Motion control system of trackless rubber-wheeled electric vehicle in coal mine

Abstract

The present invention relates to the field of motion control technology, specifically to a motion control system for a trackless rubber-wheeled electric vehicle in a coal mine, including an underground terrain data acquisition module, a speed adaptation analysis module, an anti-skid torque control module, and an obstacle processing and path planning module. In the present invention, by collecting the height and width data of the underground terrain, combined with the real-time measurement of the ground hardness and humidity, the system is allowed to perform highly adaptive navigation in narrow and complex mine passages, by continuously calculating the height difference between adjacent points, and dynamically adjusting the wheel torque to cope with changes in ground conditions, the stability of the vehicle and the ability to adapt to different mine environments are significantly improved, by real-time monitoring of the matching of the vehicle speed and the ground slope, and dynamically adjusting the direction of the vehicle according to the position of the obstacle, the vehicle's reaction ability and the accuracy of obstacle avoidance are enhanced, the dependence on manual operation is reduced, and the operation risk is greatly reduced.

Description

Technical Field

[0001] The invention relates to the technical field of motion control, in particular to a motion control system for a trackless rubber-wheeled electric vehicle in an underground coal mine. Background Art

[0002] The field of motion control technology focuses on the precise management and guidance of the motion of dynamic systems in mechanical equipment. It involves the integration of hardware (such as sensors, controllers, motors, and drivers) and software to command and adjust the speed, position, acceleration, and torque of these devices. It is widely used in industrial automation, robotics, precision positioning technology, and power system design for special vehicles. When designing a system, it is necessary not only to consider physical limitations and operational efficiency, but also to ensure the environmental adaptability and reliability of the system, especially in applications in extreme or special environments.

[0003] Among them, the motion control system of the rubber-tyred trackless electric vehicle in underground coal mines is an advanced motion control system designed for specific mining environments. The main purpose of the system is to improve the safety and efficiency of underground coal mine operations. It ensures accurate navigation and stable operation in narrow and complex mine passages by precisely controlling the movement of the rubber-tyred trackless electric vehicle. It uses specific algorithms and sensing technologies to adapt to the changing environment and road conditions underground, thereby improving the speed and safety of transportation operations and reducing the need for manual operation and related risks.

[0004] The patent with authorization announcement number CN204586544U discloses a motion control system for a trackless rubber-wheeled electric vehicle in a coal mine, including an operation module, a motion controller, a power converter, a motor and a wheel. The operation module is connected to the motion controller through an input signal isolation circuit, and the motion controller is connected to the power converter through an output signal isolation circuit. The power converter is connected to the motor, and the motor is connected to the drive wheel; the motion controller is connected to the battery, the battery is connected to the power converter, and the motor is also electrically connected to the motion controller. The wheels are equipped with wet brakes. The above technical solution avoids irregular demands on the drive control system due to special factors in coal mines, thereby improving the power performance of the whole vehicle. When braking downhill, it can effectively increase the amount of electric energy feedback of the electric vehicle, thereby increasing the cruising range of the whole vehicle and accelerating the application of trackless rubber-wheeled electric vehicles in coal mines. However, due to the lack of high adaptability to the changing environment underground, it is limited in safety and operating efficiency. Especially in terms of the balance between physical limitations and operational efficiency, existing systems are often unable to quickly adapt to sudden changes in terrain, such as unexpected slope changes or slippery ground conditions. The lack of technical limitations in highly dynamic adjustment capabilities may lead to slow responses and increase the possibility of accidents. At the same time, the lack of real-time terrain analysis and dynamic path adjustment capabilities has led to obstacle avoidance strategies that rely on manual operations, which increases labor costs and operational errors, especially in complex or unstable underground environments, which will directly affect the overall operational efficiency and safety standards. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a motion control system for a trackless rubber-tyred electric vehicle in a coal mine.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: The motion control system of the underground trackless rubber-wheeled electric vehicle in a coal mine comprises:

[0007] The underground terrain data acquisition module collects the height and width data of the underground terrain through a ground scanner, uses a ground material sensor and a humidity sensor to measure the hardness and humidity of the underground ground respectively, records the terrain changes at each measuring point, performs three-dimensional space mapping based on the change records, continuously calculates the height difference between adjacent points in the three-dimensional space, and performs comprehensive data processing to output a terrain feature data set;

[0008] The speed adaptation analysis module extracts the underground ground slope information from the terrain feature data set, monitors the current speed of the trackless rubber-tyred electric vehicle through a speed sensor, adjusts the speed setting according to the slope, matches the underground terrain slope characteristics, and outputs the speed adjustment parameters;

[0009] The anti-skid torque control module uses the speed adjustment parameter to set the starting torque value of the wheel of the rubber-tyred trackless electric vehicle, uses a torque sensor to monitor the actual output torque of each tire in real time, collects ground grip feedback synchronously based on the monitoring records, dynamically adjusts the wheel torque, and outputs the optimal torque distribution data;

[0010] The obstacle processing and path planning module collects road condition information ahead of the rubber-wheeled trackless electric vehicle's driving path through the camera, collects obstacle data, analyzes the obstacle position and size, calculates the obstacle avoidance path based on the analysis results combined with the optimal torque distribution data, adjusts the vehicle direction in real time, and forms an adaptive trackless rubber-wheeled electric vehicle obstacle avoidance path planning plan.

[0011] As a further solution of the present invention, the steps of recording the terrain changes at each measuring point are:

[0012] Deploy ground scanners at predetermined measuring points to measure the height and width of underground terrain, including , , Coordinates, using the formula:

[0013] ,

[0014] Get coordinate dataset ,in, , and Represents the measuring points The horizontal, vertical and height coordinates of is the number of measuring points;

[0015] Combine the coordinates of each point in the coordinate data set, combine with the ground material sensor and the humidity sensor, measure the ground hardness and humidity, and obtain the underground ground hardness and humidity data set;

[0016] Based on the underground ground hardness and humidity data set, the difference between the hardness data and the humidity data is compared, and the formula is used:

[0017] ,

[0018] Calculate measurement points The amount of terrain change , generate underground terrain change records, where is the specific gravity coefficient, which is used to enhance the sensitivity of terrain change records. Indicates measuring point Hardness data, Indicates measuring point Humidity data.

[0019] As a further solution of the present invention, the steps of acquiring the terrain feature data set are:

[0020] According to the underground terrain change records, three-dimensional space mapping is performed, and the linear interpolation method is used to fill the data gaps. The formula is:

[0021] ,

[0022] Calculate position Interpolated height value of , generate three-dimensional mapping results, For measuring point The terrain change value, is the distance weight, which is used to adjust the influence of the measured point on the current point interpolation;

[0023] Based on the three-dimensional mapping results, the height difference between adjacent points is calculated using the formula:

[0024] ,

[0025] Calculate position The height difference at , generate a height difference dataset, where represents the horizontal gradient value, Indicates the vertical gradient value;

[0026] The height difference dataset was statistically analyzed to assess the significant features of the terrain using the formula:

[0027] ,

[0028] Calculate the mean square of height differences , which is used to describe the overall changes in the underground terrain and obtain the terrain feature data set.

[0029] As a further solution of the present invention, the step of obtaining the speed adjustment parameter is:

[0030] Based on the terrain feature data set, the underground ground slope is analyzed using the formula:

[0031] ,

[0032] Calculate measurement points Slope angle , get the terrain slope information, where Represents the height difference, Represents the horizontal distance component from the measuring point to the reference point;

[0033] Using the terrain slope information, a weighted average is used to determine the average slope of the entire area using the formula:

[0034] ,

[0035] Calculate the average slope value ,in, is the weight coefficient, reflecting the contribution of the difference measurement point to the average value, is the number of measuring points;

[0036] Based on the average slope value, the speed of the electric vehicle measured by the speed sensor , using the formula:

[0037] ,

[0038] Calculate the speed of electric vehicle regulation , matching the slope change, generating speed adjustment parameters, where It is the speed adjustment factor, which is used to increase or decrease the speed adjustment range.

[0039] As a further solution of the present invention, the step of real-time monitoring of the actual output torque of each tire is:

[0040] Using the speed adjustment parameter, the wheel starting torque value is set according to the vehicle performance characteristics, using the formula:

[0041] ,

[0042] Calculate the starting torque value ,in, represents the torque coefficient, which is used to adjust the nonlinear response of vehicle dynamics to speed;

[0043] Use a torque sensor to monitor the actual output torque of the vehicle, combined with the starting torque value, using the formula:

[0044] ,

[0045] Calculate and output the actual torque deviation value , used to evaluate the difference between the torque output and the target torque, where Indicates the actual output torque value of the vehicle;

[0046] According to the actual torque deviation value, the torque is initially adjusted using the formula:

[0047] ,

[0048] Calculate and output the initial adjusted torque value , optimize vehicle driving performance.

[0049] As a further solution of the present invention, the step of obtaining the optimal torque distribution data is:

[0050] Collect torque sensor data and obtain ground grip feedback simultaneously, using the formula:

[0051] ,

[0052] Calculating Total Ground Grip Value ,in, It is The ground reaction force of each wheel, is the ground grip coefficient, reflecting the influence of ground conditions. Indicates the number of wheels;

[0053] Using the total ground grip value and the initially adjusted torque value, the differential wheel torque adjustment is dynamically calculated using the formula:

[0054] ,

[0055] Output wheel dynamic adjustment torque value ,in, is the theoretical maximum grip force value;

[0056] Integrate the wheel dynamic adjustment torque value, using the formula:

[0057] ,

[0058] Select the optimal torque value , optimize vehicle performance and output optimal torque distribution data.

[0059] As a further solution of the present invention, the analysis steps of the obstacle position and volume are as follows:

[0060] Use the camera to continuously collect the video stream in front of the trackless rubber-wheeled electric vehicle, perform image recognition, and mark the obstacles in the image. The formula is:

[0061] ,

[0062] Calculate preliminary obstacle recognition strength , generate preliminary obstacle identification data, where Represents the average brightness of the collected video frames, is the adjustment coefficient, which is used to adjust the recognition sensitivity. is the brightness response coefficient;

[0063] The preliminary obstacle identification data is analyzed to determine the position and volume of each obstacle using the formula:

[0064] ,

[0065] Calculate the weighted and adjusted obstacle characteristic strength value , output obstacle data analysis results, where, is the magnification factor, is the baseline shift, is the linear adjustment coefficient, used to adjust the calculation accuracy;

[0066] The obstacle data analysis results are integrated, the position and volume data of each obstacle are combined, and an obstacle comprehensive data set is output.

[0067] As a further solution of the present invention, the steps for obtaining the obstacle avoidance path planning scheme of the trackless rubber-wheeled electric vehicle are:

[0068] Combined with the obstacle comprehensive data set and the optimal torque distribution data, path planning is performed using the formula:

[0069] ,

[0070] Calculate and output the path selection index ,in, , and To adjust the parameters to balance the impact of obstacle data and torque distribution, is the scaling factor;

[0071] According to the path selection index, the vehicle direction is adjusted, the responsiveness of the direction control is optimized, and the direction adjustment result of the trackless rubber-tyred electric vehicle is output;

[0072] According to the direction adjustment result of the trackless rubber-wheeled electric vehicle, the road condition changes on the vehicle's driving path are continuously monitored, the vehicle direction is dynamically adjusted, and an adaptive trackless rubber-wheeled electric vehicle obstacle avoidance path planning scheme is formed.

[0073] Compared with the prior art, the advantages and positive effects of the present invention are:

[0074] In the present invention, by collecting height and width data of underground terrain and combining it with real-time measurement of ground hardness and humidity, the system is allowed to perform highly adaptive navigation in narrow and complex mine passages. By continuously calculating the height difference between adjacent points in three-dimensional space and dynamically adjusting wheel torque to cope with changes in ground conditions, the stability of the vehicle and its ability to adapt to different mine environments are significantly improved. By real-time monitoring of the matching of vehicle speed and ground slope and dynamically adjusting the direction of the vehicle according to the location of obstacles, the responsiveness of electric vehicles and the accuracy of obstacle avoidance are enhanced, which not only reduces dependence on manual operation but also greatly reduces operational risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 is a system flow chart of the present invention;

[0076] Figure 2 A flow chart for recording terrain changes at each measuring point of the present invention;

[0077] Figure 3 A flowchart for obtaining a terrain feature data set of the present invention;

[0078] Figure 4 The present invention is a flow chart for obtaining speed adjustment parameters;

[0079] Figure 5 This is a flow chart of the present invention for real-time monitoring of the actual output torque of each tire;

[0080] Figure 6 This is a flow chart for obtaining optimal torque distribution data of the present invention;

[0081] Figure 7 The following is a flow chart for analyzing the position and volume of obstacles of the present invention;

[0082] Figure 8 The present invention is a flowchart for obtaining the obstacle avoidance path planning scheme of the trackless rubber-wheeled electric vehicle. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0084] See also Figure 1 The motion control system of the underground trackless rubber-wheeled electric vehicle in coal mines includes:

[0085] The underground terrain data acquisition module collects the height and width data of the underground terrain through a ground scanner, uses a ground material sensor and a humidity sensor to measure the hardness and humidity of the underground ground respectively, records the terrain changes at each measuring point, performs three-dimensional space mapping based on the change records, continuously calculates the height difference between adjacent points in the three-dimensional space, and performs comprehensive data processing to output a terrain feature data set;

[0086] The speed adaptation analysis module extracts the underground ground slope information from the terrain feature data set, monitors the current speed of the trackless rubber-tyred electric vehicle through the speed sensor, adjusts the speed setting according to the slope, matches the underground terrain slope characteristics, and outputs the speed adjustment parameters;

[0087] The anti-skid torque control module uses speed adjustment parameters to set the starting torque value of the wheels of the rubber-tyred trackless electric vehicle, uses torque sensors to monitor the actual output torque of each tire in real time, collects ground grip feedback synchronously based on monitoring records, dynamically adjusts wheel torque, and outputs optimal torque distribution data;

[0088] The obstacle handling and path planning module collects road condition information ahead of the rubber-tyred trackless electric vehicle’s driving path through the camera, collects obstacle data, analyzes the obstacle location and size, calculates the obstacle avoidance path based on the analysis results combined with the optimal torque distribution data, adjusts the vehicle direction in real time, and forms an adaptive trackless rubber-tyred electric vehicle obstacle avoidance path planning solution.

[0089] The terrain feature dataset includes height features, width features, ground hardness index and humidity index. The speed adjustment parameters include speed setting value and slope adaptability. The optimal torque distribution data includes starting torque setting value, real-time torque monitoring records and grip feedback data. The adaptive trackless rubber-wheeled electric vehicle obstacle avoidance path planning scheme includes obstacle positioning results, path selection results and direction adjustment records.

[0090] See also Figure 2 , the steps for recording the terrain changes at each measuring point are:

[0091] Deploy ground scanners at predetermined measuring points to measure the height and width of underground terrain, including , , Coordinates, using the formula:

[0092] ,

[0093] Get coordinate dataset ,in, , and Represents the measuring points The horizontal, vertical and height coordinates of is the number of measuring points;

[0094] Combine the coordinates of each point in the coordinate data set with the ground material sensor and the humidity sensor to measure the ground hardness and humidity, and obtain the underground ground hardness and humidity data set;

[0095] Based on the underground ground hardness and humidity data set, the difference between hardness data and humidity data is compared, and the formula is used:

[0096] ,

[0097] Calculate measurement points The amount of terrain change , generate underground terrain change records, where is the specific gravity coefficient, which is used to enhance the sensitivity of terrain change records. Indicates the hardness data at the measuring point. Indicates measuring point Humidity data.

[0098] Three measurement points are collected, and their coordinates are (2, 3, 5), (4, 6, 8) and (7, 1, 4). Then:

[0099] ,

[0100] ,

[0101] ,

[0102] Insert the formula to get the coordinate data set .

[0103] The dataset The three-dimensional coordinates of each measuring point are listed in detail to facilitate subsequent accurate terrain analysis.

[0104] The ground material sensor and humidity sensor are used to collect the ground hardness and humidity data underground, and the following are obtained:

[0105] Hardness Dataset ,

[0106] Humidity dataset .

[0107] Setting weights , substituting into the formula, we get:

[0108] ,

[0109] ,

[0110] ,

[0111] The terrain change record is then obtained as The terrain variation value describes the amount of difference between the hardness and moisture of the ground at each point, with a low value such as 0.15 indicating almost no variation, while a higher value of 0.9 indicates a more significant terrain variation at that point.

[0112] See also Figure 3 , the steps to obtain the terrain feature dataset are:

[0113] According to the records of underground terrain changes, three-dimensional spatial mapping is performed, and linear interpolation method is used to fill in data gaps. The formula is:

[0114] ,

[0115] Calculate position Interpolated height value of , generate three-dimensional mapping results, For measuring point The terrain change value, is the distance weight, which is used to adjust the influence of the measured point on the current point interpolation;

[0116] Based on the three-dimensional mapping results, the height difference between adjacent points is calculated using the formula:

[0117] ,

[0118] Calculate position The height difference at , generate a height difference dataset, where represents the horizontal gradient value, Indicates the vertical gradient value;

[0119] Statistical analysis of the height difference dataset was performed to assess the significant features of the terrain using the formula:

[0120] ,

[0121] Calculate the mean square of height differences , which is used to describe the overall changes in the underground terrain and obtain the terrain feature data set.

[0122] Combined with the above results, the topographic changes at the three points are recorded as.

[0123] The corresponding coordinate points are (2, 3), (4, 6), and (7, 1).

[0124] Distance Weight Both are set to 1.

[0125] Take location (5, 4) as an example and calculate its interpolated height .

[0126] calculate :

[0127] ,

[0128] ,

[0129] ,

[0130] Substituting into the formula we get:

[0131] ,

[0132] The result represents the interpolated height Represents the terrain change characteristic value at the coordinate (5, 4). A lower value means that the terrain changes at this point are relatively gentle and not significantly different from other areas.

[0133] Continue using the above The value of is calculated by calculating the adjacent points (5, 5) and (6, 4) The values ​​are and .

[0134] Calculate the horizontal gradient:

[0135] ,

[0136] Calculate the vertical gradient:

[0137] ,

[0138] Substituting into the formula, we get:

[0139] ,

[0140] Height Difference Represents the rate of change of the terrain near point (5, 4). This value shows the relative slope of the terrain in this area. A lower value indicates a more moderate change.

[0141] Calculate the height difference of 10 points in the entire area, square value The values ​​are: , , , , , , , , and .

[0142] Substituting into the formula we get:

[0143] ,

[0144] Indicates the average square value of the terrain change in the area. The statistical characteristics describe the severity of the overall terrain change. A smaller value indicates that the terrain change is generally stable and not significant.

[0145] See also Figure 4 , the steps to obtain the speed adjustment parameters are:

[0146] Based on the terrain feature data set, the underground ground slope is analyzed using the formula:

[0147] ,

[0148] Calculate the slope angle of the measuring point , get the terrain slope information, where Represents the height difference, Represents the horizontal distance component from the measuring point to the reference point;

[0149] Using the terrain slope information, a weighted average is used to determine the average slope for the entire area using the formula:

[0150] ,

[0151] Calculate the average slope value ,in, is the weight coefficient, reflecting the contribution of the difference measurement point to the average value, is the number of measuring points;

[0152] Based on the average slope value, combined with the speed sensor to measure the speed of the electric vehicle , using the formula:

[0153] ,

[0154] Calculate the speed of electric vehicle regulation , matching the slope change, generating speed adjustment parameters, where It is the speed adjustment factor, which is used to increase or decrease the speed adjustment range.

[0155] Collect measurement points With the following parameters:

[0156] Height difference rice,

[0157] coordinate rice, rice,

[0158] Calculate the horizontal distance:

[0159] ,

[0160] Calculate the slope angle:

[0161] ,

[0162] Slope Angle Indicates the inclination angle of the measuring point relative to the horizontal. A larger inclination angle means a steeper slope, which may have a significant impact on the driving stability and safety of the rubber-tyred trackless electric vehicle.

[0163] By calculating the slope angles of the three measuring points, , , , the corresponding weights are 1, 2, 1.

[0164] Calculate the weighted slope sum:

[0165] ,

[0166] Calculate the sum of weights:

[0167] ,

[0168] Calculate the average slope:

[0169] ,

[0170] Average slope It reflects the slope of the entire monitoring area. This value will be used to adjust the speed of the rubber-tyred trackless electric vehicle to adapt to terrain changes.

[0171] The original speed is measured by the speed sensor m / s, set adjustment factor , average slope .

[0172] Substituting into the formula, we get:

[0173] ,

[0174] ,

[0175] Adjusted speed m / s indicates that the speed was slightly increased to accommodate the slope. m / s. This adjustment ensures that the electric vehicle can maintain a stable and safe speed on steep terrain.

[0176] See also Figure 5 , the steps for real-time monitoring of the actual output torque of each tire are:

[0177] Use the speed adjustment parameter to set the wheel starting torque value according to the vehicle performance characteristics, using the formula:

[0178] ,

[0179] Calculate the starting torque value ,in, represents the torque coefficient, which is used to adjust the nonlinear response of vehicle dynamics to speed;

[0180] Use a torque sensor to monitor the actual output torque of the vehicle, combined with the starting torque value, using the formula:

[0181] ,

[0182] Calculate and output the actual torque deviation value , used to evaluate the difference between the torque output and the target torque, where Indicates the actual output torque value of the vehicle;

[0183] According to the actual torque deviation value, make a preliminary adjustment of the torque using the formula:

[0184] ,

[0185] Calculate and output the initial adjusted torque value , optimize vehicle driving performance.

[0186] According to the above calculation results, the adjusted speed is obtained m / s.

[0187] Setting torque coefficient .

[0188] Insert the values ​​into the formula:

[0189] ,

[0190] ,

[0191] but:

[0192] ,

[0193] The starting torque obtained It shows that according to the current speed adjustment parameters, the wheels of the rubber-wheeled trackless electric vehicle need so much torque to start moving to adapt to the required power output demand.

[0194] Collect the actual torque measured by the torque sensor Nm.

[0195] Insert the values ​​into the formula:

[0196] ,

[0197] Torque difference Pointing out the small difference between actual output and preset torque shows that the actual performance of the wheel is close to the expected setting.

[0198] Continue to use the above torque difference and actual torque .

[0199] Substituting into the formula, we get:

[0200] ,

[0201] Torque value after initial adjustment This shows that through subtle torque adjustments, the performance of the wheels is closer to the design target, optimizing the stability of the vehicle's driving.

[0202] See also Figure 6 , the steps to obtain the optimal torque distribution data are:

[0203] Collect torque sensor data and obtain ground grip feedback simultaneously, using the formula:

[0204] ,

[0205] Calculating Total Ground Grip Value ,in, It is The ground reaction force of each wheel, is the ground grip coefficient, reflecting the influence of ground conditions. Indicates the number of wheels;

[0206] Using the total ground grip value and the initial adjusted torque value, the differential wheel torque adjustment is dynamically calculated using the formula:

[0207] ,

[0208] Output wheel dynamic adjustment torque value ,in, is the theoretical maximum grip force value;

[0209] Integrate the wheel dynamic adjustment torque value, using the formula:

[0210] ,

[0211] Select the optimal torque value , optimize vehicle performance and output optimal torque distribution data.

[0212] Taking a 4-wheel rubber-tyred trackless electric vehicle as an example, the ground reaction force is collected as , set the ground grip coefficient .

[0213] Calculate the total ground reaction force:

[0214] ,

[0215] Insert the values ​​into the formula:

[0216] ,

[0217] Total ground grip obtained It indicates the total reaction force of the vehicle when it contacts the ground. Taking the ground grip coefficient into consideration, it indicates that the grip effect between the ground and the wheels is good, reflecting that the vehicle has good stability and grip under the current ground conditions.

[0218] Theoretical maximum ground grip It is 4000N.

[0219] Using the initial adjusted torque calculated above and total ground grip .

[0220] Calculate the torque adjustment ratio:

[0221] ,

[0222] Insert the values ​​into the formula:

[0223] ,

[0224] Newly adjusted torque It means that under the current ground grip conditions, the torque is greatly increased to adapt to higher traction requirements, ensure the vehicle's optimal performance under different ground conditions, and increase the vehicle's traction and stability.

[0225] Through calculation and screening, we get:

[0226] ,

[0227] Represents the lowest new adjusted torque value currently obtained by all wheels, taking into account that all wheels should at least reach this torque level, that is, ensuring that all wheels can reach or exceed this torque value, maintaining a balanced power distribution of the vehicle and improving the overall driving performance.

[0228] See also Figure 7 , the analysis steps of obstacle position and volume are:

[0229] Use the camera to continuously collect the video stream in front of the trackless rubber-wheeled electric vehicle, perform image recognition, and mark the obstacles in the image. The formula is:

[0230] ,

[0231] Calculate preliminary obstacle recognition strength , generate preliminary obstacle identification data, where Represents the average brightness of the collected video frames, is the adjustment coefficient, which is used to adjust the recognition sensitivity. is the brightness response coefficient;

[0232] The preliminary obstacle identification data is analyzed to determine the location and volume of each obstacle using the formula:

[0233] ,

[0234] Calculate the weighted and adjusted obstacle characteristic strength value , output obstacle data analysis results, where, is the magnification factor, is the baseline shift, is the linear adjustment coefficient, used to adjust the calculation accuracy;

[0235] Integrate the obstacle data analysis results, combine the position and volume data of each obstacle, and output a comprehensive obstacle data set.

[0236] Collect and calculate the average brightness , set the adjustment coefficient , brightness response coefficient .

[0237] Calculate the brightness weighting term:

[0238] ,

[0239] Compute the logarithmic term:

[0240] ,

[0241] Calculate recognition strength:

[0242] ,

[0243] The initial obstacle recognition strength obtained Indicates the visual intensity of obstacles based on the current brightness conditions. A high value indicates a more obvious obstacle feature.

[0244] Set the magnification factor , baseline shift , linear adjustment coefficient .

[0245] Combined with the above calculation results, substitute into the formula,

[0246] Calculate the adjusted obstacle recognition strength:

[0247] ,

[0248] Calculate the denominator adjustment:

[0249] ,

[0250] Calculate obstacle characteristic strength:

[0251] ,

[0252] It represents the weighted and adjusted specific feature strength of obstacles, which can be used for further path planning and obstacle avoidance decisions.

[0253] See also Figure 8 ,The steps for obtaining the obstacle avoidance path planning scheme for the rubber-wheeled trackless electric vehicle are as follows:

[0254] Combine the obstacle comprehensive data set and the optimal torque distribution data to perform path planning using the formula:

[0255] ,

[0256] Calculate and output the path selection index ,in, , and To adjust the parameters to balance the impact of obstacle data and torque distribution, is the scaling factor;

[0257] According to the path selection index, adjust the vehicle direction, optimize the responsiveness of the direction control, and output the direction adjustment result of the trackless rubber-tyred electric vehicle;

[0258] According to the direction adjustment results of the rubber-tyred trackless electric vehicle, the road condition changes on the vehicle's driving path are continuously monitored, the vehicle direction is dynamically adjusted, and an adaptive trackless rubber-tyred electric vehicle obstacle avoidance path planning scheme is formed.

[0259] Set the zoom factor , amplification factor , baseline constant , torque adjustment factor .

[0260] Combined with the above calculation results, , .

[0261] Substituting into the formula:

[0262] ,

[0263] ,

[0264] ,

[0265] but:

[0266] ,

[0267] Represents the calculated path selection index, where a lower value indicates that the optimization degree of path selection is affected by torque.

[0268] Finally, combined with the specific terrain conditions underground in the coal mine, the vehicle direction of the rubber-tyred trackless electric vehicle is dynamically adjusted, and then an adaptive obstacle avoidance path planning scheme for the rubber-tyred trackless electric vehicle is output.

[0269] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. The motion control system of the underground trackless rubber-wheeled electric vehicle in coal mines is characterized by: The system comprises: The underground terrain data acquisition module collects the height and width data of the underground terrain through a ground scanner, uses a ground material sensor and a humidity sensor to measure the hardness and humidity of the underground ground respectively, records the terrain changes at each measuring point, performs three-dimensional space mapping based on the change records, continuously calculates the height difference between adjacent points in the three-dimensional space, performs comprehensive data processing, and outputs a terrain feature data set; The speed adaptation analysis module extracts the underground ground slope information from the terrain feature data set, monitors the current speed of the trackless rubber-tyred electric vehicle through a speed sensor, adjusts the speed setting according to the slope, matches the underground terrain slope characteristics, and outputs the speed adjustment parameters; The anti-skid torque control module uses the speed adjustment parameter to set the starting torque value of the wheel of the rubber-tyred trackless electric vehicle, uses a torque sensor to monitor the actual output torque of each tire in real time, collects ground grip feedback synchronously based on the monitoring records, dynamically adjusts the wheel torque, and outputs the optimal torque distribution data; The obstacle processing and path planning module collects road condition information ahead of the rubber-wheeled trackless electric vehicle's driving path through a camera, collects obstacle data, analyzes the obstacle location and size, calculates the obstacle avoidance path based on the analysis results combined with the optimal torque distribution data, adjusts the vehicle direction in real time, and forms an adaptive trackless rubber-wheeled electric vehicle obstacle avoidance path planning solution.

2. The motion control system of the underground trackless rubber-tyred electric vehicle in coal mines according to claim 1 is characterized in that: The steps for recording the terrain changes at each measuring point are: Deploy ground scanners at predetermined measuring points to measure the height and width of underground terrain, including , , Coordinates, using the formula: ， Get coordinate dataset ,in, , and Represents the measuring points The horizontal, vertical and height coordinates of is the number of measuring points; Combine the coordinates of each point in the coordinate data set, combine with the ground material sensor and the humidity sensor, measure the ground hardness and humidity, and obtain the underground ground hardness and humidity data set; Based on the underground ground hardness and humidity data set, the difference between the hardness data and the humidity data is compared, and the formula is used: ， Calculate measurement points The amount of terrain change , generate underground terrain change records, where is the specific gravity coefficient, which is used to enhance the sensitivity of terrain change records. Indicates measuring point Hardness data, Indicates measuring point Humidity data.

3. The motion control system of the underground trackless rubber-tyred electric vehicle in coal mines according to claim 2 is characterized by: The steps for obtaining the terrain feature dataset are as follows: According to the underground terrain change records, three-dimensional space mapping is performed, and the linear interpolation method is used to fill the data gaps. The formula is: ， Calculate position Interpolated height value of , generate three-dimensional mapping results, For measuring point The terrain change value, is the distance weight, which is used to adjust the influence of the measured point on the current point interpolation; Based on the three-dimensional mapping results, the height difference between adjacent points is calculated using the formula: ， Calculate position The height difference at , generate a height difference dataset, where represents the horizontal gradient value, Indicates the vertical gradient value; The height difference dataset was statistically analyzed to assess the significant features of the terrain using the formula: ， Calculate the mean square of height differences , which is used to describe the overall changes in the underground terrain and obtain the terrain feature data set.

4. The motion control system of the underground trackless rubber-tyred electric vehicle in coal mines according to claim 3 is characterized by: The steps for obtaining the speed adjustment parameters are: Based on the terrain feature data set, the underground ground slope is analyzed using the formula: ， Calculate measurement points Slope angle , get the terrain slope information, where Represents the height difference, Represents the horizontal distance component from the measuring point to the reference point; Using the terrain slope information, a weighted average is used to determine the average slope of the entire area using the formula: ， Calculate the average slope value ,in, is the weight coefficient, reflecting the contribution of the difference measurement point to the average value, is the number of measuring points; Based on the average slope value, the speed of the electric vehicle measured by the speed sensor , using the formula: ， Calculate the speed of electric vehicle regulation , matching the slope change, generating speed adjustment parameters, where It is the speed adjustment factor, which is used to increase or decrease the speed adjustment range.

5. The motion control system of the underground trackless rubber-tyred electric vehicle in coal mines according to claim 4 is characterized by: The steps of real-time monitoring of the actual output torque of each tire are as follows: Using the speed adjustment parameter, the wheel starting torque value is set according to the vehicle performance characteristics, using the formula: ， Calculate the starting torque value ,in, represents the torque coefficient, which is used to adjust the nonlinear response of vehicle dynamics to speed; Use a torque sensor to monitor the actual output torque of the vehicle, combined with the starting torque value, using the formula: ， Calculate and output the actual torque deviation value , used to evaluate the difference between the torque output and the target torque, where Indicates the actual output torque value of the vehicle; According to the actual torque deviation value, the torque is initially adjusted using the formula: ， Calculate and output the initial adjusted torque value , optimize vehicle driving performance.

6. The motion control system of the underground trackless rubber-tyred electric vehicle in coal mines according to claim 5 is characterized by: The steps for obtaining the optimal torque distribution data are as follows: Collect torque sensor data and obtain ground grip feedback simultaneously, using the formula: ， Calculating Total Ground Grip Value ,in, It is The ground reaction force of each wheel, is the ground grip coefficient, reflecting the influence of ground conditions. Indicates the number of wheels; Using the total ground grip value and the initially adjusted torque value, the differential wheel torque adjustment is dynamically calculated using the formula: ， Output wheel dynamic adjustment torque value ,in, is the theoretical maximum grip force value; Integrate the wheel dynamic adjustment torque value, using the formula: ， Select the optimal torque value , optimize vehicle performance and output optimal torque distribution data.

7. The motion control system of the underground trackless rubber-tyred electric vehicle in coal mines according to claim 6 is characterized by: The analysis steps of the obstacle position and volume are as follows: Use the camera to continuously collect the video stream in front of the trackless rubber-wheeled electric vehicle, perform image recognition, and mark the obstacles in the image. The formula is: ， Calculate preliminary obstacle recognition strength , generate preliminary obstacle identification data, where Represents the average brightness of the collected video frames, is the adjustment coefficient, which is used to adjust the recognition sensitivity. is the brightness response coefficient; The preliminary obstacle identification data is analyzed to determine the position and volume of each obstacle using the formula: ， Calculate the weighted and adjusted obstacle characteristic strength value , output obstacle data analysis results, where, is the magnification factor, is the baseline shift, is the linear adjustment coefficient, used to adjust the calculation accuracy; The obstacle data analysis results are integrated, the position and volume data of each obstacle are combined, and an obstacle comprehensive data set is output.

8. The motion control system of the underground trackless rubber-tyred electric vehicle in coal mines according to claim 7 is characterized by: The steps for obtaining the obstacle avoidance path planning scheme of the trackless rubber-wheeled electric vehicle are as follows: Combined with the obstacle comprehensive data set and the optimal torque distribution data, path planning is performed using the formula: ， Calculate and output the path selection index ,in, , and To adjust the parameters to balance the impact of obstacle data and torque distribution, is the scaling factor; According to the path selection index, the vehicle direction is adjusted, the responsiveness of the direction control is optimized, and the direction adjustment result of the trackless rubber-tyred electric vehicle is output; According to the direction adjustment result of the trackless rubber-wheeled electric vehicle, the road condition changes on the vehicle's driving path are continuously monitored, the vehicle direction is dynamically adjusted, and an adaptive trackless rubber-wheeled electric vehicle obstacle avoidance path planning scheme is formed.

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