Automatic control system of unmanned pit digging machine

Abstract

The invention discloses an automatic control system for an unmanned underground digging machine, and the system comprises a data collection module which is responsible for the collection of sensor data; the intelligent path planning module is used for generating an optimal path according to the real-time geological data and the drilling progress; the control decision module is used for generating a control instruction according to the path planning result and driving an execution mechanism of the unmanned pit digging machine; and the real-time monitoring and feedback module monitors the state of the excavator in real time and performs dynamic adjustment according to feedback. According to the invention, a geological radar, a geological sensor, a torque sensor, a position sensor, a depth sensor, a GPS module and a distance sensor are integrated, a comprehensive sensor network system is constructed, multi-dimensional data are comprehensively considered, a plurality of key factors in actual operation are combined, an accurate optimal path is generated, and the accuracy of the optimal path is improved. The working efficiency and the resource utilization rate of the excavator are remarkably improved, the system can dynamically adjust the working strategy, and it is ensured that the excavator always keeps the efficient and stable working state in the complex geological environment.

Description

Technical Field

[0001] The invention belongs to the technical field of unmanned pit excavators, and in particular relates to an automatic control system for unmanned pit excavators. Background Art

[0002] In the traditional underground construction field, the operation of excavation equipment usually relies on manual control, especially in narrow and dangerous environments, such as mines, tunnels and other underground operation scenarios. However, manual operation has many defects, such as operating errors, inefficiency and safety hazards. In addition, due to the complexity of the underground environment, the path planning of excavation equipment becomes extremely difficult, especially in the case of changeable geological conditions. How to effectively plan and adjust the excavation path to ensure the efficiency and safety of the operation has become a difficult problem that needs to be solved in the industry.

[0003] At present, although some technologies have been applied to the automated control of pit excavation, such as automatic navigation systems based on preset paths or geological condition monitoring using a single sensor, these technologies still have significant deficiencies in dealing with complex geological environments and real-time adjustments. For example, in the prior art, due to the lack of comprehensive consideration of various influencing factors when the unmanned pit excavator is working, the adopted path is often costly; and the preset path cannot dynamically respond to real-time changing geological conditions, and the data acquisition of a single sensor is limited by its installation location and measurement range, making it difficult to provide comprehensive and accurate environmental information; this results in the planning of the excavation path often being inaccurate, the excavation efficiency and resource utilization being low, and the risk of equipment damage and operation failure being increased. Based on this, the present invention hopes to provide an unmanned pit automatic control system. Summary of the invention

[0004] The object of the present invention is to provide an unmanned pit automatic control system to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An automatic control system for an unmanned pit excavator comprises: a data acquisition module, which is responsible for collecting sensor data; an intelligent path planning module, which generates an optimal path according to real-time geological data and drilling progress; a control decision module, which generates control instructions according to the path planning results and drives the actuator of the unmanned pit excavator; and a real-time monitoring and feedback module, which monitors the state of the excavator in real time and makes dynamic adjustments according to the feedback.

[0007] Preferably, in the data acquisition module, the sensors include geological radar, geological sensors and torque sensors installed on the drill bit of an unmanned pit excavator, as well as position sensors, depth sensors, GPS modules and distance sensors installed on the drilling rig; the data acquisition module is used to collect relevant data for optimal path calculation, and the relevant data include geological hardness, drilling progress, and the distance from the sampling point to the target point.

[0008] Preferably, the intelligent path planning module includes a candidate path generation unit, a path cost calculation unit and a path selection unit, the candidate path generation unit is used to generate multiple candidate paths, each path includes a number of sampling points; the path cost calculation unit performs a comprehensive path cost calculation for each generated candidate path; the path selection unit is used to sort the candidate paths according to the calculation result of the comprehensive path cost, and find the path with the minimum comprehensive path cost.

[0009] Preferably, when calculating the optimal path, the geological resistance cost item is calculated first; the specific steps are as follows:

[0010] Define the following parameters and variables:

[0011] H i (t): represents the geological hardness of the lowest i sampling points at time t;

[0012] ω1 represents the weight coefficient of geological resistance cost term;

[0013] The calculation formula for calculating the geological resistance cost is as follows:

[0014]

[0015] In this formula, the geological hardness H is expressed by using an exponential function. i (t) Impact on path cost: the harder the geology, the higher the cost; is an exponential decay function, and α is an adjustment parameter used to control the influence intensity of geological hardness.

[0016] Preferably, when calculating the optimal path, the drilling progress cost item of the drill bit is calculated first, and the specific steps are as follows:

[0017] Define the following parameters and variables:

[0018] P i (t): represents the drilling progress of the i-th sampling point at time t;

[0019] ω2 represents the weight coefficient of the drilling progress cost term;

[0020] The calculation formula of drilling progress cost is as follows:

[0021] ω2·P i (t)

[0022] In this formula, the higher the drilling progress, the lower the cost, indicating an improvement in drilling efficiency.

[0023] Preferably, when calculating the optimal path, the drilling speed cost item of the drill bit is calculated first, and the specific steps are as follows:

[0024] Define the following parameters and variables:

[0025] V i (t): represents the drilling speed of the i-th sampling point at time t;

[0026] ω4 represents the weight coefficient of the drilling speed cost term;

[0027] The calculation formula of drilling progress cost is as follows:

[0028]

[0029] In this formula, the higher the drilling speed, the lower the cost, indicating an improvement in drilling efficiency.

[0030] Preferably, when calculating the optimal path, the normalized cost item of each sampling point distance is calculated first, and the specific steps are as follows:

[0031] Define the following parameters and variables:

[0032] d i (t): represents the distance from the lowest i sampling points to the target point;

[0033] ω3 represents the weight coefficient of the distance cost term;

[0034] The calculation formula of the distance cost term is as follows:

[0035]

[0036] In this formula, the distance d is converted into i (t) is converted to a value between 0 and 1 to ensure path connectivity, where β is a tuning parameter used to control the curve speed affected by distance, and μ is a normalization parameter.

[0037] Preferably, when calculating the optimal path, the above comprehensive cost is integrated over the entire time operation T to obtain the total path cost, and then according to the calculation result, the path with the minimum comprehensive path cost is selected as the final path; the specific steps are as follows:

[0038] Define the following parameters and variables:

[0039] T: indicates the total operation time;

[0040] N: represents the number of sampling points on the path;

[0041] The calculation formula is as follows:

[0042]

[0043] The value range of this formula is a non-negative real number, which represents the total path cost after comprehensively considering geological resistance, drilling progress and path distance.

[0044] Preferably, the actuator includes a hydraulic system that provides power support for various parts of the excavator, a drive system that controls the movement of the excavator, and a drill control system that controls the drill bit. The actuator is used to generate control instructions according to the path planning results.

[0045] Preferably, the real-time monitoring and feedback module is used to continuously collect geological hardness, drilling progress and distance data in real time through the data acquisition module, and recalculate the comprehensive path cost of each candidate path based on the new real-time data. Based on the recalculated comprehensive path cost, the path with the smallest comprehensive path cost is always selected as the final path, and then the path planning is dynamically adjusted and updated based on the selected path to ensure that the path is always optimal.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] (1) The present invention provides an automatic control system for an unmanned pit excavator. The multi-sensor data acquisition module integrates geological radar, geological sensor, torque sensor, position sensor, depth sensor, GPS module and distance sensor to build a comprehensive sensor network system. Through multi-sensor data fusion processing, the system can obtain key parameters such as geological conditions and drilling progress in real time, thereby providing accurate basic data for path planning. Compared with the use of a single or a small number of sensors in the prior art, the present invention has significant advantages in data accuracy and comprehensiveness.

[0048] (2) The present invention provides an automatic control system for an unmanned pit excavator. The path planning of the intelligent path planning module not only takes into account the geological hardness and drilling progress, but also introduces multi-dimensional data such as drilling speed. By calculating the comprehensive path cost formula, an optimal path is generated that can maximize the operating efficiency and resource utilization. Compared with the existing system based on preset paths, the present invention can flexibly respond to real-time changes in complex geological environments and ensure that the excavator is always in the optimal operating state.

[0049] (3) The present invention provides an automatic control system for an unmanned pit excavator. The control decision module generates precise control instructions based on the intelligent path planning results to drive the movement and drilling operations of the excavator. The module can adjust the rotation speed and drilling depth of the drill bit in real time, further optimize the excavation process, and reduce operation time and energy consumption;

[0050] (4) The present invention provides an automatic control system for an unmanned pit excavator. The module can monitor the operating status of the excavator in real time and dynamically adjust the operating parameters according to feedback information to ensure the stability and safety of the operating process. This closed-loop control system has higher reliability and adaptability than existing open-loop or semi-open-loop systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the overall architecture structure of the present invention.

[0052] Figure 2 It is the work flow chart of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Example 1

[0055] Reference Figures 1-2 , which is the first embodiment of the present invention, provides an automatic control system for an unmanned pit excavator, comprising:

[0056] The data acquisition module is responsible for collecting sensor data; in the data acquisition module, the sensors include geological radar, geological sensors and torque sensors installed on the drill bit of the unmanned pit excavator, as well as position sensors, depth sensors, GPS modules and distance sensors installed on the drilling rig; the data acquisition module is used to collect relevant data for optimal path calculation, including geological hardness, drilling progress, and the distance from the sampling point to the target point;

[0057] The data acquisition module uses a variety of sensors, including geological radar, geological sensors, torque sensors, position sensors, depth sensors, GPS modules and distance sensors, to build a comprehensive sensor network. Through the collaborative work of multiple sensors, the system can accurately capture important parameters in the working environment; the collaborative work of these sensors enables the system to obtain detailed data in the excavator's working environment in real time, providing accurate basic data for intelligent path planning; the integrated use of this multi-sensor system not only improves the accuracy and reliability of the data, and enhances the intelligence of the entire system, but also plays a key role in real-time monitoring and feedback, enabling the system to dynamically adjust its operating strategy.

[0058] An intelligent path planning module generates an optimal path according to real-time geological data and drilling progress; the intelligent path planning module includes a candidate path generation unit, a path cost calculation unit and a path selection unit, the candidate path generation unit is used to generate multiple candidate paths, each path includes a number of sampling points; the path cost calculation unit performs a comprehensive path cost calculation for each generated candidate path;

[0059] Path cost calculation unit: The calculation formula of the comprehensive path cost not only takes into account the geological hardness and drilling progress, but also introduces the influence of drilling speed, thus more comprehensively reflecting the actual cost of each path. This calculation method makes the path selection more scientific and accurate, and can achieve the best efficiency and resource utilization under different operating conditions;

[0060] The path selection unit is used to sort the candidate paths according to the calculation results of the comprehensive path cost and find the path with the minimum comprehensive path cost; by sorting the comprehensive path costs of the candidate paths, the path with the minimum cost is finally selected; this selection method ensures that the excavator is always on the optimal path during the operation, maximizes the utilization efficiency of resources, and reduces the operation time and cost.

[0061] The intelligent path planning module generates the optimal path based on real-time geological data and drilling progress, and uses path cost calculation to evaluate the pros and cons of different candidate paths. This path planning is not just a simple path search, but a comprehensive analysis of multi-dimensional data (such as geological hardness, drilling progress, distance, etc.) to generate the optimal path that can maximize efficiency and resource utilization.

[0062] The control decision module generates control instructions according to the path planning results to drive the actuator of the unmanned pit excavator; the actuator includes a hydraulic system that provides power support for various parts of the excavator, a drive system that controls the movement of the excavator, and a drill control system that controls the drill bit. The actuator is used to generate control instructions according to the path planning results.

[0063] When calculating the optimal path, first calculate the geological resistance cost item; the specific steps are as follows:

[0064] Define the following parameters and variables: H i (t): represents the geological hardness of the lowest i sampling points at time t; ω1 represents the weight coefficient of the geological resistance cost term. In practical applications, the higher the geological hardness, the greater the drilling difficulty of the excavator. Therefore, the value of ω1 should be larger to fully reflect the impact of geological hardness on the path cost, and can be set between 0.4 and 0.6;

[0065] The calculation formula for calculating the geological resistance cost is as follows:

[0066]

[0067] In this formula, the geological hardness H is expressed by using an exponential function. i (t) Impact on path cost: the harder the address, the higher the cost; is an exponential decay function, and α (in practical applications, it can be set to a moderate value, such as between 0.1 and 0.3, to ensure that the influence of geological hardness is significant but not excessive within a certain range) is an adjustment parameter used to control the influence intensity of geological hardness.

[0068] When calculating the optimal path, the drilling progress cost of the drill bit is calculated first. The specific steps are as follows:

[0069] Define the following parameters and variables: i (t): represents the drilling progress of the i-th sampling point at time t, ω2 represents the weight coefficient of the drilling progress cost term. In practical applications, the faster the progress, the closer the drilling is to the target, and the higher the priority of path selection. It can be set between 0.3 and 0.5;

[0070] The calculation formula of drilling progress cost is as follows:

[0071] ω2·P i (t)

[0072] In this formula, the higher the drilling progress, the lower the cost, indicating an improvement in drilling efficiency.

[0073] When calculating the optimal path, the drilling speed cost of the drill bit is calculated first. The specific steps are as follows:

[0074] Define the following parameters and variables: V i (t): represents the drilling speed of the i-th sampling point at time t, ω4 represents the weight coefficient of the drilling speed cost term. The higher the drilling speed, the higher the efficiency of the excavator and the higher the priority of path selection. It can be set between 0.3 and 0.5;

[0075] The calculation formula of drilling progress cost is as follows:

[0076]

[0077] In this formula, the higher the drilling speed, the lower the cost, indicating an improvement in drilling efficiency.

[0078] When calculating the optimal path, first calculate the normalized cost term of each sampling point distance. The specific steps are as follows:

[0079] Define the following parameters and variables: i (t): represents the distance from the lowest i sampling points to the target point, ω3 represents the weight coefficient of the distance cost term. The shorter the path distance, the lower the comprehensive cost, and the higher the priority of path selection. It can be set between 0.2 and 0.4;

[0080] The calculation formula of the distance cost term is as follows:

[0081]

[0082] In this formula, the distance d is converted into i (t) is converted to a value between 0 and 1 to ensure path connectivity, where β (this parameter is used to smooth the impact of path distance and can usually be set between 0.1 and 0.5) is a tuning parameter used to control the curve speed affected by distance, and μ is a normalization parameter (this parameter is related to the average path distance or the preset target distance and is used to adjust the impact of path distance on the final path cost. It can be set according to the actual path planning goal, such as the expected length of the path);

[0083] When calculating the optimal path, the above comprehensive cost is integrated over the entire time operation T to obtain the total path cost, and then according to the calculation result, the path with the smallest comprehensive path cost is selected as the final path; the specific steps are as follows:

[0084] Define the following parameters and variables: T: represents the total operation time; N: represents the number of sampling points on the path;

[0085] The calculation formula is as follows:

[0086]

[0087] The value range of this formula is a non-negative real number, which represents the total path cost after comprehensively considering geological resistance, drilling progress and path distance;

[0088] The real-time monitoring and feedback module monitors the excavator status in real time and makes dynamic adjustments based on the feedback. The real-time monitoring and feedback module is used to continuously collect geological hardness, drilling progress and distance data in real time through the data acquisition module. According to the new real-time data, the comprehensive path cost of each candidate path is recalculated. According to the recalculated comprehensive path cost, the path with the smallest comprehensive path cost is always selected as the final path. Then, the path planning is dynamically adjusted and updated based on the selected path to ensure that the path is always optimal.

[0089] The real-time monitoring and feedback module realizes real-time monitoring of the excavator status, and dynamically adjusts the operation path and drilling strategy according to the feedback information by monitoring the excavator status in real time. This closed-loop control system ensures that the excavator can still maintain efficient and stable operation in complex geological environments, avoiding the path deviation and inefficiency caused by the lack of real-time feedback in traditional systems. The module ensures the robustness and adaptability of the system, especially when facing complex geological conditions, and can effectively avoid path deviation and potential operation failures.

[0090] In summary, this technical solution proposes a highly practical unmanned pit excavator automatic control system through the organic combination of four modules: data collection, intelligent path planning, control decision-making, and real-time monitoring and feedback. The present invention significantly improves the overall operating efficiency and resource utilization of the system through the comprehensive utilization of multi-dimensional data and the introduction of a real-time feedback mechanism.

[0091] Example 2

[0092] This is the second embodiment of the present invention. This embodiment is different from the first embodiment in that in order to verify the superiority of the automatic control system of the unmanned pit excavator proposed by the present invention, a test in a real environment was carried out. The experimental environment was selected in a mining area with complex geological conditions. The specific steps are as follows:

[0093] This solution simulates a mining area with complex geological conditions and configures an unmanned pit excavator, which is equipped with the automatic control system of the present invention, including a data acquisition module, an intelligent path planning module, a control decision module and a real-time monitoring and feedback module.

[0094] First, the geological conditions of the test area were analyzed in detail, and real-time geological data were collected through multiple sensors, including geological hardness sensors, position sensors, depth sensors, and GPS modules and distance sensors installed on the drilling rig. These data were input into the data acquisition module and used for path planning after preprocessing.

[0095] The intelligent path planning module uses the algorithm of the present invention to generate multiple candidate paths. The candidate path generation unit generates multiple paths by searching for paths between predefined starting points and target points. Each path contains several sampling points. Then, the path cost calculation unit calculates the comprehensive path cost for each candidate path. The cost not only takes into account the geological hardness, distance, drilling progress, etc., but also specifically introduces the influence of drilling speed, so that the cost calculation is more comprehensive and can reflect the actual workload and resource consumption of each path.

[0096] According to the cost calculation results, the path selection unit sorts the candidate paths, and finally selects the path with the smallest cost as the optimal path, and passes it to the control decision module. The control decision module generates control instructions according to the optimal path to drive the actuator of the unmanned pit excavator to perform the movement and drilling operations of the excavator.

[0097] The specific situation of the optimal path of the excavator generated according to the above steps is shown in Table 1:

[0098] Table 1

[0099]

[0100] As can be seen from the table above, the geological hardness, distance, drilling progress and drilling speed of different paths are significantly different. The comprehensive path cost formula is used to calculate that the comprehensive path cost of each path is different. According to the comprehensive path cost ranking, path 4 has the smallest cost of 35, while path 3 has the largest cost of 60.

[0101] In this embodiment, path 4 with the smallest comprehensive path cost is selected as the optimal path. This path has advantages in geological hardness and distance, and the drilling progress is fast and the drilling speed is moderate, so the lowest value is obtained in the comprehensive path cost calculation. This embodiment realizes the intelligent selection of the optimal path by comprehensively considering multi-dimensional data, which significantly improves the efficiency and resource utilization of the excavator operation. Compared with the prior art, it is not just a simple path search, but combines multiple key factors in actual operation to generate a more scientific and accurate optimal path. Through the real-time monitoring and feedback module, the system can dynamically adjust the operation strategy to ensure that the excavator always maintains an efficient and stable operating state in a complex geological environment, avoiding the path deviation and inefficiency caused by the lack of real-time feedback in the traditional system.

[0102] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automatic control system for an unmanned pit excavator, characterized in that: include: Data acquisition module, responsible for collecting sensor data; Intelligent path planning module, which generates the optimal path based on real-time geological data and drilling progress; The control decision module generates control instructions based on the path planning results to drive the actuators of the unmanned pit excavator. The real-time monitoring and feedback module monitors the excavator status in real time and makes dynamic adjustments based on the feedback.

2. The automatic control system for an unmanned pit excavator according to claim 1, characterized in that: In the data acquisition module, the sensors include geological radar, geological sensors and torque sensors installed on the drill bit of the unmanned pit excavator, as well as position sensors, depth sensors, GPS modules and distance sensors installed on the drilling rig; the data acquisition module is used to collect relevant data for optimal path calculation, and the relevant data include geological hardness, drilling progress, and the distance from the sampling point to the target point.

3. The automatic control system for an unmanned pit excavator according to claim 1 is characterized in that: The intelligent path planning module includes a candidate path generation unit, a path cost calculation unit and a path selection unit, wherein the candidate path generation unit is used to generate a plurality of candidate paths, each path including a plurality of sampling points; The path cost calculation unit calculates the comprehensive path cost for each generated candidate path; the path selection unit is used to sort the candidate paths according to the calculation result of the comprehensive path cost, and find the path with the minimum comprehensive path cost.

4. The automatic control system of an unmanned pit excavator according to claim 1 is characterized in that: When calculating the optimal path, first calculate the geological resistance cost item; the specific steps are as follows: Define the following parameters and variables: H i (t): represents the geological hardness of the lowest i sampling points at time t; ω1 represents the weight coefficient of geological resistance cost term; The calculation formula for calculating the geological resistance cost is as follows: In this formula, the geological hardness H is expressed by using an exponential function. i (t) Impact on path cost: the harder the geology, the higher the cost; is an exponential decay function, and α is an adjustment parameter used to control the influence intensity of geological hardness.

5. The automatic control system of an unmanned pit excavator according to claim 4 is characterized in that: When calculating the optimal path, the drilling progress cost of the drill bit is calculated first. The specific steps are as follows: Define the following parameters and variables: P i (t): represents the drilling progress of the i-th sampling point at time t; ω2 represents the weight coefficient of the drilling progress cost term; The calculation formula of drilling progress cost is as follows: ω2·Pi(t) In this formula, the higher the drilling progress, the lower the cost, indicating an improvement in drilling efficiency.

6. The automatic control system of an unmanned pit excavator according to claim 5, characterized in that: When calculating the optimal path, the drilling speed cost of the drill bit is calculated first. The specific steps are as follows: Define the following parameters and variables: V i (t): represents the drilling speed of the i-th sampling point at time t; ω4 represents the weight coefficient of the drilling speed cost term; The calculation formula of drilling progress cost is as follows: In this formula, the higher the drilling speed, the lower the cost, indicating an improvement in drilling efficiency.

7. The automatic control system of an unmanned pit excavator according to claim 6 is characterized in that: When calculating the optimal path, first calculate the normalized cost term of each sampling point distance. The specific steps are as follows: Define the following parameters and variables: d i (t): represents the distance from the lowest i sampling points to the target point; ω3 represents the weight coefficient of the distance cost term; The calculation formula of the distance cost term is as follows: In this formula, the distance d is converted into i (t) is converted to a value between 0 and 1 to ensure path connectivity, where β is a tuning parameter used to control the curve speed affected by distance, and μ is a normalization parameter.

8. The automatic control system of an unmanned pit excavator according to claim 7, characterized in that: When calculating the optimal path, the above comprehensive cost is integrated over the entire time operation T to obtain the total path cost, and then according to the calculation result, the path with the smallest comprehensive path cost is selected as the final path; the specific steps are as follows: Define the following parameters and variables: T: indicates the total operation time; N: represents the number of sampling points on the path; The calculation formula is as follows: The value range of this formula is a non-negative real number, which represents the total path cost after comprehensively considering geological resistance, drilling progress and path distance.

9. The automatic control system for an unmanned pit excavator according to claim 8, characterized in that: The actuator includes a hydraulic system that provides power support for various parts of the excavator, a drive system that controls the movement of the excavator, and a drill control system that controls the drill bit. The actuator is used to generate control instructions according to the path planning results.

10. The automatic control system for an unmanned pit excavator according to claim 1, characterized in that: The real-time monitoring and feedback module is used to continuously collect geological hardness, drilling progress and distance data in real time through the data acquisition module. According to the new real-time data, the comprehensive path cost of each candidate path is recalculated. According to the recalculated comprehensive path cost, the path with the smallest comprehensive path cost is always selected as the final path. Then, the path planning is dynamically adjusted and updated according to the selected path to ensure that the path is always optimal.

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