CN114407025B - Robot sudden stop mode automatic control method and device and robot - Google Patents
Robot sudden stop mode automatic control method and device and robot Download PDFInfo
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- CN114407025B CN114407025B CN202210314960.XA CN202210314960A CN114407025B CN 114407025 B CN114407025 B CN 114407025B CN 202210314960 A CN202210314960 A CN 202210314960A CN 114407025 B CN114407025 B CN 114407025B
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- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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Abstract
The disclosure relates to the technical field of robots, and provides a robot sudden stop mode automatic control method, a device and a robot. The method comprises the following steps: detecting whether the robot is blocked in moving forwards or not; if yes, identifying the source of the robot blockage, wherein the source comprises artificial blockage; acquiring the blocking time length and load data of the robot under the condition that the source is artificially blocked; and respectively comparing the blocked time length and the load data with respective corresponding preset thresholds, and automatically controlling the working state of the robot based on the comparison result, wherein the working state of the robot comprises an opening emergency stop mode. This is disclosed through detecting the source of the obstructed antecedent of robot, comes whether the obstructed source of intelligent recognition is the artificial implementation to control the automatic urgent mode that opens of robot, effectively solved unable intelligent effective handling in the automatic work of current robot and artificially promoted the problem, also can guarantee the operation safety of robot simultaneously.
Description
Technical Field
The disclosure relates to the technical field of robots, in particular to a robot emergency stop mode automatic control method and device and a robot.
Background
Mobile devices, such as robots for intelligent distribution services, have gradually advanced into people's daily lives, and the figure of the robot is visible in places such as intelligent buildings, hotels, and the like. Generally, when the robot is blocked or manually pushed, the robot can execute a self-avoiding function to actively avoid people or obstacles. In practical application, there is a special case that a robot needs to be pushed manually, and at this time, if the robot is pushed manually forcibly, the robot may be triggered to execute an automatic avoidance function, or a certain damage is caused to the robot, and the robot cannot be pushed manually. Therefore, the current robot cannot intelligently and effectively handle the situation of manually pushing the robot in the normal operation process.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a method and an apparatus for automatically controlling a robot in an emergency stop mode, and a robot, so as to solve the problem that in the prior art, a robot cannot be intelligently and effectively handled during a normal operation process of the robot.
In a first aspect of the embodiments of the present disclosure, a method for automatically controlling a robot in an emergency stop mode is provided, including: detecting whether the robot is blocked in moving forwards or not; if yes, identifying the source of the robot blockage, wherein the source comprises artificial blockage; acquiring the blocking time length and load data of the robot under the condition that the source is artificially blocked; and respectively comparing the blocked time length and the load data with respective corresponding preset thresholds, and automatically controlling the working state of the robot based on the comparison result, wherein the working state of the robot comprises an opening emergency stop mode.
In a second aspect of the embodiments of the present disclosure, there is provided an automatic control device for an emergency stop mode of a robot, including: a detection module configured to detect whether the robot is blocked from advancing; an identification module configured to identify a source of the robot obstruction if the robot obstruction is present, the source including an artificial obstruction; the acquisition module is configured to acquire the blocked time length and the load data of the robot under the condition that the source is artificially blocked; and the control module is used for comparing the blocked time and the load data with respective corresponding preset thresholds and automatically controlling the working state of the robot based on the comparison result, wherein the working state of the robot comprises an emergency stop mode.
In a third aspect of the disclosed embodiments, there is provided a robot comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above method when executing the computer program.
Compared with the prior art, the embodiment of the disclosure has the following beneficial effects: whether the blocked source is artificially pushed or not is intelligently identified by detecting the blocked source of the robot, so that the robot is controlled to automatically start an emergency mode, the problem that the robot cannot intelligently and effectively process artificial pushing in automatic work is effectively solved, and the running safety of the robot is ensured.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without inventive efforts.
FIG. 1 is a scenario diagram of an application scenario of an embodiment of the present disclosure;
fig. 2 is a schematic flow chart of an automatic control method for an emergency stop mode of a robot according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart diagram illustrating another method for automatically controlling an emergency stop mode of a robot according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an automatic control device in an emergency stop mode of a robot according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a robot according to an embodiment of the present disclosure.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, techniques, etc. in order to provide a thorough understanding of the disclosed embodiments. It will be apparent, however, to one skilled in the art that the present disclosure may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present disclosure with unnecessary detail.
An automatic control method and device for an emergency stop mode of a robot according to an embodiment of the present disclosure will be described in detail with reference to the accompanying drawings.
Fig. 1 is a scene schematic diagram of an application scenario of an embodiment of the present disclosure. The application scenario may include a robot 1, a server 2, and a network 3.
The robot 1 may include hardware and software. The hardware of the robot 1 may specifically include electronic devices such as a camera device, a mobile drive device, a network module, a processor, and a memory; the software of the robot 1 may include application software or a computer program installed in the electronic device as above. The software of the robot 1 may be implemented as a plurality of software or software modules, or may be implemented as a single software or software module, which is not limited by the embodiment of the present disclosure. Further, the robot 1 may have various applications installed thereon, such as an image recognition algorithm, a portrait detection algorithm, a data processing application, a search-type application, a shopping-type application, and the like.
The server 2 may be a server providing various services, for example, a backend server receiving a request sent by a robot with which a communication connection is established, and the backend server may receive and analyze the request sent by the robot and generate a processing result. The server 2 may be one server, may also be a server cluster composed of a plurality of servers, or may also be a cloud computing service center, which is not limited in this disclosure.
The server 2 may be hardware or software. When the server 2 is hardware, it may be various electronic devices that provide various services to the robot 1. When the server 2 is software, it may be multiple software or software modules providing various services for the robot 1, or may be a single software or software module providing various services for the robot 1, which is not limited in the embodiment of the present disclosure.
The network 3 may be a wired network connected by a coaxial cable, a twisted pair and an optical fiber, or may be a wireless network that can interconnect various Communication devices without wiring, for example, Bluetooth (Bluetooth), Near Field Communication (NFC), Infrared (Infrared), and the like, which is not limited in the embodiment of the present disclosure.
The user 4 may actively push the robot 1 during the normal operation of the robot 1 to assist the robot 1 to move forward or block the robot 1 from moving forward, and for the situation that the robot 1 detects that the robot 1 is blocked from moving forward, the robot 1 may determine the source of the blocked robot by using its own vision system or other detection means, for example, the robot 1 may obtain a surrounding image, then send the surrounding image to the server 2 via the network 3, request the server 2 to analyze the surrounding image, determine the source of the blocked robot 1, and return the analysis result to the robot 1; if the robot 1 is manually pushed in the blocked forward mode, the robot 1 can control to start the emergency stop mode, so that the robot 1 is prevented from automatically avoiding a pushed user, and the automatic control of the emergency stop mode of the robot is realized.
It should be noted that specific types, numbers, and combinations of the robot 1, the server 2, and the network 3 may be adjusted according to actual requirements of an application scenario, and the embodiment of the present disclosure does not limit this.
Fig. 2 is a flowchart of an automatic control method for an emergency stop mode of a robot according to an embodiment of the present disclosure. The robot scram mode automatic control method of fig. 2 may be performed by the robot 1 of fig. 1. As shown in fig. 2, the method for automatically controlling the robot in the emergency stop mode comprises the following steps:
s201, detecting whether the robot is blocked in forward movement;
s202, if yes, identifying a source of the robot blockage, wherein the source comprises artificial blockage;
s203, acquiring the blocking time and the load data of the robot under the condition that the source is artificially blocked;
and S204, respectively comparing the blocked time and the load data with respective corresponding preset thresholds, and automatically controlling the working state of the robot based on the comparison result, wherein the working state of the robot comprises an emergency stop mode.
Specifically, whether the robot is blocked in advancing can be determined by detecting whether tires of the robot are idle, skid, or displace, and the like, and certainly, the robot is blocked in advancing can be that the robot is subjected to power and cannot travel at the speed of the robot, or the robot is subjected to resistance and cannot travel normally.
The means of detecting the robot's obstructed forward travel is not unique in practice and the following examples will provide two possible solutions.
In some embodiments, identifying the source of the obstruction to the robot includes: acquiring a peripheral image of the robot; and identifying surrounding images based on an image identification algorithm, and determining the source of the robot blockage.
Specifically, the surrounding image of the robot may be one or more pictures, or may be a video, which is not limited by the embodiment of the present disclosure. The image recognition algorithm for recognizing the surrounding images can adopt a common algorithm in the field, or can train a machine learning algorithm by using image samples with marks in advance to learn and obtain a machine learning algorithm capable of automatically recognizing whether the robot is supposed to be pushed around, and the machine learning algorithm is arranged in the robot or other electronic equipment connected with a robot network to analyze and process the surrounding images of the robot to obtain a source of the robot with blocked forward movement.
According to the technical scheme provided by the embodiment of the disclosure, the surrounding images of the robot are identified through an image identification technology, so that whether the source of the robot with the forward motion resistance is manually pushed or not is automatically obtained, and the robot is very intelligent.
In some embodiments, identifying the source of the obstruction to the robot includes: acquiring laser scanning data around the robot based on a laser scanning device preset on the robot; and detecting the laser scanning data based on a human leg detection method, and determining the source of the robot blockage.
Specifically, if the robot is provided with a laser scanning device, the laser scanning data around the robot can be analyzed and processed by using a human leg laser detection technology, so as to automatically identify whether the source of the robot which is blocked from moving forwards is the situation of manual pushing. It should be noted that, since the laser detection technology for human legs belongs to the conventional technology in the field, the detailed description is omitted here.
According to the technical scheme provided by the embodiment of the disclosure, whether the source of the robot with the blocked forward motion is manually pushed or not can be rapidly and accurately detected through a human leg laser detection technology, and the detection accuracy of the source can be effectively improved.
In the embodiment of the disclosure, the blocked time period refers to the resistance duration from the detection of the robot blocking if the source is the human push, after the detection of the robot blocking, from the beginning of the detection of the robot blocking. The load data refers to load information fed back by a motor driving the robot to move forwards on the robot.
In some embodiments, the blocked time length and the load data are respectively compared with the preset threshold values corresponding to the blocked time length and the load data, and the working state of the robot is automatically controlled based on the comparison result, wherein the working state comprises the following steps: comparing the blocked time with a preset time threshold: under the condition that the blocked time length is greater than the time length threshold value, inputting the load data into a preset load judgment function to obtain an output value of the load judgment function; comparing the output value with a preset load threshold value; setting the working state of the robot to be in an emergency stop mode under the condition that the output value is larger than the load threshold value; and under the condition that the output value is less than or equal to the load threshold value, setting the working state of the robot to be in a closed emergency stop mode, wherein the working state of the robot comprises the closed emergency stop mode.
Specifically, the duration threshold and the amplitude threshold are the same, and both may be preset values set by the user according to empirical data, or may be new preset values obtained by the user adjusting the preset values according to some feedback of practical application, which is not limited in the embodiment of the present disclosure.
Specifically, the load judgment function includes using different load data of the robot as samples, and marking the samples based on blocked sources of the robot under the different load data, that is, the sources include artificial and non-artificial deductions, and performing machine learning by using the marked samples, for example, training a classification model algorithm by using the marked samples, to obtain a trained load judgment function. In addition, in order to improve the judgment accuracy of the load judgment function, the blocked time can be used as the characteristic of the sample, and the labeled sample added with the blocked time is used for machine learning, so that when the load data is judged by using the trained load judgment function, the blocked time and the load data are simultaneously input into the load judgment function, the output value of the load judgment function is a judgment result, namely the blocked source of the robot is artificially or non-artificially promoted, and a more accurate judgment result is obtained. It should be understood that the length of the blocking time is not a requirement for the training of the load judge function.
Further, the load data may be a load data sequence within a certain time, in some embodiments, before the load function is input to the load judgment function, normalization processing may be performed on the load data, then a maximum load value in the load data is obtained and input to the load judgment function, a corresponding output value is obtained at the output of the load judgment function, and since the output value is a judgment result at the maximum load value, the output value may be compared with a preset load threshold, and a control policy of the emergency stop mode of the robot may be determined according to the comparison result. In addition, in other embodiments, the load data sequence and the corresponding blocked time length may be directly input into the load judgment function, or the load data sequence is subjected to normalization processing and then input into the load judgment function together with the corresponding blocked time length, the load judgment function may perform automatic identification according to the load data sequence corresponding to the blocked time length to obtain an output value of the load judgment function, and according to the output value, it may be determined whether the blocked source is artificially pushed, so that a step of performing load threshold judgment is omitted, that is, in a case where the output value indicates that the blocked source is artificially pushed, the robot is controlled to start the emergency stop mode, and in a case where the output value indicates that the blocked source is not considered to be pushed, the robot is controlled to stop the emergency stop mode.
According to the technical scheme provided by the embodiment of the disclosure, aiming at the situation of manual pushing, in order to improve the misjudgment, the blocked time and the load data are used for judging at the same time, and only when the two data both exceed the preset threshold value, the robot is controlled to start the sudden stop mode, so that some mistouch behaviors in practice are avoided, and the accuracy of the control of the sudden stop mode of the robot is improved.
In addition, except for the condition that the blocked time and the load data simultaneously meet the condition, the robot is not controlled to start the scram mode under other conditions.
In some embodiments, after comparing the blocked time period with the preset time period threshold, the method further includes: and under the condition that the blocked time length is less than or equal to the time length threshold value, setting the working state of the robot to be in a closed emergency stop mode.
Specifically, the blocked time is judged to avoid the accidental touch of the user to the robot in practice, and in addition, the load data is judged to avoid the accidental behavior of the user to the robot in practice, for example, children have pushed some robots to the robot. Therefore, by means of the ordered judgment of the data, invalid human behavior can be effectively filtered.
In some embodiments, after detecting whether the robot is blocked from advancing, the method further includes: if not, the working state of the robot is set to be the emergency stop closing mode, wherein the working state of the robot comprises the emergency stop closing mode.
Specifically, under the condition that the robot does not detect forward blocking, the robot can be controlled to close the emergency mode, and the robot is returned to perform repeated or regular forward blocking detection, so that on one hand, the emergency mode of the robot with the emergency mode opened can be automatically closed after the manual pushing is finished, and the robot can be recovered to a normal running state; on the other hand, the monitoring of the forward blocking of the robot can also be kept.
In some embodiments, after identifying the source of the obstruction to the robot, further comprising: and in the case that the source is the non-artificial obstruction, setting the working state of the robot to be in an emergency stop closing mode, wherein the source comprises the non-artificial obstruction, and the working state of the robot comprises the emergency stop closing mode.
Specifically, since the embodiments of the present disclosure are only directed to the robot intelligent feedback mechanism under the human-induced behavior, the embodiments of the present disclosure all control the robot to turn off the emergency mode for the behavior that is not artificially induced.
In some embodiments, the robot emergency stop mode automatic control method is executed periodically after the working state of the robot is set to be the starting emergency stop mode.
Specifically, when the robot triggers the automatic emergency mode activation due to the human-induced behavior, the method steps shown in fig. 2 may be periodically executed after a period of time has elapsed, so that the robot is automatically controlled to close the emergency mode after the human-induced behavior is finished, thereby restoring the normal operation of the robot, avoiding the situation that the robot is subjected to long-term obstacle confrontation and standby, improving the utilization rate of energy, and making the robot more intelligent and energy-saving.
According to the technical scheme provided by the embodiment of the disclosure, whether the blocked source is manually pushed or not is intelligently identified by detecting the blocked source of the robot, so that the robot is controlled to automatically start an emergency mode, the problem that the robot cannot intelligently and effectively process manual pushing in automatic work of the existing robot is effectively solved, and the running safety of the robot can be ensured.
Referring to fig. 3 again, it is a flowchart of another method for automatically controlling the robot in the emergency stop mode according to the embodiment of the present disclosure, and the method may also be executed by the robot in the application scenario of fig. 1. As shown in fig. 3, the automatic control method for the robot emergency stop mode comprises the following steps:
S301, detecting whether the robot is blocked in moving forwards;
and S302, if not, setting the working state of the robot to be the emergency stop closing mode, wherein the working state of the robot comprises the emergency stop closing mode and the emergency stop opening mode, and returning to the step S301.
S303, if yes, identifying the source of the robot blockage, wherein the source comprises artificial blockage and non-artificial blockage;
s304, under the condition that the source is not blocked manually, setting the working state of the robot to be in an emergency stop closing mode;
s305, acquiring the blocking time and the load data of the robot under the condition that the source is artificially blocked;
s306, comparing the blocked time with a preset time threshold:
s307, under the condition that the blocked time length is less than or equal to the time length threshold value, setting the working state of the robot to be in a closed emergency stop mode;
s308, under the condition that the blocked time length is greater than the time length threshold, inputting the load data into a preset load judgment function to obtain an output value of the load judgment function;
s309, comparing the output value with a preset load threshold value;
s310, under the condition that the output value is larger than the load threshold value, setting the working state of the robot to be in an open emergency stop mode, and returning to the step S301 after a preset time;
And S311, setting the working state of the robot to be in an emergency stop closing mode under the condition that the output value is smaller than or equal to the load threshold value.
According to the technical scheme provided by the embodiment of the disclosure, whether the blocked source is artificially promoted or not is intelligently identified by detecting the blocked source of the robot, so that the robot is controlled to automatically start an emergency mode, the problem that the artificial promotion cannot be intelligently and effectively processed in the automatic work of the existing robot is effectively solved, and the running safety of the robot can be ensured.
All the above optional technical solutions may be combined arbitrarily to form optional embodiments of the present application, and are not described in detail herein.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 4 is a schematic diagram of an automatic control device for an emergency stop mode of a robot according to an embodiment of the present disclosure. As shown in fig. 4, the robot scram mode automatic control apparatus includes:
a detection module 401 configured to detect whether the robot is blocked from moving forward;
an identifying module 402 configured to identify a source of the robot obstruction if the robot obstruction is present, the source including an artificial obstruction;
An obtaining module 403 configured to obtain a blocked time length and load data of the robot in a case where the source is a human barrier;
and the control module 404 compares the blocked time length and the load data with respective preset thresholds, and automatically controls the working state of the robot based on the comparison result, wherein the working state of the robot comprises an open emergency stop mode.
According to the technical scheme provided by the embodiment of the disclosure, whether the blocked source is artificially pushed or not is intelligently identified by detecting the blocked source of the robot, so that the robot is controlled to automatically start an emergency mode, the problem that the artificial pushing cannot be effectively processed in the automatic work of the existing robot is effectively solved, and the running safety of the robot can be ensured.
In some embodiments, the detection module 401 in fig. 4 acquires an image of the surroundings of the robot; and identifying surrounding images based on an image identification algorithm, and determining the source of the robot blockage.
In some embodiments, the detection module 401 in fig. 4 acquires laser scanning data around the robot based on a laser scanning device preset on the robot; and detecting the laser scanning data based on a human leg detection method, and determining the source of the robot blockage.
In some embodiments, the control module 404 of FIG. 4 compares the blocked time period to a preset time period threshold: under the condition that the blocked time length is greater than the time length threshold value, inputting the load data into a preset load judgment function to obtain an output value of the load judgment function; comparing the output value with a preset load threshold value; setting the working state of the robot to be in an emergency stop mode under the condition that the output value is larger than the load threshold value; and under the condition that the output value is less than or equal to the load threshold value, setting the working state of the robot to be in a closed emergency stop mode, wherein the working state of the robot comprises the closed emergency stop mode.
In some embodiments, the control module 404 of fig. 4 sets the operating state of the robot to the off scram mode if the blocked time period is less than or equal to the time period threshold.
In some embodiments, after detecting whether the robot is blocked from forward traveling, if not, the control module 404 in fig. 4 sets the operating state of the robot to the off scram mode, where the operating state of the robot includes the off scram mode.
In some embodiments, after the control module 404 of fig. 4 identifies the source of the obstruction to the robot, the operating state of the robot is set to the off scram mode if the source is a non-human obstruction, wherein the source includes the non-human obstruction and the operating state of the robot includes the off scram mode.
In some embodiments, the robot emergency stop mode automatic control device further comprises:
and a cycle module 305 configured to periodically execute the robot emergency stop mode automatic control method after the working state of the robot is set to be the starting emergency stop mode.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present disclosure.
Fig. 5 is a schematic diagram of a robot 5 provided by an embodiment of the present disclosure. The robot 5 may be applied in the scenario of fig. 1, as shown in fig. 5, the robot 5 of this embodiment includes: a processor 501, a memory 502 and a computer program 503 stored in the memory 502 and operable on the processor 501. The steps in the various method embodiments described above are implemented when the processor 501 executes the computer program 503. Alternatively, the processor 501 implements the functions of the respective modules/units in the above-described respective apparatus embodiments when executing the computer program 503.
Illustratively, the computer program 503 may be partitioned into one or more modules/units, which are stored in the memory 502 and executed by the processor 501 to accomplish the present disclosure. One or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 503 in the robot 5.
The robot 5 may include, but is not limited to, a processor 501 and a memory 502. Those skilled in the art will appreciate that fig. 5 is merely an example of a robot 5 and does not constitute a limitation of robot 5 and may include more or fewer components than shown, or some components in combination, or different components, e.g., the robot may also include input and output devices, network access devices, buses, camera assemblies, laser assemblies, drive motors, etc.
The Processor 501 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 502 may be an internal storage unit of the robot 5, for example, a hard disk or a memory of the robot 5. The memory 502 may also be an external storage device of the robot 5, such as a plug-in hard disk provided on the robot 5, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 502 may also include both an internal storage unit and an external storage device of the robot 5. The memory 502 is used for storing computer programs and other programs and data required by the robot. The memory 502 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules, so as to perform all or part of the functions described above. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
In the embodiments provided in the present disclosure, it should be understood that the disclosed apparatus/robot and method may be implemented in other ways. For example, the above-described apparatus/robot embodiments are merely illustrative, and for example, a division of modules or units is merely a logical division, and there may be other divisions when actually implemented, and a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present disclosure may implement all or part of the flow of the method in the above embodiments, and may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the above methods and embodiments. The computer program may comprise computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain suitable additions or additions that may be required in accordance with legislative and patent practices within the jurisdiction, for example, in some jurisdictions, computer readable media may not include electrical carrier signals or telecommunications signals in accordance with legislative and patent practices.
The above examples are only intended to illustrate the technical solution of the present disclosure, not to limit it; although the present disclosure has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present disclosure, and are intended to be included within the scope of the present disclosure.
Claims (9)
1. A robot emergency stop mode automatic control method is applied to a robot of intelligent distribution service, and is characterized by comprising the following steps:
detecting whether the robot is blocked to move forward or not;
if yes, identifying the source of the robot blockage, wherein the source comprises artificial blockage and non-artificial blockage;
acquiring the blocking duration and the load data of the robot under the condition that the source is artificially blocked; respectively comparing the blocked time length and the load data with respective corresponding preset threshold values, and automatically controlling the working state of the robot based on the comparison result, wherein the blocked time length is compared with the preset time length threshold value: under the condition that the hindered duration is greater than the duration threshold, inputting the load data into a preset load judgment function to obtain an output value of the load judgment function; comparing the output value with a preset load threshold value; setting the working state of the robot to be in an emergency stop mode under the condition that the output value is larger than the load threshold value;
And under the condition that the source is not artificially blocked, setting the working state of the robot to be in an emergency stop closing mode, wherein the working state of the robot comprises an emergency stop opening mode and an emergency stop closing mode.
2. The method of claim 1, wherein the identifying a source of the robotic obstruction comprises:
acquiring a peripheral image of the robot;
and identifying the surrounding image based on an image identification algorithm, and determining the blocked source of the robot.
3. The method of claim 1, wherein the identifying a source of the robotic obstruction comprises:
acquiring laser scanning data around the robot based on a laser scanning device preset on the robot;
and detecting the laser scanning data based on a human leg detection method, and determining the source of the robot blockage.
4. The method of claim 1, wherein comparing the output value to a preset load threshold further comprises:
and setting the working state of the robot to be in an emergency stop closing mode under the condition that the output value is smaller than or equal to the load threshold value, wherein the working state of the robot comprises the emergency stop closing mode.
5. The method of claim 4, wherein after comparing the blocked time period to a preset time period threshold, further comprising:
and under the condition that the blocked time length is less than or equal to the time length threshold value, setting the working state of the robot to be in a closed emergency stop mode.
6. The method according to claim 1, wherein the detecting whether the robot is blocked in forward movement further comprises:
if not, setting the working state of the robot to be in an emergency stop closing mode, wherein the working state of the robot comprises the emergency stop closing mode.
7. The method according to any one of claims 1-6, characterized in that the robot emergency stop mode automatic control method is executed periodically after the working state of the robot is set to start emergency stop mode.
8. The utility model provides a robot scram mode automatic control device, is applied to the robot of intelligent delivery service, its characterized in that includes:
a detection module configured to detect whether the robot is blocked from advancing;
an identification module configured to identify sources of the robot obstruction if yes, wherein the sources comprise an artificial obstruction and a non-artificial obstruction;
The acquisition module is configured to acquire the blocked time length and the load data of the robot under the condition that the source is a man-made block;
the control module is used for respectively comparing the blocked time length and the load data with respective corresponding preset thresholds and automatically controlling the working state of the robot based on the comparison result, wherein the blocked time length is compared with the preset time length threshold: under the condition that the blocked time length is larger than the time length threshold value, inputting the load data into a preset load judgment function to obtain an output value of the load judgment function; comparing the output value with a preset load threshold value; setting the working state of the robot to be in an emergency stop mode under the condition that the output value is larger than the load threshold value; and under the condition that the source is not blocked by human, setting the working state of the robot to be in an emergency stop closing mode, wherein the working state of the robot comprises an emergency stop opening mode and an emergency stop closing mode.
9. A robot comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
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