CN113820985A - Risk level control method and device for robot - Google Patents
Risk level control method and device for robot Download PDFInfo
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- CN113820985A CN113820985A CN202111130043.8A CN202111130043A CN113820985A CN 113820985 A CN113820985 A CN 113820985A CN 202111130043 A CN202111130043 A CN 202111130043A CN 113820985 A CN113820985 A CN 113820985A
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention provides a method and a device for controlling risk level of a robot, comprising the steps of obtaining operation data of the robot; when determining that a sensor with an abnormal condition exists in the robot based on the operation data, determining a risk level corresponding to the sensor, wherein the risk level corresponding to the sensor is preset based on a deployment scene of the robot; and when the risk level is determined to be in the preset risk level control range, controlling the robot to execute corresponding operation based on the operation instruction corresponding to the risk level. In the scheme, a sensor with an abnormal condition is determined through real-time acquired running data of the robot; determining a risk level corresponding to the sensor; and finally, when the risk level is determined to be in the preset risk level control range, controlling the robot to execute corresponding operation by using the operation instruction corresponding to the risk level. The current robot can smoothly execute the next task, and the use experience of the user is improved.
Description
Technical Field
The invention relates to the technical field of robots, in particular to a method and a device for controlling risk levels of a robot.
Background
The robot is a machine device capable of automatically executing work according to instructions, and when the robot automatically executes work, the robot can automatically avoid obstacles through various sensors of the robot, such as a laser radar sensor, an IMU sensor, a camera sensor and the like.
Because the robot can be realized only by supplementing various sensors during execution, when any sensor has data loss, abnormality and the like, the robot cannot complete the following tasks, and the use experience of a user is poor.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for controlling a risk level of a robot, so as to solve a problem that a robot in the prior art cannot complete a subsequent task, thereby resulting in poor user experience.
In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
in an embodiment of the present invention, a method for controlling a risk level of a robot includes:
acquiring operation data of the robot, wherein the operation data at least comprises laser data acquired by a laser radar sensor, camera data acquired by a camera sensor, odometer data acquired by an odometer sensor, IMU data acquired by an inertial sensor IMU and ultrasonic data acquired by an ultrasonic sensor;
when determining that a sensor with an abnormal condition exists in the robot based on the operation data, determining a risk level corresponding to the sensor, wherein the risk level corresponding to the sensor is preset based on a deployment scene of the robot;
and when the risk level is determined to be in a preset risk level control range, controlling the robot to execute corresponding operation based on an operation instruction corresponding to the risk level.
Optionally, the sensor for determining that an abnormal condition exists in the robot based on the operation data includes:
comparing the difference between the timestamps of every two frames of data in the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data with a first preset threshold value respectively, and determining a sensor with abnormal conditions in the robot;
if the difference between the timestamps of every two frames of data in the laser data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is a laser radar sensor;
if the difference between the timestamps of every two frames of data in the camera data is greater than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is a camera sensor;
if the difference between the timestamps of every two frames of data in the odometry data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an odometry sensor;
if the difference between the time stamps of every two frames of data of the IMU data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an inertial sensor IMU;
and if the difference between the time stamps of every two frames of data in the ultrasonic data is greater than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an ultrasonic sensor.
Optionally, the sensor for determining that an abnormal condition exists in the robot based on the operation data includes:
determining whether data of a first special value or a second special value larger than a preset number exists in the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data, and determining that a sensor with an abnormal condition exists in the robot;
if data of a first special value or a second special value larger than a preset number exist in the laser data, determining that a sensor with an abnormal condition in the robot is a laser radar sensor;
if data of a first special value or a second special value larger than a preset number exists in the camera data, determining that a sensor with an abnormal condition in the robot is a camera sensor;
if data of a first special value or a second special value larger than a preset number exists in the odometer data, determining that a sensor with an abnormal condition in the robot is an odometer sensor;
if data of a first special value or a second special value larger than a preset number exist in the IMU data, determining that a sensor with an abnormal condition in the robot is an inertial sensor IMU;
and if the ultrasonic data contains data of the first special value or the second special value which is larger than the preset number, determining that the sensor with the abnormal condition in the robot is an ultrasonic sensor.
Optionally, the sensor for determining that an abnormal condition exists in the robot based on the operation data includes:
judging whether the mileage difference between adjacent frames in the mileage count is larger than a preset normal value or not based on the mileage count data;
and if so, determining that the sensor with the abnormal condition in the robot is an odometer sensor.
Optionally, the method further includes:
and when the risk level is determined not to be in a preset risk level control range, not controlling the robot to execute any protection operation.
A second aspect of an embodiment of the present invention shows a risk level control apparatus for a robot, the apparatus including:
the robot comprises an acquisition module, a data acquisition module and a data processing module, wherein the acquisition module is used for acquiring operation data of the robot, and the operation data at least comprises laser data acquired by a laser radar sensor, camera data acquired by a camera sensor, odometer data acquired by an odometer sensor, IMU data acquired by an inertial sensor IMU and ultrasonic data acquired by an ultrasonic sensor;
the determining module is used for determining a risk level corresponding to a sensor when the sensor with abnormal conditions exists in the robot based on the operation data, wherein the risk level corresponding to the sensor is preset based on a deployment scene of the robot;
and the execution module is used for controlling the robot to execute corresponding operation based on the operation instruction corresponding to the risk level when the risk level is determined to be in a preset risk level control range.
Optionally, the determining module is specifically configured to: comparing the difference between the timestamps of every two frames of data in the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data with a first preset threshold value respectively, and determining a sensor with abnormal conditions in the robot; if the difference between the timestamps of every two frames of data in the laser data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is a laser radar sensor; if the difference between the timestamps of every two frames of data in the camera data is greater than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is a camera sensor; if the difference between the timestamps of every two frames of data in the odometry data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an odometry sensor; if the difference between the time stamps of every two frames of data of the IMU data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an inertial sensor IMU; and if the difference between the time stamps of every two frames of data in the ultrasonic data is greater than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an ultrasonic sensor.
Optionally, the determining module is specifically configured to: determining whether data of a first special value or a second special value larger than a preset number exists in the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data, and determining that a sensor with an abnormal condition exists in the robot; if data of a first special value or a second special value larger than a preset number exist in the laser data, determining that a sensor with an abnormal condition in the robot is a laser radar sensor; if data of a first special value or a second special value larger than a preset number exists in the camera data, determining that a sensor with an abnormal condition in the robot is a camera sensor; if data of a first special value or a second special value larger than a preset number exists in the odometer data, determining that a sensor with an abnormal condition in the robot is an odometer sensor; if data of a first special value or a second special value larger than a preset number exist in the IMU data, determining that a sensor with an abnormal condition in the robot is an inertial sensor IMU; and if the ultrasonic data contains data of the first special value or the second special value which is larger than the preset number, determining that the sensor with the abnormal condition in the robot is an ultrasonic sensor.
Optionally, the determining module is specifically configured to: a sensor for determining the presence of an abnormal condition in the robot based on the operational data, comprising: judging whether the mileage difference between adjacent frames in the mileage count is larger than a preset normal value or not based on the mileage count data; and if so, determining that the sensor with the abnormal condition in the robot is an odometer sensor.
Optionally, the execution module is further configured to:
and when the risk level is determined not to be in a preset risk level control range, not controlling the robot to execute any protection operation.
Based on the method and the device for controlling the risk level of the robot provided by the embodiment of the invention, the method comprises the following steps: acquiring operation data of the robot, wherein the operation data at least comprises laser data acquired by a laser radar sensor, camera data acquired by a camera sensor, odometer data acquired by an odometer sensor, IMU data acquired by an inertial sensor IMU and ultrasonic data acquired by an ultrasonic sensor; when determining that a sensor with an abnormal condition exists in the robot based on the operation data, determining a risk level corresponding to the sensor, wherein the risk level corresponding to the sensor is preset based on a deployment scene of the robot; and when the risk level is determined to be in a preset risk level control range, controlling the robot to execute corresponding operation based on an operation instruction corresponding to the risk level. In the embodiment of the invention, the sensor with abnormal condition is determined by the real-time acquired running data of the robot; determining a risk level corresponding to the sensor; and finally, when the risk level is determined to be in a preset risk level control range, controlling the robot to execute corresponding operation by using an operation instruction corresponding to the risk level. The current robot can smoothly execute the next task, and the use experience of the user is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a schematic flow chart illustrating a method for controlling risk levels of an individual according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a risk level control device for a robot according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In this application, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
As can be seen from the background art, the robot needs multiple sensors to supplement each other during execution, and therefore when any one of the sensors is lost or abnormal, the robot cannot complete the following tasks, resulting in poor user experience.
The embodiment of the invention discloses a method and a device for controlling the risk level of a robot, which comprises the steps of firstly determining a sensor with an abnormal condition through real-time acquired running data of the robot; determining a risk level corresponding to the sensor; and finally, when the risk level is determined to be in a preset risk level control range, controlling the robot to execute corresponding operation by using an operation instruction corresponding to the risk level. The current robot can smoothly execute the next tasks, namely the fluency and the high efficiency of the robot tasks are improved, and the use experience of a user is further improved.
Referring to fig. 1, a schematic flow chart of a risk level control method for a robot according to an embodiment of the present invention is shown, where the method includes:
step S101: and acquiring the operation data of the robot.
In step S101, the operation data at least includes laser data acquired by a laser radar sensor, camera data acquired by a camera sensor, odometer data acquired by an odometer sensor, IMU data acquired by an inertial sensor IMU, and ultrasonic data acquired by an ultrasonic sensor.
Optionally, the robot acquires laser data acquired by the laser radar sensor, camera data acquired by the camera sensor, odometer data acquired by the odometer sensor, IMU data acquired by the inertial sensor IMU, and ultrasonic data acquired by the ultrasonic sensor in real time through a callback function.
In the process of implementing step S101 specifically, laser data, camera data, odometer data, IMU data, and ultrasound data in the robot are acquired.
It should be noted that the robot may not only obtain laser data, camera data, odometer data, IMU data, and ultrasound data, but also obtain data collected by other sensors in the robot, which is not limited in the embodiment of the present invention.
Step S102: and determining whether the robot has a sensor of an abnormal condition or not based on the operation data, if so, executing the step S103, and if not, returning to the step S101.
In the process of implementing step S102 specifically, the acquired operation data is analyzed to determine whether there is a sensor in an abnormal situation, and when it is determined that there is a sensor in an abnormal situation in the robot based on the operation data, step S103 is executed, where it is determined that there is no sensor in an abnormal situation in the robot based on the operation data, it indicates that the robot can perform a task safely, and whether there is an abnormal situation in the sensor in the robot is continuously detected, that is, the process returns to step S101.
It should be noted that the abnormal condition refers to a condition that data collected by the sensor is lost.
Step S103: a risk level corresponding to the sensor is determined.
In step S103, the risk level corresponding to the sensor is preset based on the deployment scenario of the robot.
Optionally, determining that the current robot needs to be deployed, such as: normal scenes, cross-floor scenes, falling scenes, glass scenes and other scenes requiring the robot to execute tasks. And then, setting the risk level of each sensor in the robot according to the environment condition, and configuring the self-defined risk level parameter level of the robot operating system ROS.
In the embodiment of the invention, the laser radar sensor and the odometer sensor are main sensors in all scenes of the robot, and if the laser radar sensor and the odometer sensor do not exist, the robot cannot normally complete tasks, so as long as a problem occurs, a risk control system can intervene immediately to ensure the safety of the robot, that is, the risk levels corresponding to the laser radar sensor and the odometer sensor are the highest levels.
For other sensors, the setting can be performed according to the actual scene used by the robot, such as: have the staircase or fall under the condition of scene, the trouble of camera and positioning offset all has safe danger to the robot, can regard camera sensor and nature sensor IMU as the risk level of being inferior to light radar sensor and odometer sensor this moment, can set up positioning offset or camera trouble into 4 grades, and positioning offset trouble sets up to 3 grades, and positioning offset and camera trouble set up to 2 grades, and positioning offset trouble sets up to 1 grade, and camera sensor trouble sets up to 0 grades etc..
In the process of implementing step S103 specifically, the risk level of each sensor of the robot is searched, and the risk level of the sensor in which an abnormal condition exists is determined.
Step S104: and judging whether the risk level is in a preset risk level control range, if so, executing the step S105, and otherwise, executing the step S106.
In the process of specifically implementing step S104, it is determined whether the current risk level is within a control range, that is, whether the current risk level is within a preset risk level control range, and when it is determined that the risk level is within the preset risk level control range, it indicates that the robot cannot normally complete the task, step S105 is performed, and when it is determined that the risk level is not within the preset risk level control range, it indicates that the robot can normally complete the task, and step S106 is performed.
It should be noted that the preset risk level control range is set empirically or according to actual situations, and the embodiment of the present invention is not limited thereto.
It should be further noted that, in different deployment scenarios, the preset risk level control ranges corresponding to different sensors are also different, that is, conditions for triggering the operation instruction corresponding to the risk level by different sensors in different deployment scenarios are different.
Step S105: and controlling the robot to execute corresponding operation based on the operation instruction corresponding to the risk level.
Optionally, after setting the risk level of each sensor of the robot, an operation instruction corresponding to each risk level needs to be set.
In the process of implementing step S105 specifically, an operation instruction corresponding to the risk level is searched for, and the robot is controlled to execute a corresponding operation by using the operation instruction.
Such as: and searching the operation instruction corresponding to the risk level as the zero speed forced control processing so as to execute the zero speed forced control processing on the robot by using the operation instruction corresponding to the established risk level.
Step S106: the robot is not controlled to perform any protection operations.
In the process of implementing step S106, the abnormal condition of the sensor may be temporarily ignored, and the robot is not controlled to perform any protection operation.
In the embodiment of the invention, the sensor with abnormal condition is determined by the real-time acquired running data of the robot; determining a risk level corresponding to the sensor; and finally, when the risk level is determined to be in a preset risk level control range, controlling the robot to execute corresponding operation by using an operation instruction corresponding to the risk level. The current robot can smoothly execute the next tasks, namely the fluency and the high efficiency of the robot tasks are improved, and the use experience of a user is further improved.
Based on the method for controlling the risk level of the robot shown in the above embodiment of the present invention, in the process of executing step S102 to determine whether there is a sensor in the robot in an abnormal condition based on the operation data, the method includes the following steps:
step S11: comparing the difference between the timestamps of every two frames of data in the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data with a first preset threshold respectively to determine a sensor with an abnormal condition in the robot, and executing step S12 if the difference between the timestamps of every two frames of data in the laser data is greater than the first preset threshold; if the difference between the timestamps of every two frames of data in the camera data is greater than a first preset threshold, executing step S13, and if the difference between the timestamps of every two frames of data in the odometry data is greater than the first preset threshold, executing step S14; if the difference between the time stamps of every two frames of data of the IMU data is greater than the first preset threshold, performing step S15; if the difference between the timestamps of every two frames of data in the ultrasound data is greater than the first preset threshold, step S16 is executed.
In the process of implementing the step S11 specifically, it is determined that the sensor is abnormal through the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data in the operation data, and if the difference between the timestamps of every two frames of data in the laser data is greater than a first preset threshold, the step S12 is executed; if the difference between the timestamps of every two frames of data in the camera data is greater than a first preset threshold, executing step S13, and if the difference between the timestamps of every two frames of data in the odometry data is greater than the first preset threshold, executing step S14; if the difference between the time stamps of every two frames of data of the IMU data is greater than the first preset threshold, performing step S15; if the difference between the timestamps of every two frames of data in the ultrasound data is greater than the first preset threshold, step S16 is executed.
The time stamp of each two frames of data refers to that the robot operating system ros stamps the time stamp on the data collected by the sensor according to the clock of the computer, so as to synchronize the data by using the time stamps among different data.
Step S12: and determining that the sensor in the robot with the abnormal condition is a laser radar sensor.
In the process of implementing step S12, the lidar sensor corresponding to the laser data in which the abnormal condition occurs is used as the sensor in which the abnormal condition exists.
Step S13: and determining that the sensor with the abnormal condition in the robot is a camera sensor.
In the process of implementing step S13, the camera sensor corresponding to the abnormal camera data is used as the abnormal sensor.
Step S14: the sensor that determines the presence of an abnormal condition in the robot is an odometer sensor.
In the process of specifically implementing step S14, the odometer sensor corresponding to the odometer data in which an abnormal condition has occurred is set as the sensor in which an abnormal condition exists.
Step S15: and determining that a sensor with an abnormal condition in the robot is an inertial sensor IMU.
In the process of specifically implementing step S15, the inertial sensor IMU corresponding to the IMU data in which the abnormal condition occurs is taken as the sensor in which the abnormal condition exists.
Step S16: and determining that the sensor in the robot with the abnormal condition is an ultrasonic sensor.
In the process of implementing step S16, the ultrasonic sensor corresponding to the ultrasonic data in which the abnormal condition occurs is used as the sensor in which the abnormal condition exists.
In the embodiment of the invention, the abnormal condition of the sensor is determined through the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data in the operation data; determining a risk level corresponding to the sensor; and finally, when the risk level is determined to be in a preset risk level control range, controlling the robot to execute corresponding operation by using an operation instruction corresponding to the risk level. The current robot can smoothly execute the next tasks, namely the fluency and the high efficiency of the robot tasks are improved, and the use experience of a user is further improved.
Based on the method for controlling the risk level of the robot shown in the above embodiment of the present invention, in the process of executing step S102 to determine whether there is a sensor in the robot in an abnormal condition based on the operation data, the method includes the following steps:
step S21: determining whether the laser data, the camera data, the odometry data, the IMU data and the ultrasonic data have data with a first special value or a second special value which is larger than a preset number, determining that a sensor with an abnormal condition exists in the robot, and executing a step S22 if the laser data have the data with the first special value or the second special value which is larger than the preset number; if the data of the first special value or the second special value which is larger than the preset number exists in the camera data, executing step S23; if the odometry data includes data of the first special value or the second special value which is larger than the preset number, executing step S24; if the IMU data includes data of the first special value or the second special value larger than the preset number, performing step S25; if there is data of the first special value or the second special value greater than the preset number in the ultrasound data, step S26 is executed.
In the process of specifically implementing the step S21, it is determined which sensor is abnormal through laser data, camera data, odometer data, IMU data and ultrasonic data in the operation data, and when it is determined that data larger than a preset number of first special values or second special values exists in the laser data, the step S22 is executed; when it is determined that data of the first special value or the second special value larger than the preset number exists in the camera data, executing step S23; when it is determined that data of the first special value or the second special value greater than the preset number exists in the odometry data, performing step S24; when it is determined that there is data of the first special value or the second special value greater than the preset number in the IMU data, performing step S25; upon determining that there is data of the first special value or the second special value greater than the preset number in the ultrasound data, step S26 is performed.
It should be noted that the first special value may be the information flow inf, and the second special value may be the special value nan.
Step S22: and determining that the sensor in the robot with the abnormal condition is a laser radar sensor.
In the process of implementing step S22, the lidar sensor corresponding to the laser data in which the abnormal condition occurs is used as the sensor in which the abnormal condition exists.
Step S23: and determining that the sensor with the abnormal condition in the robot is a camera sensor.
In the process of implementing step S23, the camera sensor corresponding to the abnormal camera data is used as the abnormal sensor.
Step S24: the sensor that determines the presence of an abnormal condition in the robot is an odometer sensor.
In the process of specifically implementing step S24, the odometer sensor corresponding to the odometer data in which an abnormal condition has occurred is set as the sensor in which an abnormal condition exists.
Step S25: and determining that a sensor with an abnormal condition in the robot is an inertial sensor IMU.
In the process of specifically implementing step S25, the inertial sensor IMU corresponding to the IMU data in which the abnormal condition occurs is taken as the sensor in which the abnormal condition exists.
Step S26: and determining that the sensor in the robot with the abnormal condition is an ultrasonic sensor.
In the process of implementing step S26, the ultrasonic sensor corresponding to the ultrasonic data in which the abnormal condition occurs is used as the sensor in which the abnormal condition exists.
In the embodiment of the invention, the abnormal condition of the sensor is determined through the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data in the operation data; determining a risk level corresponding to the sensor; and finally, when the risk level is determined to be in a preset risk level control range, controlling the robot to execute corresponding operation by using an operation instruction corresponding to the risk level. The current robot can smoothly execute the next tasks, namely the fluency and the high efficiency of the robot tasks are improved, and the use experience of a user is further improved.
Optionally, based on the risk level control method for a robot shown in the above embodiment, in addition to determining whether the odometer sensor is abnormal or not by using the manners of step S11 and step S21, the method may further determine whether the odometer sensor is abnormal or not by using a difference between mileage between adjacent frames in the odometer data, and specifically includes the following steps:
step S31: and judging whether the mileage difference between adjacent frames in the mileage count is larger than a preset normal value or not based on the mileage counting data, if so, executing the step S32, if not, indicating that the mileage sensor has no problem, returning to execute the step S11 or S21, and continuously predicting whether other sensors have abnormal conditions or not.
In the process of implementing step S31, it is determined whether the inter-frame mileage difference of the mileage count in the odometer data is greater than a preset normal value, if so, step S32 is executed, if not, it is determined that the odometer sensor is not in a problem, the process returns to step S11 or S21, and whether the other sensors are abnormal or not is continuously predicted.
It should be noted that the preset normal value refers to a value set empirically, and the comparison may be set according to actual situations, and the embodiment of the present invention is not limited thereto.
S32: the sensor that determines the presence of an abnormal condition in the robot is an odometer sensor.
In the specific implementation of step S32, the odometer sensor corresponding to the abnormal odometer data is set as the abnormal sensor.
In the embodiment of the invention, whether the odometer sensor is abnormal or not is determined by the mileage difference between adjacent frames of the mileage count in the odometer data; so as to determine the risk level corresponding to the sensor when determining that the sensor has a fault; and finally, when the risk level is determined to be in a preset risk level control range, controlling the robot to execute corresponding operation by using an operation instruction corresponding to the risk level. The current robot can smoothly execute the next tasks, namely the fluency and the high efficiency of the robot tasks are improved, and the use experience of a user is further improved.
In contrast to the method for controlling the risk level of the robot according to the embodiment of the present invention, the present invention also discloses a device for controlling the risk level of the robot, and as shown in fig. 2, the device is a schematic structural diagram of the device for controlling the risk level of the robot according to the embodiment of the present invention, and the device includes:
the acquisition module 201 is configured to acquire operation data of the robot, where the operation data at least includes laser data acquired by a laser radar sensor, camera data acquired by a camera sensor, odometer data acquired by an odometer sensor, IMU data acquired by an inertial sensor IMU, and ultrasonic data acquired by an ultrasonic sensor.
A determining module 202, configured to determine a risk level corresponding to a sensor when determining that there is an abnormal sensor in the robot based on the operation data, where the risk level corresponding to the sensor is preset based on a deployment scenario of the robot.
And the executing module 203 is configured to, when it is determined that the risk level is within the preset risk level control range, control the robot to execute a corresponding operation based on the operation instruction corresponding to the risk level.
Optionally, the executing module 203 is further configured to:
and when the risk level is determined not to be in a preset risk level control range, not controlling the robot to execute any protection operation.
It should be noted that, the specific principle and the implementation process of each unit in the risk level control device for a robot disclosed in the above embodiment of the present invention are the same as the risk level control method for a robot implemented in the above embodiment of the present invention, and reference may be made to corresponding parts in the risk level control method for a robot disclosed in the above embodiment of the present invention, and details are not repeated here.
In the embodiment of the invention, the sensor with abnormal condition is determined by the real-time acquired running data of the robot; determining a risk level corresponding to the sensor; and finally, when the risk level is determined to be in a preset risk level control range, controlling the robot to execute corresponding operation by using an operation instruction corresponding to the risk level. The current robot can smoothly execute the next tasks, namely the fluency and the high efficiency of the robot tasks are improved, and the use experience of a user is further improved.
Optionally, based on the risk level control apparatus for a robot shown in the foregoing embodiment of the present invention, the determining module 202 is specifically configured to: comparing the difference between the timestamps of every two frames of data in the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data with a first preset threshold value respectively, and determining a sensor with abnormal conditions in the robot; if the difference between the timestamps of every two frames of data in the laser data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is a laser radar sensor; if the difference between the timestamps of every two frames of data in the camera data is greater than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is a camera sensor; if the difference between the timestamps of every two frames of data in the odometry data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an odometry sensor; if the difference between the time stamps of every two frames of data of the IMU data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an inertial sensor IMU; and if the difference between the time stamps of every two frames of data in the ultrasonic data is greater than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an ultrasonic sensor.
Optionally, based on the risk level control apparatus for a robot shown in the foregoing embodiment of the present invention, the determining module 202 is specifically configured to: determining whether a first special value or a second special value larger than a preset number exists in the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data, and determining that a sensor with an abnormal condition exists in the robot; if the laser data contains data with a first special value or a second special value which is larger than a preset number, determining that a sensor with an abnormal condition in the robot is a laser radar sensor; if the data of the first special value or the second special value which is larger than the preset number exists in the camera data, determining that a sensor with an abnormal condition in the robot is a camera sensor; if the odometer data contains data of a first special value or a second special value which is more than a preset number, determining that a sensor with an abnormal condition in the robot is an odometer sensor; if data of a first special value or a second special value larger than a preset number exists in the IMU data, determining that a sensor with an abnormal condition in the robot is an inertial sensor IMU; and if the ultrasonic data contains data with the first special value or the second special value which is more than the preset number, determining that the sensor with the abnormal condition in the robot is an ultrasonic sensor.
Optionally, based on the risk level control apparatus for a robot shown in the foregoing embodiment of the present invention, the determining module 202 is specifically configured to: a sensor for determining the presence of an abnormal condition in the robot based on the operational data, comprising: judging whether the mileage difference between adjacent frames in the mileage count is larger than a preset normal value or not based on the mileage count data; and if so, determining that the sensor with the abnormal condition in the robot is an odometer sensor.
In the embodiment of the invention, the abnormal condition of the sensor is determined through the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data in the operation data; determining a risk level corresponding to the sensor; and finally, when the risk level is determined to be in a preset risk level control range, controlling the robot to execute corresponding operation by using an operation instruction corresponding to the risk level. The current robot can smoothly execute the next tasks, namely the fluency and the high efficiency of the robot tasks are improved, and the use experience of a user is further improved.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, the system or system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described system and system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. 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 invention.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for risk level control of a robot, the method comprising:
acquiring operation data of the robot, wherein the operation data at least comprises laser data acquired by a laser radar sensor, camera data acquired by a camera sensor, odometer data acquired by an odometer sensor, IMU data acquired by an inertial sensor IMU and ultrasonic data acquired by an ultrasonic sensor;
when determining that a sensor with an abnormal condition exists in the robot based on the operation data, determining a risk level corresponding to the sensor, wherein the risk level corresponding to the sensor is preset based on a deployment scene of the robot;
and when the risk level is determined to be in a preset risk level control range, controlling the robot to execute corresponding operation based on an operation instruction corresponding to the risk level.
2. The method of claim 1, wherein the determining a sensor of the presence of an abnormal condition in the robot based on the operational data comprises:
comparing the difference between the timestamps of every two frames of data in the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data with a first preset threshold value respectively, and determining a sensor with abnormal conditions in the robot;
if the difference between the timestamps of every two frames of data in the laser data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is a laser radar sensor;
if the difference between the timestamps of every two frames of data in the camera data is greater than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is a camera sensor;
if the difference between the timestamps of every two frames of data in the odometry data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an odometry sensor;
if the difference between the time stamps of every two frames of data of the IMU data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an inertial sensor IMU;
and if the difference between the time stamps of every two frames of data in the ultrasonic data is greater than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an ultrasonic sensor.
3. The method of claim 1, wherein determining a sensor of the robot that an abnormal condition exists based on the operational data comprises:
determining whether data of a first special value or a second special value larger than a preset number exists in the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data, and determining that a sensor with an abnormal condition exists in the robot;
if data of a first special value or a second special value larger than a preset number exist in the laser data, determining that a sensor with an abnormal condition in the robot is a laser radar sensor;
if data of a first special value or a second special value larger than a preset number exists in the camera data, determining that a sensor with an abnormal condition in the robot is a camera sensor;
if data of a first special value or a second special value larger than a preset number exists in the odometer data, determining that a sensor with an abnormal condition in the robot is an odometer sensor;
if data of a first special value or a second special value larger than a preset number exist in the IMU data, determining that a sensor with an abnormal condition in the robot is an inertial sensor IMU;
and if the ultrasonic data contains data of the first special value or the second special value which is larger than the preset number, determining that the sensor with the abnormal condition in the robot is an ultrasonic sensor.
4. The method of claim 1, wherein determining a sensor of the robot that an abnormal condition exists based on the operational data comprises:
judging whether the mileage difference between adjacent frames in the mileage count is larger than a preset normal value or not based on the mileage count data;
and if so, determining that the sensor with the abnormal condition in the robot is an odometer sensor.
5. The method of claim 1, further comprising:
and when the risk level is determined not to be in a preset risk level control range, not controlling the robot to execute any protection operation.
6. A risk level control apparatus of a robot, characterized in that the apparatus comprises:
the robot comprises an acquisition module, a data acquisition module and a data processing module, wherein the acquisition module is used for acquiring operation data of the robot, and the operation data at least comprises laser data acquired by a laser radar sensor, camera data acquired by a camera sensor, odometer data acquired by an odometer sensor, IMU data acquired by an inertial sensor IMU and ultrasonic data acquired by an ultrasonic sensor;
the determining module is used for determining a risk level corresponding to a sensor when the sensor with abnormal conditions exists in the robot based on the operation data, wherein the risk level corresponding to the sensor is preset based on a deployment scene of the robot;
and the execution module is used for controlling the robot to execute corresponding operation based on the operation instruction corresponding to the risk level when the risk level is determined to be in a preset risk level control range.
7. The apparatus of claim 6, wherein the determining module is specifically configured to: comparing the difference between the timestamps of every two frames of data in the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data with a first preset threshold value respectively, and determining a sensor with abnormal conditions in the robot; if the difference between the timestamps of every two frames of data in the laser data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is a laser radar sensor; if the difference between the timestamps of every two frames of data in the camera data is greater than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is a camera sensor; if the difference between the timestamps of every two frames of data in the odometry data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an odometry sensor; if the difference between the time stamps of every two frames of data of the IMU data is larger than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an inertial sensor IMU; and if the difference between the time stamps of every two frames of data in the ultrasonic data is greater than a first preset threshold value, determining that a sensor with an abnormal condition in the robot is an ultrasonic sensor.
8. The apparatus of claim 6, wherein the determining module is specifically configured to: determining whether data of a first special value or a second special value larger than a preset number exists in the laser data, the camera data, the odometer data, the IMU data and the ultrasonic data, and determining that a sensor with an abnormal condition exists in the robot; if data of a first special value or a second special value larger than a preset number exist in the laser data, determining that a sensor with an abnormal condition in the robot is a laser radar sensor; if data of a first special value or a second special value larger than a preset number exists in the camera data, determining that a sensor with an abnormal condition in the robot is a camera sensor; if data of a first special value or a second special value larger than a preset number exists in the odometer data, determining that a sensor with an abnormal condition in the robot is an odometer sensor; if data of a first special value or a second special value larger than a preset number exist in the IMU data, determining that a sensor with an abnormal condition in the robot is an inertial sensor IMU; and if the ultrasonic data contains data of the first special value or the second special value which is larger than the preset number, determining that the sensor with the abnormal condition in the robot is an ultrasonic sensor.
9. The apparatus of claim 6, wherein the determining module is specifically configured to: a sensor for determining the presence of an abnormal condition in the robot based on the operational data, comprising: judging whether the mileage difference between adjacent frames in the mileage count is larger than a preset normal value or not based on the mileage count data; and if so, determining that the sensor with the abnormal condition in the robot is an odometer sensor.
10. The apparatus of claim 6, wherein the execution module is further configured to:
and when the risk level is determined not to be in a preset risk level control range, not controlling the robot to execute any protection operation.
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| CN202111130043.8A CN113820985A (en) | 2021-09-26 | 2021-09-26 | Risk level control method and device for robot |
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| CN202111130043.8A CN113820985A (en) | 2021-09-26 | 2021-09-26 | Risk level control method and device for robot |
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Address after: Room 702, 7th floor, NO.67, Beisihuan West Road, Haidian District, Beijing 100080 Applicant after: Beijing Yunji Technology Co.,Ltd. Address before: Room 702, floor 7, No. 67, North Fourth Ring West Road, Haidian District, Beijing Applicant before: BEIJING YUNJI TECHNOLOGY Co.,Ltd. |
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Application publication date: 20211221 |