CN116394266B - Robot self-collision processing method and device, robot and medium - Google Patents
Robot self-collision processing method and device, robot and medium Download PDFInfo
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- CN116394266B CN116394266B CN202310672409.7A CN202310672409A CN116394266B CN 116394266 B CN116394266 B CN 116394266B CN 202310672409 A CN202310672409 A CN 202310672409A CN 116394266 B CN116394266 B CN 116394266B
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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
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
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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
- B25J9/1661—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
- 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
- B25J9/1666—Avoiding collision or forbidden zones
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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
- B25J9/1674—Programme controls characterised by safety, monitoring, diagnostic
- B25J9/1676—Avoiding collision or forbidden zones
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The application discloses a robot self-collision processing method, a device, a robot and a medium, which are applied to the technical field of robots and specifically comprise the following steps: acquiring a space coordinate conversion relation between each collision risk point and each collision risk point of the current robot operation type; based on the space coordinate conversion relation, determining the real-time space coordinates of each collision risk point by utilizing the real-time space coordinates of the center point of the tail end of the mechanical arm; determining the current distance between collision risk points with collision relation in the current robot operation link based on the real-time space coordinates of each collision risk point; based on the magnitude relation between the current distance and the preset safety distance, the mechanical arm is controlled to perform self-collision avoidance treatment, and the collision problem is reduced to be the spatial position distance relation between the limited collision risk points and is matched with the robot operation type and the robot operation link to perform collision risk point matching, so that a collision detection algorithm can be greatly simplified, and the collision detection efficiency is improved.
Description
Technical Field
The present application relates to the field of robots, and in particular, to a method and apparatus for processing a robot self-collision, a robot, and a medium.
Background
When the live working robot works aloft, because the line of a working scene is complex and the arm of the mechanical arm is limited, the robot body is close to the working position in the working process, so that the robot action is limited in the working process, the mutual limit avoidance and limit approaching between robot working mechanisms are usually needed in the working process to realize mutual matching, and the safe working space is restored by reverse movement after the limit avoidance and limit approaching, so that the self-collision conditions of the mechanical arm, such as the joint of the mechanical arm, the left mechanical arm, the right mechanical arm, the tail end tool, the mechanical arm, the airborne module, the mechanical arm, the tool table, the mechanical arm, the robot shell and the like often occur, however, the self-collision treatment of the robot in the prior art often cannot achieve the expected effect, and a large amount of calculation resources and calculation time are needed to consume, so that the self-collision treatment effect of the robot is poor and the self-collision treatment efficiency is low.
Disclosure of Invention
The application provides a robot self-collision processing method, a device, a robot and a medium, which are used for solving the problems of poor effect and low efficiency of the robot self-collision processing in the prior art, and concretely provides the following technical scheme:
In one aspect, the present application provides a robot self-collision processing method, including:
acquiring each collision risk point corresponding to the current robot operation type and a space coordinate conversion relation between each collision risk point; the space coordinate conversion relationship is used for representing a one-way transmission relationship of space coordinates of each collision risk point from the starting coordinate position of the robot to the tail end position of the mechanical arm;
based on the space coordinate conversion relation among the collision risk points, real-time space coordinates of the collision risk points are determined by utilizing real-time space coordinates of the center points of the tail ends of the mechanical arms;
determining the current distance between collision risk points with collision relations corresponding to the current robot operation link based on the real-time space coordinates of each collision risk point;
and controlling the mechanical arm to perform corresponding self-collision avoidance processing based on the magnitude relation between the current distance and the preset safety distance.
On the other hand, the application also provides a robot self-collision processing device, which comprises:
the information acquisition unit is used for acquiring each collision risk point corresponding to the current robot operation type and the space coordinate conversion relation among the collision risk points; the space coordinate conversion relationship is used for representing a one-way transmission relationship of space coordinates of each collision risk point from the starting coordinate position of the robot to the tail end position of the mechanical arm;
The coordinate determining unit is used for determining the real-time space coordinates of each collision risk point by utilizing the real-time space coordinates of the central point of the tail end of the mechanical arm based on the space coordinate conversion relation among the collision risk points;
the distance determining unit is used for determining the current distance between collision risk points with collision relations corresponding to the current robot operation link based on the real-time space coordinates of each collision risk point;
the self-collision processing unit is used for controlling the mechanical arm to perform corresponding self-collision avoidance processing based on the magnitude relation between the current distance and the preset safety distance.
On the other hand, the application also provides a live working robot, which comprises a body, a mechanical arm arranged on the body, a memory and a processor arranged in the body, wherein the memory stores a computer program which can run on the processor, and the processor realizes the self-collision processing method of the robot when executing the computer program.
In another aspect, the present application also provides a computer readable storage medium storing computer instructions that when executed by a processor implement the robot self-collision processing method described above.
The beneficial effects of the application are as follows:
according to the application, the collision problem is simplified into the spatial position distance relation among the limited collision risk points, so that the self-collision detection effect can be effectively improved, the self-collision detection algorithm can be greatly simplified, the self-collision processing efficiency is improved, the collision risk points are screened by utilizing the operation type of the robot and the operation link of the robot in the self-collision detection process, the calculated amount of the self-collision detection can be further reduced, and the self-collision processing efficiency can be further improved.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute a limitation on the application. In the drawings:
FIG. 1 is a schematic flow frame diagram of a robot self-collision processing method in an embodiment of the application;
FIG. 2 is a schematic flow chart of an overview of a robot self-collision processing method according to an embodiment of the application;
FIG. 3 is a schematic diagram of a spatial coordinate transformation relationship under a world coordinate system according to an embodiment of the present application;
FIG. 4 is a schematic view of collision relationships among collision risk points according to an embodiment of the present application;
FIG. 5 is a schematic functional structure of a robot self-collision handling device according to an embodiment of the present application;
fig. 6 is a schematic hardware structure of a charged working robot according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantageous effects of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order to facilitate a better understanding of the present application, technical terms related to the present application will be briefly described below.
The robot working mechanism is a working mechanism involved in the robot working process, and in the application, the robot working mechanism can comprise a mechanical arm, a mechanical arm tail end tool, a robot shell, an airborne module and other component mechanisms of the live working robot, and can also comprise a tool table and other component mechanisms.
The collision risk points are position points with collision risk calibrated in advance on a three-dimensional model of a robot operation mechanism, and in the application, the collision risk points are divided according to the operation type of the robot and the operation links of the robot.
After technical terms related to the application are introduced, application scenes and technical concepts of the application are briefly introduced.
At present, the following treatment measures are mainly adopted for the self-collision treatment mode of the robot in the prior art: the first mode is that an expansion layer is arranged according to a three-dimensional geometrical model of a robot to carry out self-collision avoidance motion planning; the second mode is to build a self-collision avoidance model to carry out avoidance processing, such as an artificial potential field method (APF), a rapid expansion random tree method (RRTs), a probability map method (PRMs) and the like; the third mode is to use offline learning and separation axis method to perform self-collision avoidance calculation. The first mode generally needs stronger theoretical knowledge, the self-collision avoidance movement planning based on the expansion layer setting often has a self-collision avoidance movement path which does not meet expectations, the self-collision avoidance movement planning success rate is low, the situation that obstacles are too many is not met, the small-range space cannot be successfully planned, moreover, the calculation resources occupy more, the environmental obstacle data is in direct proportion to the calculation data amount, a large amount of data calculation particularly consumes the calculation resources, and therefore operation is blocked; the second mode needs a large amount of calculation time for a high-dimensional configuration Space (C-Space) generated by high joint multi-degree of freedom, is not suitable for real-time robot motion control, and compared with the first mode, needs more theoretical support and complex formula calculation, and is often not suitable for small-Space motion control for self-collision avoidance motion planning; the third mode is to divide the robot body into different region blocks after modeling by utilizing the bounding volumes according to the three-dimensional geometric information of the robot, divide each two region blocks into a group of region pairs, offline learn candidate region pairs generating self-collision by utilizing an optimization method, and detect the self-collision state of the candidate region pairs by utilizing a separation axis method, wherein the calculation amount of the self-collision detection cannot be obviously reduced per se, the theoretical support cannot be reduced, and the self-collision avoidance motion planning is not suitable for small-space motion control.
Therefore, referring to fig. 1, the present application models each robot operation mechanism, and pre-calibrates collision risk points on a three-dimensional model of each robot operation mechanism according to different robot operation types, and then obtains and stores spatial coordinate conversion relations between each collision risk point corresponding to different robot operation types, so that in the live working robot operation process, the present distance between collision risk points with collision relations corresponding to the present robot operation link can be determined by using real-time spatial coordinates of center points of the tail ends of the mechanical arms based on the spatial coordinate conversion relations between each collision risk point corresponding to the present robot operation type, therefore, the mechanical arm can be controlled to carry out corresponding self-collision avoidance processing based on the magnitude relation between the current distance and the preset safety distance, and further, the collision problem is simplified into the spatial position distance relation between different robot operation types and limited collision risk points corresponding to different robot operation links, so that not only can the light self-collision calculation be realized on the basis of no complex theory and algorithm, but also the calculation resource consumption is greatly reduced, the self-collision detection algorithm is simplified, the self-collision processing efficiency is improved, the self-collision design customization can be realized for different robot operation types, the requirements of different robot operation types and different robot operation links are met, the self-collision situation is greatly prevented, the self-collision avoidance motion planning is not needed, the original motion control is not interfered, the self-collision detection effect can be effectively improved, in addition, in the self-collision detection process, the robot operation type and the robot operation link are utilized to screen collision risk points, so that the calculation amount of the self-collision detection can be further reduced, and the self-collision processing efficiency can be further improved.
After the application scene and the technical concept of the application are introduced, the technical scheme provided by the application is described in detail below.
The embodiment of the application provides a robot self-collision processing method, and referring to fig. 2, the general flow of the robot self-collision processing method provided by the embodiment of the application is as follows:
step 201: acquiring each collision risk point corresponding to the current robot operation type and a space coordinate conversion relation between each collision risk point; the spatial coordinate conversion relationship is used for representing a one-way transmission relationship of spatial coordinates of each collision risk point from the starting coordinate position of the robot to the tail end position of the mechanical arm.
In practical application, the user can calibrate the collision risk points in advance for different robot operation types according to the practical operation requirement, so that the robot control system can obtain each collision risk point corresponding to different robot operation types and the space coordinate conversion relation between each collision risk point, and the following modes can be adopted specifically but not limited to:
first, the robot control system models each robot work mechanism to obtain a three-dimensional model of each robot work mechanism. Specifically, a user can initiate a modeling instruction to the robot control system through the interactive terminal, when the robot control system receives the modeling instruction, the robot control system can respectively model each robot operating mechanism based on joint data of the power distribution operating robot, and the three-dimensional model of each robot operating mechanism is obtained and then sent to the interactive terminal for display, so that the user can calibrate collision risk points on the three-dimensional model of each robot operating mechanism.
Then, the robot control system acquires the collision risk points respectively calibrated on the three-dimensional model of each robot working mechanism and the spatial coordinate conversion relations among the calibrated collision risk points. Specifically, after the user calibrates the collision risk points on the three-dimensional model of each robot operation mechanism, the interactive terminal automatically generates a spatial coordinate conversion relation between each collision risk point calibrated on the three-dimensional model of each robot operation mechanism by the user, and sends each collision risk point calibrated on the three-dimensional model of each robot operation mechanism by the user and the spatial coordinate conversion relation between each calibrated collision risk point to the robot control system, and the robot control system can obtain the spatial coordinate conversion relation between each collision risk point calibrated on the three-dimensional model of each robot operation mechanism by the user and each calibrated collision risk point.
And finally, the robot control system determines each collision risk point corresponding to the different robot operation types from the calibrated collision risk points, and determines the spatial coordinate conversion relationship between each collision risk point corresponding to the different robot operation types based on the spatial coordinate conversion relationship between the calibrated collision risk points. Specifically, the user may further configure different robot operation types and each robot operation link corresponding to the different robot operation types in the interactive terminal, the interactive terminal sends each robot operation link corresponding to the different robot operation types and the different robot operation types configured by the user to the robot control system, and the robot control system may determine each collision risk point corresponding to the different robot operation types from the calibrated collision risk points, and determine a spatial coordinate conversion relationship between each collision risk point corresponding to the different robot operation types based on the spatial coordinate conversion relationship between the calibrated collision risk points.
In practical application, in the process of controlling the live working robot, the robot control system may obtain each collision risk point corresponding to the current robot working type and a spatial coordinate conversion relationship between each collision risk points from a spatial coordinate conversion relationship between each collision risk point corresponding to different robot working types, so as to perform subsequent self-collision processing based on each collision risk point corresponding to the current robot working type and a spatial coordinate conversion relationship between each collision risk point, where the spatial coordinate conversion relationship may be a TF (Trans Form, coordinate conversion) relationship between each collision risk point, for example, a spatial coordinate conversion relationship between all collision risk points corresponding to the current robot working type may be, but is not limited to, a TF relationship as shown in fig. 3, and in fig. 3, a robot start coordinate position is an origin position of a robot coordinate system, for example, a robot start coordinate position may be, but is not limited to, an origin position of a world coordinate system, and the like.
Step 202: based on the space coordinate conversion relation among the collision risk points, the real-time space coordinates of the collision risk points are determined by utilizing the real-time space coordinates of the center points of the tail ends of the mechanical arms.
In specific implementation, when the robot control system determines the real-time space coordinates of each collision risk point by using the real-time space coordinates of the center point of the tail end of the mechanical arm based on the space coordinate conversion relation between each collision risk point, the following manner may be adopted, but is not limited to:
firstly, a robot control system carries out inverse solution on the space coordinate conversion relation among all collision risk points to obtain an inverse space coordinate conversion relation among all the collision risk points; the inverse space coordinate conversion relationship is used for representing a one-way transmission relationship of space coordinates of each collision risk point between the tail end position of the mechanical arm and the starting coordinate position of the robot. For example, referring to fig. 3, assuming that TF relationships of two adjacent collision risk points in the spatial coordinate transformation relationship may be a one-way transfer relationship of spatial coordinates from a collision risk point of the left arm end tool to a collision risk point of the left arm end center point, the inverse spatial coordinate transformation relationship obtained after the inverse solution may be a one-way transfer relationship of spatial coordinates from a collision risk point of the left arm end center point to a collision risk point of the left arm end tool.
Then, the robot control system sequentially calculates real-time space coordinates of collision risk points adjacent to the known position points by using the real-time space coordinates of the known position points based on the inverse space coordinate conversion relation among the collision risk points until the real-time space coordinates of the collision risk points are calculated; the known position point is the center point of the tail end of the mechanical arm when the first calculation is performed, and is the last collision risk point when the first calculation is performed.
In practical application, when the working tool is installed at the tail end of the mechanical arm, the central point of the tail end of the mechanical arm is the central point of the tool at the tail end of the mechanical arm, and when the working tool is not installed at the tail end of the mechanical arm, the central point of the tail end of the mechanical arm is the central point of the flange at the tail end of the mechanical arm, based on the central point of the tail end of the mechanical arm, the real-time Cartesian space coordinates of the central point of the tail end of the mechanical arm can be converted into the real-time space coordinates of all the collision risk points in the world coordinate system when the real-time space coordinates of all the collision risk points are calculated by the robot control system, the real-time space coordinates of all the collision risk points are calculated by using the real-time space coordinates of the collision risk points in the world coordinate system, and the real-time space coordinates of all the collision risk points are calculated by using the real-time space coordinates of all the collision risk points.
In order to enhance the visualization of collision risk points in the robot operation process, the robot control system utilizes real-time space coordinates of center points at the tail ends of the mechanical arms to determine the real-time space coordinates of the collision risk points, and then the real-time space coordinates of the collision risk points and the space coordinate conversion relations between the collision risk points can be sent to the interaction terminal, so that the interaction terminal dynamically displays the space position relations between the collision risk points by utilizing the real-time space coordinates of the collision risk points based on the space coordinate conversion relations between the collision risk points.
Step 203: and determining the current distance between collision risk points with collision relations corresponding to the current robot operation link based on the real-time space coordinates of each collision risk point.
In specific implementation, when the robot control system determines the current distance between collision risk points with collision relation corresponding to the current robot operation link based on the real-time space coordinates of each collision risk point, the following manner may be adopted, but is not limited to:
firstly, a robot control system determines a collision risk point combination corresponding to a current robot operation link based on each collision risk point; the collision risk point combination comprises collision risk points on two pre-calibrated robot working components with collision relations.
Then, the robot control system selects a corresponding distance calculation mode according to the number of the collision risk points on the operation part of the collided robot in the collision risk point combination to calculate the current distance between the collision risk points with collision relation in the collision risk point combination. Specifically, there may be, but are not limited to, the following three cases:
first case: one collision risk point exists in the collision risk point combination and is positioned on the collided robot working component.
In this case, the robot control system may calculate the current distance between collision risk points having a collision relationship in the collision risk point combination by using a point-to-point distance calculation method based on real-time spatial coordinates of each collision risk point in the collision risk point combination. For example, referring to fig. 4, the collision risk points on the left robot working component and the right collided robot working component are calibrated in advance to have a collision relationship, and if one collision risk point a1 exists on the right collided robot working component, the current distance between the collision risk point on the left robot working component and the collision risk point on the right collided robot working component can be calculated by adopting a point-to-point distance calculation mode, namely, the current distance between the collision risk point a on the left robot working component and the collision risk point a1 on the right collided robot working component and the current distance between the collision risk point b on the left robot working component and the collision risk point a1 on the right collided robot working component are calculated respectively; the point-to-point distance calculation method may be the calculation method of the following formula (1):
… … formula (1)
In the above-mentioned formula (1),characterizing the current distance; />Representing real-time space coordinates of collision risk points on a robot operation part; />Real-time spatial coordinates characterizing collision risk points on a collided robotic work component.
Second case: two collision risk points exist in the collision risk point combination and are positioned on the collided robot working component.
In this case, the robot control system may calculate the current distance between collision risk points having a collision relationship in the collision risk point combination by using a point-to-line distance calculation method based on real-time spatial coordinates of each collision risk point in the collision risk point combination. For example, referring to fig. 4, the collision risk points on the left robot working part and the right collided robot working part are calibrated in advance to have a collision relationship, and assuming that two collision risk points, namely, a collision risk point a1 and a collision risk point a2, exist on the right collided robot working part, a point-to-line distance calculation manner is adopted for each collision risk point on the left robot working part, a current distance between the collision risk points on the right collided robot working part is calculated, namely, a point-to-line distance calculation manner is adopted, and a current distance between the collision risk point a on the left robot working part and a straight line formed by the collision risk point a1 and the collision risk point a2 on the right collided robot working part and a current distance between the collision risk point b on the left robot working part and a straight line formed by the collision risk point a1 and the collision risk point a2 on the right collided robot working part are calculated; the calculation method of the distance from the point to the line may be the calculation method of the following formula (2):
… … formula (2)
In the above-mentioned formula (2),characterizing the current distance; />Representing real-time space coordinates of collision risk points on a robot operation part; />The real-time space coordinates of the collision risk points on the robot operation component and the real-time space coordinates of the two collision risk points on the collided robot operation component are represented, and the real-time space coordinates of the feet of the collision risk points on the robot operation component on the straight line formed by the two collision risk points on the collided robot operation component are solved.
Third case: more than three collision risk points exist in the collision risk point combination and are positioned on the collided robot working part.
In this case, the robot control system may calculate the current distance between collision risk points having a collision relationship in the collision risk point combination by using a point-to-surface distance calculation method based on real-time spatial coordinates of each collision risk point in the collision risk point combination. For example, referring to fig. 4, the collision risk points on the left robot working part and the right collided robot working part are calibrated in advance to have a collision relationship, and if three or more collision risk points exist on the right collided robot working part, namely, a collision risk point a1, a collision risk point a2, a collision risk point a3 and a collision risk point a4, a point-to-face distance calculation mode is adopted for each collision risk point on the left robot working part, and a current distance between planes formed by the three or more collision risk points on the right collided robot working part, namely, a point-to-face distance calculation mode is adopted, so that a current distance between a collision risk point a on the left robot working part and a plane formed by collision risk points a1-a4 on the right collided robot working part and a current distance between a collision risk point b on the left robot working part and a plane formed by collision risk points a1-a4 on the right collided robot working part are calculated respectively; the point-to-face distance calculation method may be the calculation method of the following formula (3):
… … formula (3)
In the above-mentioned formula (3),characterizing the current distance; />Representing real-time space coordinates of collision risk points on a robot operation part; />Characterizing a plane equation of a plane formed by any three collision risk points on the collided robot working part based on real-time space coordinates of the any three collision risk points on the collided robot working part>Is a known constant in the past.
Step 204: and controlling the mechanical arm to perform corresponding self-collision avoidance processing based on the magnitude relation between the current distance and the preset safety distance.
In practical application, the preset safety distance may include a minimum safety distance and a sub-safety distance, for example, referring to fig. 4, d1 represents the minimum safety distance, and d2 represents the sub-safety distance, based on which, when the robot control system controls the mechanical arm to perform corresponding self-collision avoidance processing based on the magnitude relation between the current distance and the preset safety distance, there may be, but not limited to, the following three situations:
first case: and if the robot control system determines that the current distance is greater than the sub-safety distance, determining that the mechanical arm does not perform the self-collision avoidance processing, and controlling the mechanical arm to continue operation.
Second case: and if the robot control system determines that the current distance is greater than the minimum safety distance and less than or equal to the secondary safety distance, storing all the joint positions of the mechanical arm in the current robot operation link to obtain a joint position and position combination, and controlling the mechanical arm to continue operation.
In order to achieve the purpose that the mechanical arm can recover the original state without collision, when the robot control system determines that the current distance is larger than the minimum safety distance and smaller than or equal to the secondary safety distance, all joint positions of the mechanical arm in the current robot operation link can be stored to obtain a joint position and pose combination, so that when the position and pose state of the mechanical arm before the current robot operation link is determined to be recovered, the mechanical arm is controlled to move reversely according to the reverse sequence of the joint positions and poses in the joint position and pose combination, the consistency of the reciprocating paths can be guaranteed, and the problem of self collision when the mechanical arm recovers the state after collision avoidance is relieved is effectively solved.
Third case: and if the robot control system determines that the current distance is equal to the minimum safe distance, the robot control system controls the mechanical arm to stop moving.
In the embodiment of the application, the collision problem is simplified into the spatial position distance relation among the limited collision risk points, so that the self-collision detection effect can be effectively improved, the self-collision detection algorithm can be greatly simplified, the self-collision processing efficiency is improved, the collision risk points are screened by utilizing the operation type of the robot and the operation links of the robot in the self-collision detection process, the calculated amount of the self-collision detection can be further reduced, and the self-collision processing efficiency can be further improved.
Based on the foregoing embodiments, the embodiment of the present application further provides a robot self-collision processing apparatus, as shown in fig. 5, where the robot self-collision processing apparatus 500 provided by the embodiment of the present application at least includes:
an information obtaining unit 501, configured to obtain each collision risk point corresponding to a current robot operation type and a spatial coordinate conversion relationship between each collision risk point; the space coordinate conversion relationship is used for representing a one-way transmission relationship of space coordinates of each collision risk point from the starting coordinate position of the robot to the tail end position of the mechanical arm;
a coordinate determining unit 502, configured to determine real-time spatial coordinates of each collision risk point by using real-time spatial coordinates of a center point of an end of the mechanical arm based on spatial coordinate conversion relationships between each collision risk point;
a distance determining unit 503, configured to determine a current distance between collision risk points with a collision relationship corresponding to a current robot operation link, based on real-time spatial coordinates of each collision risk point;
the self-collision processing unit 504 is configured to control the mechanical arm to perform corresponding self-collision avoidance processing based on a magnitude relation between the current distance and a preset safety distance.
In a possible implementation manner, the robot self-collision processing device 500 provided in the embodiment of the present application further includes:
the information calibration unit 505 is configured to model each robot operation mechanism to obtain a three-dimensional model of each robot operation mechanism; acquiring collision risk points calibrated on the three-dimensional model of each robot operation mechanism and space coordinate conversion relations among the calibrated collision risk points; and determining each collision risk point corresponding to the different robot operation types from the calibrated collision risk points, and determining the space coordinate conversion relationship between each collision risk point corresponding to the different robot operation types based on the space coordinate conversion relationship between the calibrated collision risk points.
In one possible implementation manner, when determining the real-time spatial coordinates of each collision risk point by using the real-time spatial coordinates of the center point of the end of the mechanical arm based on the spatial coordinate conversion relationship between each collision risk point, the coordinate determining unit 502 is specifically configured to:
carrying out inverse solution on the space coordinate conversion relations among all the collision risk points to obtain inverse space coordinate conversion relations among all the collision risk points; the inverse space coordinate conversion relationship is used for representing a one-way transfer relationship of space coordinates of each collision risk point between the tail end position of the mechanical arm and the initial coordinate position of the robot;
Based on the inverse space coordinate conversion relation among the collision risk points, calculating the real-time space coordinates of the collision risk points adjacent to the known position points by sequentially utilizing the real-time space coordinates of the known position points until the real-time space coordinates of the collision risk points are calculated; the known position point is the center point of the tail end of the mechanical arm when the first calculation is performed, and is the last collision risk point when the first calculation is performed.
In a possible implementation manner, the robot self-collision processing device 500 provided in the embodiment of the present application further includes:
an information display unit 506, configured to dynamically display the spatial position relationship between the collision risk points by using real-time spatial coordinates of the collision risk points based on the spatial coordinate conversion relationship between the collision risk points.
In one possible implementation manner, when determining the current distance between collision risk points with collision relationships corresponding to the current robot task link based on the real-time spatial coordinates of each collision risk point, the distance determining unit 503 is specifically configured to:
based on each collision risk point, determining a collision risk point combination corresponding to the current robot operation link; the collision risk point combination comprises collision risk points on two robot operation components with collision relation;
If one collision risk point exists in the collision risk point combination and is located on the operation part of the robot to be collided, calculating the current distance between the collision risk points with collision relation in the collision risk point combination by adopting a point-to-point distance calculation mode based on real-time space coordinates of each collision risk point in the collision risk point combination;
if two collision risk points in the collision risk point combination are determined to be positioned on the operation part of the robot to be collided, calculating the current distance between the collision risk points with collision relation in the collision risk point combination by adopting a point-to-line distance calculation mode based on real-time space coordinates of each collision risk point in the collision risk point combination;
if more than three collision risk points in the collision risk point combination are located on the operation part of the robot to be collided, calculating the current distance between the collision risk points with collision relation in the collision risk point combination by adopting a point-to-surface distance calculation mode based on real-time space coordinates of each collision risk point in the collision risk point combination.
In one possible embodiment, the preset safety distance includes a minimum safety distance and a sub-safety distance; based on the magnitude relation between the current distance and the preset safety distance, when the mechanical arm is controlled to perform corresponding self-collision avoidance processing, the self-collision processing unit 504 is specifically configured to:
If the current distance is determined to be greater than the secondary safety distance, determining that the mechanical arm does not perform self-collision avoidance treatment, and controlling the mechanical arm to continue operation;
if the current distance is determined to be greater than the minimum safety distance and less than or equal to the sub-safety distance, storing all joint positions of the mechanical arm in the current robot operation link to obtain a joint position and position combination, and controlling the mechanical arm to continue operation;
and if the current distance is determined to be equal to the minimum safety distance, controlling the mechanical arm to stop moving.
In one possible implementation, after storing all the joint poses of the mechanical arm in the current robot working link to obtain the joint pose combination, the self-collision processing unit 504 is further configured to:
when the pose state of the mechanical arm before the current robot operation link is determined to be recovered, the mechanical arm is controlled to move reversely according to the reverse sequence of the joint pose in the joint pose combination.
It should be noted that, the principle of solving the technical problem of the robot self-collision processing apparatus 500 provided by the embodiment of the present application is similar to that of the robot self-collision processing method provided by the embodiment of the present application, so that the implementation of the robot self-collision processing apparatus 500 provided by the embodiment of the present application can refer to the implementation of the robot self-collision processing method provided by the embodiment of the present application, and the repetition is omitted.
After the method and the device for processing the robot self-collision provided by the embodiment of the application are introduced, the live working robot provided by the embodiment of the application is briefly introduced.
Referring to fig. 6, the live working robot provided in the embodiment of the application at least includes a body 60, a mechanical arm 61 installed on the body 60, a memory 602 and a processor 601 installed in the body 60, wherein a computer program capable of running on the processor 601 is stored in the memory 602, and the self-collision processing method of the robot provided in the embodiment of the application is implemented when the processor 601 executes the computer program.
The hot-line work machine provided by embodiments of the present application may also include a bus 603 that connects the different components, including the processor 601 and the memory 602. Where bus 603 represents one or more of several types of bus structures, including a memory bus, a peripheral bus, a local bus, and so forth.
The Memory 602 may include readable media in the form of volatile Memory such as RAM (Random Access Memory ) 6021 and/or cache Memory 6022, and may further include ROM (Read Only Memory) 6023. Memory 602 may also include a program tool 6025 having a set (at least one) of program modules 6024, program modules 6024 include, but are not limited to: an operating subsystem, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The live working robot may also communicate with one or more devices (e.g., cell phone, computer, etc.) that enable a user to interact with the live working robot, and/or with any device (e.g., router, modem, etc.) that enables the live working robot to communicate with one or more other live working robots. Such communication may occur through an I/O (Input/Output) interface 604. Also, the live working robot may communicate with one or more networks (e.g., LAN (Local Area Network, local area network), WAN (Wide Area Network ) and/or public network, such as the internet) via network adapter 605. As shown in fig. 6, the network adapter 605 communicates with other modules of the live working robot via the bus 603. It should be appreciated that although not shown in fig. 6, other hardware and/or software modules may be used in connection with the live working robot, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (Redundant Arrays of Independent Disks, disk array) subsystems, tape drives, data backup storage subsystems, and the like.
It should be noted that the live working robot shown in fig. 6 is only an example, and should not impose any limitation on the functions and the application scope of the embodiment of the present application.
In addition, the embodiment of the application also provides a computer readable storage medium, and the computer readable storage medium stores computer instructions which are executed by a processor to realize the robot self-collision processing method provided by the embodiment of the application. Specifically, the computer instruction may be built-in or installed in the processor, so that the processor may implement the robot self-collision processing method provided by the embodiment of the present application by executing the built-in or installed computer instruction.
In addition, the method for processing the robot self-collision provided by the embodiment of the application can be further implemented as a program product, and the program product comprises program codes which realize the method for processing the robot self-collision provided by the embodiment of the application when being run on a processor.
The program product provided by the embodiments of the present application may employ any combination of one or more readable media, where the readable media may be a readable signal medium or a readable storage medium, and the readable storage medium may be, but is not limited to, a system, apparatus, or device that is an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor, or any combination thereof, and more specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, RAM, ROM, EPROM (Erasable Programmable Read Only Memory, erasable programmable read-Only Memory), an optical fiber, a CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The program product provided by the embodiment of the application can adopt a CD-ROM and comprises program codes and can also run on a processor. However, the program product provided by the embodiments of the present application is not limited thereto, and in the embodiments of the present application, the 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, apparatus, or device.
It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functions of two or more of the elements described above may be embodied in one element in accordance with embodiments of the present application. Conversely, the features and functions of one unit described above may be further divided into a plurality of units to be embodied.
Furthermore, although the operations of the methods of the present application are depicted in the drawings in a particular order, this is not required to either imply that the operations must be performed in that particular order or that all of the illustrated operations be performed to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the embodiments of the present application without departing from the spirit or scope of the embodiments of the application. Thus, if such modifications and variations of the embodiments of the present application fall within the scope of the claims and the equivalents thereof, the present application is also intended to include such modifications and variations.
Claims (10)
1. A robot self-collision processing method, characterized by comprising:
acquiring space coordinate conversion relations among all collision risk points corresponding to the current robot operation type; the spatial coordinate conversion relationship is used for representing a one-way transmission relationship of spatial coordinates of each collision risk point from a starting coordinate position of the robot to an end position of the mechanical arm;
based on the space coordinate conversion relation among the collision risk points, determining the real-time space coordinates of the collision risk points by utilizing the real-time space coordinates of the center points of the tail ends of the mechanical arms;
Determining the current distance between collision risk points with collision relations corresponding to the current robot operation link based on the real-time space coordinates of each collision risk point;
and controlling the mechanical arm to perform corresponding self-collision avoidance processing based on the magnitude relation between the current distance and the preset safety distance.
2. The method for processing the self-collision of the robot according to claim 1, further comprising, before acquiring each collision risk point corresponding to the current robot job type and the spatial coordinate conversion relationship between each collision risk point:
modeling each robot operation mechanism respectively to obtain a three-dimensional model of each robot operation mechanism;
acquiring the spatial coordinate conversion relations between the collision risk points respectively calibrated on the three-dimensional model of each robot operation mechanism and the calibrated collision risk points;
and determining each collision risk point corresponding to different robot operation types from the calibrated collision risk points, and determining the space coordinate conversion relationship between each collision risk point corresponding to different robot operation types based on the space coordinate conversion relationship between the calibrated collision risk points.
3. The robot self-collision processing method according to claim 1, wherein determining real-time spatial coordinates of the collision risk points using real-time spatial coordinates of a center point of an end of the robot arm based on a spatial coordinate conversion relationship between the collision risk points, comprises:
carrying out inverse solution on the space coordinate conversion relation among the collision risk points to obtain an inverse space coordinate conversion relation among the collision risk points; the inverse space coordinate conversion relationship is used for representing a one-way transfer relationship of space coordinates of each collision risk point from the tail end position of the mechanical arm to the initial coordinate position of the robot;
based on the inverse space coordinate conversion relation among the collision risk points, calculating the real-time space coordinates of the collision risk points adjacent to the known position points by sequentially utilizing the real-time space coordinates of the known position points until the real-time space coordinates of the collision risk points are calculated; the known position point is the center point of the tail end of the mechanical arm when the mechanical arm is calculated for the first time, and is the last collision risk point when the mechanical arm is calculated for the non-first time.
4. The method for processing the self-collision of the robot according to claim 1, wherein after determining the real-time spatial coordinates of the collision risk points by using the real-time spatial coordinates of the center point of the end of the arm based on the spatial coordinate conversion relation between the collision risk points, further comprising:
based on the space coordinate conversion relation among the collision risk points, the space position relation among the collision risk points is dynamically displayed by utilizing the real-time space coordinates of the collision risk points.
5. The robot self-collision processing method according to claim 1, wherein determining a current distance between collision risk points having a collision relationship corresponding to a current robot operation link based on real-time spatial coordinates of the respective collision risk points comprises:
based on the collision risk points, determining a collision risk point combination corresponding to the current robot operation link; wherein the collision risk point combination comprises collision risk points on two robot working components with collision relation;
if one collision risk point exists in the collision risk point combination and is located on the operation part of the robot to be collided, calculating the current distance between the collision risk points with collision relation in the collision risk point combination by adopting a point-to-point distance calculation mode based on real-time space coordinates of each collision risk point in the collision risk point combination;
If two collision risk points in the collision risk point combination are determined to be positioned on the operation part of the robot to be collided, calculating the current distance between the collision risk points with collision relation in the collision risk point combination by adopting a point-to-line distance calculation mode based on real-time space coordinates of each collision risk point in the collision risk point combination;
if more than three collision risk points in the collision risk point combination are located on the operation part of the robot to be collided, calculating the current distance between the collision risk points with collision relation in the collision risk point combination by adopting a point-to-surface distance calculation mode based on real-time space coordinates of each collision risk point in the collision risk point combination.
6. The robot self-collision processing method according to any one of claims 1 to 5, wherein the preset safety distance includes a minimum safety distance and a sub-safety distance; based on the magnitude relation between the current distance and the preset safety distance, controlling the mechanical arm to perform corresponding self-collision avoidance processing, including:
if the current distance is determined to be larger than the secondary safety distance, determining that the mechanical arm does not perform self-collision avoidance processing, and controlling the mechanical arm to continue operation;
If the current distance is determined to be greater than the minimum safety distance and smaller than or equal to the secondary safety distance, storing all joint positions of the mechanical arm in the current robot operation link to obtain a joint position and position combination, and controlling the mechanical arm to continue operation;
and if the current distance is determined to be equal to the minimum safety distance, controlling the mechanical arm to stop moving.
7. The method for processing the robot self-collision according to claim 6, wherein after storing all joint positions of the mechanical arm in a current robot working link to obtain a joint position combination, further comprising:
and when determining to restore the pose state of the mechanical arm before the current robot operation link, controlling the mechanical arm to move reversely according to the reverse sequence of the joint pose in the joint pose combination.
8. A robot self-collision handling device, comprising:
the information acquisition unit is used for acquiring each collision risk point corresponding to the current robot operation type and the space coordinate conversion relation among the collision risk points; the spatial coordinate conversion relationship is used for representing a one-way transmission relationship of spatial coordinates of each collision risk point from a starting coordinate position of the robot to an end position of the mechanical arm;
The coordinate determining unit is used for determining the real-time space coordinates of each collision risk point by utilizing the real-time space coordinates of the center point of the tail end of the mechanical arm based on the space coordinate conversion relation among the collision risk points;
the distance determining unit is used for determining the current distance between collision risk points with collision relations corresponding to the current robot operation link based on the real-time space coordinates of each collision risk point;
and the self-collision processing unit is used for controlling the mechanical arm to perform corresponding self-collision avoidance processing based on the magnitude relation between the current distance and the preset safety distance.
9. A live working robot comprising a body, a robot arm mounted on the body, a memory mounted in the body, and a processor, wherein the memory stores a computer program executable on the processor, and the processor implements the robot self-collision processing method according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the robot self-collision handling method according to any of claims 1-7.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR960013819A (en) * | 1994-10-31 | 1996-05-22 | 경주현 | Vehicle Collision Hazard Alarm |
| DE19625637A1 (en) * | 1996-06-26 | 1998-01-02 | Brink Carsten Dipl Ing Ten | Robot operating method for multi-robot operation e.g. in deep sea applications |
| EP1972415A1 (en) * | 2007-03-23 | 2008-09-24 | Honda Research Institute Europe GmbH | Robots with collision avoidance functionality |
| KR20090044130A (en) * | 2007-10-31 | 2009-05-07 | 한국기계연구원 | Self collision control method for dual arm robot system |
| KR20190109332A (en) * | 2019-09-05 | 2019-09-25 | 엘지전자 주식회사 | Apparatus and method for planning path of robot, and robot for operating based on the method |
| CN110561432A (en) * | 2019-08-30 | 2019-12-13 | 广东省智能制造研究所 | safety cooperation method and device based on man-machine co-fusion |
| CN110682286A (en) * | 2019-05-28 | 2020-01-14 | 广东省智能制造研究所 | Real-time obstacle avoidance method for cooperative robot |
| CN112548986A (en) * | 2021-03-01 | 2021-03-26 | 国网瑞嘉(天津)智能机器人有限公司 | Live working robot collision detection method and live working robot |
| EP3812106A1 (en) * | 2019-10-23 | 2021-04-28 | Robert Bosch GmbH | Robot assembly, method for operating the robot assembly, computer program and machine readable storage medium |
| CN113400303A (en) * | 2021-06-01 | 2021-09-17 | 青岛悟牛智能科技有限公司 | Six-axis robot fruit and vegetable picking path planning method based on RRT (recursive least squares) algorithm |
| CN113618739A (en) * | 2021-08-24 | 2021-11-09 | 深圳市优必选科技股份有限公司 | Robot dynamic obstacle avoidance method and device and robot |
| CN114021850A (en) * | 2020-11-26 | 2022-02-08 | 广东博智林机器人有限公司 | Collision prediction method and device for construction operation equipment and construction operation equipment |
| CN115609594A (en) * | 2022-12-15 | 2023-01-17 | 国网瑞嘉(天津)智能机器人有限公司 | Planning method and device for mechanical arm path, upper control end and storage medium |
| CN115716265A (en) * | 2022-10-31 | 2023-02-28 | 中国电器科学研究院股份有限公司 | Robot double-arm collision neural reflex control method |
| WO2023029901A1 (en) * | 2021-09-01 | 2023-03-09 | 节卡机器人股份有限公司 | Robot collision prediction method, computer storage medium, and electronic device |
-
2023
- 2023-06-08 CN CN202310672409.7A patent/CN116394266B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR960013819A (en) * | 1994-10-31 | 1996-05-22 | 경주현 | Vehicle Collision Hazard Alarm |
| DE19625637A1 (en) * | 1996-06-26 | 1998-01-02 | Brink Carsten Dipl Ing Ten | Robot operating method for multi-robot operation e.g. in deep sea applications |
| EP1972415A1 (en) * | 2007-03-23 | 2008-09-24 | Honda Research Institute Europe GmbH | Robots with collision avoidance functionality |
| KR20090044130A (en) * | 2007-10-31 | 2009-05-07 | 한국기계연구원 | Self collision control method for dual arm robot system |
| CN110682286A (en) * | 2019-05-28 | 2020-01-14 | 广东省智能制造研究所 | Real-time obstacle avoidance method for cooperative robot |
| CN110561432A (en) * | 2019-08-30 | 2019-12-13 | 广东省智能制造研究所 | safety cooperation method and device based on man-machine co-fusion |
| KR20190109332A (en) * | 2019-09-05 | 2019-09-25 | 엘지전자 주식회사 | Apparatus and method for planning path of robot, and robot for operating based on the method |
| EP3812106A1 (en) * | 2019-10-23 | 2021-04-28 | Robert Bosch GmbH | Robot assembly, method for operating the robot assembly, computer program and machine readable storage medium |
| CN114021850A (en) * | 2020-11-26 | 2022-02-08 | 广东博智林机器人有限公司 | Collision prediction method and device for construction operation equipment and construction operation equipment |
| CN112548986A (en) * | 2021-03-01 | 2021-03-26 | 国网瑞嘉(天津)智能机器人有限公司 | Live working robot collision detection method and live working robot |
| CN113400303A (en) * | 2021-06-01 | 2021-09-17 | 青岛悟牛智能科技有限公司 | Six-axis robot fruit and vegetable picking path planning method based on RRT (recursive least squares) algorithm |
| CN113618739A (en) * | 2021-08-24 | 2021-11-09 | 深圳市优必选科技股份有限公司 | Robot dynamic obstacle avoidance method and device and robot |
| WO2023024279A1 (en) * | 2021-08-24 | 2023-03-02 | 深圳市优必选科技股份有限公司 | Dynamic obstacle avoidance method and apparatus for robot, and robot |
| WO2023029901A1 (en) * | 2021-09-01 | 2023-03-09 | 节卡机器人股份有限公司 | Robot collision prediction method, computer storage medium, and electronic device |
| CN115716265A (en) * | 2022-10-31 | 2023-02-28 | 中国电器科学研究院股份有限公司 | Robot double-arm collision neural reflex control method |
| CN115609594A (en) * | 2022-12-15 | 2023-01-17 | 国网瑞嘉(天津)智能机器人有限公司 | Planning method and device for mechanical arm path, upper control end and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| 六关节检修机械臂动态碰撞检测;陆军;杨秀成;朱齐丹;;应用科技(第12期);全文 * |
| 双臂机器人自碰撞检测机器运动规划;吴长征，岳义，韦宝琛;自动化技术(第1期);全文 * |
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