CN115328120A - Control method and device for automatic guided vehicle, storage medium and automatic guided vehicle - Google Patents
Control method and device for automatic guided vehicle, storage medium and automatic guided vehicle Download PDFInfo
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Abstract
The present disclosure relates to mobile robotics, and in particular, to a method and an apparatus for controlling an automatic guided vehicle, a storage medium, and an automatic guided vehicle. The control method of the automatic guided vehicle comprises the following steps: establishing a local grid map, mapping an expected running path of an automatic guided vehicle into the local grid map, determining the position of the automatic guided vehicle in the local grid map at the beginning of each control period, determining an error integral term of the automatic guided vehicle according to the position of the automatic guided vehicle in the local grid map at the beginning of each control period and the expected running path mapped in the local grid map, calculating an offset position of the automatic guided vehicle in the local grid map according to the error integral term, determining an optimal path of the automatic guided vehicle according to the offset position, and controlling the automatic guided vehicle to run according to the optimal path in the current control period.
Description
Technical Field
The present disclosure relates to mobile robotics, and in particular, to a method and an apparatus for controlling an automatic guided vehicle, a storage medium, and an automatic guided vehicle.
Background
The intelligent mobile robot works in a complex environment and has the capabilities of autonomous planning, self-organization and self-adaptation. An AGV (automatic guided vehicle) based on laser SLAM (simultaneous localization and mapping) navigation is one of the intelligent mobile robots. The AGV based on the laser SLAM navigation is increasingly used in the logistics automation link of industrial production. The laser SLAM navigation mode has the advantages of good flexibility, high flexibility, convenience in deployment and the like, and is suitable for variable and complex electronic manufacturing scenes. Generally, a laser SLAM navigation method requires a user to establish an environment map by remotely controlling an AGV, then to set a virtual path on the environment map, and then to enable the AGV to automatically run along the set path, i.e., to track the path to complete a material transportation task. The virtual path is typically given in the form of a straight line, a circular arc or a high-order bezier curve.
Currently, sampling algorithms, such as DWA (Dynamic Window approach), are widely used for AGV path tracking control. Specifically, each grid of the local grid map is assigned with a score, the grid where the expected travel path is located is set to be 0 score, the grid farther away from the grid where the expected travel path is located is set to have a higher score, and the optimal path is determined by scoring the simulated path, for example, taking the score of the grid where the path end point is located as the path score. However, due to the resolution of the grid map, static errors may occur when the given path is closer to the simulated path, as shown in FIG. 1. The overall tracking effect of the path is not ideal, and a large angle adjustment amount can be generated when the path approaches the end point, so that the stopping process of the AGV is not smooth enough. The conventional solution is to improve the resolution precision of the grid, but the improvement of the resolution of the grid map can bring about a sharp increase of the overall calculation amount, thereby affecting the real-time performance of control and causing a time lag or an unstable phenomenon.
Disclosure of Invention
The embodiment of the disclosure provides a control method and device for an automatic guided vehicle, a storage medium and the automatic guided vehicle, which can solve the problem caused by static errors.
In a first aspect, an embodiment of the present application provides a control method for an automatic guided vehicle, including: establishing a local grid map, mapping an expected running path of an automatic guided vehicle into the local grid map, determining the position of the automatic guided vehicle in the local grid map at the beginning of each control period, determining an error integral term of the automatic guided vehicle according to the position of the automatic guided vehicle in the local grid map at the beginning of each control period and the expected running path mapped in the local grid map, calculating the offset position of the automatic guided vehicle in the local grid map according to the error integral term, determining the optimal path of the automatic guided vehicle according to the offset position, and controlling the automatic guided vehicle to run according to the optimal path in the current control period.
Optionally, the determining an error integral term of the automatic guided vehicle according to the position of the automatic guided vehicle in the partial grid map at the beginning of each control cycle and the desired travel path mapped in the partial grid map comprises: and respectively calculating an error integral term of each control period according to the position of the automatic guided vehicle in the local grid map at the beginning of each control period and the expected running path mapped in the local grid map, accumulating the error integral terms of each control period, and determining the error integral term of the automatic guided vehicle.
Optionally, the step of calculating the error integral term for each control cycle according to the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the desired travel path mapped in the local grid map comprises: determining the positions of two position points closest to a first position in the position point sequence according to a first position of each control cycle, wherein the first position is the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle, determining a second position of each control cycle according to the first position and the positions of the two position points closest to the first position, wherein the second position is the position closest to the first position on the expected operation path, determining the distance between the automatic guided vehicle and the expected operation path in each control cycle according to the first position of each control cycle and the second position of each control cycle, and respectively calculating an error integral term of each control cycle, wherein for any one control cycle in each control cycle, the error integral term of the control cycle is determined according to the distance between the automatic guided vehicle and the expected operation path in the control cycle, the distance between the automatic guided vehicle and the expected operation path in the previous control cycle in the control cycle, the second position of the control cycle and the second position of the previous control cycle in the control cycle.
Optionally, the calculating an offset position of the automatic guided vehicle in the local grid map according to the error integral term includes: and calculating the inclination angle of a connecting line of the first position and the second position in the current control period, and calculating the offset position of the automatic guided vehicle in the local grid map according to the first position in the current control period of the automatic guided vehicle, the distance between the automatic guided vehicle and the expected running path in the current control period, the error integral term of the automatic guided vehicle and the inclination angle.
Optionally, determining an optimal path of the automatic guided vehicle according to the offset position, and controlling the automatic guided vehicle to travel according to the optimal path in the current control cycle includes: according to a raster path algorithm, simulating a plurality of running paths of the automatic guided vehicle in the local raster map, wherein starting points of the running paths are the offset positions, determining an optimal path in the running paths, wherein the optimal path is the path with the highest proximity degree with the expected running path in the running paths, acquiring the linear velocity and the angular velocity of the automatic guided vehicle corresponding to the optimal path, and controlling the automatic guided vehicle to run according to the linear velocity and the angular velocity in the current control period.
Optionally, before the determining an error integral term for the automated guided vehicle from the position of the automated guided vehicle in the partial grid map at the start of each control cycle and the desired travel path mapped in the partial grid map, the method comprises: and determining a first distance, wherein the first distance is the distance between the position of the automatic guided vehicle in a local grid map and the expected running path mapped in the local grid map in the current control period, and when the first distance is greater than or equal to a preset dead zone error and less than or equal to a preset integral tolerance error, the step of determining an error integral term of the automatic guided vehicle according to the position of the automatic guided vehicle in the local grid map and the expected running path in each control period is executed.
Optionally, when the first distance is greater than or equal to the dead zone error and less than or equal to the integral tolerance error, the method includes: according to a raster path algorithm, simulating a plurality of running paths of the automatic guided vehicle in the local raster map, wherein starting points of the running paths are positions of the automatic guided vehicle in the local raster map when a current control period starts, determining an optimal path in the running paths, the optimal path is a path with the highest proximity degree with the expected running path in the running paths, acquiring the linear velocity and the angular velocity of the automatic guided vehicle corresponding to the optimal path, and controlling the automatic guided vehicle to run according to the linear velocity and the angular velocity in the current control period.
In a second aspect, an embodiment of the present application provides a control device for an automatic guided vehicle, including a processor and a memory, where the memory stores computer instructions, and the computer instructions, when executed by the processor, implement the steps of the method according to any one of the above first aspects.
In a third aspect, the present application provides a storage medium having stored thereon computer instructions, which when executed by a processor, implement the steps of the method according to any one of the above first aspects.
In a fourth aspect, the present application provides an automatic guided vehicle having a processor and a memory, where the memory stores computer instructions, and the computer instructions, when executed by the processor, implement the steps of the method of any one of the first aspect
The method has the advantages that the local grid map is built, the expected running path of the automatic guided vehicle is mapped into the local grid map, the offset position of the automatic guided vehicle in the local grid map is determined according to the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the expected running path, the optimal path of the automatic guided vehicle is determined according to the offset position, and the automatic guided vehicle is controlled to run according to the optimal path in the current control cycle. By the method, when the automatic guided vehicle has static errors, the offset position of the automatic guided vehicle is calculated according to the error integral, the optimal path of the automatic guided vehicle is determined again according to the offset position, the tracking precision of the path can be improved under the condition that the resolution ratio of the grid map is not improved, and the flexibility and the balance of the AGV reaching a target point are improved.
Other features of embodiments of the present disclosure and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
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The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the embodiments of the disclosure.
Fig. 1 shows a schematic diagram of a background art example.
Fig. 2 shows a flowchart of a control method of an automatic guided vehicle according to an embodiment of the present disclosure.
Fig. 3-5 show schematic diagrams of examples of control methods of an automatic guided vehicle according to embodiments of the present disclosure.
Fig. 6 shows a block diagram of a control device of an automatic guided vehicle according to an embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
The embodiment of the application discloses a control method of an automatic guided vehicle, which comprises the steps of S11-S15 as shown in FIG. 2.
And step S11, establishing a local grid map, and mapping the expected running path of the automatic guided vehicle into the local grid map.
In an example of the embodiment, a local grid map with a specified side length and a specified resolution may be constructed, for example, a local grid map with a side length of 3m and a grid resolution of 0.05m may be specified. Based on the SLAM algorithm, the position, i.e., coordinates, of the AGV in the local coordinate system of the local grid map can be obtained.
In one example of this embodiment, the desired travel path of the automated guided vehicle is the target travel path of the AGV specified by the user, and in one example, the desired travel path may be the target travel path specified in a sequence of location points. The global expected travel path can be truncated in the local grid map, and the coordinates of each position point in the local grid map are calculated and mapped into the local grid map in the mode of a position point sequence.
Step S12, the position of the automated guided vehicle in the local grid map at the beginning of each control cycle is determined.
In an example of this embodiment, the automatic guided vehicle may periodically receive a control command and move according to the command in this period, and the control period of the automatic guided vehicle may be flexibly set according to actual situations, for example, the control period may be set to 10ms. At the beginning of each control cycle, the position of the AGV in the local grid map, i.e., the coordinates, at the beginning of the current control cycle as well as all control cycles in the past, may be obtained based on the SLAM algorithm.
And step S13, determining an error integral term of the automatic guided vehicle according to the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the expected running path mapped in the local grid map.
In one example of this embodiment, determining an error integral term for an automatically guided vehicle based on a position of the automatically guided vehicle in a local grid map at the beginning of each control cycle and a desired travel path mapped in the local grid map includes: and respectively calculating an error integral term of each control period according to the position of the automatic guided vehicle in the local grid map at the beginning of each control period and the expected running path mapped in the local grid map, accumulating the error integral terms of each control period, and determining the error integral term of the automatic guided vehicle.
In one example of the present embodiment, the step of calculating the error integral term for each control cycle based on the position of the automated guided vehicle in the local grid map at the start of each control cycle and the desired travel path mapped in the local grid map in a position point sequence includes: and determining the positions of two position points which are closest to the first position in the position point sequence according to the first position of each control cycle, wherein the first position is the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle. And determining a second position of each control cycle according to the first position and the positions of two position points closest to the first position, wherein the second position is the position closest to the first position on the expected running path. And determining the distance between the automatic guided vehicle and the expected running path in each control period according to the first position and the second position in each control period. And respectively calculating an error integral term of each control period, wherein for any control period in each control period, the error integral term of the control period is determined according to the distance between the automatic guided vehicle and the expected running path in the control period, the distance between the automatic guided vehicle and the expected running path in the control period which is the previous control period, the second position of the control period and the second position in the control period which is the previous control period.
Specifically, as shown in fig. 3, the coordinate system of fig. 3 is the coordinate system of the local grid map, for clarity, fig. 3 does not show the grid of the local grid map, and in fig. 3, the point P is shown m 、P n For a point in the desired travel path,
the control cycle is the kth control cycle and the control cycle is the (k-1) th control cycle respectively, and the position of the vehicle is automatically guided. When the control period is the kth control period, the position of the automatic guided vehicle in the grid map is started according to the kth control period
Determining the distance in the sequence of position points of the expected travel path
Two nearest position points P m 、P n Determining the two nearest position points P m 、P n Then, can be based on
P m 、P n In the local grid map coordinate system
P m ＝(x m ,y m ) And P n ＝(x n ,y n ) Determining the position nearest to the automatic guided vehicle on the expected running path in the kth control period
Specifically, according to formula (1):
the solved (x, y) is the coordinates of the second position
After the second position of the k control period is determined, the distance between the automatic guided vehicle and the expected running path, namely the distance between the first position and the second position can be determined according to the formula (2)
Wherein
In the form of a function of the sign,
is according to
Is determined. Similarly, the distance of the automated guided vehicle from the desired travel path for each control cycle can be determined.
After the distance between the automatic guided vehicle and the expected running path in each control period is determined, the error integral term of each control period can be calculated respectively. For the error integral term of the k control period, it is possibleAutomatically guiding the distance between the vehicle and the expected running path according to the kth control period
The distance between the automatic guided vehicle and the expected running path in the (k-1) th control cycle
And a second position of a k-th control period
And second position of the (k-1) th control period
An error integral term for the kth control period is determined.
Specifically, the error integral term of each control cycle is the area between the travel path of the automated guided vehicle and the desired travel path in each control cycle. As shown in fig. 3, the error integral term of the k-th control period can be calculated by solving the trapezoidal area in fig. 3 equivalent to the error integral term of the k-th control period. Specifically, as can be seen from the formula (3), the height h of the trapezoid is k The second position of the k control period can be passed
And the second position of the (k-1) th control cycle
Is determined.
From equation (4), the k control period, the error integral term I, can be determined k And similarly, the error integral term of each control period can be calculated, and the error integral term of each period is accumulated to determine the error integral term I of the automatic guided vehicle.
And S14, calculating the offset position of the automatic guided vehicle in the local grid map according to the error integral term.
In an example of the embodiment, the offset position of the automatic guided vehicle in the local grid map is a simulated position offset from the current position of the automatic guided vehicle, and is not an actual position of the automatic guided vehicle. As shown in fig. 4.
In one example of this embodiment, calculating an offset position of the automated guided vehicle in the local grid map based on the error integral term includes: and calculating the inclination angle of a connecting line of the first position and the second position of the current control period, and calculating the offset position of the automatic guided vehicle in the local grid map according to the first position of the current control period of the automatic guided vehicle, the distance between the automatic guided vehicle and the expected running path in the current control period, and the error integral term and the inclination angle of the automatic guided vehicle.
Specifically, as shown in fig. 4, the inclination angle of the line connecting the first position and the second position is the angle between the line connecting the first position and the second position and the x-axis. May be specified according to the first position
And a second position
The relative relationship in the local coordinate system is determined by equation (5):
after the inclination angle theta of the connecting line of the first position and the second position is calculated, the automatic guided vehicle can be automatically driven according to the first position of the current control cycle of the automatic guided vehicle, the distance between the automatic guided vehicle and the expected running path in the current control cycle, and the self-drivingAnd calculating the offset position of the automatic guided vehicle in the local grid map by using the error integral term and the inclination angle of the movable guided vehicle. In particular, the abscissa of the position can be offset
The product of error integral term I and cosine value of inclination angle cos theta of the automatic guided vehicle and the abscissa of the first position
Wherein the sign of the product is the distance of the automatic lead vehicle from the desired travel path for the current control cycle
Is consistent, i.e. equation (6):
ordinate of offset position
The sum of the product of the error integral term I and the sine value of the inclination angle cos theta of the automatic guided vehicle and the ordinate of the first position
Wherein the sign of the product is equal to the distance between the automatic guided vehicle and the expected travel path in the current control period
Is consistent, i.e. formula (7):
and S15, determining the optimal path of the automatic guided vehicle according to the offset position, and controlling the automatic guided vehicle to run according to the optimal path in the current control period.
In the embodiment, the local grid map is established, the expected running path of the automatic guided vehicle is mapped into the local grid map, the offset position of the automatic guided vehicle in the local grid map is determined according to the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the expected running path, the optimal path of the automatic guided vehicle is determined according to the offset position, and the automatic guided vehicle is controlled to run according to the optimal path in the current control cycle. By the method, when the automatic guided vehicle has static errors, the offset position of the automatic guided vehicle can be calculated according to the error integral, the optimal path of the automatic guided vehicle can be determined again according to the offset position, the tracking precision of the path can be improved under the condition that the grid map resolution is not improved, and the flexibility and the balance of the AGV reaching a target point are improved.
In one example of this embodiment, determining an optimal path of the automatic guided vehicle according to the offset position, and controlling the automatic guided vehicle to travel according to the optimal path in the current control cycle includes: according to a raster path algorithm, simulating a plurality of running paths of the automatic guided vehicle in a local raster map, wherein starting points of the running paths are offset positions, determining an optimal path in the running paths, wherein the optimal path is the path with the highest approaching degree with an expected running path in the running paths, acquiring the linear velocity and the angular velocity of the automatic guided vehicle corresponding to the optimal path, and controlling the automatic guided vehicle to run according to the linear velocity and the angular velocity in the current control period.
In an example of the present embodiment, the raster path algorithm may be a DWA (Dynamic Window approach) algorithm. After the cheap position of the AGV is determined, the linear speed and the angular speed are sampled in an even sampling mode by taking the offset position as a starting point and the current speed and the acceleration of the AGV as reference. For example, the current linear velocity is 0.5m/s, the acceleration is 1m/s, and for example, a control period is 10ms, in the current control period, the linear velocity can reach a maximum value of 0.51m/s and a minimum value of 0.49m/s, and in the range of the maximum value and the minimum value, uniform sampling is performed, for example, 20 linear velocities are sampled, and similarly, 20 angular velocities are sampled. At this time, 400 groups of samples of linear velocity and angular velocity are obtained, and simulation can be performed on each group of samples, so that multiple cycles of running paths, such as 400 running paths of 3 cycles, can be simulated according to requirements.
The method includes the steps of determining an optimal path in a plurality of simulation paths according to the proximity degree of the simulation paths and the expected operation paths, specifically, assigning a score to each grid of a local grid map, taking a point occupied by the expected operation paths as a 0-point reference point, wherein the score is higher for grids farther away from the expected operation paths, and as shown in fig. 5, scoring each simulation path according to the grid score of a path end point and determining the optimal path.
It should be noted that, although the example shows that the simulated path may be scored according to the grid score of the path end point, those skilled in the art may understand that the present disclosure is not limited thereto, and specifically, the method of scoring the simulated path may be flexibly set by those skilled in the art according to the actual situation, for example, scoring may be performed through an evaluation function of the target point proximity, the path proximity, and the path alignment, and the like.
After the optimal path is determined, the linear velocity and the angular velocity corresponding to the AGV corresponding to the optimal path can be determined, and the automatic guided vehicle is controlled to run according to the linear velocity and the angular velocity in the current control period, that is, the automatic guided vehicle runs from the actual position of the automatic guided vehicle by using the actual running path which is the same as the optimal path. As shown in fig. 4.
In one example of this embodiment, before determining the error integral term for the automated guided vehicle based on the position of the automated guided vehicle in the partial grid map at the beginning of each control cycle and the desired travel path mapped in the partial grid map, the method includes: and determining a first distance, wherein the first distance is the distance between the position of the automatic guided vehicle in the local grid map in the current control period and the expected running path mapped in the local grid map, and when the first distance is greater than or equal to a preset dead zone error and less than or equal to a preset integral tolerance error, the step of determining an error integral term of the automatic guided vehicle according to the position of the automatic guided vehicle in each control period in the local grid map and the expected running path is executed.
In one example of the present embodiment, the preset dead zone error E B The dead zone error E can be determined according to the resolution of the grid map B Generally less than the resolution of the grid map, may be set to one fifth of the resolution, e.g., 0.05m for a grid map resolution, dead zone error E B May be set to 0.01m. Predetermined integral tolerance E I Can be determined according to the resolution of the grid map, and the integral tolerance error E I The integral tolerance E can be set to twice the resolution of the grid map, for example, 0.05m I May be 0.1m.
Before calculating the error integral term, the distance between the position of the automatic guided vehicle in the local grid map and the expected running path mapped in the local grid map in the current control period can be determined by the method
At a first distance
Greater than or equal to dead zone error E B And is less than the integral tolerance error E I Therefore, the influence of the static error can be eliminated through the mode, the tracking precision of the path is improved under the condition that the resolution ratio of the grid map is not improved, and the flexibility and the balance of the AGV in the process of reaching a target point are improved.
In one example of the present example, when the first distance is equal to or greater than the dead zone error and equal to or less than the integral tolerance error, the method includes: according to the raster path algorithm, simulating a plurality of running paths of the automatic guided vehicle in a local raster map, wherein the starting points of the running paths are the positions of the automatic guided vehicle in the local raster map when the current control period starts, determining the optimal path in the running paths, the optimal path is the path with the highest proximity degree with the expected running path in the running paths, the linear speed and the angular speed of the automatic guided vehicle corresponding to the optimal path are obtained, and the automatic guided vehicle is controlled to run according to the linear speed and the angular speed in the current control period.
In one example of this embodiment, the distance between the location of the automated guided vehicle in the local grid map and the desired travel path mapped in the local grid map during the current control cycle
Less than dead band error E B The method comprises the following steps of (1) proving that the actual running path of the AGV is very close to the expected running path; the distance between the position of the automatic guided vehicle in the local grid map and the expected travel path in the local grid map is mapped in the current control period
Greater than or equal to integral tolerance E I The actual position of the AGV is further from the expected travel path; when the two conditions occur, the influence of static errors is basically negligible, a plurality of running paths can be simulated through a raster path algorithm, such as a DWA algorithm, the optimal path is determined, and the vehicle runs according to the optimal path.
Referring to fig. 3, the embodiment provides a control device 100 for an automatic guided vehicle, including a processor 101 and a memory 102, where the memory 102 stores computer instructions, and the computer instructions, when executed by the processor 101, implement the processes of the above-mentioned control method embodiment for an automatic guided vehicle, and can achieve the same technical effects, and are not described herein again to avoid repetition.
The present embodiment provides a computer-readable storage medium, where an executable command is stored in the storage medium, and when the executable command is executed by a processor, the executable command implements the processes of the above embodiment of the control method for an automatic guided vehicle, and can achieve the same technical effects, and in order to avoid repetition, details are not repeated here.
The embodiment provides an automatic guided vehicle, which has a processor and a memory, wherein a computer instruction is stored in the memory, and when the computer instruction is executed by the processor, the computer instruction realizes each process of the control method embodiment of the automatic guided vehicle, and can achieve the same technical effect, and in order to avoid repetition, the description is omitted here.
It should be noted that all actions of acquiring signals, information or data in the present application are performed under the premise of complying with the corresponding data protection regulation policy of the country of the location and obtaining the authorization given by the corresponding device/account owner.
The embodiments in the present disclosure are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on differences from other embodiments. In particular, as for the device and apparatus embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points.
The foregoing description of specific embodiments of the present disclosure has been described. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Embodiments of the present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement aspects of embodiments of the disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations for embodiments of the present disclosure may be assembly instructions, instruction Set Architecture (ISA) instructions, machine related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry may execute computer-readable program instructions to implement aspects of embodiments of the present disclosure by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of embodiments of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
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 disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A control method of an automatic guided vehicle is characterized by comprising the following steps:
establishing a local grid map, and mapping an expected running path of the automatic guided vehicle into the local grid map;
determining a location of the automated guided vehicle in the local grid map at the beginning of each control cycle;
determining an error integral term of the automatic guided vehicle according to the position of the automatic guided vehicle in the local grid map at the beginning of each control cycle and the expected running path mapped in the local grid map;
calculating the offset position of the automatic guided vehicle in a local grid map according to the error integral term;
and determining the optimal path of the automatic guided vehicle according to the offset position, and controlling the automatic guided vehicle to run according to the optimal path in the current control period.
2. The control method of claim 1, wherein determining an error integral term for the automated guided vehicle based on the position of the automated guided vehicle in the local grid map at the beginning of each control cycle and the desired travel path mapped in the local grid map comprises:
respectively calculating an error integral term of each control period according to the position of the automatic guided vehicle in the local grid map at the beginning of each control period and the expected running path mapped in the local grid map;
and accumulating the error integral terms of each control period to determine the error integral terms of the automatic guided vehicle.
3. The control method according to claim 2, wherein the desired travel path is mapped in the partial grid map in a sequence of position points, and the calculating of the error integral term for each control cycle based on the position of the automated guided vehicle in the partial grid map at the start of each control cycle and the desired travel path mapped in the partial grid map, respectively, comprises:
determining the positions of two position points which are closest to the first position in the position point sequence according to the first position of each control period, wherein the first position is the position of the automatic guided vehicle in the local grid map at the beginning of each control period;
determining a second position of each control cycle according to the first position and the positions of two position points closest to the first position, wherein the second position is the position closest to the first position on the expected running path;
determining the distance between the automatic guided vehicle and the expected running path in each control period according to the first position of each control period and the second position of each control period;
and respectively calculating an error integral term of each control period, wherein for any one control period in each control period, the error integral term of the control period is determined according to the distance between the automatic guided vehicle and the expected running path in the control period, the distance between the automatic guided vehicle and the expected running path in the control period before the control period, the second position of the control period and the second position in the control period before the control period.
4. The control method of claim 3, wherein the calculating an offset position of the automated guided vehicle in a local grid map based on the error integral term comprises:
calculating a tilt angle of a line connecting the first position and the second position of the current control period;
and calculating the offset position of the automatic guided vehicle in the local grid map according to the first position of the current control cycle of the automatic guided vehicle, the distance between the automatic guided vehicle and the expected running path in the current control cycle, the error integral term of the automatic guided vehicle and the inclination angle.
5. The control method according to claim 1, wherein determining an optimal path of the automatic guided vehicle according to the offset position, and controlling the automatic guided vehicle to travel according to the optimal path in a current control cycle includes:
simulating a plurality of running paths of the automatic guided vehicle in the local grid map according to a grid path algorithm, wherein the starting points of the plurality of running paths are the offset positions;
determining an optimal path in the multiple operation paths, wherein the optimal path is the path with the highest proximity degree with the expected operation path in the multiple operation paths;
and acquiring the linear velocity and the angular velocity of the automatic guided vehicle corresponding to the optimal path, and controlling the automatic guided vehicle to run according to the linear velocity and the angular velocity in the current control period.
6. The control method of claim 1, wherein before determining the error integral term for the automated guided vehicle based on the position of the automated guided vehicle in the local grid map at the beginning of each control cycle and the desired travel path mapped in the local grid map, the method comprises:
determining a first distance, wherein the first distance is the distance between the position of the automatic guided vehicle in a local grid map and the expected running path mapped in the local grid map in the current control period;
and when the first distance is greater than or equal to a preset dead zone error and less than or equal to a preset integral tolerance error, executing a step of determining an error integral term of the automatic guided vehicle according to the position of the automatic guided vehicle in each control cycle in the local grid map and the expected running path.
7. The control method according to claim 6, characterized in that when the first distance is equal to or greater than the dead zone error and equal to or less than the integration tolerance error, the method includes:
simulating a plurality of running paths of the automatic guided vehicle in the local grid map according to a grid path algorithm, wherein starting points of the plurality of running paths are positions of the automatic guided vehicle in the local grid map when a current control cycle starts;
determining an optimal path in the multiple operation paths, wherein the optimal path is the path with the highest proximity degree with the expected operation path in the multiple operation paths;
and acquiring the linear velocity and the angular velocity of the automatic guided vehicle corresponding to the optimal path, and controlling the automatic guided vehicle to run according to the linear velocity and the angular velocity in the current control period.
8. A control device for an automated guided vehicle, comprising a processor and a memory, the memory having stored therein computer instructions which, when executed by the processor, implement the steps of the method of any one of claims 1-7.
9. A storage medium having stored thereon computer instructions which, when executed by a processor, carry out the steps of the method of any one of claims 1 to 7.
10. An automated guided vehicle having a processor and a memory, the memory having stored therein computer instructions which, when executed by the processor, implement the steps of the method of any of claims 1-7.
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| CN202210892061.8A CN115328120A (en) | 2022-07-27 | 2022-07-27 | Control method and device for automatic guided vehicle, storage medium and automatic guided vehicle |
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