CN116890340A - Intelligent transfer robot for industrial manufacturing - Google Patents
Intelligent transfer robot for industrial manufacturing Download PDFInfo
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- CN116890340A CN116890340A CN202310932166.6A CN202310932166A CN116890340A CN 116890340 A CN116890340 A CN 116890340A CN 202310932166 A CN202310932166 A CN 202310932166A CN 116890340 A CN116890340 A CN 116890340A
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 25
- 238000012546 transfer Methods 0.000 title claims abstract description 16
- 230000009471 action Effects 0.000 claims abstract description 13
- 239000011159 matrix material Substances 0.000 claims description 21
- 238000012937 correction Methods 0.000 claims description 7
- 238000005265 energy consumption Methods 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000013139 quantization Methods 0.000 abstract description 3
- 238000004590 computer program Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000000034 method Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
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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
-
- 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
-
- 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
- B25J9/1679—Programme controls characterised by the tasks executed
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Abstract
The application relates to the technical field of industrial manufacturing and transportation, and particularly discloses an intelligent transfer robot for industrial manufacturing, which comprises the following components: the system comprises a control module and an analysis module, wherein the analysis module is used for acquiring task information and quantizing the task information to generate a path information stream and a first task action information stream; a preselection module for preselecting the conveying path according to the result of the analysis module; the recognition module is used for recognizing the road surface height difference, recognizing various road conditions according to the penetrability of the conveying path and setting corresponding robot gestures; and the selecting module is used for correcting the preselected conveying path according to the identifying module to generate an optimal path. The application is provided with various weight standard quantized route information, preselect the transport path according to the demand, and carry on one time to examine the preselect transport path, correct according to examining the original preselect path result, after correcting, carry on one time quantization and information flow compare, get the most suitable transport scheme.
Description
Technical Field
The application relates to the technical field of industrial manufacturing and transportation, in particular to an intelligent transfer robot for industrial manufacturing.
Background
In the industrial manufacturing field, the road surface is often unable to predict the roadblock because of frequent on-site dispatching, and the transportation path is difficult to preset, and the transportation robot frequently runs after being limited once, so if the efficiency of the preset task steps is low, the series of tasks generate batch inefficiency, resulting in the reduction of production efficiency and the loss of extra time cost.
In view of this, there is an urgent need for an intelligent transfer robot for industrial manufacturing that sets an optimal transfer path.
Disclosure of Invention
The application provides an intelligent transfer robot for industrial manufacturing, which is used for solving the technical problems of production efficiency reduction and extra time cost loss caused by low task step efficiency in the prior art.
In order to solve the technical problems, the application discloses an intelligent transfer robot for industrial manufacturing, which comprises:
the control module is used for inputting task information, wherein the task information comprises a transportation starting point and a transportation ending point;
the analysis module is used for quantifying the task information and generating a path information stream and a first task action information stream;
a preselection module for preselecting the conveying path according to the result of the analysis module;
the recognition module is used for recognizing the road surface height difference, recognizing various road conditions according to the penetrability of the conveying path and setting corresponding robot gestures;
and the selecting module is used for correcting the preselected conveying path according to the identifying module to generate an optimal path.
Preferably, the analysis module generates a path information stream according to the transport start point and the transport end point, wherein,
the analysis module sets a raised area in real time according to the geographic information system and the digital elevation model database and acquires a topographic map and elevation data of the raised area; generating a plurality of feasible conveying paths in the staging area according to the topographic map, recording the total plane distance P of the paths, acquiring the plane distance S and the height difference H between each elevation data on the conveying paths to generate a discrete aggregate, calculating the extreme difference L of the elevation data according to the discrete aggregate, quantitatively fitting the conveying path data to generate the weight A of the conveying paths,
and s1+s2+s3+ & gt+sn=p, h1+h2+h3+ & gt+hn=l, wherein S1 to Sn are the planar distance between the first elevation data to the first point elevation data and the planar distance between the elevation data to the n-1 th point elevation data, respectively; h1 to Hn are respectively the height difference between the first elevation data from the starting point to the first point elevation data and the height difference between the elevation data from the n-1 point to the n-th point;
and calculating the weight A of each conveying path, and sorting the generated path information flows A1, A2 and A3.
Preferably, the analysis module further sets a first task action weight B according to the maximum lifting height G and the unit plane moving distance V of the robot, wherein,
and calculating a first task action weight B of each conveying path, and sequencing from small to large to obtain and generate path information flows B1, B2 and B3.
Preferably, the pre-selection module is provided with a quick-handling mode, wherein,
the preselection module is also provided with a quick handling mode, and the quick handling mode defaults to select a transport path corresponding to A1 in the path information stream as the transport path of the quick handling mode
Preferably, the pre-selecting module is further provided with a calculated energy consumption matrix C, and C (a1+b1, a2+b2, a3+b3+,...
Preferably, the pre-selecting module is further configured to select, by default, a transport path corresponding to the first task action weight B1 as the transport path in the safe transport mode when the safe transport mode is selected.
Preferably, the recognition module is pre-configured with a road condition matrix T0 and a preset gesture matrix Y, and TO (T01, T02, T03, T04) is set for the road condition matrix T0, where T01 is a first road condition, T02 is a second road condition, T03 is a third road condition, and T04 is a fourth road condition, and 0 < T01 < T02 < T03 < T04 is set according TO a magnitude relation of road surface height difference; for the preset gesture matrix Y, Y (Y1, Y2, Y3, Y4) is set, wherein Y1 is a first preset gesture, Y2 is a second preset gesture, Y3 is a third preset gesture, and Y4 is a fourth preset gesture, and 0 < Y1 < Y2 < Y3 < Y4;
the recognition module is further used for determining the gesture of the robot according to the relation between the maximum pavement height difference R within the unit plane moving distance V and the preset road condition matrix T0:
when L is less than or equal to T01, selecting the first preset gesture Y1 as the gesture of the robot on the first road condition;
when T01 is more than L and less than or equal to T02, selecting the second preset gesture Y2 as the gesture of the robot on the second road condition;
when T02 is more than L and less than or equal to T03, selecting the third preset gesture Y3 as the gesture of the robot on a third road condition;
and when T03 is more than L and less than or equal to T04, selecting the fourth preset gesture Y4 as the gesture of the robot on the fourth road condition.
Preferably, the identification module is configured to determine, according to a relationship between a maximum road surface height difference R within a unit plane movement distance V and a preset road condition matrix T0, that a distance between each correction node of a robot gesture corrects a plane distance S between each elevation data in the path information stream, and according to a replacement of a height difference H between each elevation data in the path information stream corresponding to the maximum road surface height difference R, calculate a weight A1 of a corrected path, and compare the weight A1 of the corrected path with a weight A2 of a path of No. two, where the weight A1 of the corrected path of No. one is smaller than A2.
Preferably, when the weight A1 > A2 of the corrected path number one, a path corresponding to A2 is selected as the path number one to replace A1.
Compared with the prior art, the intelligent transfer robot for industrial manufacture has the beneficial effects that:
by using a map construction algorithm, the robot can create an accurate and real-time environment map, quantizes data, is provided with various weight standard quantized route information, preselects a transportation path according to requirements, performs primary examination on the preselect transportation path, corrects an original preselect path result according to the examination, and performs primary quantization and comparison with an information flow after correction to obtain the most suitable transportation scheme; the robot can preselect a plurality of conveying paths based on map information and weight standards, the paths can be set according to different requirements, such as a shortest path, a fastest path, an energy optimal path and the like, and the preselect conveying paths provide flexibility and diversified selection, so that the robot can conduct path planning according to specific requirements; and comparing the quantitative information with the information flow, and finally obtaining the most suitable conveying scheme by the robot through examination and correction. This solution will be a result of a comprehensive consideration of several factors, and obtaining the most suitable transport solution will ensure that the robot will be able to perform tasks in an optimal manner during handling.
Drawings
The accompanying drawings are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate the application and together with the embodiments of the application, serve to explain the application. In the drawings:
fig. 1 is a schematic structural view of an intelligent transfer robot according to the present application.
Detailed Description
The following describes in further detail the embodiments of the present application with reference to the drawings and examples. The following examples are illustrative of the application and are not intended to limit the scope of the application.
In the description of the present application, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application. The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
Referring to fig. 1, the present embodiment discloses an intelligent transfer robot for industrial manufacturing, comprising:
the control module is used for inputting task information, wherein the task information comprises a transportation starting point and a transportation ending point;
the analysis module is used for quantifying the task information and generating a path information stream and a first task action information stream;
a preselection module for preselecting the conveying path according to the result of the analysis module;
the recognition module is used for recognizing the road surface height difference, recognizing various road conditions according to the penetrability of the conveying path and setting corresponding robot gestures;
and the selecting module is used for correcting the preselected conveying path according to the identifying module to generate an optimal path.
Preferably, the analysis module generates a path information stream according to the transport start point and the transport end point, wherein,
the analysis module sets a raising area in real time according to the geographic information system and the digital elevation model database and acquires a topographic map and elevation data of the raising area; generating a plurality of feasible conveying paths in a raised area according to a topographic map, recording the total plane distance P of the paths, acquiring the plane distance S and the height difference H between each elevation data on the conveying paths to generate a discrete aggregate, calculating the extreme difference L of the elevation data according to the discrete aggregate, quantitatively fitting the conveying path data to generate the weight A of the conveying paths,
and s1+s2+s3+ & gt+sn=p, h1+h2+h3+ & gt+hn=l, wherein S1 to Sn are the planar distance between the first elevation data to the first point elevation data and the planar distance between the elevation data to the n-1 th point elevation data, respectively; h1 to Hn are respectively the height difference between the first elevation data from the starting point to the first point elevation data and the height difference between the elevation data from the n-1 point to the n-th point;
and calculating the weight A of each conveying path, and sorting the generated path information flows A1, A2 and A3.
Preferably, the analysis module also sets a first task action weight B according to the maximum lifting height G and the unit plane moving distance V of the robot, wherein,
and calculating a first task action weight B of each conveying path, and sequencing from small to large to obtain and generate path information flows B1, B2 and B3.
Preferably, the pre-selection module is provided with a quick-handling mode, wherein,
the preselection module is also provided with a rapid conveying mode, and the rapid conveying mode defaults to select a conveying path corresponding to A1 in the path information stream as the conveying path of the rapid conveying mode
Preferably, the preselection module is further provided with a calculated energy consumption matrix C, and C (a1+b1, a2+b2, a3+b3+,...
Preferably, the preselection module is further configured to select a transport path corresponding to the first task action weight B1 as a transport path in the safe transport mode by default when the safe transport mode is selected.
Preferably, the recognition module is pre-set with a road condition matrix T0 and a preset gesture matrix Y, and TO (T01, T02, T03, T04) is set for the road condition matrix T0, wherein T01 is a first road condition, T02 is a second road condition, T03 is a third road condition and T04 is a fourth road condition, and 0 < T01 < T02 < T03 < T04 is set according TO the magnitude relation of the road surface height difference; for the preset gesture matrix Y, Y (Y1, Y2, Y3, Y4) is set, wherein Y1 is a first preset gesture, Y2 is a second preset gesture, Y3 is a third preset gesture, and Y4 is a fourth preset gesture, and 0 < Y1 < Y2 < Y3 < Y4;
the recognition module is also used for determining the gesture of the robot according to the relation between the maximum pavement height difference R within the unit plane moving distance V and the preset road condition matrix T0:
when L is less than or equal to T01, a first preset gesture Y1 is selected as the gesture of the robot in a first road condition;
when T01 is more than L and less than or equal to T02, selecting a second preset gesture Y2 as the gesture of the robot on a second road condition;
when T02 is more than L and less than or equal to T03, selecting a third preset gesture Y3 as the gesture of the robot under a third condition;
and when T03 is more than L and less than or equal to T04, selecting a fourth preset gesture Y4 as the gesture of the robot in a fourth road condition.
Preferably, the recognition module is used for determining the distance between each correction node of the robot gesture according to the relation between the maximum road surface height difference R within the unit plane movement distance V and the preset road condition matrix T0, correcting the plane distance S corresponding to each elevation data in the path information flow, replacing the height difference H corresponding to each elevation data in the path information flow according to the maximum road surface height difference R, calculating the weight A1 of the corrected first path, comparing with the weight A2 of the second path, and when the weight A1 of the corrected first path is smaller than A2, not changing.
Preferably, when the weight A1 > A2 of the corrected path number one, the path corresponding to A2 is selected as the path number one to replace A1.
In summary, the intelligent transfer robot for industrial manufacturing that this embodiment provided, compared with prior art, its beneficial effect lies in:
by using a map construction algorithm, the robot can create an accurate and real-time environment map, quantizes data, is provided with various weight standard quantized route information, preselects a transportation path according to requirements, performs primary examination on the preselect transportation path, corrects an original preselect path result according to the examination, and performs primary quantization and comparison with an information flow after correction to obtain the most suitable transportation scheme; the robot can preselect a plurality of conveying paths based on map information and weight standards, the paths can be set according to different requirements, such as a shortest path, a fastest path, an energy optimal path and the like, and the preselect conveying paths provide flexibility and diversified selection, so that the robot can conduct path planning according to specific requirements; and comparing the quantitative information with the information flow, and finally obtaining the most suitable conveying scheme by the robot through examination and correction. This solution will be a result of a comprehensive consideration of several factors, and obtaining the most suitable transport solution will ensure that the robot will be able to perform tasks in an optimal manner during handling.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present application and not for limiting the same, and although the present application has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the application without departing from the spirit and scope of the application, which is intended to be covered by the claims.
Claims (9)
1. An intelligent transfer robot for industrial manufacturing, comprising:
the control module is used for inputting task information, wherein the task information comprises a transportation starting point and a transportation ending point;
the analysis module is used for quantifying the task information and generating a path information stream and a first task action information stream;
a preselection module for preselecting the conveying path according to the result of the analysis module;
the recognition module is used for recognizing the road surface height difference, recognizing various road conditions according to the penetrability of the conveying path and setting corresponding robot gestures;
and the selecting module is used for correcting the preselected conveying path according to the identifying module to generate an optimal path.
2. The intelligent carrier robot for industrial manufacturing according to claim 1, wherein,
the analysis module generates a path information stream according to the transport start point and the transport end point, wherein,
the analysis module sets a raised area in real time according to the geographic information system and the digital elevation model database and acquires a topographic map and elevation data of the raised area; generating a plurality of feasible conveying paths in the staging area according to the topographic map, recording the total plane distance P of the paths, acquiring the plane distance S and the height difference H between each elevation data on the conveying paths to generate a discrete aggregate, calculating the extreme difference L of the elevation data according to the discrete aggregate, quantitatively fitting the conveying path data to generate the weight A of the conveying paths,
and s1+s2+s3+ & gt+sn=p, h1+h2+h3+ & gt+hn=l, wherein S1 to Sn are the planar distance between the first elevation data to the first point elevation data and the planar distance between the elevation data to the n-1 th point elevation data, respectively; h1 to Hn are respectively the height difference between the first elevation data from the starting point to the first point elevation data and the height difference between the elevation data from the n-1 point to the n-th point;
and calculating the weight A of each conveying path, and sorting the generated path information flows A1, A2 and A3.
3. The intelligent transfer robot for industrial manufacturing according to claim 2, wherein,
the analysis module also sets a first task action weight B according to the maximum lifting height G and the unit plane moving distance V of the robot, wherein,
and calculating a first task action weight B of each conveying path, and sequencing from small to large to obtain and generate path information flows B1, B2 and B3.
4. An intelligent transfer robot for industrial manufacture according to claim 3, wherein,
the preselection module is further provided with a rapid conveying mode, and the rapid conveying mode defaults to select a conveying path corresponding to A1 in the path information flow as the conveying path of the rapid conveying mode.
5. The intelligent carrier robot for industrial manufacturing according to claim 4, wherein,
the preselection module is further provided with a calculated energy consumption matrix C, C (a1+b1, a2+b2, a3+b3+,...
6. The intelligent carrier robot for industrial manufacturing according to claim 5, wherein,
the preselection module is further provided with a transport path corresponding to the first task action weight B1 selected by default when the safe transport mode is selected as the transport path of the safe transport mode.
7. The intelligent carrier robot for industrial manufacturing according to claim 6, wherein,
the recognition module is pre-provided with a road condition matrix T0 and a preset gesture matrix Y, TO (T01, T02, T03, T04) is set for the road condition matrix T0, wherein T01 is a first road condition, T02 is a second road condition, T03 is a third road condition and T04 is a fourth road condition, and T01 is more than 0 and less than T02 and less than T03 and less than T04 are set according TO the magnitude relation of the road surface height difference; for the preset gesture matrix Y, Y (Y1, Y2, Y3, Y4) is set, wherein Y1 is a first preset gesture, Y2 is a second preset gesture, Y3 is a third preset gesture, and Y4 is a fourth preset gesture, and 0 < Y1 < Y2 < Y3 < Y4;
the recognition module is also used for determining the gesture of the robot according to the relation between the maximum road surface height difference R within the unit plane moving distance V and the preset road condition matrix T0;
when L is less than or equal to T01, selecting the first preset gesture Y1 as the gesture of the robot on the first road condition;
when T01 is more than L and less than or equal to T02, selecting the second preset gesture Y2 as the gesture of the robot on the second road condition;
when T02 is more than L and less than or equal to T03, selecting the third preset gesture Y3 as the gesture of the robot on a third road condition;
and when T03 is more than L and less than or equal to T04, selecting the fourth preset gesture Y4 as the gesture of the robot on the fourth road condition.
8. The intelligent transfer robot for industrial manufacturing according to claim 7, wherein the recognition module is configured to determine, according to a relationship between a road surface maximum height difference R within a unit plane movement distance V and a preset road condition matrix T0, that a distance between each correction node of a robot gesture corresponds to a plane distance S between each elevation data in the path information flow, correct the plane distance S, and according to the surface maximum height difference R, replace a height difference H between each elevation data in the path information flow, calculate a weight A1 of a corrected path, and compare the weight A1 of the corrected path with a weight A2 of the path No. 2, and when the weight A1 of the corrected path No. 1 is smaller than A2.
9. The intelligent carrier robot for industrial manufacturing according to claim 8, wherein when the weight A1 > A2 of the corrected path number one, the path corresponding to A2 is selected as the path number one to replace A1.
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