A robot task automatic correction method and device, electronic equipment and medium

Abstract

The present disclosure relates to the technical field of robot control, and provides a robot task automatic correction method and device, electronic equipment and medium. The method comprises: acquiring images around the robot; extracting image features of the images; acquiring a task map of the robot according to the image features; judging whether the current position of the robot matches the current map position; and replacing the current map with the task map if the current position of the robot does not match the current map position. The method can automatically correct the task map of the robot pushing the wrong charging pile, in a timely and effective manner, saves manpower, reduces maintenance costs, and avoids safety hazards after the robot pushes the wrong charging pile.

Description

Technical Field

[0001] This disclosure relates to the field of robot control technology, and in particular to a method, apparatus, electronic device, and medium for automatic correction of robot tasks. Background Technology

[0002] In practical applications of robots, multiple robots typically need to work simultaneously on-site. When a robot's battery is low, it needs to be pushed to a charging station for recharging. Since there is a one-to-one correspondence between a robot and a charging station, multiple charging stations exist on-site. With multiple robots and multiple charging stations, and since the robots are manually pushed to the charging stations, it's inevitable that robots will be pushed to the wrong station. After charging at the wrong station, when the robot tries to return to its previous task, it will become lost and continue executing the previously set task, posing not only a safety risk but also preventing it from performing the correct task. Therefore, solving the problem of task correction after a robot is pushed to the wrong station has become a key research focus in this industry.

[0003] In existing technologies, the robot task map that has been pushed to the wrong charging station is usually corrected and modified manually. This correction and modification method is not only untimely and still poses safety risks, but it is also time-consuming and labor-intensive. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and medium for automatic correction of robot tasks, including: acquiring images of the robot's surroundings; extracting image features from the images; acquiring a task map of the robot based on the image features; determining whether the robot's current position matches the current map position; if the robot's current position does not match the current map position, then replacing the current map with the task map. Compared with the prior art, this disclosure can automatically correct the task map of a robot that has pushed the wrong charging pile, which is timely and effective; it also saves manpower, reduces maintenance costs, and avoids safety hazards after the robot pushes the wrong charging pile.

[0005] To achieve the above objectives, the present disclosure adopts the following technical solution:

[0006] The first aspect of this disclosure provides a method for automatic correction of robot tasks, including:

[0007] Acquire images from around the robot.

[0008] Extract the image features from the image.

[0009] The robot's task map is obtained based on the image features.

[0010] Determine whether the robot's current position matches the current map position:

[0011] If the robot's current position does not match the current map position, then the current map is replaced with the task map.

[0012] Furthermore, the robot task automatic correction method, after replacing the current map with the task map if the robot's current position does not match the current map position, further includes:

[0013] The robot is monitored in real time. When the task being performed by the robot does not match the task map, the robot stops performing the current task and switches to the correct task map.

[0014] Furthermore, the robot task automatic correction method, after real-time monitoring of the robot and stopping the current task and switching to the correct task map when the robot's task does not match the task map, further includes:

[0015] When the robot is performing a task, it interacts with the current scene to find the correct task map.

[0016] Furthermore, the robot task automatic correction method, after the robot interacts with the current scene to find the correct task map while performing a task, further includes:

[0017] The robot is trained using a training dataset to ensure that it correctly performs all tasks.

[0018] A second aspect of this disclosure provides an automatic correction device for robot tasks, comprising:

[0019] The first acquisition unit is used to acquire images of the robot's surroundings.

[0020] An extraction unit is used to extract image features from the image.

[0021] The second acquisition unit is used to acquire the robot's task map based on the image features.

[0022] The judgment unit is used to determine whether the robot's current position matches the current map position:

[0023] If the robot's current position does not match the current map position, then the current map is replaced with the task map.

[0024] Furthermore, the aforementioned automatic robot task correction device also includes:

[0025] The monitoring unit is used to monitor the robot in real time. When the task being performed by the robot does not match the task map, it stops executing the current task and switches to the correct task map.

[0026] Furthermore, the aforementioned automatic robot task correction device also includes:

[0027] An interaction unit is used to interact with the current scene when the robot is performing a task in order to find the correct task map.

[0028] Furthermore, the aforementioned automatic robot task correction device also includes:

[0029] A training unit is used to train the robot using a training dataset to ensure that the robot correctly performs all tasks.

[0030] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0031] A fourth aspect of this disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0032] This disclosure provides a method, apparatus, electronic device, and medium for automatic correction of robot tasks, including: acquiring images of the robot's surroundings; extracting image features from the images; acquiring a task map of the robot based on the image features; determining whether the robot's current position matches the current map position; if the robot's current position does not match the current map position, then replacing the current map with the task map. Compared with the prior art, this disclosure can automatically correct the task map of a robot that has pushed the wrong charging pile, which is timely and effective; it also saves manpower, reduces maintenance costs, and avoids safety hazards after the robot pushes the wrong charging pile. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a schematic diagram of a robot task automatic correction method according to an embodiment of the present disclosure;

[0035] Figure 2 This is a schematic flowchart of another automatic robot task correction method in the embodiments of this disclosure.

[0036] Figure 3This is a schematic diagram of the composition structure of an automatic robot task correction device according to an embodiment of the present disclosure;

[0037] Figure 4 This is a schematic diagram of the composition of another automatic robot task correction device in an embodiment of this disclosure;

[0038] Figure 5 This is a schematic diagram of the composition structure of an electronic device for automatic correction of robot tasks in an embodiment of this disclosure. Detailed Implementation

[0039] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0040] Unless otherwise defined, all technical and scientific terms used in this disclosure have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs; the terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the terms “comprising” and “having” and any variations thereof in the description and claims of this disclosure and the foregoing drawings, but is intended to cover non-exclusive inclusion.

[0041] In the description of the embodiments of this disclosure, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary or secondary relationship of the indicated technical features. In the description of the embodiments of this disclosure, "a plurality of" means two or more, unless otherwise explicitly defined.

[0042] In the description of the embodiments of this disclosure, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0043] In the description of the embodiments of this disclosure, the term "multiple" refers to two or more (including two), similarly, "multiple groups" refers to two or more (including two groups), and "multiple pieces" refers to two or more (including two pieces).

[0044] In the description of the embodiments of this disclosure, the technical terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this disclosure and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this disclosure.

[0045] In the description of the embodiments of this disclosure, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this disclosure according to the specific circumstances.

[0046] Example 1

[0047] This disclosure provides an automatic correction method for robot tasks, such as... Figure 1 As shown, it includes:

[0048] 101. Obtain images of the robot's surroundings.

[0049] Specifically, a vision sensor is placed beneath the robot to capture the scene around it. This vision sensor, an instrument that uses optical elements and imaging devices to acquire image information of the external environment, is the direct source of information for the entire machine vision system. It mainly consists of one or two image sensors, sometimes accompanied by a light projector and other auxiliary equipment. The primary function of the vision sensor is to acquire sufficient raw images for the machine vision system to process. A wide variety of vision sensors are available on the market, such as laser scanners, linear and area CCD cameras, TV cameras, and digital cameras.

[0050] It should be noted that this embodiment does not limit the number of vision sensors. One rotatable vision sensor or multiple uniformly fixed vision sensors can be used, as long as they can capture images of the robot's surroundings. Furthermore, this embodiment does not limit the type of vision sensor. One or more of the above-mentioned laser scanners, linear and area CCD cameras, TV cameras, and digital cameras can be used.

[0051] 102. Extract the image features of the image.

[0052] The main image features extracted from an image include:

[0053] (1) Extracting image grayscale features: that is, converting the image into a grayscale image and extracting grayscale features.

[0054] (2) Extracting texture features of images: The main methods include gray-level difference statistics, gray-level co-occurrence matrix and gray-level gradient co-occurrence matrix.

[0055] It should be noted that when implementing this step, you can choose to extract the grayscale features of the image, or you can choose to extract the texture features of the image, or you can choose to extract both grayscale features and texture features of the image. This embodiment does not limit this, and the implementer can choose according to actual needs.

[0056] 103. Obtain the robot's task map based on the image features.

[0057] Specifically, based on image features, walls, pools, stairs, steps, etc., in the motion space are identified, the robot's walking direction is redefined, and the task map corresponding to the robot's location is then represented.

[0058] 104. Determine whether the robot's current position matches the current map position:

[0059] 1041. If the robot's current position does not match the current map position, then the current map is replaced with the task map.

[0060] When the robot's current location does not match the current map location, it can be determined that the robot has been pushed to the wrong charging station. At this time, the previous map is no longer usable and may cause the robot to get lost or collide. Therefore, the current map needs to be replaced with the task map mentioned above.

[0061] This disclosure provides an automatic robot task correction method, including: acquiring images of the robot's surroundings; extracting image features from the images; acquiring a task map of the robot based on the image features; determining whether the robot's current position matches the current map position; if the robot's current position does not match the current map position, then replacing the current map with the task map. Compared with the prior art, this disclosure can automatically correct the task map of a robot that has pushed the wrong charging pile, which is timely and effective; it also saves manpower, reduces maintenance costs, and avoids safety hazards after the robot pushes the wrong charging pile.

[0062] Example 2

[0063] This disclosure provides an automatic correction method for robot tasks, such as... Figure 2 As shown, it includes:

[0064] 201. Obtain images of the robot's surroundings.

[0065] Specifically, four digital cameras are evenly placed below the robot, enabling it to capture images from all sides.

[0066] 202. Extract the image features of the image.

[0067] Specifically, the gray-level co-occurrence matrix is ​​used to extract the texture features of the image:

[0068] The gray-level co-occurrence matrix (GLCM) reflects comprehensive information about an image's gray levels regarding direction, adjacent spacing, and magnitude of change. It forms the basis for analyzing local patterns and their arrangement rules within an image. The GLCM reveals the gray-level values ​​and their spatial distribution within an image.

[0069] The gray-level co-occurrence matrix is ​​defined as follows: Given a fixed moving distance (a,b), for any point (x,y) in the image and another point (x+a,y+b), whose gray-level values ​​are (i,j), let (x,y) move across the entire image to obtain different (i,j) values. Calculate the probability P of (i,j) appearing in the entire image. ij Thus, the gray-level co-occurrence matrix P is obtained.

[0070] 203. Obtain the robot's task map based on the image features.

[0071] The image features obtained in the above steps include various obstacles, such as pools, stairs, and steps. When obtaining the task map, these obstacles must be avoided.

[0072] 204. Determine whether the robot's current position matches the current map position:

[0073] 2041. If the robot's current position does not match the current map position, then the current map is replaced with the task map.

[0074] When the robot's current location does not match the current map location, it can be determined that the robot has been pushed to the wrong charging station. At this time, the previous map is no longer usable and may cause the robot to get lost or collide. Therefore, the current map must be replaced with the task map mentioned above.

[0075] 205. Monitor the robot in real time. When the task being performed by the robot does not match the task map, stop executing the current task and switch to the correct task map.

[0076] Specifically, an error monitoring device is installed inside the robot. When the robot performs a task that does not match the task map, it stops executing the current task and switches to the correct task map.

[0077] 206. When the robot is performing a task, it interacts with the current scene to find the correct task map.

[0078] Specifically, when a robot needs to perform a task, it interacts with the current scene, finds the task within that scene, locates the correct task map, and then executes the subsequent task.

[0079] 207. Train the robot using the training dataset to ensure that the robot correctly performs all tasks.

[0080] Specifically, by using a training dataset, a vision algorithm can be trained to ensure that the robot correctly performs all tasks. This vision algorithm is a mathematical model that attempts to help the computer understand images. It facilitates computation, allowing the computer to process specific types of image data.

[0081] This disclosure provides an automatic robot task correction method, including: acquiring images of the robot's surroundings; extracting image features from the images; acquiring a task map of the robot based on the image features; determining whether the robot's current position matches the current map position; if the robot's current position does not match the current map position, then replacing the current map with the task map. Compared with the prior art, this disclosure can automatically correct the task map of a robot that has pushed the wrong charging pile, which is timely and effective; it also saves manpower, reduces maintenance costs, and avoids safety hazards after the robot pushes the wrong charging pile.

[0082] Meanwhile, the embodiments of this disclosure monitor the robot in real time. When the robot's task does not match the task map, it will stop executing the current task and switch to the correct task map, further improving the degree of automation.

[0083] Furthermore, in this embodiment of the present disclosure, when the robot performs a task, it interacts with the current scene and can find the correct task map, thereby improving the robot's interaction efficiency.

[0084] Finally, embodiments of this disclosure use a training dataset to train the robot to ensure that the robot correctly performs all tasks, thereby more effectively correcting the robot's tasks.

[0085] Example 3

[0086] This disclosure provides an automatic correction device for robot tasks, such as... Figure 3 As shown, it includes:

[0087] The first acquisition unit 31 is used to acquire images of the robot's surroundings.

[0088] Extraction unit 32 is used to extract image features of the image.

[0089] The second acquisition unit 33 is used to acquire the robot's task map based on the image features.

[0090] Judgment unit 34 is used to determine whether the robot's current position matches the current map position:

[0091] If the robot's current position does not match the current map position, then the current map is replaced with the task map.

[0092] It should be noted that: for detailed descriptions of each component in this embodiment, please refer to the corresponding parts of other embodiments, and will not be repeated here.

[0093] This disclosure provides an automatic robot task correction device, comprising: a first acquisition unit acquiring images of the robot's surroundings; an extraction unit extracting image features from the images; a second acquisition unit acquiring a task map of the robot based on the image features; and a judgment unit determining whether the robot's current position matches the current map position. If the robot's current position does not match the current map position, the current map is replaced with the task map. Compared with the prior art, this disclosure can automatically correct the task map of a robot that has pushed the wrong charging pile, which is timely and effective. Moreover, it saves manpower, reduces maintenance costs, and avoids safety hazards after the robot pushes the wrong charging pile.

[0094] Example 4

[0095] This disclosure provides an automatic correction device for robot tasks, such as... Figure 4 As shown, it includes:

[0096] The first acquisition unit 41 is used to acquire images of the robot's surroundings.

[0097] Extraction unit 42 is used to extract image features of the image.

[0098] The second acquisition unit 43 is used to acquire the robot's task map based on the image features.

[0099] Judgment unit 44 is used to determine whether the robot's current position matches the current map position:

[0100] If the robot's current position does not match the current map position, then the current map is replaced with the task map.

[0101] Preferably, the automatic robot task correction device further includes:

[0102] The monitoring unit 45 is used to monitor the robot in real time. When the task performed by the robot does not match the task map, it stops executing the current task and switches to the correct task map.

[0103] Preferably, the automatic robot task correction device further includes:

[0104] The interaction unit 46 is used to interact with the current scene when the robot is performing a task in order to find the correct task map.

[0105] Preferably, the automatic robot task correction device further includes:

[0106] Training unit 47 is used to train the robot using a training dataset to ensure that the robot correctly performs all tasks.

[0107] It should be noted that: for detailed descriptions of each component in this embodiment, please refer to the corresponding parts of other embodiments, and will not be repeated here.

[0108] This disclosure provides an automatic robot task correction device, comprising: a first acquisition unit acquiring images of the robot's surroundings; an extraction unit extracting image features from the images; a second acquisition unit acquiring a task map of the robot based on the image features; and a judgment unit determining whether the robot's current position matches the current map position. If the robot's current position does not match the current map position, the current map is replaced with the task map. Compared with the prior art, this disclosure can automatically correct the task map of a robot that has pushed the wrong charging pile, which is timely and effective. Moreover, it saves manpower, reduces maintenance costs, and avoids safety hazards after the robot pushes the wrong charging pile.

[0109] Meanwhile, the monitoring unit of this embodiment monitors the robot in real time. When the robot's task does not match the task map, it will stop executing the current task and switch to the correct task map, further improving the degree of automation.

[0110] Furthermore, in this embodiment of the present disclosure, the interaction unit interacts with the current scene when the robot is performing a task, which enables it to find the correct task map and improves the robot's interaction efficiency.

[0111] Finally, the training unit of this embodiment uses a training dataset to train the robot to ensure that the robot correctly performs all tasks and to more effectively correct the robot's tasks.

[0112] Example 5

[0113] This disclosure provides an electronic device for automatic correction of robot tasks, such as... Figure 5As shown, it includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described automatic correction method for robot tasks.

[0114] The processing device (e.g., central processing unit, graphics processor, etc.) 51 can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 52 or the program loaded from the storage device 58 into the random access memory (RAM) 53. The RAM 53 also stores various programs and data required for the operation of the electronic device. The processing device 51, ROM 52, and RAM 53 are interconnected via a bus 54. An input / output (I / O) interface 55 is also connected to the bus 54.

[0115] Typically, the following devices can be connected to I / O interface 55: input devices 56 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 57 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 59. Communication device 59 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown. More or fewer devices may be implemented or have alternatively. Figure 5 Each box shown can represent a device or multiple devices as needed.

[0116] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 59, or installed from a storage device 58, or installed from a ROM 52. When the computer program is executed by the processing device 51, it performs the functions defined in the methods of some embodiments of this disclosure.

[0117] This disclosure provides an electronic device for automatic robot task correction, comprising: acquiring images of the robot's surroundings; extracting image features from the images; acquiring a task map of the robot based on the image features; determining whether the robot's current position matches the current map position; if the robot's current position does not match the current map position, then replacing the current map with the task map. Compared with the prior art, this disclosure can automatically correct the task map of a robot that has pushed the wrong charging pile, which is timely and effective; it also saves manpower, reduces maintenance costs, and avoids safety hazards after the robot pushes the wrong charging pile.

[0118] Meanwhile, the embodiments of this disclosure monitor the robot in real time. When the robot's task does not match the task map, it will stop executing the current task and switch to the correct task map, further improving the degree of automation.

[0119] Furthermore, in this embodiment of the present disclosure, when the robot performs a task, it interacts with the current scene and can find the correct task map, thereby improving the robot's interaction efficiency.

[0120] Finally, embodiments of this disclosure use a training dataset to train the robot to ensure that the robot correctly performs all tasks, thereby more effectively correcting the robot's tasks.

[0121] Example 6

[0122] This disclosure provides a computer-readable storage medium for automatic correction of robot tasks, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by one or more processors, it implements the above-described automatic correction method for robot tasks.

[0123] It should be noted that, in some embodiments of this disclosure, the computer-readable medium described above may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0124] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol, such as HTTP (Hypertext Transfer Protocol), and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0125] The aforementioned computer-readable medium may be included in the aforementioned device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquire images of the robot's surroundings; extract image features from the images; acquire a task map of the robot based on the image features; determine whether the robot's current position matches the current map position; if the robot's current position does not match the current map position, then replace the current map with the task map.

[0126] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0127] This disclosure provides a computer-readable storage medium for automatic robot task correction, comprising: acquiring images of the robot's surroundings; extracting image features from the images; acquiring a task map of the robot based on the image features; determining whether the robot's current position matches the current map position; if the robot's current position does not match the current map position, then replacing the current map with the task map. Compared with the prior art, this disclosure can automatically correct the task map of a robot that has pushed the wrong charging pile, which is timely and effective; it also saves manpower, reduces maintenance costs, and avoids safety hazards after the robot pushes the wrong charging pile.

[0128] Meanwhile, the embodiments of this disclosure monitor the robot in real time. When the robot's task does not match the task map, it will stop executing the current task and switch to the correct task map, further improving the degree of automation.

[0129] Furthermore, in this embodiment of the present disclosure, when the robot performs a task, it interacts with the current scene and can find the correct task map, thereby improving the robot's interaction efficiency.

[0130] Finally, embodiments of this disclosure use a training dataset to train the robot to ensure that the robot correctly performs all tasks, thereby more effectively correcting the robot's tasks.

[0131] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0132] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including a receiving unit, an information acquisition unit, a target determination unit, and a delivery unit. The names of these units do not necessarily limit the specific unit itself.

[0133] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0134] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

[0135] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention. In particular, as long as there is no structural conflict, the various technical features mentioned in the embodiments can be combined in any way. The present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for automatic correction of robot tasks, characterized in that, include: Acquire images of the robot's surroundings; Extract the image features of the image; The task map of the robot is obtained based on the image features; Determine whether the robot's current position matches the current map position: If the robot's current position does not match the current map position, then the current map is replaced with the task map; The feature is that, after replacing the current map with the task map if the robot's current position does not match the current map position, the method further includes: The robot is monitored in real time. When the task being performed by the robot does not match the task map, the robot stops performing the current task and switches to the correct task map. The feature is that, after monitoring the robot in real time and stopping the current task and switching to the correct task map when the robot's task does not match the task map, the method further includes: When the robot is performing a task, it interacts with the current scene to find the correct task map; After the robot interacts with the current scene to find the correct task map while performing a task, the method further includes: The robot is trained using a training dataset to ensure that it correctly performs all tasks.

2. An automatic correction device for robot tasks, characterized in that, include: The first acquisition unit is used to acquire images of the robot's surroundings; An extraction unit is used to extract image features from the image; The second acquisition unit is used to acquire the robot's task map based on the image features; The judgment unit is used to determine whether the robot's current position matches the current map position: If the robot's current position does not match the current map position, then the current map is replaced with the task map; Also includes: The monitoring unit is used to monitor the robot in real time. When the task being performed by the robot does not match the task map, it stops executing the current task and switches to the correct task map. Also includes: An interaction unit is used to interact with the current scene when the robot is performing a task in order to find the correct task map; Also includes: A training unit is used to train the robot using a training dataset to ensure that the robot correctly performs all tasks.

3. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in claim 1.

4. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in claim 1.

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