CN117565870B - Ultra-low vehicle speed prediction control method for ramp section of off-road unmanned vehicle - Google Patents
Ultra-low vehicle speed prediction control method for ramp section of off-road unmanned vehicle Download PDFInfo
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Abstract
The invention discloses an ultra-low vehicle speed prediction control method for a ramp section of an off-road unmanned vehicle, which comprises the following steps of: acquiring and correcting road point clouds in front of a vehicle to obtain laser point cloud data; acquiring longitudinal section data in front of the vehicle running through the laser point cloud data; calculating a slope angle function of the vehicle in the advancing direction according to the longitudinal section data; acquiring a target vehicle speed command and a current state of a vehicle, optimizing driving force/braking force control according to the slope angle function, the target vehicle speed command and the current state of the vehicle, and sending the driving force/braking force control to an execution unit; the invention is based on the sensor, the control algorithm and the vehicle dynamics prediction model, and can ensure that the vehicle can maintain excellent stability when meeting the ramp road section at extremely low speed.
Description
Technical Field
The invention relates to the technical field of intelligent driving control, in particular to a method for predicting and controlling ultralow vehicle speed of a ramp section, which is applied to the technical field of off-road unmanned vehicle control.
Background
With rapid development of science and technology, intelligent driving technology gradually becomes an important research field of modern society; the off-road unmanned vehicle is an important component in the field, and the technical background thereof relates to a plurality of disciplines including mechanical engineering, electronic engineering, computer science, control theory and the like.
However, the technical challenges faced by off-road unmanned vehicles are also considerable. Because of the complexity and variability of its operating environment, vehicles are required to have powerful perceptibility, decision making capability and control capability, which requires that the related art must achieve considerable accuracy and stability. In addition, when the off-road unmanned vehicle performs tasks such as sensing detection, inspection, environment modeling and the like, the speed of the vehicle is required to be accurately controlled, the vehicle speed is kept in an ultra-low speed interval, for example, 3-5km/h, and complex and changeable road conditions are faced, wherein the characteristics include uncertain gradient, irregular road surfaces, rapid change of terrains and the like, namely, the running load resistance is large. Aiming at the slope working conditions, the existing intelligent driving control technology has the following defects:
prior art one (CN 117022322 a): the scheme provides a method for controlling an automatic driving vehicle based on a combination of vehicle kinematics and longitudinal dynamics; the nonlinear model predictive controller is designed based on the longitudinal dynamics model of the vehicle, and the longitudinal dynamics model is introduced to enable the controller to control the characteristics of the vehicle in longitudinal movement more comprehensively, and the road adhesion coefficient is taken into consideration, so that the method can be better suitable for different road conditions, and meanwhile the slip rate can be controlled to ensure the anti-slip safety of the wheels.
The scheme utilizes a model predictive control method to control the longitudinal speed of the vehicle, takes the road adhesion coefficient as a state input quantity when a state equation is established, mainly takes the error with the target vehicle speed as a performance index when an objective function is established, but does not optimize the speed control for gradient change.
State of the art two (CN 110341715 a): the scheme provides a speed control method and device for the ramp of the unmanned vehicle; firstly, acquiring pitch angle information of a vehicle, determining gradient information by combining actual acceleration of the vehicle, preset fitting coefficients of pitch angle disturbance and acceleration of the vehicle, and then determining components of gravity acceleration in the direction of a ramp; calculating the expected acceleration of the vehicle according to the actual and planned vehicle speed of the vehicle and combining the components of the gravity acceleration in the direction of the ramp; and inquiring a preset throttle opening/braking pressure gauge according to the actual speed and the expected acceleration of the vehicle, determining throttle opening information or braking pressure information, and finally achieving the effect of improving the speed control of the automatic driving vehicle when the vehicle runs on a slope.
According to the scheme, an integral link is introduced for an automatic driving ramp speed control algorithm to offset the interference of the ramp on a vehicle motion model, and the speed control of an automatic driving vehicle ramp scene is solved by adopting an integral compensation mode; however, the scheme mainly considers the gradient of the current position of the vehicle and does not consider the influence of the gradient in front of the road on the speed control of the vehicle; as in other prior art solutions, in the ultra-low speed interval control of the unmanned vehicle, closed-loop control is usually performed for speed control errors, but when the vehicle travels to a continuous rough road section, because the required driving force/braking force varies drastically, if the power is not adjusted in advance for the ramp condition that the vehicle will pass through in the future, the speed control command sent by the unmanned vehicle is difficult to complete the closed-loop control.
Prior art three (CN 114162120 a): the scheme provides a slope road vehicle speed accurate control method of a vehicle-mounted intelligent cruising system; acquiring road map information in front of the current vehicle in the running direction of the current vehicle by acquiring the current position of the vehicle; if the map contains slope information, the cruising speed adjusting control module compensates the vehicle acceleration and deceleration request value based on the slope information, correspondingly converts the slope information into a scale factor and acts on the output request value, thereby realizing the adaptive adjustment control of the cruising speed of the slope with prediction.
The scheme utilizes the front road map information to realize compensation adjustment on the acceleration and deceleration of the vehicle, high-precision map data support is needed, and the working scene of the off-road unmanned vehicle is usually an unknown environment and is usually not supported by the high-precision map.
According to the vehicle dynamics model, in all running resistance in an ultralow speed state, the gradient resistance is most severely changed, the duty ratio is heaviest, and the influence on unmanned vehicle speed control is also greatest; according to the research of the prior art, the off-road environment is not provided with a high-precision map, and if the gradient in front of the vehicle suddenly changes, the automatic driving system of the off-road unmanned vehicle is usually corrected after the road gradient influences the actual acceleration response of the off-road unmanned vehicle, so that the hysteresis of the speed control of the off-road unmanned vehicle is caused. Particularly, under the ultra-low speed driving condition, if the driving force/braking force of the vehicle is not adjusted in advance according to the gradient change condition, two main problems may occur: (1) When the vehicle suddenly runs from flat ground to a large-gradient uphill road surface, the driving force of the vehicle is limited by the amplification of the vehicle or the limitation of parameters due to closed-loop control of the speed, so that the driving force is not increased timely, the actual running speed of the vehicle is forced to suddenly drop very low, and even the vehicle stops or slides; (2) When the vehicle suddenly runs from flat ground to a large-gradient downhill road, the braking force of the vehicle is limited by the amplification of the vehicle or the limitation of parameters due to closed-loop control of the speed, so that the braking force is not increased timely, and the vehicle is suddenly accelerated and even is in a downward movement. Such a situation affects the safety and speed stability of the unmanned vehicle, and particularly affects the functions of inspection, topography and the like required for the loading of the unmanned vehicle.
Therefore, how to optimally control the driving force/braking force by adopting a control method under the ultra-low speed driving condition of the ramp section of the off-road unmanned vehicle so as to flexibly cope with the ramp section and ensure the optimal stability of the speed closed loop is a problem to be solved urgently.
Disclosure of Invention
The invention aims to solve the problems in the prior art, and provides an ultralow vehicle speed prediction control method for a slope section of an off-road unmanned vehicle, so as to solve the problems that the off-road unmanned vehicle cannot meet the ultralow stable running speed requirement and the complex and changeable slope in the prior art.
In order to solve the technical problems, the specific technical scheme of the invention is as follows:
the invention provides an ultra-low vehicle speed prediction control method for a ramp section of an off-road unmanned vehicle, which mainly comprises the following steps:
before the application of the real vehicle, off-road unmanned vehicles are calibrated off-line, and the mapping relation between the pitch angle fed back by inertial navigation, the vehicle acceleration and the current slope angle of the vehicle is measured;
when the real vehicle is applied, the current slope angle information of the vehicle is calculated by combining inertial navigation real-time acquisition data, and the slope angle of the road surface in front of the real-time monitoring of the laser radar is corrected;
and constructing an expected driving force/braking force control sequence in a future period of time according to the corrected road surface slope angle by using a model prediction control method, and optimizing the output of the unmanned vehicle driving unit/braking unit.
As an improvement, regarding the above steps, there are more specific control steps as follows:
s100, off-line calibration of the relationship between inertial navigation and the slope angle of the position where the vehicle is located;
off-line calibrating the relation between the pitch angle of the unmanned vehicle body and the longitudinal acceleration of the unmanned vehicle body fed back by inertial navigation; even if the unmanned vehicle moves on a plane, when the unmanned vehicle has acceleration, the position of the vehicle body where inertial navigation is usually installed can also generate pitching, namely, additional angles which do not substantially reflect the slope angle exist in inertial navigation feedback data, so that the influence needs to be removed through calibration; i.e. calibrating to obtain the table look-up function relationshipVehicle longitudinal acceleration values that can be acquired from inertial navigationAcquiring the pitch angle of the current car bodyNamely, the following table look-up function relationship:
s200, sensing the gradient of the road in front of the unmanned vehicle;
s201: in the ultra-low vehicle speed prediction control, the longitudinal acceleration of the vehicle is acquired in real time through inertial navigation, and the corresponding pitch angle information of the vehicle under the current longitudinal acceleration condition of the vehicle is calculated through table lookup in step S100The current vehicle body pitch angle information combined with inertial navigation measurement is recorded asThe road surface slope angle information of the current vehicle can be obtainedAnd for subsequent pairs of lasersCompensating and correcting the measured front slope angle information;
s202: the lidar measures the forward terrain information. Front topographic information is detected through the laser radar, and point cloud data acquired according to the laser radarAnd constructing front pavement topography data. By using the information of the road surface slope angle of the current vehicleRoad surface topography information constructed by laser radar point clouds is corrected, and corrected point clouds representing real topography shapes are obtainedThe specific flow is as follows:
point cloud coordinate value acquired by laser radarConverting coordinates in a laser radar coordinate system into coordinates in a geodetic coordinate system, wherein the laser radar coordinate system isThe slope angle calculation coordinate system isThe specific point cloud coordinate position conversion calculation formula is as follows:
the set is:
correctedIs that
S203: laser point cloud in front of corrected vehicle travelIn the vehicle width direction, data in the vehicle width direction is screened to obtain longitudinal section dataObtaining a function curve of the ground shape relative to the forward displacement distance of the vehicleBy derivative calculation, the slope angle function of the vehicle advancing direction can be obtained。
Wherein,by means of the collective screening, the method can obtain,；
by traversing eachTaking the average value of the z values of the points under the same x value to obtain the pseudo code of the calculation process:
then by combiningAfter interpolation is continuous, derivative can be obtained by:
s300, vehicle driving force/braking force prediction control: according to the gradient change curve in front of the vehicleAnd the target vehicle speed command issued by the unmanned system and the obtained current actual state of the vehicle are fed back, wherein the current vehicle speed, the current driving force/braking force and the like are included to optimize the control of the driving force/braking force. It should be noted that, in general, the unmanned vehicle adopts an electric drive form, and the electric motor can drive and brake, so that the driving force/braking force is not strictly distinguished in the present application, and all refer to the combined driving force of all the longitudinal power units of the unmanned vehicle.
S301: constructing a longitudinal dynamics equation of the vehicle with a slope angle function:
wherein,is the only control input for the vehicle driving force/braking force.Is the quality of the whole vehicle,the acceleration of the gravity is that,in order to be a coefficient of rolling resistance,is the coefficient of air resistance and is used for the air resistance,for the frontal area of the vehicle,in order to achieve an air density of the air,the conversion coefficient of the rotating mass of the automobile is known parameter.
For the speed of travel of the vehicleIs used as a first derivative of (a),is a state variable.The gradient angle is changed, but the change rule is obtained through the steps. If it is desired to maintain the vehicle speedStable driving force in the running of the off-road unmanned vehicleNonlinear variation should be controlled, and control laws should be combined、And (5) equivalence.
S302: and constructing a nonlinear model predictive controller. Firstly, carrying out equation conversion on a vehicle longitudinal dynamics model to obtain a continuous state space model based on the vehicle longitudinal dynamics model:
s303: discretizing the continuous state space model to obtain a system discrete state space equation. It should be noted that, this step may optionally employ a mature discretization method, including but not limited to forward euler integration, longgnkuta method, and backward euler integration. Forward euler integration was chosen in this application:
wherein the method comprises the steps ofFor vehiclesThe speed of the moment of time,for vehiclesThe angle of the slope at the moment,is thatThe longitudinal displacement at the moment in time,to control input quantity, i.eDriving force/braking force at the moment. It should be noted that only in this modelIs a state variable that is a function of the state,andis along withThe corresponding parameter of the change is not an independent state variable. Because the unmanned vehicle is subjected to ultra-low-speed steady-state closed-loop control, the output of the model is also selected as。
S304: in order to ensure that the driving force of the vehicle output by the designed nonlinear model predictive controller accords with the actual driving force, a control constraint condition is designed:
i.e. the magnitude and rate of change of the driving/braking force are bounded, the parameters being determined in dependence on the actual vehicle situation，，，Is a value of (2).
S305: and a reasonable target function is designed. The control objective of the real vehicle is to makeI.e.The vehicle speed is kept stable,is the target vehicle speed. For this reason, three performance optimization indexes and optimization directions are designed in the application: (1) the speed tracking deviation is small; (2) The vehicle speed can not be reduced to zero by controlling the driving force change, and the penalty is larger as the vehicle speed approaches zero; (3) the rate of change of the input control signal is small. The optimization problem is the vehicle state at the current time (i.e., time 0)And a current gradient functionIn the case of (2), the optimal objective function value represented by the following formula is obtained as the minimum:
wherein,，，to adjust the parameters of the 3 sub-target impact weights,for vehiclesThe speed is desired at the moment in time,in order to predict the number of cycles of control,to prevent extremely small correction values with denominator 0, these parameters are all preferred according to the actual vehicle situation.
S306: the model predictive controller solves for the optimal control value. It should be noted that, the solving process of the nonlinear controller belongs to a conventional technical means in the field, and is not a technical problem that needs to be solved in the application, and the solving process of the nonlinear controller is referred to without detailed explanation. The final optimal control sequence is typically found by:
then the first item thereofAs a control value at the present moment, the control value is sent to the driving unit/braking unit for execution.
The technical scheme of the invention has the beneficial effects that:
according to the ultra-low vehicle speed prediction control method for the off-road unmanned vehicle ramp section, the vehicle longitudinal dynamics model with the gradient function is adopted to conduct non-linear model prediction control of the ultra-low vehicle speed, so that the driving force/braking force can be effectively optimized in advance for the change of a future slope angle, the off-road unmanned vehicle is controlled in advance in speed precision, and the speed is guaranteed to be stable;
the invention utilizes the laser radar to identify the gradient of the road ahead, replaces a high-precision map to detect the gradient information ahead, and can construct a relation function between the future driving distance and the gradient angle even if the high-precision map is not available in the off-road environment, thereby being expressed in a prediction model;
according to the invention, the model predictive control theory is utilized to carry out an optimization function of the vehicle speed predictive control, and particularly, the vehicle is ensured not to be slowed down to 0 or even slide down in an extremely severe condition when the vehicle is in an uphill working condition through additional punishment for the vehicle speed to be reduced to zero.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for predictive control of ultra-low vehicle speed on an off-road unmanned vehicle ramp section according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of a laser radar detection ramp in the method for controlling ultra-low speed prediction of an off-road unmanned vehicle ramp section according to embodiment 1 of the present invention;
fig. 3 is a schematic diagram of the point cloud coordinate position conversion in the method for controlling the ultra-low vehicle speed prediction of the off-road unmanned vehicle ramp section according to embodiment 1 of the present invention;
fig. 4 is a schematic diagram of an architecture of an ultra-low speed predictive control system for an off-road unmanned vehicle ramp section according to embodiment 2 of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making clear and defining the scope of the present invention.
In the description of the present invention, it should be noted that the described embodiments of the present invention are some, but not all embodiments of the present invention; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, apparatus, article, or device that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or device.
Embodiment 1, this embodiment provides a method for predicting and controlling an ultralow vehicle speed of a slope section of an off-road unmanned vehicle, as shown in fig. 1 to 3, mainly comprising the following steps:
before the application of the real vehicle, off-road unmanned vehicles are calibrated off-line, and the mapping relation between the pitch angle fed back by inertial navigation, the vehicle acceleration and the current slope angle of the vehicle is measured;
when the real vehicle is applied, the current slope angle information of the vehicle is calculated by combining inertial navigation real-time acquisition data, and the slope angle of the road surface in front of the real-time monitoring of the laser radar is corrected;
and constructing an expected driving force/braking force control sequence in a future period of time according to the corrected road surface slope angle by using a model prediction control method, and optimizing the output of the unmanned vehicle driving unit/braking unit.
The control method can improve the performance of the off-road unmanned vehicle on complex terrain, so that the off-road unmanned vehicle can better cope with different slopes and road conditions, and the stability and safety of the vehicle are improved.
For convenience in demonstrating the implementation principle of the present application, see fig. 2, the x-axis horizontally and forwards represents the displacement distance in front of the vehicle, the z-axis vertically and upwards represents the vertical height, and the y-axis is determined according to the right-hand rule;for consolidation with the headstock, and the xy plane is always kept horizontal, calculating a coordinate system of a group of slope angles of x towards the headstock forever; the origin and coordinate axis of the point cloud data detected by the laser radar are also defined asWherein the origin is still O, the y axis is the same, but the xy plane is consolidated by the laser radar and changes along with the pitching of the vehicle; the line drawn from the lidar in fig. 2 is a schematic line of laser light emitted from the lidar.
Further, regarding the above steps, there are more specific control steps as follows:
s100, off-line calibration of the relationship between inertial navigation and the slope angle of the position where the vehicle is located;
off-line calibrating the relation between the pitch angle of the unmanned vehicle body and the longitudinal acceleration of the unmanned vehicle body fed back by inertial navigation; even if the unmanned vehicle moves on a plane, when the unmanned vehicle has acceleration, the position of the vehicle body where inertial navigation is usually installed can also generate pitching, namely, additional angles which do not substantially reflect the slope angle exist in inertial navigation feedback data, so that the influence needs to be removed through calibration; i.e. calibrating to obtain the table look-up function relationshipVehicle longitudinal acceleration values that can be acquired from inertial navigationAcquiring the pitch angle of the current car bodyNamely, the following table look-up function relationship:
s200, sensing the gradient of the road in front of the unmanned vehicle;
s201: in the ultra-low vehicle speed prediction control, the longitudinal acceleration of the vehicle is acquired in real time through inertial navigation, and the corresponding pitch angle information of the vehicle under the current longitudinal acceleration condition of the vehicle is calculated through table lookup in step S100The current vehicle body pitch angle information combined with inertial navigation measurement is recorded asThe road surface slope angle information of the current vehicle can be obtainedThe method is used for carrying out compensation correction on the front slope angle information measured by the laser radar;
s202: the lidar measures the forward terrain information. Front topographic information is detected through the laser radar, and point cloud data acquired according to the laser radarAnd constructing front pavement topography data. By using the information of the road surface slope angle of the current vehicleRoad surface topography information constructed by laser radar point clouds is corrected, and corrected point clouds representing real topography shapes are obtainedAs shown in fig. 3, the specific flow is as follows:
point cloud coordinate value acquired by laser radarConverting coordinates in a laser radar coordinate system into coordinates in a geodetic coordinate system, wherein the laser radar coordinate system isThe slope angle calculation coordinate system isThe specific point cloud coordinate position conversion calculation formula is as follows:
the set is:
correctedIs set as
S203: laser point cloud in front of corrected vehicle travelIn the vehicle width direction, data in the vehicle width direction is screened to obtain longitudinal section dataObtaining a functional curve of the ground shape with respect to the forward displacement distance of the vehicleWire (C)By derivative calculation, the slope angle function of the vehicle advancing direction can be obtained。
Wherein,by means of the collective screening, the method can obtain,；
by traversing eachTaking the average value of the z values of the points under the same x value to obtain the pseudo code of the calculation process:
then by combiningAfter interpolation is continuous, derivative can be obtained by:
s300, vehicle driving force/braking force prediction control: according to the gradient change curve in front of the vehicleThe target vehicle speed command issued by the unmanned system and the current actual state of the obtained vehicle are fed back, wherein the current vehicle speed command comprises the current vehicle speed and the current vehicle speedFront driving force/braking force, etc., to optimize the control of the driving force/braking force. It should be noted that, in general, the unmanned vehicle adopts an electric drive form, and the electric motor can drive and brake, so that the driving force/braking force is not strictly distinguished in the present application, and all refer to the combined driving force of all the longitudinal power units of the unmanned vehicle.
S301: constructing a longitudinal dynamics equation of the vehicle with a slope angle function:
wherein,vehicle driving force/braking force is the only control input.Is the quality of the whole vehicle,the acceleration of the gravity is that,in order to be a coefficient of rolling resistance,is the coefficient of air resistance and is used for the air resistance,for the frontal area of the vehicle,in order to achieve an air density of the air,the conversion coefficient of the rotating mass of the automobile is known parameter.
For the speed of travel of the vehicleIs used as a first derivative of (a),is a state variable.The gradient angle is changed, but the change rule is obtained through the steps. If it is desired to maintain the vehicle speedStable driving force in the running of the off-road unmanned vehicleNonlinear variation should be controlled, and control laws should be combined、And (5) equivalence.
S302: and constructing a nonlinear model predictive controller. Firstly, carrying out equation conversion on a vehicle longitudinal dynamics model to obtain a continuous state space model based on the vehicle longitudinal dynamics model:
s303: discretizing the continuous state space model to obtain a system discrete state space equation. It should be noted that, this step may optionally employ a mature discretization method, including but not limited to forward euler integration, longgnkuta method, and backward euler integration. Forward euler integration was chosen in this application:
wherein the method comprises the steps ofFor vehiclesThe speed of the moment of time,for vehiclesThe angle of the slope at the moment,is thatThe longitudinal displacement at the moment in time,to control input quantity, i.eDriving force/braking force at the moment. It should be noted that only in this modelIs a state variable that is a function of the state,andis along withThe corresponding parameter of the change is not an independent state variable. Because the unmanned vehicle is subjected to ultra-low-speed steady-state closed-loop control, the output of the model is also selected as。
S304: in order to ensure that the driving force of the vehicle output by the designed nonlinear model predictive controller accords with the actual driving force, a control constraint condition is designed:
i.e. the magnitude and rate of change of the driving/braking force are bounded, the parameters being determined in dependence on the actual vehicle situation，，，Is a value of (2).
S305: and a reasonable target function is designed. The control objective of the real vehicle is to makeI.e.The vehicle speed is kept stable,is the target vehicle speed. For this reason, three performance optimization indexes and optimization directions are designed in the application: (1) the speed tracking deviation is small; (2) The vehicle speed can not be reduced to zero by controlling the driving force change, and the penalty is larger as the vehicle speed approaches zero; (3) the rate of change of the input control signal is small. The optimization problem is the vehicle state at the current time (i.e., time 0)And a current gradient functionIn the case of (2), the optimal objective function value represented by the following formula is obtained as the minimum:
wherein,，，to adjust the parameters of the 3 sub-target impact weights,for vehiclesThe speed is desired at the moment in time,in order to predict the number of cycles of control,to prevent extremely small correction values with denominator 0, these parameters are all preferred according to the actual vehicle situation.
S306: the model predictive controller solves for the optimal control value. It should be noted that, the solving process of the nonlinear controller belongs to a conventional technical means in the field, and is not a technical problem that needs to be solved in the application, and the solving process of the nonlinear controller is referred to without detailed explanation. The final optimal control sequence is typically found by:
then the first item thereofAs a control value at the present moment, the control value is sent to the driving unit/braking unit for execution.
Embodiment 2, which is based on the same inventive concept as the ultralow vehicle speed prediction control method of the off-road unmanned vehicle ramp section described in embodiment 1, provides an off-road unmanned vehicle ramp section ultralow vehicle speed prediction control system, as shown in fig. 4, comprising: laser radar, inertial navigation, vehicle state acquisition unit, control unit and drive unit/brake unit; the laser radar is used for detecting the front road point cloud; the inertial navigation is used for detecting the pitch angle and the acceleration of the current vehicle; the vehicle state acquisition unit is used for acquiring a vehicle state, and the vehicle state is used for feeding back the current vehicle speed; the drive unit actually performs driving force/braking force, etc. The laser radar is generally arranged above the vehicle head, inertial navigation is arranged at the mass center position of the vehicle, and the laser radar and the inertial navigation are possibly arranged on the vehicle frame, are also common sensors of the unmanned vehicle, and are arranged according to the installation standard of the unmanned vehicle.
As an implementation mode of the invention, before the application of the real vehicle, off-road unmanned vehicles are calibrated off-line, and the mapping relation of pitch angle fed back by inertial navigation, vehicle acceleration and the current slope angle of the vehicle is measured; when the real vehicle is applied, the current slope angle information of the vehicle is calculated by combining inertial navigation real-time acquisition data, and the slope angle of the road surface in front of the real-time monitoring of the laser radar is corrected; and constructing an expected driving force/braking force control sequence in a future period of time according to the corrected road surface slope angle by using a model prediction control method, and optimizing the output of the unmanned vehicle driving unit/braking unit.
Embodiment 3, the present embodiment provides a computer-readable storage medium for storing computer software instructions for implementing the off-road unmanned vehicle hill section ultra-low vehicle speed prediction control method described in embodiment 1 above, which includes a program for executing the program set for the off-road unmanned vehicle hill section ultra-low vehicle speed prediction control method described above; specifically, the executable program may be built in the off-road unmanned vehicle hill section ultra-low vehicle speed prediction control system described in embodiment 2, so that the off-road unmanned vehicle hill section ultra-low vehicle speed prediction control system may implement the off-road unmanned vehicle hill section ultra-low vehicle speed prediction control method described in embodiment 1 by executing the built-in executable program.
Further, the computer readable storage medium provided in the present embodiment may be any combination of one or more readable storage media, where the readable storage media includes an electric, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof.
Compared with the prior art, the method for predicting and controlling the ultralow speed of the road section of the off-road unmanned vehicle has the advantages that the ultralow speed of the road section of the off-road unmanned vehicle is detected through laser radar, inertial navigation and the like in consideration of the ultralow speed requirement of the off-road unmanned vehicle and the complex and changeable road conditions, so that the running dynamics model of the vehicle is optimized, the optimal driving force/braking force sequence in a future period of time is provided through model prediction control, the speed of the vehicle is predictively regulated, the condition that the vehicle always has enough driving force/braking force in the road section of the road section is ensured, the vehicle speed is stably controlled to the ultralow speed value required by uploading, and the method has higher application value.
It should be understood that, in the various embodiments herein, the sequence number of each process described above does not mean the sequence of execution, and the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments herein.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided herein, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices, or elements, or may be an electrical, mechanical, or other form of connection.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the elements may be selected according to actual needs to achieve the objectives of the embodiments herein.
In addition, each functional unit in the embodiments herein may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions herein are essentially or portions contributing to the prior art, or all or portions of the technical solutions may be embodied in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments herein. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes or direct or indirect application in other related technical fields are included in the scope of the present invention.
Claims (3)
1. The ultra-low vehicle speed prediction control method for the off-road unmanned vehicle ramp section is characterized by comprising the following steps of:
acquiring and correcting road point clouds in front of a vehicle to obtain laser point cloud data;
acquiring longitudinal section data in front of the vehicle running through the laser point cloud data;
calculating a slope angle function of the vehicle in the advancing direction according to the longitudinal section data;
acquiring a target vehicle speed command and a current state of a vehicle, optimizing driving force or braking force control according to the slope angle function, the target vehicle speed command and the current state of the vehicle, and sending the driving force or braking force control to an execution unit;
the step of obtaining and correcting the road point cloud in front of the vehicle to obtain laser point cloud data, further comprises the steps of:
acquiring road point cloud in front of a vehicle through a laser radar, and measuring the current road surface slope angle information of the vehicle through inertial navigation;
based on the road surface slope angle information, converting the coordinate value of the road point cloud in front of the vehicle from the coordinate in a laser radar coordinate system to the coordinate in a geodetic coordinate system to obtain the laser point cloud dataWherein->I is an E laser radar feedback single-point data corner mark set, the x-axis horizontally forwards represents the displacement distance in front of the vehicle, the z-axis vertically upwards represents the vertical height, and the y-axis is determined according to the right-hand rule;
the step of measuring the road surface slope angle information of the current position of the vehicle through inertial navigation further comprises the following steps:
acquiring a vehicle body pitch angle and a vehicle longitudinal acceleration value of a vehicle during plane movement through inertial navigation, and calibrating the relationship between the vehicle body pitch angle and the vehicle longitudinal acceleration value in an off-line manner to obtain a lookup table function relationship;
acquiring current vehicle longitudinal acceleration and current vehicle body pitch angle information through inertial navigation, acquiring first vehicle body pitch angle information through the current vehicle longitudinal acceleration and the lookup function relationship, and acquiring the road surface slope angle information based on the first vehicle body pitch angle information and the current vehicle body pitch angle information;
the acquiring the longitudinal section data of the front of the vehicle traveling through the laser point cloud data further comprises:
screening the laser point cloud dataObtaining longitudinal section data in a vehicle width along the vehicle advancing direction>，；
The calculating a slope angle function of the vehicle advancing direction according to the longitudinal section data further comprises:
traversing each longitudinal section dataTaking the average value of the z values of the points at the same x value to obtain a function curve of the ground shape relative to the forward displacement distance of the vehicle +.>；
By combiningAfter interpolation is continuous, derivative is obtained to obtain a slope angle function +.>；
；
The method includes the steps of obtaining a target vehicle speed command and a current state of a vehicle, optimizing driving force or braking force control according to the slope angle function, the target vehicle speed command and the current state of the vehicle, and sending the driving force or braking force control to an execution unit, and further comprises the steps of:
constructing a longitudinal dynamics equation of the vehicle with a slope angle function;
constructing a nonlinear model predictive controller, and converting the longitudinal dynamics equation of the vehicle into a continuous state space model based on a longitudinal kinematics model;
discretizing the continuous state space model to obtain a system discrete state space equation;
designing control constraint conditions and control optimization targets;
based on the nonlinear model predictive controller, solving an optimal control value and sending the optimal control value to an execution unit;
the longitudinal dynamics equation of the vehicle is as follows:
,
wherein,is the only control input for the driving force or braking force of the vehicle; />The quality of the whole vehicle is achieved; />Gravitational acceleration; />Is the rolling resistance coefficient; />Is the air resistance coefficient; />The windward area of the vehicle; />Is air density; />The conversion coefficient of the rotating mass of the automobile; />For vehicle speed->Is the first derivative of (a); />Is a state variable; />Is a slope angle;
the continuous state space model is:
,
discretizing the continuous state space model to obtain a system discrete state space equation, and further comprising: discretizing a continuous state space equation by adopting forward Euler integral:
,
wherein,for vehicle->Time of day speed->For vehicle->Time ramp angle->Is->Longitudinal displacement of time of day->For controlling input quantity, i.e.)>Driving force or braking force at the moment.
2. The off-road unmanned vehicle ramp section ultra-low vehicle speed prediction control method according to claim 1, wherein: the control constraint conditions are as follows:
,
the control optimization objective includes: the speed tracking deviation is smaller, the penalty is applied when the vehicle speed is reduced to zero, and the change rate of the input control signal is smaller, namely, the optimal objective function value shown in the following formula is calculated to be minimum:
,
wherein,，/>，/>for adjusting the parameters of the influence weights of the 3 sub-targets, +.>For vehicle->Time of day desired speed +.>For predicting the number of cycles of control->To prevent the denominator from being 0.
3. The off-road unmanned vehicle ramp section ultra-low vehicle speed prediction control method according to claim 2, wherein: the nonlinear model-based predictive controller solves the optimal control value and sends the optimal control value to the execution unit, and the nonlinear model-based predictive controller further comprises: and (3) obtaining an optimal control sequence:
,
to make the first item thereofAnd the control value is used as a control value at the current moment and is sent to an execution unit for execution.
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