CN106598043B - Parallel robot high-speed picking-up method for optimizing route towards multiple dynamic objects - Google Patents
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Abstract
The present invention provides a kind of parallel robot high-speed picking-up method for optimizing route towards multiple dynamic objects, including：Step 1, random number is carried out to each target location in placement region and obtains corresponding coordinate information, and sequentially chooses from capture area with the same number of target object in target location, to target object progress random number, while obtaining the current coordinate of target object；Step 2, acquired target object and target location are subjected to translocation sorting at random, constitute the long chromosome of initial population；Step 3, repeat step 2 and build initial population, and by genetic algorithm robotic end effector is picked up and to put action operating path and optimize, the shortest one group of long chromosome of output total kilometres as an optimization after robotic end effector pick up and put action operating path.Optimal crawl path can be obtained through the invention, greatly improve working efficiency.
Description
Technical field
The invention belongs to the technical field that production automation and advanced control algolithm combine, more particularly, to one kind towards
The parallel robot high-speed picking-up method for optimizing route of multiple dynamic objects.
Background technology
With the development of technology, parallel robot of the domestic application in the light industries such as medical treatment, electronics, food be increasingly
It is more, it is especially sorted on vanning assembly line in high density, the target object on conveyer belt sorted in limiting time and be placed
Into the region of specified proper alignment, case compared to manual sorting although using parallel robot, efficiency improves；But
The sequence cased in other words of sorting path for being these robots is empirically set by worker, is relatively-stationary, and
It cannot in real time be adjusted, therefore this path is not optimal path, and be installed according to the target object of random distribution on conveyer belt
Debugging cycle is long, illustrates that production efficiency also has and greatly improves space.Therefore, sorting path global optimization is to improve assembly line effect
Crucial one of the technology of rate, and research emphasis.The speed of conveyer belt, the distribution density and parallel robot of target object
The parameters such as pickup matching have that pipeline efficiency is closely related, in order to solve these problems, give full play to industrial automation and
The superiority of intelligent control, it is therefore desirable to invent it is a kind of towards high density multiple target coordinate dynamic change based on improved genetic algorithms
The method for optimizing route of method establishes the close ties of parallel robot and ambient enviroment, can not only promote parallel robot
Working efficiency and adaptability, and can advance off-line simulation, verification algorithm instruct in turn in actual production technology to transmission
The speed of service of band optimizes, and shortens the period of installation and debugging.
Invention content
In order to solve the above problem in the prior art, in order to improve parallel manipulator task efficiency.The present invention
Propose a kind of parallel robot high-speed picking-up method for optimizing route towards multiple dynamic objects.
A kind of parallel robot high-speed picking-up method for optimizing route towards multiple dynamic objects proposed by the present invention, including
Following steps：
Step 1, random number is carried out to each target location in placement region and obtains corresponding coordinate information, and from crawl
Region sequentially chooses and the same number of target object in target location, carries out random number to target object, while obtaining target
The initial coordinate of object；
Step 2, acquired target object and target location are subjected to translocation sorting at random, constitute the long dye of initial population
Colour solid；The sequence of the chromosome is that robotic end effector picks up and puts action operating path；
Step 3, it repeats step 2 and builds initial population, and robotic end effector is picked up by genetic algorithm and puts action
Operating path optimizes, and the robotic end effector after exporting the shortest one group of long chromosome of total kilometres as an optimization, which is picked up, puts
Act operating path；The genetic algorithm, object function are Min (S), and S is total kilometres.
Preferably, genetic algorithm described in step 3, select probability are
C=(Smax-Sq)/(Smax-Smin)
Wherein, SmaxFor the corresponding longest total path of long chromosome, S in current iteration populationminFor in current iteration population
The corresponding most short total path of long chromosome, SqFor the corresponding total path length of long chromosome of serial number q in current iteration population.
Preferably, genetic algorithm described in step 3, in each iterative process, by long chromosome separation at two about mesh
The short chromosome for marking object and target location, is selected, is intersected, being made a variation and insertion operation respectively, then remerges into one
New long chromosome.
Preferably, genetic algorithm described in step 3 is terminated using algorithmic statement or preset iterations as the algorithm
Condition, and the robot terminal after choosing the maximum long chromosome of fitness function in last time iteration population as an optimization
Actuator, which picks up, puts action operating path.
Preferably, genetic algorithm described in step 3, fitness function are (1/S).
Preferably, the algorithmic statement, if the kind group mean adaptive value variation in specially adjacent Q generations is less than predetermined threshold value；Q
For the iterations for judging algorithmic statement of setting.
Preferably, the computational methods of the total kilometres are：
Wherein, S indicates total kilometres, AiIndicate target object, BjIndicate that target location, i indicate the serial number of target object, j tables
Show the serial number of target location, SijIndicate the stroke between target object i and target location j, DijIndicate target object AiWith target
Position BjBetween horizontal distance, b indicate robotic end effector pick up the rising height for putting action, A (xi, yi) indicate target
Object AiCoordinate, B (xj, yj) indicate target location BjCoordinate, A (xi(ti), yi) indicate tiMoment target object AiSeat
Mark, tiTarget object A is captured for robotic end effectoriTime, (Si(j-1)+Sij) indicate that a robot terminal executes
Device, which picks up, to be put in action from position BjSet out crawl AiAfter be placed into B(j+1)Stroke.
Preferably, genetic algorithm described in step 3, long chromosome number is 50 in initial population.
Preferably, the tiMoment target object AiCoordinate A (xi(ti), yi) can be calculate by the following formula：
Wherein, v is the transmission speed of conveyer belt, ti0This target object A is shot for cameraiAt the time of, tiFor parallel manipulator
People captures target object AiAt the time of.
Preferably, in the method for the present invention, the take pictures computational methods of frequency f of target object can be：
Wherein, L is the length that target object is distributed on a moving belt in each cycle, which is passing according to target object
The target location quantity in the density for taking distribution and corresponding cycle is sent to calculate；Lc is the length that camera shoots section.
Preferably, the artificial parallel robot of the machine.
Preferably, further include the method for the optimization optimization of the transmission speed of conveyer belt after this method step 3：
Step 41, the shortest one group of long chromosome of total kilometres is corresponding has executed needed for total kilometres for output in judgment step 3
Time TSWhether the single cycle time interval [T of setting is fallen intoa, Tb]；If TS∈[Ta, Tb], then follow the steps 42；If TS< Ta,
Then follow the steps 43；If TS> Tb, then follow the steps 44；
Step 42, the transmission speed of conveyer belt is not adjusted；
Step 43, it promotes the transmission speed of conveyer belt, then re-starts to pick up by the method for step 1 to step 3 and put action
The optimization of operating path；
Step 44, it reduces the transmission speed of conveyer belt, then re-starts to pick up by the method for step 1 to step 3 and put action
The optimization of operating path.
The beneficial effects of the invention are as follows：
First：Intelligent algorithm is applied to industrial automatic control scene and solves parallel robot pickup path rule by the present invention
Optimization problem is drawn, parallel robot pickup optimal path on a sorting assembly line can be obtained, it is time-consuming most short, greatly improve
Working efficiency；
Second：Method proposed by the present invention can provide the speed of conveyer belt, the distribution density and parallel machine of target object
The relationship of device people's pickup velocity carries out the off-line simulation verification of pipelining, provides the optimal velocity of conveyer belt in advance in advance,
Shorten install debug time, optimized production process.
Description of the drawings
Fig. 1 is parallel robot pickup path locus optimization stream based on improved adaptive GA-IAGA of the present invention towards multiple target
Journey schematic diagram；
Fig. 2 is equipped with the flow production line environmental modeling schematic diagram of parallel robot；
Fig. 3 is the running orbit schematic diagram of parallel robot sorting material.
Specific implementation mode
The preferred embodiment of the present invention described with reference to the accompanying drawings.It will be apparent to a skilled person that this
A little embodiments are used only for explaining the technical principle of the present invention, it is not intended that limit the scope of the invention.
One, working environment mathematical modeling
As shown in Fig. 2, parallel robot work assembly line is set as two bit planes, global coordinate system BASE is established；This
Origin crosses origin O and the direction vertical with conveyer belt C1 is set as x-axis in O in embodiment, crosses origin O and is put down with conveyer belt C1
Capable direction is set as y-axis, and x, the positive direction of y-axis are as shown in Figure 2.
Working region D5 on assembly line, target area (the i.e. rest areas D3 are determined by the mechanism parameter of parallel robot
Domain), the multinomial performance parameter of parallel robot, such as maximum speed are inputted, acceleration picks up curve of track etc.；Setting is sat
Mark system origin O is the initial position of parallel robot end effector, set conveyer belt C1 fall into the region of working region D5 as
Capture area.
Enter the target object image of camera image acquisition region D1 with specific frequency f acquisitions by industrial camera CAM,
By image recognition technique and the transfer function of the image coordinate and physical coordinates of setting, the coordinate of each target object, mesh are obtained
Mark object AiInitial coordinate be A (xi0, yi0), coordinate A (xi0, yi0) be global coordinate system BASE under physical coordinates.
The purpose to be realized is：Parallel robot will within the time of restriction, on conveyer belt C1 conveyer belt C1 transmit
N number of target object of direction front end is moved to robot work region D5, and picks up the mesh to specific target area D3 settings
Cursor position Bj.N is consistent with the quantity of target location.
The present invention optimization algorithm in, can according to conveyer belt C1 target objects corresponding with parallel robot crawl when
Between come coordinate A (x when determining that corresponding target object is crawledi(ti), yi), it calculates as shown in formula (1)：
Wherein, v is the transmission speed of conveyer belt, ti0This target object A is shot for cameraiAt the time of, tiFor parallel manipulator
People captures target object AiAt the time of.
Multiple target object A on conveyer belt C1iWith multiple target location B of target area D3j(such as Fig. 2) is defined as
Two big collections：Target object set A={ A1,A2,A3…,AN, target location set B={ B1,B2,B3…,BN}.Because of target
The target location B of region D3jQuantity be given, so take N=M, that is, obtain target object AiWhen according to x coordinate by it is small to
It takes greatly N number of, and defines that often to fill up in the D3 of target area all target locations be a cycle, a cycle is indicated with T.
Although the target location in the D3 of target area is constant, the object in picking region in each period on conveyer belt
Body AiIt is random distribution, so the time for completing to fill up region D3 every time is different, i.e., cycle T is a variable.
Two, the mathematical modeling of path planning
(target object is placed into target location from conveyer belt by the above working environment mathematical modeling and to vanning flow
Flow) analysis, parallel robot end effector pick up conveyer belt on a target object AiIt is put into the mesh of target area D3
Cursor position Bj, then go to pick up next target object A again(i+1)It is put into target location B(j+1), and so on move；It can be seen that
The reciprocatory movement is an approximate traveling salesman problem (Travelling Salesman Problem, TSP), but practical
Situation is but very different with TSP；Assuming that the end effector of parallel robot is as travelling salesman, target object set A and
Target location set B can regard two groups of cities as, and it is approximately travelling that the end effector of parallel robot, which carries out Pick-and-Place operations,
Quotient it is each it is intercity walk, it is characterized in that target location BjThe target object A intercepted on (j=1,2 ... M) and conveyer belti(i=
1,2 ... N) correspond, parallel robot end effector be not moved between arbitrary two cities, and must be
Alternating movement (i.e. the alternating movement between set A and B), two intercity distance D between corresponding two groups of cities of A and BijSuch as
Shown in formula (2)：
Wherein A (xi, yi) indicate target object AiCoordinate, B (xj, yj) indicate target location BjCoordinate.
Because parallel robot end effector pickup track use when gate track (as shown in Figure 3), door is a height of
B is captured or is placed each time, and the stroke of parallel robot end effector is approximately Sij=Dij+2*b；And mesh in set A
Mark object AiCoordinate A (xi, yi) it is the i.e. coordinate x as conveyer belt C1 travels forward togetheriIt is corresponding for the function of time t
Target object AiCoordinate can be expressed as A (xi(t), yi)。
Pick-and-Place operations, as shown by dotted lines in figure 3：Parallel robot end effector is placing A(i-1)To B(j-1)Afterwards, it opens
Begin to capture next target Ai, and it is put into target location Bj, it can be seen that end effector is while crawl, target AiAlso with
It conveyer belt to travel forward with speed v, i.e., to complete crawl end effector and target AiSame position is reached simultaneously；It is practical
It is equivalent to and is moved between 3 cities, such as from position B(j-1)Set out crawl AiAfterwards, it is placed into Bj, the trip is represented by formula
(3)：
It understands shown in total kilometres S such as formula (4)
Wherein, S indicates total kilometres, AiIndicate target object, BjIndicate that target location, i indicate the serial number of target object, j tables
Show the serial number of target location, SijIndicate the stroke between target object i and target location j, DijIndicate target object AiWith target
Position BjBetween horizontal distance, b indicates that robotic end effector picks up the rising height (i.e. door height) for putting action, A (xi, yi)
Indicate target object AiCoordinate, B (xj, yj) indicate target location BiCoordinate, A (xi(ti), yi) indicate tiMoment target object
AiCoordinate, tiTarget object A is captured for robotic end effectoriTime, (Si(j-1)+Sij) indicate a robot terminal
Actuator, which picks up, to be put in action from position BjSet out crawl AiAfter be placed into B(j+1)Stroke.
Min (S) is the object function of this mathematical model.Complete same number of vanning task, the fortune of end effector
Capable total stroke is smaller, then the spent time is shorter, i.e., working efficiency is higher.
Three, the path optimization of each cycle period
Through above analysis it is found that the operating path of robotic end effector, which is origin O, starts crawl AiIt is put into Bj, such as
This is reciprocal, is filled up until target area, completes the task of a cycle.Entire path process can pass through such as next long dye
Colour solid is indicated：
O→Ai→Bj→Am→Bk…AN→BM
A kind of parallel robot high-speed picking-up method for optimizing route towards multiple dynamic objects of the embodiment of the present invention, packet
Include following steps：
Step 1, random number is carried out to each target location in placement region and obtains corresponding coordinate information, and from crawl
Region sequentially chooses and the same number of target object in target location, carries out random number to target object, while obtaining target
The initial coordinate of object.
In the step, random number is carried out to target object set A and target location set B respectively, obtains { A1,A2,
A3…,AN}、{B1,B2,B3…,BM}。
Step 2, acquired target object and target location are subjected to translocation sorting at random, constitute the long dye of initial population
Colour solid；The sequence of the chromosome is that robotic end effector picks up and puts action operating path；
It can be according to { A1,A2,A3…,AN}、{B1,B2,B3…,BMCarry out the long chromosome of initial population structure, for example,
One long chromosome of initial population can be
O→A1→B1→A2→B2→A3→B3…BM-1→AN→BM
It can be seen that, it is known that a long chromosome can divide to obtain two groups about target object set A and target location
The short chromosome of set B.
Step 3, it repeats step 2 and builds initial population, and robotic end effector is picked up by genetic algorithm and puts action
Operating path optimizes, and the robotic end effector after exporting the shortest one group of long chromosome of total kilometres as an optimization, which is picked up, puts
Act operating path.
Step 31, multiple long chromosomes can be built according to the method for building long chromosome in step 2, constitutes initial kind
Group sets the item number of long chromosome in initial population in the present embodiment as 50.
Step 32, initial population is as parent, with Min (S) for object function, using C as select probability, carries out genetic algorithm
Selection, intersect, variation and weight insertion operation, the condition terminated using algorithmic statement or preset iterations as the algorithm,
And the robot terminal after choosing the maximum long chromosome of fitness function in last time iteration population as an optimization executes
Device, which picks up, puts action operating path, and the maximum long chromosome of the fitness function carries out Pick-and-Place operations for controlling parallel robot.
In the present embodiment, in object function Min (S), shown in S such as formula (4), fitness function 1/S, select probability C
As shown in formula (5)：
C=(Smax-Sq)/(Smax-Smin) (5)
Wherein, SmaxFor the corresponding longest total path of long chromosome, S in current iteration populationminFor in current iteration population
The corresponding most short total path of long chromosome, SqFor the corresponding total path length of long chromosome of serial number q in current iteration population.
In the present embodiment, in order to which the cross arrangement for ensureing stringent A and B in every chromosome in each step operating process is suitable
Sequence, in each iteration, by long chromosome separation at the two short chromosome about target object A and target location B, respectively into
Then row selection, intersection, variation and insertion operation remerge into a new long chromosome.
In the present embodiment, if the kind group mean adaptive value variation in algorithmic statement specially adjacent Q generations is less than predetermined threshold value；Q
For the iterations for judging algorithmic statement of setting.
Target object A in the present embodimentiCoordinate real-time change, obtain target object on conveyer belt C1 in recycling each time
The number of A, according to the ascending interception of x coordinate so that N=M (quantity of target object A is consistent with the quantity of target location B)；
It can so be calculated according to the target location quantity in the target object A density being distributed on conveyer belt C1 and corresponding cycle
Go out the length L that cycle target object A is distributed on a moving belt every time, so as to optimize filming frequency f (the i.e. objects of camera
The filming frequency of body), it can specifically be calculated by formula (6)：
Wherein, Lc is the length that camera shoots section.
By the optimization of the filming frequency of camera, control system can be reduced to the performance requirement of processor and can be ensured
The real-time of intelligent algorithm.
The present embodiment is after step 3, the step of being additionally provided with the optimization optimization of the transmission speed of conveyer belt, specific to wrap
It includes：
Step 41, the shortest one group of long chromosome of total kilometres is corresponding has executed needed for total kilometres for output in judgment step 3
Time TSWhether the single cycle time interval [T of setting is fallen intoa, Tb]；If TS∈[Ta, Tb], then follow the steps 42；If TS< Ta,
Then follow the steps 43；If TS> Tb, then follow the steps 44；
Step 42, the transmission speed of conveyer belt is not adjusted；
Step 43, it promotes the transmission speed of conveyer belt, then re-starts to pick up by the method for step 1 to step 3 and put action
The optimization of operating path；
Step 44, it reduces the transmission speed of conveyer belt, then re-starts to pick up by the method for step 1 to step 3 and put action
The optimization of operating path.
The optimization optimization method of the transmission speed of above-mentioned conveyer belt, the path optimization side based on the present embodiment in the present embodiment
The path planning mathematical model that is built in method proposes, to optimize the efficiency of assembly line as target, by advance off-line simulation, in turn
It instructs the speed of service in actual production technology to transmission belt to optimize, shortens the period of installation and debugging.
Those skilled in the art should be able to recognize that, side described in conjunction with the examples disclosed in the embodiments of the present disclosure
Method step, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate electronic hardware and
The interchangeability of software generally describes each exemplary composition and step according to function in the above description.These
Function is executed with electronic hardware or software mode actually, depends on the specific application and design constraint of technical solution.
Those skilled in the art can use different methods to achieve the described function each specific application, but this reality
Now it should not be considered as beyond the scope of the present invention.
So far, it has been combined preferred embodiment shown in the drawings and describes technical scheme of the present invention, still, this field
Technical staff is it is easily understood that protection scope of the present invention is expressly not limited to these specific implementation modes.Without departing from this
Under the premise of the principle of invention, those skilled in the art can make the relevant technologies feature equivalent change or replacement, these
Technical solution after change or replacement is fallen within protection scope of the present invention.
Claims (11)
1. a kind of parallel robot high-speed picking-up method for optimizing route towards multiple dynamic objects, which is characterized in that including with
Lower step：
Step 1, random number is carried out to each target location in placement region and obtains corresponding coordinate information, and from capture area
Sequentially selection and the same number of target object in target location carry out random number to target object, while obtaining target object
Initial coordinate；
Step 2, acquired target object and target location are subjected to translocation sorting at random, constitute the long dyeing of initial population
Body；The sequence of the chromosome is that robotic end effector picks up and puts action operating path；
Step 3, it repeats step 2 and builds initial population, and robotic end effector is picked up by genetic algorithm and puts action operation
Path optimizes, and the robotic end effector after exporting the shortest one group of long chromosome of total kilometres as an optimization, which is picked up, puts action
Operating path；The genetic algorithm, object function are Min (S), and S is total kilometres；The computational methods of the total kilometres are：
Wherein, S indicates total kilometres, AiIndicate target object, BjIndicate that target location, i indicate that the serial number of target object, j indicate mesh
The serial number of cursor position, SijIndicate the stroke between target object i and target location j, DijIndicate target object AiWith target location
BjBetween horizontal distance, b indicate robotic end effector pick up the rising height for putting action, A (xi, yi) indicate target object
AiCoordinate, B (xj, yj) indicate target location BjCoordinate, A (xi(ti), yi) indicate tiMoment target object AiCoordinate, ti
Target object A is captured for robotic end effectoriTime, (Si(j-1)+Sij) indicate that a robotic end effector is picked up and put
From position B in actionjSet out crawl AiAfter be placed into B(j+1)Stroke.
2. according to the method described in claim 1, it is characterized in that, genetic algorithm described in step 3, select probability are
C=(Smax-Sq)/(Smax-Smin)
Wherein, SmaxFor the corresponding longest total path of long chromosome, S in current iteration populationminFor long dye in current iteration population
The corresponding most short total path of colour solid, SqFor the corresponding total path length of long chromosome of serial number q in current iteration population.
3. according to the method described in claim 2, it is characterized in that, genetic algorithm described in step 3, in each iterative process,
By long chromosome separation at the two short chromosome about target object and target location, selected, intersected respectively, make a variation with
Then insertion operation remerges into a new long chromosome.
4. according to the method described in claim 3, it is characterized in that, genetic algorithm described in step 3, with algorithmic statement or presets
The condition that is terminated as the algorithm of iterations, and the maximum long dye of fitness function is chosen from last time iteration population
Colour solid as an optimization after robotic end effector pick up and put action operating path.
5. according to the method described in claim 4, it is characterized in that, genetic algorithm described in step 3, fitness function 1/
S。
6. according to the method described in reference claim 5, which is characterized in that the algorithmic statement, if the kind in specially adjacent Q generations
The variation of group mean adaptive value is less than predetermined threshold value；Q is the iterations for judging algorithmic statement of setting.
7. according to reference claim 1-6 any one of them methods, which is characterized in that genetic algorithm described in step 3, initially
Long chromosome number is 50 in population.
8. according to reference claim 1-6 any one of them methods, which is characterized in that the tiMoment target object AiSeat
Mark A (xi(ti), yi) can be calculate by the following formula：
Wherein, v is the transmission speed of conveyer belt, ti0This target object A is shot for cameraiAt the time of, tiIt is captured for parallel robot
Target object AiAt the time of.
9. according to reference claim 1-6 any one of them methods, which is characterized in that in this method, target object is taken pictures frequently
The computational methods of rate f can be：
Wherein, L is the length that target object is distributed on a moving belt in each cycle, and the length is according to target object in conveyer belt
Target location quantity in the density of upper distribution and corresponding cycle calculates；Lc is the length that camera shoots section.
10. according to reference claim 1-6 any one of them methods, which is characterized in that the artificial parallel manipulator of the machine
People.
11. according to reference claim 1-6 any one of them methods, which is characterized in that after this method step 3, further include
The method of the optimization optimization of the transmission speed of conveyer belt：
Step 41, the corresponding T the time required to having executed total kilometres of the shortest one group of long chromosome of total kilometres is exported in judgment step 3S
Whether the single cycle time interval [Ta, Tb] of setting is fallen into；If TS∈[Ta, Tb], then follow the steps 42；If TS< Ta, then hold
Row step 43；If TS> Tb, then follow the steps 44；
Step 42, the transmission speed of conveyer belt is not adjusted；
Step 43, it promotes the transmission speed of conveyer belt, then re-starts to pick up by the method for step 1 to step 3 and put action operation
The optimization in path；
Step 44, it reduces the transmission speed of conveyer belt, then re-starts to pick up by the method for step 1 to step 3 and put action operation
The optimization in path.
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| CN107272706A (en) * | 2017-08-01 | 2017-10-20 | 上海辛格林纳新时达电机有限公司 | A kind of conveyer belt tracking, robot and computer-readable recording medium |
| CN107807651B (en) * | 2017-11-29 | 2021-02-19 | 北京康力优蓝机器人科技有限公司 | Self-charging control system and method for mobile robot |
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| CN109834712B (en) * | 2019-03-01 | 2021-03-12 | 广东工业大学 | Method for optimizing grabbing sequence of robot |
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| CN113751339B (en) * | 2021-09-23 | 2023-05-23 | 浙江工业大学 | Multi-robot task allocation method based on intelligent sorting assembly line |
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