CN113625713A - Control method and device for automatically guiding transport vehicle - Google Patents
Control method and device for automatically guiding transport vehicle Download PDFInfo
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Abstract
The invention discloses a control method and a control device for an automatic guided transport vehicle, and relates to the technical field of computers. One embodiment of the method comprises: determining parking deviation data of the automatic guided vehicle on the target path; determining whether the parking deviation data meets an adjustment condition; and acquiring the current parameters of the automatic guided transport vehicle, determining the adjustment parameters of the automatic guided transport vehicle according to the parking deviation data and the current parameters, and performing guide control on the automatic guided transport vehicle by using the adjustment parameters. According to the embodiment, the parking precision of the AGV can be improved, and the normal operation of the transportation operation is ensured.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a control method and a control device for an automatic guide transport vehicle.
Background
An Automated Guided Vehicle (AGV) is an Automated Vehicle that can be automatically guided and driven by a person. If the parking accuracy of the AGV is not enough, not only the service realization of the AGV scene can be influenced, but also the collision risk can be caused. In addition, if the parking accuracy of the AGV is not high, the parking may be deviated, which may cause other devices cooperating with the AGV to fail to operate, thereby making the transport job related to the AGV unable to be performed normally.
Disclosure of Invention
In view of this, embodiments of the present invention provide a control method and device for automatically guiding a transport vehicle, which can improve the parking accuracy of an AGV and ensure the normal operation of transport operations.
In a first aspect, an embodiment of the present invention provides a control method for automatically guiding a transport vehicle, including:
determining parking deviation data of the automatic guided vehicle on the target path;
determining whether the parking deviation data meets an adjustment condition;
and under the condition that the parking deviation data accords with the adjustment condition, acquiring the current parameters of the automatic guided transport vehicle, determining the adjustment parameters of the automatic guided transport vehicle according to the parking deviation data and the current parameters, and performing guide control on the automatic guided transport vehicle by using the adjustment parameters.
Optionally, the parking deviation data comprises: a parking accuracy value;
the determining that the parking deviation data meets an adjustment condition includes:
determining whether the parking precision value is outside a preset range;
the obtaining the current parameters of the automatic guided vehicle under the condition that the parking deviation data accords with the adjustment condition, and determining the adjustment parameters of the automatic guided vehicle according to the parking deviation data and the current parameters, wherein the method comprises the following steps:
under the condition that the parking precision value is out of the preset range, acquiring current parameters, vehicle information and path information of the target path of the automatic guided vehicle;
and determining an adjusting parameter of the automatic guided vehicle according to the parking precision value, the current parameter, the vehicle information and the path information.
Optionally, the parking deviation data comprises: parking deviation distribution data, the current parameters including: current gyroscope parameters, the adjustment parameters including: adjusting the parameters of the gyroscope;
the determining that the parking deviation data meets an adjustment condition includes:
determining whether the parking precision value is distributed in a skewed state or not according to the parking deviation distribution data;
the obtaining the current parameters of the automatic guided vehicle under the condition that the parking deviation data accords with the adjustment condition, and determining the adjustment parameters of the automatic guided vehicle according to the parking deviation data and the current parameters, wherein the method comprises the following steps:
under the condition that the parking precision values are distributed in a skewed state, acquiring path information of the target path;
determining the flatness of the area where the target path is located according to the parking deviation distribution data and the path information;
and acquiring the current gyroscope parameters of the automatic guided vehicle, and determining the adjusted gyroscope parameters of the automatic guided vehicle according to the flatness and the current gyroscope parameters.
After determining whether the parking deviation data meets the adjustment condition, the method further includes:
under the condition that the parking deviation data accords with the adjustment condition, acquiring a road surface image of an area where the target path is located;
detecting whether obstacle information exists in the road surface image;
and transmitting obstacle prompt information when the obstacle information exists in the road surface image.
Optionally, the parking deviation data comprises: the parking accuracy value is that the end point of the target path is a target end point;
the determining parking deviation data of the automatic guided vehicle on the target path comprises:
determining coordinate values of the automated guided vehicle for a plurality of actual end points of the target path;
and determining the parking precision value of the automatic guided vehicle according to the coordinate values of the actual end points and the coordinate value of the target end point.
Optionally, the parking deviation data comprises: parking deviation distribution data, wherein the end point of the target path is a target end point;
the determining parking deviation data of the automatic guided vehicle on the target path comprises:
determining coordinate values of the automated guided vehicle for a plurality of actual end points of the target path;
respectively calculating distance values between coordinate values of the actual end points and coordinate values of the target end points;
and determining parking deviation distribution data of the automatic guided vehicle according to the distance value.
Optionally, the coordinate value of the actual end point is obtained from log data of the automated guided vehicle.
In a second aspect, an embodiment of the present invention provides a control device for automatically guiding a transport vehicle, including:
the data determination module is used for determining parking deviation data of the automatic guided vehicle on the target path;
the condition determining module is used for determining whether the parking deviation data meets an adjusting condition;
and the strategy execution module is used for acquiring the current parameters of the automatic guided vehicle under the condition that the parking deviation data accords with the adjustment conditions, determining the adjustment parameters of the automatic guided vehicle according to the parking deviation data and the current parameters, and performing guide control on the automatic guided vehicle by using the adjustment parameters.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any of the embodiments described above.
In a fourth aspect, an embodiment of the present invention provides a computer-readable medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method of any one of the above embodiments.
One embodiment of the above invention has the following advantages or benefits: under the condition that the parking deviation data of the automatic guided transporting vehicle AGV accord with the adjustment conditions, according to the parking deviation data and the current AGV parameters, the AGV adjustment parameters which accord with the current conditions can be accurately determined, the AGV is controlled to run through the adjustment parameters, the parking precision of the AGV can be improved, and the normal operation of the transporting operation is ensured.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
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The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic view of a flow of a control method for automatically guiding a transport vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a target path provided by one embodiment of the present invention;
FIG. 3 is a schematic diagram of a coordinate comparison between a target endpoint and an actual endpoint according to an embodiment of the present invention;
FIG. 4 is a schematic illustration of a flow of another control method for an automated guided vehicle according to one embodiment of the present invention;
FIG. 5 is a schematic illustration of a flow of yet another method of controlling an automated guided vehicle according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a control device for an automatic guided vehicle according to an embodiment of the present invention;
fig. 7 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram illustrating a flow of a control method for automatically guiding a transportation vehicle according to an embodiment of the present invention. As shown in fig. 1, the method includes:
step 101: parking deviation data for an automated guided vehicle on a target path is determined.
The parking deviation data is related to parking deviation of the AGV automatically guiding the transport vehicle on the target path. The parking deviation data may include: parking accuracy values, parking deviation distribution data, etc. The parking deviation distribution data may include: the parking deviation kurtosis, the parking deviation skewness, the parking deviation coefficient and the like.
The parking deviation data can be determined by the following method, the AGV is controlled to run for multiple times from the starting point to the end point of the target path, and the coordinate values of the corresponding multiple actual end points are obtained; and determining parking deviation data of the automatic guided vehicle according to the coordinate values of the plurality of actual end points and the coordinate value of the target end point.
The parking precision value is used for representing a deviation degree value of an actual terminal of the AGV parking from a target terminal. Specifically, distance values between a plurality of actual end points and a target end point are respectively acquired, and an average value, a median, a mode, and the like of the plurality of distance values are taken as parking accuracy values. The method also can calculate the average value of the coordinate values of a plurality of actual end points, determine the coordinate of the expected end point of the AGV according to the average value of the coordinate values, calculate the difference value between the expected end point and the target end point, and determine the difference value as the parking accuracy value.
The parking deviation distribution data is used for representing the distribution characteristics of the parking precision values of the AGV. Specifically, distance values between a plurality of actual end points and a target end point are respectively obtained, and parking deviation distribution data are calculated according to the distance values.
The coordinate value of the actual end point can be obtained from AGV log data, high-precision equipment tracking the AGV movement track and the like. The coordinate value of the actual end point may also be determined by: firstly, two-dimensional codes accurately paved on the ground are arranged at the starting point and the ending point of a target path, and a fixed point position on an AGV body, such as a point A at the upper right corner of a frame, is selected. At the start of the target path, the coordinate difference of point a with respect to the start is recorded by a clock hammer. When the AGV travels to the terminal, the coordinate difference of the point A relative to the terminal is obtained through the clock hammer. And obtaining the actual coordinate of the whole AGV according to the coordinate difference of the point A relative to the starting point and the end point.
Step 102: it is determined whether the parking deviation data meets the adjustment condition.
The adjustment conditions can be set according to specific requirements. For example, the adjustment conditions may include: whether the parking accuracy value is greater than the maximum accuracy threshold value or not, whether the parking accuracy value is less than the minimum accuracy threshold value or not, and the like. If the parking deviation data accords with the adjustment conditions, the parking accuracy of the AGV is low, and the control parameters of the AGV need to be adjusted so as to reduce the parking error of the AGV.
Step 103: and under the condition that the parking deviation data accords with the adjustment condition, acquiring the current parameters of the automatic guided transport vehicle, determining the adjustment parameters of the automatic guided transport vehicle according to the parking deviation data and the current parameters, and performing guide control on the automatic guided transport vehicle by using the adjustment parameters.
The current parameters are related parameters for guiding and controlling the AGV currently, such as: current gyroscope parameters, current AGV load, current maximum speed value, current acceleration point, current deceleration point, current acceleration, current deceleration and the like.
The AGV parking control method has the advantages that the AGV is guided and controlled by the current parameters, the parking accuracy of the AGV is low, and the current parameters of the AGV need to be adjusted to reduce the parking error of the AGV. The adjustment parameters are related parameters for guiding and controlling the AGV after adjustment, such as: the gyroscope parameters after adjustment, the AGV loads after adjustment, the maximum speed value after adjustment, the acceleration point after adjustment, the deceleration point after adjustment, the acceleration after adjustment, the deceleration after adjustment and the like.
In the embodiment of the invention, under the condition that the parking deviation data of the AGV of the automatic guided transport vehicle meets the adjustment conditions, the AGV adjustment strategy meeting the current condition can be accurately determined according to the parking deviation data, so that the parking precision of the AGV is improved, and the risk that the transport operation cannot be normally carried out is reduced.
As described above, the parking deviation data for the AGVs can be read by the AGV controller log, the AGV motion trajectory can be tracked by the high-precision device, and the like. The method for reading the parking accuracy through the log of the AGV controller is a main means at the present stage, but the method for reading the log through the AGV controller cannot truly reflect the posture of the AGV, for example, the displacement caused by wheel slip cannot be recorded. The method for tracking the motion track of the AGV through the high-precision equipment is very high in cost, and the selling price of the high-precision equipment is millions; in addition, the apparatus can record the trajectory, but how to evaluate the parking accuracy using the trajectory data has not formed an evaluation criterion.
Based on this, the embodiment of the invention records the position data of the AGV through a method with stronger operability, calculates the parking deviation data of the AGV, and determines the adjustment parameters of the AGV by using the parking deviation data so as to improve the parking accuracy of the AGV. The method comprises the following steps:
step S01: on a standard path with the distance S, the distance S ensures that the acceleration can be finished to the maximum speed and then the deceleration is stopped to the terminal point. Fig. 2 is a schematic diagram of a target path according to an embodiment of the present invention. As shown in FIG. 2, the AGV is controlled to travel j times from the start point to the end point, the actual coordinates of the AGV are recorded each time, and the coordinates are recorded by measuring the X, Y distance from the AGV to the origin point.
Step S02: and after the AGV stops, recording the actual terminal position information of the AGV stopping. Fig. 3 is a schematic diagram of a coordinate comparison between a target endpoint and an actual endpoint according to an embodiment of the present invention. As shown in FIG. 3, the two points in FIG. 3 represent the target endpoint and the actual endpoint of the jth stop, respectively. Target endpoint coordinate is noted as (X)0，Y0，Z0) The actual end point coordinate of the AGV from the jth run is noted as (X)j，Yj，Zj). It should be noted that the coordinate Z represents the height of the slope, and in the case that the operation area is a horizontal plane, the coordinate Z may not be set or the user may sit on the horizontal planeThe index Z is set to 0.
Step S03: and calculating a parking precision evaluation index. After the AGV trolley is controlled to stop for j times, calculating the average value of the actual terminal coordinates of the AGV stopping:
the parking accuracy is set as A and is calculated by the following formula:
step S04: taking the following test data as an example, assuming that the target end point coordinate is X (1000.0, 1000.0, 0), after 30 parking tests (for convenience of explanation, 30 test data are taken), the actual end point coordinate of the jth AGV stop is recorded as (X)j，Yj，Zj) As shown in table 1 below.
TABLE 1 actual end point coordinates for AGV parking
| J | X | Y | Z |
| 1 | 1000.00 | 1000.00 | 0.00 |
| 2 | 1009.08 | 1002.73 | 0.00 |
| 3 | 1014.50 | 1009.01 | 0.00 |
| 4 | 1007.74 | 1016.09 | 0.00 |
| 5 | 1005.56 | 1012.88 | 0.00 |
| 6 | 1017.36 | 1007.25 | 0.00 |
| 7 | 1000.17 | 1003.59 | 0.00 |
| 8 | 1009.01 | 1014.81 | 0.00 |
| 9 | 1006.82 | 1014.87 | 0.00 |
| 10 | 1002.75 | 1000.90 | 0.00 |
| 11 | 998.19 | 1000.62 | 0.00 |
| 12 | 1013.20 | 1004.72 | 0.00 |
| 13 | 1016.54 | 1002.59 | 0.00 |
| 14 | 1003.17 | 1004.34 | 0.00 |
| 15 | 1001.57 | 1017.21 | 0.00 |
| 16 | 1006.97 | 1007.57 | 0.00 |
| 17 | 1013.86 | 1012.97 | 0.00 |
| 18 | 1008.70 | 1007.20 | 0.00 |
| 19 | 1010.93 | 1012.11 | 0.00 |
| 20 | 1000.68 | 1001.69 | 0.00 |
| 21 | 998.26 | 1007.83 | 0.00 |
| 22 | 1006.01 | 1016.09 | 0.00 |
| 23 | 998.76 | 1011.11 | 0.00 |
| 24 | 1016.03 | 1004.24 | 0.00 |
| 25 | 1015.70 | 1012.36 | 0.00 |
| 26 | 999.66 | 1010.13 | 0.00 |
| 27 | 1016.97 | 1012.14 | 0.00 |
| 28 | 1006.09 | 1010.30 | 0.00 |
| 29 | 1006.78 | 1013.38 | 0.00 |
| 30 | 1013.63 | 999.07 | 0.00 |
At this time, the parking accuracy of the AGVs can be determined to be 11.2, and for a plurality of AGVs, the value of a is measured for each of the AGVs in the same field, with a smaller value being better.
Step S05: and performing auxiliary evaluation by using the log data. The coordinate log data of the AGV encoder can deviate along with the change of a field, such as wheel train slip, but when the data volume is large, the evaluation basis with higher accuracy can be provided by utilizing the clutter and fault tolerance of large data. Constructing a parking accuracy data model comprising a code j, a parking coordinate (X)j，Yj，Zj) Offset by an angle alpha. Through a scheduling program, the AGV continuously and automatically repeats parking actions, and based on the conventional distance of 1 meter of the current storage AGV two-dimensional code and the combination of the normal speed of 1m/s, the data volume which can be obtained more than 5000 times per day is predicted. Calculating the coordinate deviation value of each point:
the skewness deviation α j for each point is recorded. The evaluation is aided by the distribution characteristics of Aj and α j.
Step S06: through data analysis of parking accuracy, data within the accuracy requirement range are in accordance with normal scores, the expectation of coordinate deviation Aj is 0, and the variance is +/-5 mm. But when the distribution characteristic of Aj is calculated, the expectation of Aj is not 0 (Aj is a large data volume statistical result, and the large data has fault tolerance and can exclude the influence of accidental factors), the method can be compensated by the following method:
step S061: the ground flatness defect is checked, if the inclination occurs, and the gradient is greater than the preset gradient, such as 3 degrees, and the problem needs to be solved by physically leveling the ground.
Step S062: when the ground slope is less than or equal to the preset slope, the problem is solved by adjusting the parameters of the gyroscope of the controller. The values of the X, Y coordinates of the AGV car are compensated for.
And in the running process of the AGV, the expected value is returned to 0 through the deviation rectifying function of the gyroscope. In general, parameter adjustment is not in place for one or two times, and correction can be continuously performed through a CNN (Convolutional Neural Networks) algorithm and a machine learning manner until a suitable parameter is found.
In an embodiment of the present invention, after determining whether the parking deviation data meets the adjustment condition, the method further includes: under the condition that the parking deviation data accords with the adjustment condition, acquiring a road surface image of an area where the target path is located; detecting whether obstacle information exists in the road surface image; when obstacle information exists in the road surface image, obstacle presenting information is transmitted. And sending prompt information under the condition of detecting that the obstacle exists in the road surface, so that related workers can clean the road surface in time, and the AGV can work normally.
In addition, if the friction force on the road surface can be reduced by the obstacles in the road surface, the AGV can slide wheels and the like in the parking process, the parking accuracy value of the AGV will become large, and the parking accuracy value of the AGV can be distributed in a positive deviation manner. Such obstacles may include: water, oil, etc. If the friction force of the road surface can be increased by the obstacles in the road surface, the AGV can be stopped quickly in the stopping process, the parking accuracy value of the AGV is reduced, and the parking accuracy value of the AGV can be distributed in a negative state. Such obstacles may include: stones, dropped goods, etc.
Fig. 4 is a schematic diagram of a flow of another control method for automatically guiding a transport vehicle according to an embodiment of the present invention. As shown in fig. 4, the method includes:
step 401: determining parking deviation data for an automated guided vehicle on a target path, the parking deviation data comprising: a parking accuracy value.
The parking precision value is used for representing a deviation degree value of an actual terminal of the AGV parking from a target terminal.
Step 402: it is determined whether the parking accuracy value is outside a preset range.
The preset range can be set according to specific requirements, such as the precision requirement of the AGV cooperative equipment. For example, the predetermined range may be-5 mm-5mm, -10mm-10mm, or-10 mm-15mm, etc.
Step 403: and under the condition that the parking precision value is out of the preset range, acquiring the vehicle information, the current parameters and the path information of the target path of the automatic guided transport vehicle.
The vehicle information is used to characterize the relevant information of the AGV vehicles. The vehicle information may include: load weight, self weight, remaining capacity, AGV performance parameters, etc.
The current parameters are related parameters for guiding and controlling the AGV in the current state, such as: current gyroscope parameters, current AGV load, current maximum speed value, current acceleration point, current deceleration point, current acceleration, current deceleration and the like.
The path information is used to characterize the relevant information of the target path. The path information may include: path length, path flatness, path shape, coefficient of friction, and the like.
Step 404: and determining an adjusting parameter of the automatic guided transport vehicle according to the parking precision value, the vehicle information, the current parameter and the path information, and performing guide control on the automatic guided transport vehicle by using the adjusting parameter.
The operation of the guided vehicle is controlled by the current parameters, so that the guided vehicle has larger deviation, and therefore, adjustment parameters need to be determined, and the guided vehicle is controlled by the adjustment parameters. The tuning parameters for the automated guided vehicle may be determined in a number of ways. The parking precision value, the vehicle information, the current parameters, the path information and other information can be prestored in the system, and the corresponding relation between the parking precision value, the vehicle information, the current parameters, the path information and other information and the adjustment parameters can be determined according to the corresponding relation.
Adjustment parameters may also be determined using an AGV parameter model. Specifically, information such as a parking accuracy value, vehicle information, current parameters, path information, and the like is determined. And performing numerical conversion on the parking precision value, the vehicle information, the current parameter, the path information and the like, taking the converted parking precision value, the vehicle information, the current parameter, the path information and the like as the input of an AGV parameter model, and determining the adjustment parameters of the automatic guided transport vehicle by using the output result of the AGV parameter model. The AGV parameter model can be obtained through the training of a prediction model.
In the embodiment of the invention, the parking precision value, the vehicle information, the current parameter, the path information and other information of the AGV are comprehensively considered, so that the determined adjusting parameter of the automatic guided transport vehicle is more accurate, and the risk caused by the fact that the AGV cannot be parked accurately can be reduced.
Fig. 5 is a schematic diagram illustrating a flow of a control method for automatically guiding a transportation vehicle according to an embodiment of the present invention. As shown in fig. 5, the method includes:
step 501: determining parking deviation data for an automated guided vehicle on a target path, the parking deviation data comprising: parking deviation distribution data.
The parking deviation distribution data is data that may be used to characterize the distribution of parking accuracy values for the AGVs. The parking deviation distribution data may include: the parking deviation kurtosis, the parking deviation skewness, the parking deviation coefficient, the mean value of the parking precision values, the mode of the parking precision values and the like.
Step 502: and determining whether the parking precision value is in the skewed distribution or not according to the parking deviation distribution data.
For example, when the parking deviation factor is within a range relatively close to 0, the parking accuracy value is normally distributed. And when the parking deviation coefficient is larger than the maximum coefficient threshold value, the parking precision value is in positive deviation distribution. And when the parking deviation coefficient is smaller than the minimum coefficient threshold value, the parking accuracy value is in a negative attitude distribution.
For another example, when the difference between the mean of the parking accuracy values and the mode of the parking accuracy values is greater than the maximum difference threshold, the parking accuracy values are in a positive skewed distribution. When the difference between the mean value of the parking accuracy values and the mode of the parking accuracy values is smaller than the minimum difference threshold value, the parking accuracy values are distributed in a negative attitude mode.
If there is certain roughness in the direction from the starting point of target route to the terminal point, this roughness also can be understood as the slope, if the roughness is the positive value, then the AGV dolly is because the gravitation effect, and the parking precision value of AGV will grow, and the parking precision value of AGV can present positive skewed state and distribute, and the roughness is bigger, and the difference is bigger between the actual terminal point that the AGV stopped and the target terminal point.
If the roughness is the negative value, then the AGV dolly is because the gravitation effect, and the AGV can stop at the in-process very fast stopping, and the parking accuracy value of AGV will diminish, and the parking accuracy value of AGV can present negative attitude distribution, and the absolute value of roughness is bigger, and the difference is bigger between the actual terminal point that the AGV stopped and the target terminal point. Therefore, the parking precision value of the AGV and the flatness of the area where the target path is located have an association relationship.
Step 503: under the condition that the parking precision values are distributed in a skewed state, acquiring path information of a target path; and determining the flatness of the area where the target path is located according to the parking deviation distribution data and the path information.
The path information is related information of the target path. The path information may include: path length, path shape, coefficient of friction, etc. The system can be preset with the corresponding relation or the calculation model of the parking deviation distribution data, the path information and the flatness, and the flatness of the area where the target path is located can be determined according to the corresponding relation or the calculation model. The obtaining of the vehicle information of the AGV may further include: load weight, self weight, speed information, etc. And calculating the flatness of the area where the target path is located according to the vehicle information, the path length and the parking deviation distribution data by using a mechanical principle.
Step 504: acquiring current gyroscope parameters of the automatic guided transport vehicle; and determining the adjusted gyroscope parameters of the automatic guided vehicle according to the flatness and the current gyroscope parameters, and performing guide control on the automatic guided vehicle by using the adjusted gyroscope parameters.
The adjusted gyroscope parameters of the automated guided vehicle may be determined in a variety of ways, such as empirical values, pre-set models, and the like. Through the parameter of the built-in gyroscope in the control AGV automobile body, adjustment AGV to a reasonable skew angle can solve because the regional problem that has the slope in target route place well, makes AGV stop in a reasonable position department, makes things convenient for AGV to accomplish work such as transportation or with other equipment cooperations.
Fig. 6 is a schematic structural diagram of a control device for an automatic guided vehicle according to an embodiment of the present invention. As shown in fig. 6, the apparatus includes:
a data determining module 601, configured to determine parking deviation data of an automatic guided vehicle on a target path;
a condition determining module 602, configured to determine whether the parking deviation data meets an adjustment condition;
and the strategy executing module 603 is configured to, when the parking deviation data meets the adjustment condition, obtain a current parameter of the automatic guided vehicle, determine an adjustment parameter of the automatic guided vehicle according to the parking deviation data and the current parameter, and perform guidance control on the automatic guided vehicle by using the adjustment parameter.
Optionally, the parking deviation data comprises: a parking accuracy value;
the condition determining module 602 is specifically configured to: determining whether the parking precision value is outside a preset range;
the policy execution module 603 is specifically configured to: under the condition that the parking precision value is out of the preset range, acquiring vehicle information, current parameters and path information of the target path of the automatic guided vehicle;
and determining an adjusting parameter of the automatic guided vehicle according to the parking precision value, the vehicle information, the current parameter and the path information.
Optionally, the parking deviation data comprises: parking deviation distribution data, the adjustment parameters including: a gyroscope parameter;
the condition determining module 602 is specifically configured to: determining whether the parking precision value is distributed in a skewed state or not according to the parking deviation distribution data;
the policy execution module 603 is specifically configured to: under the condition that the parking precision values are distributed in a skewed state, determining the flatness of the area where the target path is located according to the parking deviation distribution data;
and determining the gyroscope parameters of the automatic guided vehicle according to the flatness.
Optionally, the apparatus further comprises:
a prompt module 604, configured to obtain a road surface image of an area where the target path is located when the parking deviation data meets the adjustment condition;
detecting whether obstacle information exists in the road surface image;
and transmitting obstacle prompt information when the obstacle information exists in the road surface image.
Optionally, the parking deviation data comprises: the parking accuracy value is that the end point of the target path is a target end point;
the data determining module 601 is specifically configured to: determining coordinate values of the automated guided vehicle for a plurality of actual end points of the target path;
and determining the parking precision value of the automatic guided vehicle according to the coordinate values of the actual end points and the coordinate value of the target end point.
Optionally, the parking deviation data comprises: parking deviation distribution data, wherein the end point of the target path is a target end point;
the data determining module 601 is specifically configured to: determining coordinate values of the automated guided vehicle for a plurality of actual end points of the target path;
and determining parking deviation distribution data of the automatic guided vehicle according to the coordinate values of the actual end points and the coordinate value of the target end point.
Optionally, the coordinate value of the actual end point is obtained from log data of the automated guided vehicle.
An embodiment of the present invention provides an electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method of any of the embodiments described above.
Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 701.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: the device comprises a data determining module, a condition determining module and a strategy executing module. The names of these modules do not in some cases constitute a definition of the module itself, for example, the data determination module may also be described as a "module that determines parking deviation data of an automated guided vehicle on a target path".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise:
determining parking deviation data of the automatic guided vehicle on the target path;
determining whether the parking deviation data meets an adjustment condition;
and under the condition that the parking deviation data accords with the adjustment condition, acquiring the current parameters of the automatic guided transport vehicle, determining the adjustment parameters of the automatic guided transport vehicle according to the parking deviation data and the current parameters, and performing guide control on the automatic guided transport vehicle by using the adjustment parameters. .
According to the technical scheme of the embodiment of the invention, under the condition that the parking deviation data of the AGV of the automatic guided transport vehicle meets the adjustment conditions, the AGV adjustment strategy meeting the current condition can be accurately determined according to the parking deviation data, so that the parking precision of the AGV is improved, and the normal operation of the transport operation is ensured.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A control method for automatically guiding a transport vehicle, comprising:
determining parking deviation data of the automatic guided vehicle on the target path;
determining whether the parking deviation data meets an adjustment condition;
and under the condition that the parking deviation data accords with the adjustment condition, acquiring the current parameters of the automatic guided transport vehicle, determining the adjustment parameters of the automatic guided transport vehicle according to the parking deviation data and the current parameters, and performing guide control on the automatic guided transport vehicle by using the adjustment parameters.
2. The method of claim 1, wherein the parking deviation data comprises: a parking accuracy value;
the determining that the parking deviation data meets an adjustment condition includes:
determining whether the parking precision value is outside a preset range;
the obtaining the current parameters of the automatic guided vehicle under the condition that the parking deviation data accords with the adjustment condition, and determining the adjustment parameters of the automatic guided vehicle according to the parking deviation data and the current parameters, wherein the method comprises the following steps:
under the condition that the parking precision value is out of the preset range, acquiring current parameters, vehicle information and path information of the target path of the automatic guided vehicle;
and determining an adjusting parameter of the automatic guided vehicle according to the parking precision value, the current parameter, the vehicle information and the path information.
3. The method of claim 1, wherein the parking deviation data comprises: parking deviation distribution data, the current parameters including: current gyroscope parameters, the adjustment parameters including: adjusting the parameters of the gyroscope;
the determining that the parking deviation data meets an adjustment condition includes:
determining whether the parking precision value is distributed in a skewed state or not according to the parking deviation distribution data;
the obtaining the current parameters of the automatic guided vehicle under the condition that the parking deviation data accords with the adjustment condition, and determining the adjustment parameters of the automatic guided vehicle according to the parking deviation data and the current parameters, wherein the method comprises the following steps:
under the condition that the parking precision values are distributed in a skewed state, acquiring path information of the target path;
determining the flatness of the area where the target path is located according to the parking deviation distribution data and the path information;
and acquiring the current gyroscope parameters of the automatic guided vehicle, and determining the adjusted gyroscope parameters of the automatic guided vehicle according to the flatness and the current gyroscope parameters.
4. The method of claim 1, wherein after determining whether the parking deviation data meets an adjustment condition, further comprising:
under the condition that the parking deviation data accords with the adjustment condition, acquiring a road surface image of an area where the target path is located;
detecting whether obstacle information exists in the road surface image;
and transmitting obstacle prompt information when the obstacle information exists in the road surface image.
5. The method of claim 1, wherein the parking deviation data comprises: the parking accuracy value is that the end point of the target path is a target end point;
the determining parking deviation data of the automatic guided vehicle on the target path comprises:
determining coordinate values of the automated guided vehicle for a plurality of actual end points of the target path;
and determining the parking precision value of the automatic guided vehicle according to the coordinate values of the actual end points and the coordinate value of the target end point.
6. The method of claim 1, wherein the parking deviation data comprises: parking deviation distribution data, wherein the end point of the target path is a target end point;
the determining parking deviation data of the automatic guided vehicle on the target path comprises:
determining coordinate values of the automated guided vehicle for a plurality of actual end points of the target path;
respectively calculating distance values between coordinate values of the actual end points and coordinate values of the target end points;
and determining parking deviation distribution data of the automatic guided vehicle according to the distance value.
7. The method according to claim 5 or 6, wherein the coordinate value of the actual end point is acquired from log data of the automated guided vehicle.
8. A control device for automatically guiding a transport vehicle, comprising:
the data determination module is used for determining parking deviation data of the automatic guided vehicle on the target path;
the condition determining module is used for determining whether the parking deviation data meets an adjusting condition;
and the strategy execution module is used for acquiring the current parameters of the automatic guided vehicle under the condition that the parking deviation data accords with the adjustment conditions, determining the adjustment parameters of the automatic guided vehicle according to the parking deviation data and the current parameters, and performing guide control on the automatic guided vehicle by using the adjustment parameters.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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