A fuzzy control-based speed control method for unmanned vehicles

Abstract

The present disclosure relates to a kind of unmanned speed control method based on fuzzy control.The method includes: based on the preset transport trajectory of unmanned vehicle, the trajectory planning of unmanned vehicle is carried out, the expected speed of the operation scene planning transport section of unmanned vehicle is generated;Respectively based on the positioning module of unmanned vehicle, line control chassis real-time acquisition feedback information of unmanned vehicle;With the expected speed of unmanned vehicle, feedback information as input, based on preset fuzzy control table, the throttle / brake opening information of unmanned vehicle is calculated and generated, and the speed control of unmanned vehicle is completed based on the throttle / brake opening information.The present disclosure realizes the speed control in the automatic driving motion control of unmanned vehicle, improves the degree of automation in mining area, eliminates personnel safety risk, and improves transport efficiency.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of unmanned driving, and in particular, to an unmanned driving speed control method and device based on fuzzy control, an electronic device, and a computer readable storage medium. BACKGROUND

[0002] At present, in an actual production in an open pit mine area, wide-body dump trucks in unmanned vehicles are widely used. The wide-body dump trucks come and go in different work areas such as loading areas and unloading areas. The working characteristics are short transport distance and heavy load. The transport section is often an uphill and downhill road.

[0003] The drive system of the wide-body dump truck has a series power system of engine + generator + drive motor + gearbox + retarder or engine + gearbox + retarder. The speed is reduced by two ways of electric braking and mechanical braking. The mechanical braking uses brake discs for braking, and the electric braking realizes electric coasting through braking energy recovery. In general, the speed is controlled by electric coasting during the transportation process.

[0004] AMT is a common transmission on wide-body dump trucks, which has simple structure, high transmission efficiency, can meet the requirements of reliability and fuel economy of engineering vehicles, and is low in price, so it is widely used. At present, except for a few electric vehicles, most engineering vehicles use clutches for shifting and starting, which will cause non-power shifting due to power interruption. When in some working conditions such as uphill, repeated downhill may occur due to power interruption, which will increase the control difficulty and need to be optimized through control strategy.

[0005] Transportation task is the most common task for wide-body dump trucks. In order to realize the automation of mine production and further realize the intelligentization, the wide-body dump truck needs to realize unmanned driving during the transportation process. The unmanned driving motion control of the wide-body dump truck can be decoupled into longitudinal speed control and lateral position control. Given the desired speed, the controller needs to give the control amount according to the feedback speed, gear position, slope and other information of the vehicle to form a closed-loop control.

[0006] The wide-body dump truck has the characteristics of heavy weight, strong nonlinearity of power system, large power transmission delay, complex running conditions, and power interruption. The existing technical solutions usually calculate through mechanical balance equation, and directly convert the target throttle control amount and electric braking control amount through the longitudinal control algorithm by simply linearly converting the current speed and target acceleration, which has low accuracy and poor adaptability.

[0007] In the prior art, a speed following control method for an unmanned electric mining truck is disclosed. This method includes a planning module, a control module, and a positioning module, comprising the following steps: S1: The planning module outputs planned acceleration and planned speed, and calculates the target acceleration based on the planned acceleration, planned speed, and the current speed of the mining truck; S2: The slope pitch angle is calculated based on the pose information output by the positioning module; S3: The control module calculates the vehicle resistance and calculates the target traction force according to Newton's second law; S4: Based on the target traction force F and combined with the vehicle speed information, a table is looked up from the external characteristic curve of the motor / generator to calculate the vehicle throttle / electric braking control command.

[0008] This method is mainly applicable to electric-driven mining trucks without gearboxes. It relies on looking up the external characteristic curves of a single motor / generator, which cannot be applied to the power characteristics under different gears. When applied to wide-body dump trucks with multiple gears, it will cause control unevenness at the shift points. It cannot solve the problem of wide-body dump trucks slipping on slopes due to power interruption during AMT shifts based on information such as current speed, slope, and gear. It cannot be applied or adapted to more multi-dimensional information data input. The pitch angle calculation method is also different.

[0009] A method for controlling vehicle pedal opening includes: S1, dividing the pedal opening into M segments and calculating M accelerations corresponding to each of the M pedal opening segments at the current vehicle speed; S2, acquiring a preset acceleration and calculating a first pedal opening corresponding to the preset acceleration based on the M pedal opening segments, the M accelerations, and the preset acceleration; S3, acquiring the actual acceleration and compensating the first pedal opening based on the difference between the actual acceleration and the preset acceleration to obtain a second pedal opening; S4, controlling the pedal opening according to a preset rule so that the pedal opening is equal to the second pedal opening. The method expands its applicability by calculating the relationship between vehicle speed, acceleration, and pedal opening, improves control performance by performing PID control based on actual acceleration, and enhances overall vehicle driving performance by filtering the pedal opening.

[0010] This method also suffers from the problem that, for the same speed and acceleration, the pedal opening is a fixed value according to preset rules. However, when a wide-body dump truck using AMT is at a shift point, the pedal opening for the same speed and acceleration in adjacent gears is not the same, resulting in uneven speed control. It also cannot solve the problem of wide-body dump trucks rolling backwards due to power interruption during AMT shifts based on information such as current speed, slope, and gear. Furthermore, it cannot apply or adapt to more multi-dimensional information data input.

[0011] A control system and method for limiting the gear position of an unmanned mining truck are provided, comprising: an automatic transmission control unit, a first speed sensor, an engine management system, a vehicle controller, an electronic braking system, a second speed sensor, an intelligent driving controller, a decision-making and planning system, and a perception and positioning system. This invention uses information feedback from the first and second speed sensors and the engine management system to monitor the status of the unmanned mining truck in real time during operation. Based on the vehicle status and driving scenario or operating conditions, the highest gear position of the mining truck is limited in real time. This ensures normal vehicle operation while improving the safety performance of the unmanned mining truck, adapting to the complex and harsh driving environment of mining areas and the differences between empty and heavy-load driving, avoiding certain driving safety hazards, and improving the safety redundancy of the unmanned mining truck system.

[0012] This invention is a gear-limiting control system. Based on a pre-planned vehicle speed, it determines the highest gear that the automatic transmission can reach at that time and limits it to this highest gear. However, when this invention is applied to a transmission where power is interrupted during gear shifting, it cannot solve the problem of gear skipping that may occur before reaching the highest gear on a slope. For example, if the highest gear is limited to 4th gear, but the vehicle repeatedly skips gears between 2nd and 3rd gear on a slope due to power interruption, then this gear-limiting strategy will not work.

[0013] A deep reinforcement learning-based unmanned mining truck tracking control system and method are disclosed. In the learning phase, environmental state information and control action information are received through a simulation platform to simulate the tracking process of the unmanned mining truck. The state of the unmanned mining truck at each moment on the preset route is collected. The state at each moment is used as input, and the control action information at each moment is used as output for deep reinforcement learning training to obtain the algorithm kernel. In the application phase, the current state and the target state of the unmanned mining truck at the next moment are obtained and fed into the algorithm kernel. Based on the algorithm kernel, the control action information at the current moment is predicted. This invention enables precise control of the unmanned mining truck's trajectory. It can automatically track under different working conditions, environments, and states according to the algorithm trained by deep reinforcement learning, exhibiting highly intelligent, self-learning, and self-adaptive characteristics. It improves the efficiency of mining truck tracking control and reduces fuel consumption.

[0014] The problems with this invention are: 1. The control method generated through learning is a black box, making it difficult for designers to intuitively understand what rules are used for control, and reliability is hard to guarantee; 2. The control effect is heavily dependent on the learning samples and the controlled vehicle. If the learning samples are multiple mining areas, the control effect for a single mining area may not be guaranteed. If the learning samples are a single mining area, they need to be relearned when reused in other mining areas. The same applies to the controlled vehicle. The control effect may still be different for vehicles of the same brand and model under different working conditions. Even for the same vehicle, the control effect may not be guaranteed at different stages of its working life cycle, resulting in poor reusability.

[0015] Therefore, one or more methods are needed to solve the above problems.

[0016] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0017] The purpose of this disclosure is to provide a method, apparatus, electronic device, and computer-readable storage medium for unmanned driving based on fuzzy control, thereby overcoming at least to some extent one or more problems caused by the limitations and defects of related technologies.

[0018] According to one aspect of this disclosure, a speed control method for autonomous driving based on fuzzy control is provided, comprising:

[0019] Based on the preset transportation trajectory of the autonomous vehicle, trajectory planning is performed on the autonomous vehicle to generate the expected speed of the planned transportation route for the autonomous vehicle's operation scenario.

[0020] The autonomous vehicle's feedback information is obtained in real time based on its positioning module and drive-by-wire chassis. The feedback information includes position slope, speed change rate, and gear information.

[0021] Using the desired speed and feedback information of the autonomous vehicle as input, and based on a preset fuzzy control table, the throttle / electric brake opening information of the autonomous vehicle is calculated and generated, and the speed control of the autonomous vehicle is completed based on the throttle / electric brake opening information.

[0022] In one exemplary embodiment of this disclosure, the method further includes:

[0023] Based on the preset transportation trajectory, road information, and preset work site production regulations of the unmanned vehicle, trajectory planning is performed on the unmanned vehicle to generate discrete trajectory points, which include latitude, longitude, elevation, north angle, vehicle speed, and road curvature.

[0024] In one exemplary embodiment of this disclosure, the method further includes:

[0025] Based on the discrete trajectory points of the autonomous vehicle for trajectory planning and the preset work site production regulations of the autonomous vehicle, the speed limit coefficient of the autonomous vehicle is generated.

[0026] Based on the speed limit coefficient of the unmanned vehicle, the expected speed of the planned transportation route for the unmanned vehicle's operation scenario is generated.

[0027] In one exemplary embodiment of this disclosure, the method further includes:

[0028] The latitude, longitude, elevation, and northward angle information of the unmanned vehicle are obtained based on the positioning module of the unmanned vehicle.

[0029] Based on the latitude, longitude, elevation, and north angle information of the unmanned vehicle, the nearest point of the trajectory and the forward aiming point of the unmanned vehicle are obtained, and the desired speed is generated based on the speed information of the nearest point of the trajectory and the forward aiming point.

[0030] The position slope is obtained by comparing the difference between the current elevation of the autonomous vehicle and the elevation of the pre-aiming point with the distance between the current position of the autonomous vehicle and the pre-aiming point, using an inverse trigonometric function.

[0031] In one exemplary embodiment of this disclosure, the method further includes:

[0032] Based on the current speed and gear information of the autonomous vehicle's drive-by-wire chassis, the difference between the current speed and the desired speed, and the rate of change of speed are calculated and generated based on the current speed and the desired speed of the autonomous vehicle.

[0033] In one exemplary embodiment of this disclosure, the method further includes:

[0034] Based on the difference between the current speed and the desired speed in the current gear of the unmanned vehicle, the rate of change of speed, the current speed and the position slope, multiple preset fuzzy control tables are established.

[0035] Using the desired speed and feedback information of the autonomous vehicle as input, the control information in the multiple preset fuzzy control tables under different gears of the autonomous vehicle is weighted and summed to calculate the throttle / electric brake opening information of the autonomous vehicle, and the speed control of the autonomous vehicle is completed based on the throttle / electric brake opening information.

[0036] In one exemplary embodiment of this disclosure, the method further includes:

[0037] The position slope of the autonomous vehicle is determined. If the position slope of the autonomous vehicle is greater than the first preset position slope, the gear lock control is triggered to lock the gearbox of the autonomous vehicle in the preset gear.

[0038] When the slope of the autonomous vehicle's position is less than the second preset slope, an unlock signal is generated to release the lock control.

[0039] In one aspect of this disclosure, a fuzzy control-based unmanned driving speed control device is provided, comprising:

[0040] The expected speed generation module is used to perform trajectory planning for the unmanned vehicle based on the preset transportation trajectory of the unmanned vehicle, and generate the expected speed of the unmanned vehicle's planned transportation route in the operation scenario.

[0041] The feedback information generation module is used to obtain feedback information of the autonomous vehicle in real time based on the positioning module and the drive-by-wire chassis of the autonomous vehicle, respectively. The feedback information includes position slope, speed change rate, and gear information.

[0042] The fuzzy control module is used to calculate and generate the throttle / electric brake opening information of the unmanned vehicle based on the desired speed and feedback information of the unmanned vehicle and a preset fuzzy control table, and to complete the speed control of the unmanned vehicle based on the throttle / electric brake opening information.

[0043] In one aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory storing computer-readable instructions that, when executed by the processor, implement the method according to any one of the preceding claims.

[0044] In one aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method according to any one of the preceding claims.

[0045] An exemplary embodiment of this disclosure provides a fuzzy control-based speed control method for unmanned vehicles. The method includes: planning the trajectory of the unmanned vehicle based on a preset transport trajectory to generate a desired speed for the planned transport segment of the unmanned vehicle's operating scenario; acquiring feedback information from the unmanned vehicle in real time based on its positioning module and drive-by-wire chassis; using the desired speed and feedback information as input, calculating throttle / electric brake opening information based on a preset fuzzy control table, and controlling the speed of the unmanned vehicle based on this throttle / electric brake opening information. This disclosure achieves speed control in the motion control of autonomous driving vehicles, improving the automation level of mining areas, eliminating personnel safety risks, and improving transportation efficiency.

[0046] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0047] The above and other features and advantages of this disclosure will become more apparent from the detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0048] Figure 1 A flowchart of an autonomous driving speed control method based on fuzzy control according to an exemplary embodiment of the present disclosure is shown;

[0049] Figure 2 A speed planning flowchart of an autonomous driving speed control method based on fuzzy control according to an exemplary embodiment of the present disclosure is shown.

[0050] Figure 3 A flowchart illustrating the gear locking control process of an autonomous driving speed control method based on fuzzy control according to an exemplary embodiment of the present disclosure is shown.

[0051] Figure 4 A schematic block diagram of an autonomous driving speed control device based on fuzzy control according to an exemplary embodiment of the present disclosure is shown;

[0052] Figure 5 A block diagram of an electronic device according to an exemplary embodiment of the present disclosure is schematically shown; and

[0053] Figure 6 The illustration shows a schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0054] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0055] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, materials, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, materials, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0056] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0057] In this example embodiment, a speed control method for autonomous driving based on fuzzy control is first provided; see reference. Figure 1 As shown, this autonomous driving speed control method based on fuzzy control may include the following steps:

[0058] Step S110: Based on the preset transportation trajectory of the unmanned vehicle, perform trajectory planning for the unmanned vehicle to generate the expected speed of the planned transportation route for the unmanned vehicle's operation scenario.

[0059] Step S120: Based on the positioning module and drive-by-wire chassis of the autonomous vehicle, the feedback information of the autonomous vehicle is obtained in real time. The feedback information includes position slope, speed change rate, and gear information.

[0060] Step S130: Using the desired speed and feedback information of the unmanned vehicle as input, calculate and generate the throttle / electric brake opening information of the unmanned vehicle based on a preset fuzzy control table, and complete the speed control of the unmanned vehicle based on the throttle / electric brake opening information.

[0061] An exemplary embodiment of this disclosure provides a fuzzy control-based speed control method for unmanned vehicles. The method includes: planning the trajectory of the unmanned vehicle based on a preset transport trajectory to generate a desired speed for the planned transport segment of the unmanned vehicle's operating scenario; acquiring feedback information from the unmanned vehicle in real time based on its positioning module and drive-by-wire chassis; using the desired speed and feedback information as input, calculating throttle / electric brake opening information based on a preset fuzzy control table, and controlling the speed of the unmanned vehicle based on this throttle / electric brake opening information. This disclosure achieves speed control in the motion control of autonomous driving vehicles, improving the automation level of mining areas, eliminating personnel safety risks, and improving transportation efficiency.

[0062] The following will further explain an autonomous driving speed control method based on fuzzy control in this example embodiment.

[0063] In this example embodiment, the wide-body vehicle with AMT transmission is a special type of autonomous vehicle, characterized by its large weight, strong nonlinearity of the power system, large power transmission lag, complex operating conditions, and the ability to switch to different gears depending on the operating conditions, as well as the interruption of power. Compared with other ordinary autonomous vehicles, the wide-body vehicle based on AMT transmission requires additional autonomous driving speed control based on fuzzy control.

[0064] In step S110, the autonomous vehicle can be trajectory planned based on its preset transport trajectory to generate the expected speed of the planned transport segment for the autonomous vehicle's operation scenario.

[0065] In this example embodiment, the method further includes:

[0066] Based on the preset transportation trajectory, road information, and preset work site production regulations of the unmanned vehicle, trajectory planning is performed on the unmanned vehicle to generate discrete trajectory points, which include latitude, longitude, elevation, north angle, vehicle speed, and road curvature.

[0067] In this example embodiment, the method further includes:

[0068] Based on the discrete trajectory points of the autonomous vehicle for trajectory planning and the preset work site production regulations of the autonomous vehicle, the speed limit coefficient of the autonomous vehicle is generated.

[0069] Based on the speed limit coefficient of the unmanned vehicle, the expected speed of the planned transportation route for the unmanned vehicle's operation scenario is generated.

[0070] In the embodiments of this example, as Figure 2 As shown, road information is collected manually during transport. Based on the road information and work site production regulations, trajectory planning is performed to generate a series of discrete trajectory points, which are saved in a txt file. Each line in the file stores one trajectory point, and each line sequentially stores information such as latitude, longitude, elevation, north angle, vehicle speed, and road curvature. The storage format is shown in the table below. The collected information is processed by the planning module to output the trajectory. Discrete points are taken at intervals to obtain the latitude, longitude, elevation, and north angle of the discrete points. Then, the road curvature at the discrete points is calculated based on the trajectory curve. The desired vehicle speed is determined according to the work site production regulations, with a maximum vehicle speed v. max and minimum vehicle speed v min The desired velocity v passes through the discrete points at the slopes θ and the maximum slope θ, respectively. max and road curvature curvature curvature max The amplitude limiting coefficient is calculated, and the amplitude is limited by multiplying the maximum speed by a coefficient k less than 1. One method for linearly calculating the coefficient k is as follows: If the speed after the limit is less than the minimum speed, then it is the minimum speed.

[0071] Track file example table

[0072] latitude longitude elevation northing vehicle speed road curvature 39.4738774 115.6254204 87.82 54.76 20 0 39.4738784 115.6254223 87.82 54.76 20 0 39.4738795 115.6254242 87.82 54.76 20 0 39.4738805 115.625426 87.82 54.76 20 0 39.4738815 115.6254279 87.82 54.76 20 0 39.4738826 115.6254298 87.82 54.76 20 0 39.4738836 115.6254317 87.82 54.76 20 0 39.4738846 115.6254336 87.82 54.76 20 0 39.4738857 115.6254355 87.82 54.76 20 0 39.4738867 115.6254374 87.82 54.76 20 0 39.4738877 115.6254393 87.82 54.76 20 0 39.4738888 115.6254412 87.82 54.76 20 0 39.4738898 115.6254431 87.82 54.76 20 0 39.4738908 115.625445 87.82 54.76 20 0 39.4738919 115.6254469 87.82 54.76 20 0 39.4738929 115.6254488 87.82 54.76 20 0 39.4738939 115.6254507 87.82 54.76 20 0 39.473895 115.6254526 87.82 54.76 20 0 39.473896 115.6254545 87.82 54.76 20 0

[0073] In step S120, feedback information of the autonomous vehicle can be obtained in real time based on the positioning module and the drive-by-wire chassis of the autonomous vehicle. The feedback information includes position slope, speed change rate, and gear information.

[0074] In this example embodiment, the method further includes:

[0075] The latitude, longitude, elevation, and northward angle information of the unmanned vehicle are obtained based on the positioning module of the unmanned vehicle.

[0076] Based on the latitude, longitude, elevation, and north angle information of the unmanned vehicle, the nearest point of the trajectory and the forward aiming point of the unmanned vehicle are obtained, and the desired speed is generated based on the speed information of the nearest point of the trajectory and the forward aiming point.

[0077] The position slope is obtained by comparing the difference between the current elevation of the autonomous vehicle and the elevation of the pre-aiming point with the distance between the current position of the autonomous vehicle and the pre-aiming point, using an inverse trigonometric function.

[0078] In this example embodiment, the method further includes:

[0079] Based on the current speed and gear information of the autonomous vehicle's drive-by-wire chassis, the difference between the current speed and the desired speed, and the rate of change of speed are calculated and generated based on the current speed and the desired speed of the autonomous vehicle.

[0080] In this example embodiment, further calculations are performed using the positioning module and feedback information from the drive-by-wire chassis to obtain the input quantities required for subsequent fuzzy logic:

[0081] The positioning module obtains the vehicle's latitude, longitude, elevation, and northward angle. Calculations are performed to obtain the nearest point on the trajectory and the forward target point, yielding the current expected speed. The slope θ at the current position is approximated using the inverse trigonometric function arctan, by comparing the difference between the current vehicle's elevation and the elevation of the forward target point with the distance between the current vehicle's position and the forward target point. now .

[0082] The current vehicle speed and gear are obtained through the drive-by-wire chassis, and the difference between the current vehicle speed and the desired speed and the rate of change of speed are further calculated.

[0083] In step S130, the desired speed and feedback information of the unmanned vehicle can be used as inputs. Based on a preset fuzzy control table, the throttle / electric brake opening information of the unmanned vehicle can be calculated and generated. Based on the throttle / electric brake opening information, the speed control of the unmanned vehicle can be completed.

[0084] In this example embodiment, the method further includes:

[0085] Based on the difference between the current speed and the desired speed in the current gear of the unmanned vehicle, the rate of change of speed, the current speed and the position slope, multiple preset fuzzy control tables are established.

[0086] Using the desired speed and feedback information of the autonomous vehicle as input, the control information in the multiple preset fuzzy control tables under different gears of the autonomous vehicle is weighted and summed to calculate the throttle / electric brake opening information of the autonomous vehicle, and the speed control of the autonomous vehicle is completed based on the throttle / electric brake opening information.

[0087] In this example embodiment, the characteristic curves corresponding to throttle / brake opening, vehicle speed, and acceleration under different gears are tested in the test site using gear lock control. The fuzzy rule tables for all N gears of the transmission are then modified based on the characteristic curves. For a given gear, there are M different fuzzy rule tables. Here, M=2 is taken as an example. The input quantities are speed, speed difference, speed change rate, and gradient. The fuzzy control table using speed difference and speed change rate as input quantities can remain unchanged when the control accuracy requirements are not strict. The fuzzy rule tables are shown in Table 2-3. The basic universe of discourse for the vehicle speed difference E is set to [-6,6], the basic universe of discourse for the rate of change of the vehicle speed signal EC is set to [-1,1], and the basic universe of discourse for the control quantity throttle output value U1 is set to [-1,1]. The fuzzy set uses 7 linguistic values: {NB,NM,NS,ZO,PS,PM,PB}. Using a fuzzy rule table with slope and speed as inputs requires calculations based on the characteristic curves corresponding to throttle / brake opening, vehicle speed, and acceleration, combined with the gravitational acceleration provided by the slope, to obtain the relationship between slope and vehicle speed at 0 throttle / brake. The original fuzzy rule table is offset according to the new 0 opening line, as shown in Table 3. Let the basic universe of discourse for slope θ be [-6,6], the basic universe of discourse for vehicle speed signal V be [-6,6], and the basic universe of discourse for control quantity throttle output value U2 be [-1,1]. The fuzzy set takes 7 linguistic values ​​{NB,NM,NS,ZO,PS,PM,PB}.

[0088] In this example embodiment, at a specific gear position, the aforementioned fuzzy control input is calculated, and a fuzzy relation table R1 is established using Table 1. This is achieved by traversing the fuzzy values ​​E of E. * The fuzzy value of EC and EC * , using the formula U1=(E * ×EC * The fuzzy values ​​are selected and the fuzzy output value U1 is calculated by iterating through all universes of discourse of E and EC. A fuzzy relation table R2 is established using Table 2-3, and the fuzzy values ​​θ and V are iterated through. * The fuzzy value of V and V * , using the formula U2=(θ * ×V * R iterates through all universes of discourse of θ and V, selects fuzzy values, and calculates the fuzzy output value U2. The final output U is a weighted sum of outputs U1, U2, etc. If U is greater than 0, the throttle opening is output; if U is less than 0, the brake opening is output.

[0089] Table 2. Fuzzy rule table with speed difference and speed change rate as inputs.

[0090]

[0091] Table 1. Fuzzy rule table with speed and current slope as input.

[0092]

[0093] In this example embodiment, in addition to Tables 2 and 3 in the example above, the preset fuzzy control table can also be expanded to M x N tables by creating a table for every two available input variables under different gear positions. Here, M refers to the number of power gears of the AMT transmission. For example, in an eight-speed transmission, excluding the Nth gear, M is 7. There are N fuzzy control tables under each gear position. In this case, N is 2. It can be further expanded so that under each gear position and variable, the throttle / electric brake opening information of the unmanned vehicle can be generated based on the preset fuzzy control table, and the speed control of the unmanned vehicle can be completed based on the throttle / electric brake opening information.

[0094] This is a feature that distinguishes it from other patents. For example, without differentiating based on gear position, using the same set of N fuzzy control tables, with M specifically set to 1, and processing the output throttle / electric brake opening using the transmission ratio under different gear positions, can achieve a similar effect. However, this method does not control the smoothness of gear shifting as effectively.

[0095] In this example embodiment, the method further includes:

[0096] The position slope of the autonomous vehicle is determined. If the position slope of the autonomous vehicle is greater than the first preset position slope, the gear lock control is triggered to lock the gearbox of the autonomous vehicle in the preset gear.

[0097] When the slope of the autonomous vehicle's position is less than the second preset slope, an unlock signal is generated to release the lock control.

[0098] In this example embodiment, the gear lock control implements the function of the AMT transmission of the autonomous vehicle. When in a specific gear, it switches to manual mode and the transmission no longer shifts gears automatically.

[0099] In the embodiments of this example, as Figure 3 As shown, when an AMT (Automated Manual Transmission) wide-body dump truck is driving on a slope, a gear lock control is implemented, meaning the transmission is locked in a specific gear, preventing upshifts and downshifts to avoid power interruption caused by gear shifting on the slope. When sending control commands, the slope angle θ at the current position is considered. now The system makes a judgment, setting an entry threshold θ1 and an exit threshold θ2, where the absolute value of θ1 is greater than θ2. For example, if θ1 = 4° and θ2 = 2°, then θ... now When the absolute value of θ is greater than 4°, the locking state is triggered until θ nowIf the absolute value is less than 2°, the gear lock state is disengaged to prevent repeated triggering of the lock state due to terrain bumps. When in the lock state, a lock gear is determined based on the desired speed. On this slope, the transmission is locked in this gear, and the lock signal and control signal are sent together until the lock state is disengaged and a release signal is sent.

[0100] In this example embodiment, the present disclosure enables speed control in the motion control of an AMT wide-body dump truck's autonomous driving system, replacing manual driving, improving the automation level of the mining area, eliminating personnel safety risks, and increasing transportation efficiency. Applying this method achieves high speed control accuracy, smooth speed control during gear shifts, stable slope control, and avoids frequent gear skipping and loss of power. It also exhibits high robustness on slopes and bumpy roads, while maintaining high economic efficiency. This method has good information dimension scalability and good reusability for different models and types of unmanned vehicles in mining areas.

[0101] In this example embodiment, the present disclosure outputs the target throttle control quantity / electric brake control quantity in a manner more adapted to the operating conditions of wide-body dump trucks. This has the following advantages:

[0102] 1. By planning the transportation route and speed in advance, the wide-body dump truck can achieve safe, smooth, and efficient transportation speeds on slopes and curves, with reasonable gear control and high fuel economy.

[0103] 2. By using speed, gradient, speed difference, speed change rate, gear, and load as inputs and throttle / electric brake opening as output, the input information has a high dimension and makes greater use of the vehicle's status information. This enables closed-loop speed control of AMT transmission wide-body dump trucks, allowing the wide-body dump trucks to reach the planned desired speed.

[0104] 3. To address the uncertainties, nonlinearities, and time-varying nature of AMT (Automated Manual Transmission), a fuzzy control method is employed. For a single gear, M fuzzy rule tables are established. The output is obtained by calculating the input values ​​of each fuzzy rule table, and the outputs are weighted and summed. Each fuzzy rule table has two input values, such as slope-speed and speed difference-speed change rate. For different gears, corresponding fuzzy rule tables are established, and different combinations of fuzzy rule tables are used to address changes in the transmission ratio of different gears. An N (number of gears) × M (number of fuzzy rule tables for a single gear) fuzzy controller, independent of a precise mathematical model, is designed. This fuzzy control effectively reduces shift shock, decreases clutch wear, and improves the smoothness of the shifting process.

[0105] 4. A locking gear control method is used to prevent wide-body dump trucks from rolling back on slopes due to insufficient AMT electronic control software. A locking gear strategy is formulated based on the wide-body dump truck's AMT shifting strategy and the operating conditions of the transport section. For example, if a wide-body dump truck is going uphill and the AMT electronic control software is insufficient, and the power interruption time is relatively long, the vehicle speed will decrease during upshifting, and then enter an upshift-downshift-downshift-upshift cycle, causing the wide-body dump truck to roll back downhill. On slopes, the locking gear strategy keeps the gear in the optimal position, preventing power loss during uphill and downhill shifts, ensuring smooth driving and fuel economy.

[0106] 5. Good reusability: Mining areas have various brands of wide-body dump trucks, and even within a single brand, the mechanical conditions of wide-body dump trucks differ. This method is applicable to longitudinal control in unmanned driving motion control for wide-body dump trucks in different conditions. It is easy to adapt and can be reused without recalibrating the fuzzy rule table. If better control is desired, only minor modifications to the simple calibration are required.

[0107] 6. It has good scalability. For a single gear, M fuzzy rule tables can be formulated. Each control table has two input quantities, such as slope-speed and speed difference-speed change rate. When more dimensional input data can be obtained, such as load and slip ratio, fuzzy rule tables can be expanded and weighted summation can be performed to achieve more dimensional data input and more precise control.

[0108] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0109] Furthermore, in this example embodiment, a speed control device for autonomous driving based on fuzzy control is also provided. (Refer to...) Figure 4 As shown, the unmanned driving speed control device 400 based on fuzzy control may include: a desired speed generation module 410, a feedback information generation module 420, and a fuzzy control module 430. Wherein:

[0110] The expected speed generation module 410 is used to perform trajectory planning for the unmanned vehicle based on the preset transportation trajectory of the unmanned vehicle, and generate the expected speed of the unmanned vehicle's planned transportation route in the operation scenario.

[0111] The feedback information generation module 420 is used to obtain feedback information of the unmanned vehicle in real time based on the positioning module and the drive-by-wire chassis of the unmanned vehicle, respectively. The feedback information includes position slope, speed change rate, and gear information.

[0112] The fuzzy control module 430 is used to calculate and generate the throttle / electric brake opening information of the unmanned vehicle based on the desired speed and feedback information of the unmanned vehicle and a preset fuzzy control table, and to complete the speed control of the unmanned vehicle based on the throttle / electric brake opening information.

[0113] The specific details of each of the above-mentioned unmanned driving speed control device modules based on fuzzy control have been described in detail in the corresponding unmanned driving speed control method based on fuzzy control, so they will not be repeated here.

[0114] It should be noted that although several modules or units of an unmanned driving speed control device 400 based on fuzzy control have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0115] Furthermore, in an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0116] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented as entirely hardware embodiments, entirely software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, collectively referred to herein as “circuit,” “module,” or “system.”

[0117] The following reference Figure 5 To describe an electronic device 500 according to such an embodiment of the present invention. Figure 5 The electronic device 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0118] like Figure 5 As shown, the electronic device 500 is manifested in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), and a display unit 540.

[0119] The storage unit stores program code that can be executed by the processing unit 510, causing the processing unit 510 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 510 can perform actions such as... Figure 1 Steps S110 to S130 are shown in the diagram.

[0120] Storage unit 520 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 5201 and / or cache memory 5202, and may further include a read-only memory (ROM) 5203.

[0121] Storage unit 520 may also include a program / utility 5204 having a set (at least one) program module 5203, such program module 5205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0122] Bus 550 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0123] Electronic device 500 can also communicate with one or more external devices 570 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 500, and / or with any device that enables electronic device 500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 550. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. As shown, network adapter 560 communicates with other modules of electronic device 500 via bus 550. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0124] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0125] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section above.

[0126] refer to Figure 6 As shown, a program product 600 for implementing the above-described method according to an embodiment of the present invention is described. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0127] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0128] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0129] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0130] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0131] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0132] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0133] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A speed control method for unmanned driving based on fuzzy control, characterized in that, The method includes: Based on the preset transportation trajectory of the autonomous vehicle, trajectory planning is performed on the autonomous vehicle to generate the expected speed of the planned transportation route for the autonomous vehicle's operation scenario. The autonomous vehicle's feedback information is obtained in real time based on its positioning module and drive-by-wire chassis. The feedback information includes position slope, speed change rate, and gear information. Based on the drive-by-wire chassis of the unmanned vehicle, the current speed and gear information of the unmanned vehicle, the difference between the current speed and the expected speed and the speed change rate are calculated and generated based on the current speed and the expected speed of the unmanned vehicle. Using the desired speed and feedback information of the unmanned vehicle as input, and based on a preset fuzzy control table, the throttle / electric brake opening information of the unmanned vehicle is calculated and generated, and the speed control of the unmanned vehicle is completed based on the throttle / electric brake opening information. Based on the difference between the current speed and the desired speed in the current gear of the unmanned vehicle, the rate of change of speed, the current speed and the position slope, multiple preset fuzzy control tables are established. Using the desired speed and feedback information of the autonomous vehicle as input, the control information in the multiple preset fuzzy control tables under different gears of the autonomous vehicle is weighted and summed to calculate the throttle / electric brake opening information of the autonomous vehicle, and the speed control of the autonomous vehicle is completed based on the throttle / electric brake opening information.

2. The method as described in claim 1, characterized in that, The method further includes: Based on the preset transportation trajectory, road information, and preset work site production regulations of the unmanned vehicle, trajectory planning is performed on the unmanned vehicle to generate discrete trajectory points, which include latitude, longitude, elevation, north angle, vehicle speed, and road curvature.

3. The method as described in claim 2, characterized in that, The method further includes: Based on the discrete trajectory points of the autonomous vehicle for trajectory planning and the preset work site production regulations of the autonomous vehicle, the speed limit coefficient of the autonomous vehicle is generated. Based on the speed limit coefficient of the unmanned vehicle, the expected speed of the planned transportation route for the unmanned vehicle's operation scenario is generated.

4. The method as described in claim 1, characterized in that, The method further includes: The latitude, longitude, elevation, and northward angle information of the unmanned vehicle are obtained based on the positioning module of the unmanned vehicle. Based on the latitude, longitude, elevation, and north angle information of the unmanned vehicle, the nearest point of the trajectory and the forward aiming point of the unmanned vehicle are obtained, and the desired speed is generated based on the speed information of the nearest point of the trajectory and the forward aiming point. The position slope is obtained by comparing the difference between the current elevation of the autonomous vehicle and the elevation of the pre-aiming point with the distance between the current position of the autonomous vehicle and the pre-aiming point, using an inverse trigonometric function.

5. The method as described in claim 1, characterized in that, The method further includes: The position slope of the autonomous vehicle is determined. If the position slope of the autonomous vehicle is greater than the first preset position slope, the gear lock control is triggered to lock the gearbox of the autonomous vehicle in the preset gear. When the slope of the autonomous vehicle's position is less than the second preset slope, an unlock signal is generated to release the lock control.

6. An unmanned driving speed control device based on fuzzy control, characterized in that, Based on the method according to any one of claims 1-5, the apparatus comprises: The expected speed generation module is used to perform trajectory planning for the unmanned vehicle based on the preset transportation trajectory of the unmanned vehicle, and generate the expected speed of the unmanned vehicle's planned transportation route in the operation scenario. The feedback information generation module is used to obtain feedback information of the autonomous vehicle in real time based on the positioning module and the drive-by-wire chassis of the autonomous vehicle, respectively. The feedback information includes position slope, speed change rate, and gear information. The fuzzy control module is used to calculate and generate the throttle / electric brake opening information of the unmanned vehicle based on the desired speed and feedback information of the unmanned vehicle and a preset fuzzy control table, and to complete the speed control of the unmanned vehicle based on the throttle / electric brake opening information.

7. An electronic device, characterized in that, The method includes a processor; and a memory storing computer-readable instructions that, when executed by the processor, implement the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 5.

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