CN112621754A - Design method for multi-robot-cooperated assembly line safety layout - Google Patents

Design method for multi-robot-cooperated assembly line safety layout Download PDF

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CN112621754A
CN112621754A CN202011460174.8A CN202011460174A CN112621754A CN 112621754 A CN112621754 A CN 112621754A CN 202011460174 A CN202011460174 A CN 202011460174A CN 112621754 A CN112621754 A CN 112621754A
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robot
layout
safety
equipment
model
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郭锐锋
王帅
王鸿亮
苏谟
张晓星
于金刚
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Shenyang Institute of Computing Technology of CAS
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1679—Programme controls characterised by the tasks executed
    • B25J9/1682—Dual arm manipulator; Coordination of several manipulators
    • 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
    • B25J9/1674—Programme controls characterised by safety, monitoring, diagnostic
    • B25J9/1676—Avoiding collision or forbidden zones
    • 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
    • B25J9/1687—Assembly, peg and hole, palletising, straight line, weaving pattern movement

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  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Automation & Control Theory (AREA)
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Abstract

The invention relates to a multi-objective optimization method, in particular to an optimization method for robot assembly line safety layout, which aims at the problem of unsafe cooperation caused by lack of height information of optimized layout equipment among irregular equipment in the application of multi-robot-coordinated assembly line layout design. A modeling rule of irregular equipment of a robot is defined, a safe assembly line optimization model is established, the minimum logistics cost and the minimum floor area are used as optimization targets, a combined algorithm of a differential evolution strategy and a non-dominated sorting genetic algorithm II is used for carrying out layout optimization design on equipment to be distributed on an assembly line, and then quantitative safety indexes are used as triggers of three-dimensional collision detection to ensure that a design scheme of safe layout is obtained.

Description

Design method for multi-robot-cooperated assembly line safety layout
Technical Field
The invention belongs to the technical field of multi-robot cluster cooperative manufacturing, relates to an assembly line safety layout design method, and particularly relates to a safety layout method based on a heuristic random search method and a genetic method and a collision detection method.
Background
In the context of "industry 4.0", market demand is constantly changing and the need for personalized customization by customers is a trend. Conventional assembly lines are in urgent need of updating to accommodate new production needs. In the early days of designing production systems, the layout design of assembly lines is an important issue, which affects not only the effectiveness of the project stage, but also the subsequent productivity, including material flow, space utilization, energy consumption, and safe working environment. The safe and effective assembly line layout design can ensure the overall productivity and improve the manufacturing efficiency, and is the key for improving the performance of the manufacturing system.
Along with the popularization of automation, large-scale industrial robots are applied to assembly lines in a large number, the dependence on manpower is reduced, assembly tasks such as lifting and carrying of heavy objects can be completed through human-computer cooperation, the working efficiency of the assembly lines is improved, and the intelligent assembly lines are constructed. However, the robot has a large working range, a large number of degrees of freedom, and an arbitrary moving direction during working. This presents challenges to the early layout of the assembly line. Therefore, it is important to consider the safe layout of the assembly line of irregular equipment typified by robots.
The related research of the layout optimization design of the current assembly line has limitations and needs urgent research:
1. layout optimization lacks consideration of three-dimensional height information: the existing assembly line layout without a robot is mainly designed for two-dimensional layout of regular-shape equipment and regular-shape areas, and three-dimensional height information is not considered. Especially, when an assembly line is provided with irregular-shaped equipment represented by machines, a layout plan only considering two-dimensional layout is obviously insufficient, and collision is easily caused by neglecting height information of the irregular equipment, so that the insecurity in the actual assembly work in the later stage of layout is increased.
2. Limitations of robot and irregular device modeling: the layout of an assembly line with robots is to approximately consider the components in the assembly line as rectangles, and usually takes the maximum motion range of the robot base or the robot as an optimization target. Such an assumption undoubtedly has two limitations, namely, assuming that a rectangle is used as an optimization entity with the robot base as the layout optimization target, and the robot arm is not considered at all in the previous layout. In actual assembly, the operation of the robot arm can exceed the range of the base, physical collision between the robot arms is easy to occur, and meanwhile, collision between the robot arms and equipment is easy to occur. Secondly, the maximum working range of the robot is assumed to be rectangular as a layout optimization entity, the target requirement of the minimum occupied area cannot be met, the cooperative assembly among the robot arms cannot be met, and space resources cannot be saved.
3. Lack of three-dimensional verification of the layout of each device of the overall assembly line: the actual assembly line layout only aims at the self collision detection verification of the robot, and the safety of the whole assembly line equipment layout for determining the process flow is not considered. After the assembly line layout plan is generated, the feasibility and the safety of the physical layout of the assembly line can be ensured by the three-dimensional collision detection verification of the whole assembly line layout.
4. The assembly line multi-objective optimization method needs to be upgraded: a large number of researches prove that the genetic algorithm and the improved algorithm for the multi-objective optimization problem of the production line layout are effective, but the convergence effect and the diversity of the solution of the algorithm in the assembly line layout optimization still have a space for improvement.
Disclosure of Invention
To address the practical requirements of blank and actual layout designs in the above prior art studies. The invention provides an optimization method of safety layout.
And establishing an assembly line safety layout model, and considering logistics cost and assembly line area. Based on the high quality performance of the genetic algorithm (NSGA-II), a heuristic random search algorithm (DE) is integrated to reduce repetitive planning and effectively increase the diversity of solutions. And considering the height information of the equipment to be distributed and the safety of the fixed assembly process flow, and ensuring the output of a safe layout scheme of an assembly line by taking the minimum logistics cost and floor area as optimization targets.
The technical scheme adopted by the invention for realizing the purpose is as follows:
a design method of multi-robot cooperative assembly line safety layout comprises the following steps:
constructing a point cloud model of an assembly range according to the operation state and the operation range of the simulation robot, and constructing a regular robot model from an irregular robot model through a modeling rule;
an assembly line optimization model is formulated through a regular robot model, and modeling objects comprise the regular robot model, a machine tool model and an assembly station model;
taking an assembly line optimization model as the input of a multi-objective optimization combination algorithm to obtain an initial safety layout scheme, bringing the initial safety layout scheme into a safety index and judging whether the safety index meets a threshold value, if so, carrying out fine collision detection on the multiple robots, otherwise, carrying out rough collision detection on the multiple robots;
and judging whether the multiple robots collide according to the detection result, if so, giving up the initial safety layout scheme, and otherwise, taking the initial safety layout scheme as a final safety layout scheme.
The method for approximating the irregular robot model to the regular robot model through the modeling rule specifically comprises the following steps:
and (3) building a robot simulation model, simulating the actual physical process of a robot working space by using a Monte Carlo method, building a working space point cloud model, and optimizing the robot model with quadrilateral, namely regular entities.
The assembly line optimization model comprises: the system comprises an entity of a regular robot model or a machine tool model or an assembly station model, a constraint condition, an objective function and a decision variable parameter.
The objective function is the minimum logistics cost and the minimum floor area, and the expression is as follows:
Figure BDA0002831247340000031
Figure BDA0002831247340000032
wherein: dij＝|xi-xj|+|yi-yj|，Ld＝(Lr+Ll)/2+(xr-xl)，Wd＝(Wr+Wl)/2+(yr-yl) N is the number of devices to be laid out in a workshop, t is time, i, j is the serial number i, j ∈ of the devices to be laid out {1, 2,.., n }; cijUnit material handling cost per unit distance from equipment i to equipment j, DijDistance from device i to device j; pijFor the planned period t, the material transfer amount from equipment i to equipment j, LC is the logistics cost, alpha is the floor area, LdIs the length of the device d, WdIs the width, x, of the device diIs the physical abscissa, y, of the center point of the device iiIs the physical ordinate, x, of the center point of the device ijIs the physical abscissa, y, of the center point of the device jjIs the physical ordinate of the center point of the device j, LdIs the maximum abscissa x of the layoutrWith the smallest abscissa xlA difference in distance of WdAs the maximum ordinate y of the layoutrWith the smallest ordinate ylThe distance difference of (2).
The decision variable parameters include: equipment clearance, safety index, equipment quantity.
The multi-objective optimization combination algorithm specifically comprises the following steps:
firstly, optimizing the serial number of the assembly equipment by using a genetic algorithm, then optimizing the equipment spacing by using a heuristic random search algorithm based on a differential optimization strategy, comparing constraint conditions corresponding to the output equipment spacing with safety indexes to verify the safety of the entity layout, verifying the feasibility of the entity layout by three-dimensional collision detection, and outputting an initial safety layout scheme.
The safety index
Figure BDA0002831247340000042
Comprises the following steps:
Figure BDA0002831247340000041
wherein: riIs the maximum working radius, R, of the device ijIs the maximum working radius, δ, of the device jiFor a clear spacing of the apparatus, SirDistance between equipment and robot, FSiThe safety factor is.
The rough collision detection is AABB level bounding box collision detection; the accurate collision detection is a mesh collision detection method and a implicit function collision detection method of a complex curved surface.
The invention has the following beneficial effects and advantages:
1. and defining an irregular-shaped equipment modeling rule, simulating an actual physical process of a robot working space, and constructing an assembly space point cloud model. The reasonable modeling rule lays a foundation for safe layout and improves the accuracy of the algorithm.
2. The safety index judges the assembly state of the irregular robot, and is regularly coupled with the modeling of the irregular-shaped equipment to realize the safe layout on the three-dimensional height, so that the accuracy and the efficiency of the algorithm are improved.
3. The collision detection method three-dimensionally verifies the safety of the overall assembly line layout, couples with safety indexes to ensure the physical safety layout of each device in the overall assembly line, and prevents potential safety hazards caused by irregular device movement.
4. The fusion of the multi-objective optimization method DE and the NSGAII algorithm enhances the convergence effect of the algorithm, reduces repeated calculation, improves the time performance and ensures the diversity of algorithm solution sets.
Drawings
FIG. 1 is a schematic diagram of a robot entity and a robot simulation model;
FIG. 2 is a schematic diagram of a KUKA DH parameter table (KR 210R 2700) of a robot;
FIG. 3a is a schematic view of two robot assembly range point clouds;
FIG. 3b is a schematic view of two robot assembly range point clouds;
FIG. 3c is a schematic view of two robot assembly range point clouds;
FIG. 3d is a schematic view of two robot assembly range point clouds;
FIG. 4 is a schematic diagram of a secure assembly line optimization model;
FIG. 5 is a flow chart of a multi-objective optimization combining algorithm;
fig. 6 is an overall flow chart of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The present invention comprises the following steps (as shown in fig. 6):
step 1, modeling rules of irregular equipment such as robots. The irregular-shaped equipment on the assembly line comprises an industrial robot and a special-shaped machine tool. The reasonable modeling of the three-dimensional irregular target optimization entity is the basis of safety index quantification. Irregular equipment typified by industrial robots is applied in assembly lines in large numbers. To illustrate the modeling rules of irregular equipment of the robot, taking the KUKA KR 210R 2700 robot in an experimental case as an example, the robot entity and the simulation model are shown in fig. 1, an industrial robot simulation model is built, and the actual physical process of the robot working space is simulated by using the monte carlo method. And constructing a working space point cloud model according to a DH parameter table in factory configuration of the robot, as shown in figure 2.
A three-dimensional point cloud of two cooperative robot assembly ranges is shown in fig. 3 (a). The point cloud overlapping area is a cooperative assembly area of the double-arm robot, and the initial position of the robot layout influences the size of the cooperative assembly area. Fig. 3(b) (c) (d) is a ground projection, which has a large range of motion. Fig. 3(b) when two horizontally adjacent robots are cooperatively assembled, the maximum range of the assembly area of one robot passes through the center of the other robot, and the two robots cooperatively assembled at the moment have the maximum range and the best dexterity. Fig. 3(c) the layout of the two-arm robot is too close and the effective assembly area is reduced, and fig. 3(d) the layout of the two-arm robot is too far and the effective assembly area is small. The greater the overlap of the assembly areas, the greater the dexterity of the robot arm and, conversely, the weaker. So the length of the robot is selected L during modelingr＝A′B′＝C′D′＝E’F’＝G’H’＝Ri. The line spacing is considered in the vertical y direction during modeling, and the safety layout requirement is met, soThe quadrilateral width of the robot optimized entity is Wr＝E′H′＝F′G′＝2R0，R0Is the base radius of the robot. RiIs the maximum radius of motion in the actual assembly of the robot. The optimized entity of the robot is a quadrilateral E 'F' G 'H' as shown in FIG. 4.
Special-shaped machine tool MPAnd MqThe shaded irregularities shown in fig. 4 represent a shaped contact area of the irregular device with the ground. Regarding the irregular shadow area as the effective area for the irregular equipment, taking half r of the longest diagonal lineiDrawing a circle by taking the radius and the center of the equipment as the center of the circle, and taking the circumscribed rectangle of the circle as a bounding box, namely the optimization model of the equipment with the irregular contact surface.
And 2, optimizing the model of the safe assembly line. The layout design assumptions for the optimization model are as follows: physical information is known for all equipment, unit distance between equipment and unit material handling costs are determined, assembly requirements are known, and process routes for each product are known. The center points of the devices in the same row are on the same horizontal line. The assembly line layout diagram is shown in fig. 4, which includes the representation and association of each parameter involved in the mathematical model. Wherein M is1For laying out the devices l in the lower left corner, with the smallest abscissa x1And a minimum ordinate y1The apparatus of (1). MrFor laying out the equipment r at the upper right corner, with the maximum abscissa xrAnd the maximum ordinate yr. The safety layout model is crucial to obtaining a safety layout plan. Firstly, establishing a modeling rule for equipment needing layout planning on an assembly line, and then taking the minimum logistics cost and the minimum actual occupied area as optimization targets. General constraint conditions ensure that layout equipment is not overlapped, and the basic property of layout safety is ensured.
Figure BDA0002831247340000061
Safety indexes are restricted, so that the safety of the layout equipment in height is further guaranteed, and three-dimensional safety layout is realized. And finally, outputting the safety layout model.
When the initial layout of the assembly production line is carried out, from the practical engineering point of view, if the maximum bounding box of the robot is used for carrying out multi-objective optimization and outputting a layout scheme. The distance between the robots is too large, so that the cooperative assembly cannot be realized, and the cost and the time cost are increased undoubtedly in the secondary adjustment. Not only waste of layout space is caused, but also the smooth proceeding of the assembly task is influenced. If the base of the robot is used for multi-objective optimization and outputting a layout scheme. The base center point is too close, so that the possibility of unsafe collision is increased, and the problem of small cooperative assembly area is caused. From an algorithmic implementation perspective, the inconsistency of the physical input shapes is not easy for the design of encoding and decoding. Therefore, the optimization entities are all quadrilateral.
And establishing a layout model with the logistics cost LC and the actual floor area alpha minimum as optimization targets. And when the minimum value is simultaneously taken during multi-objective optimization, the optimization effect is better, the logistics cost LC and the actual floor area alpha are obtained, and the mathematical expression is shown as the formula (3).
Figure BDA0002831247340000071
Figure BDA0002831247340000072
Wherein: dij＝|xi-xj|+|yi-yj|，Ld＝(Lr+Ll)/2+(xr-xl)，Wd＝(Wr+Wl)/2+(yr-yl) N is the number of devices to be arranged in the workshop, MiAnd numbering i e {1, 2., n } for the equipment to be laid out. CijUnit material handling cost per unit distance from equipment i to equipment j, DijDistance from device i to device j; pijThe material transfer volume from equipment i to equipment j is planned for period t. LC is the cost of the material flow (in ten thousand yuan), and alpha is the occupied area. L is the length of the layout-able area (in meters), W is the width of the layout-able area (in meters), LiIs the length of the device i (in meters), WiIs the width of the device i (in meters). A is the width of the channel between the devicesIn meters, A0The width (in meters) of the first row of devices and the nearest edge of the placeable space. m is the number of rows of the layout output scheme. RiThe maximum working radius of the device. R0Is the base radius of the robot. x is the number ofiIs the physical abscissa, y, of the center point of the device iiIs the physical ordinate, x, of the center point of the device ijIs the physical abscissa, y, of the center point of the device jjIs the physical ordinate of the center point of the device j, δiIs the net distance, L, between device i and device jdIs the maximum abscissa x of the layoutrWith the smallest abscissa xlA difference in distance of WdAs the maximum ordinate y of the layoutrWith the smallest ordinate ylThe distance difference of (2). Wherein M islFor laying out the devices l in the lower left corner, with the smallest abscissa xlAnd a minimum ordinate ylThe apparatus of (1). MrFor laying out the equipment r at the upper right corner, with the maximum abscissa xrAnd the maximum ordinate yr。
The constraint conditions are classified into three types, namely machine tools and machine tools, machine tools and robots, and robots.
And (3) common constraint: two kinds of equipment with context association cannot be arranged in rows; the devices are not overlapped; and space boundary constraint, wherein the sum of the areas of all the devices, the minimum safety distance and the channel width is smaller than the total area capable of being distributed, and the total length and the total width of a workshop distribution area are reasonably not exceeded.
Customizing constraint: set as S for the constraint distance of the robot from other devicesir. Constraint condition S between robotsrrThe task requirement of cooperative assembly exists between the two robots. It is necessary to ensure that the two robots coordinate the assembly area SrrIncluding the workpiece dimension WS. It is necessary to ensure that the two robots coordinate the assembly area SrrIs larger than the workpiece dimension WS. Mathematical expression of the width of the cooperative assembly area: srr＝WS×βrr. Beta. because of the high dexterity requirements of the robot for cooperative assemblyrrValue ratio of (B)mrIs large. Constraint condition S between robot and machine tool or stationmrMachine for drillingThe robot has the task requirement of grabbing workpieces in a machine tool, and the mathematical expression is Smr＝WS×βmr，βmrThe value is small, and the workpiece can be successfully grabbed. Beta is aijFor safety factor, it is a constant, empirical range (1, 1.5). When the beta value is larger, the assembly work is more complicated, and the cooperation flexibility of the two robots is higher; as the beta value is smaller, the assembly work is simpler, and the dexterity of the two robots to cooperate is weaker.
And 3, combining algorithm of multi-objective optimization. The improved bonding algorithm flow chart is shown in fig. 5. The core idea is to generate a preliminary solution set and a solution set verification screening. Firstly, introducing a differential optimization strategy to cross and mutate the space between the devices based on an NSGA-II algorithm to generate a next generation population, and finishing population merging. The number of the differential optimization strategy references and the number of the genetic parents are large, the global search capability is effectively improved, and the situation that the local optimization is trapped is avoided. The fusion of the algorithm can effectively reduce the number of repeated individuals in the pareto solution set and enhance the convergence and the distribution of the NSGA-II method. And then, carrying out safety index judgment on the output initial optimization solution set, determining an unsafe layout scheme through collision detection, and carrying out safety distance adjustment to ensure that the output optimal solution set is a safe layout scheme. Redefining layout optimization problem and optimization target for optimization problem type of continuous parameters of layout design, and defining chromosome sequence as [ { M ] by real number coding1，...，MI，...，Mm，...，Mj，...，Mn}，{δ1，...，δI，...，δm，...，δj，...，δn-1}]。MiThe set of (1) is the placement sequence of the devices, δiIs a sequence of inter-device spacings. The layout adopts an automatic line feed mode, so that the layout of the equipment is ensured to be within the width W of the layout area, namely A0+ a (m-1) + W/2 > 0, when f (p) is equal to T, T being a constant. The fitness value of the non-dominant ordering within the population is stored for a chroma _ obj _ record [ p ]][i]In (1). The logistics cost F objective function is the minimum value, the area utilization rate alpha objective function is the maximum value, so the T is subtracted from the minimum target adaptive value, the T is added to the maximum target adaptive value, unqualified chromosomes can be eliminated, and the method is ensuredLeaving good quality chromosomes. Solving formula (4) of the coordinates of the center point of the equipment:
xj＝xi+(Li+Lj)/2+δij
yj＝yi(m-1)×A+A0 (4)
2) cross variation
The arrangement sequence of the equipment is optimized by partially mapping the double-point intersection. Spacing of devices by DE strategy deltaijAnd (5) performing cross mutation. And a differential optimization strategy is introduced, so that the convergence speed of the algorithm is accelerated, and the diversity of the algorithm solution set is ensured. The optimizing capability and the searching efficiency of the differential evolution algorithm are mainly determined by three core parameters, namely a scaling factor F and cross mutation operators CR and M, which are population scales. The boundary is [0.1, 1.8 ]]And judging the boundary to avoid crossing the boundary. Abandoning the out-of-range operator and regenerating.
And 4, quantifying safety indexes. The safety index is the trigger button setting of collision detection
Figure BDA0002831247340000095
Is a condition for judging the robot assembly state. When the safety index reaches the critical triggering condition, collision detection is carried out through point cloud calculation, the quantitative expression of the safety index is shown as (5), and the parameters are shown as fig. 4. The collision detection level is determined according to the assembly task and the shape of the layout element entity. When in use
Figure BDA0002831247340000096
In the process, the robot has no cooperative assembly and only needs to carry out rough detection; when in use
Figure BDA0002831247340000094
In time, the robot is cooperatively assembled or the robot takes a workpiece in a machine tool, and fine collision detection is required.
Figure BDA0002831247340000091
Figure BDA0002831247340000092
The safety level is the product of a safety factor and the equipment interval, the safety factor is set to be FS (factor of safety), the shape condition of the layout optimization entity is reflected, and the quantitative expression is shown as (6). The greater the safety factor, the more irregular the layout entity, and the higher the level of the corresponding required collision detection and the greater the accuracy.
FSiOptimization model area/actual area is more than or equal to 1 (6)
Step 5 triggers three-dimensional collision detection. As can be known from safe layout modeling, the layout object is horizontal to a world coordinate system, an AABB method is used for rough collision detection, and fine tasks such as irregular equipment cooperative assembly are performed, so that the AABB bounding box collision detection obviously cannot meet the precision requirement and needs to be performed finely. When the mechanical arm is cooperatively assembled or used for grabbing complex curved surface objects, a grid collision detection method can be used, and when the mechanical arm is used for grabbing concave objects, a implicit function collision detection method of the complex curved surface can be used. Can prevent the phenomenon of body element point leakage monitoring. the parameterized expression of the scanning implicit function curved surface at the time t is as follows:
Figure BDA0002831247340000093
Tt(T) a transformation matrix representing the translation of the tool surface detection voxel from an initial time to time T, Tx(t) a transformation matrix representing the rotation of the tool surface detection voxel from the initial time to time t, Ms(S) represents a set of tool surface detection voxels. According to the Gaussian theorem, the n-time complex coefficient polynomial equation has only n roots, whether the scanning body collides with the workpiece is judged according to the equation solution condition, the number of the roots can determine the collision times, and if the scanning body does not collide with the workpiece, the collision does not occur.

Claims (8)

1. A design method for multi-robot cooperative assembly line safety layout is characterized by comprising the following steps:
constructing a point cloud model of an assembly range according to the operation state and the operation range of the simulation robot, and constructing a regular robot model from an irregular robot model through a modeling rule;
an assembly line optimization model is formulated through a regular robot model, and modeling objects comprise the regular robot model, a machine tool model and an assembly station model;
taking an assembly line optimization model as the input of a multi-objective optimization combination algorithm to obtain an initial safety layout scheme, bringing the initial safety layout scheme into a safety index and judging whether the safety index meets a threshold value, if so, carrying out fine collision detection on the multiple robots, otherwise, carrying out rough collision detection on the multiple robots;
and judging whether the multiple robots collide according to the detection result, if so, giving up the initial safety layout scheme, and otherwise, taking the initial safety layout scheme as a final safety layout scheme.
2. The method as claimed in claim 1, wherein the method for designing the multi-robot collaborative assembly line safety layout is characterized in that the method for approximating an irregular robot model as a regular robot model by modeling rules specifically comprises:
and (3) building a robot simulation model, simulating the actual physical process of a robot working space by using a Monte Carlo method, building a working space point cloud model, and optimizing the robot model with quadrilateral, namely regular entities.
3. The method of claim 1 or 2, wherein the assembly line optimization model comprises: the system comprises an entity of a regular robot model or a machine tool model or an assembly station model, a constraint condition, an objective function and a decision variable parameter.
4. The method as claimed in claim 3, wherein the objective function is a minimum logistics cost and a minimum floor space, and the expression is:
Figure FDA0002831247330000011
Figure FDA0002831247330000012
wherein: dij＝|xi-xj|+|yi-yj|，Ld＝(Lr+Ll)/2+(xr-xl)，Wd＝(Wr+Wl)/2+(yr-yl) N is the number of the devices to be laid out in the workshop, t is time, i, j is the serial number i, j is the device to be laid out, and the serial number i, j belongs to {1, 2, …, n }; cijUnit material handling cost per unit distance from equipment i to equipment j, DijDistance from device i to device j; pijFor the planned period t, the material transfer amount from equipment i to equipment j, LC is the logistics cost, alpha is the floor area, LdIs the length of the device d, WdIs the width, x, of the device diIs the physical abscissa, y, of the center point of the device iiIs the physical ordinate, x, of the center point of the device ijIs the physical abscissa, y, of the center point of the device jjIs the physical ordinate of the center point of the device j, LdIs the maximum abscissa x of the layoutrWith the smallest abscissa xlA difference in distance of WdAs the maximum ordinate y of the layoutrWith the smallest ordinate ylThe distance difference of (2).
5. The method of claim 3, wherein the decision variable parameters comprise: equipment clearance, safety index, equipment quantity.
6. The method as claimed in claim 1, wherein the multi-objective optimization and combination algorithm is specifically:
firstly, optimizing the serial number of the assembly equipment by using a genetic algorithm, then optimizing the equipment spacing by using a heuristic random search algorithm based on a differential optimization strategy, comparing constraint conditions corresponding to the output equipment spacing with safety indexes to verify the safety of the entity layout, verifying the feasibility of the entity layout by three-dimensional collision detection, and outputting an initial safety layout scheme.
7. The method as claimed in claim 1, wherein the safety index is a safety index
Figure FDA0002831247330000021
Comprises the following steps:
Figure FDA0002831247330000022
wherein: riIs the maximum working radius, R, of the device ijIs the maximum working radius, δ, of the device jiFor a clear spacing of the apparatus, SirDistance between equipment and robot, FSiThe safety factor is.
8. The method of claim 1, wherein the coarse collision detection is AABB level bounding box collision detection; the accurate collision detection is a mesh collision detection method and a implicit function collision detection method of a complex curved surface.
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