CN114083533B - Data processing method and device based on mechanical arm - Google Patents
Data processing method and device based on mechanical arm Download PDFInfo
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- CN114083533B CN114083533B CN202111320039.8A CN202111320039A CN114083533B CN 114083533 B CN114083533 B CN 114083533B CN 202111320039 A CN202111320039 A CN 202111320039A CN 114083533 B CN114083533 B CN 114083533B
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- 238000000605 extraction Methods 0.000 claims description 9
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 210000000349 chromosome Anatomy 0.000 claims description 3
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Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
- B25J9/1697—Vision controlled systems
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Abstract
The invention discloses a data processing method and device based on a mechanical arm, wherein the method comprises the following steps: acquiring image information of a target object; determining a target track information set according to the target object image information and a preset track planning rule; determining a control instruction set according to the target track information set; the control instruction set comprises a plurality of target control instructions; the target control command is used for indicating the adjustment control of the mechanical arm. Therefore, the method and the device can determine the target track information from the target object image information by utilizing the track planning rule, and process the target track information to obtain the target control instruction for indicating the adjustment control of the mechanical arm, thereby being beneficial to realizing the smooth control of the mechanical arm and improving the control efficiency of the mechanical arm.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a data processing method and apparatus based on a mechanical arm.
Background
In the control process of the mechanical arm, the smooth control of the mechanical arm is generally difficult to realize due to the limitation of the opening authority of the port. Therefore, it is important to provide a data processing method and device based on a mechanical arm to realize smooth control of the mechanical arm, so as to improve the control efficiency of the mechanical arm.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a data processing method and a device based on a mechanical arm, which can determine target track information from target object image information by utilizing a track planning rule, and process the target track information to obtain a target control instruction for indicating to regulate and control the mechanical arm, thereby being beneficial to realizing smooth control of the mechanical arm and improving the control efficiency of the mechanical arm.
In order to solve the technical problem, a first aspect of the embodiment of the present invention discloses a data processing method based on a mechanical arm, the method comprising:
acquiring image information of a target object;
determining a target track information set according to the target object image information and a preset track planning rule;
determining a control instruction set according to the target track information set; the control instruction set comprises a plurality of target control instructions; the target control instruction is used for indicating adjustment control of the mechanical arm.
In an optional implementation manner, in a first aspect of the embodiment of the present invention, the determining, according to the target object image information and a preset track planning rule, a target track information set includes:
According to the image information of the target object, determining barycentric coordinate information corresponding to the target object;
determining initial attitude information of the mechanical arm according to the barycentric coordinate information; the initial gesture information of the mechanical arm is used for configuring the initial gesture of the mechanical arm;
and determining a target track information set according to the initial gesture information of the mechanical arm and a preset track planning model.
In an optional implementation manner, in a first aspect of the embodiment of the present invention, the determining, according to the image information of the target object, barycentric coordinate information corresponding to the target object includes:
processing the image information of the target object according to a preset target detection rule to obtain a target area;
processing the target area according to a preset feature extraction rule to obtain a pixel coordinate information set; the pixel coordinate information includes at least 2 pixel coordinate information;
and calculating the pixel coordinate information set to obtain barycentric coordinate information corresponding to the target object.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the initial pose information of the mechanical arm includes initial pose information of a camera and initial pose information of a joint angle;
Determining the initial attitude information of the mechanical arm according to the barycentric coordinate information comprises the following steps:
acquiring initial coordinate information of the mechanical arm and structural information of the mechanical arm;
calculating the barycentric coordinate information and the mechanical arm initial coordinate information to obtain the camera initial attitude information;
and calculating the initial pose information of the camera and the mechanical arm structure information to obtain the initial pose information of the joint angle.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the trajectory planning model includes a first planning model and a second planning model;
the determining the target track information set according to the initial gesture information of the mechanical arm and a preset track planning model comprises the following steps:
calculating the initial attitude information of the mechanical arm to obtain initial point information;
acquiring a target point information set; the target point information set comprises L pieces of target point information; l is a positive integer greater than or equal to 2;
calculating the initial point information and the target point information set by using the first planning model to obtain an initial track information set; the initial track information set comprises the L initial track information;
Determining running time information according to the initial track information set;
and processing the running time information by using the second planning model to obtain a target track information set.
In an optional implementation manner, in a first aspect of the embodiment of the present invention, the determining a control instruction set according to the target track information set includes:
determining a target control model set according to the target track information set; the target control model set comprises a plurality of target control models;
determining attitude position information according to the target object image information and a preset attitude analysis model;
and processing the attitude and position information by using the target control model set to obtain a control instruction set.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the determining, according to the target track information set, a target control model set includes:
for any target track information, matching track characteristic information corresponding to the target track information with model characteristic information corresponding to all the to-be-selected control models in a preset to-be-selected control model set to obtain a matching result; the track characteristic information comprises M track information elements; m is an odd positive integer; the model characteristic information comprises N model information elements; the N is an odd positive integer; track element information corresponding to the track information element at the middle position in the track characteristic information is matched with track element information corresponding to the track information element at the tail position; track element information corresponding to the track information element at the second position in sequence in the track characteristic information is matched with track element information corresponding to the track information element at the last position;
And when the matching result indicates that the target model characteristic information matched with the track characteristic information corresponding to the target track information exists in the to-be-selected control model set, determining the to-be-selected control model corresponding to the target model characteristic information as a target control model.
The second aspect of the embodiment of the invention discloses a data processing device based on a mechanical arm, which comprises:
the acquisition module is used for acquiring image information of the target object;
the first determining module is used for determining a target track information set according to the target object image information and a preset track planning rule;
the second determining module is used for determining a control instruction set according to the target track information set; the control instruction set comprises a plurality of target control instructions; the target control instruction is used for indicating adjustment control of the mechanical arm.
As one such alternative implementation, in the second aspect of the embodiment of the present invention, the first determining module includes a first determining sub-module, a second determining sub-module, and a third determining sub-module, where:
the first determining submodule is used for determining barycentric coordinate information corresponding to the target object according to the image information of the target object;
The second determining submodule is used for determining initial gesture information of the mechanical arm according to the barycenter coordinate information; the initial gesture information of the mechanical arm is used for configuring the initial gesture of the mechanical arm;
the third determining submodule is used for determining a target track information set according to the initial gesture information of the mechanical arm and a preset track planning model.
In a second aspect of the embodiment of the present invention, the specific manner of determining, by the first determining submodule, the barycentric coordinate information corresponding to the target object according to the target object image information is:
processing the image information of the target object according to a preset target detection rule to obtain a target area;
processing the target area according to a preset feature extraction rule to obtain a pixel coordinate information set; the pixel coordinate information includes at least 2 pixel coordinate information;
and calculating the pixel coordinate information set to obtain barycentric coordinate information corresponding to the target object.
As one such optional implementation manner, in the second aspect of the embodiment of the present invention, the mechanical arm initial pose information includes camera initial pose information and joint angle initial pose information;
The second determining submodule determines the specific mode of the initial gesture information of the mechanical arm according to the barycenter coordinate information as follows:
acquiring initial coordinate information of the mechanical arm and structural information of the mechanical arm;
calculating the barycentric coordinate information and the mechanical arm initial coordinate information to obtain the camera initial attitude information;
and calculating the initial pose information of the camera and the mechanical arm structure information to obtain the initial pose information of the joint angle.
As one such alternative implementation, in a second aspect of the embodiment of the present invention, the trajectory planning model includes a first planning model and a second planning model;
the third determining submodule determines the specific mode of the target track information set according to the initial gesture information of the mechanical arm and a preset track planning model as follows:
calculating the initial attitude information of the mechanical arm to obtain initial point information;
acquiring a target point information set; the target point information set comprises L pieces of target point information; l is a positive integer greater than or equal to 2;
calculating the initial point information and the target point information set by using the first planning model to obtain an initial track information set; the initial track information set comprises the L initial track information;
Determining running time information according to the initial track information set;
and processing the running time information by using the second planning model to obtain a target track information set.
In a second aspect of this embodiment of the present invention, the specific manner in which the second determining module determines the control instruction set according to the target track information set is:
determining a target control model set according to the target track information set; the target control model set comprises a plurality of target control models;
determining attitude position information according to the target object image information and a preset attitude analysis model;
and processing the attitude and position information by using the target control model set to obtain a control instruction set.
In a second aspect of this embodiment of the present invention, the specific manner of determining, by the second determining module, the target control model set according to the target track information set is:
for any target track information, matching track characteristic information corresponding to the target track information with model characteristic information corresponding to all the to-be-selected control models in a preset to-be-selected control model set to obtain a matching result; the track characteristic information comprises M track information elements; m is an odd positive integer; the model characteristic information comprises N model information elements; the N is an odd positive integer; track element information corresponding to the track information element at the middle position in the track characteristic information is matched with track element information corresponding to the track information element at the tail position; track element information corresponding to the track information element at the second position in sequence in the track characteristic information is matched with track element information corresponding to the track information element at the last position;
And when the matching result indicates that the target model characteristic information matched with the track characteristic information corresponding to the target track information exists in the to-be-selected control model set, determining the to-be-selected control model corresponding to the target model characteristic information as a target control model.
The third aspect of the invention discloses another data processing device based on a mechanical arm, which comprises:
a memory storing executable program code;
a processor coupled to the memory;
the processor invokes the executable program code stored in the memory to execute some or all of the steps in the data processing method based on the mechanical arm disclosed in the first aspect of the embodiment of the present invention.
A fourth aspect of the present invention discloses a computer storage medium storing computer instructions for executing part or all of the steps in the data processing method based on the mechanical arm disclosed in the first aspect of the present invention when the computer instructions are called.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, the image information of the target object is obtained; determining a target track information set according to the target object image information and a preset track planning rule; determining a control instruction set according to the target track information set; the control instruction set comprises a plurality of target control instructions; the target control command is used for indicating the adjustment control of the mechanical arm. Therefore, the method and the device can determine the target track information from the target object image information by utilizing the track planning rule, and process the target track information to obtain the target control instruction for indicating the adjustment control of the mechanical arm, thereby being beneficial to realizing the smooth control of the mechanical arm and improving the control efficiency of the mechanical arm.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a data processing method based on a mechanical arm according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of another data processing method based on a mechanical arm according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a data processing device based on a mechanical arm according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of another data processing device based on a mechanical arm according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a data processing device based on a mechanical arm according to another embodiment of the present invention.
Detailed Description
In order to make the present invention better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or device that comprises a list of steps or elements is not limited to the list of steps or elements but may, in the alternative, include other steps or elements not expressly listed or inherent to such process, method, article, or device.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The invention discloses a data processing method and a data processing device based on a mechanical arm, which can determine target track information from target object image information by utilizing a track planning rule, and process the target track information to obtain a target control instruction for indicating adjustment control of the mechanical arm, so that smooth control of the mechanical arm is facilitated, and the control efficiency of the mechanical arm is improved. The following will describe in detail.
Example 1
Referring to fig. 1, fig. 1 is a flow chart of a data processing method based on a mechanical arm according to an embodiment of the invention. The data processing method based on the mechanical arm described in fig. 1 is applied to a data processing system, such as a local server or a cloud server for data processing based on the mechanical arm, which is not limited in the embodiments of the present invention. As shown in fig. 1, the data processing method based on the mechanical arm may include the following operations:
101. and acquiring image information of the target object.
102. And determining a target track information set according to the target object image information and a preset track planning rule.
103. And determining a control instruction set according to the target track information set.
In the embodiment of the present invention, the control instruction set includes a plurality of target control instructions.
In the embodiment of the invention, the target control instruction is used for indicating the adjustment control of the mechanical arm.
Optionally, the target image information includes a plurality of target images.
Optionally, the target image is acquired by a camera located at the end of the mechanical arm.
Optionally, the mechanical arm includes a plurality of control units.
Optionally, the number of control units corresponds to the number of target control instructions.
Therefore, the data processing method based on the mechanical arm described by the embodiment of the invention can determine the target track information from the target object image information by utilizing the track planning rule, and then process the target track information to obtain the target control instruction for indicating the adjustment control of the mechanical arm, thereby being beneficial to realizing the smooth control of the mechanical arm and improving the control efficiency of the mechanical arm.
In an optional embodiment, the determining the target track information set in step 102 according to the target object image information and the preset track planning rule includes:
according to the image information of the target object, determining barycentric coordinate information corresponding to the target object;
determining initial attitude information of the mechanical arm according to the barycentric coordinate information; the initial gesture information of the mechanical arm is used for configuring the initial gesture of the mechanical arm;
and determining a target track information set according to the initial gesture information of the mechanical arm and a preset track planning model.
Optionally, the barycenter coordinate information includes three coordinate position information of a barycenter corresponding to the target object.
Optionally, the initial pose information of the mechanical arm includes a plurality of initial position information.
Optionally, the number of the initial position information is consistent with the number corresponding to the control unit.
Therefore, the data processing method based on the mechanical arm described by the embodiment of the invention can process the image information of the target object to obtain the barycentric coordinate information corresponding to the target object, then determine the initial posture information of the mechanical arm for configuring the initial posture of the mechanical arm, and determine the target track information through the track planning model, thereby being beneficial to realizing smooth control of the mechanical arm and improving the control efficiency of the mechanical arm.
In another optional embodiment, the determining barycentric coordinate information corresponding to the target object according to the image information of the target object includes:
processing the image information of the target object according to a preset target detection rule to obtain a target area;
processing the target area according to a preset feature extraction rule to obtain a pixel coordinate information set; the pixel coordinate information includes at least 2 pixel coordinate information;
and calculating the pixel coordinate information set to obtain barycentric coordinate information corresponding to the target object.
In this optional embodiment, as an optional implementation manner, the specific manner of processing the image information of the target object according to the preset target detection rule to obtain the target area is:
Processing the image information of the target object according to a preset characteristic threshold value to obtain a binary image;
searching the binary image by using a preset search window frame and search step length information to determine a standby area and the number of standby feature points corresponding to the standby area;
judging whether the number of the standby feature points corresponding to the standby area is larger than or equal to a preset feature point number threshold value or not, and obtaining a threshold value judgment result;
and when the threshold judgment result indicates that the number of the standby feature points corresponding to the standby area is larger than or equal to a preset feature point number threshold, determining the standby area as a target area.
Optionally, the search step information is used to instruct the search window frame to move in the binary image.
In this optional embodiment, as another optional implementation manner, the specific manner of processing the target area according to the preset feature extraction rule to obtain the pixel coordinate information set is as follows:
performing edge detection processing on the target area to obtain a target object edge image;
performing linear detection processing on the edge image of the target object to obtain linear segment information;
fitting the straight line segment information to obtain a pixel coordinate information set.
Optionally, the edge detection process includes a gaussian filtering process, and/or a gradient magnitude and direction calculation process, and/or a non-maximum suppression process, and/or a dual-threshold screening strong and weak edge process, and/or a boundary tracking process, which are not limited in the embodiment of the present invention.
Optionally, the above straight line detection process is implemented based on Hough transform.
Therefore, the data processing method based on the mechanical arm described by the embodiment of the invention can process the image information of the target object by utilizing the target detection rule to obtain the target area, and then determine a plurality of pixel coordinate information by utilizing the feature extraction rule to further determine and obtain the barycenter coordinate information corresponding to the target object, thereby providing a realization path for determining the barycenter coordinate information, being beneficial to realizing smooth control of the mechanical arm and further improving the control efficiency of the mechanical arm.
In yet another optional embodiment, the mechanical arm initial pose information includes camera initial pose information and joint angle initial pose information;
above-mentioned according to barycentric coordinate information, confirm the initial gesture information of arm, include:
acquiring initial coordinate information of the mechanical arm and structural information of the mechanical arm;
Calculating the barycentric coordinate information and the mechanical arm initial coordinate information to obtain camera initial attitude information;
and calculating and processing the initial posture information of the camera and the mechanical arm structure information to obtain the initial posture information of the joint angle.
Optionally, the initial coordinate information of the mechanical arm includes a plurality of initial coordinate information of joints and a plurality of basic position information.
Optionally, the number of the initial coordinate information of the joint is greater than or equal to the number of the control units.
Optionally, the base position information includes pitch angle information of a camera at the end of the mechanical arm and the target object, and/or distance information of the camera at the end of the mechanical arm and the target object, which is not limited in the embodiment of the present invention.
Therefore, the data processing method based on the mechanical arm described by the embodiment of the invention can determine the initial posture information of the camera through the calculation processing of the barycentric coordinate information and the initial coordinate information of the mechanical arm, and determine the initial posture information of the joint angle by utilizing the structural information of the mechanical arm, thereby being more beneficial to realizing the smooth control of the mechanical arm and further improving the control efficiency of the mechanical arm.
In yet another alternative embodiment, the trajectory planning model includes a first planning model and a second planning model;
The determining the target track information set according to the initial gesture information of the mechanical arm and the preset track planning model includes:
calculating the initial attitude information of the mechanical arm to obtain initial point information;
acquiring a target point information set; the target point information set comprises L pieces of target point information; l is a positive integer greater than or equal to 2;
calculating the initial point information and the target point information set by using a first planning model to obtain an initial track information set; the initial track information set comprises L initial track information;
determining running time information according to the initial track information set;
and processing the running time information by using the second planning model to obtain a target track information set.
In this optional embodiment, as an optional implementation manner, the calculating processing is performed on the initial point information and the target point information set by using the first planning model, and a specific manner of obtaining the initial track information set is as follows:
and for any two information points, carrying out track design on the two information points by using a first planning model to obtain initial track information corresponding to the two information points.
Optionally, the two information points include initial point information and target point information, or any two target point information, which is not limited in the embodiment of the present invention.
Preferably, the first planning model is a planning model based on third-order B-spline interpolation.
In this optional embodiment, as another optional implementation manner, the specific implementation manner of determining the running time information according to the initial track information set is:
for any initial track information, calculating initial time information corresponding to the initial track information;
and summing all the initial time information to obtain the running time information.
In this optional embodiment, as a further optional implementation manner, the processing the runtime information by using the second planning model, to obtain the target track information set, is specifically implemented as follows:
optimizing the initial track information set by using a second planning model to obtain a standby track information set; the track set comprises a plurality of standby track information sets;
determining a standby running time set according to the track set; the standby running time set comprises a plurality of standby running time information;
processing the standby running time set and the running time information according to preset preferable termination conditions, and determining target running time;
And determining the track information set corresponding to the target running time as a target track information set.
Optionally, the track information set includes a standby track information set, or an initial track information set, which is not limited in the embodiment of the present invention.
Optionally, the target track information set includes a plurality of target track information.
Preferably, the second planning model is a planning model based on an improved genetic algorithm.
Optionally, the second planning model includes binary-based chromosome coding, and/or an initial population, and/or a selection operator, and/or a crossover operator, and/or a mutation operator, which are not limited in the embodiments of the present invention.
Optionally, the initial population is obtained by calculating the mean variance of the fitness of the individuals and the global fitness, sorting the mean variances from small to large, and selecting the first K individuals.
Optionally, K is a positive integer.
Optionally, the crossover operator is determined based on the average distribution density and fitness variance of the population.
Therefore, the data processing method based on the mechanical arm described by the embodiment of the invention can determine the target track information by processing the initial gesture information and the target point information of the mechanical arm, which is more beneficial to realizing smooth control of the mechanical arm, thereby improving the control efficiency of the mechanical arm.
Example two
Referring to fig. 2, fig. 2 is a flow chart of another data processing method based on a mechanical arm according to an embodiment of the invention. The data processing method based on the mechanical arm described in fig. 2 is applied to a data processing system, such as a local server or a cloud server for data processing based on the mechanical arm, which is not limited in the embodiments of the present invention. As shown in fig. 2, the data processing method based on the mechanical arm may include the following operations:
201. and acquiring image information of the target object.
202. And determining a target track information set according to the target object image information and a preset track planning rule.
In the embodiment of the present invention, for specific technical details and technical term explanations of the steps 201 to 202, reference may be made to the detailed descriptions of the steps 101 to 102 in the first embodiment, and the detailed descriptions of the embodiment of the present invention are omitted.
203. And determining a target control model set according to the target track information set.
In the embodiment of the present invention, the set of target control models includes a plurality of target control models.
204. And determining the gesture position information according to the target object image information and a preset gesture analysis model.
205. And processing the attitude and position information by using the target control model set to obtain a control instruction set.
Optionally, the gesture analysis model includes an analysis model based on deep learning, and/or an analysis model based on a neural network, and/or an analysis model based on a jacobian matrix, which is not limited in the embodiment of the present invention.
Optionally, the above-mentioned gesture position information includes displacement information, and/or angle information, and/or linear velocity information, and/or angular velocity information, and/or acceleration information, which are not limited in the embodiment of the present invention.
Optionally, the number of the target control models is consistent with the number of the target track information.
Optionally, the target control model includes a control model based on fuzzy PID, and/or a control model based on a synovial membrane control algorithm, and/or a control algorithm based on a neural network, and/or a control model based on a state observer, and/or a control model based on an impedance control algorithm, which are not limited in the embodiment of the present invention.
Therefore, the data processing method based on the mechanical arm described by the embodiment of the invention can determine the target track information from the target object image information by utilizing the track planning rule, process the target track information to obtain the target control model, process the target object image information by utilizing the gesture analysis model to determine the gesture position information, and process the gesture position information by utilizing the target control model to obtain the target control instruction for indicating the adjustment control of the mechanical arm, thereby being beneficial to realizing the smooth control of the mechanical arm and further improving the control efficiency of the mechanical arm.
In an alternative embodiment, determining the set of target control models according to the set of target track information in step 203 includes:
for any target track information, matching track characteristic information corresponding to the target track information with model characteristic information corresponding to all the to-be-selected control models in a preset to-be-selected control model set to obtain a matching result; the track characteristic information comprises M track information elements; m is an odd positive integer; the model characteristic information comprises N model information elements; n is an odd positive integer; track element information corresponding to the track information element at the middle position in the track characteristic information is matched with track element information corresponding to the track information element at the tail position; track element information corresponding to the track information element at the second position in sequence in the track characteristic information is matched with track element information corresponding to the track information element at the last position;
when the matching result indicates that the target model characteristic information matched with the track characteristic information corresponding to the target track information exists in the to-be-selected control model set, determining the to-be-selected control model corresponding to the target model characteristic information as a target control model.
Optionally, the track feature information includes track number information, and/or track name information, and/or track priority information, which is not limited in the embodiment of the present invention.
Optionally, M is a positive integer greater than or equal to 7.
Optionally, M is an odd number.
Optionally, the track characteristic information is in the form of A1-A2-A3-A2-b, wherein A1, A2, A3, a and b are track element information corresponding to the track information element.
Alternatively, a consists of A1 and A2. Further, a is in the form of A2-A1-A1-A2-A2.
Alternatively, b is composed of A2 and A3. Further, b is in the form of A2-A3-A3-A2-A2.
Optionally, A1 is track number information.
Optionally, A2 is track name information.
Optionally, the A3 is track priority information.
Therefore, the data processing method based on the mechanical arm described by the embodiment of the invention can determine the target control model through matching the track characteristic information and the model characteristic information, and provides a realization path for determining the target control model, which is more beneficial to realizing smooth control of the mechanical arm, thereby improving the control efficiency of the mechanical arm.
Example III
Referring to fig. 3, fig. 3 is a schematic structural diagram of a data processing device based on a mechanical arm according to an embodiment of the invention. The apparatus described in fig. 3 can be applied to a data processing system, such as a local server or a cloud server for data processing based on a mechanical arm, which is not limited in the embodiments of the present invention.
As shown in fig. 3, the apparatus may include:
an acquiring module 301, configured to acquire image information of a target object;
the first determining module 302 is configured to determine a target track information set according to the target object image information and a preset track planning rule;
a second determining module 303, configured to determine a control instruction set according to the target track information set; the control instruction set comprises a plurality of target control instructions; the target control command is used for indicating the adjustment control of the mechanical arm.
Therefore, implementing the data processing device based on the mechanical arm described in fig. 3 can determine the target track information from the target object image information by using the track planning rule, and then process the target track information to obtain the target control instruction for indicating to regulate and control the mechanical arm, which is beneficial to realizing smooth control of the mechanical arm, thereby improving the control efficiency of the mechanical arm.
In another alternative embodiment, as shown in fig. 4, the first determining module 302 includes a first determining sub-module 3021, a second determining sub-module 3022, and a third determining sub-module 3023, wherein:
a first determining submodule 3021, configured to determine barycentric coordinate information corresponding to the target object according to the image information of the target object;
a second determining submodule 3022, configured to determine initial pose information of the mechanical arm according to barycentric coordinate information; the initial gesture information of the mechanical arm is used for configuring the initial gesture of the mechanical arm;
and a third determining submodule 3023, configured to determine the target track information set according to the initial pose information of the mechanical arm and the preset track planning model.
Therefore, the data processing device based on the mechanical arm described in fig. 4 can process the image information of the target object to obtain the barycentric coordinate information corresponding to the target object, determine the initial posture information of the mechanical arm for configuring the initial posture of the mechanical arm, and determine the target track information through the track planning model, thereby being beneficial to realizing smooth control of the mechanical arm and improving the control efficiency of the mechanical arm.
In yet another alternative embodiment, as shown in fig. 4, the specific manner of determining, by the first determining submodule 3021, barycentric coordinate information corresponding to the target object according to the image information of the target object is:
Processing the image information of the target object according to a preset target detection rule to obtain a target area;
processing the target area according to a preset feature extraction rule to obtain a pixel coordinate information set; the pixel coordinate information includes at least 2 pixel coordinate information;
and calculating the pixel coordinate information set to obtain barycentric coordinate information corresponding to the target object.
Therefore, the data processing device based on the mechanical arm described in fig. 4 can process the image information of the target object by using the target detection rule to obtain the target area, and then determine the coordinate information of a plurality of pixels by using the feature extraction rule, and further determine the coordinate information of the center of gravity corresponding to the target object.
In yet another alternative embodiment, as shown in fig. 4, the mechanical arm initial pose information includes camera initial pose information and joint angle initial pose information;
the second determining submodule 3022 determines the initial pose information of the mechanical arm according to the barycentric coordinate information in the following specific manner:
acquiring initial coordinate information of the mechanical arm and structural information of the mechanical arm;
Calculating the barycentric coordinate information and the mechanical arm initial coordinate information to obtain camera initial attitude information;
and calculating and processing the initial posture information of the camera and the mechanical arm structure information to obtain the initial posture information of the joint angle.
Therefore, the data processing device based on the mechanical arm described in fig. 4 can be implemented to determine the initial pose information of the camera through the calculation processing of the gravity center coordinate information and the initial coordinate information of the mechanical arm, and determine the initial pose information of the joint angle by using the structural information of the mechanical arm, which is more beneficial to realizing smooth control of the mechanical arm, thereby improving the control efficiency of the mechanical arm.
In yet another alternative embodiment, as shown in FIG. 4, the trajectory planning model includes a first planning model and a second planning model;
the third determining submodule 3023 determines, according to the initial pose information of the mechanical arm and the preset trajectory planning model, a specific manner of determining the target trajectory information set is as follows:
calculating the initial attitude information of the mechanical arm to obtain initial point information;
acquiring a target point information set; the target point information set comprises L pieces of target point information; l is a positive integer greater than or equal to 2;
calculating the initial point information and the target point information set by using a first planning model to obtain an initial track information set; the initial track information set comprises L initial track information;
Determining running time information according to the initial track information set;
and processing the running time information by using the second planning model to obtain a target track information set.
Therefore, the data processing device based on the mechanical arm described in fig. 4 can determine the target track information by processing the initial gesture information and the target point information of the mechanical arm, which is more beneficial to realizing smooth control of the mechanical arm, thereby improving the control efficiency of the mechanical arm.
In yet another alternative embodiment, as shown in fig. 4, the second determining module 303 determines, according to the target track information set, a specific manner of determining the control instruction set is:
determining a target control model set according to the target track information set; the target control model set comprises a plurality of target control models;
determining attitude position information according to the image information of the target object and a preset attitude analysis model;
and processing the attitude and position information by using the target control model set to obtain a control instruction set.
Therefore, the data processing device based on the mechanical arm described in fig. 4 can determine the target track information from the target object image information by using the track planning rule, process the target track information to obtain the target control model, process the target object image information by using the gesture analysis model to determine the gesture position information, and process the gesture position information by using the target control model to obtain the target control instruction for instructing to regulate and control the mechanical arm, thereby being beneficial to realizing smooth control on the mechanical arm and improving the control efficiency of the mechanical arm.
In yet another alternative embodiment, as shown in fig. 4, the second determining module 303 determines the set of target control models according to the set of target trajectory information in the following specific manner:
for any target track information, matching track characteristic information corresponding to the target track information with model characteristic information corresponding to all the to-be-selected control models in a preset to-be-selected control model set to obtain a matching result; the track characteristic information comprises M track information elements; m is an odd positive integer; the model characteristic information comprises N model information elements; n is an odd positive integer; track element information corresponding to the track information element at the middle position in the track characteristic information is matched with track element information corresponding to the track information element at the tail position; track element information corresponding to the track information element at the second position in sequence in the track characteristic information is matched with track element information corresponding to the track information element at the last position;
when the matching result indicates that the target model characteristic information matched with the track characteristic information corresponding to the target track information exists in the to-be-selected control model set, determining the to-be-selected control model corresponding to the target model characteristic information as a target control model.
Therefore, the implementation of the data processing device based on the mechanical arm described in fig. 4 can determine the target control model by matching the track characteristic information and the model characteristic information, which provides a realization path for determining the target control model, and is more beneficial to realizing smooth control of the mechanical arm, thereby improving the control efficiency of the mechanical arm.
Example IV
Referring to fig. 5, fig. 5 is a schematic structural diagram of a data processing apparatus based on a mechanical arm according to an embodiment of the present invention. The apparatus described in fig. 5 can be applied to a data processing system, such as a local server or a cloud server for data processing based on a mechanical arm, which is not limited in the embodiments of the present invention. As shown in fig. 5, the apparatus may include:
a memory 401 storing executable program codes;
a processor 402 coupled with the memory 401;
the processor 402 invokes executable program codes stored in the memory 401 for performing the steps in the robot-based data processing method described in the first or second embodiment.
Example five
The embodiment of the invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps in the data processing method based on the mechanical arm described in the first embodiment or the second embodiment.
Example six
Embodiments of the present invention disclose a computer program product comprising a non-transitory computer readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the robot-based data processing method described in embodiment one or embodiment two.
The apparatus embodiments described above are merely illustrative, in which the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), one-time programmable Read-Only Memory (OTPROM), electrically erasable programmable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Finally, it should be noted that: the embodiment of the invention discloses a data processing method and device based on a mechanical arm, which are only disclosed as a preferred embodiment of the invention, and are only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.
Claims (5)
1. A method for processing data based on a mechanical arm, the method comprising:
acquiring image information of a target object;
determining a target track information set according to the target object image information and a preset track planning rule;
determining a control instruction set according to the target track information set; the control instruction set comprises a plurality of target control instructions; the target control instruction is used for indicating the adjustment control of the mechanical arm;
the determining the target track information set according to the target object image information and a preset track planning rule comprises the following steps:
According to the image information of the target object, determining barycentric coordinate information corresponding to the target object;
determining initial attitude information of the mechanical arm according to the barycentric coordinate information; the initial gesture information of the mechanical arm is used for configuring the initial gesture of the mechanical arm;
determining a target track information set according to the initial gesture information of the mechanical arm and a preset track planning model;
the determining the barycentric coordinate information corresponding to the target object according to the target object image information comprises the following steps:
processing the image information of the target object according to a preset target detection rule to obtain a target area;
processing the target area according to a preset feature extraction rule to obtain a pixel coordinate information set; the pixel coordinate information includes at least 2 pixel coordinate information;
calculating the pixel coordinate information set to obtain barycentric coordinate information corresponding to the target object;
the determining the target track information set according to the initial gesture information of the mechanical arm and a preset track planning model comprises the following steps:
calculating the initial attitude information of the mechanical arm to obtain initial point information;
Acquiring a target point information set; the target point information set comprises L pieces of target point information; l is a positive integer greater than or equal to 2;
calculating the initial point information and the target point information set by using a first planning model to obtain an initial track information set; the initial track information set comprises the L initial track information;
determining running time information according to the initial track information set;
processing the running time information by using a second planning model to obtain a target track information set;
the calculating the initial point information and the target point information set by using the first planning model to obtain an initial track information set comprises the following steps:
for any one initial point information and any one target point information, carrying out track design on the any one initial point information and the any one target point information by using a first planning model to obtain initial track information corresponding to the any one initial point information and the any one target point information;
the processing the running time information by using the second planning model to obtain a target track information set includes:
Optimizing the initial track information set by using a second planning model to obtain a standby track information set;
determining a standby running time set according to the standby track information set; the standby run time set comprises a plurality of standby run time information;
processing the standby running time set and the running time information according to preset preferable termination conditions, and determining target running time;
determining a track information set corresponding to the target running time as a target track information set;
the target track information set comprises a plurality of target track information, the first planning model is a planning model based on third-order B spline interpolation, the second planning model is a planning model based on an improved genetic algorithm, and the second planning model comprises binary chromosome coding and/or initial population and/or selection operator and/or crossover operator and/or mutation operator;
after determining the target track information set according to the initial gesture information of the mechanical arm and a preset track planning model, the method further comprises:
determining a target control model set according to the target track information set;
Determining attitude position information according to the target object image information and a preset attitude analysis model;
processing the attitude and position information by using the target control model set to obtain a control instruction set;
the gesture analysis model comprises an analysis model based on deep learning and/or an analysis model based on a neural network and/or an analysis model based on a Jacobian matrix;
the determining a control instruction set according to the target track information set comprises the following steps:
determining a target control model set according to the target track information set; the target control model set comprises a plurality of target control models;
determining attitude position information according to the target object image information and a preset attitude analysis model;
processing the attitude and position information by using the target control model set to obtain a control instruction set;
the determining a target control model set according to the target track information set includes:
for any target track information, matching track characteristic information corresponding to the target track information with model characteristic information corresponding to all the to-be-selected control models in a preset to-be-selected control model set to obtain a matching result; the track characteristic information comprises M track information elements; m is an odd positive integer; the model characteristic information comprises N model information elements; the N is an odd positive integer; track element information corresponding to the track information element at the middle position in the track characteristic information is matched with track element information corresponding to the track information element at the tail position; track element information corresponding to the track information element at the second position in sequence in the track characteristic information is matched with track element information corresponding to the track information element at the last position;
When the matching result indicates that target model characteristic information matched with track characteristic information corresponding to the target track information exists in the to-be-selected control model set, determining that the to-be-selected control model corresponding to the target model characteristic information is a target control model;
the track characteristic information comprises track number information and/or track name information and/or track priority information;
wherein the number corresponding to the target control model is consistent with the number corresponding to the target track information;
the target control model comprises a control model based on fuzzy PID, and/or a control model based on a synovial membrane control algorithm, and/or a control algorithm based on a neural network, and/or a control model based on a state observer, and/or a control model based on an impedance control algorithm.
2. The method for processing data based on the mechanical arm according to claim 1, wherein the mechanical arm initial pose information includes camera initial pose information and joint angle initial pose information;
determining the initial attitude information of the mechanical arm according to the barycentric coordinate information comprises the following steps:
acquiring initial coordinate information of the mechanical arm and structural information of the mechanical arm;
Calculating the barycentric coordinate information and the mechanical arm initial coordinate information to obtain the camera initial attitude information;
and calculating the initial pose information of the camera and the mechanical arm structure information to obtain the initial pose information of the joint angle.
3. A robotic-based data processing device, the device comprising:
the acquisition module is used for acquiring image information of the target object;
the first determining module is used for determining a target track information set according to the target object image information and a preset track planning rule;
the second determining module is used for determining a control instruction set according to the target track information set; the control instruction set comprises a plurality of target control instructions; the target control instruction is used for indicating the adjustment control of the mechanical arm;
the first determination module includes a first determination sub-module, a second determination sub-module, and a third determination sub-module, wherein:
the first determining submodule is used for determining barycentric coordinate information corresponding to the target object according to the image information of the target object;
the second determining submodule is used for determining initial gesture information of the mechanical arm according to the barycenter coordinate information; the initial gesture information of the mechanical arm is used for configuring the initial gesture of the mechanical arm;
The third determining submodule is used for determining a target track information set according to the initial gesture information of the mechanical arm and a preset track planning model;
the first determining submodule determines the specific mode of barycentric coordinate information corresponding to the target object according to the image information of the target object as follows:
processing the image information of the target object according to a preset target detection rule to obtain a target area;
processing the target area according to a preset feature extraction rule to obtain a pixel coordinate information set; the pixel coordinate information includes at least 2 pixel coordinate information;
calculating the pixel coordinate information set to obtain barycentric coordinate information corresponding to the target object;
the third determining submodule determines the specific mode of the target track information set according to the initial gesture information of the mechanical arm and a preset track planning model as follows:
calculating the initial attitude information of the mechanical arm to obtain initial point information;
acquiring a target point information set; the target point information set comprises L pieces of target point information; l is a positive integer greater than or equal to 2;
calculating the initial point information and the target point information set by using a first planning model to obtain an initial track information set; the initial track information set comprises the L initial track information;
Determining running time information according to the initial track information set;
processing the running time information by using a second planning model to obtain a target track information set;
the calculating the initial point information and the target point information set by using the first planning model to obtain an initial track information set comprises the following steps:
for any one initial point information and any one target point information, carrying out track design on the any one initial point information and the any one target point information by using a first planning model to obtain initial track information corresponding to the any one initial point information and the any one target point information;
the processing the running time information by using the second planning model to obtain a target track information set includes:
optimizing the initial track information set by using a second planning model to obtain a standby track information set;
determining a standby running time set according to the standby track information set; the standby run time set comprises a plurality of standby run time information;
processing the standby running time set and the running time information according to preset preferable termination conditions, and determining target running time;
Determining a track information set corresponding to the target running time as a target track information set;
the target track information set comprises a plurality of target track information, the first planning model is a planning model based on third-order B spline interpolation, the second planning model is a planning model based on an improved genetic algorithm, and the second planning model comprises binary chromosome coding and/or initial population and/or selection operator and/or crossover operator and/or mutation operator;
after the third determining submodule determines a target track information set according to the initial gesture information of the mechanical arm and a preset track planning model, the device is further used for:
determining a target control model set according to the target track information set;
determining attitude position information according to the target object image information and a preset attitude analysis model;
processing the attitude and position information by using the target control model set to obtain a control instruction set;
the gesture analysis model comprises an analysis model based on deep learning and/or an analysis model based on a neural network and/or an analysis model based on a Jacobian matrix;
The second determining module determines the specific mode of the control instruction set according to the target track information set as follows:
determining a target control model set according to the target track information set; the target control model set comprises a plurality of target control models;
determining attitude position information according to the target object image information and a preset attitude analysis model;
processing the attitude and position information by using the target control model set to obtain a control instruction set;
the determining a target control model set according to the target track information set includes:
for any target track information, matching track characteristic information corresponding to the target track information with model characteristic information corresponding to all the to-be-selected control models in a preset to-be-selected control model set to obtain a matching result; the track characteristic information comprises M track information elements; m is an odd positive integer; the model characteristic information comprises N model information elements; the N is an odd positive integer; track element information corresponding to the track information element at the middle position in the track characteristic information is matched with track element information corresponding to the track information element at the tail position; track element information corresponding to the track information element at the second position in sequence in the track characteristic information is matched with track element information corresponding to the track information element at the last position;
When the matching result indicates that target model characteristic information matched with track characteristic information corresponding to the target track information exists in the to-be-selected control model set, determining that the to-be-selected control model corresponding to the target model characteristic information is a target control model;
the track characteristic information comprises track number information and/or track name information and/or track priority information;
wherein the number corresponding to the target control model is consistent with the number corresponding to the target track information;
the target control model comprises a control model based on fuzzy PID, and/or a control model based on a synovial membrane control algorithm, and/or a control algorithm based on a neural network, and/or a control model based on a state observer, and/or a control model based on an impedance control algorithm.
4. A robotic-based data processing device, the device comprising:
a memory storing executable program code;
a processor coupled to the memory;
the processor invokes the executable program code stored in the memory to perform the robotic-based data processing method of any of claims 1-2.
5. A computer storage medium storing computer instructions which, when invoked, are adapted to perform the robotic-based data processing method of any one of claims 1-2.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102613041A (en) * | 2012-04-13 | 2012-08-01 | 浙江工业大学 | Grape bagging robot system based on machine vision |
| CN109398688A (en) * | 2018-11-16 | 2019-03-01 | 湖南大学 | A kind of rotor flying double mechanical arms target positioning grasping system and method |
| CN110065064A (en) * | 2018-01-24 | 2019-07-30 | 南京机器人研究院有限公司 | A kind of robot sorting control method |
| EP3603370A1 (en) * | 2018-08-03 | 2020-02-05 | Lg Electronics Inc. | Moving robot, method for controlling moving robot, and moving robot system |
| CN210616515U (en) * | 2019-06-19 | 2020-05-26 | 西北工业大学 | Six-degree-of-freedom autonomous sorting mechanical arm |
| CN111203893A (en) * | 2018-11-22 | 2020-05-29 | 天津工业大学 | Intelligent shearing robot |
| CN111347426A (en) * | 2020-03-26 | 2020-06-30 | 季华实验室 | Mechanical arm accurate placement track planning method based on 3D vision |
| CN113211447A (en) * | 2021-05-27 | 2021-08-06 | 山东大学 | Mechanical arm real-time perception planning method and system based on bidirectional RRT algorithm |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3587042A1 (en) * | 2018-06-25 | 2020-01-01 | Siemens Aktiengesellschaft | Method, apparatus and system for determining a trajectory of a robot's end effector |
| CN111959495B (en) * | 2020-06-29 | 2021-11-12 | 阿波罗智能技术(北京)有限公司 | Vehicle control method and device and vehicle |
-
2021
- 2021-11-09 CN CN202111320039.8A patent/CN114083533B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102613041A (en) * | 2012-04-13 | 2012-08-01 | 浙江工业大学 | Grape bagging robot system based on machine vision |
| CN110065064A (en) * | 2018-01-24 | 2019-07-30 | 南京机器人研究院有限公司 | A kind of robot sorting control method |
| EP3603370A1 (en) * | 2018-08-03 | 2020-02-05 | Lg Electronics Inc. | Moving robot, method for controlling moving robot, and moving robot system |
| CN109398688A (en) * | 2018-11-16 | 2019-03-01 | 湖南大学 | A kind of rotor flying double mechanical arms target positioning grasping system and method |
| CN111203893A (en) * | 2018-11-22 | 2020-05-29 | 天津工业大学 | Intelligent shearing robot |
| CN210616515U (en) * | 2019-06-19 | 2020-05-26 | 西北工业大学 | Six-degree-of-freedom autonomous sorting mechanical arm |
| CN111347426A (en) * | 2020-03-26 | 2020-06-30 | 季华实验室 | Mechanical arm accurate placement track planning method based on 3D vision |
| CN113211447A (en) * | 2021-05-27 | 2021-08-06 | 山东大学 | Mechanical arm real-time perception planning method and system based on bidirectional RRT algorithm |
Non-Patent Citations (4)
| Title |
|---|
| 基于最小外接矩形和Hough变换的定位算法;黄超, 茅健, 徐斌;《组合机床与自动化加工技术》(第8期);正文第2-5页 * |
| 基于视觉定位的机械臂运动控制;陈茜;《中国优秀硕士学位论文全文数据库信息科技辑》;摘要，正文第4-6, 15, 25-27，38-44，46, 50-68页 * |
| 基于视觉定位的机械臂运动控制;陈茜;《中国优秀硕士学位论文全文数据库信息科技辑I138-620》;摘要，正文第4-6,15,25-27，38-44，46,50-68页 * |
| 基于视觉的物体识别与抓取运动控制技术的研究;郝晗;《中国优秀硕士学位论文全文数据库 (信息科技辑)》;摘要，正文第8-10, 16-19, 39-42, 47-49, 53-54页 * |
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