Trapped escape method, device and system for mine autonomous driving vehicle

Abstract

The present application relates to the technical field of mine vehicle escape, and specifically discloses a trapped escape method, device and system for a mine automatic driving vehicle, comprising: obtaining driving state data of the mine automatic driving vehicle; determining whether the mine automatic driving vehicle is trapped according to the driving state data; if it is determined that the mine automatic driving vehicle is trapped, determining the trapped type of the mine automatic driving vehicle according to the driving state data; when it is determined that the trapped type of the mine automatic driving vehicle is a tire subsidence type of trapped, performing a hierarchical response escape according to the trapped level of the tire subsidence type of trapped; and when it is determined that the trapped type of the mine automatic driving vehicle is an overhead type of trapped, sending a first escape request instruction to a server. The trapped escape method for the mine automatic driving vehicle can realize accurate trapped judgment and hierarchical response escape of the electric self-unloading vehicle in a complex mine operation scene.

Description

Technical Field

[0001] This invention relates to the field of mining vehicle escaping technology, and in particular to a method for escaping from entrapment for autonomous mining vehicles, a device for escaping from entrapment for autonomous mining vehicles, a control system for escaping from entrapment, and a system for escaping from entrapment for autonomous mining vehicles. Background Technology

[0002] In recent years, with the development of electrification and intelligent technology, pure electric dump trucks have gradually replaced traditional fuel vehicles and have been widely used in mining transportation, engineering construction, urban cleaning and transportation. However, electric dump trucks often face complex road conditions in actual operation, such as muddy, slippery, undulating or soft road surfaces. These environmental factors can easily cause vehicles to get stuck. The so-called "getting stuck" refers to a situation where a vehicle, with driving force, is unable to continue moving forward or get out of its current state due to external terrain or road conditions. This is common in unpaved road environments such as mines, tunnels, and construction sites. This problem can be divided into two typical situations: (1) The first type is tire sinking type, which means that during the vehicle's operation, some or all of the wheels sink into soft ground (such as mud, sand pits, etc.). Although the motor continues to output torque, the overall position of the vehicle remains almost unchanged, making it difficult to get out of the predicament; (2) The second type is suspended type, which means that due to the ground being severely uneven or suspended, some wheels are lifted off the ground or lose their adhesion, resulting in the phenomenon of "wheel spinning freely". This type of predicament typically manifests as follows: the motor output is normal, the wheel speed is high, but the overall position of the vehicle does not change, and some tires are not in contact with the ground load.

[0003] Currently, traditional methods of handling vehicles stuck in obstacles rely heavily on human experience and intervention, resulting in problems such as slow response, low efficiency, and high costs. While some solutions exist, such as the invention patent application No. 202411143120.7, which proposes control based on escape commands and utilizes suspension vibration to reduce reliance on tire traction, this approach presupposes a confirmed receipt of escape control commands—that is, assuming the system can accurately identify the obstacle. However, in practical applications, especially in autonomous driving scenarios, the state of being stuck is often sudden and changeable, making human identification or remote intervention untimely. Therefore, the lack of an online intelligent detection mechanism for stuck states severely limits its practicality in autonomous vehicle operating environments, failing to meet the closed-loop requirements of autonomous systems for "autonomous state recognition - response decision-making - execution control." Furthermore, the core control method of this solution is to adjust the frequency and amplitude of the active suspension system to achieve vehicle vibration, thereby reducing the tire traction requirements for obstacles. However, this solution is primarily geared towards passenger vehicle platforms equipped with electronically controlled active suspension. In actual engineering scenarios, heavy-duty pure electric dump trucks used in mines typically employ passive leaf springs or hydraulic suspension structures. Their mechanical structures lack the ability to perform such active frequency adjustment actions. Therefore, this technology has extremely poor adaptability to engineering vehicles such as dump trucks or mining transport vehicles, and lacks practical engineering feasibility. For example, the invention patent application with patent number 202410792544.X proposes determining whether a vehicle has entered an escape mode based on its operating status, thereby achieving escape control. This solution relies solely on "operating status" or wheel speed difference to determine whether a vehicle is stuck, which is prone to misjudgment in non-stuck situations such as starting on a slope, load fluctuations, and steering. Furthermore, this solution's escape control relies mainly on empirical parameters and lacks scenario recognition capabilities, making it unsuitable for escape scenarios of mining vehicles in complex operating environments.

[0004] Therefore, how to provide accurate identification of electric dump trucks getting stuck and graded response for getting out of trouble in complex mining operation scenarios has become a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a method for getting stuck and escaping from a mine autonomous driving vehicle, a device for getting stuck and escaping from a mine autonomous driving vehicle, a control system for getting stuck and escaping from a mine autonomous driving vehicle, and a system for getting stuck and escaping from a mine autonomous driving vehicle, solving the problem in related technologies that it is impossible to achieve accurate judgment of getting stuck and graded response for escaping from a mine in complex operation scenarios.

[0006] As a first aspect of the present invention, a method for resolving entrapment in autonomous vehicles used in mining operations is provided, comprising:

[0007] Acquire driving status data of autonomous vehicles in mining areas. The driving status data includes at least real-time wheel speed, vehicle geographical location information and displacement trajectory information, vehicle attitude information, and real-time output torque of the vehicle motor.

[0008] Based on the driving status data, determine whether the current autonomous driving vehicle in the mine is stuck;

[0009] If it is determined that the current autonomous driving vehicle in the mine is stuck, the type of stuck vehicle is determined based on the driving status data. The type of stuck vehicle includes tire sinking stuck and overhead stuck.

[0010] When it is determined that the current stuck type of the autonomous vehicle in the mine is a tire sinking stuck, a graded response to get out of the stuck is carried out according to the stuck level of the tire sinking stuck;

[0011] When it is determined that the current mine autonomous driving vehicle is stuck in an elevated situation, a first escape request command is sent to the server.

[0012] Furthermore, based on the driving status data, the current type of entanglement for the autonomous driving vehicle in the mine is determined, including:

[0013] The real-time rotational speed of the wheels and the real-time output torque of the vehicle motor are compared with the preset no-load state data of the autonomous driving vehicle in the mine.

[0014] If the real-time rotational speed of the wheels and the real-time output torque of the vehicle motor are consistent with the preset unloaded state data of the autonomous driving vehicle in the mine, then the current entrapment type of the autonomous driving vehicle in the mine is determined to be an overhead entrapment.

[0015] If the real-time rotational speed of the wheels and the real-time output torque of the vehicle motor are inconsistent with the preset unloaded state data of the autonomous driving vehicle in the mine, then the current entrapment type of the autonomous driving vehicle in the mine is determined to be tire sinking entrapment.

[0016] Furthermore, when it is determined that the current stuck type of the autonomous driving vehicle in the mine is a tire-diving stuck situation, a graded response for getting out of the stuck situation is performed according to the stuck situation level of the tire-diving stuck situation, including:

[0017] When it is determined that the current autonomous driving vehicle in the mine is stuck due to tire sinking, the current stuck level of the autonomous driving vehicle in the mine is determined according to the driving status data and the preset stuck level threshold. The stuck level includes Level 1 stuck and Level 2 stuck, and the severity of Level 2 stuck is greater than that of Level 1 stuck.

[0018] The corresponding escaping control strategy is triggered according to different levels of entrapment. The escaping control strategy for Level 1 entrapment includes at least a vehicle motor torque output enhancement strategy, and the escaping control strategy for Level 2 entrapment includes sending a second escaping request command to the server.

[0019] Further, the current entrapment level of the autonomous driving vehicle in the mine is determined based on the driving status data and a preset entrapment level threshold, including:

[0020] The real-time output torque of the vehicle motor is compared with a preset torque threshold, the real-time rotation speed of the wheel is compared with a preset wheel speed threshold, and the vehicle geographical location information is compared with a preset displacement threshold within a first preset duration.

[0021] If the real-time output torque of the vehicle motor is greater than a preset torque threshold within a first preset duration, and the real-time rotational speed of the wheel is lower than a preset wheel speed threshold within a first preset duration, and the vehicle position movement is less than a preset displacement threshold within a first preset duration, then the current mine autonomous driving vehicle is determined to be trapped at level one.

[0022] Determine whether the current autonomous driving vehicle in the mine has moved to the preset displacement range within the second preset duration after executing the escape control strategy corresponding to the first level of difficulty;

[0023] If the current autonomous driving vehicle in the mine fails to move to the preset displacement range within the second preset duration after executing the escape control strategy corresponding to Level 1 difficulty, then the current autonomous driving vehicle's entrapment level is determined to be upgraded to Level 2 entrapment.

[0024] Furthermore, if the current autonomous driving vehicle in the mine moves to a preset displacement range within the second preset duration after executing the escape control strategy corresponding to the first-level difficulty, then the current autonomous driving vehicle in the mine is determined to have successfully escaped the difficulty.

[0025] Furthermore, the traction control strategy corresponding to the first level of entrapment includes a vehicle motor torque output enhancement strategy, which triggers corresponding traction control strategies according to different entrapment levels, including:

[0026] When the current entrapment level of the autonomous driving vehicle in the mine is determined to be Level 1 entrapment (tire sinking), a vehicle motor torque output enhancement strategy is triggered, wherein the vehicle motor torque output enhancement strategy includes:

[0027] The vehicle motor torque is increased in stages, where the expression for the incremental vehicle motor torque is:

[0028] ,

[0029] in, This indicates the current real-time output torque of the vehicle's motor. This indicates the basic torque of the vehicle's motor. This indicates the maximum safe output torque of the vehicle's motor. This represents the torque increment coefficient per unit time, and t represents the cumulative control time from the moment the trap is identified.

[0030] Furthermore, the escape control strategy corresponding to the first-level entrapment also includes a periodic disturbance-driven strategy, which triggers the corresponding escape control strategy according to different entrapment levels, and further includes:

[0031] Determine whether the displacement of the current autonomous driving vehicle in the mine meets the preset displacement requirements within a preset time period after the vehicle motor torque output enhancement strategy is implemented;

[0032] If the displacement of the current autonomous driving vehicle in the mine does not meet the preset displacement requirement within a preset time period, the periodic disturbance driving strategy is triggered.

[0033] The periodic disturbance drive strategy includes generating a non-stationary acceleration control signal to control wheel slippage, wherein the non-stationary acceleration control signal includes at least an oscillating acceleration command, a drive speed command, and a wheel-end speed command.

[0034] The expression for the oscillation acceleration command is:

[0035] ,

[0036] The expression for the drive speed command is:

[0037] ,

[0038] The expression for the wheel end speed command is:

[0039] ,

[0040] in, This represents the amplitude of the oscillating acceleration. This represents the disturbance frequency, and r represents the tire radius of the wheel. This indicates the initial wheel speed.

[0041] As another aspect of the present invention, a device for escaping entrapment of autonomous vehicles in mining is provided, for implementing the aforementioned method for escaping entrapment of autonomous vehicles in mining, comprising:

[0042] The acquisition module is used to acquire driving status data of autonomous vehicles in mining. The driving status data includes at least the real-time wheel speed, vehicle geographical location information and displacement trajectory information, vehicle attitude information, and real-time output torque of the vehicle motor.

[0043] The entrapment detection module is used to determine whether the current autonomous driving vehicle in the mine is entrapped based on the driving status data;

[0044] The entrapment type analysis module is used to determine the entrapment type of the current autonomous driving vehicle in the mine based on the driving status data if it is determined that the current autonomous driving vehicle in the mine is entrapped. The entrapment type includes tire sinking entrapment and overhead entrapment.

[0045] The tire sinking-type entrapment control module is used to respond in a graded manner to extricate the driver from the entrapment when it is determined that the current entrapment type of the autonomous driving vehicle in the mine is tire sinking-type entrapment.

[0046] The overhead-type entrapment and escape control module is used to send a first escape request command to the server when it is determined that the current mine autonomous driving vehicle is entrapped in an overhead-type entrapment.

[0047] In another aspect, a scrambling and escaping control system is provided, comprising: a sensing device, a calculation and decision-making device, and an execution control device. The sensing device and the execution control device are both communicatively connected to the calculation and decision-making device. The calculation and decision-making device includes the scrambling and escaping device for autonomous mining vehicles described above.

[0048] The sensing device is used to sense the driving status data of autonomous vehicles in the mine.

[0049] The calculation and decision-making device is used to determine the current stuck state of the autonomous driving vehicle in the mine based on the driving status data, and to generate corresponding escape control commands and / or escape request commands when the stuck type of the current autonomous driving vehicle in the mine is determined.

[0050] The execution control device is used to generate an escape drive control signal according to the escape control command.

[0051] As another aspect of the present invention, a system for escaping and retrieving stuck vehicles used in mining operations is provided, comprising: a server and the aforementioned system for escaping and retrieving stuck vehicles, wherein the server is communicatively connected to the system for escaping and retrieving stuck vehicles.

[0052] The entrapment and escape control system is used to determine the entrapment status based on the driving status data of the autonomous driving vehicle in the mine and generate an escape drive control signal based on the generated escape control command, as well as generate an escape request command.

[0053] The server is used to remotely control the trapped escape control system and / or send request signals to the user according to the escape request command.

[0054] The present invention provides a method for escaping and retrieving stuck vehicles for autonomous vehicles in mines. This method acquires the driving status data of the autonomous vehicle and determines whether the vehicle is stuck based on this data. When stuck, the method identifies the type of entrapment based on the driving status data. If the entrapment is determined to be a tire-sinking type, a graded response is implemented based on the entrapment level. If the entrapment is determined to be an overhead type, a first escaping request command is sent to the server. This method, combined with the driving status data, achieves multi-strategy collaborative escaping control, enabling accurate identification and graded response to vehicle entrapment. It also supports manual intervention and fault information reporting. This method enables accurate entrapment judgment and graded escaping response for vehicles in complex mining operation scenarios, improving the intelligent escaping capability, safety, and operational efficiency of autonomous vehicles in automated and unmanned operating environments. Attached Figure Description

[0055] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the following detailed description to explain the invention, but do not constitute a limitation thereof.

[0056] Figure 1 The flowchart illustrates the method for escaping from entrapment in autonomous vehicles used in mining, as provided by this invention.

[0057] Figure 2 The flowchart provided by the present invention is for determining the current entrapment type of an autonomous vehicle in a mine.

[0058] Figure 3 This invention provides a flowchart for graded response and extrication from tire-sinking entrapment.

[0059] Figure 4 The flowchart provided by this invention is for determining the current entrapment level of an autonomous driving vehicle in a mine.

[0060] Figure 5 The structural block diagram of the device for getting stuck and escaping from autonomous vehicles in mines provided by the present invention.

[0061] Figure 6 This is a structural block diagram of the trapping and escape control system provided by the present invention.

[0062] Figure 7 The structural block diagram of the system for getting stuck and escaping from autonomous vehicles in mines provided by the present invention. Detailed Implementation

[0063] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0064] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0065] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0066] This embodiment provides a method for resolving entrapment issues in autonomous vehicles used in mining operations. Figure 1 This is a flowchart of a method for retrieving and extricating autonomous vehicles from entrapment in mining, provided by an embodiment of the present invention. Figure 1 As shown, it includes:

[0067] S100. Obtain driving status data of autonomous vehicles in the mine. The driving status data includes at least the real-time wheel speed, vehicle geographical location information and displacement trajectory information, vehicle attitude information and real-time output torque of the vehicle motor.

[0068] In this embodiment of the invention, a sensing device is installed on the autonomous mining vehicle. This sensing device can perceive the driving status of the autonomous mining vehicle in real time and obtain driving status data. Specifically, the driving status data may include real-time wheel speed, vehicle geographical location information and displacement trajectory information, vehicle attitude information, and real-time output torque of the vehicle motor. Based on the real-time wheel speed, it can be determined whether the autonomous mining vehicle is slipping or spinning freely. Based on the vehicle geographical location information and displacement trajectory information, it can determine the displacement changes of the autonomous mining vehicle. Based on the vehicle attitude information, it can identify whether the vehicle body is tilted, raised, or exhibits other abnormal postures. Based on the real-time output torque of the vehicle motor, it can detect whether the drive motor is continuously outputting high torque but the vehicle is not moving, thus assisting in the determination of being stuck.

[0069] S200: Determine whether the current autonomous driving vehicle in the mine is stuck based on the driving status data;

[0070] In this embodiment of the invention, it is possible to determine whether the current autonomous driving vehicle in the mine is stuck based on the above driving status data.

[0071] S300. If it is determined that the current autonomous driving vehicle in the mine is stuck, the type of stuck vehicle in the mine is determined according to the driving status data. The type of stuck vehicle includes tire sinking type stuck and overhead type stuck.

[0072] It should be understood that if it is determined that the current autonomous driving vehicle in the mine is stuck based on the above judgment results, the type of stuck vehicle is determined according to the driving status data of the current autonomous driving vehicle in the mine. The types of stuck vehicles in this embodiment of the invention include tire sinking stuck and overhead stuck.

[0073] S400. When it is determined that the current mine autonomous driving vehicle is stuck in a tire sinking type of entrapment, a graded response to escape the entrapment is performed according to the entrapment level of the tire sinking type of entrapment.

[0074] In this embodiment of the invention, when it is determined that the current mine autonomous driving vehicle is stuck in a tire sinking type of entrapment, the entrapment level of this type of entrapment can be classified, and then a graded response can be carried out based on the corresponding entrapment level to achieve extrication.

[0075] S500: When it is determined that the current mine autonomous driving vehicle is stuck in an elevated position, a first escape request command is sent to the server.

[0076] In this embodiment of the invention, when it is determined that the current entanglement type of the autonomous mining vehicle is an overhead entanglement, a first escape request command is directly sent to the server. It should be understood that since the current entanglement type of the autonomous mining vehicle is determined to be an overhead entanglement, an escape request command can be directly sent to the server. Because it is an overhead entanglement, the autonomous mining vehicle cannot escape on its own. Upon receiving the first escape request command, the server will display a request for manual intervention to the user, thus achieving the escape operation for the overhead entanglement through manual intervention.

[0077] In summary, the method for escaping and getting out of trouble for autonomous vehicles in mines provided by this invention acquires the driving status data of the autonomous vehicles and determines whether the vehicles are stuck based on this data. When a vehicle is stuck, the method determines the type of entrapment based on the driving status data. If the entrapment is determined to be a tire sinking entrapment, a graded response is performed based on the entrapment level. If the entrapment is determined to be an overhead entrapment, a first escape request command is sent to the server. This method for escaping and getting out of trouble for autonomous vehicles in mines combines the driving status data of the vehicles to achieve multi-strategy collaborative escape control. It can accurately identify and grade the vehicle's entrapment status and supports manual intervention and fault information reporting. It can accurately determine vehicle entrapment and graded escape response in complex mining operation scenarios, improving the intelligent escape capability, safety, and operational efficiency of autonomous vehicles in automated and unmanned operation environments.

[0078] In this embodiment of the invention, after obtaining the driving status data of the autonomous driving vehicle in the mine, it is possible to determine whether the autonomous driving vehicle in the mine has become stuck based on the driving status data. For example, the position change of the vehicle can be determined by the vehicle's geographical location information and displacement trajectory information, and then combined with the real-time rotation speed of the wheels to determine whether the autonomous driving vehicle in the mine has become stuck. Alternatively, a lightweight neural network or decision tree model (such as LightGBM) can be trained based on historical operating data to identify the stuck situation, automatically learning the temporal change features of speed, position, torque, etc., to improve adaptability and discrimination accuracy.

[0079] In this embodiment of the invention, the type of entrapment for the current autonomous driving vehicle in the mine is determined based on the driving status data, such as... Figure 2 As shown, it includes:

[0080] S310. Compare the real-time rotational speed of the wheels and the real-time output torque of the vehicle motor with the preset no-load state data of the mining autonomous driving vehicle.

[0081] It should be understood that the real-time wheel speed and the real-time output torque of the vehicle motor are compared with the preset unloaded state data of the mining autonomous vehicle. Here, the preset unloaded state data of the mining autonomous vehicle refers to the real-time wheel speed and the output torque of the vehicle motor when the vehicle motor of the mining vehicle has a constant torque output and the wheel speed exists but no displacement occurs.

[0082] S320. If the real-time rotational speed of the wheels and the real-time output torque of the vehicle motor are consistent with the preset unloaded state data of the mining autonomous driving vehicle, then the current mining autonomous driving vehicle is determined to be trapped in an elevated state.

[0083] It should be understood that if the real-time wheel speed and the real-time output torque of the vehicle motor are consistent with the preset data of the unloaded state of the autonomous driving vehicle in the mine, then the current type of entrapment of the autonomous driving vehicle in the mine is determined to be an overhead entrapment.

[0084] It should be noted that due to severely uneven or suspended ground, some wheels may lift off the ground or lose traction, resulting in "wheel spinning." This type of entrapment typically manifests as: normal motor output and high wheel speed, but no change in the overall vehicle position, and some tires lacking ground contact. Therefore, based on a combination of signals such as abnormal wheel speed (some wheel speeds above average), positional changes <0.2m, and abnormal vehicle posture (abrupt changes in IMU angle), it is determined to be an overhead entrapment. Considering that overhead entrapment often involves road condition reconstruction or human intervention, the autonomous driving vehicle in the mine does not actively control the drive system. Instead, it triggers an entrapment alarm and sends an initial entrapment request command to the server, indicating the need for human intervention or engineering machinery assistance.

[0085] S330. If the real-time rotational speed of the wheels and the real-time output torque of the vehicle motor are inconsistent with the preset unloaded state data of the mining autonomous driving vehicle, then the current entrapment type of the mining autonomous driving vehicle is determined to be tire sinking entrapment.

[0086] It should be noted that the comparison of real-time wheel speed and real-time output torque of the vehicle motor with the preset unloaded state data of the automated mining vehicle is conducted under the premise that the current automated mining vehicle is stuck. Therefore, based on this premise, when it is determined that both the real-time wheel speed and the real-time output torque of the vehicle motor are inconsistent with the preset unloaded state data of the automated mining vehicle, the current stuck type of the automated mining vehicle is determined to be a tire-sinking stuck. That is, after excluding the above-mentioned overhead stuck, this stuck is a tire-sinking stuck.

[0087] In embodiments of the present invention, such as Figure 3As shown, when the current stuck type of the autonomous driving vehicle in the mine is determined to be tire sinking stuck, a graded response for getting out of the stuck situation is performed according to the stuck level of the tire sinking stuck, including:

[0088] S410. When it is determined that the current mine autonomous driving vehicle is stuck in a tire sinking-in type of stagnation, the current mine autonomous driving vehicle is stuck in a stagnation level based on the driving status data and the preset stagnation level threshold. The stagnation level includes a first-level stagnation and a second-level stagnation, and the severity of the second-level stagnation is greater than that of the first-level stagnation.

[0089] In this embodiment of the invention, during the operation of an autonomous driving vehicle in a mine, some or all of its wheels become stuck in soft ground (such as mud, sand pits, etc.). Although the electric motor continues to output torque, the overall position of the vehicle remains almost unchanged, making it difficult to get out of trouble.

[0090] Specifically, the current entrapment level of the autonomous driving vehicle in the mine is determined based on the driving status data and a preset entrapment level threshold, such as... Figure 4 As shown, it includes:

[0091] S411. The real-time output torque of the vehicle motor is compared with a preset torque threshold, the real-time rotation speed of the wheel is compared with a preset wheel speed threshold, and the vehicle geographical location information is compared with a preset displacement threshold within a first preset duration.

[0092] S412. If the real-time output torque of the vehicle motor is greater than a preset torque threshold within a first preset duration, and the real-time rotation speed of the wheel is lower than a preset wheel speed threshold within a first preset duration, and the vehicle position movement is less than a preset displacement threshold within a first preset duration, then the current mine autonomous driving vehicle is determined to be trapped at level one.

[0093] S413. Determine whether the current mine autonomous driving vehicle has moved to the preset displacement range within the second preset duration after executing the first-level difficulty escape control strategy;

[0094] S414. If the current autonomous driving vehicle in the mine fails to move to the preset displacement range within the second preset duration after executing the escape control strategy corresponding to the first-level entrapment, then the entrapment level of the current autonomous driving vehicle in the mine is determined to be upgraded to the second-level entrapment.

[0095] It should be noted that if the current autonomous driving vehicle in the mine moves to the preset displacement range within the second preset duration after executing the escape control strategy corresponding to the first level of difficulty, then the current autonomous driving vehicle in the mine is determined to have successfully escaped the difficulty.

[0096] In addition, embodiments of the present invention can also determine the level of entrapment based on the ground adhesion coefficient. Specifically, the ground adhesion coefficient can be dynamically estimated based on the wheel speed difference and vehicle acceleration. A low adhesion coefficient is judged as a high-risk state of entrapment.

[0097] In this embodiment of the invention, by continuously monitoring changes in vehicle speed and position, and combining this with motor torque output information, under the conditions that "the given torque is greater than a set threshold (e.g., >10000 Nm)", "the speed is continuously lower than 0.1 m / s", and "the position movement is less than 0.2 m", the vehicle is determined to be in a first-level entrapment within 4 seconds. Subsequently, the automatic "torque boosting" control logic is activated, which increases the driving torque and optimizes the motor response to enable the vehicle to attempt a strong escape along the original path. If the vehicle successfully moves to a position above the set displacement range within the next 4 seconds, the entrapment is lifted; otherwise, it is upgraded to a second-level entrapment.

[0098] S420. Trigger the corresponding escaping control strategy according to different levels of entrapment. The escaping control strategy for Level 1 entrapment includes at least a vehicle motor torque output enhancement strategy, and the escaping control strategy for Level 2 entrapment includes sending a second escaping request command to the server.

[0099] In this embodiment of the invention, different traction control strategies are triggered for different levels of entrapment. Specifically, the traction control strategy for Level 1 entrapment prioritizes increasing the output torque of the vehicle's motor. This is achieved by progressively increasing the torque of the drive motor to enhance traction and attempt to overcome the adhesion limit between the tires and the ground. The traction control strategy for Level 2 entrapment specifically includes sending a second traction request command to the server. This second traction request command may include requesting remote control by maintenance personnel. If remote control by maintenance personnel still fails to resolve the entrapment, on-site intervention is required to achieve traction.

[0100] Specifically, the traction control strategy corresponding to the first level of entrapment includes a vehicle motor torque output enhancement strategy, which triggers corresponding traction control strategies according to different levels of entrapment, including:

[0101] 1) When the current entrapment level of the autonomous driving vehicle in the mine is determined to be Level 1 entrapment (tire sinking), a vehicle motor torque output enhancement strategy is triggered, wherein the vehicle motor torque output enhancement strategy includes:

[0102] 2) The vehicle motor torque is increased in stages, where the expression for the incremental vehicle motor torque is:

[0103] ,

[0104] in, This indicates the current real-time output torque of the vehicle's motor. This indicates the basic torque of the vehicle's motor. This indicates the maximum safe output torque of the vehicle's motor. This represents the torque increment coefficient per unit time, and t represents the cumulative control time from the moment the trap is identified.

[0105] It should be noted that if the displacement If the first stage of the entrapment is successful, the escape is considered successful; otherwise, the next stage of the strategy is initiated. This means that the escape control strategy corresponding to the first stage of entrapment can be further supplemented by a periodic disturbance drive strategy. If the vehicle motor torque output enhancement strategy still fails to escape the entrapment, the periodic disturbance drive strategy will be further attempted.

[0106] Specifically, the escape control strategy corresponding to the first-level entrapment also includes a periodic disturbance driving strategy, which triggers the corresponding escape control strategy according to different entrapment levels, and further includes:

[0107] 1) Determine whether the displacement of the current mine autonomous driving vehicle within a preset time period after the execution of the vehicle motor torque output enhancement strategy meets the preset displacement requirements;

[0108] 2) If the displacement of the current mine autonomous driving vehicle does not meet the preset displacement requirement within a preset time period, the periodic disturbance driving strategy is triggered.

[0109] The periodic disturbance drive strategy includes generating a non-stationary acceleration control signal to control wheel slippage, wherein the non-stationary acceleration control signal includes at least an oscillating acceleration command, a drive speed command, and a wheel-end speed command.

[0110] The expression for the oscillation acceleration command is:

[0111] ,

[0112] The expression for the drive speed command is:

[0113] ,

[0114] The expression for the wheel end speed command is:

[0115] ,

[0116] in, This represents the amplitude of the oscillating acceleration. This represents the disturbance frequency, and r represents the tire radius of the wheel. This indicates the initial wheel speed.

[0117] To prevent the torque from falling into the "pure idling" range, periodic disturbances are applied to the drive motor, and non-steady acceleration is used to stimulate micro-slippage of the tire to form micro-displacement.

[0118] Apply oscillatory acceleration command:

[0119] ;

[0120] The drive speed command is:

[0121] ;

[0122] Wheel end speed command:

[0123] ;

[0124] in: This represents the amplitude of the oscillation acceleration (unit: m / s²), with an initial value set as, for example, 0.2. The disturbance frequency (rad / s) is indicated, and the recommended range is 1Hz to 3Hz. Indicates the tire radius (in meters). This indicates the initial wheel speed (speed before getting stuck).

[0125] It should be noted that in vehicles equipped with independent front and rear axle drive systems, the differential lock can be engaged or the vehicle can be switched to four-wheel drive output after a slump is detected, in order to improve the vehicle's ability to get out of trouble. For models equipped with active suspension or air pressure chassis adjustment systems, the vehicle body can be temporarily raised in a slump to change the tire contact force distribution and assist in getting out of trouble.

[0126] In summary, the entrapment and escape method for autonomous vehicles in mining provided by this invention effectively improves the accuracy and real-time performance of entrapment identification and reduces false positives and false negatives by integrating multi-source sensor data such as vehicle speed, displacement, torque, attitude, and position, and combining it with a sliding window mechanism for entrapment status judgment. It sets first-level and second-level entrapment levels according to the severity of the entrapment, achieving graded response control. This improves the autonomous escape efficiency under mild entrapment and automatically reports faults under severe entrapment, ensuring the safe and reliable operation of the system. The embodiments of this invention support multiple entrapment control strategies such as motor torque control, front and rear swaying, direction adjustment, and braking coordination, automatically matching the optimal operation under complex road conditions to improve the vehicle's actual entrapment ability and terrain adaptability. Furthermore, this invention has data interaction capabilities with a server, supports entrapment status reporting and manual intervention command reception, and is suitable for autonomous vehicles or remote operation scenarios, improving remote controllability and operational safety.

[0127] As another embodiment of the present invention, a device 100 for escaping entrapment of autonomous vehicles in mining is provided, for implementing the aforementioned method for escaping entrapment of autonomous vehicles in mining, wherein, as... Figure 5 As shown, it includes:

[0128] The acquisition module 110 is used to acquire driving status data of the autonomous driving vehicle in the mine. The driving status data includes at least the real-time wheel speed, vehicle geographical location information and displacement trajectory information, vehicle attitude information and real-time output torque of the vehicle motor.

[0129] The entrapment detection module 120 is used to determine whether the current autonomous driving vehicle in the mine is entrapped based on the driving status data.

[0130] The entrapment type analysis module 130 is used to determine the entrapment type of the current autonomous driving vehicle in the mine based on the driving status data if it is determined that the current autonomous driving vehicle in the mine is entrapped. The entrapment type includes tire sinking entrapment and overhead entrapment.

[0131] The tire sinking-type entrapment control module 140 is used to perform graded response and entrapment according to the entrapment level when it is determined that the current entrapment type of the autonomous driving vehicle in the mine is tire sinking-type entrapment.

[0132] The overhead-type entrapment and escape control module 150 is used to send a first escape request command to the server when it is determined that the current mine autonomous driving vehicle is entrapped in an overhead-type entrapment.

[0133] The present invention provides a device for escaping and getting out of trouble for autonomous vehicles in mines. This device acquires the driving status data of the autonomous vehicle and determines whether the vehicle is stuck based on this data. When a stuck situation is identified, the device determines the type of entrapment based on the driving status data. If the entrapment is determined to be a tire-sinking type, a graded response is implemented based on the entrapment level. If the entrapment is determined to be an overhead type, a first escape request command is sent to the server. This device, combined with the driving status data, achieves multi-strategy collaborative escape control, enabling accurate identification and graded response to vehicle entrapment situations. It also supports manual intervention and fault information reporting. This allows for accurate entrapment judgment and graded escape response in complex mining operation scenarios, improving the intelligent escape capability, safety, and operational efficiency of autonomous vehicles in automated and unmanned operating environments.

[0134] The specific working principle of the device for getting stuck and escaping from autonomous vehicles in mining provided by this invention can be referred to the description of the method for getting stuck and escaping from autonomous vehicles in mining above, and will not be repeated here.

[0135] As another embodiment of the present invention, a entrapment and escape control system 10 is provided, wherein, as Figure 6As shown, it includes: a sensing device 11, a computing and decision-making device 12, and an execution control device 13. Both the sensing device 11 and the execution control device 13 are communicatively connected to the computing and decision-making device 12. The computing and decision-making device 12 includes the aforementioned obstacle-trapping and escape device 100 for autonomous vehicles in mines.

[0136] The sensing device 11 is used to sense the driving status data of the autonomous driving vehicle in the mine.

[0137] The calculation and decision-making device 12 is used to determine the current stuck state of the autonomous driving vehicle in the mine based on the driving state data, and generate corresponding escape control commands and / or escape request commands when determining the current stuck type of the autonomous driving vehicle in the mine.

[0138] The execution control device 13 is used to generate an escape drive control signal according to the escape control command.

[0139] In this embodiment of the invention, the sensing device 11 can collect vehicle operating status and external environmental information in real time, providing a data foundation for entrapment identification and control decisions. It mainly includes: a wheel speed sensor, which collects wheel rotation speed in real time to determine whether the vehicle is slipping or spinning freely; a GPS positioning module, which obtains the vehicle's position coordinates and calculates displacement changes; an IMU (Inertial Measurement Unit), which collects attitude data such as acceleration and angular velocity to identify abnormal postures such as vehicle tilting or lifting; a motor torque sensor, which detects whether the drive motor continuously outputs high torque but the vehicle does not move, assisting in entrapment identification; and a camera / LiDAR, used to perceive terrain and detect muddy or sunken areas, suitable for advanced environmental recognition systems.

[0140] In this embodiment of the invention, the computing and decision-making device 12 is based on an on-board edge computing platform and integrates a stalemate identification and control decision-making algorithm. It mainly includes the stalemate and escape device 100 for autonomous vehicles in mines, as described above. By analyzing parameters such as speed (<0.1 m / s) and displacement (<0.2 m), it comprehensively judges the vehicle's stalemate status and classifies it into three levels: "normal," "Level 1 stalemate," and "Level 2 stalemate." For Level 1 stalemate vehicles, it automatically generates control commands, such as "increase torque output" and "rear-to-rear easing" strategies to attempt escape. It maintains a data cache with a sliding time window for continuously judging stalemate trends and abnormal changes. It realizes information interaction with a remote server and automatically reports information such as stalemate level, processing results, and vehicle operating status.

[0141] In this embodiment of the invention, the execution control device 13 can receive control commands issued by the calculation and decision-making device 12 and act on the relevant actuators of the vehicle body to achieve traction control. It mainly includes: a motor drive controller: adjusting the motor torque output according to system commands (such as torque increase, deceleration buffer); a braking control system: achieving instantaneous braking in a free-wheeling or slipping state to improve traction or stabilize the posture; and a steering execution system: realizing steering assistance operation when it is necessary to adjust the traction angle or perform a reversing action.

[0142] Therefore, the entrapment and escape control system provided by the present invention adopts the aforementioned entrapment and escape device for autonomous vehicles in mines, which can accurately identify and respond in a graded manner to the vehicle's entrapment state, and supports manual command intervention and fault information reporting. It can realize accurate entrapment judgment and graded escape response of vehicles in complex mining operation scenarios, thereby improving the intelligent escape capability, safety and operation efficiency of autonomous vehicles in automated and unmanned operation environments.

[0143] The specific working principle of the entrapment and escape control system provided by this invention can be referred to the description of the entrapment and escape method for autonomous vehicles in mines above, and will not be repeated here.

[0144] As another embodiment of the present invention, a system 1 for getting stuck and escaping from a mine's automated driving vehicle is provided, wherein, as Figure 7 As shown, it includes: a server 20 and the aforementioned entrapment and escape control system 10, wherein the server 20 is communicatively connected to the entrapment and escape control system 10.

[0145] The entrapment and escape control system 10 is used to determine the entrapment status based on the driving status data of the autonomous driving vehicle in the mine and generate an escape drive control signal based on the generated escape control command, as well as generate an escape request command.

[0146] The server 20 is used to remotely control the trapped escape control system and / or send request signals to the user according to the escape request instruction.

[0147] It should be understood that the trapping and escape control system 10 can be installed on the mine's autonomous driving vehicle. The server can receive the operating status and trapping alarm information of the mine's autonomous driving vehicle, and realize functions such as real-time visualization, remote intervention, and historical data tracking. Specifically, it includes: a trapping alarm and fault reporting interface: when the system identifies a level 2 trapping situation, it automatically reports to the platform through the communication module and issues multi-mode (sound / light / image) alarms; a status visualization module: graphically displays the vehicle's current operating status, GPS trajectory, trapping level, and execution results; a remote control and manual intervention interface: in emergency situations, it allows maintenance personnel to send remote commands, such as stopping vehicle operation, restarting the system, or manually resuming operations; and a historical data management module: used to record trapping events, processing procedures, and system response logs to assist in later optimization and analysis.

[0148] Therefore, the entrapment and escape system for autonomous vehicles in mining provided by this invention adopts the aforementioned entrapment and escape control system, enabling accurate identification and graded response to vehicle entrapment status. It also supports manual intervention and fault information reporting, achieving intelligent perception, accurate judgment, and efficient handling of entrapment status for unmanned electric dump trucks, thus improving the reliability and escape capability of vehicles in complex scenarios such as mines, construction sites, and mountainous areas. Furthermore, the entrapment and escape system for autonomous vehicles in mining also possesses data interaction capabilities between the vehicle and server sides, supporting entrapment status reporting and receiving manual intervention commands. It is suitable for autonomous vehicles or remote operation scenarios, improving the system's remote controllability and operational safety. In addition, this invention provides a complete data recording and log interface, enabling full-process storage and backtracking of the entrapment process, providing data support for subsequent system optimization, model training, and fault diagnosis. This invention adopts a hardware and software separation design, offering flexible deployment options and applicability to various types of electric commercial vehicles, possessing good compatibility, scalability, and engineering application prospects.

[0149] The specific working principle of the entrapment and escape system for autonomous vehicles in mining provided by this invention can be referred to the description of the entrapment and escape method for autonomous vehicles in mining above, and will not be repeated here.

[0150] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled 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 to be within the scope of protection of the present invention.

Claims

1. A method for escaping from entrapment in autonomous vehicles used in mining operations, characterized in that, include: Acquire driving status data of autonomous vehicles in mining areas. The driving status data includes at least real-time wheel speed, vehicle geographical location information and displacement trajectory information, vehicle attitude information, and real-time output torque of the vehicle motor. Based on the driving status data, determine whether the current autonomous driving vehicle in the mine is stuck; If it is determined that the current autonomous driving vehicle in the mine is stuck, the type of stuck vehicle is determined based on the driving status data. The type of stuck vehicle includes tire sinking stuck and overhead stuck. When it is determined that the current stuck type of the autonomous vehicle in the mine is a tire sinking stuck, a graded response to get out of the stuck is carried out according to the stuck level of the tire sinking stuck; When it is determined that the current mine autonomous driving vehicle is stuck in an overhead ditch, a first escape request command is sent to the server. The method for determining the current entrapment type of the autonomous driving vehicle in the mine based on the driving status data includes: The real-time rotational speed of the wheels and the real-time output torque of the vehicle motor are compared with the preset no-load state data of the autonomous driving vehicle in the mine. If the real-time rotational speed of the wheels and the real-time output torque of the vehicle motor are consistent with the preset unloaded state data of the autonomous driving vehicle in the mine, then the current entrapment type of the autonomous driving vehicle in the mine is determined to be an overhead entrapment. If the real-time rotational speed of the wheels and the real-time output torque of the vehicle motor are inconsistent with the preset unloaded state data of the mining autonomous driving vehicle, then the current mining autonomous driving vehicle is determined to be stuck in a tire sinking type of stuck. Specifically, when the current autonomous driving vehicle in the mine is determined to be stuck in a tire-diving stalemate, a graded response for escape is performed based on the stalemate level, including: When it is determined that the current autonomous driving vehicle in the mine is stuck due to tire sinking, the current stuck level of the autonomous driving vehicle in the mine is determined according to the driving status data and the preset stuck level threshold. The stuck level includes Level 1 stuck and Level 2 stuck, and the severity of Level 2 stuck is greater than that of Level 1 stuck. The corresponding escaping control strategy is triggered according to different levels of entrapment. The escaping control strategy for Level 1 entrapment includes at least a vehicle motor torque output enhancement strategy, and the escaping control strategy for Level 2 entrapment includes sending a second escaping request command to the server.

2. The method for getting stuck and escaping from a mine's automated driving vehicle according to claim 1, characterized in that, The current entrapment level of the autonomous vehicle in the mine is determined based on the driving status data and a preset entrapment level threshold, including: The real-time output torque of the vehicle motor is compared with a preset torque threshold, the real-time rotation speed of the wheel is compared with a preset wheel speed threshold, and the vehicle geographical location information is compared with a preset displacement threshold within a first preset duration. If the real-time output torque of the vehicle motor is greater than a preset torque threshold within a first preset duration, and the real-time rotational speed of the wheel is lower than a preset wheel speed threshold within a first preset duration, and the vehicle position movement is less than a preset displacement threshold within a first preset duration, then the current mine autonomous driving vehicle is determined to be trapped at level one. Determine whether the current autonomous driving vehicle in the mine has moved to the preset displacement range within the second preset duration after executing the escape control strategy corresponding to the first level of difficulty; If the current autonomous driving vehicle in the mine fails to move to the preset displacement range within the second preset duration after executing the escape control strategy corresponding to Level 1 difficulty, then the current autonomous driving vehicle's entrapment level is determined to be upgraded to Level 2 entrapment.

3. The method for getting stuck and escaping from difficult situations for automated driving vehicles in mines according to claim 2, characterized in that, If the current autonomous driving vehicle in the mine moves to the preset displacement range within the second preset duration after executing the escape control strategy corresponding to the first level of difficulty, then the current autonomous driving vehicle in the mine is determined to have successfully escaped the difficulty.

4. The method for resolving entrapment in autonomous vehicles used in mining operations according to claim 1, characterized in that, The traction control strategy corresponding to the first level of entrapment includes a vehicle motor torque output enhancement strategy, which triggers corresponding traction control strategies according to different entrapment levels, including: When the current entrapment level of the autonomous driving vehicle in the mine is determined to be Level 1 entrapment (tire sinking), a vehicle motor torque output enhancement strategy is triggered, wherein the vehicle motor torque output enhancement strategy includes: The vehicle motor torque is increased in stages, where the expression for the incremental vehicle motor torque is: ， in, This indicates the current real-time output torque of the vehicle's motor. This indicates the basic torque of the vehicle's motor. This indicates the maximum safe output torque of the vehicle's motor. This represents the torque increment coefficient per unit time, and t represents the cumulative control time from the moment the trap is identified.

5. The method for getting stuck and escaping from a mine's automated driving vehicle according to claim 4, characterized in that, The escape control strategy corresponding to the first-level entrapment also includes a periodic disturbance-driven strategy, which triggers the corresponding escape control strategy according to different entrapment levels, and further includes: Determine whether the displacement of the current autonomous driving vehicle in the mine meets the preset displacement requirements within a preset time period after the vehicle motor torque output enhancement strategy is implemented; If the displacement of the current autonomous driving vehicle in the mine does not meet the preset displacement requirement within a preset time period, the periodic disturbance driving strategy is triggered. The periodic disturbance drive strategy includes generating a non-stationary acceleration control signal to control wheel slippage, wherein the non-stationary acceleration control signal includes at least an oscillating acceleration command, a drive speed command, and a wheel-end speed command. The expression for the oscillation acceleration command is: ， The expression for the drive speed command is: ， The expression for the wheel end speed command is: ， in, This represents the amplitude of the oscillating acceleration. This represents the disturbance frequency, and r represents the tire radius of the wheel. This indicates the initial wheel speed.

6. A device for escaping entrapment of an automated mining vehicle, used to implement the method for escaping entrapment of an automated mining vehicle as described in any one of claims 1 to 5, characterized in that, include: The acquisition module is used to acquire driving status data of autonomous vehicles in mining. The driving status data includes at least the real-time wheel speed, vehicle geographical location information and displacement trajectory information, vehicle attitude information, and real-time output torque of the vehicle motor. The entrapment detection module is used to determine whether the current autonomous driving vehicle in the mine is entrapped based on the driving status data; The entrapment type analysis module is used to determine the entrapment type of the current autonomous driving vehicle in the mine based on the driving status data if it is determined that the current autonomous driving vehicle in the mine is entrapped. The entrapment type includes tire sinking entrapment and overhead entrapment. The tire sinking-type entrapment control module is used to respond in a graded manner to extricate the driver from the entrapment when it is determined that the current entrapment type of the autonomous driving vehicle in the mine is tire sinking-type entrapment. The overhead-type entrapment and escape control module is used to send a first escape request command to the server when it is determined that the current mine autonomous driving vehicle is entrapped in an overhead-type entrapment.

7. A system for controlling getting stuck and escaping, characterized in that, include: The system comprises a sensing device, a computing and decision-making device, and an execution control device, wherein the sensing device and the execution control device are both communicatively connected to the computing and decision-making device, and the computing and decision-making device includes the entrapment and escape device for autonomous vehicles in mines as described in claim 6. The sensing device is used to sense the driving status data of autonomous vehicles in the mine. The calculation and decision-making device is used to determine the current stuck state of the autonomous driving vehicle in the mine based on the driving status data, and to generate corresponding escape control commands and / or escape request commands when the stuck type of the current autonomous driving vehicle in the mine is determined. The execution control device is used to generate an escape drive control signal according to the escape control command.

8. A system for getting stuck and escaping from difficult situations in autonomous vehicles used in mining, characterized in that, include: The server and the entrapment and escape control system according to claim 7, wherein the server is communicatively connected to the entrapment and escape control system. The entrapment and escape control system is used to determine the entrapment status based on the driving status data of the autonomous driving vehicle in the mine and generate an escape drive control signal based on the generated escape control command, as well as generate an escape request command. The server is used to remotely control the trapped escape control system and / or send request signals to the user according to the escape request command.

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