CN111966103B - Method, device, equipment and medium for dynamically correcting zero deflection angle of unmanned forklift - Google Patents
Method, device, equipment and medium for dynamically correcting zero deflection angle of unmanned forklift Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device, equipment and a medium for dynamically correcting a zero deflection angle of an unmanned forklift. The method comprises the following steps: the method comprises the steps of obtaining a current angle value of a zero deflection angle of a rear steering wheel of the unmanned forklift at regular time; and correcting and updating the current angle value of the zero deflection angle of the rear steering wheel according to the current running speed of the unmanned forklift, the distance average deviating from the pilot line of the unmanned forklift in a preset time period, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the number of running fluctuation. By the aid of the technical scheme, the zero deflection angle is dynamically corrected in the process of driving of the unmanned forklift, and the zero deflection angle is corrected to a smaller accurate range, so that the unmanned forklift can drive linearly as much as possible.
Description
Technical Field
The embodiment of the invention relates to the technical field of unmanned control, in particular to a method, a device, equipment and a medium for dynamically correcting a zero deflection angle of an unmanned forklift.
Background
In the running control of the unmanned forklift, the angle and the speed of the rear steering wheel can be fed back to the controller, so that the controller controls the front steering wheel according to the angle feedback signal and the speed feedback signal.
Wherein, whether the angle feedback signal and the speed feedback signal are accurate or not can influence the calculation of the advancing mileage of the unmanned forklift. In general, the velocity feedback signal does not have the problem of cumulative accuracy. However, regarding the angle feedback signal, there may be a loss of the pulse signal, and further the actual physical angle of the steering wheel of the unmanned forklift is inconsistent with the angle fed back by the rear steering wheel, which is specifically represented as: the angle feedback signal should cause the drone forklift to travel in a zero position direction (direction along the lead line) with the rear steering wheel not in the zero position direction (e.g., actual physical position at 5 degrees). If the angle value of the zero deflection angle cannot be accurately determined and the zero deflection angle is accumulated on the angle value fed back by the rear steering wheel, the unmanned forklift can frequently adjust the direction when running and runs an S-shaped route.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a medium for dynamically correcting a zero deflection angle of an unmanned forklift, so that the zero deflection angle is dynamically corrected in the running process of the unmanned forklift, the zero deflection angle is corrected to be within a smaller accurate range, and the unmanned forklift can run linearly as much as possible.
In a first aspect, an embodiment of the present invention provides a method for dynamically correcting a zero deflection angle of an unmanned forklift, including:
the method comprises the steps of obtaining a current angle value of a zero deflection angle of a rear steering wheel of the unmanned forklift at regular time;
and correcting and updating the current angle value of the zero deflection angle of the rear steering wheel according to the current running speed of the unmanned forklift, the distance mean value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of the unmanned forklift within a preset time period.
In a second aspect, an embodiment of the present invention further provides a device for dynamically correcting a zero deflection angle of an unmanned forklift, where the device includes:
the rear steering wheel zero deflection angle timing acquisition module is set to acquire the current angle value of the rear steering wheel zero deflection angle of the unmanned forklift at fixed time;
and the rear steering wheel zero deflection angle correction updating module is set to correct and update the current angle value of the rear steering wheel zero deflection angle according to the current running speed of the unmanned forklift, and the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of the unmanned forklift within a preset time period.
In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes:
one or more processors;
a memory for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method for dynamically correcting the zero drift angle of an unmanned forklift according to any embodiment.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for dynamically correcting the zero drift angle of the unmanned forklift according to any embodiment.
According to the technical scheme provided by the embodiment of the invention, the current angle value of the zero deflection angle of the rear steering wheel of the unmanned forklift is obtained at regular time, and the current angle value of the zero deflection angle of the rear steering wheel is corrected and updated according to the current running speed of the unmanned forklift, the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the number of running fluctuation times of the unmanned forklift in a preset time period, so that the zero deflection angle is dynamically corrected in the running process of the unmanned forklift, the zero deflection angle is corrected to be within a smaller accurate range, and the unmanned forklift can run linearly as much as possible.
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Fig. 1 is a flowchart of a method for dynamically correcting a zero deflection angle of an unmanned forklift according to a first embodiment of the present invention;
fig. 2 is a flowchart of a method for dynamically correcting a zero deflection angle of an unmanned forklift in the second embodiment of the present invention;
fig. 3 is a flowchart of a method for dynamically correcting a zero deflection angle of an unmanned forklift in the third embodiment of the present invention;
fig. 4 is an exemplary diagram of an implementation result of a method for dynamically correcting a zero deflection angle of an unmanned forklift in the third embodiment of the present invention;
fig. 5 is a schematic block structure diagram of a zero deflection angle dynamic correction device of an unmanned forklift in the fourth embodiment of the present invention;
fig. 6 is a schematic structural diagram of a computer device in the fifth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a method for dynamically correcting a zero deflection angle of an unmanned forklift according to an embodiment of the present invention. The present embodiment is applicable to the case of dynamically correcting the zero offset angle in the traveling process of the unmanned forklift, and the method may be performed by the dynamic correction apparatus for the zero offset angle of the unmanned forklift provided in any embodiment of the present invention, and the apparatus may be composed of hardware and/or software, and may be generally integrated in a computer device, for example, a controller of the unmanned forklift.
As shown in fig. 1, the method for dynamically correcting the zero deflection angle of the unmanned forklift provided by this embodiment includes the following steps:
s110, acquiring the current angle value of the zero deflection angle of the rear steering wheel of the unmanned forklift at regular time.
The rear steering wheel zero deflection angle refers to an angle value which is added to an angle feedback value of the rear steering wheel so that the unmanned forklift can run linearly as much as possible according to a leading line.
It should be noted that, the rear steering wheel of the unmanned forklift related to this embodiment is only one, and an angle feedback value is sent to the controller of the unmanned forklift, so that the controller controls the advancing angles of the two front steering wheels (i.e., the driven wheels) according to the angle feedback value.
Before the technical scheme provided by the embodiment is implemented, the current angle value of the zero deflection angle of the rear steering wheel, which is acquired for the first time, can be a preset value or a default value of the zero deflection angle of the rear steering wheel; after the technical scheme provided by this embodiment is implemented, the current angle value of the zero-off angle of the rear steering wheel obtained each time refers to the angle value of the zero-off angle of the rear steering wheel obtained through the last dynamic correction.
And S120, correcting and updating the current angle value of the zero deflection angle of the rear steering wheel according to the current running speed of the unmanned forklift, and the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of the unmanned forklift within a preset time period.
The current running speed refers to a running speed of the unmanned forklift at the current time, and may be determined based on a speed feedback value of the rear steering wheel, for example.
The mean value of the distances from the piloted path refers to the mean value of the distance values from the unmanned forklift to the preset piloted path. Correspondingly, the mean value of the distances from the piloting line of the unmanned forklift within the preset time period refers to the mean value of the distances from the piloting line determined according to the driving route of the unmanned forklift within the preset time period, for example, the mean value of the distance values from the unmanned forklift to the preset piloting line within 5 seconds. The preset navigation line refers to a navigation route planned for the unmanned forklift, And may be a real line or an SLAM (Simultaneous Localization And Mapping) virtual line. The distance value from the unmanned forklift to the preset navigation line can be obtained by calculation according to the positioning information of the unmanned forklift and the positioning information of the preset navigation line.
When the unmanned forklift runs along the preset navigation line, the unmanned forklift usually runs according to a given angle of a forklift motion model, and the state of deviation from the preset navigation line during running is usually slightly fluctuated back and forth around the preset navigation line. When a zero-yaw angle of the rear steering wheel exists, the unmanned forklift will likely not travel along the preset lead line, typically including two situations: one is that after the unmanned forklift deviates from a preset leading line, the unmanned forklift is quickly corrected back to the preset leading line, which shows that the unmanned forklift has larger fluctuation when running; the other type is that the unmanned forklift moves in a way of deviating from a whole body to be parallel to a preset navigation line, and when the zero deflection angle of the rear steering wheel is opposite to the deviation direction of the forklift, the driving route of the unmanned forklift is distributed in a bilateral symmetry mode just by taking the preset navigation line as the center.
And the driving slope value deviating from the pilot line refers to the deviation degree of the driving route of the unmanned forklift relative to the preset pilot line. Correspondingly, the running slope value of the unmanned forklift deviating from the leading line within the preset time period refers to a running slope value of the deviating leading line determined according to the running route of the unmanned forklift within the preset time period, and for example, may be determined according to a tangent value of an included angle between a starting and stopping point connecting line of the unmanned forklift and the preset leading line within the preset time period, or may be determined according to a ratio of a mean value of distances of the unmanned forklift deviating from the leading line within the preset time period to a traveling distance along the preset leading line.
The number of times of driving the piloted path refers to the number of intersections of the driving path of the unmanned forklift and the preset piloted path. Correspondingly, the number of times of driving the unmanned forklift to pass the leading line in the preset time period refers to the number of times of driving the leading line determined according to the driving route of the unmanned forklift in the preset time period.
The number of running fluctuations refers to the number of times that the running route of the unmanned forklift fluctuates. Wherein, the unmanned forklift driving away from and approaching to the preset leading line can be called as a wave. Optionally, the running fluctuation times refer to the times of target fluctuation of the running route of the unmanned forklift, and the target fluctuation refers to fluctuation of which the fluctuation amplitude is larger than a preset fluctuation threshold value. Accordingly, the number of traveling fluctuations of the unmanned forklift in the preset time period refers to the number of traveling fluctuations determined according to the traveling route of the unmanned forklift in the preset time period.
Optionally, the preset time period refers to a preset time period before the current time, and may be 5 seconds, for example. Alternatively, the "timing" in S110 is matched with the preset time period, and assuming that the preset time period is 5 seconds, the "timing" may be once every 5 seconds.
Optionally, in the running process of the unmanned forklift, the distance average value deviating from the leading line, the running slope value deviating from the leading line, the number of times of running across the leading line and the running fluctuation number of the unmanned forklift within a preset time period are counted in real time.
In an optional implementation manner, after acquiring the current running speed of the unmanned forklift, and the distance average value deviating from the leading line, the running slope value deviating from the leading line, the number of times of running through the leading line, and the number of running fluctuation of the unmanned forklift within a preset time period, these parameters are input into a statistical model generated by pre-training, an angle value of the zero-offset angle of the rear steering wheel output by the statistical model is acquired, and the current angle value of the zero-offset angle of the rear steering wheel is updated by using the angle value, that is, the angle value is used as a new current angle value of the zero-offset angle of the rear steering wheel.
Optionally, the sample data used for training the statistical model is historical driving data of the unmanned forklift. Each group of sample data comprises the current running speed of the unmanned forklift, the distance mean value deviating from the leading line of the unmanned forklift within a preset time period, the running slope value deviating from the leading line, the number of times of running through the leading line, the number of running fluctuation and the corrected angle value of the zero deflection angle of the rear steering wheel. The correction angle value of the zero deflection angle of the rear steering wheel can be obtained through a test measurement mode. With the increase of sample data, the accuracy of the statistical model is higher and higher.
In an optional implementation manner, after acquiring the current running speed of the unmanned forklift, and the distance mean value deviating from the leading line, the running slope value deviating from the leading line, the number of times of running through the leading line, and the number of running fluctuation of the unmanned forklift within a preset time period, these parameters are input into a zero offset angle correction model generated by pre-training, an angle value of the zero offset angle of the rear steering wheel output by the zero offset angle correction model is acquired, and the current angle value of the zero offset angle of the rear steering wheel is updated by using the angle value, that is, the angle value is used as a new current angle value of the zero offset angle of the rear steering wheel.
Optionally, the sample data used by the zero-bias correction model is historical driving data of the unmanned forklift. Each group of sample data comprises the current running speed of the unmanned forklift, the distance mean value deviating from the leading line of the unmanned forklift within a preset time period, the running slope value deviating from the leading line, the number of times of running through the leading line, the number of running fluctuation and the corrected angle value of the zero deflection angle of the rear steering wheel. The correction angle value of the zero deflection angle of the rear steering wheel can be obtained through a test measurement mode.
And performing curve fitting on the multiple groups of sample data to obtain a zero deviation angle correction model, wherein the zero deviation angle correction model can exist in a polynomial form. With the increase of sample data, the accuracy of the zero-bias correction model is higher and higher.
Furthermore, after the current angle value of the zero deflection angle of the rear steering wheel is corrected and updated, the running angle of the unmanned forklift is controlled according to the angle feedback value of the rear steering wheel and the current angle value of the zero deflection angle of the rear steering wheel. Optionally, the current angle value of the zero drift angle of the rear steering wheel is superposed on the angle feedback value of the rear steering wheel to be used as the angle of the unmanned forklift for controlling the front steering wheel to travel, so that the travel route of the unmanned forklift is adjusted. And repeatedly executing the steps, and adjusting the driving route of the unmanned forklift at regular time, so that the unmanned forklift can drive along or parallel to the preset navigation line as far as possible.
It is worth pointing out that the current angle value of the zero-offset angle of the rear steering wheel after one correction and update may be equal to the current angle value of the zero-offset angle of the rear steering wheel before the current correction and update, which indicates that the angle value of the zero-offset angle of the rear steering wheel is more accurate in the corresponding preset time period, and the unmanned forklift is continuously controlled according to the angle value and the angle feedback value of the rear steering wheel.
It should be noted that the driving angle of the unmanned forklift may also be related to some mechanical structures, and the embodiment is not particularly limited with respect to the processing manner of such an angle error.
According to the technical scheme provided by the embodiment of the invention, the current angle value of the zero deflection angle of the rear steering wheel of the unmanned forklift is obtained at regular time, and the current angle value of the zero deflection angle of the rear steering wheel is corrected and updated according to the current running speed of the unmanned forklift, the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the number of running fluctuation times of the unmanned forklift in a preset time period, so that the zero deflection angle is dynamically corrected in the running process of the unmanned forklift, the zero deflection angle is corrected to be within a smaller accurate range, and the unmanned forklift can run linearly as much as possible.
Example two
Fig. 2 is a flowchart of a dynamic correction method for a zero deflection angle of an unmanned forklift according to a second embodiment of the present invention. The present embodiment is embodied on the basis of the above embodiment, wherein before the current angle value is updated, the method further includes:
determining a current driving mode of the unmanned forklift, wherein the driving mode comprises a high-speed driving mode and a low-speed driving mode;
correspondingly, according to the current running speed of the unmanned forklift, and the distance average deviating from the leading line, the running slope value deviating from the leading line, the number of times of running through the leading line and the running fluctuation number of the unmanned forklift within a preset time period, the current angle value is corrected and updated, and the method comprises the following steps:
if the current running mode is a low-speed running mode, correcting and updating the current angle value by using a zero deflection angle correction low-speed model based on the current running speed of the unmanned forklift, and the distance average value deviating from the leading line, the running slope value deviating from the leading line, the number of times of running through the leading line and the running fluctuation number of the unmanned forklift within a preset time period;
and if the current running mode is a high-speed running mode, correcting the high-speed model by using a zero deflection angle or correcting the low-speed model by using the zero deflection angle, and correcting and updating the current angle value based on the current running speed of the unmanned forklift, and the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the number of running fluctuation times of the unmanned forklift within a preset time period.
Further, the method provided by this embodiment further includes: and generating a zero deflection angle correction low-speed model and a zero deflection angle correction high-speed model in advance according to historical driving data of the unmanned forklift.
The zero deflection angle correction low-speed model and the zero deflection angle correction high-speed model are two zero deflection angle correction models which are generated by respectively carrying out curve fitting according to historical driving data of the unmanned forklift and are suitable for different scenes. The zero deflection angle correction low-speed model is suitable for correcting and updating the zero deflection angle value of the rear steering wheel under the low-speed running condition of the unmanned forklift, and the zero deflection angle correction high-speed model is suitable for correcting and updating the zero deflection angle value of the rear steering wheel under the high-speed running condition of the unmanned forklift. Whether the unmanned forklift is running at a low speed or a high speed can be determined according to the current running speed of the unmanned forklift, and the embodiment is not particularly limited with respect to the boundary for dividing the high speed and the low speed. For example, if the current running speed is less than 0.5m/s, the unmanned forklift is considered to be running at a low speed; and if the current running speed is more than 1m/s, the unmanned forklift is considered to be running at a high speed.
When the zero deflection angle correction low-speed model is generated, historical driving data corresponding to the low-speed driving of the unmanned forklift is obtained, and sample data corresponding to the low-speed driving is constructed. Each group of sample data comprises the current running speed of the unmanned forklift, the distance mean value deviating from the leading line of the unmanned forklift within a preset time period, the running slope value deviating from the leading line, the number of times of running through the leading line, the number of running fluctuation and the corrected angle value of the zero deflection angle of the rear steering wheel. The correction angle value of the zero deflection angle of the rear steering wheel can be obtained through a test measurement mode. And performing curve fitting on the multiple groups of sample data to obtain a zero-deflection-angle low-speed correction model.
When the zero deflection angle correction high-speed model is generated, historical driving data corresponding to high-speed driving of the unmanned forklift is obtained, and sample data corresponding to the high-speed driving are constructed. Each group of sample data comprises the current running speed of the unmanned forklift, the distance mean value deviating from the leading line of the unmanned forklift within a preset time period, the running slope value deviating from the leading line, the number of times of running through the leading line, the number of running fluctuation and the corrected angle value of the zero deflection angle of the rear steering wheel. The correction angle value of the zero deflection angle of the rear steering wheel can be obtained through a test measurement mode. And performing curve fitting on the multiple groups of sample data to obtain a zero deflection angle high-speed correction model.
Optionally, the zero deflection angle correction low-speed model and the zero deflection angle correction high-speed model are polynomial models, and the difference is that coefficients of parameters in the polynomial are different.
As shown in fig. 2, the method for dynamically correcting the zero deflection angle of the unmanned forklift provided by this embodiment includes the following steps:
s210, acquiring the current angle value of the zero deflection angle of the rear steering wheel of the unmanned forklift at regular time.
And S220, determining the current running mode of the unmanned forklift.
The driving mode comprises a high-speed driving mode and a low-speed driving mode, and can be determined according to a control command sent by a controller of the unmanned forklift, or can be determined according to related configuration information in the controller.
For example, if the control command issued by the controller is a high speed command, the running mode of the unmanned forklift is a high speed running mode, and if the control command issued by the controller is a low speed command, the running mode of the unmanned forklift is a low speed running mode.
In the low-speed running mode, the running speed of the unmanned forklift is always maintained at a low speed, for example, 0.2m/s, 0.3m/s, or the like.
In the high speed running mode, the running speed of the unmanned forklift may be maintained at a high speed, for example, 2m/s or the like at all times, and may be high speed or low speed at all times.
For example, after the controller sends a high-speed instruction, the unmanned forklift starts to run, the rear steering wheel has an incorrect zero offset angle, the unmanned forklift inevitably deviates to a certain side of a preset leading line when running, when the distance of the unmanned forklift deviates from a larger value of the preset leading line, the controller reduces the running speed of the unmanned forklift to a low speed so as to prevent the unmanned forklift from deviating from a cruising limit range (the cruising limit range is the limit range deviating from the preset leading line), the unmanned forklift tends to be close to the preset leading line after running for a certain distance at the low speed, the controller determines that the unmanned forklift meets the high-speed running condition, adjusts the running speed of the unmanned forklift to the high speed, further deviates again, and the unmanned forklift reciprocates in such a way.
Furthermore, in the high-speed travel mode, the unmanned forklift may have two travel states: one is that the vehicle cannot continuously run at a high speed, but repeatedly switches between a high speed and a low speed; the other mode is normal high-speed driving, but the unmanned forklift does not use a preset leading line as a center during driving (for example, the unmanned forklift runs parallel to the preset leading line), and if the unmanned forklift uses the original turning angle during turning, the unmanned forklift fails to turn.
S230, determining whether the current driving mode is the low-speed driving mode, if so, executing S240, and if not, executing S250.
S240, correcting the low-speed model by using the zero deflection angle, and correcting and updating the current angle value based on the current running speed of the unmanned forklift, the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of the unmanned forklift in a preset time period.
And under the condition that the current running mode is a low-speed running mode, inputting the current running speed of the unmanned forklift, the mean value of the distance of the unmanned forklift deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number into a zero-deviation-angle correction low-speed model within a preset time period, wherein the angle value output by the zero-deviation-angle correction low-speed model is a correction updated value of the current angle value, and the angle value is the current angle value of the corrected rear steering wheel zero-deviation angle.
And S250, correcting the high-speed model or the low-speed model by using a zero deflection angle, and correcting and updating the current angle value based on the current running speed of the unmanned forklift, the distance average value deviating from the leading line, the running slope value deviating from the leading line, the number of times of running through the leading line and the running fluctuation number of the unmanned forklift in a preset time period.
And under the condition that the current running mode is a high-speed running mode, selecting a zero deflection angle correction high-speed model or the zero deflection angle correction low-speed model according to the actual running condition of the unmanned forklift to correct and update the current angle value.
Optionally, if the actual running condition of the unmanned forklift is continuous high-speed running, the current angle value is corrected and updated by using a zero-deflection correction high-speed model; and if the actual running condition of the unmanned forklift is high-speed and low-speed running switching, correcting and updating the current angle value by using a zero deflection angle correction low-speed model.
As an optional implementation manner, if the current driving mode is a high-speed driving mode, determining whether the unmanned forklift continuously drives at a high speed;
if so, correcting and updating the current angle value by using the zero deflection angle correction high-speed model based on the current running speed of the unmanned forklift, and the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of the unmanned forklift within a preset time period;
and if not, the unmanned forklift is clamped to be driven at a low speed, the low-speed model is corrected by using the zero deflection angle, and the current angle value is corrected and updated on the basis of the current driving speed of the unmanned forklift, the distance average value deviating from the leading line, the driving slope value deviating from the leading line, the number of driving passing the leading line and the number of driving fluctuation of the unmanned forklift within a preset time period.
And under the condition that the current running mode is a high-speed running mode, if the unmanned forklift is determined to run continuously at a high speed, inputting the current running speed of the unmanned forklift, the mean value of the distance deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running across the pilot line and the running fluctuation number of the unmanned forklift within a preset time period into a zero-deviation-angle correction high-speed model, wherein the angle value output by the zero-deviation-angle correction high-speed model is a corrected updated value of the current angle value, and the angle value is the current angle value of the corrected rear steering wheel zero-deviation angle.
Under the condition that the current running mode is a high-speed running mode, if it is determined that the unmanned forklift does not continuously run at a high speed and also switches to run at a high speed and a low speed, firstly, the unmanned forklift is clamped to run at a low speed, and after the unmanned forklift runs for a period of time at the low speed, the current running speed of the unmanned forklift, the distance average value deviating from the pilot line of the unmanned forklift, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number are input into a zero-offset-angle correction low-speed model in a preset time period, an angle value output by the zero-offset-angle correction low-speed model is a corrected updated value of the current angle value, and the angle value is the current angle value of the zero offset angle of the rear steering wheel after correction.
As an alternative embodiment, the unmanned forklift may be clamped to a low speed, which may be specifically:
and if the continuous times of acceleration and deceleration crossing of the unmanned forklift within the preset running distance reach a first preset time threshold value, or the running slope value of the unmanned forklift deviating from the leading line is greater than a preset slope threshold value, the unmanned forklift is clamped to run at a low speed.
The term "acceleration-deceleration crossover" refers to deceleration immediately after acceleration or acceleration immediately after deceleration. Here, the statistics is the number of consecutive times that the unmanned forklift accelerates, or decelerates, after acceleration. When the continuous times are greater than a first preset time threshold value, for example, 3 times, it indicates that the unmanned forklift continuously switches between high speed and low speed and is in a state of driving an S-shaped route, so that the speed of the unmanned forklift is firstly clamped to be driven at low speed, and the current angle value is corrected and updated by using a zero deflection angle correction low speed model.
When the driving slope value of the unmanned forklift deviating from the leading line is larger than the preset slope threshold value, the degree angle of the unmanned forklift deviating from the preset leading line is indicated, so that the speed of the unmanned forklift is limited to low speed driving, and the current angle value is corrected and updated by using the zero-deflection-angle correction low-speed model to approach to the actual zero-deflection-angle value.
Further, after the unmanned forklift is clamped to a low speed for running, the method further comprises the following steps:
and if the average value of the distances of the unmanned forklift deviating from the leading line within the preset driving distance is smaller than the preset distance threshold value and the times of the unmanned forklift driving through the leading line is larger than a second preset time threshold value, cancelling the operation of clamping the unmanned forklift to low-speed driving.
The average value of the distance of the unmanned forklift deviating from the leading line within the preset driving distance is smaller than a preset distance threshold value, and the fact that the driving route of the unmanned forklift tends to be normal is shown; and the times that the unmanned forklift drives the navigation line are greater than a second preset time threshold value, which indicates that the unmanned forklift can return to the preset navigation line. When the two conditions are met, the operation of clamping the unmanned forklift to low-speed running can be cancelled, so that the unmanned forklift can recover high-speed running.
For those parts of this embodiment that are not explained in detail, reference is made to the aforementioned embodiments, which are not repeated herein.
In the technical scheme, because interference factors are more in the multiple running processes of the unmanned forklift, and the odometer mode in the existing theory cannot be used when the zero-offset angle value is inaccurate, two zero-offset angle correction models are provided, and the method is suitable for correcting the zero-offset angle value of the rear steering wheel in different running scenes of the unmanned forklift, so that the zero-offset angle value of the rear steering wheel approaches to the actual zero-offset angle value, and the unmanned forklift runs linearly as much as possible.
Further, on the basis of the above technical solution, the above method further includes:
if the current running mode is the high-speed running mode, counting the low-speed correction times for correcting and updating the current angle value by using the zero deflection angle correction low-speed model and the high-speed correction times for correcting and updating the current angle value by using the zero deflection angle correction high-speed model within a target time period;
determining the optimization direction of the zero deflection angle correction low-speed model and/or the zero deflection angle correction high-speed model according to the low-speed correction times and the high-speed correction times;
the target time period is a time period from the driving starting moment of the unmanned forklift to the current angle value correction finishing moment of the zero deflection angle of the rear steering wheel, and the current angle value is in a preset angle range at the current angle value correction finishing moment of the zero deflection angle of the rear steering wheel. Namely, the target time period is the time period from the beginning of correction of the current angle value of the zero deflection angle of the rear steering wheel to the completion of correction.
In one case, if the low-speed correction times are higher than a third preset time threshold, and the high-speed correction times are lower than a fourth preset time threshold, it is indicated that the correction of the zero deflection angle of the rear steering wheel is completed almost only by using the zero deflection angle correction low-speed model, and then the zero deflection angle correction high-speed model is to be optimally adjusted. On the contrary, if the high-speed correction times are higher than the third preset time threshold, and the low-speed correction times are lower than the fourth preset time threshold, it indicates that the correction of the zero deflection angle of the rear steering wheel is completed almost only by using the zero deflection angle correction high-speed model, and further the zero deflection angle correction low-speed model is to be optimally adjusted.
In another case, if the low-speed correction frequency is higher than a fifth preset frequency threshold, or the high-speed correction frequency is higher than the fifth preset frequency threshold, indicating that the correction frequency is excessive, it may be determined that the zero-deflection-angle correction low-speed model or the zero-deflection-angle correction high-speed model needs to be optimally adjusted.
EXAMPLE III
Fig. 3 is a flowchart of a method for dynamically correcting a zero deflection angle of an unmanned forklift according to a third embodiment of the present invention. The present embodiment provides a specific implementation manner based on the above embodiment, wherein the instruction initiated by the controller is a high-speed reverse instruction, so that the running mode of the unmanned forklift is a high-speed running mode.
As shown in fig. 3, the method for dynamically correcting the zero deflection angle of the unmanned forklift provided by this embodiment includes the following steps:
s310, acquiring the current angle value of the zero deflection angle of the rear steering wheel of the unmanned forklift at regular time.
And S320, determining that the current running mode of the unmanned forklift is a high-speed running mode.
And S330, judging whether the unmanned forklift continuously runs at a high speed, if so, executing S340, and if not, executing S360.
When the step is executed, whether the unmanned forklift continuously runs at a high speed or not is judged according to the running data of the unmanned forklift in the previous preset time period.
And S340, judging whether the current first driving range of the unmanned forklift reaches a first corrected range, if so, executing S350, and if not, executing S340.
And setting a first correction mileage to count the average distance value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the number of running fluctuation of the unmanned forklift in the mileage so as to serve as a reference parameter for correcting the zero deflection angle by the zero deflection angle correction high-speed model.
And the current first driving range can be re-counted after the conclusion that the unmanned forklift continuously drives at the high speed is obtained.
And S350, correcting and updating the current angle value by using the zero deflection angle correction high-speed model based on the current driving speed of the unmanned forklift, and the distance average value deviating from the pilot line, the driving slope value deviating from the pilot line, the number of times of driving through the pilot line and the driving fluctuation number of the unmanned forklift in a preset time period, and executing S310.
And S360, the unmanned forklift is clamped to run at a low speed.
Optionally, if the number of continuous times of acceleration and deceleration crossing of the unmanned forklift within the preset driving distance reaches a first preset number threshold, or the driving slope value of the unmanned forklift deviating from the leading line is greater than a preset slope threshold, the unmanned forklift is clamped to be driven at a low speed.
And S370, judging whether the current second driving mileage of the unmanned forklift reaches the second corrected mileage, if so, executing S380, and if not, executing S370.
And setting a second correction mileage for counting the average distance value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the number of running fluctuation of the unmanned forklift in the mileage so as to be used as a reference parameter for correcting the zero deflection angle by the zero deflection angle correction low-speed model. Optionally, the second corrected mileage is smaller than the first corrected mileage.
The current second driving range can be recalculated after the unmanned forklift is clamped to be driven at a low speed or the current angle value is corrected and updated by using the zero-deflection correction low-speed model.
And S380, correcting the low-speed model by using a zero deflection angle, and correcting and updating the current angle value based on the current running speed of the unmanned forklift, the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of the unmanned forklift in a preset time period.
And S390, judging whether the unmanned forklift runs normally, if so, executing S3100, and if not, executing S370.
Optionally, if the average value of the distances of the unmanned forklift deviating from the leading line within the preset driving distance is smaller than a preset distance threshold value and the number of times that the unmanned forklift drives through the leading line is larger than a second preset number threshold value, it is determined that the unmanned forklift drives normally.
S3100, the operation of clamping the unmanned forklift to the low speed running is cancelled, and S310 is executed.
Fig. 4 shows an example of a result of adjusting the zero drift angle of the rear steering wheel by using the method for dynamically correcting the zero drift angle of the unmanned forklift according to the embodiment. In fig. 4, the ordinate of the curve L1 represents the value of the distance between the unmanned forklift and the preset leading line, the ordinate of the curve L2 represents the current value of the angle of zero-offset of the rear steering wheel (where the initial value of the angle is 5 degrees), the ordinate of the curve L3 represents the current travel speed of the unmanned forklift, the ordinate of the curve L4 represents the value of the travel slope of the unmanned forklift away from the leading line, the ordinate of the curve L5 represents the number of times the unmanned forklift travels through the leading line, the ordinate of the curve L6 represents the number of times the unmanned forklift travel fluctuation, the ordinate of the curve L7 represents the mean value of the distance between the unmanned forklift (e.g., within 5 s) away from the leading line, the ordinate of the curve L8 represents the number of high-speed corrections, the ordinate of the curve L9 represents the number of low-speed corrections, and the ordinate of the curve L10 represents the low-speed gripping state ("1" represents low-speed gripping cancellation, "0" represents low-speed gripping cancellation). The abscissa of the curves L1-L10 is time and corresponds to coincidence. To facilitate comparative analysis of the curves L1-L10, the abscissas of the curves L1-L10 are only identified uniformly at the lowermost level in FIG. 4.
For those parts of this embodiment that are not explained in detail, reference is made to the aforementioned embodiments, which are not repeated herein.
Example four
Fig. 5 is a schematic block structure diagram of a zero deflection angle dynamic correction device for an unmanned forklift according to a fourth embodiment of the present invention. The present embodiment is applicable to the case of dynamically correcting the zero deflection angle in the traveling of the unmanned forklift, and the apparatus may be implemented in a software and/or hardware manner, and may be generally integrated in a computer device, for example, a controller of the unmanned forklift. As shown in fig. 5, the apparatus includes: a rear steering wheel zero offset angle timing acquisition module 410 and a rear steering wheel zero offset angle correction updating module 420. Wherein the content of the first and second substances,
a rear steering wheel zero offset angle timing acquisition module 410 which is set to acquire the current angle value of the rear steering wheel zero offset angle of the unmanned forklift at a timing;
and the rear steering wheel zero offset angle correction updating module 420 is configured to correct and update the current angle value of the rear steering wheel zero offset angle according to the current running speed of the unmanned forklift, and the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of the unmanned forklift within a preset time period.
According to the technical scheme provided by the embodiment of the invention, the current angle value of the zero deflection angle of the rear steering wheel of the unmanned forklift is obtained at regular time, and the current angle value of the zero deflection angle of the rear steering wheel is corrected and updated according to the current running speed of the unmanned forklift, the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the number of running fluctuation times of the unmanned forklift in a preset time period, so that the zero deflection angle is dynamically corrected in the running process of the unmanned forklift, the zero deflection angle is corrected to be within a smaller accurate range, and the unmanned forklift can run linearly as much as possible.
Further, the above apparatus further comprises: a current running mode determining module configured to determine a current running mode of the unmanned forklift before the current angle value is updated, where the running mode includes a high-speed running mode and a low-speed running mode;
accordingly, the rear steering wheel zero slip angle correction updating module 420 includes:
a rear steering wheel zero-offset-angle first correction updating unit which is set to use a zero-offset-angle correction low-speed model if the current running mode is a low-speed running mode, and corrects and updates the current angle value based on the current running speed of the unmanned forklift, and the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of times of the unmanned forklift within a preset time period;
and the rear steering wheel zero deflection angle second correction updating unit is set to use a zero deflection angle correction high-speed model or a zero deflection angle correction low-speed model if the current running mode is a high-speed running mode, and corrects and updates the current angle value based on the current running speed of the unmanned forklift, and the distance average value deviating from the leading line, the running slope value deviating from the leading line, the number of times of running over the leading line and the running fluctuation number of times of the unmanned forklift within a preset time period.
Further, the second correction updating unit for the zero deflection angle of the rear steering wheel is set to judge whether the unmanned forklift continuously runs at a high speed or not if the current running mode is a high-speed running mode; if so, correcting and updating the current angle value by using the zero deflection angle correction high-speed model based on the current running speed of the unmanned forklift, and the distance average deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of the unmanned forklift within a preset time period; and if not, the unmanned forklift is clamped to be driven at a low speed, the low-speed model is corrected by using the zero deflection angle, and the current angle value is corrected and updated on the basis of the current driving speed of the unmanned forklift, and the distance average value deviating from the pilot line, the driving slope value deviating from the pilot line, the number of times of driving through the pilot line and the driving fluctuation number of times of the unmanned forklift within a preset time period.
Optionally, when the rear steering wheel zero-deflection-angle second correction updating unit clamps the unmanned forklift to the low-speed running, specifically setting that if the continuous times of acceleration, deceleration and crossing of the unmanned forklift within a preset running distance reach a first preset time threshold value, or the running slope value of the unmanned forklift deviating from the leading line is greater than a preset slope threshold value, the unmanned forklift is clamped to the low-speed running.
Optionally, the second correction updating unit for zero deflection angle of the rear steering wheel is further configured to cancel the operation of clamping the unmanned forklift to low-speed driving if the average value of the distance from the unmanned forklift to the leading line within the preset driving distance is smaller than a preset distance threshold and the number of times that the unmanned forklift drives through the leading line is greater than a second preset number threshold after the unmanned forklift is clamped to low-speed driving.
Further, the above apparatus further comprises: and the model generation module is set to generate the zero deflection angle correction low-speed model and the zero deflection angle correction high-speed model in advance according to the historical driving data of the unmanned forklift.
As an optional implementation, the apparatus further includes: the model optimization direction determining module is set to count the low-speed correction times for correcting and updating the current angle value by using the zero deflection angle correction low-speed model and the high-speed correction times for correcting and updating the current angle value by using the zero deflection angle correction high-speed model in a target time period if the current driving mode is a high-speed driving mode; determining the optimization direction of the zero deflection angle correction low-speed model and/or the zero deflection angle correction high-speed model according to the low-speed correction times and the high-speed correction times; the target time period is a time period from the driving starting moment of the unmanned forklift to the current angle value correction finishing moment of the zero deflection angle of the rear steering wheel, and the current angle value is in a preset angle range at the current angle value correction finishing moment of the zero deflection angle of the rear steering wheel.
The device for dynamically correcting the zero deflection angle of the unmanned forklift provided by the embodiment of the invention can execute the method for dynamically correcting the zero deflection angle of the unmanned forklift provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
EXAMPLE five
Fig. 6 is a schematic structural diagram of a computer device according to a fifth embodiment of the present invention, as shown in fig. 6, the computer device includes a processor 50, a memory 51, an input device 52, and an output device 53; the number of processors 50 in the computer device may be one or more, and one processor 50 is taken as an example in fig. 6; the processor 50, the memory 51, the input device 52 and the output device 53 in the computer apparatus may be connected by a bus or other means, and the connection by the bus is exemplified in fig. 6.
The memory 51 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the dynamic correction method for the zero offset angle of the unmanned forklift in the embodiment of the present invention (for example, the rear steering wheel zero offset angle timing acquisition module 410 and the rear steering wheel zero offset angle correction updating module 420 in the dynamic correction device for the zero offset angle of the unmanned forklift shown in fig. 5). The processor 50 executes various functional applications and data processing of the computer device by running software programs, instructions and modules stored in the memory 51, so as to implement the above-mentioned dynamic correction method for the zero drift angle of the unmanned forklift.
The memory 51 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the computer device, and the like. Further, the memory 51 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 51 may further include memory located remotely from the processor 50, which may be connected to a computer device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 52 is operable to receive input numeric or character information and to generate key signal inputs relating to user settings and function controls of the computer apparatus. The output device 53 may include a display device such as a display screen.
EXAMPLE six
An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which when executed by a computer processor is configured to execute a method for dynamically correcting a zero deflection angle of an unmanned forklift, the method including:
the method comprises the steps of obtaining a current angle value of a zero deflection angle of a rear steering wheel of the unmanned forklift at regular time;
and correcting and updating the current angle value of the zero deflection angle of the rear steering wheel according to the current running speed of the unmanned forklift, the distance mean value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of the unmanned forklift within a preset time period.
Of course, the computer program of the computer-readable storage medium storing the computer program provided in the embodiment of the present invention is not limited to the above method operations, and may also perform related operations in the method for dynamically correcting the zero drift angle of the unmanned forklift provided in any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods of the embodiments of the present invention.
It should be noted that, in the embodiment of the device for dynamically correcting a zero deflection angle of an unmanned forklift, each included unit and module are only divided according to functional logic, but are not limited to the above division, as long as corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments illustrated herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (8)
1. A dynamic correction method for a zero deflection angle of an unmanned forklift is characterized by comprising the following steps:
the method comprises the steps of obtaining a current angle value of a zero deflection angle of a rear steering wheel of the unmanned forklift at regular time;
correcting and updating the current angle value of the zero deflection angle of the rear steering wheel according to the current running speed of the unmanned forklift, and the distance average deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of the unmanned forklift within a preset time period;
before the current angle value is updated, the method further includes:
determining a current driving mode of the unmanned forklift, wherein the driving mode comprises a high-speed driving mode and a low-speed driving mode;
according to the current running speed of the unmanned forklift, and the distance mean value deviating from the leading line, the running slope value deviating from the leading line, the number of times of running through the leading line and the running fluctuation number of the unmanned forklift within a preset time period, the current angle value is corrected and updated, and the method comprises the following steps:
if the current running mode is the low-speed running mode, correcting and updating the current angle value by using a zero deflection angle correction low-speed model based on the current running speed of the unmanned forklift, and the distance average value deviating from the leading line, the running slope value deviating from the leading line, the number of times of running through the leading line and the running fluctuation number of the unmanned forklift within a preset time period;
if the current running mode is a high-speed running mode, correcting a high-speed model or correcting a low-speed model by using a zero deflection angle, and correcting and updating the current angle value based on the current running speed of the unmanned forklift, and the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the number of running fluctuation of the unmanned forklift within a preset time period;
if the current running mode is a high-speed running mode, a zero deflection angle correction high-speed model or a zero deflection angle correction low-speed model is used, and based on the current running speed of the unmanned forklift, and the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running over the pilot line and the number of running fluctuation of the unmanned forklift within a preset time period, the current angle value is corrected and updated, and the method comprises the following steps:
if the current driving mode is a high-speed driving mode, judging whether the unmanned forklift continuously drives at a high speed or not;
if so, correcting and updating the current angle value by using the zero deflection angle correction high-speed model based on the current running speed of the unmanned forklift, and the distance average deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of the unmanned forklift within a preset time period;
and if not, the unmanned forklift is clamped to be driven at a low speed, the low-speed model is corrected by using the zero deflection angle, and the current angle value is corrected and updated on the basis of the current driving speed of the unmanned forklift, and the distance average value deviating from the pilot line, the driving slope value deviating from the pilot line, the number of times of driving through the pilot line and the driving fluctuation number of times of the unmanned forklift within a preset time period.
2. The method of claim 1, wherein clamping the unmanned forklift to low speed travel comprises:
and if the continuous times of acceleration and deceleration crossing of the unmanned forklift within the preset running distance reach a first preset time threshold value, or the running slope value of the unmanned forklift deviating from the leading line is greater than a preset slope threshold value, the unmanned forklift is clamped to run at a low speed.
3. The method according to claim 1 or 2, further comprising, after clamping the unmanned forklift to low speed travel:
and if the average value of the distances of the unmanned forklift deviating from the leading line is smaller than a preset distance threshold value and the times of the unmanned forklift driving through the leading line is larger than a second preset time threshold value within a preset driving distance, cancelling the operation of clamping the unmanned forklift to low-speed driving.
4. The method of claim 1, further comprising:
and generating the zero deflection angle correction low-speed model and the zero deflection angle correction high-speed model in advance according to the historical driving data of the unmanned forklift.
5. The method of claim 4, further comprising:
if the current driving mode is a high-speed driving mode, counting the times of low-speed correction for correcting and updating the current angle value by using the zero deflection angle correction low-speed model and the times of high-speed correction for correcting and updating the current angle value by using the zero deflection angle correction high-speed model within a target time period;
determining the optimization direction of the zero deflection angle correction low-speed model and/or the zero deflection angle correction high-speed model according to the low-speed correction times and the high-speed correction times;
the target time period is a time period from the driving starting moment of the unmanned forklift to the current angle value correction finishing moment of the zero deflection angle of the rear steering wheel, and the current angle value is in a preset angle range at the current angle value correction finishing moment of the zero deflection angle of the rear steering wheel.
6. The utility model provides a zero off-angle's of unmanned fork truck dynamic correction device which characterized in that includes:
the rear steering wheel zero deflection angle timing acquisition module is set to acquire the current angle value of the rear steering wheel zero deflection angle of the unmanned forklift at fixed time;
the rear steering wheel zero deflection angle correction updating module is set to correct and update the current angle value of the rear steering wheel zero deflection angle according to the current running speed of the unmanned forklift, and the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of the unmanned forklift within a preset time period;
the rear steering wheel zero deflection angle correction updating module comprises:
the rear steering wheel zero-offset-angle first correction updating unit is set to use a zero-offset-angle correction low-speed model if the current running mode is a low-speed running mode, and corrects and updates the current angle value based on the current running speed of the unmanned forklift, and the distance average value deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of times of the unmanned forklift within a preset time period;
a second correction updating unit of the rear steering wheel zero deflection angle, which is set to use a zero deflection angle correction high-speed model or a zero deflection angle correction low-speed model if the current running mode is a high-speed running mode, and corrects and updates the current angle value based on the current running speed of the unmanned forklift, and the distance average value deviating from the leading line, the running slope value deviating from the leading line, the number of times of running over the leading line and the running fluctuation number of the unmanned forklift within a preset time period;
the second correction updating unit for the zero deflection angle of the rear steering wheel is set to judge whether the unmanned forklift continuously runs at a high speed or not if the current running mode is a high-speed running mode;
if so, correcting and updating the current angle value by using the zero deflection angle correction high-speed model based on the current running speed of the unmanned forklift, and the distance average deviating from the pilot line, the running slope value deviating from the pilot line, the number of times of running through the pilot line and the running fluctuation number of the unmanned forklift within a preset time period;
and if not, the unmanned forklift is clamped to be driven at a low speed, the low-speed model is corrected by using the zero deflection angle, and the current angle value is corrected and updated on the basis of the current driving speed of the unmanned forklift, and the distance average value deviating from the pilot line, the driving slope value deviating from the pilot line, the number of times of driving through the pilot line and the driving fluctuation number of times of the unmanned forklift within a preset time period.
7. A computer device, characterized in that the computer device comprises:
one or more processors;
a memory for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method for dynamic correction of zero drift angle for unmanned forklift trucks of any of claims 1-5.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method for dynamically correcting a zero drift angle of an unmanned forklift according to any one of claims 1 to 5.
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