CN112356075B - Test method and device, test equipment and storage medium - Google Patents
Test method and device, test equipment and storage medium Download PDFInfo
- Publication number
- CN112356075B CN112356075B CN202011360840.0A CN202011360840A CN112356075B CN 112356075 B CN112356075 B CN 112356075B CN 202011360840 A CN202011360840 A CN 202011360840A CN 112356075 B CN112356075 B CN 112356075B
- Authority
- CN
- China
- Prior art keywords
- robot
- tested
- preset
- test
- motion state
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
- B25J19/0095—Means or methods for testing manipulators
Abstract
The embodiment of the disclosure relates to a test method and device, test equipment and a storage medium. When a tested robot moves on a test platform, acquiring the motion state of the tested robot; and determining a stability result of the tested robot based on the motion state. The stability of the tested robot can be determined by positioning the motion position of the tested robot, and the test efficiency and the test experience are favorably improved.
Description
Technical Field
The present disclosure relates to the field of testing technologies, and in particular, to a testing method and apparatus, a testing device, and a storage medium.
Background
At present, the robot needs to be subjected to a stability test before leaving the factory. The existing test is to set a glass surface at an angle of 10 ° to the horizontal and then guide the robot to move down the glass surface at an angle of 10 ° to the horizontal and measure its speed again. When the speed of the robot does not exceed more than 10% of the initially measured speed, the stability of the robot is satisfied.
However, the above-described test method has a problem that only acceleration acting on the service robot without brake when going downhill is considered. In practical application, under dynamic conditions, the service robot can brake and limit the speed when the robot goes downhill, for example, the sweeping robot can automatically decelerate when going downhill, the electric balance car can move back to the center when going downhill due to human operation, so as to slow down the speed, and the like, so that it is meaningless that the test speed is not more than 10% higher than the initial measurement speed.
Disclosure of Invention
The present disclosure provides a testing method and apparatus, a testing device, and a storage medium, so as to solve the deficiencies of the related art.
According to a first aspect of embodiments of the present disclosure, there is provided a test method, including:
when a tested robot moves on a test platform, acquiring the motion state of the tested robot;
and determining a stability result of the tested robot based on the motion state.
Optionally, the surface of the test platform is provided with a positioning device for determining the movement position of the tested robot in the test process.
Optionally, the positioning device includes a preset track pattern, and the preset track pattern includes tracks of horizontal lines, vertical lines, and oblique lines;
the transverse line is parallel to the intersection line of the test platform and the horizontal plane;
the vertical line is perpendicular to the intersecting line;
the included angle between the oblique line and the intersecting line is greater than 0 degree and less than 90 degrees.
Optionally, when the test platform is in a static state and/or a motion state, the preset trajectory graph includes a triangle, a trapezoid, an irregular polygon, and a reciprocating straight line graph.
Optionally, acquiring a motion state of the robot under test includes:
acquiring the real-time position of the robot to be tested;
Acquiring the real-time distance between the real-time position and the preset track graph;
when the real-time distance is larger than or equal to a preset distance threshold, determining that the motion state of the robot to be tested is an abnormal state; or when the real-time distance is smaller than or equal to the preset distance threshold, determining that the motion state of the robot to be tested is a normal state.
Optionally, acquiring a motion state of the robot under test includes:
acquiring an actual motion track of the robot to be detected;
obtaining a deflection angle between the actual motion track and the preset track graph;
when the deflection angle is larger than or equal to a preset angle threshold value, determining that the motion state of the tested robot is an abnormal state; or when the deflection angle is smaller than or equal to the preset angle threshold, determining that the motion state of the tested robot is a normal state.
Optionally, when the number of motion states is a group, determining a stability result of the robot under test based on the motion states includes:
when the motion state is an abnormal state, determining that the stability result of the tested robot is unstable; when the motion state is a normal state, determining that the stability result of the tested robot is stable;
Alternatively, the first and second electrodes may be,
when the number of the motion states is multiple groups, determining a stability result of the tested robot based on the motion states, including:
counting the proportion of the normal state according to the plurality of groups of motion states;
when the proportion exceeds a preset proportion, determining that the stability result of the tested robot is stable;
alternatively, the first and second electrodes may be,
and when the proportion is less than or equal to the preset proportion, determining that the stability result of the tested robot is unstable.
According to a second aspect of the embodiments of the present disclosure, there is provided a test apparatus including:
the motion state acquisition module is used for acquiring the motion state of the tested robot when the tested robot moves on the test platform;
and the stable result acquisition module is used for determining the stable result of the tested robot based on the motion state.
According to a third aspect of the embodiments of the present disclosure, a testing apparatus is provided, which includes a testing base and a testing platform, wherein the testing platform is fixed on the testing base, and an included angle between the testing platform and a horizontal plane is a preset angle;
and a positioning device for determining the motion position of the tested robot in the test process is arranged on the surface of the test platform.
Optionally, the positioning device comprises a preset track pattern.
Optionally, an angle adjusting device is further included; the angle adjusting device is fixed on the test base, and the test platform is fixed on the angle adjusting device; the angle adjusting device is used for adjusting an included angle between the test platform and the horizontal plane to be a preset angle.
Optionally, the adjustment range of the angle adjustment device is 0 ° to 45 °.
Optionally, the test device further comprises a rotation adjusting device, wherein the rotation adjusting device is fixed between the angle adjusting device and the test platform and is used for controlling the test platform to rotate at a preset speed at the preset angle.
Optionally, an image acquisition device is further included; the image acquisition device is used for acquiring an image of the tested robot in the test platform, and the image is used for determining a stability result of the tested robot.
According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic apparatus including:
a processor;
a memory for storing a computer program executable by the processor;
wherein the at least one processor is configured to execute the computer program in the at least one memory to implement the above-described method.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects:
in the embodiment of the disclosure, the motion state of the robot to be tested can be acquired when the robot to be tested moves on the test platform; then, a stability result of the robot under test is determined based on the motion state. Like this, this embodiment can confirm the stability of being surveyed the robot through the motion position of location being surveyed the robot, is favorable to promoting efficiency of software testing and test experience.
The test equipment provided in the embodiment of the disclosure can comprise a test base and a test platform, wherein the test platform is fixed on the test base, and an included angle between the test platform and a horizontal plane is a preset angle; and a positioning device is arranged on the surface of the test platform and used for positioning the motion position of the tested robot in the test process. Therefore, the stability of the tested robot can be determined according to the movement position of the tested robot in the embodiment, and the test efficiency and the test experience can be improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a side view of a testing device shown in accordance with an exemplary embodiment.
FIG. 2 is a front view of a testing apparatus shown according to an exemplary embodiment.
FIG. 3 is a diagram illustrating 7 preset trajectory patterns in a static state, according to an exemplary embodiment.
FIG. 4 is a side view of another test apparatus shown in accordance with an exemplary embodiment.
FIG. 5 is a side view of yet another test apparatus shown in accordance with an exemplary embodiment.
FIG. 6 is a diagram illustrating one preset trajectory pattern in a dynamic state, according to an exemplary embodiment.
FIG. 7 is a side view of yet another test apparatus shown in accordance with an exemplary embodiment.
FIG. 8 is a flow chart illustrating a testing method according to an exemplary embodiment.
FIG. 9 is a flow chart illustrating a method of obtaining a motion state according to an exemplary embodiment.
FIG. 10 is a flow diagram illustrating another method of obtaining a motion state according to an exemplary embodiment.
FIG. 11 is a flow diagram illustrating a method of obtaining stability results according to an example embodiment.
FIG. 12 is a block diagram illustrating a test apparatus according to an example embodiment.
FIG. 13 is a block diagram illustrating an electronic device in accordance with an example embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The following exemplary described embodiments do not represent all embodiments consistent with the present disclosure. Rather, they are merely examples of devices consistent with certain aspects of the present disclosure as recited in the claims below.
The device under test referred to by the embodiments of the present disclosure may be any device capable of moving. For the purpose of illustration only, in the embodiments of the present disclosure, a robot (e.g., a model 1 robot, a model 2 robot) is taken as an example of the device to be tested.
To solve the above technical problem, referring to fig. 1 and 2, an embodiment of the present disclosure provides a testing apparatus including a testing base 10 and a testing platform 20. The testing platform 20 is fixed on the testing base 10, and an included angle a between the testing platform 20 and the horizontal plane is a predetermined angle. In one example, the preset angle is 10 °.
The surface of the test platform 20 is provided with a positioning device 30, and the positioning device 30 can be used for positioning the movement position of the tested robot during the test process. It should be noted that the positioning device 30 may include a preset track pattern, where the preset track pattern 30 is used as a reference track of the tested robot during the testing process, and at this time, the positioning device 30 is used as an identifier to assist in positioning the motion position. Of course, the positioning device may further include a pressure sensor array, such as a combination of a plurality of pressure sensors forming the shape of the preset track pattern, and the positioning device 30 may directly position the location instead of the mark; projection means may be included, for example, the projection means may project the predetermined track pattern, and the positioning means 30 may directly position the position instead of the mark. It can be understood that, in the case of being able to directly position or assist in positioning the movement position of the tested robot, the corresponding positioning device falls into the protection scope of the present disclosure.
It should be noted that the above-mentioned positioning device 30 may also include a variety of devices, such as: a position sensor, a pressure sensor, an acceleration sensor and an angular displacement sensor; the embodiments of the present disclosure are not limited thereto.
In this embodiment, the stiffness of the test platform 20 may be greater than or equal to a preset stiffness, where the preset stiffness is a unit moment corresponding to deformation caused by the maximum load bearing of the test platform. That is to say, the technician can count the weight of the tested device with the largest weight according to a plurality of types of tested devices to be tested, that is, the largest load bearing of the test platform can be obtained. The size, material, etc. of the test platform are determined based on the maximum load bearing, and the test platform can be made of, but not limited to, tempered glass, steel plates or stone plates, taking materials as an example. And the minimum stiffness of the test platform can be calculated when dimensions and materials are determined. In actual production, the rigidity of the test platform is only required to exceed the minimum rigidity, so that the tested equipment can be ensured not to deform when the test platform moves.
It should be noted that, in practical applications, the surface of the testing platform is flat and has no unevenness, that is, the surface of the testing platform has no influence on the testing result of the device under test. In addition, the test platform can not generate vibration due to factors such as self weight, rotation or motion of the tested device and the like in a static state or a motion state.
In one example, the preset track pattern 30 may include tracks of horizontal lines, vertical lines, and diagonal lines. Wherein, the transverse line is parallel to the intersection line of the test platform and the horizontal plane; the vertical line is vertical to the intersecting line; the included angle between the oblique line and the intersecting line is more than 0 degree and less than 90 degrees. Referring to fig. 2, the test platform 20 (in the plane of the test platform) has an intersection 21 with the horizontal plane. The predetermined track pattern 30 includes a horizontal line 31, a vertical line 32, and a slant line 33, and the slant line 33 and the intersecting line 21 form an included angle of (0, 90 degrees) based on the horizontal line 31 being parallel to the intersecting line 21 and the vertical line 32 being perpendicular to the intersecting line 21.
In the testing process, the user may adopt a visual test, that is, when the tested robot may move on the testing platform, if the deviation preset track graph 30 exceeds a set distance (for example, 5-10cm), and abnormal states such as toppling or falling, it indicates that the stability result of the tested robot is unstable, and if the tested robot normally moves along the preset track graph 30, it indicates that the stability result of the tested robot is stable.
The preset track pattern 30 includes a horizontal line, a vertical line and an oblique line, so that the complexity of the preset track pattern is increased, and the actual motion track of the robot to be tested can be reflected more objectively. It will be appreciated that the predetermined trace pattern 30 may also include curves, arcs, etc. to further increase the complexity of the trace pattern. Of course, the preset track pattern 30 includes at least one of a horizontal line, a vertical line, and a diagonal line, and taking the example that the preset track pattern 30 includes a horizontal line, a vertical line, and a diagonal line respectively, during actual measurement, the track reciprocating by the horizontal line may be measured once, the track reciprocating by the vertical line may be measured once, and the track reciprocating by the diagonal line may be measured once. That is to say, the embodiment of the present disclosure is not limited to the specific structure of the preset track pattern, and different schemes may be combined under the condition that the track of the actual work of the device under test can be objectively reflected, and the corresponding schemes also fall within the protection scope of the present disclosure.
When the test platform is in a static state and/or a motion state, the preset track graph comprises a triangle, a trapezoid, an irregular polygon and a reciprocating straight line graph. For example: when the test platform is in a static state and/or a motion state, the preset track graph comprises a triangle, a trapezoid and an irregular polygon. For example: when the test platform is in a motion state, the preset track graph comprises a reciprocating straight line graph.
Based on the design concept of the preset trajectory graph 30, a part of the preset trajectory graph is provided in the present example, and the side line of each graph is used as the movement route of the device under test. Referring to fig. 3, a shows a right trapezoid, the legs of which are parallel to the intersection line 21, and the hypotenuse of which may be closer to the intersection line 21 or further away from the intersection line 21. b illustrates a polygon with a rectangle with one corner removed (i.e., a missing corner), and the right-angled side away from the missing corner may be close to or away from the intersection line 21 and parallel to the intersection line 21. c illustrates a polygon with two adjacent corners (i.e., 2 unfilled corners) removed from the rectangle, and the right-angled side away from the unfilled corner can be close to or away from the intersection 21 and parallel to the intersection 21. d shows a polygon with two opposite corners (i.e., 2 missing corners) removed from the rectangle, and a side line parallel to the intersection line 21. The e-diagram shows a trapezoid with one edge parallel or perpendicular to the intersection line 21. The f diagram shows a pattern of two opposing triangular vertices (like an hourglass shape) and three triangular vertex lines, one parallel to the intersection line 21. The g diagram shows a right triangle with one of the legs parallel to the intersection line 21.
In an embodiment, the test equipment may further comprise an angle adjustment device. The angle adjusting device can be fixed on the testing base, and the testing platform can be fixed on the angle adjusting device. Therefore, the included angle between the test platform and the horizontal plane can be adjusted to be a preset angle by adjusting the angle adjusting device. The preset angle may be 10 °. For example, the angle adjustment device 40 may include a knob 41, a sleeve 42, and a tray 43. The sleeve 42 includes an outer barrel and an inner barrel, and the outer diameter of the inner barrel is smaller than the inner diameter of the outer barrel so that the inner barrel can be placed inside the outer barrel. The knob 41 may be a mechanical knob or an electrical knob (e.g., a motor). When the knob 41 is rotated, the inner cylinder can move inside the outer cylinder, so as to drive the tray 43 to move back and forth, and further adjust the inclination angle of the test platform. That is, the angle between the test platform and the horizontal plane can be adjusted by adjusting the angle adjusting device in this embodiment. In consideration of practical application scenarios, in this embodiment, the adjustment range of the angle adjustment device may be 0 to 45 degrees, so as to ensure that the included angle between the test platform and the horizontal plane is a preset angle.
It should be noted that the angle adjusting device 40 shown in fig. 4 is only an example, and in practical applications, the sleeve 42 may also be vertically disposed or the outer cylinder and the testing base are integrally formed, and at this time, the inner cylinder may move up and down in the outer cylinder to drive the testing platform to move.
In an embodiment, the test equipment may further comprise a rotation adjustment device. The rotation adjusting device is fixed between the angle adjusting device and the test platform and used for controlling the test platform to rotate at a preset angle according to a preset speed. Referring to fig. 5, the rotation adjusting device 50 may rotate at a preset speed w, so as to rotate the testing platform 10 around the imaginary axis at the preset speed w. In other words, the surface of the test platform 10 remains in the same plane during rotation. Therefore, the dynamic scene of the robot to be tested, which needs to work, can be objectively reflected through the rotation of the test platform, and the accuracy of the detection result can be favorably ensured.
It should be noted that, when the test platform rotates, the robot under test has already substantially faced various scenes such as a horizontal line, a vertical line, or a diagonal line, and at this time, the preset track pattern may be set to be a straight reciprocating pattern, and the effect is shown in fig. 6. It can be understood that, when the platform is rotating, i.e. in a dynamic state, the testing platform can also perform the test by using the preset track pattern shown in fig. 3.
In an embodiment, the test device may further comprise an image acquisition device. The image capturing device may include one or more of a camera, a radar, a laser sensor, or a pressure sensor, and may be configured to capture an image of the robot under test in the test platform, where the image may be used to determine a stability result of the robot under test, and a method for determining the stability result according to the image may refer to the contents of the subsequent method embodiment, which will not be described herein before. Referring to fig. 7, taking a camera as an example, after the position of the camera is calibrated, the calibration relationship between the camera and the test platform is stored. The image acquisition device can acquire an image of a designated area of the test platform. The designated area may cover the entire area of the test platform or the area where the preset trace pattern is located. After the tested device is placed at one of the starting points (such as a vertex of the graph in fig. 3) of the preset track graph of the test platform, the camera can acquire the image of the tested robot in real time or according to a set period and upload the image to a subsequent processor or an upper computer, so that the processor or the upper computer can conveniently determine the stability of the tested robot by using the image.
In an embodiment, the testing apparatus may further include a processor, which may be connected to the image acquisition device, and configured to acquire an image uploaded by the image acquisition device, and determine a position, a displacement, or an offset of the robot under test according to the calibration relationship and the image, so as to determine the stability of the robot under test.
The test equipment provided in the embodiment of the present disclosure may include a test base and a test platform, wherein the test platform is fixed on the test base, and an included angle between the test platform and a horizontal plane is a preset angle; and a positioning device is arranged on the surface of the test platform and used for positioning the motion position of the tested robot in the test process. Therefore, the stability of the tested robot can be determined according to the movement position of the tested robot in the embodiment, and the test efficiency and the test experience can be improved.
On the basis of the test equipment provided by the above embodiments, the embodiments of the present disclosure provide a test method, and fig. 8 is a flowchart illustrating a detection method according to an exemplary embodiment. Referring to fig. 8, a testing method includes steps 81 to 82:
In step 81, when the tested robot moves on the test platform, the motion state of the tested robot is obtained.
In this embodiment, when the testing platform is adjusted to a preset angle (e.g., 10 degrees), the testing platform may not be rotated, i.e., the tested robot is tested under a static condition; the test platform can also be rotated, i.e. the tested robot is tested in a dynamic situation. Subsequent embodiments will no longer distinguish between static or dynamic cases. After the tested robot is placed on the test platform, the tested robot can move according to the preset track pattern.
In one example, referring to fig. 9, in step 91, the processor may obtain a real-time position of the robot under test.
In this step, the manner of acquiring the real-time position by the processor may include, but is not limited to: image processing, point cloud processing, pressure processing, laser processing, and the like.
Taking an image mode as an example, the image acquisition device is a camera, and in the motion process of the tested robot, the camera can adopt the image of the designated area of the test platform and upload the image to the processor. The processor can calculate the position of the tested robot in the image according to the image and the calibration relation (obtained when the camera is calibrated), and converts the position into the real-time position in the test platform.
Taking a point cloud processing mode as an example, the image acquisition device can be a laser radar or an ultrasonic radar, and in the motion process of the robot to be tested, the radar can transmit a pulse signal to a specified area of the test platform and then acquire an echo signal of the pulse signal to obtain the point cloud. The processor can reconstruct an image of the designated area according to the point cloud, so as to obtain the position of the tested robot in the image, namely obtain the real-time position of the tested robot in the test platform.
Taking the pressure processing manner as an example, the image capturing device may be a pressure sensor device or a pressure sensor array. The pressure sensor devices may be evenly distributed on the test platform. During the movement of the tested robot, the pressed pressure sensor outputs a pressure value. The processor can determine the real-time position of the tested robot in the test platform according to the distribution of the pressure sensors.
For example, a laser processing method may be adopted, in which a row of laser emitters and receivers are respectively disposed around the test platform, and the emitters may transmit optical signals to the receivers. In the movement process of the robot to be tested, the optical signal at the corresponding position can be blocked by the robot to be tested, so that the receiver cannot receive the optical signal. The processor can obtain signals output by the receiver, obtain transverse and vertical areas without signals respectively, and obtain the real-time position of the tested robot in the test platform by combining the transverse and vertical areas.
In step 92, the processor may obtain a real-time distance between the real-time position and the preset track pattern. The processor may calculate a real-time distance from the real-time position to the preset trajectory graph, and may adopt a calculation manner of a point-to-straight line distance or a point-to-point distance in the related art, which is not described herein again.
In step 93, when the real-time distance is greater than or equal to the preset distance threshold, the processor may determine that the motion state of the robot under test is an abnormal state; or, when the real-time distance is less than or equal to the preset distance threshold, the processor may determine that the motion state of the robot under test is a normal state. In this step, the preset distance threshold may be set to 5-15 cm, and in one example, the value is 5 cm. If the real-time distance is larger than or equal to the preset distance threshold value, the situation that the detected robot is abnormal in displacement, toppling or falling possibly occurs is shown, and therefore the motion state of the detected robot can be determined to be an abnormal state. Otherwise, after the detected robot is in an abnormal state and the whole preset track graph is finished, namely the detected robot returns to the starting point, the motion state of the detected robot can be determined to be a normal state.
In this another example, referring to fig. 10, in step 101, the processor may acquire a real-time motion trajectory of the robot under test. It will be appreciated that the real-time position of the robot under test is obtained in step 91, and the real-time motion trajectory may be obtained together with all or part of the previous historical positions. In step 102, the processor may obtain a deflection angle of the actual motion trajectory from the preset trajectory pattern. For example, assuming that the actual motion trajectory is a straight line, and a section of the preset trajectory graph corresponding to the actual motion trajectory is also a straight line, an included angle between the two straight lines can be obtained, and the deflection angle can be obtained. Or, rotating the preset track graph by taking the starting point as a reference, and taking the rotation angle of the preset track graph as a deflection angle when the preset track graph is superposed with the real-time motion track or superposed with a part of motion track corresponding to the real-time position. In step 103, when the deflection angle is greater than or equal to a preset angle threshold (e.g., 3 to 10 degrees), the processor may determine that the motion state of the robot under test is an abnormal state; when the deflection angle is smaller than or equal to the preset angle threshold, the processor can determine that the motion state of the tested robot is a normal state.
In step 82, a stability result of the robot under test is determined based on the motion state.
In this embodiment, the processor may determine the stability result of the robot under test according to the motion state, where the stability refers to physical characteristics of the robot such as no deviation, no fall, no rollover, and the like in a stable traveling process, and refers to that an action can be completed according to a predetermined route without error, deviation of a traveling track due to a change in environment, or other adverse traveling situations.
In an example, the processor may determine the stability result of the robot under test from a set of motion states: when the motion state is an abnormal state, the processor can determine that the stability result of the tested robot is unstable; when the motion state is a normal state, the processor may determine that the stability result of the robot under test is stable. Considering the scene that the test platform comprises a static state and a dynamic state, the test can be performed once respectively, and the stability result is determined to be stable only when the test platform is in a normal state twice, otherwise, the test platform is not stable. In this way, the present example can improve detection efficiency.
In another example, the processor may determine the stability result of the robot under test from a plurality of sets of motion states: referring to fig. 11, in step 111, the processor may count the proportion of the normal state according to the plurality of sets of motion states. For example, in the motion state of the group, the number of times of occurrence of the normal state in the motion state is 3, and then the proportion of the normal state is 3/5 × 100 — 60%. The scene of the test platform including the static state and the dynamic state is combined, the test can be performed once respectively, and the proportion of the normal state is counted. In step 112, when the ratio exceeds a preset ratio (e.g., 70%), the processor may determine that the stability result of the robot under test is stable; when the ratio is less than or equal to the preset ratio, the processor may determine that the stability result of the robot under test is unstable. In this way, the stability result is determined in a statistical manner in this example, which is beneficial to eliminating the influence on the test result due to other reasons.
Therefore, in the embodiment of the disclosure, when the robot to be tested moves on the test platform, the motion state of the robot to be tested can be acquired; then, a stability result of the tested robot is determined based on the motion state. Therefore, the stability of the tested robot can be determined according to whether the motion position of the tested robot deviates from the preset track pattern or not, and the test efficiency and the test experience are favorably improved.
On the basis of the test equipment provided by the above embodiment, the embodiment of the present disclosure provides a test method, when the test platform is adjusted to a preset angle (e.g., 10 degrees), the test platform may not be rotated, that is, the tested robot is tested under a static condition; the test platform can also be rotated, i.e. the tested robot is tested in a dynamic situation. In the motion process of the tested robot, the tester can observe the position of the tested robot, when the tested robot is observed to have abnormal displacement, toppling or falling and the like, the tester can determine that the stability result of the tested robot is unstable, and when the tested robot can complete the preset track graph, the stability result of the tested robot can be determined to be stable. Therefore, the stability result of the tested robot can be determined in a visual detection mode, and the detection efficiency can be improved.
On the basis of the above-mentioned detection method, referring to fig. 12, the embodiment further provides a testing apparatus, including:
the motion state acquisition module 121 is configured to acquire a motion state of the robot to be tested when the robot to be tested moves on the test platform;
and a stability result obtaining module 122, configured to determine a stability result of the robot under test based on the motion state.
In an embodiment, a positioning device for determining the movement position of the tested robot in the testing process is arranged on the testing platform.
In an embodiment, the positioning device comprises a predetermined track pattern. The preset track graph comprises tracks of transverse lines, vertical lines and oblique lines;
the transverse line is parallel to the intersection line of the test platform and the horizontal plane;
the vertical line is perpendicular to the intersecting line;
the included angle between the oblique line and the intersecting line is greater than 0 degree and less than 90 degrees.
In an embodiment, when the test platform is in a static state and/or a moving state, the preset trajectory pattern includes at least one of the following: triangles, trapezoids, irregular polygons, and reciprocating rectilinear patterns.
In one embodiment, the motion state acquisition module includes:
The real-time position acquisition unit is used for acquiring the real-time position of the tested robot;
the real-time distance acquisition unit is used for acquiring the real-time distance between the real-time position and the preset track graph;
the motion state determining unit is used for determining that the motion state of the robot to be tested is an abnormal state when the real-time distance is greater than or equal to a preset distance threshold;
alternatively, the first and second electrodes may be,
and when the real-time distance is smaller than or equal to the preset distance threshold, determining that the motion state of the robot to be tested is a normal state.
In one embodiment, the motion state acquisition module includes:
the actual track acquisition unit is used for acquiring the actual motion track of the tested robot;
the group angle acquisition unit is used for acquiring the deflection angle of the actual motion track and the preset track graph;
the motion state determining unit is used for determining that the motion state of the robot to be tested is an abnormal state when the deflection angle is larger than or equal to a preset angle threshold; or when the deflection angle is smaller than or equal to the preset angle threshold, determining that the motion state of the robot to be tested is a normal state;
alternatively, the first and second electrodes may be,
When the number of motion states is one group, the stable result obtaining module includes:
a first result determining unit, configured to determine that a stability result of the robot under test is unstable when the motion state is an abnormal state;
and the second result determining unit is used for determining that the stability result of the tested robot is stable when the motion state is a normal state.
In an embodiment, when the number of the motion states is a plurality of groups, the stable result obtaining module includes:
the proportion acquisition unit is used for counting the proportion of the normal state according to the plurality of groups of motion states;
the result determining unit is used for determining that the stability result of the tested robot is stable when the proportion exceeds a preset proportion; or, when the proportion is less than or equal to the preset proportion, determining that the stability result of the tested robot is unstable.
It can be understood that the apparatus provided in the embodiments of the present disclosure corresponds to the method described above, and specific contents may refer to the contents of each embodiment of the method, which are not described herein again.
FIG. 13 is a block diagram illustrating an electronic device in accordance with an example embodiment. For example, the electronic device 1300 may be a smartphone, a computer, a digital broadcast terminal, a tablet device, a medical device, a fitness device, a personal digital assistant, and so forth.
Referring to fig. 13, electronic device 1300 may include one or more of the following components: a processing component 1302, a memory 1304, a power component 1306, a multimedia component 1308, an audio component 1310, an input/output (I/O) interface 1312, a sensor component 1314, a communication component 1316, and an image acquisition component 1318.
The processing component 1302 generally controls overall operation of the electronic device 1300, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 1302 may include one or more processors 1320 to execute computer programs. Further, the processing component 1302 can include one or more modules that facilitate interaction between the processing component 1302 and other components. For example, the processing component 1302 may include a multimedia module to facilitate interaction between the multimedia component 1308 and the processing component 1302.
The memory 1304 is configured to store various types of data to support operation at the electronic device 1300. Examples of such data include computer programs, contact data, phonebook data, messages, pictures, videos, etc. for any application or method operating on the electronic device 1300. The memory 1304 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 1306 provides power to the various components of the electronic device 1300. The power components 1306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 1300. The power supply component 1306 may include a power chip, and the controller may communicate with the power chip to control the power chip to turn the switching device on or off to allow the battery to supply power or not supply power to the motherboard circuitry.
The multimedia component 1308 includes a screen between the electronic device 1300 and the target object that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a target object. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.
The audio component 1310 is configured to output and/or input audio signals. For example, the audio component 1310 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 1300 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 1304 or transmitted via the communication component 1316. In some embodiments, the audio component 1310 also includes a speaker for outputting audio signals.
The I/O interface 1312 provides an interface between the processing component 1302 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc.
The sensor assembly 1314 includes one or more sensors for providing various aspects of state assessment for the electronic device 1300. For example, the sensor assembly 1314 may detect an open/closed state of the electronic device 1300, the relative positioning of components, such as a display and keypad of the electronic device 1300, the sensor assembly 1314 may also detect a change in the position of the electronic device 1300 or one of the components, the presence or absence of a target object in contact with the electronic device 1300, orientation or acceleration/deceleration of the electronic device 1300, and a change in the temperature of the electronic device 1300. In this example, the sensor assembly 1314 may include a magnetic sensor, a gyroscope, and a magnetic field sensor, wherein the magnetic field sensor includes at least one of: hall sensor, thin film magneto resistance sensor, magnetic liquid acceleration sensor.
The communication component 1316 is configured to facilitate communications between the electronic device 1300 and other devices in a wired or wireless manner. The electronic device 1300 may access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G, 5G, or a combination thereof. In an exemplary embodiment, the communication component 1316 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communications component 1316 also includes a Near Field Communications (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an example embodiment, the electronic device 1300 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors, or other electronic components.
In an exemplary embodiment, a non-transitory readable storage medium is also provided that includes an executable computer program, such as the memory 1304 that includes instructions, that are executable by the processor. The readable storage medium may be, among others, ROM, Random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (13)
1. A method of testing, comprising:
when a tested robot moves on a test platform, acquiring the motion state of the tested robot;
determining a stability result of the robot under test based on the motion state;
wherein, obtain the motion state of the robot under test, include:
acquiring the real-time position of the robot to be tested;
acquiring the real-time distance between the real-time position and a preset track graph;
when the real-time distance is larger than or equal to a preset distance threshold, determining that the motion state of the robot to be tested is an abnormal state; or when the real-time distance is smaller than or equal to the preset distance threshold, determining that the motion state of the robot to be tested is a normal state;
or the like, or, alternatively,
wherein, obtain the motion state of the robot under test, include:
acquiring an actual motion track of the robot to be detected;
acquiring a deflection angle between the actual motion track and a preset track graph;
When the deflection angle is larger than or equal to a preset angle threshold value, determining that the motion state of the tested robot is an abnormal state; or when the deflection angle is smaller than or equal to the preset angle threshold, determining that the motion state of the tested robot is a normal state.
2. The testing method according to claim 1, wherein the surface of the testing platform is provided with a positioning device for determining the movement position of the tested robot during the testing process.
3. The test method according to claim 2, wherein the positioning device comprises a preset track pattern comprising tracks of horizontal lines, vertical lines and oblique lines;
the transverse line is parallel to the intersection line of the test platform and the horizontal plane;
the vertical line is perpendicular to the intersection line;
the included angle between the oblique line and the intersecting line is greater than 0 degree and less than 90 degrees.
4. The testing method according to any one of claims 1, 2 or 3, wherein when the testing platform is in a static state and/or a motion state, the preset trajectory pattern comprises at least one of: triangular, trapezoidal, irregular polygonal.
5. The testing method of claim 1, wherein when the number of motion states is a group, determining the stability result of the robot under test based on the motion states comprises:
When the motion state is an abnormal state, determining that the stability result of the tested robot is unstable; when the motion state is a normal state, determining that the stability result of the tested robot is stable;
alternatively, the first and second electrodes may be,
when the number of the motion states is multiple groups, determining a stability result of the tested robot based on the motion states, including:
counting the proportion of the normal state according to the plurality of groups of motion states;
when the proportion exceeds a preset proportion, determining that the stability result of the tested robot is stable; or when the proportion is smaller than or equal to the preset proportion, determining that the stability result of the tested robot is unstable.
6. A test apparatus, comprising:
the motion state acquisition module is used for acquiring the motion state of the tested robot when the tested robot moves on the test platform;
the stable result acquisition module is used for determining a stable result of the tested robot based on the motion state;
wherein, the motion state acquisition module acquires the motion state of the robot to be tested, and comprises:
acquiring the real-time position of the robot to be tested;
Acquiring the real-time distance between the real-time position and a preset track graph;
when the real-time distance is larger than or equal to a preset distance threshold, determining that the motion state of the robot to be tested is an abnormal state; or when the real-time distance is smaller than or equal to the preset distance threshold, determining that the motion state of the robot to be tested is a normal state;
or the like, or, alternatively,
wherein, the motion state acquisition module acquires the motion state of the robot to be tested, and comprises:
acquiring an actual motion track of the robot to be detected;
acquiring a deflection angle between the actual motion track and a preset track graph;
when the deflection angle is larger than or equal to a preset angle threshold value, determining that the motion state of the robot to be tested is an abnormal state; or when the deflection angle is smaller than or equal to the preset angle threshold, determining that the motion state of the tested robot is a normal state.
7. The test equipment is characterized by comprising a test base and a test platform, wherein the test platform is fixed on the test base, and an included angle between the test platform and the horizontal plane is a preset angle;
the surface of the test platform is provided with a positioning device, and the positioning device is used for positioning the motion position of the tested robot in the test process so as to further acquire the motion state of the tested robot;
Wherein, obtain the motion state of the robot under test, include:
acquiring the real-time position of the robot to be tested;
acquiring the real-time distance between the real-time position and a preset track graph;
when the real-time distance is larger than or equal to a preset distance threshold, determining that the motion state of the robot to be tested is an abnormal state; or when the real-time distance is smaller than or equal to the preset distance threshold, determining that the motion state of the robot to be tested is a normal state;
or the like, or a combination thereof,
wherein, obtain the motion state of the robot under test, include:
acquiring an actual motion track of the robot to be detected;
acquiring a deflection angle between the actual motion track and a preset track graph;
when the deflection angle is larger than or equal to a preset angle threshold value, determining that the motion state of the robot to be tested is an abnormal state; or when the deflection angle is smaller than or equal to the preset angle threshold, determining that the motion state of the tested robot is a normal state.
8. The test apparatus of claim 7, wherein the positioning device comprises a predetermined track pattern.
9. The test apparatus of claim 7, further comprising an angle adjustment device; the angle adjusting device is fixed on the test base, and the test platform is fixed on the angle adjusting device; the angle adjusting device is used for adjusting an included angle between the test platform and the horizontal plane to be a preset angle.
10. The test apparatus of claim 9, wherein the adjustment range of the angle adjustment device is 0 ° to 45 °.
11. The test apparatus of claim 9, further comprising a rotation adjustment device fixed between the angle adjustment device and the test platform for controlling the test platform to rotate at a preset speed at the preset angle.
12. The test apparatus of claim 7, further comprising an image capture device; the image acquisition device is used for acquiring an image of the tested robot in the test platform, and the image is used for determining a stability result of the tested robot.
13. An electronic device, comprising:
a processor;
a memory for storing a computer program executable by the processor;
wherein the at least one processor is configured to execute the computer program in the at least one memory to implement the method of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011360840.0A CN112356075B (en) | 2020-11-27 | 2020-11-27 | Test method and device, test equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011360840.0A CN112356075B (en) | 2020-11-27 | 2020-11-27 | Test method and device, test equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112356075A CN112356075A (en) | 2021-02-12 |
| CN112356075B true CN112356075B (en) | 2022-07-29 |
Family
ID=74535466
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011360840.0A Active CN112356075B (en) | 2020-11-27 | 2020-11-27 | Test method and device, test equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112356075B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113175932A (en) * | 2021-04-27 | 2021-07-27 | 上海景吾智能科技有限公司 | Robot navigation automation test method, system, medium and equipment |
| CN113686563B (en) * | 2021-09-01 | 2024-04-19 | 中煤科工集团重庆智慧城市科技研究院有限公司 | Test method and system for robot rollover test |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103776635A (en) * | 2012-10-18 | 2014-05-07 | 上海理工大学 | Wheelchairs static stability test device |
| CN105387872A (en) * | 2014-09-09 | 2016-03-09 | 中国科学院沈阳自动化研究所 | Navigation and positioning performance testing device and method for autonomous mobile robot |
| CN106625778A (en) * | 2017-01-19 | 2017-05-10 | 长安大学 | Test platform and test method for movement control of foot type bio-robot and test method |
| CN109724824A (en) * | 2019-03-07 | 2019-05-07 | 重庆德新机器人检测中心有限公司 | Mobile robot ramp and integrated test platform of turning on one's side |
| CN110281274A (en) * | 2019-06-28 | 2019-09-27 | 北京云迹科技有限公司 | Robot whole process test platform |
| KR20200042871A (en) * | 2018-10-16 | 2020-04-24 | 세종대학교산학협력단 | Integrated testing apparatus for a self balancing robot |
-
2020
- 2020-11-27 CN CN202011360840.0A patent/CN112356075B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103776635A (en) * | 2012-10-18 | 2014-05-07 | 上海理工大学 | Wheelchairs static stability test device |
| CN105387872A (en) * | 2014-09-09 | 2016-03-09 | 中国科学院沈阳自动化研究所 | Navigation and positioning performance testing device and method for autonomous mobile robot |
| CN106625778A (en) * | 2017-01-19 | 2017-05-10 | 长安大学 | Test platform and test method for movement control of foot type bio-robot and test method |
| KR20200042871A (en) * | 2018-10-16 | 2020-04-24 | 세종대학교산학협력단 | Integrated testing apparatus for a self balancing robot |
| CN109724824A (en) * | 2019-03-07 | 2019-05-07 | 重庆德新机器人检测中心有限公司 | Mobile robot ramp and integrated test platform of turning on one's side |
| CN110281274A (en) * | 2019-06-28 | 2019-09-27 | 北京云迹科技有限公司 | Robot whole process test platform |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112356075A (en) | 2021-02-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112356075B (en) | Test method and device, test equipment and storage medium | |
| US11044402B1 (en) | Power management for optical position tracking devices | |
| US9341471B2 (en) | Calibrated hardware sensors for estimating real-world distances | |
| JP6502525B2 (en) | Object measuring apparatus and object measuring method | |
| KR101639312B1 (en) | Method, apparatus, program and storage medium for positioning and navigating | |
| US10963100B2 (en) | Interactive object control method and apparatus, terminal, and computer-readable storage medium | |
| US20170085781A1 (en) | Method and apparatus for controlling positioning of camera device | |
| CN102428431A (en) | Portable electronic apparatus including a display and method for controlling such an apparatus | |
| CN103702029A (en) | Method and device for prompting focusing during shooting | |
| CN106200225A (en) | The trapezoidal distortion correction method of projector and device | |
| US10564250B2 (en) | Device and method for measuring flight data of flying objects using high speed video camera and computer readable recording medium having program for performing the same | |
| US20220244081A1 (en) | Method and apparatus for calibrating joystick and remote control device | |
| CN111813290B (en) | Data processing method and device and electronic equipment | |
| CN102980517A (en) | Monitoring measurement method | |
| US20170019656A1 (en) | Automatic calibration system and related automatic calibration method applied to a camera | |
| CN109116368A (en) | System for monitoring displacement and method | |
| CN106503682A (en) | Crucial independent positioning method and device in video data | |
| US9584640B2 (en) | Portable information processing device, output control method for a projector, and recording medium | |
| EP3327363B1 (en) | Method and apparatus for air purification | |
| CN111829651B (en) | Method, device and equipment for calibrating light intensity value and storage medium | |
| JP2022145440A (en) | Survey information management system, survey information management method, and survey information management program | |
| CN110044309B (en) | Measuring method and device | |
| US20210025705A1 (en) | Target positioning with electronic distance measuring and bundle adjustment | |
| CN111998871A (en) | Drift detection method and system of VR equipment | |
| US20130155189A1 (en) | Object measuring apparatus and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |