CN117930661A - Differential AGV control method, device, trolley and readable storage medium - Google Patents
Differential AGV control method, device, trolley and readable storage medium Download PDFInfo
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Abstract
The embodiment of the application provides a differential AGV control method, a differential AGV control device, a differential AGV and a differential AGV readable storage medium, and belongs to the technical field of automatic control. The method comprises the following steps: the upper layer planner plans the target track; the controller establishes a differential AGV kinematic model; performing linear processing on the kinematic model; acquiring a linear kinematic error model according to a target track position corresponding to the current position, and performing discrete processing; determining a corresponding optimal control sequence in a preset period; determining a constraint range of an optimal control sequence, and mapping the optimal control sequence into the constraint range; and converting the mapped result into a differential AGV wheel speed signal through decoupling, and sending the differential AGV wheel speed signal to the lower computer, wherein the lower computer controls the differential AGV to track the target track. The scheme provided dynamically adjusts according to the actual running state of the AGV, so that the AGV is better suitable for different environments and conditions.
Description
Technical Field
The application relates to the field of automatic control, in particular to a differential AGV control method, a differential AGV control device, a differential AGV trolley and a readable storage medium.
Background
The differential automatic guided vehicle (Automated Guided Vehicle, AGV) is a common automated logistics device and is widely applied to occasions such as warehouses, factories and distribution centers. The differential AGV is generally provided with differential wheels, the moving direction and speed of the differential AGV can be controlled by adjusting the rotating speeds of the left wheel and the right wheel, the current modeling method for the differential AGV is complex, comprises a dynamic model and the like, the control performance is emphasized too much, the simplification of the model and the real-time performance of control calculation are not considered sufficiently, and engineering realization is not easy.
Disclosure of Invention
In order to solve the technical problems, the embodiment of the application provides a differential AGV control method, a differential AGV control device, a differential AGV control trolley and a differential AGV readable storage medium.
In a first aspect, an embodiment of the present application provides a differential AGV control method, which is applied to a differential AGV control system, where the differential AGV control system includes an upper layer planner, a controller, and a lower computer, and the method includes:
the upper layer planner plans a target track of the differential AGV according to the obstacle information;
The controller establishes a differential AGV kinematic model;
Performing linear processing on the differential AGV kinematic model according to the current position of the differential AGV;
acquiring a linear kinematic error model according to a target track position corresponding to the current position of the differential AGV, and performing discrete processing on the linear kinematic error model;
determining a corresponding optimal control sequence in a preset period according to the result after the discrete processing;
determining a constraint range of the optimal control sequence based on a wheel speed stabilizing the differential AGV, and mapping the optimal control sequence into the constraint range;
And converting the mapped result into a differential AGV wheel speed signal through decoupling, and sending the differential AGV wheel speed signal to the lower computer, wherein the lower computer controls the differential AGV to track the target track.
In one embodiment, the controller builds a differential AGV kinematic model including equation (1), equation (2) and equation (3):
wherein, For the differential AGV body's lateral displacement x derivative with respect to time,/>For the derivative of the longitudinal displacement y of the differential AGV body with respect to time,/>The yaw angle θ of the differential AGV body is derived over time, v is the linear velocity of the differential AGV body, and w is the angular velocity about the center of the differential AGV body.
In one embodiment, the differential AGV control system further includes a positioner, wherein the current position of the differential AGV is obtained by the positioner, and the differential AGV control system includes a lateral displacement x r, a longitudinal displacement y r, a yaw angle θ r, a linear velocity v r, and an angular velocity w r around the center of the differential AGV, and the linear processing is performed on the differential AGV kinematic model according to the current position of the differential AGV, and the differential AGV control system includes:
Performing taylor expansion on the formula (1) under the conditions of theta r and v r to obtain a formula (4):
performing taylor expansion on the formula (2) under the conditions of theta r and v r to obtain a formula (5):
Substituting w r into the formula (3) results in formula (6):
Integrating the formula (4), the formula (5) and the formula (6) to obtain a kinematic linear model (7) of the current position of the differential AGV:
Wherein the method comprises the steps of
In one embodiment, the obtaining a linear kinematic error model according to the current position of the differential AGV, and performing discrete processing on the linear kinematic error model includes:
Substituting the transverse displacement x ref, the longitudinal displacement y ref, the yaw angle theta ref, the linear speed v ref and the angular speed w ref around the center of the vehicle body of the differential AGV vehicle body of the target track corresponding to the current position into the kinematic linear model (7) to obtain a kinematic linear model (8) of the target track:
Wherein the method comprises the steps of
And (3) a simultaneous model (7) and a model (8), and enabling X e＝X-Xref,ue＝u-uref to obtain a linear kinematic error model (9):
Discretizing the linear kinematic error model (9) to obtain a discrete kinematic error model (10) at n times:
Wherein the method comprises the steps of T is the control period.
In one embodiment, determining the optimal control sequence corresponding to the preset period according to the result after the discrete processing includes:
constructing a cost function model (11) in the prediction period NT time:
The discrete kinematic error models (10) are combined to obtain an optimal control sequence U= (U (0), U (1), U (2), … … and U (N)) in the prediction period NT time;
Wherein Q is a deviation weight matrix of the current position and the corresponding target track position, R is a deviation weight matrix of the speed of the current position and the speed of the corresponding target track, and Q f is a deviation weight matrix of the final position and the corresponding target track position.
In one embodiment, the determining the constraint range of the optimal control sequence based on the wheel speed stabilizing the differential AGV, mapping the optimal control sequence into the constraint range, includes:
setting a constraint range of control quantity in the optimal control sequence, wherein the maximum value of the constraint range is The minimum value of the constraint range is/>
The optimal control sequence is subjected to speed mapping to obtain
The method further comprises the steps of:
and mapping the out-of-range control quantity in the optimal control sequence into the constraint range by a scaling method.
In one embodiment, converting the mapped result into differential AGV wheel speed signals by decoupling includes:
Decoupling the speed mapping result through a formula (12), converting the speed mapping result into speed signals of a left wheel and a right wheel of the differential AGV, and transmitting the speed signals to the lower computer;
wherein, w r is the rotation speed of the motor of the right wheel, w l is the rotation speed of the motor of the left wheel, r is the radius of the wheel, and d is the wheelbase of the vehicle body.
In a second aspect, an embodiment of the present application provides a differential AGV control device, which is applied to a differential AGV control system, where the differential AGV control system includes an upper layer planner, a controller, and a lower computer, and the device includes:
the planning module is used for planning a target track of the differential AGV according to the obstacle information through the upper layer planner;
The modeling module is used for establishing a differential AGV kinematic model through the controller;
The processing module is used for performing linear processing on the differential AGV kinematic model according to the current position of the differential AGV, acquiring a linear kinematic error model according to a target track position corresponding to the current position of the differential AGV, and performing discrete processing on the linear kinematic error model;
The mapping module is used for determining a corresponding optimal control sequence in a preset period according to a discrete processing result, determining a constraint range of the optimal control sequence based on a wheel speed enabling the differential AGV to be stable, and mapping the optimal control sequence into the constraint range;
And the execution module is used for converting the mapped result into differential AGV wheel speed signals through decoupling, sending the differential AGV wheel speed signals to the lower computer, and controlling the differential AGV to track the target track by the lower computer.
In a third aspect, an embodiment of the present application provides an automatic guided vehicle, including a memory and a processor, where the memory is configured to store a computer program, and the computer program executes the differential AGV control method provided in the first aspect when the processor runs.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program that when run on a processor performs the differential AGV control method provided in the first aspect.
According to the differential AGV control method provided by the application, the upper layer planner plans a target track of the differential AGV according to the obstacle information; the controller establishes a differential AGV kinematic model; performing linear processing on the differential AGV kinematic model according to the current position of the differential AGV; acquiring a linear kinematic error model according to a target track position corresponding to the current position of the differential AGV, and performing discrete processing on the linear kinematic error model; determining a corresponding optimal control sequence in a preset period according to the result after the discrete processing; determining a constraint range of the optimal control sequence based on a wheel speed stabilizing the differential AGV, and mapping the optimal control sequence into the constraint range; and converting the mapped result into a differential AGV wheel speed signal through decoupling, and sending the differential AGV wheel speed signal to the lower computer, wherein the lower computer controls the differential AGV to track the target track. According to the scheme, the nonlinear kinematic model of the differential AGV is subjected to linearization and discretization, so that the complexity of a control model is simplified, and the calculation efficiency of a control algorithm is improved. Meanwhile, the processing method enables the model parameters to be dynamically adjusted according to the actual running state of the AGV, and is better suitable for different environments and conditions.
Drawings
In order to more clearly illustrate the technical solutions of the present application, the drawings that are required for the embodiments will be briefly described, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope of the present application. Like elements are numbered alike in the various figures.
FIG. 1 is a flow chart of a differential AGV control method according to an embodiment of the present application;
FIG. 2 illustrates a schematic diagram of a differential AGV provided by an embodiment of the present application;
FIG. 3 is a schematic flow chart of a differential AGV control method according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a differential AGV control device according to an embodiment of the present application.
Icon: 400-differential AGV control, 401-planning module, 402-modeling module, 403-processing module, 404-mapping module, 405-execution module.
Detailed Description
Example 1
The embodiment of the application provides a differential AGV (Automated Guided Vehicle, automatic guided vehicle) control method which is applied to a differential AGV control system.
Referring to FIG. 1, the differential AGV control method includes:
S101: the upper layer planner plans a target track of the differential AGV according to the obstacle information.
In this embodiment, the upper layer planner obtains the information of the obstacle in the working environment, including the information of the position, the size, the shape, and the like of the obstacle, through the sensor or the communication interface, and in the upper layer planner, according to the information of the position, the size, the shape, and the like of the obstacle, calculates the expected track of the differential AGV, that is, the optimal track avoiding the obstacle, through the differential control algorithm.
S102: the controller establishes a differential AGV kinematic model.
In this embodiment, the kinematic model is a mathematical model describing the motion law of the robot or vehicle. For differential AGVs, the kinematic model mainly describes the motion of the AGVs in a plane, including the change rules of parameters such as transverse displacement, longitudinal displacement, yaw angle, linear speed and the like.
To build a kinematic model of a differential AGV, its kinematic characteristics first need to be defined. Differential AGVs typically consist of two independently driven wheels that are steered by controlling the speed differential between the left and right wheels. Therefore, the kinematic model needs to consider the characteristics of such differential drive.
Referring to fig. 2, first, a global coordinate system (usually a world coordinate system) and a local coordinate system, also called a vehicle body coordinate system (usually fixedly connected with a differential AGV) are required to be defined, in the global coordinate system, the target track of the differential AGV and the position of the obstacle can be defined, parameters in the model are determined according to the structure and the motion characteristics of the differential AGV, the parameters generally include the length, the width, the wheel spacing, the initial position, the posture and the like of the vehicle body, and the mathematical relationship among the transverse and longitudinal displacement, the yaw angle, the linear velocity and the time of the vehicle body is established based on the principle of kinematics.
In one embodiment, the controller builds a differential AGV kinematic model including equation (1), equation (2) and equation (3):
wherein, For the differential AGV body's lateral displacement x derivative with respect to time,/>For the derivative of the longitudinal displacement y of the differential AGV body with respect to time,/>The yaw angle θ of the differential AGV body is derived over time, v is the linear velocity of the differential AGV body, and w is the angular velocity about the center of the differential AGV body. By constructing a kinematic model of the differential AGV, the kinematic state and behavior of the differential AGV are accurately described, and the change rules of parameters such as transverse displacement, longitudinal displacement, yaw angle, linear speed and the like are included.
S103: and carrying out linear processing on the differential AGV kinematic model according to the current position of the differential AGV.
In this embodiment, the nonlinear kinematic model of the differential AGV is converted into the linear kinematic model at the current position point of the differential AGV through taylor expansion, in an actual controller, the computing resources are usually limited, and for the nonlinear model, the direct computation may be very complex and time-consuming, especially for a real-time control system, by linearization, the nonlinear problem can be converted into the linear problem, so that the computation complexity is greatly simplified, and the computing efficiency is improved.
In one embodiment, the differential AGV control system further includes a positioner, wherein the current position of the differential AGV is obtained by the positioner, and the differential AGV control system includes a lateral displacement v r, a longitudinal displacement y r, a yaw angle θ r, a linear velocity v r, and an angular velocity w r around the center of the differential AGV, and the linear processing is performed on the differential AGV kinematic model according to the current position of the differential AGV, and the differential AGV control system includes:
Performing taylor expansion on the formula (1) under the conditions of theta r and v r to obtain a formula (4):
performing taylor expansion on the formula (2) under the conditions of theta r and v r to obtain a formula (5):
Substituting w r into the formula (3) results in formula (6):
Integrating the formula (4), the formula (5) and the formula (6) to obtain a kinematic linear model (7) of the current position of the differential AGV:
Wherein the method comprises the steps of Is the derivative of the kinematic linear model with respect to time.
S104: and acquiring a linear kinematic error model according to a target track position corresponding to the current position of the differential AGV, and performing discrete processing on the linear kinematic error model.
In this embodiment, first, a linear kinematic model is constructed according to a target track position corresponding to a current position, a linear kinematic error model is constructed by comparing the current position of the differential AGV with the corresponding target track position, discretization is performed on the linear kinematic model, continuous time and state space are divided into a series of discrete points, continuous time and state space are converted into discrete time steps and state points, necessary data is provided for subsequent controller design, and a controller can generate control instructions according to the discrete error information, so that the differential AGV can track a target track more accurately.
In one embodiment, the obtaining a linear kinematic error model according to the current position of the differential AGV, and performing discrete processing on the linear kinematic error model includes:
Substituting the transverse displacement x ref, the longitudinal displacement y ref, the yaw angle theta ref, the linear speed v ref and the angular speed w ref around the center of the vehicle body of the differential AGV vehicle body of the target track corresponding to the current position into the kinematic linear model (7) to obtain a kinematic linear model (8) of the target track:
Wherein the method comprises the steps of
And (3) a simultaneous model (7) and a model (8), and enabling X e＝X-Xref,ue＝u-uref to obtain a linear kinematic error model (9):
Discretizing the linear kinematic error model (9) to obtain a discrete kinematic error model (10) at n times:
Wherein the method comprises the steps of T is the control period.
In this embodiment, the euler forward difference method is used to discretize the linear kinematic error model, where the error between the differential AGV and the target track at time n may also be derived from the difference between the target track error at time n and the target track error at time n-1 to the control period to obtain the model (13): the simultaneous model (9) and the model (13) can discretize the linear kinematic error model (9) to obtain a discrete kinematic error model (10) at the time n.
S105: and determining a corresponding optimal control sequence in a preset period according to the result after the discrete processing.
In this embodiment, an optimal control sequence in a preset period is determined according to a result after discrete processing, so as to realize effective control of the differential AGV, the optimal control sequence is a control input sequence for enabling a system state to reach an optimal state by giving the preset period, the differential AGV can track a preset track more accurately through the optimal control sequence, and the control sequence calculates an optimal input instruction according to a deviation between a current position and a target track, so that the differential AGV adjusts a speed and a direction to reduce the deviation.
In one embodiment, determining the optimal control sequence corresponding to the preset period according to the result after the discrete processing includes:
constructing a cost function model (11) in the prediction period NT time:
The discrete kinematic error models (10) are combined to obtain an optimal control sequence U= (U (0), U (1), U (2), … … and U (N)) in the prediction period NT time;
Wherein Q is a deviation weight matrix of the current position and the corresponding target track position, R is a deviation weight matrix of the speed of the current position and the speed of the corresponding target track, and Q f is a deviation weight matrix of the final position and the corresponding target track position.
In this embodiment, the matrix Q, R, Q f is a positive definite symmetric matrix. In the construction of the cost function model (11), X e(k)TQXe (k) measures the state deviation from the target trajectory, U e(k)TRue (k) measures the input deviation from the target speed, and X e(N)TQfXe (N) measures the final state deviation, so that the tracking problem of the actual trajectory and the target trajectory is converted into a set of control inputs u= (U (0), U (1), U (2), … …, U (N)) which minimizes the cost function model J (U).
In this embodiment, the above-mentioned optimization problem is solved by adopting the minimum value principle, and the hash function (14) and the synergy equation (15) of the valence function model (11) are respectively:
The cross-sectional condition (16) and the control equation (17) are respectively:
Assuming λ (K) =k (K) X e (K), li Kadi difference equation (18) can be obtained: The terminal condition of the licarpi equation is K (N) =q f, K (N) =q f is set as an iteration initial value, K (K), namely K (N-1), K (N-2), …, K (0) are obtained by inverse recursion from k=n-1, …,0, and u (K) = -L (K) X e(k)+uref (19) are obtained by (16) and (17), u (K) is the optimal control input u (K) at the kth moment in the prediction period NT in the cost function model, wherein L (K) is the optimal feedback gain matrix,/>
S106: and determining a constraint range of the optimal control sequence based on the wheel speed for stabilizing the differential AGV, and mapping the optimal control sequence into the constraint range.
In this embodiment, in order to smooth the wheel speed of the differential AGV, it is necessary to determine the constraint range of the optimal control sequence, the wheel speed limits being derived from the rotational speed of the wheels themselves, the maximum linear speed, and the mechanical limitations of the differential AGV, which determine the possible range of wheel speeds, and therefore need to be considered in determining the optimal control sequence. After determining the constraint range, the optimal control sequence is mapped into this constraint range. By adjusting or scaling the control sequence to ensure that it meets all constraints. If the control sequence is outside the constraint at some time steps, we may need to readjust or optimize the control sequence so that it meets the constraint at all time steps.
In one embodiment, the determining the constraint range of the optimal control sequence based on the wheel speed stabilizing the differential AGV, mapping the optimal control sequence into the constraint range, includes:
setting a constraint range of control quantity in the optimal control sequence, wherein the maximum value of the constraint range is The minimum value of the constraint range is/>
The optimal control sequence is subjected to speed mapping to obtain
In this embodiment, the AGV may have a limit on the maximum speed during the transport for safety reasons, and an acceleration limit may be considered for stability of the operation of the AGV. Deriving a control constraint maximum value of the cost function model by considering speed and acceleration constraintsAnd minimum/>The deduction process is as formula (20):
U(k)∈[Umin(k),Umax(k)],Umax(k)＝min(Usetmax,Uaccmax(k)),Umin(k)＝max(Usetmin,Uaccmin(k)),k＝1,2,…,N.
Wherein the method comprises the steps of Maximum linear velocity and maximum angular velocity set for the user; To obtain maximum speed and maximum angular velocity through acceleration constraints, where a max is the differential AGV maximum linear acceleration and α max is the maximum angular acceleration; /(I) Maximum linear velocity and maximum angular velocity set for the user; /(I) Maximum speed and maximum angular velocity are obtained for constraint by acceleration.
In one embodiment, the method further comprises: and mapping the out-of-range control quantity in the optimal control sequence into the constraint range by a scaling method.
In this embodiment, referring to fig. 3, after determining the constrained speed range, the control amount exceeding the range is mapped into the constrained range by a scaling method, and the flow of the method is as shown in fig. 4, and each group of control amounts in the control sequence is traversed to respectively determine whether the linear speed and angular speed constraint ranges are exceeded, and if the linear speed and angular speed constraint ranges are exceeded, scaling is performed. It should be noted that after the proportional mapping, the control sequence is not the optimal solution, but according to the optimization principle, the mapped solution is a suboptimal solution; the curvature of the track before and after mapping can be kept unchanged by the proportional mapping method, so that the method meets the requirements of engineering application.
S107: and converting the mapped result into a differential AGV wheel speed signal through decoupling, and sending the differential AGV wheel speed signal to the lower computer, wherein the lower computer controls the differential AGV to track the target track.
In this embodiment, the mapped optimal control sequence is converted from a unified form to differential AGV wheel speed signals. The control sequence is decomposed into speed commands of two differential wheels through decoupling control, and the aim of decoupling is to better adapt to the characteristics of the differential AGV, ensure that each wheel can independently receive the speed commands and adjust the speed accordingly. The decoupled speed command is converted into a specific wheel speed signal, the generated wheel speed signal is then sent to a lower computer, and the lower computer is a hardware device for directly controlling the differential AGV to move and is responsible for receiving and executing the command sent by the upper computer so as to ensure that the AGV moves according to the expected speed and direction. Under the control of the lower computer, the differential AGV starts to execute corresponding actions and track the target track. This process typically involves adjusting the wheel speed and direction in real time to accommodate changes in external environment and internal conditions, ensuring that the differential AGV can accurately track the target track.
In one embodiment, converting the mapped result into differential AGV wheel speed signals by decoupling includes: decoupling the speed mapping result through a formula (12), converting the speed mapping result into speed signals of a left wheel and a right wheel of the differential AGV, and transmitting the speed signals to the lower computer;
wherein, w r is the rotation speed of the motor of the right wheel, w l is the rotation speed of the motor of the left wheel, r is the radius of the wheel, and d is the wheelbase of the vehicle body.
According to the differential AGV control method provided by the embodiment, the upper layer planner plans a target track of the differential AGV according to the obstacle information; the controller establishes a differential AGV kinematic model; performing linear processing on the differential AGV kinematic model according to the current position of the differential AGV; acquiring a linear kinematic error model according to a target track position corresponding to the current position of the differential AGV, and performing discrete processing on the linear kinematic error model; determining a corresponding optimal control sequence in a preset period according to the result after the discrete processing; determining a constraint range of the optimal control sequence based on a wheel speed stabilizing the differential AGV, and mapping the optimal control sequence into the constraint range; and converting the mapped result into a differential AGV wheel speed signal through decoupling, and sending the differential AGV wheel speed signal to the lower computer, wherein the lower computer controls the differential AGV to track the target track. According to the scheme, the nonlinear kinematic model of the differential AGV is subjected to linearization and discretization, so that the complexity of a control model is simplified, and the calculation efficiency of a control algorithm is improved. Meanwhile, the processing method enables the model parameters to be dynamically adjusted according to the actual running state of the AGV, and is better suitable for different environments and conditions.
Example 2
In addition, the embodiment of the application provides a differential AGV control device which is applied to an automatic guided vehicle.
As shown in fig. 4, the differential AGV control apparatus 400 includes:
the planning module 401 is configured to plan a target track of the differential AGV according to the obstacle information by using the upper layer planner;
A modeling module 402 for establishing a differential AGV kinematic model by the controller;
The processing module 403 is configured to perform linear processing on the differential AGV kinematic model according to a current position of the differential AGV, obtain a linear kinematic error model according to a target track position corresponding to the current position of the differential AGV, and perform discrete processing on the linear kinematic error model;
the mapping module 404 is configured to determine, by determining an optimal control sequence corresponding to a preset period according to a result after the discrete processing, a constraint range of the optimal control sequence based on a wheel speed that stabilizes the differential AGV, and map the optimal control sequence into the constraint range;
And the execution module 405 is configured to convert the mapped result into differential AGV wheel speed signals through decoupling, send the differential AGV wheel speed signals to the lower computer, and control the differential AGV to track the target track by the lower computer.
Optionally, the modeling module 402 is further configured to establish a differential AGV kinematic model by using the controller, where the model includes equation (1), equation (2), and equation (3):
wherein, For the differential AGV body's lateral displacement x derivative with respect to time,/>For the derivative of the longitudinal displacement y of the differential AGV body with respect to time,/>The yaw angle θ of the differential AGV body is derived over time, v is the linear velocity of the differential AGV body, and w is the angular velocity about the center of the differential AGV body.
Optionally, the processing module 403 is further configured to control the differential AGV control system further includes a positioner, and obtain the current position of the differential AGV through the positioner, where the current position includes a lateral displacement x r, a longitudinal displacement y r, a yaw angle θ r, a linear velocity v r, and an angular velocity w r around a center of the differential AGV body, and the taylor expansion is performed on the equation (1) under θ r and v r to obtain an equation (4):
performing taylor expansion on the formula (2) under the conditions of theta r and v r to obtain a formula (5):
Substituting w r into the formula (3) results in formula (6):
Integrating the formula (4), the formula (5) and the formula (6) to obtain a kinematic linear model (7) of the current position of the differential AGV:
Wherein the method comprises the steps of
Optionally, the illustrated processing module 403 is further configured to substitute the lateral displacement x ref, the longitudinal displacement y ref, the yaw angle θ ref, the linear velocity v ref, and the angular velocity w ref about the center of the vehicle body of the differential AGV vehicle body of the target track corresponding to the current position into the kinematic linear model (7) to obtain a kinematic linear model (8) of the target track:
Wherein the method comprises the steps of
And (3) a simultaneous model (7) and a model (8), and enabling X e＝X-Xref,ue＝u-uref to obtain a linear kinematic error model (9):
Discretizing the linear kinematic error model (9) to obtain a discrete kinematic error model (10) at n times:
Wherein the method comprises the steps of T is the control period.
Optionally, the mapping module 404 is further configured to control, during the prediction period NT time, constructing the cost function model (11):
The discrete kinematic error models (10) are combined to obtain an optimal control sequence U= (U (0), U (1), U (2), … … and U (N)) in the prediction period NT time;
Wherein Q is a deviation weight matrix of the current position and the corresponding target track position, R is a deviation weight matrix of the speed of the current position and the speed of the corresponding target track, and Q f is a deviation weight matrix of the final position and the corresponding target track position.
Optionally, the mapping module 404 is further configured to set a constraint range of the control amount in the optimal control sequence, where a maximum value of the constraint range isThe minimum value of the constraint range is
The optimal control sequence is subjected to speed mapping to obtain
The method further comprises the steps of:
and mapping the out-of-range control quantity in the optimal control sequence into the constraint range by a scaling method.
Optionally, the executing module 405 is further configured to decouple the result after the speed mapping by using formula (12), convert the result into speed signals of a left wheel and a right wheel of the differential AGV, and send the speed signals to the lower computer;
wherein, w r is the rotation speed of the motor of the right wheel, w l is the rotation speed of the motor of the left wheel, r is the radius of the wheel, and d is the wheelbase of the vehicle body.
The differential AGV control device 400 provided in this embodiment can implement the differential AGV control method provided in embodiment 1, and in order to avoid repetition, a detailed description is omitted here.
According to the differential AGV control device provided by the embodiment, the upper layer planner plans a target track of the differential AGV according to the obstacle information; the controller establishes a differential AGV kinematic model; performing linear processing on the differential AGV kinematic model according to the current position of the differential AGV; acquiring a linear kinematic error model according to a target track position corresponding to the current position of the differential AGV, and performing discrete processing on the linear kinematic error model; determining a corresponding optimal control sequence in a preset period according to the result after the discrete processing; determining a constraint range of the optimal control sequence based on a wheel speed stabilizing the differential AGV, and mapping the optimal control sequence into the constraint range; and converting the mapped result into a differential AGV wheel speed signal through decoupling, and sending the differential AGV wheel speed signal to the lower computer, wherein the lower computer controls the differential AGV to track the target track. According to the scheme, the nonlinear kinematic model of the differential AGV is subjected to linearization and discretization, so that the complexity of a control model is simplified, and the calculation efficiency of a control algorithm is improved. Meanwhile, the processing method enables the model parameters to be dynamically adjusted according to the actual running state of the AGV, and is better suitable for different environments and conditions.
Example 3
In addition, an embodiment of the present application provides an automatic guided vehicle, including a memory and a processor, where the memory stores a computer program that when run on the processor performs the differential AGV control method provided in embodiment 1.
The steps of the differential AGV control method provided in the foregoing method embodiment 1 may be executed by the automatic guided vehicle provided in the embodiment of the present invention, and in order to avoid repetition, details are not repeated here.
Example 4
The present application also provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the differential AGV control method provided by embodiment 1.
In the present embodiment, the computer readable storage medium may be a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk, an optical disk, or the like.
The computer readable storage medium provided in this embodiment can implement the differential AGV control method provided in embodiment 1, and in order to avoid repetition, a detailed description is omitted here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are to be protected by the present application.
Claims (10)
1. The utility model provides a differential AGV control method, characterized in that is applied to differential AGV control system, differential AGV control system includes upper strata planner, controller and lower computer, the method includes:
the upper layer planner plans a target track of the differential AGV according to the obstacle information;
The controller establishes a differential AGV kinematic model;
Performing linear processing on the differential AGV kinematic model according to the current position of the differential AGV;
acquiring a linear kinematic error model according to a target track position corresponding to the current position of the differential AGV, and performing discrete processing on the linear kinematic error model;
determining a corresponding optimal control sequence in a preset period according to the result after the discrete processing;
determining a constraint range of the optimal control sequence based on a wheel speed stabilizing the differential AGV, and mapping the optimal control sequence into the constraint range;
And converting the mapped result into a differential AGV wheel speed signal through decoupling, and sending the differential AGV wheel speed signal to the lower computer, wherein the lower computer controls the differential AGV to track the target track.
2. The differential AGV control method according to claim 1, wherein the controller builds a differential AGV kinematic model including equation (1), equation (2) and equation (3):
wherein, For the differential AGV body's lateral displacement x derivative with respect to time,/>For the derivative of the longitudinal displacement y of the differential AGV body with respect to time,/>The yaw angle θ of the differential AGV body is derived over time, v is the linear velocity of the differential AGV body, and w is the angular velocity about the center of the differential AGV body.
3. The differential AGV control method according to claim 2, wherein the differential AGV control system further comprises a positioner by which the differential AGV current position is obtained, including a lateral displacement x r of a differential AGV body, a longitudinal displacement y r, a yaw angle θ r, a linear velocity v r, and an angular velocity w r around a center of the differential AGV body, the performing linear processing on the differential AGV kinematic model according to the differential AGV current position, comprising:
Performing taylor expansion on the formula (1) under the conditions of theta r and v r to obtain a formula (4):
performing taylor expansion on the formula (2) under the conditions of theta r and v r to obtain a formula (5):
Substituting w r into the formula (3) results in formula (6):
Integrating the formula (4), the formula (5) and the formula (6) to obtain a kinematic linear model (7) of the current position of the differential AGV:
Wherein the method comprises the steps of
4. The method of claim 3 wherein said obtaining a linear kinematic error model from a differential AGV current position, performing discrete processing on said linear kinematic error model, comprises:
Substituting the transverse displacement x ref, the longitudinal displacement y ref, the yaw angle theta ref, the linear speed v ref and the angular speed w ref around the center of the vehicle body of the differential AGV vehicle body of the target track corresponding to the current position into the kinematic linear model (7) to obtain a kinematic linear model (8) of the target track:
Wherein the method comprises the steps of
And (3) a simultaneous model (7) and a model (8), and enabling X e＝X-Xref,ue＝u-uref to obtain a linear kinematic error model (9):
Discretizing the linear kinematic error model (9) to obtain a discrete kinematic error model (10) at n times:
Wherein the method comprises the steps of T is the control period.
5. The method of claim 1, wherein determining the corresponding optimal control sequence for the predetermined period based on the result of the discrete processing comprises:
constructing a cost function model (11) in the prediction period NT time:
Combining the discrete kinematic error models (10) to obtain an optimal control sequence u= (U (0), U (1), U (2)) and/or U (N)) over the prediction period NT time;
Wherein Q is a deviation weight matrix of the current position and the corresponding target track position, R is a deviation weight matrix of the speed of the current position and the speed of the corresponding target track, and Q f is a deviation weight matrix of the final position and the corresponding target track position.
6. The method of claim 5 wherein the determining a constraint range for the optimal control sequence based on wheel speeds that smooth the differential AGV, mapping the optimal control sequence into the constraint range, comprises:
setting a constraint range of control quantity in the optimal control sequence, wherein the maximum value of the constraint range is The minimum value of the constraint range is/>
The optimal control sequence is subjected to speed mapping to obtain
The method further comprises the steps of:
and mapping the out-of-range control quantity in the optimal control sequence into the constraint range by a scaling method.
7. The method of claim 6 wherein converting the mapped results into differential AGV wheel speed signals by decoupling includes:
Decoupling the speed mapping result through a formula (12), converting the speed mapping result into speed signals of a left wheel and a right wheel of the differential AGV, and transmitting the speed signals to the lower computer;
wherein, w r is the rotation speed of the motor of the right wheel, w l is the rotation speed of the motor of the left wheel, r is the radius of the wheel, and d is the wheelbase of the vehicle body.
8. Differential AGV controlling means, its characterized in that is applied to differential AGV control system, differential AGV control system includes upper strata planner, controller and lower computer, the device includes:
the planning module is used for planning a target track of the differential AGV according to the obstacle information through the upper layer planner;
The modeling module is used for establishing a differential AGV kinematic model through the controller;
The processing module is used for performing linear processing on the differential AGV kinematic model according to the current position of the differential AGV, acquiring a linear kinematic error model according to a target track position corresponding to the current position of the differential AGV, and performing discrete processing on the linear kinematic error model;
The mapping module is used for determining a corresponding optimal control sequence in a preset period according to a discrete processing result, determining a constraint range of the optimal control sequence based on a wheel speed enabling the differential AGV to be stable, and mapping the optimal control sequence into the constraint range;
And the execution module is used for converting the mapped result into differential AGV wheel speed signals through decoupling, sending the differential AGV wheel speed signals to the lower computer, and controlling the differential AGV to track the target track by the lower computer.
9. An automatic guided vehicle comprising a memory and a processor, the memory storing a computer program that when run by the processor performs the differential AGV control method of any of claims 1-7.
10. A computer readable storage medium, characterized in that it stores a computer program which, when run on a processor, performs the differential AGV control method according to any one of claims 1 to 7.
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