Intelligent mine unmanned driving system and method

Abstract

The embodiment of the invention provides an intelligent mine unmanned driving control system and method which are applied to an unmanned driving mine card, and the method comprises the steps: receiving planning path data and operation parameter data issued by a cloud platform; generating an initial driving path based on the current loading position, the target unloading position and the planned path data; according to the received operation parameter data, generating a corresponding fuel consumption optimization strategy; identifying the size and the type of an obstacle by receiving sensing data fused by multiple sensors, and generating a target driving path by combining the initial driving path; driving to the target loading position according to the target driving path on the basis of the current loading position, and executing the fuel consumption optimization strategy in the driving process; and performing local path planning at the target unloading position according to the retaining wall position so as to accurately stop at the target unloading point. And the safety and efficiency of mining area operation are improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of driverless technology, and in particular, to an intelligent mine driverless system and method. Background Art

[0002] In traditional mine transportation operations, there are problems such as great potential safety hazards and low efficiency. With the development of driverless technology, how to achieve efficient and safe driverless in the mine environment has become an urgent problem to be solved. Summary of the Invention

[0003] The embodiments of the present invention aim to at least solve one of the technical problems existing in the prior art, and provide an intelligent mine driverless system and method.

[0004] In a first aspect, the embodiments of the present invention provide an intelligent mine driverless control method, which is applied to a driverless mining truck. The method includes:

[0005] Receiving the planned path data and operation parameter data sent from the cloud platform;

[0006] Generating an initial driving path based on the current loading position, the target unloading position, and the planned path data;

[0007] Generating a corresponding fuel consumption optimization strategy according to the received operation parameter data;

[0008] Identifying the size and type of obstacles by receiving the perception data of multi-sensor fusion, and generating a target driving path in combination with the initial driving path;

[0009] Driving from the current loading position to the target loading position according to the target driving path, and executing the fuel consumption optimization strategy during the driving process;

[0010] Performing local path planning according to the retaining wall position at the target unloading position to accurately dock at the target unloading point.

[0011] In some possible embodiments, the generating an initial driving path based on the current loading position, the target unloading position, and the planned path data includes:

[0012] Obtaining the current loading position of the driverless mining truck through a GPS positioning system or a mine map coordinate system, and obtaining the target unloading position from the cloud platform;

[0013] Using a path planning algorithm, and generating the initial driving path in combination with the planned path data, the current loading position, and the target unloading position.

[0014] In some possible embodiments, the operating parameter data includes vehicle speed, rotational speed, and throttle opening. Generating a corresponding fuel consumption optimization strategy based on the received operating parameter data includes:

[0015] Establishing a relationship model between the vehicle speed, rotational speed, throttle opening, and fuel quantity;

[0016] Based on the relationship model, determining the target vehicle speed, target rotational speed, and throttle target opening corresponding to the optimal fuel consumption to obtain a fuel consumption optimization strategy.

[0017] In some possible embodiments, identifying the size and type of obstacles by receiving perception data of multi-sensor fusion and generating a target driving path in combination with the initial driving path includes:

[0018] Based on a convolutional neural network model, identifying the size and type of obstacles from the received perception data;

[0019] Evaluating the collision risk according to the size and type of the obstacle and the initial driving path;

[0020] Based on the risk assessment result, formulating a corresponding obstacle avoidance strategy and generating the target driving path.

[0021] In some possible embodiments, performing local path planning according to the retaining wall position at the target unloading position to accurately dock at the target unloading point includes:

[0022] Calculating a local path that can avoid the retaining wall and guide the driverless mining truck to accurately dock according to the current position of the driverless mining truck, the target unloading point, and the position of the retaining wall;

[0023] Driving according to the local path, continuously monitoring the position and attitude of the driverless mining truck during the process of approaching the target unloading point, and dynamically adjusting the driving route as needed until accurately docking at the target unloading point.

[0024] In a second aspect, an embodiment of the present invention provides an intelligent mine driverless control system applied to a driverless mining truck. The system includes:

[0025] A receiving module, configured to receive the planned path data and operating parameter data sent from the cloud platform;

[0026] A first generating module, configured to generate an initial driving path based on the current loading position, the target unloading position, and the planned path data;

[0027] A second generating module, configured to generate a corresponding fuel consumption optimization strategy according to the received operating parameter data;

[0028] A third generation module, configured to receive the perception data of multi-sensor fusion, identify the size and type of obstacles, and generate a target driving path in combination with the initial driving path;

[0029] A control module, configured to drive to the target loading position according to the target driving path based on the current loading position, and execute the fuel consumption optimization strategy during the driving process;

[0030] A planning module, configured to perform local path planning according to the retaining wall position at the target unloading position to accurately dock at the target unloading point.

[0031] In some possible embodiments, the first generation module is further specifically configured to:

[0032] Obtain the current loading position of the driverless mining truck through a GPS positioning system or a mining area map coordinate system, and obtain the target unloading position from the cloud platform;

[0033] Use a path planning algorithm to generate the initial driving path in combination with the planned path data, the current loading position, and the target unloading position.

[0034] In some possible embodiments, the second generation module is further specifically configured to:

[0035] Establish a relationship model between the vehicle speed, rotational speed, throttle opening, and fuel quantity;

[0036] Based on the relationship model, determine the target vehicle speed, target rotational speed, and throttle target opening corresponding to the optimal fuel consumption to obtain a fuel consumption optimization strategy.

[0037] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0038] One or more processors;

[0039] A storage unit, configured to store one or more programs, and when the one or more programs are executed by the one or more processors, enable the one or more processors to implement the method described above.

[0040] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it can implement the method described above.

[0041] In a fifth aspect, an embodiment of the present invention provides a computer program product, including a computer program, and when the computer program is executed by a processor, it can implement the method described above.

[0042] The intelligent mine driverless control method and system according to the embodiments of the present invention can perceive the surrounding environment in real time through multi-sensor fusion technology, identify the size and type of obstacles, and dynamically adjust the driving path, greatly reducing the accident risk, especially in complex and dangerous mine environments. In addition, generating an optimized fuel consumption strategy based on operation parameter data helps reduce fuel usage, lower operating costs, and also reduces carbon emissions, which is beneficial to environmental protection. The automated path planning and adjustment can ensure that the mining truck efficiently and accurately reaches the unloading point from the loading point, reducing the time for unnecessary stops and re-planning the path, and improving the overall operation efficiency. Conducting local path planning according to the retaining wall position at the target unloading position enables the driverless mining truck to accurately dock at the designated unloading point, enhancing the operation accuracy, especially suitable for scenarios with limited space or requiring high-precision operations. By sending the planned path data and operation parameter data through the cloud platform, remote management and control of the driverless mining truck are achieved, enhancing the flexibility and response speed of mine operations. Moreover, the path planning and driving strategy can be flexibly adjusted according to different mine environmental conditions (such as terrain, weather, etc.), showing strong adaptability.

[0043] In summary, this intelligent mine driverless control method not only improves the safety and efficiency of mine operations, but also contributes to environmental protection and economy. By integrating advanced sensing technologies, path planning algorithms, and cloud data processing capabilities, this method represents a major advancement in the field of mine transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is a flowchart of the intelligent mine driverless control method according to the embodiments of the present invention;

[0046] Figure 2 It is a schematic structural diagram of the intelligent mine driverless control system according to the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] To enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0048] Unless otherwise specifically stated, the technical terms or scientific terms used in the embodiments of the present invention should have the ordinary meaning understood by those with ordinary skills in the field to which the present invention pertains. The use of "including" or "comprising" and the like in the embodiments of the present invention neither limits the mentioned shapes, numbers, steps, actions, operations, components, elements and / or their groups, nor excludes the occurrence or addition of one or more other different shapes, numbers, steps, actions, operations, components, elements and / or their groups, or the addition of these. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity and order of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise specifically and clearly defined.

[0049] Unless otherwise specifically stated, the relative settings, numerical expressions and numerical values of the components and steps described in these embodiments do not limit the scope of the present invention. At the same time, it should be understood that, for the sake of convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. For technologies, methods and devices known to those of ordinary skill in the relevant field, they may not be discussed in detail, but where appropriate, the shown technologies, methods and devices should be regarded as part of the authorization specification. In all the examples shown and discussed here, any specific other example may have different values. It should be noted that: similar symbols and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0050] In the description of the embodiments of the present invention, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In the embodiments of the present invention, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in the embodiments of the present invention and the features of different embodiments or examples.

[0051] Next, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited by the exemplary embodiments described herein.

[0052] Figure 1 It is a flowchart of an intelligent mine driverless control method according to an embodiment of the present invention. As Figure 1 shown, the embodiment of the present invention relates to an intelligent mine driverless control method applied to a driverless mining truck. The method includes the following steps S101 to step S106:

[0053] Step S101, receive the planned path data and operation parameter data sent from the cloud platform.

[0054] Step S102, generate an initial driving path based on the current loading position, the target unloading position, and the planned path data.

[0055] Specifically, in this step, the current loading position of the driverless mining truck is obtained through a GPS positioning system or a mining area map coordinate system, and the target unloading position is obtained from the cloud platform; a path planning algorithm is used to combine the planned path data, the current loading position, and the target unloading position to generate the initial driving path.

[0056] Step S103, generate a corresponding fuel consumption optimization strategy according to the received operation parameter data.

[0057] Specifically, in this step, the operation parameter data includes vehicle speed, engine speed, and throttle opening. Generating a corresponding fuel consumption optimization strategy according to the received operation parameter data includes: establishing a relationship model between the vehicle speed, engine speed, throttle opening, and fuel quantity; determining the target vehicle speed, target engine speed, and throttle target opening corresponding to the optimal fuel consumption based on the relationship model to obtain the fuel consumption optimization strategy.

[0058] Step S104: By receiving the perception data fused by multiple sensors, identify the size and type of obstacles, and generate a target driving path in combination with the initial driving path.

[0059] Specifically, in this step, based on the convolutional neural network model, identify the size and type of obstacles from the received perception data; evaluate the collision risk according to the size, type of the obstacles and the initial driving path; based on the risk assessment result, formulate a corresponding obstacle avoidance strategy, and generate the target driving path.

[0060] Step S105: Drive to the target loading position according to the target driving path based on the current loading position, and execute the fuel consumption optimization strategy during the driving process.

[0061] Step S106: Perform local path planning according to the position of the retaining wall at the target unloading position to accurately dock at the target unloading point.

[0062] Specifically, in this step, calculate a local path that can avoid the retaining wall and guide the driverless mining truck to accurately dock according to the current position of the driverless mining truck, the target unloading point and the position of the retaining wall; drive according to the local path, and continuously monitor its position and attitude during the process of the driverless mining truck approaching the target unloading point, and dynamically adjust the driving route as needed until it accurately docks at the target unloading point.

[0063] The intelligent mine driverless control method of the embodiment of the present invention can real-time sense the surrounding environment through multi-sensor fusion technology, identify the size and type of obstacles, and dynamically adjust the driving path, greatly reducing the accident risk, especially in complex and dangerous mining area environments. In addition, generating a fuel consumption optimization strategy based on the operation parameter data helps to reduce the fuel usage, reduce the operation cost while also reducing the carbon emissions, which is beneficial to environmental protection. The automatic path planning and adjustment can ensure that the mining truck efficiently and accurately reaches the unloading point from the loading point, reducing the time of unnecessary parking and re-planning the path, and improving the overall operation efficiency. Performing local path planning according to the position of the retaining wall at the target unloading position enables the driverless mining truck to accurately dock at the designated unloading point, improving the operation accuracy, especially suitable for scenarios with limited space or requiring high-precision operations. By sending the planned path data and operation parameter data through the cloud platform, the remote management and control of the driverless mining truck are realized, enhancing the flexibility and response speed of the mining area operation. And, it can also flexibly adjust its path planning and driving strategy according to different mining area environmental conditions (such as terrain, weather, etc.), showing strong adaptability.

[0064] In summary, this intelligent mine driverless control method not only improves the safety and efficiency of mining area operations, but also contributes to environmental protection and economy. By integrating advanced sensing technologies, path planning algorithms, and cloud data processing capabilities, this method represents a major advancement in the field of mine transportation.

[0065] Figure 2 FIG. is a schematic structural diagram of the intelligent mine driverless control system according to an embodiment of the present invention, as Figure 2 shown, an embodiment of the present invention provides an intelligent mine driverless control system, which is applied to a driverless mining truck. The system includes: a receiving module 201, a first generating module 202, a second generating module 203, a third generating module 204, a control module 205, and a planning module 206.

[0066] Specifically, the receiving module 201 is configured to receive the planned path data and the operating parameter data sent from the cloud platform. The first generating module 202 is configured to generate an initial driving path based on the current loading position, the target unloading position, and the planned path data. The second generating module 203 is configured to generate a corresponding fuel consumption optimization strategy according to the received operating parameter data. The third generating module 204 is configured to identify the size and type of obstacles by receiving the perception data of multi-sensor fusion, and generate a target driving path in combination with the initial driving path. The control module 205 is configured to drive from the current loading position to the target loading position according to the target driving path, and execute the fuel consumption optimization strategy during the driving process. The planning module 206 is configured to perform local path planning according to the retaining wall position at the target unloading position to accurately park at the target unloading point.

[0067] The intelligent unmanned control system for mines in the embodiments of the present invention uses multi-sensor fusion technology to perceive the surrounding environment in real time, identify the size and type of obstacles, and dynamically adjust the driving path, greatly reducing the accident risk, especially in complex and dangerous mine environments. In addition, an optimized fuel consumption strategy is generated based on the operating parameter data, which helps to reduce the fuel usage, lower the operating cost, and also reduce the carbon emissions, being beneficial to environmental protection. The automated path planning and adjustment can ensure that the mining truck efficiently and accurately reaches the unloading point from the loading point, reducing the time for unnecessary stops and re-planning the path, and improving the overall operation efficiency. Local path planning is carried out according to the position of the retaining wall at the target unloading position, enabling the unmanned mining truck to accurately dock at the designated unloading point, improving the operation accuracy, especially applicable to scenarios with limited space or requiring high-precision operations. By sending the planned path data and operating parameter data through the cloud platform, remote management and control of the unmanned mining truck are achieved, enhancing the flexibility and response speed of mine operations. Moreover, the path planning and driving strategy can be flexibly adjusted according to different mine environmental conditions (such as terrain, weather, etc.), showing strong adaptability.

[0068] In summary, such an intelligent unmanned control system for mines not only improves the safety and efficiency of mine operations, but also contributes to environmental protection and economy. By integrating advanced sensing technologies, path planning algorithms, and cloud data processing capabilities, this method represents a major advancement in the field of mine transportation.

[0069] In some embodiments, as Figure 2 shown, the first generation module 202 is specifically further configured to: obtain the current loading position of the unmanned mining truck through the GPS positioning system or the mine map coordinate system, and obtain the target unloading position from the cloud platform; use a path planning algorithm, in combination with the planned path data, the current loading position, and the target unloading position, to generate the initial driving path.

[0070] In some embodiments, as Figure 2 shown, the second generation module 203 is specifically further configured to: establish a relationship model between the vehicle speed, rotational speed, throttle opening, and fuel quantity; based on the relationship model, determine the target vehicle speed, target rotational speed, and throttle target opening corresponding to the optimal fuel consumption, and obtain an optimized fuel consumption strategy.

[0071] Based on the same inventive concept, embodiments of the present invention provide an electronic device, including:

[0072] One or more processors;

[0073] A storage unit for storing one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the method described above.

[0074] Based on the same inventive concept, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, it can implement the method described above.

[0075] Among them, the computer-readable medium may be included in the device, equipment, or system of the present invention, or may exist separately.

[0076] Among them, the computer-readable storage medium may be any tangible medium that contains or stores a program, and it may be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment. More specific examples include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, an optical fiber, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0077] Among them, the computer-readable storage medium may also include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Specific examples include, but are not limited to, electromagnetic signals, optical signals, or any suitable combination thereof.

[0078] Based on the same inventive concept, an embodiment of the present invention provides a computer program product including a computer program, and when the computer program is executed by a processor, it can implement the method described above.

[0079] It can be understood that the above embodiments are merely exemplary embodiments adopted to illustrate the principles of the present invention, but the present invention is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered within the protection scope of the present invention.

Claims

1. An intelligent mine unmanned driving control method, characterized in that: Applied to unmanned mining trucks, the method includes: Receive planning path data and operating parameter data from the cloud platform; generating an initial driving path based on the current loading position, the target unloading position, and the planned path data; generating a corresponding fuel consumption optimization strategy based on the received operating parameter data; By receiving multi-sensor fusion perception data, identifying the size and type of obstacles, and combining the initial driving path, generating a target driving path; driving to the target loading position according to the target driving path based on the current loading position, and executing the fuel consumption optimization strategy during driving; At the target unloading position, local path planning is performed according to the position of the retaining wall to accurately dock at the target unloading point.

2. The method according to claim 1, characterized in that The generating of the initial driving path based on the current loading position, the target unloading position and the planned path data includes: Obtaining the current loading position of the unmanned mining truck through the GPS positioning system or the mine map coordinate system, and obtaining the target unloading position from the cloud platform; The initial driving path is generated by using a path planning algorithm in combination with the planned path data, the current loading position, and the target unloading position.

3. The method according to claim 1, characterized in that The operating parameter data includes vehicle speed, rotation speed, and throttle opening. Generating a corresponding fuel consumption optimization strategy based on the received operating parameter data includes: Establishing a relationship model between the vehicle speed, rotation speed, throttle opening and fuel quantity; The target vehicle speed, target rotation speed and target throttle opening corresponding to the optimal fuel consumption are determined based on the relationship model to obtain a fuel consumption optimization strategy.

4. The method according to any one of claims 1 to 3, characterized in that The method of receiving multi-sensor fusion perception data, identifying the size and type of obstacles, and generating a target driving path based on the initial driving path includes: Based on the convolutional neural network model, the obstacle size and type are identified from the received perception data; Assessing collision risk based on the size and type of the obstacle and the initial driving path; Based on the risk assessment results, a corresponding obstacle avoidance strategy is formulated and the target driving path is generated.

5. The method according to any one of claims 1 to 3, characterized in that The local path planning is performed at the target unloading position according to the position of the retaining wall to accurately dock at the target unloading point, including: Based on the current position of the unmanned mining truck, the target unloading point, and the location of the retaining wall, a local path is calculated that can avoid the retaining wall and guide the unmanned mining truck to an accurate stop. The truck drives along the local path and continuously monitors its position and posture as it approaches the target unloading point, and dynamically adjusts the driving route as needed until it accurately stops at the target unloading point.

6. An intelligent unmanned mine driving control system, characterized in that: Applied to unmanned mining trucks, the system includes: The receiving module is used to receive the planned path data and operating parameter data sent from the cloud platform; A first generating module, configured to generate an initial driving path based on a current loading position, a target unloading position, and the planned path data; a second generating module, configured to generate a corresponding fuel consumption optimization strategy based on the received operating parameter data; A third generation module is used to identify the size and type of obstacles by receiving multi-sensor fusion perception data, and generate a target driving path based on the initial driving path; a control module, configured to drive to the target loading position according to the target driving path based on the current loading position, and execute the fuel consumption optimization strategy during driving; The planning module is used to perform local path planning at the target unloading position according to the position of the retaining wall so as to accurately dock at the target unloading point.

7. The system according to claim 6, characterized in that The first generating module is further configured to: Obtaining the current loading position of the unmanned mining truck through the GPS positioning system or the mine map coordinate system, and obtaining the target unloading position from the cloud platform; The initial driving path is generated by using a path planning algorithm in combination with the planned path data, the current loading position, and the target unloading position.

8. The system according to claim 6 or 7, characterized in that The second generating module is further configured to: Establishing a relationship model between the vehicle speed, rotation speed, throttle opening and fuel quantity; The target vehicle speed, target rotation speed and target throttle opening corresponding to the optimal fuel consumption are determined based on the relationship model to obtain a fuel consumption optimization strategy.

9. An electronic device, characterized in that: include: one or more processors; A storage unit, configured to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the method according to any one of claims 1 to 5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 can be implemented.

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