CN113885511B - Track optimization method of flexible assembly robot of circuit breaker - Google Patents
Track optimization method of flexible assembly robot of circuit breaker Download PDFInfo
- Publication number
- CN113885511B CN113885511B CN202111233563.1A CN202111233563A CN113885511B CN 113885511 B CN113885511 B CN 113885511B CN 202111233563 A CN202111233563 A CN 202111233563A CN 113885511 B CN113885511 B CN 113885511B
- Authority
- CN
- China
- Prior art keywords
- circuit breaker
- joint
- flexible
- assembly robot
- interpolation
- 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.)
- Active
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 117
- 238000000034 method Methods 0.000 title claims abstract description 59
- 239000002245 particle Substances 0.000 claims abstract description 102
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 50
- 230000001133 acceleration Effects 0.000 claims abstract description 19
- 230000036461 convulsion Effects 0.000 claims abstract description 11
- 230000007246 mechanism Effects 0.000 claims description 62
- 238000013178 mathematical model Methods 0.000 claims description 7
- 230000003044 adaptive effect Effects 0.000 claims description 5
- 230000006835 compression Effects 0.000 claims description 5
- 238000007906 compression Methods 0.000 claims description 5
- 230000006870 function Effects 0.000 description 52
- 230000008569 process Effects 0.000 description 20
- 238000010586 diagram Methods 0.000 description 14
- 238000004364 calculation method Methods 0.000 description 11
- 230000000739 chaotic effect Effects 0.000 description 10
- 230000002068 genetic effect Effects 0.000 description 9
- 238000004519 manufacturing process Methods 0.000 description 8
- 230000000694 effects Effects 0.000 description 6
- 210000000078 claw Anatomy 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000006872 improvement Effects 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000011218 segmentation Effects 0.000 description 2
- 241000251468 Actinopterygii Species 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000000052 comparative effect Effects 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 239000012636 effector Substances 0.000 description 1
- 238000005562 fading Methods 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000003801 milling Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000035939 shock Effects 0.000 description 1
- 230000009469 supplementation Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Manipulator (AREA)
Abstract
The invention relates to a track optimization method of a flexible assembly robot of a circuit breaker, which comprises the following steps: interpolating the motion trail of the flexible assembly robot of the circuit breaker by adopting a quintic polynomial to obtain the motion trail of the flexible assembly robot of the circuit breaker; the fifth order polynomial is a function of the position, the movement speed, the acceleration and the jerk of each joint of the flexible assembly robot of the circuit breaker with respect to time; constructing an objective function based on the fifth-order polynomial; and optimizing an objective function by adopting an improved particle swarm algorithm, optimizing initialized particles which do not meet a first constraint condition, after calculating the fitness value of the particle swarm and determining the current individual extremum and the population extremum, optimizing the particles which do not meet a second constraint condition on the basis of the fitness function of the switch, and recalculating the fitness value of the particle swarm to determine the current individual extremum and the population extremum until the fitness value of the particle swarm meets the second constraint condition, thereby obtaining the total movement time after the optimization of each joint. The invention improves the track optimization speed and optimization precision.
Description
Technical Field
The invention relates to the technical field of circuit breaker assembly and manufacturing, in particular to a track optimization method of a flexible circuit breaker assembly robot.
Background
The circuit breaker is an important electrical device in a power distribution network, and has wide application in the fields of industry, civilian use and the like. The circuit breaker has more parts and more complex components, the production of the current circuit breaker mainly uses manpower, the rigidity in an automatic assembly unit is higher, and the flexible assembly process is lost, so that the assembly process is complicated, and the production and assembly efficiency and the product reliability are restricted. The industrial robot has the advantages of high working efficiency, stability, reliability and the like, is increasingly applied in the manufacturing industry, combines the industrial robot with the automatic manufacturing of the circuit breaker, researches a novel circuit breaker assembly method and system taking flexibility as a main characteristic, and has important significance for improving the product performance and the production efficiency.
In the practical application of the industrial robot, the working efficiency and the reliability are important indexes for measuring the performance of the robot, and the improvement of the working efficiency and the reliability of the industrial robot is a critical problem which needs to be solved in the application of the industrial robot. The track planning is a fundamental problem in robot application, and mainly aims to define a smooth optimal motion track passing through a working task point based on corresponding calculation rules and meeting boundary constraint conditions under the condition of a given working task point, so as to determine the working efficiency and the motion performance of the robot.
At present, more researches on robot track planning are carried out, and the researches mainly focus on two aspects of polynomial track interpolation and track algorithm optimization. In the aspect of polynomial track interpolation, an interpolation mode of carrying out track interpolation by adopting a cubic polynomial takes joint position and speed as constraint conditions, the method is essentially to carry out track planning by adopting an off-line mode and then track tracking by adopting an on-line real-time mode, and has simple structure and convenient application, but the method does not consider the constraints of acceleration, joint moment and the like of the operation of a robot joint, and can cause the problems of abrupt acceleration, obvious shock impact of a joint of a mechanical arm and the like. The interpolation method for carrying out track interpolation by adopting the sextuple polynomial can solve the problem of discontinuous motion process in low-order polynomial segmentation interpolation, avoid the problems of abrupt acceleration, mechanical arm operation impact and the like, but the polynomial has over-high order, complex calculation and easy cause the phenomenon of 'Dragon' of interpolation results deviating from an original function due to over-high order. The method for carrying out robot interpolation by using NURBS curves has the characteristics of continuous derivative, good segmentation processing effect, strong local support and the like, but when the robot path is complex, the method also faces the problems of complex process and large calculation amount. In the aspect of track algorithm optimization, more researches are conducted on the aspect of artificial intelligence algorithm, for example, the method for planning the optimal time track of the robot by adopting a genetic algorithm has low calculation requirement and good universality, and is more advantageous in the aspect of processing the problems of fewer optimization targets and constraint conditions. The locus planning of the milling cutter shaft at the side of the conical cutter is researched by adopting a standard PSO algorithm, the standard PSO has advantages in the aspects of optimization range, robustness and expansibility, and the searching mode is a random searching based on the whole population, so that the local optimum trap is not easy to fall into. The chaotic sequence is introduced into a PSO algorithm, and the optimization algorithm based on the chaotic theory has better ergodic performance, and can adaptively realize track optimization according to random given time intervals, so that the time optimal track optimization of the robot under the dynamic constraint condition is realized, but the algorithm performance of the robot can face the problems of fading and reduced optimization precision along with the increase of the optimizing space.
Disclosure of Invention
The invention aims to provide a track optimization method of a flexible assembly robot of a circuit breaker, which improves track optimization speed and optimization precision.
In order to achieve the above object, the present invention provides the following solutions:
a track optimization method of a flexible assembly robot of a circuit breaker comprises the following steps:
obtaining D-H parameters of a connecting rod structure formed by joints of the flexible assembly robot of the circuit breaker;
constructing a mathematical model of the flexible assembly robot of the circuit breaker according to the D-H parameters;
interpolating a motion track of the flexible assembly robot of the circuit breaker by adopting a quintic polynomial based on the mathematical model to obtain the motion track of the flexible assembly robot of the circuit breaker; the fifth order polynomial is a function of the position, the movement speed, the acceleration and the jerk of each joint of the flexible assembly robot of the circuit breaker with respect to time;
based on the penta polynomial, constructing an objective function by taking the motion speed as a constraint condition and taking the total motion time of each joint as an optimization target;
optimizing the objective function by adopting an improved particle swarm algorithm, judging the interpolation time of initialized particles based on a first constraint condition, optimizing the initialized particles which do not meet the first constraint condition, after calculating the fitness value of the particle swarm and determining the current individual extremum and group extremum, optimizing the particles which do not meet a second constraint condition based on the switch-type fitness function, and recalculating the fitness value of the particle swarm to determine the current individual extremum and group extremum until the fitness value of the particle swarm meets the second constraint condition, so as to obtain the total motion time of each joint after optimization;
and determining the motion trail of the flexible assembly robot of the circuit breaker according to the total motion time of each joint after optimization and the quintic polynomial.
Optionally, the fitness function of the switch type is expressed as:
f 1 (t)＝min(t 1 +t 2 +t 3 )；
wherein f fitness (t) is a fitness function of a switch type, t 1 Interpolation time, t, of a first segment of flexible assembly robot for said circuit breaker 2 Interpolation time, t, of the second segment of flexible assembly robot for said circuit breaker 3 Interpolation time, e, of the third segment of flexible assembly robot for said circuit breaker 1 E, for the interpolation time when the first interpolation time does not meet the second constraint condition 2 E, for the interpolation time when the second interpolation time does not meet the second constraint condition 3 For the interpolation time when the third interpolation time does not meet the second constraint condition, V j1 (t) velocity, V, during the first interpolation time for the j-th joint j2 (t) is the velocity, V, of the j-th joint during the second interpolation time j3 (t) is the speed, V, of the j-th joint during the third interpolation time max Is the maximum constraint speed.
Optionally, the particle swarm speed update formula in the improved particle swarm algorithm is:
wherein,indicating updated particle group velocity, +.>Represents the particle group velocity before update, k represents the current iteration number, +.>Represents the individual extremum after the kth iteration, < ->Population extremum after the kth iteration, +.>Representing particle position after the kth iteration，/> For the adaptive compression factor, iterofcur is the current iteration number, NGer is the total iteration number, μ is a positive integer, e is a positive integer, ω is a weight, c 1 As a first weight factor, c 2 Is a second weight factor, r 1 For the first random number sum r 2 Is a second random number, r 1 And r 2 The value ranges of the (E) are all 0,1]。
Alternatively, NGer is 100, μ is 8,e is 10, ω is 0.5, c 1 2, c 2 2.
Optionally, the fifth degree polynomial is expressed as:
wherein h is j1 (t) represents the 1 st segment interpolation function of the j-th joint, h j2 (t) represents the 2 nd interpolation function of the j-th joint, h j3 (t) represents the 3 rd-stage interpolation function of the j-th joint, j is the joint number, j ε {1,2, …,6}, a j1i The ith coefficient, a, representing the jth joint trajectory, segment 1 interpolation function j2i The ith coefficient, a, representing the jth joint trajectory, segment 2 interpolation function j3i The ith coefficient, t, representing the jth joint trajectory, 3 rd segment interpolation function 1 The interpolation time, t, of the interpolation function of the 1 st segment 2 Interpolation time, t, representing the interpolation function of segment 2 3 The interpolation time of the interpolation function of paragraph 3 is indicated.
Optionally, the track optimization method of the flexible assembly robot of the circuit breaker is applied to a circuit breaker part posture adjustment device, and the circuit breaker part posture adjustment device comprises a six-axis robot, a flexible clamping jaw, a circuit breaker assembly platform, a loading tray, a positioning carrier and an auxiliary adjustment mechanism;
the base of the six-axis robot is fixed on the circuit breaker assembly platform, the execution end of the six-axis robot is connected with a plurality of flexible clamping jaws, and the feeding tray, the positioning carrier and the auxiliary adjusting mechanism are all arranged on the circuit breaker assembly platform;
the feeding tray is used for placing parts to be assembled; the positioning carrier is used for placing the part to be assembled into a target posture; the auxiliary adjusting mechanism is used for adjusting the pose of the part to be assembled, which is placed on the auxiliary adjusting mechanism;
the flexible clamping jaw is used for adjusting the pose of the clamped part to be assembled; the flexible clamping jaw is further used for clamping the part to be assembled from the feeding tray to the positioning carrier, or clamping the part to be assembled from the feeding tray to the auxiliary adjusting mechanism, and further used for clamping the part to be assembled from the auxiliary adjusting mechanism to the positioning carrier.
Optionally, the auxiliary adjusting mechanism comprises an arc extinguish chamber pose auxiliary adjusting mechanism, a magnetic assembly pose auxiliary adjusting mechanism, a magnetic yoke auxiliary adjusting mechanism, a handle auxiliary adjusting mechanism and a large U auxiliary adjusting mechanism.
Optionally, the part to be assembled includes an arc extinguishing chamber, a handle, a magnetic yoke, a magnetic assembly and a large U, and the flexible clamping jaw includes a first flexible clamping jaw, a second flexible clamping jaw, a third flexible clamping jaw and a fourth flexible clamping jaw.
Optionally, the D-H parameters include joint rotation angles, joint offsets, link lengths, and link torsion angles of joints of the circuit breaker flexible assembly robot.
Optionally, the flexible assembly robot for the circuit breaker is a ROKAEXB4 six-axis mechanical arm.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
according to the method, an improved particle swarm algorithm is adopted to optimize an objective function of a motion track, on one hand, interpolation time of initialized particles is selected and optimized based on constraint conditions, the problem of optimization efficiency reduction caused by most particles which do not meet constraint conditions due to randomness of particle initial value generation is avoided, so that rationality of random values of each initial particle is ensured, on the other hand, judgment and optimization are carried out on particles after the fitness value of a particle swarm is calculated and current individual extremum and group extremum are determined, and on the other hand, an optimization mechanism based on a switch fitness function can avoid occupation of particles which do not meet constraint conditions on follow-up iteration optimization time, so that follow-up iteration calculation efficiency is ensured, and track optimization speed and optimization precision are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of a track optimization method of a flexible assembly robot for a circuit breaker;
fig. 2 is a schematic diagram of the internal structure of the circuit breaker;
fig. 3 is a schematic diagram of various parts of the circuit breaker to be assembled;
FIG. 4 is a schematic diagram of a circuit breaker part attitude adjustment apparatus of the present invention;
FIG. 5 is a schematic view of the structure of the flexible gripper of the robot of the present invention;
FIG. 6 is a schematic diagram of an auxiliary adjustment mechanism according to the present invention;
fig. 7 is a schematic view of a flexible assembly process of various circuit breaker components of the present invention;
FIG. 8 is a schematic view of a positioning carrier for final placement of parts according to the present invention;
FIG. 9 is a schematic view of a robot link structure according to the present invention;
FIG. 10 is a schematic diagram of a mathematical model of a robot of the present invention;
FIG. 11 is a schematic diagram of a standard PSO optimization flow;
FIG. 12 is a schematic diagram of an improved PSO optimization flow in accordance with the present invention;
FIG. 13 is a schematic view of a robot segment job path of the present invention;
FIG. 14 is a plot of pre-optimization position of the present invention;
FIG. 15 is a graph of velocity profile prior to optimization in accordance with the present invention;
FIG. 16 is a graph of pre-optimized acceleration of the present invention;
FIG. 17 is a graph of pre-optimization jerk curve of the present invention;
FIG. 18 is a comparative schematic diagram of iterative process before and after improvement of PSO algorithm of the present invention;
FIG. 19 is a plot of the position after optimization in accordance with the present invention;
FIG. 20 is a graph of velocity after optimization in accordance with the present invention;
FIG. 21 is an optimized acceleration profile of the present invention;
FIG. 22 is a graph of jerk after optimization in accordance with the present invention;
fig. 23 is a schematic diagram of a robot motion trajectory according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a track optimization method of a flexible assembly robot of a circuit breaker, which improves track optimization speed and optimization precision.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Fig. 1 is a schematic flow chart of a track optimization method of a flexible assembly robot of a circuit breaker, as shown in fig. 1, the track optimization method of the flexible assembly robot of the circuit breaker comprises the following steps:
step 101: D-H parameters of a connecting rod structure formed by joints of the flexible assembly robot of the circuit breaker are obtained.
Step 102: and constructing a mathematical model of the flexible assembly robot of the circuit breaker according to the D-H parameters.
Step 103: interpolating the motion trail of the flexible assembly robot of the circuit breaker by adopting a quintic polynomial based on a mathematical model to obtain the motion trail of the flexible assembly robot of the circuit breaker; the fifth order polynomial is a function of the position, the movement speed, the acceleration and the jerk of each joint of the flexible assembly robot of the circuit breaker with respect to time.
Step 104: based on the fifth-order polynomial, the objective function is constructed by taking the movement speed as a constraint condition and taking the total movement time of each joint as an optimization target.
Step 105: optimizing the objective function by adopting an improved particle swarm algorithm, judging the interpolation time of the initialized particles based on a first constraint condition, optimizing the initialized particles which do not accord with the first constraint condition, after calculating the fitness value of the particle swarm and determining the current individual extremum and the population extremum, optimizing the particles which do not accord with a second constraint condition based on the switch-type fitness function, and recalculating the fitness value of the particle swarm to determine the current individual extremum and the population extremum until the fitness value of the particle swarm accords with the second constraint condition, thereby obtaining the total motion time after the optimization of each joint.
The first constraint condition is specifically a time constraint condition, and is used to optimize whether the interpolation time of the initial particle meets a normal time value.
Optimizing the initialization particles which do not meet the first constraint condition specifically comprises the following steps: after the interpolation time of the initialized particles is judged based on the first constraint condition, if the initialized interpolation time of part of the particles does not accord with the time constraint condition, the initialized particles need to be removed, and after the initialized particles are removed, the particles with corresponding quantity need to be regenerated for supplementation. Such as: initializing 50 particles, judging that the interpolation time of 20 particles does not accord with the time constraint condition, removing, subsequently regenerating 20 particles to carry out constraint condition judgment until 20 particles accord with the condition exist, and finally obtaining 50 particles accord with the condition.
Step 106: and determining the motion trail of the flexible assembly robot of the circuit breaker according to the total motion time and the quintic polynomial after the optimization of each joint.
The fitness function of the switch is expressed as:
f 1 (t)＝min(t 1 +t 2 +t 3 )；
wherein f fitness (t) is a fitness function of a switch type, t 1 First period of interpolation time t of flexible assembly robot for circuit breaker 2 Second-stage interpolation time t of flexible assembly robot for circuit breaker 3 Interpolation time, e, of the third section of flexible assembly robot for circuit breaker 1 E, for the interpolation time when the first interpolation time does not meet the second constraint condition 2 E, for the interpolation time when the second interpolation time does not meet the second constraint condition 3 For the interpolation time when the third interpolation time does not meet the second constraint condition, V j1 (t) velocity, V, during the first interpolation time for the j-th joint j2 (t) is the velocity, V, of the j-th joint during the second interpolation time j3 (t) is the speed, V, of the j-th joint during the third interpolation time max Is the maximum constraint speed.
The particle swarm speed update formula in the improved particle swarm algorithm is as follows:
wherein,indicating updated particle group velocity, +.>Represents the particle group velocity before update, k represents the current iteration number, +.>Represents the individual extremum after the kth iteration, < ->Population extremum after the kth iteration, +.>Represents the particle position after the kth iteration, < >> For the adaptive compression factor, iterofcur is the current iteration number, the meaning of k is the same, NGer is the total iteration number, mu is a positive integer, e is a positive integer, omega is a weight, c 1 As a first weight factor, c 2 Is a second weight factor, r 1 For the first random number sum r 2 Is a second random number, r 1 And r 2 The value ranges of the (E) are all 0,1]。
The fifth degree polynomial is expressed as:
wherein h is j1 (t) represents the 1 st segment interpolation function of the j-th joint, h j2 (t) represents the 2 nd interpolation function of the j-th joint, h j3 (t) represents the 3 rd-stage interpolation function of the j-th joint, j is the joint number, j ε {1,2, …,6}, a j1i The ith coefficient, a, representing the jth joint trajectory, segment 1 interpolation function j2i The ith coefficient, a, representing the jth joint trajectory, segment 2 interpolation function j3i The ith coefficient, t, representing the jth joint trajectory, 3 rd segment interpolation function 1 The interpolation time of the interpolation function of segment 1 is represented,t 2 interpolation time, t, representing the interpolation function of segment 2 3 The interpolation time of the interpolation function of paragraph 3 is indicated. Under Cartesian system X ji The joint angle (position) is calculated for the space coordinates of the mechanical arm motion through inverse kinematics. The conditions are known: the known condition is the starting point X of each segment of the j-th joint j0 Intermediate point X j1 And X j2 Last point X j3 Acceleration and speed (generally taken as 0) of the position, start point and end point, and speed and acceleration between the path points are continuously equal. According to the known condition, the columns write a matrix of track coefficients that satisfies the condition, as shown in equation (2):
a＝A -1 *N(2)
in the above formula, a is a track curve coefficient solution, A is a polynomial matrix related to time t, 18 equations (a fifth order polynomial) are added, and N is related to X ji Is specifically shown as a matrix of a formula (3):
the track optimization method of the flexible assembly robot of the circuit breaker is applied to a gesture adjustment device of the circuit breaker part, and as shown in fig. 4, the gesture adjustment device of the circuit breaker part comprises a six-axis robot, a flexible clamping jaw, a circuit breaker assembly platform, a feeding tray, a positioning carrier and an auxiliary adjustment mechanism.
The circuit breaker has relatively complex component parts, the specific internal structure is shown in figure 2, the circuit breaker comprises a shell, a magnetic assembly, a handle, an arc-extinguishing chamber and other parts, and the handle, the large U, the magnetic yoke, the magnetic core, the magnetic assembly main body and the arc-extinguishing chamber are selected as assembly objects (figure 3) to realize flexible automatic assembly of 6 parts by a single robot.
The base of six robots is fixed on circuit breaker assembly platform, and a plurality of flexible clamping jaws are connected to six robots's execution end, and material loading tray, positioning carrier and supplementary guiding mechanism all set up on circuit breaker assembly platform.
The feeding tray is used for placing parts to be assembled; the positioning carrier is used for placing the part to be assembled to a target posture; the auxiliary adjusting mechanism is used for adjusting the pose of the part to be assembled placed on the auxiliary adjusting mechanism.
The flexible clamping jaw is used for adjusting the pose of the clamped part to be assembled; the flexible clamping jaw is also used for clamping the part to be assembled from the feeding tray to the positioning carrier or clamping the part to be assembled from the feeding tray to the auxiliary adjusting mechanism, and the flexible clamping jaw is also used for clamping the part to be assembled from the auxiliary adjusting mechanism to the positioning carrier.
Fig. 5 shows a designed flexible multi-jaw mechanism of the robot, 4 jaws with different strokes are designed in the mechanism according to different part sizes and assembly process requirements, and the strokes of jaw cylinders are respectively 11-17mm (first jaw), 16-22mm (second jaw), 3-9mm (third jaw) and 0-6mm (fourth jaw), so that the clamping range and the positioning accuracy are matched. In order to effectively clamp parts with different sizes and shapes, different sizes are designed at the tail ends of the claw hands, the clamping range of the magnetic yoke and the magnetic assembly main body is 1mm, the clamping range of a large U is 1.2mm, the clamping precision requirement of the type of parts is high, the tail ends of the claw hands are designed to be of an inverted trapezoid structure (the upper bottom is 6mm, the lower bottom is 1.5mm and the height is 9 mm), the tail ends of the other claw hands are designed to be rectangular (the height is 9mm and the width is 6 mm), and the clamping position width of the parts and the reliability during grabbing are ensured. The device is matched with the claw and also comprises an end effector connecting mechanism, a sliding block cylinder and a clamping jaw cylinder connecting structure. Through the flexible design of the multiple clamping jaws, the stroke range and the clamping width of the clamping jaws are matched with the sizes of the parts, so that the pose adjustment of different poses of various parts is realized.
The flexible assembly process of the internal parts of the circuit breaker is complex, in order to realize flexible assembly, an auxiliary pose adjusting mechanism shown in fig. 6 is designed, auxiliary cooperation in the assembly process is provided, and finally flexible assembly of the parts is realized.
The auxiliary adjusting mechanism comprises an arc extinguish chamber pose auxiliary adjusting mechanism, a magnetic assembly pose auxiliary adjusting mechanism, a magnetic yoke auxiliary adjusting mechanism, a handle auxiliary adjusting mechanism and a large U auxiliary adjusting mechanism.
The part to be assembled comprises an arc extinguishing chamber, a handle, a magnetic yoke, a magnetic assembly and a large U, wherein the flexible clamping jaw comprises a first flexible clamping jaw (clamping jaw I), a second flexible clamping jaw (clamping jaw II), a third flexible clamping jaw (clamping jaw III) and a fourth flexible clamping jaw (clamping jaw IV).
On the basis of the robot assembly frame and the corresponding mechanism shown in fig. 4-6, the invention provides a small-sized breaker flexible assembly method and a process as follows: the arc extinguishing chamber is adjusted in position by the cooperation of the clamping jaw I and the clamping jaw II, the magnetic yoke is adjusted in position by the cooperation of the clamping jaw IV and the clamping jaw II, the big U is adjusted in position by the clamping jaw IV, the magnetic assembly system is adjusted in position by the cooperation of the clamping jaw III and the clamping jaw IV, and the handle is adjusted in position by the cooperation of the clamping jaw III and the clamping jaw IV. Taking pose adjustment of the arc extinguishing chamber as an example, the flexible assembly process of the robot is described. The gesture adjustment of the arc extinguish chamber part under different gestures is mainly classified into three conditions, wherein a clamping jaw I and a clamping jaw II are needed in the gesture adjustment process, and the specific flow is shown in (a) of fig. 7. Under the condition of the gesture 1, the arc extinguish chamber is placed on the upper surface and the lower surface, and is directly placed in an arc extinguish chamber carrier by only clamping the arc extinguish chamber by using a clamping jaw II to rotate around a Z axis for a certain angle for adjustment; under the condition of the gesture 2, the arc-extinguishing chamber is placed at the front and the back, firstly clamped by a clamping jaw I, then is put into an arc-extinguishing chamber adjusting mechanism after being rotated anticlockwise around a Y axis and clockwise around a Z axis in a compound manner by a certain angle, and finally is put into an arc-extinguishing chamber carrier by a clamping jaw II; under the condition of the gesture 3, the arc-extinguishing chamber is placed on the left and right sides, the arc-extinguishing chamber is clamped by the first clamping jaw, then is placed in the arc-extinguishing chamber adjusting mechanism after rotating around the Y axis clockwise and Z axis anticlockwise in a compound mode for a certain angle, and finally is placed in the arc-extinguishing chamber carrier by the second clamping jaw.
In fig. 7, (b) is a schematic diagram of magnetic yoke posture adjustment, (c) is a schematic diagram of large U posture adjustment, (d) is a schematic diagram of magnetic component posture adjustment, (e) is a schematic diagram of handle posture adjustment, posture adjustment can be performed according to a method similar to that of the arc extinguishing chamber in fig. 7 (a), and finally different parts are placed in the part carrier in a specified posture, wherein the arc extinguishing chamber adjustment mechanism in fig. 7 is an arc extinguishing chamber posture auxiliary adjustment mechanism, the magnetic yoke adjustment mechanism is a magnetic yoke posture auxiliary adjustment mechanism, the large U adjustment mechanism is a large U posture auxiliary adjustment mechanism, and the handle adjustment mechanism is a handle posture auxiliary adjustment mechanism. The final part-determined pose carrier is shown in fig. 8.
The D-H parameters comprise joint rotation angles, joint offset and connecting rods of all joints of the flexible assembly robot for the circuit breakerLength and link torsion angle. The flexible assembly robot of the circuit breaker is a ROKAEXB4 six-axis mechanical arm, and the connecting rod structure of the flexible assembly robot of the circuit breaker is shown in figure 9. The D-H parameters of the flexible assembly robot for a circuit breaker are shown in Table 1 below, where θ i Represents the rotation angle, d, of the joint i i Represents the offset of joint i, a i Representing the length of the connecting rod of joint i, alpha i The link torsion angle of joint i is indicated.
TABLE 1D-H parameter Table
For a six-axis mechanical arm, the simplified kinematic model has 7 coordinate systems, and among the 7 coordinate systems: the six coordinate systems (x 0, y0, z 0) - (x 5, y5, z 5) are coordinate systems of six joint rotation axes of the mechanical arm, and x6, y6, z6 are coordinate systems of the end clamping jaw, as shown in fig. 9, the coordinate systems are first determined by the base coordinate systems x0, y0, z0, and then the rest 1,2, 3, 4, 5, 6 coordinate systems are sequentially deduced, and the purpose of the coordinate transformation relation between the joints of the mechanical arm is to be determined.
In order to ensure the running stability and the execution efficiency of the robot in the flexible assembly process of the miniature circuit breaker, the motion trail of the miniature circuit breaker needs to be optimally planned and designed. The particle swarm algorithm is a global evolution optimization algorithm based on swarm intelligence, and is an approximate simulation of the swarm predation and movement process of fish swarms and bird swarms. Assume that in a D-dimensional search space, a population x= (X) consisting of n particles 1 ,X 2 ,…,X n ) Wherein the ith particle is represented as a vector X in D-dimension i ＝(x i1 ,x i2 ,…,x iD ) T Representing the position of the ith particle in the D-dimensional search space and also representing one potential solution to the problem. The position X of each particle can be calculated according to the objective function i Corresponding fitness value. The speed of the ith particle is V i ＝(V i1 ,V i2 ,…,V iD ) T Its individual extremum is P i ＝(P i1 ,P i2 ,…,P iD ) T Population extremum of population P g ＝(P g1 ,P g2 ,…,P gD ) T . In each iteration, the particle updates its own velocity and position through the individual extremum and population extremum, i.e.
Where ω is the inertial weight, d=1, 2, D, i=1, 2,..n, k is the current iteration number; v (V) id Is the particle velocity; c 1 And c 2 Non-negative constants, called acceleration factors, r 1 And r 2 Is distributed in [0,1 ]]Random number of interval, in order to prevent blind searching of particles, its searching range is set up, in which the positionBelonging to the interval [ -X max ,X ma x]Speed->Belonging to the interval [ -0.1.X max ,0.1·X max ]。
The key of optimizing the particle swarm optimization based on the quintic polynomial interpolation function is that the optimal time is obtained by selecting the independent variable of the particle optimizationThe invention is directly carried out at the time t to be optimized 1 、t 2 And t 3 The search space of the particle swarm is optimized, the search dimension of the particle swarm is reduced to 3 dimensions, and the derivation of complex mapping relations is avoided. Meanwhile, in order to enable the joint movement speed to converge as soon as possible, multiple optimization is adopted to carry out optimization control of the fitness function, and after the kinematic constraint is met, optimization iteration of time optimization is carried out, and the fitness function is determined as follows:
f(t)＝min(t 1 +t 2 +t 3 )(16)
equation (17) is a constraint of the objective function, in which V j1 、V j2 、V j3 And V max The real-time speed and the maximum limiting speed (maximum constraint) of the ith joint, respectively.
According to the method, a multiple optimizing mechanism is introduced into a PSO algorithm, a condition judging optimizing mechanism based on a switching function is provided in the multiple optimizing process, a corresponding switching function is established according to a particle fitness value result in the optimizing process, and the results are judged, compared and optimized according to the switching function, so that the computing efficiency is improved. The standard PSO optimization flow is shown in FIG. 11 below, and the improved PSO optimization flow of the present invention is shown in FIG. 12 below.
Fig. 11 is a detailed standard PSO optimization flow. The method comprises the steps of initializing particles, determining basic parameters, calculating fitness values of the particles through a fitness function after the initialization is finished, determining individual and group optimal values, updating the particles according to a speed and position formula of the particles after the extreme value determination is finished, judging the updating condition of the particles according to constraint conditions, carrying out subsequent iterative optimization if the constraint conditions are met, finally outputting an optimal solution, carrying out constraint condition optimization again if the constraint conditions are not met, carrying out calculation of the fitness values of the particles again until the constraint conditions are met, carrying out subsequent iterative optimization, and finally outputting the optimal solution.
Compared with the standard particle swarm optimization flow of FIG. 11, the invention introduces a multiple optimizing mechanism in the standard PSO algorithm, and simultaneously provides a condition judgment optimizing mechanism based on a switching function in the multiple optimizing process. As shown in FIG. 12, the invention adds two optimizations on the basis of the standard PSO flow to form a multiple optimizing mechanism. On the one hand, after the particle swarm is initialized and the basic parameters are determined, the interpolation time period of the initialized particles is subjected to good and bad selection based on constraint conditions, so that the problem of optimization efficiency reduction caused by the fact that the particle population which does not meet the constraint conditions is caused by the randomness of the initial particle value generation is avoided, and the rationality of random values of each initial particle is ensured; on the other hand, after calculating the fitness value of the particle swarm and determining the current individual extremum and the group extremum, the invention provides a switch-type fitness function and a rapid optimizing algorithm thereof, and the fitness function is defined as follows:
f 1 (t)＝min(t 1 +t 2 +t 3 )(19)
as shown in the formula (18), the switch-type fitness function provided by the invention performs segment selection of the particle fitness value by establishing condition selection. Equation (18) is the final objective function of the present invention, and if the speeds of three segments all satisfy the constraint of equation (17), f in equation (18) is directly performed 1 Calculating the fitness of (t), and then carrying out subsequent algorithm iteration; if the particle is at t 1 、t 2 、t 3 If any section of the three-section interpolation curve has a constraint condition that the function value of the curve does not meet the formula (17), then re-constraint optimization is performed based on the constraint condition of the formula (17), and the speed value of the section is reduced until f in the formula (18) is met on the basis of ensuring the stability of the interpolation curve 2 The requirement of (t). Formula (20) is f 2 The seven calculation cases of (t) introduce a time variable e under the condition of non-compliance aiming at the seven judgment cases of non-compliance with the constraint condition 1 、e 2 、e 3 And then, carrying out corresponding fitness value calculation. Combining the flow of fig. 12 and formulas (18) - (20), the switch fitness function mainly performs condition screening on the initial fitness value of the particles, and two types of particles are screened out through constraint condition judgment, one type is dominant particle, so that subsequent iterative optimization can be directly performed, and the efficiency is improved; the other type is a disadvantaged particle,the subsequent optimizing process is the same as the standard PSO, and the subsequent iterative optimization can be performed after constraint optimization is needed and the constraint condition is reached. The optimization mechanism based on the switch fitness function can avoid the occupation of particles which do not meet constraint conditions to the optimization time of subsequent iteration, so that the efficiency of the subsequent iteration calculation is ensured.
In terms of optimizing algorithm parameters, the number m of particle groups is set to be 50, the initial particle positions are random numbers of [0,5], and the maximum flying speed of particles is between [ -0.5,0.5 ]. Meanwhile, in order to adjust the weight of the PSO algorithm and ensure the global exploration capacity at the initial stage and the local searching capacity at the later stage of the algorithm, an adaptive compression factor is added in the PSO, and a particle swarm velocity update formula is shown in the following formula (21).
In the formula (22)For the adaptive compression factor, iterofcur is the current iteration number, NGer is the total iteration number 100, μ is a positive integer (set to 8), e is a positive integer 10, the weight ω is 0.5, the weight factor c 1 ＝2，c 2 ＝2，r 1 And r 2 Is [0,1]Is a random number of (a) in the memory.
As shown in fig. 13, the flexible assembly of the circuit breaker parts can be divided into four operating processes: the AB section indicates that the mechanical arm (flexible assembly robot for the circuit breaker) clamps the part to be assembled from an initial point to a feeding area (a feeding tray), the BC section indicates that the mechanical arm clamps the part to be assembled and puts the part to be assembled into an auxiliary adjusting mechanism, the CD section indicates that the mechanical arm adjusts the part to be assembled to a fixed posture on the auxiliary adjusting mechanism to wait for final assembly, and the DE section indicates that the mechanical arm puts the part to be assembled with the posture adjusted into a part carrier (a positioning carrier).
The invention takes the AB section running path as an example to carry out detailed process optimization verification. Interpolation points and interpolation time periods for the 6 joint angles of the AB segment are shown in tables 2 and 3 below, and the joint kinematics constraints are shown in table 4 below.
Table 2 AB section Joint angle interpolation points
Table 3 AB section Pre-optimization run time
Table 4 AB section kinematic constraints
The position, velocity, acceleration, jerk interpolation curves for 6 joints under the polynomial interpolation of the first 5 th order are shown in fig. 14, 15, 16, 17 below.
As can be seen from the position curve of FIG. 14, at T 1 、T 2 、T 3 Polynomial interpolation curve passes a given interpolation point X over a period of time 5 th order 1 、X 2 、X 3 、X 4 And the curve is continuous and smooth, which proves that the selected 5 th order polynomial interpolation is effective. As is clear from fig. 15, 16, and 17, the non-optimized velocity, acceleration, and jerk curves are continuous, and the peak point satisfies the constraint conditions of table 4, but there is room for approaching an extremum.
In order to perform comparison and verification on the optimization result, the proposed optimizing algorithm is respectively subjected to optimization and comparison with a standard PSO, a genetic algorithm and a chaotic algorithm, wherein the chaotic algorithm is respectively designed with 2 different optimizing spaces, and the comparison result is shown in figure 18. As can be seen from fig. 18, from the number of iterative convergence on the abscissa, the modified PSO converged to the optimum value at the 8 th time, the standard PSO converged to the optimum value at the 33 th time, the genetic algorithm converged to the optimum value at the 17 th time, and the first and second iterative optimizations of the chaotic algorithm converged to the optimum values at the 20 th and 56 th times, respectively. As shown by the analysis of the convergence times, the improved PSO has a convergence rate which is improved by about 76 percent compared with the standard PSO, 53 percent compared with the genetic algorithm, and 60 percent compared with the chaotic algorithm 1 (optimizing space 100); compared with the chaotic algorithm 2 (optimizing space 200), the convergence rate is improved by about 85.7 percent. According to analysis of optimal fitness values of ordinate iteration convergence, the improved PSO is improved by about 1.1% compared with a genetic algorithm in optimization effect, the optimization precision of the improved PSO and the genetic algorithm is approximately the same, the optimization precision of the improved PSO and the genetic algorithm is improved by about 4.4% compared with the optimization precision of a standard PSO, and the optimization effects of the improved PSO and the genetic algorithm are respectively improved by about 15.7% and 12.2% compared with the first optimization effect and the second optimization effect of a chaotic algorithm. Because the performance of the chaotic optimization algorithm is obviously reduced along with the increase of the optimizing space, the convergence speed and the convergence precision of the improved PSO are obviously improved compared with the improved PSO under the condition that the optimizing space is increased.
The running time of each joint of the AB segment optimized by the improved PSO algorithm is shown in the following table 5.
Table 5 AB section six joint optimization time comparison
Because each joint moves in the same time in the running process of the robot, each interpolation time in the table 5 takes the maximum value of each section of each joint, T 1 ＝max{T i1 }，T 2 ＝max{T i2 }，T 3 ＝max{T i3 T is }, then 1 ＝2.79s，T 2 ＝3.31s，T 3 =1.79 s. The comparison of the run time before the optimization of the AB segment in Table 3 shows that the total time of the AB segment is shortened from 12s to 7.89s and the time optimization is 34.25%.
From the optimized time: t (T) 1 ＝2.79s，T 2 ＝3.31s，T 3 The position, velocity, acceleration, jerk curve of the joints of the mechanical arm 6 after optimization is obtained by =1.79 s, as shown in fig. 19, 20, 21, 22 below.
As can be seen from comparing the curves of the joints before and after optimization, as shown in FIGS. 19, 20, 21 and 22, the joint is improvedPSO algorithm optimized joint curve still passes through interpolation point X 1 、X 2 、X 3 、X 4 And all the conditions meet the kinematic constraint conditions of the table 4, the curve extremum after optimization is more approximate to the constraint value, and the acceleration curve of the mechanical arm is continuous and stable. As can be seen in FIG. 21, the maximum acceleration of the six joints is-0.8 rad/s 2 Within the acceleration constraint range of table 3; as can be seen in FIG. 22, the jerk (joint impact of the associated robotic arm) of the six joints is a maximum of 1.51rad/s 3 Within the jerk constraint of table 3. After PSO optimization is improved, the mechanical arm shortens the assembly running time and improves the running efficiency.
The four-segment path final time optimization comparison after optimization by the improved PSO algorithm is shown in Table 6 below.
Four path times before and after the algorithm of table 6 is improved
From table 6 above, after the optimization of the improved PSO algorithm, the time of the AB segment was shortened by 2.13s, the time of the bc segment was shortened by 1.61s, the time of the cd segment was shortened by 1.95s, the time of the de segment was shortened by 1.21s, the overall time was shortened by 6.9s, and the overall efficiency was improved by 16.7%.
FIG. 23 shows a simulation curve of the robot joint space trajectory plan, and (a), (b), and (c) in FIG. 23 are improved and optimized T for the AB segment 1 、T 2 、T 3 A three-period time interpolation curve. As can be seen from (a), (b) and (c) in fig. 21, the AB interpolation track is continuous and reasonable, the mechanical arm operation curve is continuous and stable, and the operation time completely corresponds to the final optimized T 1 、T 2 、T 3 The time optimization control proves the effectiveness of the PSO algorithm time optimal track planning.
Aiming at the problems of high equipment rigidity and lack of flexible assembly technology in the current breaker production, the invention provides a flexible automatic assembly technology and method for a breaker robot, which can complete flexible automatic assembly of multiple parts such as an arc extinguishing chamber, a handle, a magnetic assembly and the like through a single robot. On the basis, the invention provides a circuit breaker flexible assembly robot track optimization method based on an improved particle swarm optimization, a multiple optimizing mechanism is introduced into a PSO algorithm, an acceleration optimizing method based on a switch fitness function is constructed, the result is judged, compared and optimized according to the switch function, and the calculation efficiency is improved. Simulation calculation and experimental verification are carried out respectively, and comparison results show that compared with PSO before improvement, the optimization rate is improved by about 76%, and the optimization accuracy is improved by about 4%; compared with a genetic algorithm, the optimization rate is improved by about 53%, and the precision is improved by 1.1%; compared with a chaotic algorithm, the optimization speed is improved by about 60% under the condition of optimizing the space 100, and the optimization accuracy is improved by about 15.7%; the optimization rate is improved by about 85.7% in the case of optimizing the space 200, and the optimization accuracy is improved by about 12.2%. The flexible assembly robot assembly process for the circuit breaker and the track optimization method thereof based on the improved particle swarm optimization are beneficial to realizing flexible assembly and production of the circuit breaker and are beneficial to improving the working performance and efficiency of the flexible assembly robot.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (9)
1. The track optimization method of the flexible assembly robot of the circuit breaker is characterized by comprising the following steps of:
obtaining D-H parameters of a connecting rod structure formed by joints of the flexible assembly robot of the circuit breaker;
constructing a mathematical model of the flexible assembly robot of the circuit breaker according to the D-H parameters;
interpolating a motion track of the flexible assembly robot of the circuit breaker by adopting a quintic polynomial based on the mathematical model to obtain the motion track of the flexible assembly robot of the circuit breaker; the fifth order polynomial is a function of the position, the movement speed, the acceleration and the jerk of each joint of the flexible assembly robot of the circuit breaker with respect to time;
based on the penta polynomial, constructing an objective function by taking the motion speed as a constraint condition and taking the total motion time of each joint as an optimization target;
optimizing the objective function by adopting an improved particle swarm algorithm, judging the interpolation time of initialized particles based on a first constraint condition, optimizing the initialized particles which do not meet the first constraint condition, after calculating the fitness value of the particle swarm and determining the current individual extremum and group extremum, optimizing the particles which do not meet a second constraint condition based on the switch-type fitness function, and recalculating the fitness value of the particle swarm to determine the current individual extremum and group extremum until the fitness value of the particle swarm meets the second constraint condition, so as to obtain the total motion time of each joint after optimization;
determining the motion trail of the flexible assembly robot of the circuit breaker according to the total motion time of each joint after optimization and the penta-order polynomial;
the fitness function of the switch type is expressed as:
f 1 (t)＝min(t 1 +t 2 +t 3 )；
wherein f fitness (t) is a fitness function of a switch type, t 1 First-stage plug of flexible assembly robot for circuit breakerValue time, t 2 Interpolation time, t, of the second segment of flexible assembly robot for said circuit breaker 3 Interpolation time, e, of the third segment of flexible assembly robot for said circuit breaker 1 E, for the interpolation time when the first interpolation time does not meet the second constraint condition 2 E, for the interpolation time when the second interpolation time does not meet the second constraint condition 3 For the interpolation time when the third interpolation time does not meet the second constraint condition, V j1 (t) velocity, V, during the first interpolation time for the j-th joint j2 (t) is the velocity, V, of the j-th joint during the second interpolation time j3 (t) is the speed, V, of the j-th joint during the third interpolation time max Is the maximum constraint speed.
2. The track optimization method of a flexible assembly robot for a circuit breaker according to claim 1, wherein the particle swarm speed update formula in the improved particle swarm algorithm is:
wherein,indicating updated particle group velocity, +.>Represents the particle group velocity before update, k represents the current iteration number, +.>Represents the individual extremum after the kth iteration, < ->Population extremum after the kth iteration, +.>Represents the particle position after the kth iteration, < >> For the adaptive compression factor, iterofcur is the current iteration number, NGer is the total iteration number, μ is a positive integer, e is a positive integer, ω is a weight, c 1 As a first weight factor, c 2 Is a second weight factor, r 1 For the first random number sum r 2 Is a second random number, r 1 And r 2 The value ranges of the (E) are all 0,1]。
3. The trajectory optimization method of a flexible assembly robot for circuit breakers of claim 2, wherein ngar is 100, μ is 8,e is 10, ω is 0.5, c 1 2, c 2 2.
4. The trajectory optimization method of a flexible assembly robot for a circuit breaker according to claim 1, wherein the fifth order polynomial is expressed as:
wherein h is j1 (t) represents the 1 st segment interpolation function of the j-th joint, h j2 (t) represents the 2 nd interpolation function of the j-th joint, h j3 (t) represents the 3 rd-stage interpolation function of the j-th joint, j is the joint number, j ε {1,2, …,6}, a j1i The ith coefficient, a, representing the jth joint trajectory, segment 1 interpolation function j2i The ith coefficient, a, representing the jth joint trajectory, segment 2 interpolation function j3i The ith coefficient, t, representing the jth joint trajectory, 3 rd segment interpolation function 1 The interpolation time, t, of the interpolation function of the 1 st segment 2 Interpolation time, t, representing the interpolation function of segment 2 3 Representing the 3 rd phase insertInterpolation time of the value function.
5. The track optimization method of the flexible assembly robot for the circuit breaker according to claim 1, wherein the track optimization method of the flexible assembly robot for the circuit breaker is applied to a gesture adjustment device of a circuit breaker part, and the gesture adjustment device of the circuit breaker part comprises a six-axis robot, a flexible clamping jaw, a circuit breaker assembly platform, a feeding tray, a positioning carrier and an auxiliary adjustment mechanism;
the base of the six-axis robot is fixed on the circuit breaker assembly platform, the execution end of the six-axis robot is connected with a plurality of flexible clamping jaws, and the feeding tray, the positioning carrier and the auxiliary adjusting mechanism are all arranged on the circuit breaker assembly platform;
the feeding tray is used for placing parts to be assembled; the positioning carrier is used for placing the part to be assembled into a target posture; the auxiliary adjusting mechanism is used for adjusting the pose of the part to be assembled, which is placed on the auxiliary adjusting mechanism;
the flexible clamping jaw is used for adjusting the pose of the clamped part to be assembled; the flexible clamping jaw is further used for clamping the part to be assembled from the feeding tray to the positioning carrier, or clamping the part to be assembled from the feeding tray to the auxiliary adjusting mechanism, and further used for clamping the part to be assembled from the auxiliary adjusting mechanism to the positioning carrier.
6. The trajectory optimization method of a flexible assembly robot for a circuit breaker of claim 5, wherein the auxiliary adjustment mechanism comprises an arc extinguishing chamber pose auxiliary adjustment mechanism, a magnet assembly pose auxiliary adjustment mechanism, a magnet yoke auxiliary adjustment mechanism, a handle auxiliary adjustment mechanism, and a large U auxiliary adjustment mechanism.
7. The trajectory optimization method of a flexible assembly robot for a circuit breaker of claim 5, wherein the part to be assembled comprises an arc chute, a handle, a yoke, a magnet assembly, and a large U, and the flexible jaws comprise a first flexible jaw, a second flexible jaw, a third flexible jaw, and a fourth flexible jaw.
8. The trajectory optimization method of a flexible assembly robot for a circuit breaker of claim 1, wherein the D-H parameters include joint rotation angles, joint offsets, link lengths, and link torsion angles of each joint of the flexible assembly robot for a circuit breaker.
9. The trajectory optimization method of a flexible assembly robot for a circuit breaker of claim 1, wherein the flexible assembly robot for a circuit breaker is a ROKAE XB4 six-axis mechanical arm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111233563.1A CN113885511B (en) | 2021-10-22 | 2021-10-22 | Track optimization method of flexible assembly robot of circuit breaker |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111233563.1A CN113885511B (en) | 2021-10-22 | 2021-10-22 | Track optimization method of flexible assembly robot of circuit breaker |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113885511A CN113885511A (en) | 2022-01-04 |
| CN113885511B true CN113885511B (en) | 2023-12-12 |
Family
ID=79004332
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111233563.1A Active CN113885511B (en) | 2021-10-22 | 2021-10-22 | Track optimization method of flexible assembly robot of circuit breaker |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113885511B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103020331A (en) * | 2012-10-15 | 2013-04-03 | 宁波大学 | Power consumption optimizing method for mixed polarity XNOR/OR circuit |
| CN107861511A (en) * | 2017-11-07 | 2018-03-30 | 河北科技大学 | A kind of control device and its control method for ground handling trolley |
| CN108646734A (en) * | 2018-04-27 | 2018-10-12 | 天津大学 | Nonlinear model predictive control method based on quanta particle swarm optimization |
| CN111152224A (en) * | 2020-01-10 | 2020-05-15 | 温州大学 | Dual-optimization robot motion trajectory optimization method |
| CN112070413A (en) * | 2020-09-17 | 2020-12-11 | 上海应用技术大学 | Reliability research method of traction power supply system based on small sample condition |
| EP3872520A2 (en) * | 2020-02-25 | 2021-09-01 | Origin Wireless, Inc. | Device and method for vehicle related wireless monitoring based on multipath channel information |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2479862A1 (en) * | 2011-01-25 | 2012-07-25 | Thomson Licensing | Management of the power supply of a local power transport grid |
| US10613489B2 (en) * | 2017-06-20 | 2020-04-07 | Baidu Usa Llc | Method and system for determining optimal coefficients of controllers for autonomous driving vehicles |
-
2021
- 2021-10-22 CN CN202111233563.1A patent/CN113885511B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103020331A (en) * | 2012-10-15 | 2013-04-03 | 宁波大学 | Power consumption optimizing method for mixed polarity XNOR/OR circuit |
| CN107861511A (en) * | 2017-11-07 | 2018-03-30 | 河北科技大学 | A kind of control device and its control method for ground handling trolley |
| CN108646734A (en) * | 2018-04-27 | 2018-10-12 | 天津大学 | Nonlinear model predictive control method based on quanta particle swarm optimization |
| CN111152224A (en) * | 2020-01-10 | 2020-05-15 | 温州大学 | Dual-optimization robot motion trajectory optimization method |
| EP3872520A2 (en) * | 2020-02-25 | 2021-09-01 | Origin Wireless, Inc. | Device and method for vehicle related wireless monitoring based on multipath channel information |
| CN112070413A (en) * | 2020-09-17 | 2020-12-11 | 上海应用技术大学 | Reliability research method of traction power supply system based on small sample condition |
Non-Patent Citations (8)
| Title |
|---|
| An improved particle filter for mobile robot localization based on particle swarm optimization;Zhang Qi-bin等;《Expert Systems with Applications》;全文 * |
| Mariani, V.C. ; 等.Differential evolution with dynamic adaptation of mutation factor applied to inverse heat transfer problem.《2010 IEEE Congress on Evolutionary Computation》.2010,全文. * |
| PSO combined with LPA for the detection of overlapping community;Qi Zhang等;《2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)》;全文 * |
| 农村电网变电站优化规划――基于GIS和改进混合遗传算法;刘清海;等;农机化研究(第02期);全文 * |
| 双重寻优的机器人轨迹规划及其在断路器柔性装配中的应用研究;舒亮，等;《温州大学学报(自然科学版)》;第42卷(第1期);全文 * |
| 基于AGA的时间最优机械臂轨迹规划算法;付荣;居鹤华;;计算机应用研究;第28卷(第09期);全文 * |
| 基于群智能求解带约束问题的时域鲁棒优化算法;黄元君;等;控制与决策;第35卷(第03期);全文 * |
| 基于量子行为粒子群算法的含分布式电源的配电网故障定位;刘煜;等;智慧电力(第08期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113885511A (en) | 2022-01-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108932216B (en) | Robot inverse kinematics solving method based on particle swarm optimization algorithm | |
| CN109676610B (en) | Circuit breaker assembly robot and method for realizing work track optimization | |
| Huang et al. | Particle swarm optimization for solving the inverse kinematics of 7-DOF robotic manipulators | |
| CN111070206B (en) | Station layout method for reducing robot motion energy consumption | |
| WO2021184655A1 (en) | Method for planning motion along trajectory of end of hyper-redundant mechanical arm | |
| CN105138000A (en) | Seven-freedom-degree space manipulator track planning method optimizing position and posture disturbance of pedestal | |
| CN111300420B (en) | Method for solving minimum path of joint space corner of mechanical arm | |
| Liu et al. | Online time-optimal trajectory planning for robotic manipulators using adaptive elite genetic algorithm with singularity avoidance | |
| CN110598285A (en) | Method and device for solving inverse kinematics of manipulator track and storage medium | |
| CN113478489B (en) | Mechanical arm track planning method | |
| Zhang et al. | Learning insertion primitives with discrete-continuous hybrid action space for robotic assembly tasks | |
| CN114147708B (en) | Mechanical arm obstacle avoidance path planning method based on improved longhorn beetle whisker search algorithm | |
| CN115302502A (en) | Mechanical arm trajectory planning method combining grabbing network and grabbing pose selection and adjustment | |
| Ruchanurucks et al. | Humanoid robot motion generation with sequential physical constraints | |
| CN113885511B (en) | Track optimization method of flexible assembly robot of circuit breaker | |
| CN113319863B (en) | Workpiece clamping pose optimization method and system for robot grinding and polishing machining of blisk | |
| CN109015657A (en) | A kind of final state network optimized approach towards redundant mechanical arm repeating motion planning | |
| CN116690557A (en) | Method and device for controlling humanoid three-dimensional scanning motion based on point cloud | |
| CN115741688A (en) | Six-degree-of-freedom mechanical arm track optimization method based on improved genetic algorithm | |
| Li et al. | Dynamics and workspace analysis of a multi-robot collaborative towing system with floating base | |
| Jiang et al. | Time optimal trajectory planning of five degrees of freedom manipulator based on PSO algorithm | |
| Hussein et al. | A hybrid intelligent active force controller for robot arms using evolutionary neural networks | |
| CN115689049A (en) | Multi-target workshop scheduling method for improving gray wolf optimization algorithm | |
| CN109857110A (en) | Motion planning method, device, equipment and computer readable storage medium | |
| CN113043278A (en) | Mechanical arm track planning method based on improved whale searching method |
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 |