CN118269110A - Stacking mechanism path optimization method and system for automobile floor longitudinal beam production - Google Patents
Stacking mechanism path optimization method and system for automobile floor longitudinal beam production Download PDFInfo
- Publication number
- CN118269110A CN118269110A CN202410711550.8A CN202410711550A CN118269110A CN 118269110 A CN118269110 A CN 118269110A CN 202410711550 A CN202410711550 A CN 202410711550A CN 118269110 A CN118269110 A CN 118269110A
- Authority
- CN
- China
- Prior art keywords
- stacking
- mechanical arm
- target
- obstacle avoidance
- obstacle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000007246 mechanism Effects 0.000 title claims abstract description 82
- 238000005457 optimization Methods 0.000 title claims abstract description 80
- 238000000034 method Methods 0.000 title claims abstract description 79
- 238000004519 manufacturing process Methods 0.000 title claims description 23
- 230000003044 adaptive effect Effects 0.000 claims abstract description 60
- 230000009466 transformation Effects 0.000 claims abstract description 53
- 238000009499 grossing Methods 0.000 claims abstract description 27
- 230000008569 process Effects 0.000 claims description 47
- 230000033001 locomotion Effects 0.000 claims description 19
- 238000010586 diagram Methods 0.000 claims description 13
- 230000015654 memory Effects 0.000 claims description 13
- 238000012545 processing Methods 0.000 claims description 13
- 206010000117 Abnormal behaviour Diseases 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 8
- 230000004888 barrier function Effects 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 6
- 230000006978 adaptation Effects 0.000 claims description 4
- 230000008859 change Effects 0.000 description 12
- 238000006243 chemical reaction Methods 0.000 description 12
- 238000013519 translation Methods 0.000 description 10
- 238000004891 communication Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 5
- 239000000463 material Substances 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012549 training Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000012550 audit Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 239000000725 suspension Substances 0.000 description 1
Classifications
-
- 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
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Manipulator (AREA)
Abstract
The application provides a method and a system for optimizing a path of a stacking mechanism for producing an automobile floor longitudinal beam, which are used for determining a guide adaptive entropy of target guidance quality under each target bias value in historical stacking data of the stacking mechanism, and determining a bias optimization quantity through each target bias value and the corresponding guide adaptive entropy; determining obstacle avoidance distances between the mechanical arm and each stacking obstacle point, and performing constraint optimization on the obstacle avoidance path of the mechanical arm based on all the obstacle avoidance distances and the bias optimization quantity to obtain a stacking obstacle avoidance path; carrying out smoothness comparison on joint angles of the mechanical arm through historical transformation records in historical stacking data and a stacking obstacle avoidance path to obtain joint smoothing factors; when the mechanical arm is piled according to the obstacle avoidance path, the joint angle of the mechanical arm during piling is dynamically optimized through the joint smoothing factor. According to the scheme, the stacking obstacle avoidance path of the mechanical arm in the stacking mechanism can be dynamically optimized, so that the smoothness of the stacking obstacle avoidance path of the mechanical arm in the stacking mechanism is improved.
Description
Technical Field
The application relates to the technical field of path optimization, in particular to a stacking mechanism path optimization method and system for automobile floor longitudinal beam production.
Background
In the production of automotive parts, palletizing techniques are used to efficiently and accurately stack and arrange automotive parts. The palletizing technology plays an important role in the automobile manufacturing industry, can improve production efficiency, reduce cost and ensure quality and safety in the production process, and utilizes automation equipment such as robots or automatic stackers to automatically arrange and stack automobile parts (such as automobile floor stringers, automobile door outer plates and the like) according to a preset stacking mode.
The stacking mechanism is provided with a precise sensor and a vision system, the stacking mechanism can accurately identify and position automobile parts, accuracy and stability in the stacking process are guaranteed, the stacking mechanism can ensure that the mechanical arm of the stacking mechanism can avoid collision and damage parts when performing stacking operation through path planning and collision detection, a preset stacking path is usually adopted for the stacking path of the mechanical arm in the stacking mechanism in the prior art, but the preset stacking path cannot adapt to a complex stacking working environment, a large amount of time is required for adjustment when a real-time scene changes, so that production efficiency is influenced, and therefore, dynamic optimization is carried out on the stacking obstacle avoidance path of the mechanical arm in the stacking mechanism, so that the smoothness of the stacking obstacle avoidance path of the mechanical arm in the stacking mechanism is a difficult problem faced by the industry.
Disclosure of Invention
The application provides a method and a system for optimizing a stacking mechanism path for producing an automobile floor longitudinal beam, which can dynamically optimize a stacking obstacle avoidance path of a mechanical arm in a stacking mechanism, so that the smoothness of the stacking obstacle avoidance path of the mechanical arm in the stacking mechanism is improved.
In a first aspect, the present application provides a palletizing mechanism path optimization method for producing an automotive floor stringer, comprising:
acquiring a stacking record of a mechanical arm in a target stacking mechanism on an automobile floor longitudinal beam, and further acquiring historical stacking data;
Extracting all target offset values from the historical stacking data, further determining the guiding adaptive entropy of the target guidance quality under each target offset value, and determining the offset optimization amount of the mechanical arm in the target stacking mechanism in the stacking process through each target offset value and the corresponding guiding adaptive entropy;
Collecting all stacking obstacle points in a path space between a stacking point of a target automobile floor longitudinal beam and the mechanical arm, further determining an obstacle avoidance distance between the mechanical arm and each stacking obstacle point, and performing constraint optimization on an obstacle avoidance path of the mechanical arm based on all obstacle avoidance distances and the bias optimization quantity to obtain a stacking obstacle avoidance path;
Extracting historical transformation records of joint angles of the mechanical arm in each stacking process from the historical stacking data, and carrying out smoothness comparison on the joint angles of the mechanical arm through all the historical transformation records and the stacking obstacle avoidance path to obtain joint smoothness factors of the mechanical arm in the stacking process;
And when the mechanical arm is piled according to the obstacle avoidance path, dynamically optimizing the joint angle of the mechanical arm during piling through the joint smoothing factor.
In some embodiments, determining the steering adaptation entropy of the target steering at each target bias value specifically includes:
for each target bias value, acquiring all stacking records corresponding to the target bias value from the historical stacking data;
Acquiring the abnormal behavior times and the mechanical arm joint rotation times in each stacking record;
Determining the stacking success proportion of the target offset value through all stacking records;
And determining the guiding adaptive entropy of the target guidance quality under the target offset value through all abnormal behavior times, the stacking success proportion and all mechanical arm joint rotation times, and further obtaining the guiding adaptive entropy of the target guidance quality under each target offset value.
In some embodiments, determining the bias optimization measure of the mechanical arm in the stacking process of the target stacking mechanism through each target bias value and the corresponding guide adaptive entropy specifically comprises:
Comparing each guide adaptive entropy with a preset guide adaptive threshold value to obtain all optimized guide adaptive entropy;
taking the target bias values corresponding to the optimized guide adaptive entropy as the compliance bias values, and further obtaining all the compliance bias values;
and determining the bias optimization quantity of the mechanical arm in the target stacking mechanism in the stacking process according to all the compliance bias values.
In some embodiments, determining the obstacle avoidance distance between the robotic arm and each palletizing obstacle point specifically includes:
For each stacking obstacle point, enveloping the mechanical arm and the stacking obstacle point to obtain an enveloping structure diagram;
acquiring a spatial range relation between the mechanical arm and a stacking obstacle point in the envelope structure diagram;
And determining the obstacle avoidance distance between the mechanical arm and the stacking obstacle points based on the spatial range relation, and further obtaining the obstacle avoidance distance between each stacking obstacle point and the mechanical arm.
In some embodiments, performing constraint optimization on the obstacle avoidance path of the mechanical arm based on all obstacle avoidance distances and the bias optimization quantity, and obtaining the stacking obstacle avoidance path specifically includes:
marking each stacking obstacle point through all obstacle avoidance distances to obtain an obstacle marking sequence;
For each adjacent stacking obstacle point in the obstacle marking sequence, determining adjacent obstacle avoidance paths between the adjacent stacking obstacle points, and taking the adjacent obstacle avoidance paths as branch paths in the obstacle avoidance paths of the mechanical arm;
orthogonal constraint is carried out on the branch paths through the bias optimization quantity, so that optimized branch paths are obtained, and then the optimized branch paths of each adjacent stacking barrier point in the barrier mark sequence are determined;
and determining a stacking obstacle avoidance path according to all the optimized branch paths.
In some embodiments, the dynamic optimization of the joint angle of the mechanical arm during stacking through the joint smoothing factor specifically includes: and in the motion of the mechanical arm according to the initialized stacking path, when the motion direction is changed, adjusting the joint angle of the mechanical arm by using the joint smoothing factor, and repeating the steps until the mechanical arm reaches the stacking point of the target automobile floor longitudinal beam.
In some embodiments, the target palletizing mechanism is a robotic stacker.
In a second aspect, the application provides a path optimization system of a palletizing mechanism for producing automobile floor stringers, comprising a path optimization unit, wherein the path optimization unit comprises:
the acquisition module is used for acquiring the stacking record of the mechanical arm in the target stacking mechanism on the automobile floor longitudinal beam so as to acquire historical stacking data;
The processing module is used for extracting all target offset values from the historical stacking data, further determining the guiding adaptive entropy of the target guiding performance under each target offset value, and determining the offset optimization amount of the mechanical arm in the target stacking mechanism in the stacking process through each target offset value and the corresponding guiding adaptive entropy;
The processing module is also used for collecting all stacking obstacle points in a path space between a stacking point of the target automobile floor longitudinal beam and the mechanical arm, further determining an obstacle avoidance distance between the mechanical arm and each stacking obstacle point, and carrying out constraint optimization on the obstacle avoidance path of the mechanical arm based on all obstacle avoidance distances and the bias optimization quantity to obtain a stacking obstacle avoidance path;
The processing module is also used for extracting historical transformation records of joint angles of the mechanical arm in each stacking process from the historical stacking data, and carrying out smoothness comparison on the joint angles of the mechanical arm through all the historical transformation records and the stacking obstacle avoidance path to obtain joint smoothness factors of the mechanical arm in the stacking process;
and the execution module is used for dynamically optimizing the joint angle of the mechanical arm during stacking through the joint smoothing factor when the mechanical arm stacks according to the obstacle avoidance path.
In a third aspect, the present application provides a computer device comprising a memory for storing a computer program and a processor for calling and running the computer program from the memory, such that the computer device performs the palletising mechanism path optimisation method for the production of automotive floor stringers as described above.
In a fourth aspect, the present application provides a computer readable storage medium having instructions or codes stored therein, which when executed on a computer cause the computer to perform the palletizing mechanism path optimization method for producing an automotive floor stringer as described above.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
According to the method and the system for optimizing the path of the stacking mechanism for producing the automobile floor longitudinal beam, which are provided by the application, the stacking record of the mechanical arm in the target stacking mechanism on the automobile floor longitudinal beam is obtained, and then the historical stacking data are obtained; extracting all target offset values from the historical stacking data, further determining the guiding adaptive entropy of the target guidance quality under each target offset value, and determining the offset optimization amount of the mechanical arm in the target stacking mechanism in the stacking process through each target offset value and the corresponding guiding adaptive entropy; collecting all stacking obstacle points in a path space between a stacking point of a target automobile floor longitudinal beam and the mechanical arm, further determining an obstacle avoidance distance between the mechanical arm and each stacking obstacle point, and performing constraint optimization on an obstacle avoidance path of the mechanical arm based on all obstacle avoidance distances and the bias optimization quantity to obtain a stacking obstacle avoidance path; extracting historical transformation records of joint angles of the mechanical arm in each stacking process from the historical stacking data, and carrying out smoothness comparison on the joint angles of the mechanical arm through all the historical transformation records and the stacking obstacle avoidance path to obtain joint smoothness factors of the mechanical arm in the stacking process; and when the mechanical arm is piled according to the obstacle avoidance path, dynamically optimizing the joint angle of the mechanical arm during piling through the joint smoothing factor.
Therefore, in the application, when the mechanical arm is piled according to the obstacle avoidance path, the joint angle of the mechanical arm during piling is dynamically optimized through the joint smoothing factor; the method comprises the steps that an obstacle avoidance path can be obtained through combining an obstacle avoidance distance between a mechanical arm and each stacking obstacle point with an offset optimization amount, namely, a position deviation range (namely, the offset optimization amount) and the obstacle avoidance distance in a stacking or stacking process are comprehensively considered, the obstacle avoidance path of the mechanical arm reaching a target point is planned, so that an initial path of the mechanical arm when stacking is obtained, the initial path error range of the obstacle avoidance path of the mechanical arm reaching the target point is ensured to be in a controllable range, and joint angles of the mechanical arm can be adjusted to different degrees through the obstacle avoidance distance, so that smoothness of joint angle conversion of the mechanical arm is improved; then, comparing the smoothness in the history transformation record of the joint angle of the mechanical arm with the smoothness of the stacking obstacle avoidance path when the mechanical arm is stacked, and obtaining the association degree (namely joint smoothness factor) between the smoothness degree of the motion of the mechanical arm in stacking according to the stacking obstacle avoidance path and the joint angle of the mechanical arm; finally, when the mechanical arm stacks according to the obstacle avoidance path, the joint smoothing factors are used for adjusting the part which is not smooth enough in the joint angle of the mechanical arm during stacking, so that the smoothness of the obstacle avoidance path is further improved; in summary, by adopting the scheme, the stacking obstacle avoidance path of the mechanical arm in the stacking mechanism can be dynamically optimized, so that the smoothness of the stacking obstacle avoidance path of the mechanical arm in the stacking mechanism is improved.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is an exemplary flow chart of a palletizing mechanism path optimization method for use in the production of automotive floor stringers, according to some embodiments of the application;
FIG. 2 is a block diagram of an envelope shown in some embodiments according to the application;
FIG. 3 is a schematic diagram of exemplary hardware and/or software of a path optimization unit shown in accordance with some embodiments of the application;
Fig. 4 is a schematic overall perspective view of a stacking mechanism (symmetrically arranged) for producing an automobile floor longitudinal beam according to an embodiment of the present application when stacking workpieces to be processed;
fig. 5 is a schematic overall perspective view of a palletizing mechanism (single structure) for producing an automobile floor longitudinal beam according to an embodiment of the present application;
FIG. 6 is a schematic diagram of an assembly structure of a jacking assembly and a translational adjustment assembly according to an embodiment of the present application;
Fig. 7 is a schematic structural view of a computer device for implementing a palletizing mechanism path optimization method for producing automotive floor stringers according to some embodiments of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. 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.
The embodiment of the application provides a stacking mechanism path optimization method and system for producing automobile floor longitudinal beams, wherein the core of the method is that the guiding adaptive entropy of target guidance quality under each target offset value in the historical stacking data of a stacking mechanism is determined, and the offset optimization quantity is determined through each target offset value and the corresponding guiding adaptive entropy; determining obstacle avoidance distances between the mechanical arm and each stacking obstacle point, and performing constraint optimization on the obstacle avoidance path of the mechanical arm based on all the obstacle avoidance distances and the bias optimization quantity to obtain a stacking obstacle avoidance path; carrying out smoothness comparison on joint angles of the mechanical arm through historical transformation records in historical stacking data and a stacking obstacle avoidance path to obtain joint smoothing factors; when the mechanical arm is piled according to the obstacle avoidance path, the joint angle of the mechanical arm during piling is dynamically optimized through the joint smoothing factor. According to the scheme, the stacking obstacle avoidance path of the mechanical arm in the stacking mechanism can be dynamically optimized, so that the smoothness of the stacking obstacle avoidance path of the mechanical arm in the stacking mechanism is improved.
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments. Referring to fig. 1, which is an exemplary flowchart of a palletizing mechanism path optimization method for producing an automotive floor rail according to some embodiments of the present application, the palletizing mechanism path optimization method for producing an automotive floor rail mainly includes the steps of:
In step 101, a stacking record of a mechanical arm in a target stacking mechanism on an automobile floor longitudinal beam is obtained, and historical stacking data are further obtained.
In the application, the target stacking mechanism is a mechanical arm type stacking machine, the mechanical arm type stacking machine is an automatic device for stacking or stacking automobile floor stringers in a tray or a container according to a preset mode, and the target stacking mechanism consists of a mechanical arm, a sensing vision system, a control system, a tray conveying device and a safety device; in specific implementation, the last 100 stacking records can be obtained in a control system of the target stacking mechanism, and the set of all the stacking records can be used as historical stacking data; in the application, the stacking record comprises a target offset value, an abnormal behavior, a key movement process of the mechanical arm and a stacking result which are set in each stacking process, wherein the abnormal behavior comprises a movement behavior with collision, rollback and suspension in the stacking process.
In step 102, all target offset values are extracted from the historical stacking data, so that the guiding adaptive entropy of the target guiding performance under each target offset value is determined, and the offset optimization amount of the mechanical arm in the target stacking mechanism in the stacking process is determined through each target offset value and the corresponding guiding adaptive entropy.
In the specific implementation, the target bias value in each stacking record in the historical stacking data is obtained and all target bias values are de-duplicated, and it is to be noted that in the application, all target bias values used later are target bias values after de-duplication, the target bias values are quantized values of a position deviation range in a stacking or stacking process allowed and set in a target stacking mechanism, the target bias values are used for controlling target guidance, the greater the target bias values are, the longer the movement path of a mechanical arm of the stacking mechanism is, the worse the target guidance is, and the higher the deviation degree of the searching direction from the shortest path direction of the starting point and the end point is.
In some embodiments, determining the steering adaptation entropy of the target steering at each target bias value may be accomplished by:
for each target bias value, acquiring all stacking records corresponding to the target bias value from the historical stacking data;
Acquiring the abnormal behavior times and the mechanical arm joint rotation times in each stacking record;
Determining the stacking success proportion of the target offset value through all stacking records;
And determining the guiding adaptive entropy of the target guidance quality under the target offset value through all abnormal behavior times, the stacking success proportion and all mechanical arm joint rotation times, and further obtaining the guiding adaptive entropy of the target guidance quality under each target offset value.
The guiding adaptive entropy represents the adaptive degree between the target bias value and the target guiding property in the target stacking mechanism; when the method is specifically implemented, firstly, a target offset value is selected as a comparison offset value, all the stacking records with the same target offset value as the comparison offset value in the historical stacking data are obtained, and all the stacking records can be used as all the stacking records corresponding to the comparison offset value (namely, the target offset value); secondly, the total times of abnormal behaviors in all stacking records corresponding to the comparison offset value can be counted to be used as the times of the abnormal behaviors; the total number of times of angle adjustment of the mechanical arm joints in all stacking records corresponding to the comparison offset value can be counted and used as the number of times of rotation of the mechanical arm joints; then, counting the total number of successful stacking times in stacking results in all stacking records corresponding to the comparison offset value as the total number of successful stacking times, taking the total number of all stacking records corresponding to the comparison offset value as the total number of successful stacking times, and taking the ratio of the total number of successful stacking times to the total number of successful stacking times as the ratio of successful stacking times of the target offset value; and finally, initializing a target guide model, wherein all abnormal behavior times can be used as a training data set of the target guide model, the stacking success proportion is used as an adjustment coefficient of a final evaluation result of the target guide model, the target guide model is used for carrying out unstable evaluation on the target guide property under the target bias value, so that the guide adaptive entropy of the target guide property under the target bias value can be obtained, and the guide adaptive entropy of the target guide property under each target bias value can be obtained through the mode.
In some embodiments, determining the bias optimization amount of the mechanical arm in the stacking process of the target stacking mechanism through each target bias value and the corresponding guide adaptive entropy can be achieved by adopting the following steps:
Comparing each guide adaptive entropy with a preset guide adaptive threshold value to obtain all optimized guide adaptive entropy;
taking the target bias values corresponding to the optimized guide adaptive entropy as the compliance bias values, and further obtaining all the compliance bias values;
and determining the bias optimization quantity of the mechanical arm in the target stacking mechanism in the stacking process according to all the compliance bias values.
In the application, the bias optimization quantity represents a target bias value applicable to the current stacking process of the target stacking mechanism; optimizing the guiding adaptive entropy to show that the adaptive degree of the target bias value and the target guiding property in the target stacking mechanism reaches the guiding adaptive entropy of the system requirement; when the method is specifically implemented, firstly, a guide adaptive entropy is selected, the guide adaptive entropy is compared and judged with a preset guide adaptive threshold, if the guide adaptive entropy is larger than or equal to the preset guide adaptive threshold, the guide adaptive entropy is used as an optimized guide adaptive entropy, if the guide adaptive entropy is smaller than the preset guide adaptive threshold, the comparison and judgment of the rest guide adaptive entropy are continued until the comparison and judgment of all guide adaptive entropy are completed, and all the optimized guide adaptive entropy can be obtained through the mode, wherein the guide adaptive threshold is a quantitative value of a qualification standard of the adaptation degree between a target bias value and a target guidance property, and the guide adaptive threshold can be preset through historical experience; then, taking target bias values corresponding to the optimized guide adaptive entropy as compliance bias values, and further obtaining all the compliance bias values; and finally, taking all the compliance bias values as the value range of the bias optimization quantity of the mechanical arm in the stacking process in the target stacking mechanism.
In step 103, all stacking obstacle points in a path space between a stacking point of the target automobile floor longitudinal beam and the mechanical arm are collected, further, the obstacle avoidance distance between the mechanical arm and each stacking obstacle point is determined, and constraint optimization is performed on the obstacle avoidance path of the mechanical arm based on all obstacle avoidance distances and the bias optimization quantity, so that a stacking obstacle avoidance path is obtained.
In the specific implementation, a sensing vision system in a stacking mechanism can be used for collecting all stacking obstacle points in a path space between a mechanical arm and a stacking point of a target automobile floor longitudinal beam; in the application, the stacking point of the target automobile floor longitudinal beam represents the target point of the mechanical arm for grabbing the target automobile floor longitudinal beam, and the stacking point of the target automobile floor longitudinal beam can be obtained according to historical experience; the path space represents the allowable space range of the stacking path of the mechanical arm in the stacking mechanism.
In some embodiments, determining the obstacle avoidance distance between the robotic arm and each palletizing obstacle point may be accomplished by:
For each stacking obstacle point, enveloping the mechanical arm and the stacking obstacle point to obtain an enveloping structure diagram;
acquiring a spatial range relation between the mechanical arm and a stacking obstacle point in the envelope structure diagram;
And determining the obstacle avoidance distance between the mechanical arm and the stacking obstacle points based on the spatial range relation, and further obtaining the obstacle avoidance distance between each stacking obstacle point and the mechanical arm.
In specific implementation, first, for each stacking obstacle point, an existing enveloping technology is used for enveloping the mechanical arm and the stacking obstacle point to obtain an enveloping structure diagram, and the enveloping structure diagram is shown in the following figure 2; then, as shown in fig. 2, the positional relationship between the mechanical arm and the stacking obstacle point is divided into 4 cases: the first is when the mechanical arm is in the space range of 3 directions of the stacking obstacle point; the second is when the mechanical arm is in the space range of 2 directions of the stacking obstacle point; thirdly, the mechanical arm is in the space range of 1 direction of the stacking obstacle point; fourthly, when the mechanical arm is not in the space range of the stacking obstacle point; finally, as shown in fig. 2, the positional relationship between the mechanical arm and the stacking obstacle point on the mechanical arm is divided into 4 cases: the first is that when the mechanical arm is in the space range of 3 directions of the stacking obstacle point, the obstacle avoidance distance between the stacking obstacle point and the mechanical arm is 0; the second is that when the mechanical arm is in the space range of 2 directions of the stacking obstacle point, the obstacle avoidance distance between the stacking obstacle point and the mechanical arm is the minimum vertical distance between the mechanical arm and a plane formed by the two directions; thirdly, when the mechanical arm is in the space range of 1 direction of the stacking obstacle point, the obstacle avoidance distance between the stacking obstacle point and the mechanical arm is the minimum vertical distance between the coordinate mechanical arm and the side length of the cube in the direction; and fourthly, when the mechanical arm is not in the space range of the stacking obstacle points, the obstacle avoidance distance between the stacking obstacle points and the mechanical arm is the minimum distance between the mechanical arm and the cube vertex, and the obstacle avoidance distance between each stacking obstacle point and the mechanical arm can be obtained through the mode.
In some embodiments, constraint optimization is performed on the obstacle avoidance path of the mechanical arm based on all obstacle avoidance distances and the bias optimization amount, and the obtaining of the stacking obstacle avoidance path can be achieved by the following steps:
marking each stacking obstacle point through all obstacle avoidance distances to obtain an obstacle marking sequence;
For each adjacent stacking obstacle point in the obstacle marking sequence, determining adjacent obstacle avoidance paths between the adjacent stacking obstacle points, and taking the adjacent obstacle avoidance paths as branch paths in the obstacle avoidance paths of the mechanical arm;
orthogonal constraint is carried out on the branch paths through the bias optimization quantity, so that optimized branch paths are obtained, and then the optimized branch paths of each adjacent stacking barrier point in the barrier mark sequence are determined;
and determining a stacking obstacle avoidance path according to all the optimized branch paths.
When the method is specifically implemented, firstly, the obstacle avoidance distance of each stacking obstacle point is used as the marking information of the corresponding stacking obstacle point, and all the stacking obstacle points are arranged according to the sequence from small to large of the corresponding obstacle avoidance distances to be used as an obstacle marking sequence; secondly, for each adjacent stacking obstacle point in the obstacle marking sequence, determining an adjacent obstacle avoidance path between the adjacent stacking obstacle points by using an existing path planning algorithm; then initializing an orthogonal constraint model, wherein the bias optimization quantity can be used as a weight adjustment coefficient of the orthogonal constraint model, the adjacent obstacle avoidance path is used as an initial path of the orthogonal constraint model, the consistency and feasibility of the path after orthogonal constraint and the initial path are kept as far as possible while the path bypasses the obstacle, the orthogonal constraint model is used for completing positive constraint on the adjacent obstacle avoidance path to obtain an optimized branch path, and the optimized branch path of each adjacent stacking obstacle point in the obstacle marking sequence can be obtained through the mode; and finally, connecting all the optimized branch paths according to the sequence of the obstacle marking sequences to serve as a stacking obstacle avoidance path.
In step 104, a history transformation record of the joint angle of the mechanical arm in each stacking process is extracted from the history stacking data, and the joint angle of the mechanical arm is subjected to smoothness comparison through all the history transformation records and the stacking obstacle avoidance path, so that a joint smoothness factor of the mechanical arm in the stacking process is obtained.
In the specific implementation, for each stacking process, a stacking record corresponding to the stacking process is obtained in historical stacking data, then the movement process of the mechanical arm joint in the stacking record is obtained, the movement process of the mechanical arm joint is used as a historical transformation record of the mechanical arm joint angle in the stacking process, and the historical transformation record of the mechanical arm joint angle in each stacking process can be obtained through the mode; in the present application, the joint angle of the mechanical arm refers to an angle value describing the position and posture of each joint of the mechanical arm, and in the multi-joint mechanical arm, each joint can rotate around its axis, and the joint angle is used to describe the rotation angle of each joint relative to its reference position.
In some embodiments, the smoothness comparison is performed on the joint angles of the mechanical arm through all history transformation records and the stacking obstacle avoidance path, and the obtained joint smoothness factors of the mechanical arm in the stacking process can be realized by adopting the following steps:
determining a path transformation scale when the path direction changes each time through the stacking obstacle avoidance path;
for each history transformation record, extracting a history transformation scale of the joint angle of the mechanical arm when the movement direction of the mechanical arm is changed in each history transformation record, and further determining the history transformation smoothness corresponding to each history transformation scale;
Comparing all the historical transformation scales with each path transformation scale according to all the historical transformation smoothness to obtain path transformation smoothness corresponding to each path transformation scale;
determining joint smooth values between the history transformation records and the stacking obstacle avoidance path according to all the history transformation smoothness and all the path transformation smoothness, and further obtaining joint smooth values between each history transformation record and the stacking obstacle avoidance path;
and determining joint smoothing factors of the mechanical arm in the stacking process based on all joint smoothing values.
In the application, the joint smoothing factor represents the association degree between the smoothing degree of the shutdown motion of the mechanical arm and the joint angle of the mechanical arm in the stacking process according to the stacking obstacle avoidance path; when the method is specifically implemented, firstly, the corners of all path directions in the stacking obstacle avoidance path are counted, the corners of each path direction change can be used as the path transformation scale of the corresponding path direction change, and the path transformation scale of each path direction change can be obtained; and secondly, for each history conversion record, acquiring an angle change value and angle change time (namely time used for angle conversion) of the joint angle of the mechanical arm when the movement direction of each mechanical arm is changed in the history conversion record, wherein the angle change value of the joint angle of the mechanical arm when the movement direction of each mechanical arm is changed can be used as a history conversion scale, one history conversion scale is selected, the ratio of the angle change value of the joint angle of the mechanical arm corresponding to the history conversion scale to the angle change time can be used as the history conversion smoothness corresponding to each history conversion scale, and the history conversion smoothness corresponding to each history conversion scale can be obtained by repeating the steps.
In addition, in the concrete implementation, firstly, for each path transformation scale, a joint angle prediction model of a mechanical arm is initialized, the historical transformation smoothness corresponding to each historical transformation scale can be used as a training resource of the joint angle prediction model, each historical transformation scale and the path transformation scale are used as initialization parameters in the joint angle prediction model, the path transformation smoothness corresponding to the path transformation scale can be obtained by using the joint angle prediction model after training, and the path transformation smoothness corresponding to each path transformation scale can be obtained by the mode; then, the ratio of the average value of all the historical transformation smoothness to the average value of all the path transformation smoothness can be used as a joint smoothness value between the stacking obstacle avoidance path and the historical transformation record, wherein the joint smoothness value represents the association degree of the joint angle of each mechanical arm and the corresponding transformation smoothness, and the joint smoothness value between each historical transformation record and each historical transformation record can be obtained through the mode; and finally, taking all the joint smoothing values as the value range of the joint smoothing factors of the mechanical arm in the stacking process.
In step 105, when the mechanical arm stacks according to the obstacle avoidance path, the joint angle of the mechanical arm during stacking is dynamically optimized through the joint smoothing factor.
In some embodiments, the dynamic optimization of the joint angle of the mechanical arm during stacking through the joint smoothing factor can be achieved by the following steps:
and in the motion of the mechanical arm according to the initialized stacking path, when the motion direction is changed, adjusting the joint angle of the mechanical arm by using the joint smoothing factor, and repeating the steps until the mechanical arm reaches the stacking point of the target automobile floor longitudinal beam.
When the method is specifically implemented, firstly, the obstacle avoidance path is used as an initialization stacking path of the mechanical arm; then, when the movement direction of the mechanical arm changes according to the initialized stacking path, acquiring an angle change value and angle change time of the joint angle of the mechanical arm, matching a joint smooth value corresponding to the angle change value in all values of a joint smooth factor by using a matching model in the prior art, if the joint smooth value is higher than a preset smooth threshold value, not processing, continuing to move according to the initialized stacking path, and if the joint smooth value is lower than the preset smooth threshold value, adjusting down or increasing the angle change time of the joint angle of the mechanical arm until the joint smooth value is higher than the preset smooth threshold value.
According to the application, when the mechanical arm is used for stacking according to the obstacle avoidance path, the joint angle of the mechanical arm during stacking is dynamically optimized through the joint smoothing factor, so that the dynamic optimization of the stacking obstacle avoidance path of the mechanical arm in the stacking mechanism can be realized, wherein the obstacle avoidance path can be obtained through the combination of the obstacle avoidance distance between the mechanical arm and each stacking obstacle point and the offset optimization quantity, namely, the planning of the obstacle avoidance path of the mechanical arm reaching the target point can be comprehensively considered by considering the position deviation range (namely, the offset optimization quantity) and the obstacle avoidance distance in the stacking process, so that the initial path of the mechanical arm during stacking is obtained, the initial path error range of the obstacle avoidance path of the mechanical arm reaching the target point can be ensured to be within a controllable range, and the joint angle of the mechanical arm can be regulated to different degrees through the obstacle avoidance distance, so that the smoothness of the mechanical arm during joint angle conversion is improved; then, comparing the smoothness in the history transformation record of the joint angle of the mechanical arm with the smoothness of the stacking obstacle avoidance path when the mechanical arm is stacked, and obtaining the association degree (namely joint smoothness factor) between the smoothness degree of the motion of the mechanical arm in stacking according to the stacking obstacle avoidance path and the joint angle of the mechanical arm; finally, when the mechanical arm stacks according to the obstacle avoidance path, the joint smoothing factors are used for adjusting the part which is not smooth enough in the joint angle of the mechanical arm during stacking, so that the smoothness of the obstacle avoidance path of the stacking can be further improved; in summary, the above scheme can realize the dynamic optimization of the stacking obstacle avoidance path of the mechanical arm in the stacking mechanism, thereby improving the smoothness of the stacking obstacle avoidance path of the mechanical arm in the stacking mechanism.
In addition, in another aspect of the present application, in some embodiments, the present application provides a palletizing mechanism path optimization system for producing automobile floor stringers, the palletizing mechanism path optimization system for producing automobile floor stringers further comprising a path optimization unit, referring to fig. 3, which is a schematic diagram of exemplary hardware and/or software of the path optimization unit according to some embodiments of the present application, the path optimization unit comprising: the acquisition module 201, the processing module 202, and the execution module 203 are respectively described as follows:
The acquisition module 201 is mainly used for acquiring the stacking record of the mechanical arm to the automobile floor longitudinal beam in the target stacking mechanism, so that historical stacking data are obtained;
The processing module 202 is used for extracting all target offset values from the historical stacking data, further determining the guide adaptive entropy of the target guidance quality under each target offset value, and determining the offset optimization amount of the mechanical arm in the target stacking mechanism in the stacking process through each target offset value and the corresponding guide adaptive entropy;
It should be noted that, the processing module 202 is further configured to collect all stacking obstacle points in a path space between a stacking point of the target automobile floor stringer and the mechanical arm, further determine an obstacle avoidance distance between the mechanical arm and each stacking obstacle point, and perform constraint optimization on an obstacle avoidance path of the mechanical arm based on all the obstacle avoidance distances and the bias optimization amount to obtain a stacking obstacle avoidance path;
In addition, the processing module 202 is further configured to extract a history transformation record of a joint angle of the mechanical arm in each stacking process from the history stacking data, and perform smoothness comparison on the joint angle of the mechanical arm through all history transformation records and the stacking obstacle avoidance path, so as to obtain a joint smoothness factor of the mechanical arm in the stacking process;
The execution module 203, in the present application, the execution module 203 is mainly configured to dynamically optimize the joint angle of the mechanical arm during stacking through the joint smoothing factor when the mechanical arm stacks according to the obstacle avoidance path.
In some embodiments, the target palletizer is a mechanical arm palletizer, and the mechanical arm palletizer is configured as shown in fig. 4-6, and in fig. 4-6: 1-stacking a bracket; 2-stacking platforms; 3-an unpowered delivery assembly; 4-stacking and clamping components; 5-positioning and guiding components; 6-jacking assembly; 8-a translational adjustment assembly; 9-to-be-machined parts; 31-unpowered inclined slide rail; 32-unpowered mounting support pad; 41-stacking and placing a clamp; 42-stacking and clamping the bottom plate; 51-positioning a guide cover plate; 52-positioning a guide plate; 61-jacking the cylinder; 62-lifting the bottom plate; 81-a translation adjustment cylinder; 82-translational adjustment mounting plate.
The translation adjusting component 8 is arranged on the stacking bracket 1; the movable end of the translation adjusting component 8 is connected with the jacking component 6; the translation adjusting assembly 8 comprises a translation adjusting cylinder 81 and a translation adjusting mounting plate 82; the translation adjusting cylinder 81 is arranged on the stacking bracket 1; the translation adjusting mounting plate 82 is mounted on a piston rod of the translation adjusting cylinder 81; the jacking cylinder 61 is mounted on the translational adjustment mounting plate 82. When the position of the jacking component 6 needs to be adjusted (the position of the manipulator for grabbing the material needs to be adjusted), the front and rear positions of the jacking component 6 are adjusted through the telescopic movement of the translation adjusting cylinder 81, so that the application range of the manipulator for grabbing the material is further improved.
The working principle of the mechanical arm type stacker crane is as follows: the to-be-machined piece 9 is conveyed to the input end of the unpowered conveying assembly 3 through the conveying belt, the to-be-machined piece 9 enters the unpowered conveying assembly 3 and is orderly piled on the unpowered inclined sliding rail 31 under the action of the positioning guide assembly 5, the unpowered inclined sliding rail 31 is in an inclined state, the to-be-machined piece 9 slides from the smooth unpowered inclined sliding rail 31 to the clamping position of the piling clamping assembly 4 under the action of gravity, the function of unpowered conveying materials is achieved, energy conservation and emission reduction are achieved, the piling and placing clamp 41 is used for clamping the to-be-machined piece 9 conveyed by the unpowered inclined sliding rail 31, after the piling and placing clamp 41 clamps the to-be-machined piece 9, a signal is sent to the jacking assembly 6, the to-be-machined piece 9 is conveyed to a mechanical arm grabbing station through the jacking assembly 6, after the jacking assembly 6 receives the jacking signal, the jacking cylinder 61 works, the jacking bottom plate 62 is driven to ascend, the piling and placing clamp 41 is driven to ascend to a mechanical arm grabbing material driving position, when the mechanical arm in the mechanical arm reaches the grabbing position, the piling and placing clamp 41 loosens the to-be-machined piece 9, and the mechanical arm in the mechanical arm is transferred to the next station or the next work is welded; at this time, the jacking cylinder 61 works, the piston rod of the jacking cylinder 61 retracts to drive the stacking clamp 41 to reset to the initial position to receive the next workpiece 9 to be processed, and the above process is repeated, so that the function of automatic feeding of the workpiece 9 to be processed is realized.
The above describes in detail an example of a palletizing mechanism path optimizing method and system for producing an automotive floor stringer provided by the embodiment of the present application, and it may be understood that, in order to implement the above-mentioned functions, the corresponding device includes a hardware structure and/or a software module for executing the corresponding functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In some embodiments, the present application also provides a computer device, the computer device including a memory for storing a computer program and a processor for calling and running the computer program from the memory, so that the computer device performs the palletizing mechanism path optimization method for producing the automobile floor longitudinal beam.
In some embodiments, reference is made to fig. 7, in which the dashed line indicates that the unit or the module is optional, which is a schematic structural diagram of a computer device implementing a palletizing mechanism path optimization method for producing automotive floor stringers according to an embodiment of the present application. The palletizing mechanism path optimization method for the production of the automotive floor stringers described in the above embodiments may be implemented by a computer device shown in fig. 7, which includes at least one processor 301, a memory 302 and at least one communication unit 305, and which may be a terminal device or a server or a chip.
Processor 301 may be a general purpose processor or a special purpose processor. For example, the processor 301 may be a central processing unit (central processing unit, CPU) which may be used to control, execute and process data of a software program for a computer device, which may further comprise a communication unit 305 for enabling input (reception) and output (transmission) of signals.
For example, the computer device may be a chip, the communication unit 305 may be an input and/or output circuit of the chip, or the communication unit 305 may be a communication interface of the chip, which may be an integral part of a terminal device or a network device or other devices.
For another example, the computer device may be a terminal device or a server, the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.
The computer device may include one or more memories 302 having a program 304 stored thereon, the program 304 being executable by the processor 301 to generate instructions 303 such that the processor 301 performs the methods described in the method embodiments described above in accordance with the instructions 303. Optionally, data (e.g., a goal audit model) may also be stored in memory 302. Alternatively, the processor 301 may also read data stored in the memory 302, which may be stored at the same memory address as the program 304, or which may be stored at a different memory address than the program 304.
The processor 301 and the memory 302 may be provided separately or may be integrated together, for example, on a System On Chip (SOC) of the terminal device.
It should be understood that the steps of the above-described method embodiments may be accomplished by logic circuitry in hardware or instructions in software in the processor 301, and the processor 301 may be a CPU, digital signal processor (DIGITAL SIGNAL processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field programmable gate array (field programmable GATE ARRAY, FPGA), or other programmable logic device, such as discrete gates, transistor logic, or discrete hardware components.
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.
For example, in some embodiments, the present application further provides a computer readable storage medium having instructions or codes stored therein, which when executed on a computer, cause the computer to implement the palletizing mechanism path optimization method for producing an automotive floor rail as described above.
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 present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The path optimization method of the stacking mechanism for producing the automobile floor longitudinal beam is characterized by comprising the following steps of:
acquiring a stacking record of a mechanical arm in a target stacking mechanism on an automobile floor longitudinal beam, and further acquiring historical stacking data;
Extracting all target offset values from the historical stacking data, further determining the guiding adaptive entropy of the target guidance quality under each target offset value, and determining the offset optimization amount of the mechanical arm in the target stacking mechanism in the stacking process through each target offset value and the corresponding guiding adaptive entropy;
Collecting all stacking obstacle points in a path space between a stacking point of a target automobile floor longitudinal beam and the mechanical arm, further determining an obstacle avoidance distance between the mechanical arm and each stacking obstacle point, and performing constraint optimization on an obstacle avoidance path of the mechanical arm based on all obstacle avoidance distances and the bias optimization quantity to obtain a stacking obstacle avoidance path;
Extracting historical transformation records of joint angles of the mechanical arm in each stacking process from the historical stacking data, and carrying out smoothness comparison on the joint angles of the mechanical arm through all the historical transformation records and the stacking obstacle avoidance path to obtain joint smoothness factors of the mechanical arm in the stacking process;
And when the mechanical arm is piled according to the obstacle avoidance path, dynamically optimizing the joint angle of the mechanical arm during piling through the joint smoothing factor.
2. The method of claim 1, wherein determining the steering adaptation entropy for the target steering at each target bias value comprises:
for each target bias value, acquiring all stacking records corresponding to the target bias value from the historical stacking data;
Acquiring the abnormal behavior times and the mechanical arm joint rotation times in each stacking record;
Determining the stacking success proportion of the target offset value through all stacking records;
And determining the guiding adaptive entropy of the target guidance quality under the target offset value through all abnormal behavior times, the stacking success proportion and all mechanical arm joint rotation times, and further obtaining the guiding adaptive entropy of the target guidance quality under each target offset value.
3. Method according to claim 1, wherein determining the amount of bias optimization of the robotic arm during palletizing in the target palletizing mechanism by means of the respective target bias values and the corresponding guide-adapted entropy comprises in particular:
Comparing each guide adaptive entropy with a preset guide adaptive threshold value to obtain all optimized guide adaptive entropy;
taking the target bias values corresponding to the optimized guide adaptive entropy as the compliance bias values, and further obtaining all the compliance bias values;
and determining the bias optimization quantity of the mechanical arm in the target stacking mechanism in the stacking process according to all the compliance bias values.
4. Method according to claim 1, wherein determining the obstacle avoidance distance between the robotic arm and each palletizing obstacle point comprises in particular:
For each stacking obstacle point, enveloping the mechanical arm and the stacking obstacle point to obtain an enveloping structure diagram;
acquiring a spatial range relation between the mechanical arm and a stacking obstacle point in the envelope structure diagram;
And determining the obstacle avoidance distance between the mechanical arm and the stacking obstacle points based on the spatial range relation, and further obtaining the obstacle avoidance distance between each stacking obstacle point and the mechanical arm.
5. The method of claim 1, wherein performing constraint optimization on the obstacle avoidance path of the mechanical arm based on all obstacle avoidance distances and the bias optimization amount, and obtaining a palletized obstacle avoidance path specifically comprises:
marking each stacking obstacle point through all obstacle avoidance distances to obtain an obstacle marking sequence;
For each adjacent stacking obstacle point in the obstacle marking sequence, determining adjacent obstacle avoidance paths between the adjacent stacking obstacle points, and taking the adjacent obstacle avoidance paths as branch paths in the obstacle avoidance paths of the mechanical arm;
orthogonal constraint is carried out on the branch paths through the bias optimization quantity, so that optimized branch paths are obtained, and then the optimized branch paths of each adjacent stacking barrier point in the barrier mark sequence are determined;
and determining a stacking obstacle avoidance path according to all the optimized branch paths.
6. The method according to claim 1, wherein dynamically optimizing the joint angle of the manipulator during palletizing by the joint smoothing factor comprises: and in the motion of the mechanical arm according to the initialized stacking path, when the motion direction is changed, the joint smoothing factor is used for adjusting the joint angle of the mechanical arm, and the steps are repeated until the mechanical arm reaches the stacking point of the target automobile floor longitudinal beam.
7. A method according to claim 1, wherein the target palletising mechanism is a robotic stacker.
8. The utility model provides a pile up neatly mechanism route optimizing system of automobile floor longeron production usefulness, this pile up neatly mechanism route optimizing system of automobile floor longeron production usefulness is including route optimizing unit, its characterized in that, route optimizing unit includes:
the acquisition module is used for acquiring the stacking record of the mechanical arm in the target stacking mechanism on the automobile floor longitudinal beam so as to acquire historical stacking data;
The processing module is used for extracting all target offset values from the historical stacking data, further determining the guiding adaptive entropy of the target guiding performance under each target offset value, and determining the offset optimization amount of the mechanical arm in the target stacking mechanism in the stacking process through each target offset value and the corresponding guiding adaptive entropy;
The processing module is also used for collecting all stacking obstacle points in a path space between a stacking point of the target automobile floor longitudinal beam and the mechanical arm, further determining an obstacle avoidance distance between the mechanical arm and each stacking obstacle point, and carrying out constraint optimization on the obstacle avoidance path of the mechanical arm based on all obstacle avoidance distances and the bias optimization quantity to obtain a stacking obstacle avoidance path;
The processing module is also used for extracting historical transformation records of joint angles of the mechanical arm in each stacking process from the historical stacking data, and carrying out smoothness comparison on the joint angles of the mechanical arm through all the historical transformation records and the stacking obstacle avoidance path to obtain joint smoothness factors of the mechanical arm in the stacking process;
and the execution module is used for dynamically optimizing the joint angle of the mechanical arm during stacking through the joint smoothing factor when the mechanical arm stacks according to the obstacle avoidance path.
9. A computer device, characterized in that it comprises a memory for storing a computer program and a processor for calling and running the computer program from the memory, so that the computer device performs the palletizing mechanism path optimization method for the production of an automobile floor rail according to any one of claims 1 to 7.
10. A computer readable storage medium having instructions or code stored therein which, when run on a computer, cause the computer to perform the palletising mechanism path optimisation method for the production of automotive floor stringers as claimed in any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410711550.8A CN118269110A (en) | 2024-06-04 | 2024-06-04 | Stacking mechanism path optimization method and system for automobile floor longitudinal beam production |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410711550.8A CN118269110A (en) | 2024-06-04 | 2024-06-04 | Stacking mechanism path optimization method and system for automobile floor longitudinal beam production |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN118269110A true CN118269110A (en) | 2024-07-02 |
Family
ID=91634477
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410711550.8A Pending CN118269110A (en) | 2024-06-04 | 2024-06-04 | Stacking mechanism path optimization method and system for automobile floor longitudinal beam production |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN118269110A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102773858A (en) * | 2012-07-17 | 2012-11-14 | 北京航空航天大学 | Obstacle avoidance method of robot palletizer |
| US20140277691A1 (en) * | 2013-03-15 | 2014-09-18 | Cybernet Systems Corporation | Automated warehousing using robotic forklifts |
| CN117490715A (en) * | 2023-11-01 | 2024-02-02 | 浙江中烟工业有限责任公司 | Path planning method for tobacco industry finished product warehouse palletizing robot |
-
2024
- 2024-06-04 CN CN202410711550.8A patent/CN118269110A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102773858A (en) * | 2012-07-17 | 2012-11-14 | 北京航空航天大学 | Obstacle avoidance method of robot palletizer |
| US20140277691A1 (en) * | 2013-03-15 | 2014-09-18 | Cybernet Systems Corporation | Automated warehousing using robotic forklifts |
| CN117490715A (en) * | 2023-11-01 | 2024-02-02 | 浙江中烟工业有限责任公司 | Path planning method for tobacco industry finished product warehouse palletizing robot |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109436820B (en) | Destacking method and destacking system for goods stack | |
| US11794346B2 (en) | Robotic system with error detection and dynamic packing mechanism | |
| DE102019130048B4 (en) | A robotic system with a sack loss management mechanism | |
| JP6704157B1 (en) | Robot system with dynamic packing mechanism | |
| US9149932B2 (en) | Robot picking system, control device, and method of manufacturing a workpiece | |
| CN108818530B (en) | Mechanical arm grabbing scattered stacking piston motion planning method based on improved RRT algorithm | |
| DE102019212839A1 (en) | Object handling device and computer program product | |
| JP2017520418A (en) | Real-time determination of object metrics for trajectory planning | |
| JP5196156B2 (en) | Work picking method in bulk picking equipment | |
| JP2016144841A (en) | Transportation robot system equipped with three-dimensional sensor | |
| LU501953B1 (en) | High-accuracy method for controlling grabbing position of grab with radar feedback | |
| CN114266956A (en) | Machine learning-based decision making for robotic article handling | |
| CN107671856A (en) | A kind of robotic arm path planning method and device based on space coordinates | |
| JP2012135820A (en) | Automatic picking device and automatic picking method | |
| CN114273726A (en) | 3D visual guide groove cutting method, device, equipment, system and storage medium | |
| CN210824350U (en) | Transfer robot | |
| CN116087979A (en) | Container loading and unloading robot positioning navigation method, device, equipment and medium | |
| CN118269110A (en) | Stacking mechanism path optimization method and system for automobile floor longitudinal beam production | |
| CN111788134A (en) | Method, device and storage device for automatic palletizing | |
| CN107512591B (en) | stacking piece layout method and device based on mechanical arm | |
| CN108958244A (en) | Motion planning method, device, equipment and computer readable storage medium | |
| CN112830279A (en) | Automatic rotating device and method for loading and unloading materials | |
| CN116835334B (en) | Disordered stacking method, disordered stacking device, disordered stacking medium and disordered stacking equipment based on 3D vision | |
| CN107538491B (en) | Construction method of space coordinate system based on mechanical arm | |
| JPS6126110A (en) | Robot control system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination |