CN111745653A - Planning method for hull plate curved surface forming cooperative processing based on double mechanical arms - Google Patents
Planning method for hull plate curved surface forming cooperative processing based on double mechanical arms Download PDFInfo
- Publication number
- CN111745653A CN111745653A CN202010654350.5A CN202010654350A CN111745653A CN 111745653 A CN111745653 A CN 111745653A CN 202010654350 A CN202010654350 A CN 202010654350A CN 111745653 A CN111745653 A CN 111745653A
- Authority
- CN
- China
- Prior art keywords
- processing
- heating
- curved surface
- heating path
- path
- 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.)
- Granted
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/1605—Simulation of manipulator lay-out, design, modelling of manipulator
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
- B25J9/1682—Dual arm manipulator; Coordination of several manipulators
Landscapes
- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Automation & Control Theory (AREA)
- Manipulator (AREA)
Abstract
The invention provides a planning method for forming and collaborative processing of a hull plate curved surface based on two mechanical arms, which is used for solving the problems that the existing multi-task planning method lacks a unified analysis and task planning model and does not consider the constraint condition in the actual task allocation process, and specifically comprises the following steps: s1, modeling and analyzing; s2, determining the starting and ending points of each heating path by determining the heating direction of the heating path, and calculating the distance cost between the heating path and the heating path; s3, selecting and defining variables; s4, determining constraint conditions; s5, determining a double-mechanical-arm optimization target for forming the curved surface of the hull plate; s6, improving the artificial bee colony algorithm, and distributing the processing tasks of the double mechanical arms for forming the curved surface of the hull plate by adopting the improved artificial bee colony algorithm. The invention adopts the improved artificial bee colony algorithm to solve the task planning, and effectively solves the task allocation problem of the cooperative processing of the two mechanical arms.
Description
Technical Field
The invention relates to the technical field of planning of forming and processing of a curved surface of a hull plate, in particular to a planning method of forming and processing the curved surface of the hull plate based on two mechanical arms.
Background
The ship body is formed by a complex and inextensible space curved surface, and a ship steel plate is processed into the curved surface shape of the ship body outer plate. At present, most of the methods adopted by shipyards in various countries in the world are linear water-fire ship plate curved surface forming processing technologies. The principle of the process is that the bending deformation of the whole steel plate is achieved by utilizing the hot elastic-plastic deformation generated after the local part of the steel plate is cooled at high temperature. The hull plate curved surface forming is a process mode for finishing processing by utilizing the elastic-plastic deformation of a steel plate, and the technology is finished by skilled workers with abundant experience through manual operation because the operation of forming the hull plate curved surface has certain dangerousness and has high requirements on the experience of workers. With the increasingly fierce competition of the modern shipbuilding market, more and more shipbuilding enterprises consider how to improve the speed and the efficiency of shipbuilding while ensuring the quality, obviously, the ship hull outer plate curved surface forming process which must be manually operated by skilled workers is the biggest obstacle for improving the speed and the efficiency of shipbuilding, so the realization of the automation of the ship hull outer plate curved surface forming process has great significance.
In the industrial field of hull plate curved surface forming, the mechanical arm is applied to ship manufacturing due to the characteristics of high working efficiency, reliable performance and the like. However, in the process of processing complex and diversified tasks in the hull plate curved surface forming process, a single mechanical arm increasingly shows insufficient capacity, and double mechanical arms can complete complex work tasks through cooperative processing, so that the work efficiency is improved.
At present, multi-task planning is mainly performed by classifying tasks on a workpiece level, and the motion of each mechanical arm in the system is calculated through the motion transformation of a relevant frame with a coordination relationship, but a unified analysis and task planning model is still lacked. In the actual task allocation process, a large number of constraint conditions are not taken into consideration, such as cooperation and competition of the double mechanical arms for forming the curved surface of the hull plate in actual processing, so that the solution is not accurate enough and cannot be well applied to practice. Therefore, it is very important to develop a planning method for the hull plate curved surface forming cooperative processing based on the double mechanical arms.
Disclosure of Invention
The invention provides a planning method for forming and collaborative processing of a hull plate curved surface based on two mechanical arms, which aims to solve the problem that the prior multi-task planning method lacks a unified analysis and task planning model and does not consider constraint conditions in the actual task allocation process, so that the applicability of the solution is poor, so as to fully consider the constraint conditions, effectively solve the task allocation problem of the two mechanical arms collaborative processing, and realize that the task planning method can be well applied to practice.
The technical scheme of the invention is realized as follows:
the planning method for the forming and collaborative processing of the hull plate curved surface based on the double mechanical arms specifically comprises the following steps:
s1, modeling analysis: according to the requirement of the ship hull plate curved surface forming co-processing technology and the extraction of the heating path, obtaining various characteristics of the heating path;
s2, determining the starting and ending points of each heating path by determining the heating direction of the heating path, and calculating the distance cost between the heating path and the heating path;
s3, selection and definition of variables: the method comprises the steps of known constant definition, variable definition and mechanical arm intermediate variable definition;
s4, determining constraint conditions;
s5, determining a double-mechanical-arm optimization target for forming the curved surface of the hull plate; deducing the path cost of each mechanical arm in the working process according to various intermediate variables in the processing process of the heating paths of the two mechanical arms, calculating the idle running time and the processing time according to the path cost, finally determining the time cost, and forming the optimization target of the two mechanical arms for the hull outer plate curved surface by using the minimum total time;
s6, improving the artificial bee colony algorithm, and distributing the processing tasks of the double mechanical arms for forming the curved surface of the hull plate by adopting the improved artificial bee colony algorithm.
In step S1, the heating path includes synchronous processing of the two robots, competitive processing according to an optimization target, processing time staggering, and cooperative processing.
Further optimizing the technical solution, the step S2 includes the following steps:
s21, if S1And s2Is within reach of one of the robots, belonging to adjacent heating paths of the same robot, A1、B1Is a heating path s1End point of (A)2、B2Is a heating path s2The endpoint of (1);
s22, assuming that the heating path S is processed first1Reprocessing s2；
S23, if S1The machine direction of (A)1→B1，s2In the machine direction ofA 2→B2The processing order of the heating paths is B1→A1；
S24, if S1The machine direction of (A)1→B1，s2In the machine direction of B2→A2The processing order of the heating paths is B1→B2；
S25, if S1In the machine direction of B1→A1，s2The machine direction of (A)2→B2The processing order of the heating paths is A1→A2；
S26, if S1In the machine direction of B1→A1，s2In the machine direction of B2→A2The processing order of the heating paths is A1→B2。
To further optimize the solution, in step S3, the known variable definition includes defining a process heating pathID. The length l of each processing heating path and the two mechanical arms are defined as R1AndR 2defining the movement speed V of the mechanical arm;
the variable definition comprises the definition of the heating path processing direction d, the heating path processing sequence x and the mechanical arm r to which the heating path belongsiHeating path start processing time tiHeating path end processing time TiHeating path waiting time τi；
The definition of the middle variable of the mechanical arm comprises a heating path sequence Sj processed by each mechanical arm and a heating path length sequence L of each mechanical armjHeating path processing direction sequence D of each mechanical armjHeating path processing start and end time sigma of each mechanical armjWaiting time psi for each robot armj。
Further optimizing the technical scheme, in the step S4, the constraint conditions include a synchronous processing constraint condition, a safe time constraint condition, a reachable space constraint condition, a collision constraint condition, and a kinematics constraint condition;
the synchronous processing constraint condition means that the start time and the end time of two heating paths needing synchronous processing are consistent; the safe time constraint condition refers to that two heating paths which cannot be processed at the same time need to be separated by a period of time in the middle of processing; the reachable space constraint means that each robot must be space-reachable for all its assigned heating paths; the collision constraint condition refers to the collision between the mechanical arms and the processing material; the kinematics constraint condition means that the actual speed and acceleration constraints of the mechanical arm must be considered in task planning.
In step S5, the time cost includes a processing time, an idle time, and a waiting time of the robot arm.
Further optimizing the technical solution, the step S6 includes the following steps:
s61, in the initial stage, NP feasible solutions (x) are randomly generated1,x2,…,xNP) As an initial heating path for the movement of the processing robot armEach set of processing paths that move may be denoted as XiNP represents the number of food sources, and the number of leading bees and following bees is equal to the number of food sources;
s62, leading bees, wherein each leading bee corresponds to one food source, a new food source is obtained by searching around the leading bee according to the formula, and the processing path moved by the processing mechanical arm is updated in real time;
after the positions of the leading bees in the food sources are updated, comparing the nectar richness degree of the candidate food sources with that of the original food sources, if the fitness value of the candidate food sources is higher than that of the original food sources, replacing the original food sources with the candidate food sources, otherwise, maintaining the positions of the original food sources unchanged;
s63, improving the artificial bee colony algorithm, namely, improving a search equation of the artificial bee colony algorithm by introducing a differential evolution search strategy idea into the artificial bee colony algorithm;
s64, a bee following stage, namely selecting the searched food source in a roulette mode by the following bee according to the information of the richness degree of the food source fed back by the leading bee or the fitness function value, and searching the processing path of the mechanical arm according to the search equation in the step S63;
s65, executing a scout bee stage, namely judging whether all leading bees and following bees finish searching tasks, if so, converting the leading bees into scout bees, randomly searching a new food source in the space to replace the original food source, and if not, reserving the original food source;
s66, after all bees finish the search task, judging whether a termination condition is reached, if the termination condition is met, recording the processing path of the mechanical arm, terminating the algorithm, otherwise, turning to the step S62, starting to search again for the bee colony, and finally connecting the bee colony sequentially through all heating paths in the mechanical arm working environment to obtain the optimal solution for the task planning of the double mechanical arms for forming the hull outer plate curved surface.
Further optimizing the technical solution, in the step S62, the search mode is as follows:
vij＝xij+rij(xij-xkj)
wherein i is 1,2, … NP, vijIs a candidate food source, xkjIs a randomly selected artificial bee, k ∈ (1,2, …, NP) and k ≠ i, j ∈ [1,2, …, D]And is the dimension of the solution, all other variables will be inherited from the old food source, rijIs [ -1,1 [ ]]The radius of the neighborhood is gradually reduced along with the continuous increase of the iteration number, and the optimal solution is finally obtained.
In step S62, the honey abundance of the candidate food sources and the original food sources is compared with the fitness function value of each group of processing paths of the comparison mechanical arm, and the fitness function value of each food source is calculated according to the following formula:
further optimizing the technical solution, in the step S63, the search equation adopted is as follows:
vij＝xij+η[(c1xpj+c2xqj+c3xrj-xij)+(xbj-xij)]
wherein x ispj、xqjAnd xrjIs three known solutions chosen at random, c1、c2And c3Is determined by the fitness function value of three known solutions, and c1+c2+c3＝1，xbjIs the current optimal food source position, T represents the number of current iterations, η is the differential variation factor as follows:
in the formula, a represents a constant, and the degree of variation of the differential variation factor η can be changed by adjusting the value of a.
By adopting the technical scheme, the invention has the beneficial effects that:
the invention models the multitask planning of the double mechanical arms into a complex multi-constraint TSP problem, fully considers the cooperative control problems of cooperation, competition and the like of the double mechanical arms for forming the curved surface of the hull plate in actual processing, establishes a double-mechanical-arm cooperative processing task planning model by analyzing various characteristics of the heating path for forming the curved surface of the hull plate, and adopts an improved artificial bee colony algorithm to distribute the processing tasks of the double mechanical arms for forming the curved surface of the hull plate.
According to the method, firstly, the requirements of the forming process of the curved surface of the hull plate are analyzed, the task planning and modeling analysis of the double mechanical arms is carried out, and then the objective function of the task planning of the double mechanical arms for forming the curved surface of the hull plate is established by optimizing the objective in the shortest working time. Then aiming at the problems that the basic artificial bee colony algorithm is premature, is easy to fall into local optimum, has low iterative later convergence speed and the like, a differential evolution search strategy idea is introduced into the artificial bee colony algorithm to improve a search equation of the artificial bee colony algorithm, and an original search equation is improved into a complex polynomial form containing the current optimum solution. Because the current optimal solution guide population exists, a new candidate solution is generated in the neighborhood adjacent to the optimal solution, and the development capability of the algorithm is enhanced.
In the initial stage of iteration under the influence of the differential evolution idea, the differential variation factor is large, so that the search space is favorably expanded, the search capability of the algorithm is improved, the diversity of solutions is increased, the development capability of the population is improved, and in the later stage of iteration, the differential variation factor is gradually reduced, so that the algorithm is favorably converged to a local optimal position, and the convergence precision is improved. The improved artificial bee colony algorithm is adopted to solve the task planning, so that the task allocation problem of the collaborative processing of the two mechanical arms is effectively solved, constraint conditions are fully considered, the task planning method can be well applied to practice, and the method has important significance for realizing the automation of the forming of the hull plate curved surface.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic view of a dual robot heating path assembly according to the present invention;
FIG. 3 illustrates the present invention processing adjacent heating path combinations;
FIG. 4 illustrates adjacent heating paths defining a machine direction according to the present invention;
FIG. 5 is a schematic view of the present invention illustrating simultaneous processing;
fig. 6 is a detailed flow chart of the improved artificial bee colony algorithm of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A planning method for forming and collaborative processing of a hull plate curved surface based on two mechanical arms is shown in a combined manner in figures 1 to 6, and specifically comprises the following steps:
and S1, modeling and analyzing. And obtaining various characteristics of the heating path according to the requirements of the ship hull plate curved surface forming co-processing technology and the extraction of the heating path.
In step S1, the characteristics of the heating path include synchronous processing of the two robots, competitive processing according to an optimization target, staggered processing time, and cooperative processing.
Specifically, the characteristic relationships between the heating paths are mainly:
1) some symmetrical heating paths need two mechanical arms for simultaneous deformation;
2) some heating paths are in a public reachable area of the two mechanical arms, and the mechanical arms compete for processing according to an optimization target;
3) some heating paths cannot be processed simultaneously due to process requirements (e.g., preventing deformation of the sheet), and thus require staggering the processing times from one another;
4) some heating paths are longer and exceed the motion range of any mechanical arm, and at the moment, two mechanical arms are needed to cooperate to process;
5) some heating paths are crossed, and the machining time of the mechanical arm needs to be staggered.
As shown in fig. 2, step S1 specifically includes the following contents:
R1、R2the heating paths in the shared reachable space are divided into a competitive heating path, a synchronous heating path, a time exclusive heating path and a cross heating path according to the processing technology requirements, and the specific analysis is as follows:
and S2, determining the heating direction of the heating path. And determining the heating direction of each heating path, determining the starting and ending points of each heating path, and calculating the distance cost between the heating paths. After the processing direction of the heating paths is determined, the distance cost w(s) between the heating paths can be uniquely determinedi,sj)。
As shown in fig. 3 and 4, step S2 includes the following steps:
s21, if S1And s2Is within reach of one of the robots, belonging to adjacent heating paths of the same robot, A1、B1Is a heating path s1End point of (A)2、B2Is a heating path s2The endpoint of (1).
S22, assuming that the heating path S is processed first1Reprocessing s2The heating path is shown in fig. 2.
S23, if S1The machine direction of (A)1→B1，s2The machine direction of (A)2→B2The processing order of the heating paths is B1→A1。
S24, if S1The machine direction of (A)1→B1，s2In the machine direction of B2→A2The processing order of the heating paths is B1→B2。
S25, if S1In the machine direction of B1→A1，s2The machine direction of (A)2→B2The processing order of the heating paths is A1→A2。
S26, if S1In the machine direction of B1→A1，s2In the machine direction of B2→A2The processing order of the heating paths is A1→B2。
Step S26 determines the direction of each heating path to determine the starting and ending points of each heating path, and calculates the heating path S1And a heating path s2Distance cost between, noted as w(s)1,s2) After the processing direction of the heating paths is determined, the distance cost between the heating paths can be uniquely determined. When s is determined, as shown in FIG. 31The machine direction of the heating path is A1→B1，s2The machine direction of the heating path is A2→B2Then s can be determined1、s2Processing sequence B of two heating paths1→A1。
S3, selection and definition of variables: the method comprises known constant definition, variable definition and mechanical arm intermediate variable definition.
In step S3, the known variable definitions include ID defining the processing heating path, length l of each processing heating path, and two robots defined as R1And R2And defining the motion speed V of the mechanical arm.
The variable definition comprises the definition of the heating path processing direction d, the heating path processing sequence x and the mechanical arm r to which the heating path belongsiHeating path start processing time tiHeating path end processing time TiHeating path waiting time τi。
The method comprises the following steps of defining intermediate variables of the mechanical arms, a heating path sequence processed by each mechanical arm, a heating path length sequence of each mechanical arm, a heating path processing direction sequence of each mechanical arm, the heating path processing start and end time of each mechanical arm and the waiting time of each mechanical arm.
Step S3 specifically includes the following steps:
s31, known constant definition. The main constants are defined as follows:
and S32, variable definition. The main variables are defined as follows:
and S33, defining intermediate variables of the mechanical arm. The intermediate variables are defined as follows:
and S4, determining constraint conditions. In step S4, the constraint conditions include a synchronous processing constraint condition, a safe time constraint condition, a reachable space constraint condition, a collision constraint condition, and a kinematic constraint condition.
The synchronous processing constraint condition means that the start time and the end time of two heating paths needing synchronous processing are consistent; the safe time constraint condition refers to that two heating paths which cannot be processed at the same time need to be separated by a period of time in the middle of processing; the reachable space constraint means that each robot must be space-reachable for all its assigned heating paths; the collision constraint condition refers to the collision between the mechanical arms and the processing material; the kinematics constraint condition means that the actual speed and acceleration constraints of the mechanical arm must be considered in task planning.
The constraints included in the target optimization in step S4 are as follows:
step S4 includes the following steps:
s41, synchronous processing constraint
And (4) determining. In order to ensure the requirement of the processing technology, some heating paths need to be processed synchronously, and a synchronous processing schematic diagram is shown in fig. 4. Synchronous machining requires that the start and end times of the two heating paths coincide, and the synchronous heating path s is defined asi,sjRequest ti＝tj,Ti＝TjWhere i, j ∈ S.
S42, safety time constraint
And (4) determining. The requirement of the forming and processing process of the hull plate curved surface prevents the material of the plate from being damaged due to over high local temperature, certain heating paths cannot be processed at the same time, and a period of time is needed in the middle of the processing of the heating paths. Therefore, when these heating paths are processed simultaneously, one of the robots is required to stop waiting for the completion of the processing of the other robot. In actual processing, the formula (1) needs to be satisfied for the heating path which is time-exclusive.
Ti＜tj‖Tj＜ti(i, j ∈ S) formula (1)
S43, reachable space constraint
And (4) determining. In the task planning of the two mechanical arms, all heating paths distributed to each mechanical arm must be accessible in space, and for measuring whether one heating path is accessible, the method is adopted to obtain a series of discrete heating path characteristic points when the heating path information is extracted, then whether each point is in the accessible space of the mechanical arm distributed to the heating path is calculated according to the D-H parameters and the inverse solution of the mechanical arm, and if each point is in the accessible space of the mechanical arm, the heating path is in the accessible space of the mechanical arm. For the cooperative heating path, the cooperative heating path can be divided into two sections of heating paths, and then the spaces can be reached by the corresponding mechanical arms respectively. Recording heating path siThe reachable space of the mechanical arm is Rj(q), q is the motion range of the joint of the mechanical arm, and needs to satisfy: si∈Rj(q) wherein i ∈ S, j is 1, 2.
S44 collision restraint
And (4) determining. In actual processing, the mechanical arms and the substitute processing materials are rigid objects, so that the mechanical arms are damaged once collision occurs, and the collision of the two mechanical arms for forming the curved surface of the hull plate comprises the collision between the mechanical arms and the processing materials. Let the space occupied by the processing material be RwpThe space where the robot arm 1 is located at this time is R1(q1)，q1The current joint vector of the mechanical arm 1 and the space where the mechanical arm 2 is located at the moment are R2(q2)，q2For the current joint vector of the robot arm 2, equation (2) needs to be satisfied.
Rwp∩R1(q1)＝φ，Rwp∩R2(q2)＝φ，R1(q1)∩R2(q2) Phi type (2)
S45, kinematic constraint
Determination of (d), let vij(t) is the current velocity of the ith joint of the mechanical arm, VijFor the ith j joint speed range, a of the robot armij(t) is the current acceleration of the ith j joint of the mechanical arm, AijIn order to satisfy the j-th joint acceleration range of the robot i, equation (3) is required, where i is 1 or 2, and j ∈ represents the number of the i-th joint angles of the robot.
vij(t)∈Vij，aij(t)∈AijFormula (3).
S5, determining a double-mechanical-arm optimization target for forming the curved surface of the hull plate; according to various intermediate variables in the processing process of the heating paths of the two mechanical arms, the path cost of each mechanical arm in the working process is deduced, the idle running time and the processing time are calculated according to the path cost, the time cost is finally determined, and the minimum total time is used as the optimization target of the hull plate curved surface forming two mechanical arms. In the step, firstly, the path cost U of each mechanical arm in the working process is determinedWjAnd a time cost UTjAnd then determining an optimization target U of the double-mechanical-arm task planning.
In step S5, the time cost includes a processing time, an idle time, and a waiting time of the robot arm.
Step S5 includes the following steps:
s51, according to intermediate variables in the processing process of the heating paths of the two mechanical arms, the path cost U of each mechanical arm in the working process can be deducedWjAnd a time cost UTjThe path cost is the sum of the paths taken by the robot arm, and after the path of the robot arm is determined, the processing time and the idle running time can be determined according to the processing speed and the idle running speed of the robot arm, as shown in equations (4) and (5).
S52, determining an optimization target of task planning of the two mechanical arms, wherein for a certain heating path combination, the processing time is determined by the mechanical arm with the longest processing time in the two mechanical arms, the processing time of the mechanical arm is the time of the whole processing process as shown in the formula (6), and the optimization target is to minimize U, namely to solve minU.
U＝maxj＝1、2{UTj(r, x, d, l, v, τ) } formula (6)
S6, the basic artificial bee colony algorithm has the problems of being premature, being easy to fall into local optimum, being low in iterative later convergence speed and the like, so that the artificial bee colony algorithm is improved, and the improved artificial bee colony algorithm is adopted to distribute the processing tasks of the double mechanical arms for forming the curved surface of the hull plate. The improved artificial bee colony algorithm task planning is adopted for solving, an algorithm flow chart is shown in fig. 5, the difference thought is introduced in the following bee stage of the artificial bee colony algorithm, the search equation of the artificial bee colony algorithm is improved, the original search equation is improved into a complex polynomial form containing the current optimal solution, the optimization is carried out on the multi-task planning of the double mechanical arms for forming the curved surface of the hull outer plate by improving the search mode of the following bee stage, and the optimal double mechanical arm processing task distribution mode is obtained.
Step S6 includes the following steps:
s61, in the initial stage, NP feasible solutions (x) are randomly generated1,x2,…,xNP) As the initial heating path for the movement of the processing robot, each set of processing paths for the movement of the robot may be denoted as XiAs shown in equation (5), NP represents the number of food sources, and the number of leading bees and following bees is equal to the number of food sources. Each set of moving machining paths XiCorresponds to a heating path in the established mission planning model.
Xi＝(xi1,xi2,…,xiD) (i-1, 2, …, NP) formula (5).
And S62, leading bees, wherein each leading bee corresponds to a food source, a new food source is obtained by searching around the leading bee according to the formula, and the processing path moved by the processing mechanical arm is updated in real time, and the searching mode is shown as the formula (6).
vij＝xij+rij(xij-xkj) Formula (6)
Wherein i is 1,2, … NP, vijIs a candidate food source, xkjIs a randomly selected artificial bee, k ∈ (1,2, …, NP) and k ≠ i, j ∈ [1,2, …, D]And is the dimension of the solution, all other variables will be inherited from the old food source, rijIs [ -1,1 [ ]]The radius of the neighborhood is gradually reduced along with the continuous increase of the iteration number, and the optimal solution is finally obtained.
And after the positions of the food sources are updated, the honey abundance degrees of the candidate food sources and the original food sources are compared, if the fitness values of the candidate food sources are higher than those of the original food sources, the candidate food sources are used for replacing the original food sources, otherwise, the positions of the original food sources are maintained unchanged.
In step S62, the nectar richness of the candidate food sources and the original food sources is compared with the fitness function value of each group of processing paths of the comparison robot arm, and the fitness function value of each food source is calculated by the following formula:
s63, improving an artificial bee colony algorithm, wherein in an original artificial bee colony algorithm, the following bees and the leading bees all adopt the same search strategy, the search strategy has better global search capability, the local search performance of the algorithm is neglected, in order to further improve the search capability of the artificial bee colony algorithm and increase the diversity of the population, the idea of variation in the differential evolution algorithm is introduced into the search strategy, and a new search equation is obtained as shown in the formula (8);
vij＝xij+η[(c1xpj+c2xqj+c3xrj-xij)+(xbj-xij)]formula (8)
Wherein xpj、xqjAnd xrjIs three known solutions chosen at random, c1、c2And c3Is determined by the fitness function value of three known solutions, and c1+c2+c3＝1，xbjIs the current optimal food source position, T represents the number of current iterations, and η is the differential variation factor, as in equation (9).
In the equation (9), a represents a constant, and the degree of variation of the differential variation factor η can be changed by adjusting the value of a.
From the new search equation, c is determined by fitness function values based on three known solutions chosen at random1、c2And c3The value of (a) greatly increases the diversity of the population; due to the existence of the current optimal solution x of the populationbjThe difference variation factor η is gradually reduced along with the increase of the iteration times, the smaller the difference variation factor is, the smaller the search range of the bee colony is.
And S64, selecting the searched food source by the following bees in a roulette mode according to the information of the richness degree of the food source fed back by the leading bees or the size of the fitness function value, and searching the processing path of the mechanical arm according to the search equation in the step S63.
And S65, executing a bee investigation stage, namely judging whether the search task is finished by all leading bees and following bees, and judging whether the search task is greater than the mining limit. If yes, leading the bees to be converted into detecting bees, randomly searching a new food source in the space to replace the original food source, and if not, keeping the original food source.
And S66, after all the bees finish the search task, judging whether the termination condition is reached. The termination condition is reaching a maximum number of iterations or reaching a target accuracy. If the termination condition is met, recording the processing path of the mechanical arm, terminating the algorithm, otherwise, turning to the step S62, searching the bee colony again, and finally connecting the bee colony through each heating path in the mechanical arm working environment in sequence to obtain the optimal solution of the task planning of the double mechanical arms for forming the hull plate curved surface.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. The planning method for the forming and collaborative processing of the hull plate curved surface based on the double mechanical arms is characterized by comprising the following steps:
s1, modeling analysis: according to the requirement of the ship hull plate curved surface forming co-processing technology and the extraction of the heating path, obtaining various characteristics of the heating path;
s2, determining the starting and ending points of each heating path by determining the heating direction of the heating path, and calculating the distance cost between the heating path and the heating path;
s3, selection and definition of variables: the method comprises the steps of known constant definition, variable definition and mechanical arm intermediate variable definition;
s4, determining constraint conditions;
s5, determining a double-mechanical-arm optimization target for forming the curved surface of the hull plate; deducing the path cost of each mechanical arm in the working process according to various intermediate variables in the processing process of the heating paths of the two mechanical arms, calculating the idle running time and the processing time according to the path cost, finally determining the time cost, and forming the optimization target of the two mechanical arms for the hull outer plate curved surface by using the minimum total time;
s6, improving the artificial bee colony algorithm, and distributing the processing tasks of the double mechanical arms for forming the curved surface of the hull plate by adopting the improved artificial bee colony algorithm.
2. The planning method for forming and co-processing the curved surface of the hull plate based on the two robot arms as claimed in claim 1, wherein the characteristics of the heating path in step S1 include synchronous processing of the two robot arms, competing processing according to an optimization target, staggering processing time, and co-processing.
3. The planning method for the curved surface forming and collaborative processing of the ship hull plate based on the double mechanical arms as claimed in claim 1, wherein the step S2 includes the steps of:
s21, if S1And s2Is within reach of one of the robots, belonging to adjacent heating paths of the same robot, A1、B1Is a heating path s1End point of (A)2、B2Is a heating path s2The endpoint of (1);
s22, assuming that the heating path S is processed first1Reprocessing s2；
S23, if S1The machine direction of (A)1→B1，s2The machine direction of (A)2→B2The processing order of the heating paths is B1→A1；
S24, if S1The machine direction of (A)1→B1，s2In the machine direction of B2→A2The processing order of the heating paths is B1→B2；
S25, if S1In the machine direction of B1→A1，s2The machine direction of (A)2→B2The processing order of the heating paths is A1→A2；
S26, if S1In the machine direction of B1→A1，s2In the machine direction of B2→A2The processing order of the heating paths is A1→B2。
4. The planning method for curved surface forming and collaborative processing of ship hull plates based on two robots as claimed in claim 1, wherein in the step S3, the known variable definitions include ID for defining processing heating paths, length l for each processing heating path, and R for defining two robots1And R2Defining the movement speed V of the mechanical arm;
the variable definition comprises the definition of the heating path processing direction d, the heating path processing sequence x and the mechanical arm r to which the heating path belongsiHeating path start processing time tiHeating path end processing time TiHeating path waiting time τi；
The definition of the middle variable of the mechanical arm comprises a heating path sequence Sj processed by each mechanical arm and a heating path length sequence L of each mechanical armjHeating path processing direction sequence D of each mechanical armjHeating path processing start and end time sigma of each mechanical armjWaiting time psi for each robot armj。
5. The planning method for the curved surface forming and collaborative processing of the ship hull plate based on the double mechanical arms as recited in claim 1, wherein in the step S4, the constraint conditions include a synchronous processing constraint condition, a safe time constraint condition, a reachable space constraint condition, a collision constraint condition, and a kinematic constraint condition;
the synchronous processing constraint condition means that the start time and the end time of two heating paths needing synchronous processing are consistent; the safe time constraint condition refers to that two heating paths which cannot be processed at the same time need to be separated by a period of time in the middle of processing; the reachable space constraint means that each robot must be space-reachable for all its assigned heating paths; the collision constraint condition refers to the collision between the mechanical arms and the processing material; the kinematics constraint condition means that the actual speed and acceleration constraints of the mechanical arm must be considered in task planning.
6. The planning method for the curved surface forming and collaborative processing of the ship hull plate based on the double mechanical arms as claimed in claim 1, wherein the time cost in step S5 includes processing time, idle running time and waiting time of the mechanical arm.
7. The planning method for the curved surface forming and collaborative processing of the ship hull plate based on the double mechanical arms as claimed in claim 1, wherein the step S6 includes the steps of:
s61, in the initial stage, NP feasible solutions (x) are randomly generated1,x2,...,xNP) As the initial heating path for the movement of the processing robot, each set of processing paths for the movement of the robot may be denoted as XiNP represents the number of food sources, and the number of leading bees and following bees is equal to the number of food sources;
s62, leading bees, wherein each leading bee corresponds to one food source, a new food source is obtained by searching around the leading bee according to the formula, and the processing path moved by the processing mechanical arm is updated in real time;
after the positions of the leading bees in the food sources are updated, comparing the nectar richness degree of the candidate food sources with that of the original food sources, if the fitness value of the candidate food sources is higher than that of the original food sources, replacing the original food sources with the candidate food sources, otherwise, maintaining the positions of the original food sources unchanged;
s63, improving the artificial bee colony algorithm, namely, improving a search equation of the artificial bee colony algorithm by introducing a differential evolution search strategy idea into the artificial bee colony algorithm;
s64, a bee following stage, namely selecting the searched food source in a roulette mode by the following bee according to the information of the richness degree of the food source fed back by the leading bee or the fitness function value, and searching the processing path of the mechanical arm according to the search equation in the step S63;
s65, executing a scout bee stage, namely judging whether all leading bees and following bees finish searching tasks, if so, converting the leading bees into scout bees, randomly searching a new food source in the space to replace the original food source, and if not, reserving the original food source;
s66, after all bees finish the search task, judging whether a termination condition is reached, if the termination condition is met, recording the processing path of the mechanical arm, terminating the algorithm, otherwise, turning to the step S62, starting to search again for the bee colony, and finally connecting the bee colony sequentially through all heating paths in the mechanical arm working environment to obtain the optimal solution for the task planning of the double mechanical arms for forming the hull outer plate curved surface.
8. The planning method for the curved surface forming and cooperative processing of the ship hull plate based on the double mechanical arms as claimed in claim 7, wherein in the step S62, the searching manner is as follows:
vij＝xij+rij(xij-xkj)
wherein i ═ 1, 2.. NP, vijIs a candidate food source, xkjIs a randomly selected artificial bee, k ∈ (1,2,.., NP) and k ≠ i, j ∈ [1,2,..., D)]And is the dimension of the solution, all other variables will be inherited from the old food source, rijIs [ -1,1 [ ]]The radius of the neighborhood is gradually reduced along with the continuous increase of the iteration number, and the optimal solution is finally obtained.
9. The planning method for collaborative processing of curved surface forming of ship hull plate based on two robots as claimed in claim 7, wherein in step S62, the degree of richness of nectar of the candidate food sources and the original food sources is compared with the fitness function value of each group of processing paths of the comparison robot, and the fitness function value of each food source is calculated by the following formula:
10. the planning method for the curved surface forming and cooperative processing of the ship hull plate based on the double mechanical arms as claimed in claim 7, wherein the search equation adopted in the step S63 is as follows:
vij＝xij+η[(c1xpj+c2xqj+c3xrj-xij)+(xbj-xij)]
wherein x ispj、xqjAnd xrjIs three known solutions chosen at random，c1、c2And c3Is determined by the fitness function value of three known solutions, and c1+c2+c3＝1，xbjIs the current optimal food source position, T represents the number of current iterations, η is the differential variation factor as follows:
in the formula, a represents a constant, and the degree of variation of the differential variation factor η can be changed by adjusting the value of a.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010654350.5A CN111745653B (en) | 2020-07-09 | 2020-07-09 | Planning method for hull plate curved surface forming cooperative processing based on double mechanical arms |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010654350.5A CN111745653B (en) | 2020-07-09 | 2020-07-09 | Planning method for hull plate curved surface forming cooperative processing based on double mechanical arms |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111745653A true CN111745653A (en) | 2020-10-09 |
| CN111745653B CN111745653B (en) | 2022-01-14 |
Family
ID=72710033
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010654350.5A Active CN111745653B (en) | 2020-07-09 | 2020-07-09 | Planning method for hull plate curved surface forming cooperative processing based on double mechanical arms |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111745653B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113985899A (en) * | 2021-11-25 | 2022-01-28 | 江苏科技大学 | Underwater robot global path planning method based on interval multi-objective optimization |
| CN114700944A (en) * | 2022-04-06 | 2022-07-05 | 南京航空航天大学 | Heterogeneous task-oriented double-robot collaborative path planning method |
| CN116968037A (en) * | 2023-09-21 | 2023-10-31 | 杭州芯控智能科技有限公司 | Multi-mechanical-arm cooperative task scheduling method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101134196B1 (en) * | 2011-01-28 | 2012-04-09 | 강원대학교산학협력단 | Optimal designing method and device of location area planning using artifical bee colony in wireless communication network |
| CN104808665A (en) * | 2015-04-16 | 2015-07-29 | 上海大学 | Multi robot path planning method based on multi-target artificial bee colony algorithm |
| CN109159127A (en) * | 2018-11-20 | 2019-01-08 | 广东工业大学 | A kind of double welding robot intelligence paths planning methods based on ant group algorithm |
| CN110743976A (en) * | 2019-10-21 | 2020-02-04 | 江苏科技大学 | Ship body outer plate curved surface forming equipment based on double mechanical arms and implementation method thereof |
-
2020
- 2020-07-09 CN CN202010654350.5A patent/CN111745653B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101134196B1 (en) * | 2011-01-28 | 2012-04-09 | 강원대학교산학협력단 | Optimal designing method and device of location area planning using artifical bee colony in wireless communication network |
| CN104808665A (en) * | 2015-04-16 | 2015-07-29 | 上海大学 | Multi robot path planning method based on multi-target artificial bee colony algorithm |
| CN109159127A (en) * | 2018-11-20 | 2019-01-08 | 广东工业大学 | A kind of double welding robot intelligence paths planning methods based on ant group algorithm |
| CN110743976A (en) * | 2019-10-21 | 2020-02-04 | 江苏科技大学 | Ship body outer plate curved surface forming equipment based on double mechanical arms and implementation method thereof |
Non-Patent Citations (2)
| Title |
|---|
| 周晟: "双臂协作票据处理机器人设计及运动轨迹优化", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 王郑拓 等: "基于人工蜂群算法的双机器人路径规划分析", 《焊接学报》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113985899A (en) * | 2021-11-25 | 2022-01-28 | 江苏科技大学 | Underwater robot global path planning method based on interval multi-objective optimization |
| CN113985899B (en) * | 2021-11-25 | 2023-09-22 | 江苏科技大学 | Underwater robot global path planning method based on interval multi-objective optimization |
| CN114700944A (en) * | 2022-04-06 | 2022-07-05 | 南京航空航天大学 | Heterogeneous task-oriented double-robot collaborative path planning method |
| CN114700944B (en) * | 2022-04-06 | 2023-11-24 | 南京航空航天大学 | Heterogeneous task-oriented double-robot cooperative path planning method |
| CN116968037A (en) * | 2023-09-21 | 2023-10-31 | 杭州芯控智能科技有限公司 | Multi-mechanical-arm cooperative task scheduling method |
| CN116968037B (en) * | 2023-09-21 | 2024-01-23 | 杭州芯控智能科技有限公司 | Multi-mechanical-arm cooperative task scheduling method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111745653B (en) | 2022-01-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111745653B (en) | Planning method for hull plate curved surface forming cooperative processing based on double mechanical arms | |
| CN111113409B (en) | Multi-robot multi-station cooperative spot welding planning method based on step-by-step optimization | |
| WO2022174658A1 (en) | Rapid optimization and compensation method for rotation shaft spatial localization error of five-axis numerically controlled machine tool | |
| CN112692826B (en) | Industrial robot track optimization method based on improved genetic algorithm | |
| CN106503373A (en) | The method for planning track that a kind of Dual-robot coordination based on B-spline curves is assembled | |
| CN108399451A (en) | A kind of Hybrid Particle Swarm Optimization of combination genetic algorithm | |
| Zhou et al. | Multi-robot multi-station cooperative spot welding task allocation based on stepwise optimization: An industrial case study | |
| CN110134062B (en) | Multi-axis numerical control machine tool machining path optimization method based on reinforcement learning | |
| Han et al. | Ant colony optimization for assembly sequence planning based on parameters optimization | |
| CN109676610B (en) | Circuit breaker assembly robot and method for realizing work track optimization | |
| CN109623818B (en) | Mechanical arm joint track optimization method based on time grouping | |
| CN113050640A (en) | Industrial robot path planning method and system based on generation of countermeasure network | |
| CN110488810B (en) | Optimal path planning method for welding robot based on improved particle swarm optimization | |
| CN111738499A (en) | Job shop batch scheduling method based on novel neighborhood structure | |
| Larsen et al. | Path planning of cooperating industrial robots using evolutionary algorithms | |
| CN111752151A (en) | Adaptive tracking and compensating method and system for grinding and polishing industrial blade | |
| CN110216673A (en) | The non-dominant neighborhood immune genetic Multipurpose Optimal Method of electro-hydraulic joint of robot track | |
| CN113325799A (en) | Spot welding robot operation space smooth path planning method for curved surface workpiece | |
| CN114669916A (en) | Double-robot cooperative welding task planning method based on improved genetic algorithm | |
| CN115122338B (en) | Multi-robot cooperation arc welding task planning method based on multi-objective optimization | |
| CN114290335B (en) | Robot track planning method | |
| CN115169798A (en) | Distributed flexible job shop scheduling method and system with preparation time | |
| CN111515954B (en) | Method for generating high-quality motion path of mechanical arm | |
| CN104749956A (en) | Structure optimization method of industrial robot based on harmony search algorithm | |
| Wang et al. | A Modified Genetic Algorithm (GA) for Optimization of Process Planning. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |