CN115488864A - Robot teaching track optimization method and device, electronic equipment and storage medium - Google Patents
Robot teaching track optimization method and device, electronic equipment and storage medium Download PDFInfo
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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
- B25J9/0081—Programme-controlled manipulators with master teach-in means
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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- B25J9/1661—Programme controls characterised by programming, planning systems for manipulators characterised by task planning, object-oriented languages
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
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- 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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Abstract
The application discloses a robot teaching track optimization method, a device, electronic equipment and a storage medium, which are applied to the technical field of robot teaching, wherein the robot teaching track optimization method comprises the following steps: acquiring robot teaching information of a teaching robot when the teaching robot executes a teaching task; positioning a track optimization node point of a teaching total track of the teaching robot according to the robot teaching information; and optimizing the teaching total track according to the track optimization node position to obtain an optimized total track, wherein the optimized total track has a preset teaching effect. The teaching robot solves the technical problem that teaching effects of teaching robots are poor.
Description
Technical Field
The application relates to the technical field of robot teaching, in particular to a robot teaching track optimization method and device, an electronic device and a readable storage medium.
Background
With the continuous development of science and technology, the teaching robot relies on the advantage in the aspects of high operation precision and low error rate, and has gained extensive application in each industry, for example on spraying, welding, assembly and many work production lines such as point are glued, can both see the teaching robot of automatic operation, at present, the user is usually controlled the teaching robot through the teaching box and is carried out disposable teaching, in order to realize reappearing of teaching orbit, but, in the in-process of disposable teaching, because there is the limitation of naked eye when the user carries out teaching, make the adjustment to teaching orbit have the deviation, that is, the accuracy of teaching orbit is low, so, the teaching effect of present teaching robot is poor.
Disclosure of Invention
The application mainly aims to provide a robot teaching track optimization method, a robot teaching track optimization device, electronic equipment and a readable storage medium, and aims to solve the technical problem that teaching effects of teaching robots in the prior art are poor.
In order to achieve the above object, the present application provides a robot teaching trajectory optimization method, where the robot teaching trajectory optimization method includes:
acquiring robot teaching information of a teaching robot when the teaching robot executes a teaching task;
positioning a track optimization node point of a teaching total track of the teaching robot according to the robot teaching information;
and optimizing the teaching total track according to the track optimization node position to obtain an optimized total track, wherein the optimized total track has a preset teaching effect.
Optionally, the optimizing the teaching total trajectory according to the trajectory optimization node point to obtain an optimized total trajectory includes:
determining a first teaching sub-track corresponding to the teaching total track and a second teaching sub-track corresponding to the teaching total track according to the track optimization node point, wherein the first teaching sub-track has a preset teaching effect, and the second teaching sub-track does not have the preset teaching effect;
acquiring a historical teaching sub-track corresponding to the second teaching sub-track;
and splicing the historical teaching sub-track and the first teaching sub-track into the optimized total track.
Optionally, the step of determining a first teaching partial trajectory corresponding to the teaching total trajectory and a second teaching partial trajectory corresponding to the teaching total trajectory according to the trajectory optimization node point includes:
acquiring a track starting point of the teaching total track;
taking a track formed by the track starting point and the track optimizing node bit as the first teaching partial track;
and fitting the second teaching sub-track according to the track fitting point corresponding to the track optimization node.
Optionally, the step of fitting the second teaching score trajectory according to the trajectory fitting point corresponding to the trajectory optimization node point includes:
according to the track optimization node position and the first teaching sub-track, inquiring a corresponding historical teaching total track in a preset historical track library;
selecting at least one fitting track point from the historical teaching total track, and fitting at least one corresponding section of fitting track based on the pose information of each fitting track point;
and splicing the fitting tracks into the second teaching partial track.
Optionally, after the step of splicing the historical teaching part track and the first teaching part track into the optimized total track, the robot teaching track optimization method further includes:
acquiring at least one track splicing node position corresponding to the optimized total track;
according to the track cycle node position corresponding to each track splicing node position, respectively carrying out track planning on the historical teaching branch track and the first teaching branch track to obtain a corresponding connection track;
and replacing the cycle branch track corresponding to the cycle node bit of each track with each connecting track.
Optionally, the step of positioning a trajectory optimization node of a total trajectory taught by the teaching robot according to the robot teaching information includes:
detecting whether track deviation exists in a teaching total track of the teaching robot or not according to the robot teaching information;
if so, taking a track deviation point of the teaching total track as the track optimization node point;
and if not, taking a preset teaching track point of the teaching total track as the track optimization node point.
Optionally, the robot teaching information includes robot teaching image information and robot teaching stress information,
the step of acquiring robot teaching information of the teaching robot when the teaching robot executes a teaching task comprises the following steps:
when the teaching robot is detected to execute a teaching task, determining whether the teaching task is a one-time teaching task;
if the teaching task is the one-time teaching task, acquiring robot teaching image information and robot teaching stress information respectively;
and if the teaching task is not the one-time teaching task, acquiring teaching stress information of the robot.
In order to achieve the above object, the present application further provides a robot teaching trajectory optimization device, including:
the acquisition module is used for acquiring robot teaching information of the teaching robot when the teaching robot executes a teaching task;
the positioning module is used for positioning a track optimization node point of a teaching total track of the teaching robot according to the robot teaching information;
and the optimization module is used for optimizing the teaching total track according to the track optimization node position to obtain an optimized total track, wherein the optimized total track has a preset teaching effect.
Optionally, the optimization module is further configured to:
determining a first teaching sub-track corresponding to the teaching total track and a second teaching sub-track corresponding to the teaching total track according to the track optimization node point, wherein the first teaching sub-track has a preset teaching effect, and the second teaching sub-track does not have the preset teaching effect;
acquiring a historical teaching sub-track corresponding to the second teaching sub-track;
and splicing the historical teaching sub-track and the first teaching sub-track into the optimized total track.
Optionally, the optimization module is further configured to:
acquiring a track starting point of the teaching total track;
taking a track formed by the track starting point and the track optimizing node bit as the first teaching partial track;
and fitting the second teaching branch track according to the track fitting point corresponding to the track optimization node.
Optionally, the optimization module is further configured to:
according to the track optimization node positions and the first teaching sub-tracks, querying corresponding historical teaching total tracks in a preset historical track library;
selecting at least one fitting track point from the historical teaching total track, and fitting at least one corresponding section of fitting track based on the pose information of each fitting track point;
and splicing the fitting tracks into the second teaching partial track.
Optionally, the robot teaching trajectory optimization device is further configured to:
acquiring at least one track splicing node position corresponding to the optimized total track;
according to the track cycle node position corresponding to each track splicing node position, respectively carrying out track planning on the historical teaching branch track and the first teaching branch track to obtain a corresponding connection track;
and replacing the periodic sub-tracks corresponding to the periodic node positions of each track with the connection tracks.
Optionally, the positioning module is further configured to:
detecting whether a teaching total track of the teaching robot has track deviation or not according to the robot teaching information;
if so, taking the track deviation point of the teaching total track as the track optimization node point;
and if not, taking a preset teaching track point of the teaching total track as the track optimization node point.
Optionally, the robot teaching information includes robot teaching image information and robot teaching stress information, and the obtaining module is further configured to:
when the teaching robot is detected to execute a teaching task, determining whether the teaching task is a one-time teaching task;
if the teaching task is the one-time teaching task, respectively acquiring robot teaching image information and robot teaching stress information;
and if the teaching task is not the one-time teaching task, acquiring teaching stress information of the robot.
The present application further provides an electronic device, the electronic device including: a memory, a processor and a program of the robot teaching trajectory optimization method stored on the memory and executable on the processor, the program of the robot teaching trajectory optimization method being executable by the processor to implement the steps of the robot teaching trajectory optimization method as described above.
The present application also provides a computer-readable storage medium having stored thereon a program for implementing a robot teaching trajectory optimization method, the program for implementing the robot teaching trajectory optimization method implementing the steps of the robot teaching trajectory optimization method as described above when executed by a processor.
The present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the robot teaching trajectory optimization method as described above.
The application provides a robot teaching track optimization method, a device, electronic equipment and a readable storage medium, namely, robot teaching information of a teaching robot during execution of a teaching task is obtained; positioning a track optimization node point of a teaching total track of the teaching robot according to the robot teaching information; and optimizing the teaching total track according to the track optimization node position to obtain an optimized total track, wherein the optimized total track has a preset teaching effect, namely, the purpose of optimizing the teaching total track of the teaching robot through the track optimization node position is realized. Because the track optimization node position is through robot teaching information location when teaching robot carries out the teaching task, that is, the user can receive in real time teaching track point that teaching robot was automatic positioning in the teaching process, and then need not the user and rely on past experience to carry out online teaching or rely on established teaching procedure to teach, can realize in time adjusting robot teaching in-process teaching track's purpose, and not rely on the user to carry out the manual adjustment of teaching track in the disposable teaching process, so overcome because there is the limitation of naked eye when the user teaches, make the adjustment to teaching track have the deviation, that is, teaching track's low precision's technical defect, so, teaching robot's teaching effect has been promoted.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive labor.
FIG. 1 is a schematic flowchart of a first embodiment of a robot teaching trajectory optimization method according to the present application;
FIG. 2 is a schematic flowchart of a robot teaching trajectory optimization method according to a second embodiment of the present application;
FIG. 3 is a schematic diagram of an embodiment of a robot teaching trajectory optimization apparatus according to the present application;
fig. 4 is a schematic structural diagram of an electronic device related to a robot teaching trajectory optimization method in an embodiment of the present application.
The objectives, features, and advantages of the present application will be further described with reference to the accompanying drawings.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanying figures are described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Firstly, it should be understood that the basic working principle of robot teaching is teaching track reproduction, an industrial robot is guided by a user to operate once according to a teaching task, and then the robot automatically memorizes the position of each action taught in the process, and automatically generates a program for continuously executing all operations, after the teaching is completed, the user only needs to give a starting command to the robot, the robot can accurately execute the teaching task, so that the user guides the industrial robot to execute the teaching track of the teaching task, which is enough to influence the teaching effect of the teaching robot during teaching.
In a first embodiment of the robot teaching trajectory optimization method of the present application, referring to fig. 1, the robot teaching trajectory optimization method includes:
step S10, robot teaching information of a teaching robot during execution of a teaching task is acquired;
s20, positioning a track optimization node point of a teaching total track of the teaching robot according to the robot teaching information;
in this embodiment, it should be noted that the teaching robot is used for characterizing an industrial robot capable of automatically operating according to teaching by a teaching person, and specifically may be a spraying robot, a dispensing robot, a welding robot, a transfer robot, and the like, and the teaching task is set by the teaching person according to actual production requirements, for example, in an implementable manner, if the teaching robot is a transfer robot, it can be determined that the teaching person teaches the transfer robot to transfer a workpiece to be processed from an initial position a to an end position B as a teaching task.
Additionally, it should be noted that the robot teaching track optimization method is applied to a robot teaching track optimization method, the robot teaching track optimization system is disposed in a teach pendant, the teach pendant may be specifically a handheld teach pendant or a fixed teach pendant, the robot teaching optimization system is further disposed with an information collecting sensor besides components such as a display, a processor and an operation panel, the information collecting sensor is configured to collect robot teaching information, the robot teaching information is used to represent teaching parameters that change in real time during teaching of the teaching robot, specifically teaching point attitude information, teaching point stress information, teaching point time information, and the like.
Additionally, it should be noted that the trajectory optimization node is used for representing a trajectory starting node for optimizing a teaching total trajectory of the teaching robot, and because in the process of teaching a workpiece, the workpiece itself may have a flaw or a part of the teaching trajectory needs to be adjusted, the trajectory starting node for optimizing may be positioned in the teaching total trajectory of the teaching robot, and the teaching total trajectory is optimized according to a preset trajectory adjustment period, so that the adjusted teaching total trajectory has a preset teaching effect, where the preset trajectory adjustment period may be set by a demonstrator according to an actual need.
Additionally, it should be noted that the teaching box is provided with a human-computer interaction interface, a teaching person can read robot teaching information of any teaching track point of the teaching robot on a teaching total track in real time through the human-computer interaction interface, a historical teaching total track for teaching the teaching task and initial teaching information of any teaching track point on the historical teaching total track are stored in a storage device of the teaching box, and then when the current pose of the teaching robot is fixed to a certain teaching track point, whether the teaching track point is located as a track optimization node point or not can be determined through comparing the initial teaching track with the robot teaching information.
As an example, steps S10 to S20 include: the teaching robot comprises an information acquisition sensor, a robot controller, a teaching robot and a teaching robot, wherein the information acquisition sensor is used for acquiring teaching information of the robot when the teaching robot executes a teaching task; and positioning a track optimization node point of a teaching total track of the teaching robot by comparing the robot teaching information with the initial teaching information.
The step of positioning the track optimization node point of the teaching total track of the teaching robot according to the robot teaching information comprises the following steps:
step A10, detecting whether a teaching total track of the teaching robot has track deviation or not according to the robot teaching information;
step A20, if yes, taking a track deviation point of the teaching total track as the track optimization node point;
and A30, if not, taking a preset teaching track point of the teaching total track as the track optimization node point.
In this embodiment, it should be noted that, because robot teaching information and initial teaching information are specific parameter values, and then a teach person can compare information manually in real time at the human-computer interaction section of the teach box, and also can be in when inputting the control command corresponding to the track optimization on the teach box, teach box automatic acquisition the teaching robot is located robot teaching information and teaching initial information of a certain preset teaching track point, and when the information is inconsistent, judge that the teaching robot will have a track deviation if still teaching with this teaching track point, so, the track deviation is for the teaching robot executes the historical teaching total track that the teaching task produced, wherein, historical teaching total track both can be the historical teaching total track that the teaching robot produced when executing the teaching task last time, also can be the historical teaching total track that the teaching robot produced when executing the teaching task for the first time, the track deviation point is used for representing the starting point of the track deviation in the teaching total track, and specifically can be any preset teaching total track point in the teaching total track.
As an example, the steps a10 to a30 include: determining whether a teaching total track of the teaching robot has track deviation or not by detecting whether robot teaching information of the teaching robot is consistent with the initial teaching information when a teaching track point is preset; if the robot teaching information corresponding to the preset teaching track point is inconsistent with the corresponding initial teaching information, determining that the teaching total track of the teaching robot has track deviation, and taking the preset teaching track point as the track optimization node point; and if the robot teaching information corresponding to the preset teaching track point is consistent with the corresponding initial teaching information, taking the preset teaching track point of the teaching total track as the track optimization node point.
The robot teaching information comprises robot teaching image information and robot teaching stress information, and the step of acquiring the robot teaching information of the teaching robot in the teaching task execution process comprises the following steps:
step B10, when the teaching robot is detected to execute a teaching task, determining whether the teaching task is a one-time teaching task;
step B20, if the teaching task is the one-time teaching task, respectively acquiring robot teaching image information and robot teaching stress information;
and B30, if the teaching task is not the one-time teaching task, acquiring teaching stress information of the robot.
In this embodiment, it should be noted that the robot teaching image information and the robot teaching stress information are both the robot teaching information, the robot teaching image information is used to represent pose parameters of the teaching robot changing in real time during the teaching process, the robot teaching stress information is used to represent forces generated by an end operator of the teaching robot changing in real time during the teaching process, the former can collect image data through a vision sensor and can analyze specific poses of the teaching robot in a cartesian space based on the image data, the latter can capture specific stresses (sizes and directions) of the teaching robot in the cartesian space based on the force sensor, and for different teaching tasks, the requirement on the accuracy of teaching trajectories is different, for example, assuming that a preset teaching task is a teaching total track corresponding to a teaching track point E to a teaching track point F executed by a teaching robot aiming at a workpiece, if the workpiece is a spraying workpiece, the teaching total track is a teaching total track from the teaching track point E to the teaching track point F on the surface of the spraying workpiece, at this time, the teaching accuracy is required to be ensured to be 100%, if the workpiece is a carrying workpiece, the teaching total track is a teaching total track from the teaching track point E to the teaching track point F in the space, and due to the adjustability of the space, the teaching accuracy is not required to be ensured to be 100%, therefore, the acquisition range of the teaching information of the robot can be adjusted according to the different requirements of the teaching accuracy, and further the operation pressure is reduced, for example, if a serving object of a teaching robot is a teaching task which is not disposable, a vision sensor is not required to operate when a teaching box of the teaching robot is configured, further, even if there is a slight positioning error of the trajectory optimization node, the adjustment can be performed by repeating the teaching.
As an example, steps B10 to B30 include: when the teaching robot is detected to execute a teaching task, determining whether the teaching task is a one-time teaching task or not according to a robot identifier of the teaching robot, wherein the robot identifier can be a robot nameplate, a factory number or the like; if the teaching task is the one-time teaching task, acquiring teaching image information of the robot through the visual sensor, and acquiring teaching stress information of the robot through the force sensor; and if the teaching task is not the one-time teaching task, acquiring teaching stress information of the robot through the force sensor.
And S30, optimizing the teaching total track according to the track optimization node positions to obtain an optimized total track, wherein the optimized total track has a preset teaching effect.
As an example, step S30 includes: and optimizing the teaching total track according to the track optimization node position to obtain an optimized total track, wherein the optimized total track has a preset teaching effect.
The teaching total track is optimized according to the track optimization node positions, and the step of obtaining the optimized total track comprises the following steps:
step C10, according to the track optimization node position, determining a first teaching partial track corresponding to the teaching total track and a second teaching partial track corresponding to the teaching total track, wherein the first teaching partial track has a preset teaching effect, and the second teaching partial track does not have the preset teaching effect;
step C20, obtaining a historical teaching sub-track corresponding to the second teaching sub-track;
and step C30, splicing the historical teaching sub-track and the first teaching sub-track into the optimized total track.
In this embodiment, it should be noted that the first teaching partial trajectory is a partial trajectory in the teaching total trajectory, where a trajectory starting point of the teaching total trajectory is taken as a starting point, the trajectory optimization node point is taken as an end point, the second teaching partial trajectory is a partial trajectory in the teaching total trajectory, where a trajectory optimization node point of the teaching total trajectory is taken as a starting point, and a trajectory end point of the teaching total trajectory is taken as an end point, and the preset teaching effect may be determined by a demonstrator according to a historical teaching total trajectory before the teaching trajectory is optimized, for example, in an implementable manner, a section of teaching partial trajectory is intercepted from the historical teaching total trajectory, and the intercepted teaching partial trajectory has a preset teaching effect.
Additionally, it should be noted that, after a second teaching partial trajectory in the teaching total trajectories is determined, a historical teaching partial trajectory corresponding to the second teaching partial trajectory in the historical teaching total trajectories may be obtained according to the trajectory optimization node position, and then the historical teaching partial trajectory may be directly multiplexed to optimize the teaching total trajectory, for example, in an implementable manner, it is assumed that the teaching total trajectory is divided into partial trajectories A1, A2, B1, B2, and B3 by the trajectory optimization node position, where A1 and A2 are the first teaching partial trajectory, B1, B2, and B3 are the second teaching partial trajectory, and since the second teaching partial trajectory B1, B2, and B3 are obtained by different trajectory optimization node position locations, the second teaching partial trajectory B1, B2, and B3 are replaced with and updated to corresponding three sections of historical teaching partial trajectories C1, C2, and C3, and then A1, A2, C1, and C2, and C3 are jointly optimized as the total trajectory.
As an example, steps C10 to C30 include: segmenting the teaching total track according to at least one track optimization node point to obtain at least one first teaching sublevel track and at least one second teaching sublevel track; acquiring a historical teaching sub-track corresponding to the second teaching sub-track; and jointly splicing at least one historical teaching sub-track and each first teaching sub-track into the optimized total track according to time information corresponding to each track optimization node bit, wherein the time information is used for determining the splicing sequence of each section of sub-track when the sub-tracks are spliced into the optimized total track.
The step of determining a first teaching part track corresponding to the teaching total track and a second teaching part track corresponding to the teaching total track according to the track optimization node position comprises the following steps:
step D10, obtaining a track starting point of the teaching total track;
step D20, taking a track formed by the track starting point and the track optimizing node bit as the first teaching partial track;
and D30, fitting the second teaching branch track according to the track fitting point corresponding to the track optimization node.
In this embodiment, it should be noted that, in a teaching task, when the teaching robot is located at the trajectory optimization node, a teaching trajectory of the teaching robot during subsequent teaching needs to be adjusted in time, that is, the second teaching sub-trajectory does not really appear, and then the second teaching sub-trajectory can be obtained through trajectory fitting point fitting, where the trajectory fitting point is a fitting sampling point in the historical teaching total trajectory, and the fitting manner may be a manner of fitting a spline curve based on the fitting sampling point.
As an example, steps D10 to D30 include: acquiring a track starting point of the teaching total track; taking a track formed by the track starting point and the track optimizing node bit as the first teaching partial track; and acquiring a track fitting point corresponding to the track optimization node position in the historical teaching total track, and fitting to obtain the second teaching partial track according to the track fitting point and a preset spline function, wherein the preset spline function can be a cubic B-spline basis function.
After the step of splicing the historical teaching sub-trajectory and the first teaching sub-trajectory into the optimized total trajectory, the robot teaching trajectory optimization method further includes:
step E10, acquiring at least one track splicing node position corresponding to the optimized total track;
step E20, respectively carrying out trajectory planning on the historical teaching sub-trajectory and the first teaching sub-trajectory according to the trajectory period node position corresponding to each trajectory splicing node position to obtain a corresponding joining trajectory;
and E30, replacing the period sub-tracks corresponding to the node positions of the track periods with the connection tracks.
In this embodiment, it should be noted that after the optimized total trajectory is obtained, the sub-trajectories are spliced into the optimized total trajectory according to a time sequence, but since the speeds near the start point and near the end point of each section of the teaching sub-trajectory are both close to zero, and thus a trajectory node point which does not meet the process requirement exists between adjacent sub-trajectories, the optimized total trajectory may be secondarily optimized, that is, teaching trajectory points near the start point and near the end point of each section of the teaching sub-trajectory are discarded, and a new trajectory section obtained by a preset trajectory planning method is linked to improve the trajectory accuracy of the optimized total trajectory, wherein the trajectory splicing trajectory point is used to represent the trajectory start point and the end point of the linked trajectory, and the preset trajectory planning method may be a PTP trajectory planning method.
Additionally, it should be noted that the track-period node position is a track node position corresponding to a preset teaching period to be optimized in a teaching period of the optimized total track, where a sub-track corresponding to the preset teaching period to be optimized is the teaching sub-track, and is specifically determined by a teach pendant according to teaching experience, for example, assuming that the teaching period of the optimized total track is 4ms (milliseconds), the period sub-track may be specifically a first hundred preset teaching periods (0.4 s) and a last hundred preset periods (0.4 s) of the optimized total track.
As an example, steps E10 to E30 include: acquiring at least one track splicing node position corresponding to the optimized total track; according to the track period node positions corresponding to the track splicing node positions, respectively carrying out track planning on the track starting and stopping node positions of the historical teaching sub-tracks and the starting and stopping node positions of the first teaching sub-tracks to obtain corresponding joining tracks; and replacing the cycle branch track corresponding to the cycle node bit of each track with each connecting track.
The embodiment of the application provides a robot teaching track optimization method, namely robot teaching information of a teaching robot during execution of a teaching task is obtained; positioning a track optimization node point of a teaching total track of the teaching robot according to the robot teaching information; and optimizing the teaching total track according to the track optimization node position to obtain an optimized total track, wherein the optimized total track has a preset teaching effect, namely, the purpose of optimizing the teaching total track of the teaching robot through the track optimization node position is realized. Because the track optimization node position is through robot teaching information location when teaching robot carries out the teaching task, that is, the user can receive in real time teaching track point that teaching robot was automatic positioning in the teaching process, and then need not the user and rely on past experience to carry out online teaching or rely on established teaching procedure to teach, can realize in time adjusting robot teaching in-process teaching track's purpose, and not rely on the user to carry out the manual adjustment of teaching track in the disposable teaching process, so overcome because there is the limitation of naked eye when the user teaches, make the adjustment to teaching track have the deviation, that is, teaching track's low precision's technical defect, so, teaching robot's teaching effect has been promoted.
Example two
Further, referring to fig. 2, in another embodiment of the present application, the same or similar contents as those in the first embodiment may refer to the above description, and are not repeated herein. On this basis, the step of fitting the second teaching partial trajectory according to the trajectory fitting point corresponding to the trajectory optimization node point includes:
step F10, according to the track optimization node position and the first teaching sub-track, inquiring a corresponding historical teaching total track in a preset historical track library;
f20, selecting at least one fitting track point from the historical teaching total track, and fitting at least one corresponding section of fitting track based on the pose information of each fitting track point;
and F30, splicing the fitting tracks into the second teaching partial track.
In this embodiment, it should be noted that at least one section of the historical teaching total track is stored in the preset historical track library, and before fitting of a second teaching partial track of a one-time teaching task, a historical teaching total track having a mapping relationship is queried in the preset historical track library based on a track query identifier formed by the track optimization node point and the first teaching partial track, so that it is possible to avoid that the fitting accuracy of the second teaching partial track is affected due to different selection of the historical teaching total track.
As an example, steps F10 to F30 include: inquiring a corresponding historical teaching total track in a preset historical track library by taking a track inquiry identifier corresponding to the track optimization node point and the first teaching sub-track as an index; selecting at least one corresponding fitting track point from the historical teaching total track according to the track optimization node point, and fitting at least one corresponding section of fitting track based on pose information of each fitting track point, wherein the fitting track is infinitely differentiable in an interval corresponding to an adjacent fitting track point and has local support; and splicing the fitted tracks into the second teaching sub-track, wherein the splicing mode can refer to the splicing mode corresponding to the teaching track optimization method, and details are not repeated here.
The embodiment of the application provides a second teaching sublevel generation method, namely, according to the track optimization node point and the first teaching sublevel, a corresponding historical teaching total track is inquired in a preset historical track library; selecting at least one fitting track point from the historical teaching total track, and fitting at least one corresponding section of fitting track based on the pose information of each fitting track point; and splicing the fitting tracks into the second teaching partial track. Compared with a mode of randomly selecting a historical teaching total track for track fitting when a second teaching partial track is fitted, the embodiment of the application inquires a corresponding historical teaching total track according to the first teaching partial track and a track optimization node bit when the second teaching partial track is fitted, and then carries out track fitting according to the historical teaching total track, namely, the technical defect that the fitting precision of the second teaching partial track is low due to the fact that different historical teaching total track selections cause influence is overcome, and therefore a foundation is laid for improving the teaching effect of the teaching robot.
EXAMPLE III
The embodiment of the present application further provides a teaching trajectory optimization device of a robot, refer to fig. 3, the teaching trajectory optimization device of a robot includes:
an obtaining module 101, configured to obtain robot teaching information of a teaching robot when the teaching robot executes a teaching task;
the positioning module 102 is configured to position a trajectory optimization node point of a teaching total trajectory of the teaching robot according to the robot teaching information;
and the optimizing module 103 is configured to optimize the teaching total trajectory according to the trajectory optimizing node position to obtain an optimized total trajectory, where the optimized total trajectory has a preset teaching effect.
Optionally, the optimization module 103 is further configured to:
according to the track optimization node position, determining a first teaching sub-track corresponding to the teaching total track and a second teaching sub-track corresponding to the teaching total track, wherein the first teaching sub-track has a preset teaching effect, and the second teaching sub-track does not have the preset teaching effect;
acquiring a historical teaching sub-track corresponding to the second teaching sub-track;
and splicing the historical teaching sub-track and the first teaching sub-track into the optimized total track.
Optionally, the optimization module 103 is further configured to:
acquiring a track starting point of the teaching total track;
taking a track formed by the track starting point and the track optimizing node bit as the first teaching partial track;
and fitting the second teaching branch track according to the track fitting point corresponding to the track optimization node.
Optionally, the optimization module 103 is further configured to:
according to the track optimization node position and the first teaching sub-track, inquiring a corresponding historical teaching total track in a preset historical track library;
selecting at least one fitting track point from the historical teaching total track, and fitting at least one corresponding section of fitting track based on the pose information of each fitting track point;
and splicing the fitting tracks into the second teaching partial track.
Optionally, the robot teaching trajectory optimization device is further configured to:
acquiring at least one track splicing node position corresponding to the optimized total track;
according to the track cycle node position corresponding to each track splicing node position, respectively carrying out track planning on the historical teaching branch track and the first teaching branch track to obtain a corresponding connection track;
and replacing the cycle branch track corresponding to the cycle node bit of each track with each connecting track.
Optionally, the positioning module 102 is further configured to:
detecting whether a teaching total track of the teaching robot has track deviation or not according to the robot teaching information;
if so, taking a track deviation point of the teaching total track as the track optimization node point;
and if not, taking a preset teaching track point of the teaching total track as the track optimization node point.
Optionally, the obtaining module 101 is further configured to:
when the teaching robot is detected to execute a teaching task, determining whether the teaching task is a one-time teaching task;
if the teaching task is the one-time teaching task, acquiring robot teaching image information and robot teaching stress information respectively;
and if the teaching task is not the one-time teaching task, acquiring teaching stress information of the robot.
The robot teaching track optimization device provided by the invention adopts the robot teaching track optimization method in the embodiment, so that the technical problem of poor teaching effect of the teaching robot is solved. Compared with the prior art, the beneficial effects of the robot teaching trajectory optimization device provided by the embodiment of the invention are the same as the beneficial effects of the robot teaching trajectory optimization method provided by the embodiment, and other technical characteristics of the robot teaching trajectory optimization device are the same as those disclosed by the embodiment method, which are not repeated herein.
Example four
An embodiment of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the robot teaching trajectory optimization method in the first embodiment.
Referring now to FIG. 4, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 4, the electronic device may include a processing means 1001 (e.g., a central processing unit, a graphic processor, etc.) which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage means 1003 into a Random Access Memory (RAM) 1004. In the RAM1004, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus.
Generally, the following systems may be connected to the I/O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, or the like; output devices 1008 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, or the like; a storage device 1003 including, for example, a magnetic tape, a hard disk, or the like; and a communication device 1009. The communication means may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While the figures illustrate an electronic device with various systems, it is to be understood that not all illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 1009, or installed from the storage means 1003, or installed from the ROM 1002. The computer program, when executed by the processing device 1001, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
According to the electronic equipment provided by the invention, the robot teaching track optimization method in the embodiment is adopted, and the technical problem of poor teaching effect of the teaching robot is solved. Compared with the prior art, the beneficial effects of the electronic device provided by the embodiment of the invention are the same as the beneficial effects of the robot teaching track optimization method provided by the embodiment, and other technical features of the electronic device are the same as those disclosed by the embodiment method, which are not repeated herein.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the foregoing description of embodiments, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present invention, and shall cover the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
EXAMPLE five
The present embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon for executing the robot teaching trajectory optimization method in the above-described embodiments.
The computer readable storage medium provided by the embodiments of the present invention may be, for example, a USB flash disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination thereof. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer-readable storage medium may be embodied in an electronic device; or may be present alone without being incorporated into the electronic device.
The computer-readable storage medium carries one or more programs which, when executed by an electronic device, cause the electronic device to: acquiring robot teaching information of a teaching robot when the teaching robot executes a teaching task; positioning a track optimization node point of a teaching total track of the teaching robot according to the robot teaching information; and optimizing the teaching total track according to the track optimization node position to obtain an optimized total track, wherein the optimized total track has a preset teaching effect.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented by software or hardware. Wherein the names of the modules do not in some cases constitute a limitation of the unit itself.
The computer readable storage medium provided by the invention stores the computer readable program instruction for executing the robot teaching track optimization method, and solves the technical problem of poor teaching effect of the teaching robot. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the embodiment of the invention are the same as the beneficial effects of the robot teaching trajectory optimization method provided by the embodiment, and are not repeated herein.
EXAMPLE six
The present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the robot teaching trajectory optimization method as described above.
The computer program product solves the technical problem that the teaching effect of the teaching robot is poor. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the invention are the same as the beneficial effects of the robot teaching trajectory optimization method provided by the embodiment, and are not repeated herein.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all equivalent structures or equivalent processes, which are directly or indirectly applied to other related technical fields, and which are not limited by the present application, are also included in the scope of the present application.
Claims (10)
1. A robot teaching track optimization method is characterized by comprising the following steps:
acquiring robot teaching information of a teaching robot when the teaching robot executes a teaching task;
positioning a track optimization node point of a teaching total track of the teaching robot according to the robot teaching information;
and optimizing the teaching total track according to the track optimization node position to obtain an optimized total track, wherein the optimized total track has a preset teaching effect.
2. The robot teaching trajectory optimization method according to claim 1, wherein said step of optimizing said teaching trajectory overall according to said trajectory optimization node, and obtaining an optimized trajectory overall comprises:
determining a first teaching sub-track corresponding to the teaching total track and a second teaching sub-track corresponding to the teaching total track according to the track optimization node point, wherein the first teaching sub-track has a preset teaching effect, and the second teaching sub-track does not have the preset teaching effect;
acquiring a historical teaching sub-track corresponding to the second teaching sub-track;
and splicing the historical teaching sub-track and the first teaching sub-track into the optimized total track.
3. The robot teaching trajectory optimization method according to claim 2, wherein the step of determining a first teaching partial trajectory and a second teaching partial trajectory corresponding to the teaching total trajectory based on the trajectory optimization node point includes:
acquiring a track starting point of the teaching total track;
taking a track formed by the track starting point and the track optimizing node bit as the first teaching partial track;
and fitting the second teaching sub-track according to the track fitting point corresponding to the track optimization node.
4. The robot teaching trajectory optimization method according to claim 3, wherein the step of fitting the second teaching partial trajectory according to the trajectory fitting point corresponding to the trajectory optimization node comprises:
according to the track optimization node position and the first teaching sub-track, inquiring a corresponding historical teaching total track in a preset historical track library;
selecting at least one fitting track point from the historical teaching total track, and fitting at least one corresponding section of fitting track based on the pose information of each fitting track point;
and splicing the fitting tracks into the second teaching partial track.
5. The robot teaching trajectory optimization method of claim 2, wherein after the step of stitching the historical teaching part trajectory and the first teaching part trajectory into the optimized total trajectory, the robot teaching trajectory optimization method further comprises:
acquiring at least one track splicing node position corresponding to the optimized total track;
according to the track cycle node position corresponding to each track splicing node position, respectively carrying out track planning on the historical teaching sub-track and the first teaching sub-track to obtain a corresponding joining track;
and replacing the cycle branch track corresponding to the cycle node bit of each track with each connecting track.
6. The robot teaching trajectory optimization method according to claim 1, wherein the step of positioning a trajectory optimization node point of a teaching overall trajectory of the teaching robot based on the robot teaching information includes:
detecting whether a teaching total track of the teaching robot has track deviation or not according to the robot teaching information;
if so, taking a track deviation point of the teaching total track as the track optimization node point;
and if not, taking a preset teaching track point of the teaching total track as the track optimization node point.
7. The robot teaching trajectory optimization method according to claim 1, wherein the robot teaching information includes robot teaching image information and robot teaching force information,
the step of acquiring robot teaching information of the teaching robot when the teaching robot executes a teaching task comprises the following steps:
when the teaching robot is detected to execute a teaching task, determining whether the teaching task is a one-time teaching task;
if the teaching task is the one-time teaching task, respectively acquiring robot teaching image information and robot teaching stress information;
and if the teaching task is not the one-time teaching task, acquiring teaching stress information of the robot.
8. A robot teaching trajectory optimization device, characterized by comprising:
the acquisition module is used for acquiring robot teaching information of the teaching robot when the teaching robot executes a teaching task;
the positioning module is used for positioning a track optimization node point of a teaching total track of the teaching robot according to the robot teaching information;
and the optimization module is used for optimizing the teaching total track according to the track optimization node position to obtain an optimized total track, wherein the optimized total track has a preset teaching effect.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the robot teaching trajectory optimization method of any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program for implementing a robot teaching trajectory optimization method, the program being executed by a processor to implement the steps of the robot teaching trajectory optimization method according to any one of claims 1 to 7.
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