Terms such as “comprising” or “including” mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as “connected” or “linked” are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as “upper,” “lower,” “left,” “right,” “top,” or “bottom,” etc., are used only to indicate relative positional relationships, which may change accordingly when the absolute position of the described object changes. When an element such as a layer, film, region, or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element, or there may be intermediate elements present.

[0026] Figure 1 The steps of a method for determining the loading and stopping point of an unmanned mining truck according to an embodiment of the present invention are shown. Figure 2 The specific execution steps of the loading docking point finding process according to an embodiment of the present invention are shown. Figure 3 A schematic diagram of the loading area and material area according to an embodiment of the present invention is shown.

[0027] like Figure 1 As shown, the method for determining the loading and stopping point of an unmanned mining truck according to an embodiment of the present invention includes steps S1-S9.

[0028] In step S1, the mining area map information is obtained. As an example, the mining area map information can be obtained as follows: First, the mining area is surveyed on-site using surveying equipment to obtain basic geographic information such as terrain, roads, and buildings. Then, the survey data is processed to generate a standard format map file, such as an OSM (OpenStreetMap) file. Finally, the OSM map file is parsed to generate a node.csv file containing node information, a relation.csv file containing relationship information, and a way.csv file containing path information. These files contain the coordinates, attribute information, and interrelationships of various areas within the mining area, providing basic data support for subsequent loading area determination.

[0029] In step S2, the loading area 10 and the boundary line L of the loading area 10 are determined from the mining area map information. As an example, such as... Figure 3As shown, based on the mining area map information obtained in step S1, loading area 10 can be identified and determined. For example, loading area 10 refers to a designated area within the mining area specifically for unmanned mining trucks to perform loading operations. This area typically has flat ground, sufficient space, and terrain conditions conducive to the operation and movement of excavators and mining trucks. The extent of loading area 10 can be automatically identified by parsing the area attribute identifiers in the map information, and the boundary line L of this area can be extracted. The boundary line L can be the surrounding outline of loading area 10. For example, the boundary line L can consist of a series of continuous coordinate points that accurately define the spatial range of loading area 10.

[0030] In step S3, the material area 20 identified by the excavator's sensors is acquired. Typically, excavators are equipped with various sensors, including but not limited to LiDAR, cameras, and infrared sensors. These sensors can scan and identify environmental information around the excavator in real time. As an example, the excavator's sensor system can identify the material accumulation area, i.e., material area 20, within the current working area using image recognition technology and point cloud data processing technology. Material area 20 may include the mineral material to be loaded, and its location information can be recorded in coordinate form, including the center coordinates, boundary coordinates, and height of the material accumulation. The location information of material area 20 identified by the excavator's sensors is transmitted to the cloud control platform in real time, providing a basis for subsequent selection of loading line D.

[0031] In another example, multiple loading areas may be identified from the mining area map information in step S2. In this case, the loading area 10 to be loaded can be determined from the multiple identified loading areas based on the location information of the material area 20 obtained in step S3.

[0032] In step S4, based on the material area 20, a portion of the boundary line L of the loading area 10 is selected as the loading line D. For example, as shown... Figure 3 As shown, based on the location information of the material area 20 obtained in step S3, the relative positional relationship between the material area 20 and the loading area 10 can be analyzed. Since the material area 20 is located outside the loading area 10, a portion of the boundary line L of the loading area 10 closest to the material area 20 is selected as the loading line D. For example, the distance from each point on the boundary line L to the material area 20 can be calculated, and the closest continuous line segment can be selected as the loading line D. Alternatively, a distance range can be set so that continuous line segments within the set distance range are selected as the loading line D.

[0033] In step S5, the driverless mining truck is driven to loading area 10 and parked outside loading area 10 to obtain the driving trajectory line S of the driverless mining truck. As an example, such as... Figure 3As shown, based on the initiation of the loading task, the unmanned mining truck can receive an instruction to proceed to loading area 10 and activate its autonomous driving system, traveling along a preset path to the vicinity of loading area 10. During the truck's journey, the onboard positioning system (e.g., a satellite positioning system such as GPS or BeiDou) continuously records the truck's location information, including latitude and longitude coordinates and timestamps. When the truck reaches the periphery of loading area 10 and stops, the complete driving path from the starting position to the final stopping position can be recorded as a driving trajectory line S. For example, the driving trajectory line S can consist of a series of coordinate points arranged in chronological order, and connecting these coordinate points forms the actual driving path of the truck.

[0034] It should be noted that the execution process of the method according to the embodiments of this application is not limited to the steps exemplified herein, but the execution order of some steps can be changed without affecting the execution effect of the method of this application. For example, step S5 does not necessarily have to be executed after S4, but can be executed after determining the loading region 10 in step S2, or can be executed in parallel with steps S3 and S4.

[0035] In this article, "unmanned mining truck" can refer to unmanned mining trucks that receive materials from excavators for loading, such as large mining dump trucks with a load capacity of 100 to 300 tons, or other types of transport vehicles, such as unmanned articulated dump trucks, unmanned rigid dump trucks, or unmanned tracked transport vehicles.

[0036] In step S6, the endpoint of the driving trajectory line S is recorded as loading guidance point A, which includes heading information and location information. The endpoint of the driving trajectory line S can be understood as the final stopping position of the unmanned mining truck, and this position is recorded as loading guidance point A. The location information of loading guidance point A can be latitude and longitude coordinates. For example, the latitude and longitude coordinates of loading guidance point A can be obtained through GPS.

[0037] The heading information for loading guide point A can be calculated using the latitude and longitude coordinates of loading guide point A and its adjacent point B on the driving trajectory line S. For example, the calculation process is as follows.

[0038] Assuming the latitude and longitude coordinates of loading guide point A are (120.5°E, 31.2°N), and the latitude and longitude coordinates of the adjacent point B of loading guide point A are (121.0°E, 31.5°N), the steps are as follows.

[0039] a. Convert latitude and longitude to radians:

[0040]

[0041] b. Calculate the difference between latitude and longitude:

[0042]

[0043] c. Substitute into the formula:

[0044]

[0045] d. Calculation results:

[0046] This translates to approximately 63.6°. Therefore, the heading information for loading guide point A is 63.6°.

[0047] In step S7, a loading docking point search process can be performed based on the loading line D and the loading guide point A to obtain a set of loading docking points. This will be referred to below. Figure 2-3 Describe the process of finding the loading dock. For example... Figure 2 As shown, the loading docking point search process may include steps S701-S706.

[0048] In step S701, a point acquisition process is performed on the loading line D at a first predetermined interval d1. For example, the first predetermined interval d1 can be set to 5m. This allows the acquisition of multiple points PD1, PD2, PD3, PD4, PD5, PD6, PD7, and PD8 on the loading line D. Each point PD can include its own position information and heading information. Then, the position information a and heading information b of each point PD can be recorded as information item X{a, b} for each point PD. Based on the information items X1{a1, b1}, X2{a2, b2}, X3{a3, b3}, X4{a4, b4}, X5{a5, b5}, X6{a6, b6}, X7{a7, b7}, and X8{a8, b8} of all points PD1, PD2, PD3, PD4, PD5, PD6, PD7, and PD8, the first information item set J1{X1, X2, …, X8} can be generated.

[0049] In this step, the calculation of the heading information for each point on loading line D can be similar to the calculation of the heading information described above for loading guide point A. That is, the heading information of each point can be calculated using the latitude and longitude coordinates of each point on loading line D and the latitude and longitude coordinates of adjacent points, and the calculation process can be similar to the calculation process shown above. Specifically, for the selection of adjacent points, the next adjacent point can be selected according to a pre-set orientation (e.g., the left-to-right direction on a map).

[0050] Furthermore, the point selection process in this step can be implemented as follows. We can assume that the two endpoints of the loading line D are points A and B, and that the coordinates of point A are A... The coordinates of point B are B The goal is to generate one point every 5 meters. The calculation process is as follows.

[0051] a. Calculate the straight-line distance between two points.

[0052] b. Determine the number of locations

[0053] Assuming the interval distance is s = 5m, the number of positions... Satisfies: n = \left\lfloor \frac{d}{s}\right\rfloor.

[0054] If d < s, then n = 0, meaning no intermediate points are generated.

[0055] c. Generate point coordinates

[0056] The direction vector from point A to point B is: ;

[0057] Unit vector:

[0058] The i-th point P i The coordinates (i=1,2,…,n) are: ,

[0059] Right now: .

[0060] Therefore, as Figure 3 As shown, eight points PD1, PD2, PD3, PD4, PD5, PD6, PD7, and PD8 can be obtained on loading line D through the above point acquisition process.

[0061] In step S702, the first information item set J1 {X1, X2, …, X9} can be expanded to obtain the second information item set J2. For example, the expansion process may include rotating the heading angle of each point PD sequentially to the right by multiple angles such as 30 degrees, 60 degrees, 90 degrees, 120 degrees, and 150 degrees, so that each point PD has multiple heading information. In this way, the unmanned mining truck can perform multi-angle docking verification, thereby verifying the surrounding situation in multiple dimensions, and thus performing docking verification with full load as much as possible, especially in spaces where there are three relationships between materials, excavators, and unmanned vehicles.

[0062] To better understand this application, this document describes a rotation to the right by two angles (e.g., 30 degrees and 90 degrees), but the implementation of this application is not limited to this. Therefore, in the case of a two-angle right rotation, each point PD can have multiple heading information b0, b1, and b2. Taking point PD2 as an example, the original heading information is b0, and after rotation, additional heading information b1 (original heading + 30 degrees) and b2 (original heading + 90 degrees) are obtained. Then, based on the multiple heading information b0, b1, and b2 for each point, multiple information items X0{a, b0}, X1{a, b1}, and X2{a, b2} for each point PD are obtained. For example, the information item corresponding to point PD2 is X0{a, b0}. 20 {a, b0}、X 21 {a, b1}、X 22 {a, b2}. Finally, based on the multiple information items of each point, a second information item set J2{X} is generated. 10 X 11 , X 12 X 20 , …, X 90 X 91 X 92}

[0063] In step S703, the second information item set J2{X} can be traversed. 10 X 11, X 12, X 20 , …, X 90, X 91, X 92 For each information item in the}, based on the position and heading information of the loading guide point A, the angle θ between the rays of the two is calculated. Specifically, the heading information of the loading guide point A determines the direction of ray E1 originating from that point, while the heading information of each information item determines the direction of ray E2 originating from the corresponding point. Thus, the angle θ between ray E1 and ray E2 can be calculated.

[0064] Then, angles that satisfy the first set of conditions and their corresponding information items are filtered. For example, the first set of conditions could be 30°≤θ≤90°. According to an embodiment of the present invention, the first set of conditions is to consider the vehicle's turning radius constraint to exclude points that do not meet the turning radius requirement. Thus, information item X corresponding to ray E2 with an angle θ that meets the first set of conditions can be filtered out. Then, information items that satisfy the conditions are included in a third set of information items. As an example, such as Figure 3 As shown, the information item X of point PD2 20The angle between the ray from point PD5 and the ray from loading guide point A is greater than 90°, therefore the first setting condition is not met. And the information item X of point PD5... 51 The angle between the ray from point A and the ray from loading guide point A is greater than 30° and less than 90°, thus satisfying the first setting condition. As an example, it can be assumed that information item X is selected. 21 , X 50 , X 51 And thereby generate a third set of information items J3{X} 21 , X 50 , X 51}

[0065] In step S704, the third information item set J3{X} can be obtained. 21 , X 50 , X 51 Each information item X in} 21 , X 50 , X 51 The corresponding point. That is, information item X. 21 The corresponding point is PD2, and the information item X 50 , X 51 The corresponding point is PD5. Therefore, each information item X 21 , X 50 , X 51 The corresponding points are PD2 and PD5. Then, iterate through each of these points PD2 and PD5, calculating the straight-line distance k1 between each point and the loading guide point A. For example, ... Figure 3 As shown, only the straight-line distance k1 between point PD5 and loading guide point A is displayed. Similarly, the straight-line distance k1 between another point PD2 and loading guide point A can also be calculated. For example, the calculation of the straight-line distance k1 is based on the latitude and longitude coordinates of the two points.

[0066] Then, based on the second set of conditions, distances and corresponding points that meet the conditions are filtered. For example, the second set of conditions could be k1 ≥ 40m. According to an embodiment of the present invention, the second set of conditions also considers the turning radius. For example, if the spatial distance is small, the vehicle itself may not be able to complete the turning maneuver. Therefore, points that meet the second set of conditions can be filtered from the aforementioned points. As an example, in this document, it can be assumed that the straight-line distances between points PD2 and PD5 and the loading guide point A both meet the second set of conditions. Then, a set of filtered points P1 {PD2, PD5} is generated based on the filtered points.

[0067] In step S705, each screening point in the screening point set P1{PD2, PD5} can be traversed, and a point-taking process is performed on the line C connecting the screening point and the loading guide point A according to a second predetermined interval d2. For example, the second predetermined interval d2 can be set to d2=13m. The point-taking process here can be similar to the point-taking process on the loading line D described above, only the interval setting is different. Therefore, the point-taking process will not be described in detail here. As an example, such as Figure 3 As shown, candidate points PC1, PC2, PC3, and PC4 can be obtained on line C connecting point PD2 and loading guide point A. Candidate points PC5, PC6, PC7, PC8, and PC9 can be obtained on line C connecting point PD5 and loading guide point A. Therefore, a candidate point set P2{PC1, PC2, …, PC8, PC9} can be generated.

[0068] Then, the straight-line distance k2 between each candidate point in the candidate point set P2{PC1, PC2, …, PC8, PC9} and its nearest boundary line L can be calculated. As an example, such as... Figure 3 As shown, only the straight-line distance k2 between point PC2 and its nearest boundary line L is displayed. Other candidate points can then be obtained in a similar manner. In this paper, "straight-line distance k2" can be understood as the closest distance between each candidate point and the boundary line L. Then, candidate points that meet the conditions are selected according to a third set of criteria. As an example, the third set of criteria is k2 ≥ 13m. This ensures that candidate points maintain a safe distance from the boundary line L of the loading area, avoiding safety risks caused by unmanned mining trucks approaching the boundary too closely when parking, and ensuring that each loading docking point has sufficient safety margin, providing safety guarantees for the parking and loading operations of the mining trucks. For example, for ease of description, assume that candidate points PC2, PC6, and PC7 meet the third set of criteria and are determined as loading docking points that can be used for unmanned mining trucks, and generate a loading docking point set P3{PC2, PC6, PC7}.

[0069] In step S706, after obtaining the loading docking point set P3{PC2, PC6, PC7}, the loading docking point closest to the material area 20 can be selected as the optimal loading docking point POP based on the location information of the material area 20. As an example, the straight-line distance from each docking point in the loading docking point set P3 to the center point of the material area 20 can be calculated, and the loading docking point with the smallest straight-line distance can be selected as the optimal loading docking point POP. However, the embodiments of this application are not limited to this, and different distance calculation methods can be used according to actual needs, as long as the loading docking point closest to the material area 20 and most convenient for loading operations is obtained.

[0070] As an example, for ease of explanation, assume that loading docking point PC7 is closest to material area 20 and is therefore selected as the optimal loading docking point POP. In this case, the heading information of loading docking point PC7 can be calculated based on the latitude and longitude coordinates of loading docking point PC7 and its adjacent point (e.g., PC8). The calculation method is similar to that for the heading information of loading guide point A described above, so the specific calculation process for the heading information of loading docking point PC7 will not be repeated here. Thus, the location information (e.g., latitude and longitude coordinates) and heading information (e.g., orientation angle) of loading docking point PC7, i.e., the optimal loading docking point POP, can be obtained.

[0071] At this point, after obtaining the optimal loading docking point (POP), the loading docking point search process in S7 is complete, and then we can proceed to S8.

[0072] In step S8, after determining the optimal loading docking point (POP), the information transmission and mine car guidance process can be performed. Specifically, the location information and heading information of the optimal loading docking point (POP) are transmitted to the onboard control system of the unmanned mine car via a wireless communication network. For example, the location information is transmitted in the form of precise latitude and longitude coordinates to ensure that the mine car can accurately locate the target docking position. The heading information can be transmitted in the form of angle values ​​to indicate the orientation that the mine car should maintain when arriving at the docking point.

[0073] After receiving the optimal loading docking point (POP) information, the onboard control system of the unmanned mining truck immediately activates the path planning module. Based on the current location and the target docking point's location information, it calculates the optimal driving path. During path planning, factors such as terrain conditions, obstacle distribution, and turning radius are comprehensively considered to generate a smooth and safe driving trajectory. Simultaneously, the truck's final parking posture can be adjusted based on heading information to ensure that the truck's orientation at parking matches the loading operation requirements. This guides the truck accurately to the designated location and parking according to the correct heading, achieving efficient and safe loading operations.

[0074] Advantageously, the method for determining loading and parking points of unmanned mining trucks according to this application provides an automatic point selection method by constructing a point selection model that matches the logic of the autonomous driving algorithm, thereby significantly improving the adaptability of the driving algorithm and operational efficiency. This automatic point selection method accurately generates loading and parking points that conform to the vehicle's kinematic characteristics. During the point selection process, core algorithm parameters such as vehicle turning radius and path planning complexity are given priority, eliminating the need for frequent adjustments to the autonomous vehicle's trajectory and reducing unnecessary travel distances. Compared to the potential for insufficient algorithm adaptability issues with manual point selection, the method of this application can effectively shorten the operation cycle time and improve overall operational efficiency.

[0075] Secondly, the method of this application can fully unleash the advantages of autonomous driving technology and eliminate interference from human factors. Traditional manual point selection relies entirely on human factors such as driver fatigue and operating habits, leading to instability in the point selection logic. The method of this application can standardize and automate the point selection logic, avoid fluctuations in operational efficiency caused by human judgment errors, and break through the limitations of traditional manual point selection, which is restricted by the subjective experience of operators.

[0076] Furthermore, the method described in this application utilizes high-precision sensors to collect real-time operational environment data, combined with advanced algorithms to scientifically plan loading points and headings, minimizing guidance errors. Even in harsh environments, it can stably output precise guidance commands, significantly improving the success rate of unmanned vehicles entering designated areas. Precise loading point and heading guidance allows unmanned vehicles to accurately enter designated locations in one go, greatly reducing vehicle waiting and adjustment time. This enables efficient collaborative operation between excavators and unmanned vehicles, reducing equipment idle time and significantly increasing the amount of material loaded and transported per unit time.

[0077] Finally, the method described in this application can ensure that unmanned vehicles travel on safe paths through precise guidance, effectively avoiding the risk of collisions with other equipment. Scientifically planned loading points ensure uniform material loading, improve vehicle stability, provide more reliable safety guarantees for mining operations, and reduce the accident rate. Simultaneously, the increased success rate of entry reduces equipment downtime, lowers fuel costs, slows equipment wear, and correspondingly reduces maintenance costs. The efficient workflow shortens project cycles, saves manpower and time costs, and reduces mining operating costs from multiple aspects, bringing significant economic benefits to the enterprise.

[0078] According to another method of this application, a system for determining the loading and stopping point of an unmanned mining truck is also provided. This system can adopt a modular design, and through the coordinated operation of various functional modules, it can automatically determine the loading and stopping point and guide the mining truck.

[0079] Figure 4A schematic diagram of a system 1000 for determining the loading and stopping point of an unmanned mining truck according to an embodiment of the present invention is shown.

[0080] like Figure 4 As shown, system 1000 may include information receiving module 100, information processing module 200, computing module 300 and communication module 400.

[0081] The information receiving module 100 serves as the data input interface for the system 1000, responsible for acquiring necessary information from multiple data sources. The information receiving module 100 receives mining area map information through a standardized data interface, including OSM format map files and parsed node, relationship, and path data. Simultaneously, the information receiving module 100 can establish a communication connection with the excavator's sensor system to acquire real-time information about the material area 20 identified by the sensors, including parameters such as the material's location coordinates, distribution range, and accumulation height. Furthermore, the information receiving module 100 can maintain a data link with the unmanned mining truck's onboard system, continuously receiving the unmanned mining truck's driving trajectory data, including the driving trajectory line S, location coordinate sequence, and timestamp information.

[0082] The information processing module 200 can possess the core data processing functions of the system 1000 to analyze and process the received raw data. For example, the information processing module 200 first parses the mining area map information, identifies and extracts the boundary of the loading area 10 through a geographic information system algorithm, and generates precise boundary line L coordinate data. Then, based on the location information of the material area 20, it calculates the distance relationship between the material area 20 and the boundary line L of the loading area 10, and selects the optimal boundary line segment as the loading line D. In addition, the information processing module 200 also identifies the endpoint and calculates the heading of the unmanned mining truck's trajectory line S, generating a data structure including location information and heading information for the loading guidance point A.

[0083] The calculation module 300 can implement the core algorithm functions of system 1000, performing complex loading docking point search processes. Following a preset algorithm flow, the calculation module 300 can perform a series of calculations on loading line D, including point selection, expansion, and filtering. Through multiple filtering based on angle, distance, and safety distance conditions, it generates a set of loading docking points that meet technical requirements. Then, based on the location information of material area 20, a distance optimization algorithm is used to determine the optimal loading docking point POP from the loading docking point set P3, ensuring that the selected docking point can achieve the most efficient loading operation.

[0084] The communication module 400 performs the information output and communication functions of the system 1000, transmitting calculation results to the unmanned mining truck. The communication module 400 employs a reliable wireless communication protocol, sending the location and heading information of the optimal loading docking point (POP) in a standardized data format to the truck's onboard control system. Simultaneously, it monitors the data transmission status, ensuring the accurate delivery of critical information and providing data retransmission and acknowledgment mechanisms when necessary. This guarantees that the unmanned mining truck receives complete and accurate guidance information, thereby achieving precise automatic docking.

[0085] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. 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 disclosed in this invention can be achieved, and this is not limited herein.

[0086] 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 determining the loading and stopping points of unmanned mining trucks, characterized in that, include: Obtain mining area map information; The loading area and its boundary line are determined from the mining area map information; Acquire the material area identified by the excavator's sensors; Based on the material area, a portion of the boundary line of the loading area is selected as the loading line; The driverless mining truck is driven to the loading area and parked outside the loading area to obtain the driving trajectory of the driverless mining truck; The endpoint of the driving trajectory line is recorded as the loading guidance point, which includes heading information and location information; A loading docking point search process is performed based on the loading line and the loading guide point to obtain a set of loading docking points; Based on the material area, determine the optimal loading docking point from the set of loading docking points; as well as The location and heading information of the optimal loading and docking point are sent to the unmanned mining truck to guide its docking.

2. The method according to claim 1, characterized in that, The loading docking point locating process includes: A point-taking process is performed on the loading line at first predetermined intervals to obtain multiple points on the loading line, wherein each point includes its own position information and heading information; and The position and heading information of each point are recorded as information items for each point, and a first information item set is generated based on the information items of all points.

3. The method according to claim 2, characterized in that, The loading docking point locating process also includes: The first set of information items is expanded to obtain a second set of information items; and The augmentation process includes: rotating the heading angle of each point to the right by multiple angles to give each point multiple heading information, and obtaining multiple information items for each point based on the multiple heading information. The expansion process further includes generating the second set of information items based on the multiple information items of each of all points.

4. The method according to claim 3, characterized in that, The loading docking point locating process also includes: Iterate through each information item in the second information item set, and calculate the angle between the rays of the loading guide point and the guide point based on their position and heading information; and Based on the first set conditions, the included angles that meet the conditions and the corresponding information items are filtered out, so that the filtered information items are included in the third information item set.

5. The method according to claim 4, characterized in that, The loading docking point locating process also includes: Obtain the point corresponding to each information item in the third information item set; Iterate through each point in the plurality of points corresponding to the third information item set, and calculate the straight-line distance between each point and the loading guide point; and Based on the second set of conditions, filter the distances that meet the conditions and the corresponding points, and generate a set of filtered points based on the filtered points.

6. The method according to claim 5, characterized in that, The loading docking point locating process also includes: Traverse each filter point in the filter point set, and perform a point-taking process on the line connecting the filter point and the loading guide point at a second predetermined interval to generate a candidate point set; Calculate the straight-line distance between each candidate point in the candidate point set and its nearest boundary line; and Based on the third set of conditions, candidate points that meet the conditions are selected, and the selected candidate points are used as loading and docking points that can be used for the unmanned mining truck, and the set of loading and docking points is generated.

7. The method according to claim 6, characterized in that, Determining the optimal loading docking point also includes: Based on the material area, the loading docking point closest to the material area is selected from the set of loading docking points as the optimal loading docking point; and The optimal loading docking point includes location information and heading information, and the unmanned mining truck docks according to the location information and heading information of the optimal loading docking point.

8. The method according to any one of claims 1-7, characterized in that, The material area is located outside the loading area; and The loading line is selected as a portion of the boundary line of the loading area that is close to the material area.

9. The method according to any one of claims 1-7, characterized in that, Obtaining the heading information for each of the loading guidance point, the points on the loading line, and the optimal loading docking point includes: calculating the heading information based on the position information of each point and its adjacent points, wherein the position information is latitude and longitude information.

10. A system for determining the loading and stopping point of an unmanned mining truck, the system performing the method according to any one of claims 1-9, and the system comprising: The information receiving module is configured to receive mining area map information, obtain material areas from the excavator, and obtain the driving trajectory lines from the unmanned mining vehicle. The information processing module is configured to determine the loading area and the boundary line of the loading area from the mining area map information, select a portion of the boundary line of the loading area as the loading line according to the material area, and record the end point of the driving trajectory line as the loading guide point, wherein the loading guide point includes heading information and location information. The calculation module is configured to perform a loading docking point search process based on the loading line and the loading guide point to obtain a set of loading docking points, and determine the optimal loading docking point from the set of loading docking points based on the material area; A communication module is configured to send the location information and heading information of the optimal loading docking point to the unmanned mining truck to guide the unmanned mining truck to dock.

11. A computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-9.

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