CN109676610B - Circuit breaker assembly robot and method for realizing work track optimization - Google Patents
Circuit breaker assembly robot and method for realizing work track optimization Download PDFInfo
- Publication number
- CN109676610B CN109676610B CN201910072697.6A CN201910072697A CN109676610B CN 109676610 B CN109676610 B CN 109676610B CN 201910072697 A CN201910072697 A CN 201910072697A CN 109676610 B CN109676610 B CN 109676610B
- Authority
- CN
- China
- Prior art keywords
- mechanical arm
- circuit breaker
- joint
- robot
- track
- 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
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1679—Programme controls characterised by the tasks executed
- B25J9/1687—Assembly, peg and hole, palletising, straight line, weaving pattern movement
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
Landscapes
- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Manipulator (AREA)
Abstract
The invention provides a method for realizing working track optimization of a circuit breaker assembly robot, which comprises the steps of determining starting conditions, working tracks of mechanical arm movement after starting and a plurality of decomposed sectional working tracks; constructing a three-dimensional model of the mechanical arm, acquiring the poses of the connecting rods and the joints when the mechanical arm corresponds to the starting and ending positions of each segmented working track according to the starting and ending positions of each segmented working track, and simulating the change condition of the real-time poses; constructing an objective function, and converting the pose change condition of the tail end of the mechanical arm into an angle position change function of each joint on the mechanical arm based on time change under the condition of the objective function; based on a particle swarm algorithm, solving the optimal solution of the angle position of each joint of the mechanical arm, making a motion curve of the mechanical arm, and storing the motion curve as an optimized working track; and after the starting condition is reached, the mechanical arm runs according to the optimized working track. By implementing the invention, the circuit breaker assembly robot can improve the working efficiency by optimizing the working track.
Description
Technical Field
The invention relates to the technical field of intelligent robots, in particular to a circuit breaker assembling robot and a method for optimizing a working track of the circuit breaker assembling robot.
Background
The circuit breaker is an important protective component in a power distribution system, has wide application in the fields of industry, civil use and the like, is suitable for power lines with alternating current of 50/60Hz, rated voltage of 230/400V and maximum rated current of 63A, and has the functions of leakage protection, overload protection, short-circuit protection and the like.
The circuit breaker requires a lot of labor in the manufacturing process. Taking a miniature circuit breaker as an example, the assembly link with the largest manual demand is an assembly link, and comprises the processes of installation of a thermal system, a magnetic system, a linkage shaft and an adjusting screw, shell carrying, combination and the like, and one production line needs 40-50 assembly workers. Because the internal structure of the miniature circuit breaker is complex, the sizes of parts are small, the shapes of the parts are irregular, and the realization of full-automatic assembly has great difficulty. At present, domestic low-voltage electrical apparatus manufacturing enterprises basically rely on manual assembly in the assembly link, and the degree of automation is extremely low.
Even if the assembly robot is adopted on a production line to solve the problem of a circuit breaker digital manufacturing system, due to the fact that the number of parts is large, the assembly process involves complex factors such as hardware equipment, assembly objects and part constraint assembly sequences, and different working tracks of the assembly robot are formed. Therefore, in order to improve the working efficiency of the assembling robot on the production line, it is necessary to optimize the working track of the assembling robot, however, a corresponding solution is not available at present.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a circuit breaker assembly robot and a method for implementing work track optimization thereof, so that the circuit breaker assembly robot can improve work efficiency by optimizing a work track.
In order to solve the technical problem, an embodiment of the present invention provides a method for implementing work track optimization for a circuit breaker assembly robot, where the method includes the following steps:
step S1, determining the starting condition of the breaker assembling robot and the working track of the mechanical arm after starting, and decomposing the determined working track into a plurality of sectional working tracks;
step S2, constructing a mechanical arm three-dimensional model of the circuit breaker assembly robot, acquiring the positions of joints on each connecting rod when a mechanical arm in the mechanical arm three-dimensional model corresponds to the positions of the starting points and the ending points of each segmented working track according to the positions of the starting points and the ending points of each segmented working track, and further simulating the real-time position change conditions of each connecting rod on the mechanical arm and each segmented working track corresponding to each joint in the mechanical arm three-dimensional model according to the positions of joints on each connecting rod when the mechanical arm in the mechanical arm three-dimensional model corresponds to the positions of the starting points and the ending points of each segmented working track;
s3, constructing an objective function based on the motion time of the mechanical arm, and converting the pose change condition of the tail end of the mechanical arm into an angle position change function based on time change of each joint on the mechanical arm under the condition of the constructed objective function according to the simulated real-time pose change condition of each segmented working track corresponding to each connecting rod and each joint on the mechanical arm;
step S4, optimizing the angle position change function of each joint on the mechanical arm with respect to time based on a particle swarm optimization to obtain an optimal solution of the angle position of each joint of the mechanical arm, making a motion curve of the mechanical arm of the breaker assembly robot according to the obtained optimal solution of the angle position of each joint of the mechanical arm, and further storing the motion curve of the mechanical arm of the breaker assembly robot as the optimized working track of the breaker assembly robot;
and S5, after the starting condition of the circuit breaker assembling robot is met, the mechanical arm of the circuit breaker assembling robot runs according to the stored optimized working track.
The breaker assembling robot is a four-axis robot, and a mechanical arm three-dimensional model of the breaker assembling robot is a model established according to D-H parameters.
The method comprises the following steps of optimizing an obtained angular position change function of each joint on the mechanical arm with respect to time based on a particle swarm optimization algorithm, wherein the specific step of obtaining an optimal solution of the angular position of each joint of the mechanical arm comprises the following steps:
s41, determining parameters of the particle swarm; the parameters comprise the initial particle swarm number, particle swarm convergence radius, particle swarm exclusion radius, individual learning factors, swarm learning factors and inertia weight;
s42, initializing a particle swarm, wherein the maximum iteration number and the initial iteration number are set to be 0, the speed and the corresponding speed direction of each particle are randomly set, and the spatial position of each particle in the particle swarm is randomly set;
s43, acquiring the current iteration frequency, and judging whether the acquired current iteration frequency is less than the maximum iteration frequency;
s44, if yes, adding one to the obtained current iteration number, performing traversal update on each particle in the particle swarm, and obtaining the speed of each particle after traversal update according to the preset individual learning factor, the preset swarm learning factor and the preset inertial weight, the optimal value of a single particle in the particle swarm in the current traversal evolution track and the optimal values of all particles in the particle swarm in the current traversal evolution track; updating the positions of the particles according to the obtained traversal updated particle speeds, performing exclusion judgment processing and convergence judgment processing on the particle swarm after the positions are updated in sequence, and returning to the step S43;
and S45, if not, terminating traversal updating of each particle in the particle swarm, screening out the optimal value of each particle in the particle swarm in the previous evolutionary trajectory after traversal is terminated, and further converting the screened optimal value of the particle swarm into the optimal solution of the angle position of each joint of the mechanical arm to be output.
The embodiment of the invention also provides a circuit breaker assembling robot, which comprises:
the circuit breaker assembling robot comprises an initial track acquisition unit, a control unit and a control unit, wherein the initial track acquisition unit is used for determining starting conditions of the circuit breaker assembling robot and a working track of mechanical arm movement after starting, and decomposing the determined working track into a plurality of sectional working tracks;
the circuit breaker assembling robot comprises a mechanical arm pose simulation unit, a circuit breaker assembling robot control unit and a circuit breaker assembling robot control unit, wherein the mechanical arm pose simulation unit is used for constructing a mechanical arm three-dimensional model of the circuit breaker assembling robot, acquiring the poses of joints on each connecting rod when a mechanical arm in the mechanical arm three-dimensional model corresponds to the starting and ending positions of each segmented working track according to the starting and ending positions of each segmented working track, and further simulating the real-time pose change conditions of each connecting rod and each joint on the mechanical arm corresponding to each segmented working track in the mechanical arm three-dimensional model according to the poses of the joints on each connecting rod when the mechanical arm in the mechanical arm three-dimensional model corresponds;
the target function construction unit is used for constructing a target function based on the motion time of the mechanical arm, and converting the tail end pose of the mechanical arm into an angle position change function of each joint on the mechanical arm based on time change under the constructed target function condition according to the simulated real-time pose change condition of each connecting rod and each joint on the mechanical arm corresponding to each segmented working track;
the working track optimizing unit is used for optimizing the angle position change function of each joint on the mechanical arm with respect to time based on a particle swarm algorithm to obtain an optimal solution of the angle position of each joint of the mechanical arm, and making a motion curve of the mechanical arm of the circuit breaker assembling robot according to the obtained optimal solution of the angle position of each joint of the mechanical arm, and further storing the motion curve of the mechanical arm of the circuit breaker assembling robot as the optimized working track of the circuit breaker assembling robot;
and the operation unit is used for operating the circuit breaker assembly robot according to the stored optimized working track after the starting condition of the circuit breaker assembly robot is reached.
The breaker assembling robot is a four-axis robot, and a mechanical arm three-dimensional model of the breaker assembling robot is a model established according to D-H parameters.
The embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, the working track of the mechanical arm motion is subdivided into a plurality of segmental working tracks, the real-time pose change condition of each joint is simulated by the aid of the constructed mechanical arm three-dimensional model at the starting and ending positions of the mechanical arm in each segmental working track, the real-time pose change condition is converted into the angle position change function of each joint on the mechanical arm based on time change under the condition of a target function, the optimal solution is solved by the aid of a particle swarm algorithm, the optimal angle position of each joint based on time change is obtained, the optimal running track of the mechanical arm is manufactured, and the aim of improving the working efficiency of the circuit breaker assembling robot is fulfilled.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a flowchart of a method for implementing work track optimization by a circuit breaker assembly robot according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating that a four-axis robot assembles a circuit breaker to be assembled in an application scenario of the method for realizing work track optimization by a circuit breaker assembling robot according to the embodiment of the present invention;
fig. 3 is a schematic view of a working trajectory curve of the movement of the mechanical arm before the working trajectory of the four-axis robot is optimized in an application scenario of the method for realizing the working trajectory optimization by the breaker assembling robot according to the embodiment of the present invention;
fig. 4 is a comparison diagram of the running times of four joints before and after the four-axis robot work trajectory optimization in an application scenario of the method for realizing work trajectory optimization by a breaker assembly robot according to the embodiment of the present invention; wherein 4a is a running time comparison diagram of the joint 1, 4b is a running time comparison diagram of the joint 2, 4c is a running time comparison diagram of the joint 3, and 4d is a running time comparison diagram of the joint 4;
fig. 5a to 5d are operation track comparison diagrams of four joints before and after the four-axis robot work track optimization in an application scenario of the method for realizing work track optimization by a circuit breaker assembly robot according to the embodiment of the present invention; wherein, 5a is a moving track comparison diagram of the joint 1, 5b is a moving track comparison diagram of the joint 2, 5c is a moving track comparison diagram of the joint 3, and 5d is a moving track comparison diagram of the joint 4;
fig. 6 is a schematic system structure diagram of a circuit breaker assembling robot according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, in an embodiment of the present invention, a method for implementing work track optimization for a circuit breaker assembly robot is provided, where the method includes the following steps:
step S1, determining the starting condition of the breaker assembling robot and the working track of the mechanical arm after starting, and decomposing the determined working track into a plurality of sectional working tracks;
the specific process is that the starting condition of the circuit breaker assembly robot can be preset differently according to actual conditions, for example, a camera or a camera shoots a certain position on a production line where a circuit breaker to be assembled is reached, or a trigger is installed on the production line and triggers and forms a trigger signal when the circuit breaker to be assembled arrives, and the circuit breaker assembly robot is started as long as the circuit breaker assembly robot receives a picture or judges the trigger signal.
At this time, the circuit breaker assembling robot needs to perform a plurality of processes to complete the installation of the parts on the circuit breaker to be assembled, for example, the parts are extracted from the initial position, then enter from a certain fixed position of the assembly line, then complete the installation of the parts on the circuit breaker to be assembled at another fixed position of the assembly line, and finally return to the final position or the initial position. Therefore, the working track and the sectional working track of the mechanical arm movement can be determined according to the working process of the mechanical arm of the circuit breaker assembling robot.
It can be understood that optimizing the segmented working trajectory of the mechanical arm movement can optimize the working trajectory of the mechanical arm movement to improve the working efficiency. However, the segmented working track of the mechanical arm motion can be optimized by solving the extreme point of the segmented working track, so that the optimal solution can be solved by adopting a particle swarm algorithm to obtain the extreme point of multiple points on the curve.
Step S2, constructing a mechanical arm three-dimensional model of the circuit breaker assembly robot, acquiring the positions of joints on each connecting rod when a mechanical arm in the mechanical arm three-dimensional model corresponds to the positions of the starting points and the ending points of each segmented working track according to the positions of the starting points and the ending points of each segmented working track, and further simulating the real-time position change conditions of each connecting rod on the mechanical arm and each segmented working track corresponding to each joint in the mechanical arm three-dimensional model according to the positions of joints on each connecting rod when the mechanical arm in the mechanical arm three-dimensional model corresponds to the positions of the starting points and the ending points of each segmented working track;
the robot arm is formed by connecting a series of connecting rods through joint points, each joint and the corresponding connecting rod have a coordinate system, and the relative position and direction relation between the coordinate systems is established and described through homogeneous transformation to construct a kinematic equation of the robot. Therefore, a three-dimensional virtual reality model of each mechanical arm with controllable joint motion in the circuit breaker assembly robot is established, SolidWorks three-dimensional drawing software is used for drawing each part of the mechanical arm of the circuit breaker assembly robot, a complete mechanical arm three-dimensional model is assembled, the mechanical arm three-dimensional model of the circuit breaker assembly robot is introduced into a unity3d scene for motion simulation, and virtual motion of the circuit breaker assembly robot is achieved.
The movement speed of the circuit breaker assembly robot in the operation process is controllable, so that path points in a working track can be converted into joint vector angle values by adopting inverse kinematics. Therefore, firstly, the starting and ending point positions of each segmented working track are determined; secondly, the pose (namely the motion pose, the angle position, the motion distance and the like) of the joint on each connecting rod when the mechanical arm corresponds to the starting and ending positions of each segmented working track in the three-dimensional model of the mechanical arm is obtained, so that the real-time pose change condition of each connecting rod and each joint on the mechanical arm corresponding to each segmented working track is simulated in the three-dimensional model of the mechanical arm, namely the change condition of the variables such as the real-time position, the real-time speed, the real-time acceleration and the like of each connecting rod and each joint on the mechanical arm is determined by reversely solving each initial segmented working track through the kinematics of the robot according to the known pose of each joint on each connecting rod when the starting and ending positions of each segmented.
S3, constructing an objective function based on the motion time of the mechanical arm, and converting the pose change condition of the tail end of the mechanical arm into an angle position change function based on time change of each joint on the mechanical arm under the condition of the constructed objective function according to the simulated real-time pose change condition of each segmented working track corresponding to each connecting rod and each joint on the mechanical arm;
the specific process is that an objective function based on the motion time of the mechanical arm is constructed, the objective function can be converted into a speed smooth function which is fit for each joint and is related to time, the speed smooth function is enabled to sequentially pass through all path points from a starting point, and finally the target point is reached. Because joint track interpolation calculation methods are more, the circuit breaker assembly robot in the embodiment of the invention adopts track planning of point-to-point motion, namely the starting point of the circuit breaker assembly robot is 0, after the circuit breaker assembly robot moves to 1 point to complete a certain task, the 1 point is taken as the starting point, the circuit breaker assembly robot moves to 2 points to complete a target work task, and then the circuit breaker assembly robot continues to move by taking the 2 points as the starting points.
Step S4, optimizing the angle position change function of each joint on the mechanical arm with respect to time based on a particle swarm optimization to obtain an optimal solution of the angle position of each joint of the mechanical arm, making a motion curve of the mechanical arm of the breaker assembly robot according to the obtained optimal solution of the angle position of each joint of the mechanical arm, and further storing the motion curve of the mechanical arm of the breaker assembly robot as the optimized working track of the breaker assembly robot;
firstly, optimizing an obtained angular position change function of each joint on the mechanical arm with respect to time based on a particle swarm algorithm to obtain an optimal solution of the angular position of each joint of the mechanical arm, and specifically comprising the following steps:
s41, determining parameters of the particle swarm; the parameters comprise the initial particle swarm number, particle swarm convergence radius, particle swarm exclusion radius, individual learning factors, swarm learning factors and inertia weight;
s42, initializing a particle swarm, wherein the maximum iteration number and the initial iteration number are set to be 0, the speed and the corresponding speed direction of each particle are randomly set, and the spatial position of each particle in the particle swarm is randomly set;
s43, acquiring the current iteration frequency, and judging whether the acquired current iteration frequency is less than the maximum iteration frequency;
s44, if yes, adding one to the obtained current iteration number, performing traversal update on each particle in the particle swarm, and obtaining the speed of each particle after traversal update according to the preset individual learning factor, the preset swarm learning factor and the preset inertial weight, the optimal value of a single particle in the particle swarm in the current traversal evolution track and the optimal values of all particles in the particle swarm in the current traversal evolution track; updating the positions of the particles according to the obtained traversal updated particle speeds, performing exclusion judgment processing and convergence judgment processing on the particle swarm after the positions are updated in sequence, and returning to the step S43;
and S45, if not, terminating traversal updating of each particle in the particle swarm, screening out the optimal value of each particle in the particle swarm in the previous evolutionary trajectory after traversal is terminated, and further converting the screened optimal value of the particle swarm into the optimal solution of the angle position of each joint of the mechanical arm to be output.
Secondly, according to the obtained optimal solution of the angle position of each joint of the mechanical arm, a motion curve of the mechanical arm of the circuit breaker assembling robot is made, and the motion curve of the mechanical arm of the circuit breaker assembling robot is stored as the optimized working track of the circuit breaker assembling robot.
And S5, after the starting condition of the circuit breaker assembling robot is met, the mechanical arm of the circuit breaker assembling robot runs according to the stored optimized working track.
The specific process is that the starting condition of the breaker assembling robot is reached and then started, and the mechanical arm runs according to the stored optimized working track. For example, when a circuit breaker to be assembled on a conveyor belt is detected, a camera is requested to determine part coordinates, the camera photographs calculated grabbing point coordinates and sends the grabbing point coordinates to a controller on a circuit breaker assembling robot, the controller on the circuit breaker assembling robot generates a track after track planning, a mechanical arm completes a task according to track motion of specified speed and acceleration, and finally whether assembly is successful or not is judged through vision and whether the circuit breaker arrives or not is continuously monitored.
As shown in fig. 2, an application scenario of the method for implementing the work track optimization by the circuit breaker assembly robot in the embodiment of the present invention is further described:
the circuit breaker assembly robot adopts the four-axis robot, and the circuit breaker tray and wait to adorn the circuit breaker and arrange in conveyer belt 1 this moment, and after the sensor detected waiting to adorn the circuit breaker on conveyer belt 1, the four-axis robot snatched the part from A point, and in keeping away the circuit breaker that waiting to adorn that barrier B packed the part into C department, moved the 3 departments of part of D point at last.
Firstly, in order to optimize the motion track of a four-axis robot in the automatic assembly process of the circuit breaker, the track in the assembly process is specifically divided into an AB section, a BC section and a CD section to be respectively planned. .
Secondly, constructing a mechanical arm three-dimensional model of the circuit breaker assembly robot into a D-H parameter model; wherein the D-H parameters are shown in Table 1 below.
TABLE 1
The D-H modeling method is a modeling method proposed by Denavit and Hartenberg, and is mainly used in robot kinematics to realize the transformation of coordinates on two connecting rods through homogeneous coordinate transformation. For any joint connecting rod j, thetajIs the variation of joint angle, djIs the distance, a, of two common perpendiculars along the axis of the joint jj-1The distance of the two joint axes along the common vertical line, i.e. the length of the connecting rod, alphaj-1Is at a vertical angle aj-1The included angle of the two axes in the plane of (a), namely the torsional angle of the connecting rod. Wherein the variable range is theta in the joint 1, the joint 2 and the joint 4jThe maximum rotation range of. The joint 3 is a mobile joint, and the length thereof is 150mm, so that the range of the joint is-150 mm to 0 mm.
And then, the position and posture of the four-axis robot is converted into the position, the speed and the acceleration of each joint of the four-axis robot through inverse solution, the working parameters of the four-axis robot can be known through kinematic analysis of the four-axis robot, and the tail end position and posture of the four-axis robot, the speed, the acceleration and other parameters of each joint can be controlled, so that the four-axis robot can stably and efficiently move.
According to the known D-H parameters, a relative pose equation of each connecting rod can be established. According to
General formula (1):
a transformation matrix of each connecting rod of the four-axis robot can be obtained, and the formula (2) is the transformation matrix of the four-axis robot:
and multiplying the transformation matrixes of the connecting rods to obtain a positive kinematics equation (3) of the four-axis robot. Where s represents sin θ, c represents cos θ, and link angle θjJ in (a) represents the angle of the j-th link.
Through the positive kinematic formula derivation of the robot, the posture and the position of the end effector can be determined according to the known joint variables, and the inverse motion of the end effector needs to be calculated, namely the joint variables corresponding to the joints are solved under the condition that the posture and the position of the end effector are known.
Assuming that a homogeneous transformation matrix, namely the pose of the end effector of the four-axis robot is known, the corresponding joint variable theta can be obtained through the formula (4)1、θ2、θ3And theta4. In the formula nx、ny、nz，ox、oy、oz，ax、ay、az，px、py、pzRespectively, as a normal vector in the x-direction, a direction vector in the y-direction,the z-direction approach vector, the gripper position vector.
Through the building, simulation and forward/inverse kinematics operation of the model, the position, speed, acceleration and other parameters of the mechanical arm in the motion process can be obtained, and a basic model basis is provided for particle swarm optimization.
Next, the movement time of the robot arm is taken as the position x of the particleiThe moving speed of the robot arm is taken as the speed v of the particlesi. Because the motion time is optimized, the constructed objective function of the mechanical arm is converted into the angle position change function of each joint on the mechanical arm based on time change, and therefore the optimized objective function of each joint is recorded as:
f(t)＝min(tj1+tj2+tj3) (6)
the defined constraints on position and velocity (time) are as follows:
where j denotes the jth joint.
Then constructing a 4-3-4 degree polynomial interpolation function and applying a particle swarm algorithm to optimize the optimal time tji. The 4-3-4 degree polynomial interpolation function is as follows:
in the formula, the coefficients a, b and c are the coefficients of the interpolation functions of the first, second and third sections of each joint respectively, and thetajiRepresenting the angular position of each joint with respect to time, i.e. each joint is divided into three stages of acceleration, stabilization and deceleration
A movement process, and
respectively showing the position, the speed and the acceleration of the ith section of the jth joint. From the polynomial the following equation can be obtained:
[θ]＝[M][K] (9)
wherein the content of the first and second substances,
[K]＝[aj0 aj1 aj2 aj3 aj4 bj0 bj1 bj2 bj3 cj0 cj1 cj2 cj3 cj4]T (12)
then, 1) initializing a particle group, and randomly generating N (N ═ 20) particles, each of which is divided by tj1、tj2、tj3Three sections (the total number is 3N), wherein j represents a joint, and the three sections are an acceleration section, a uniform speed break section and a deceleration section respectively;
2) calculating the fitness value of the first generation of particle swarm, taking each fitness value of the first generation as an individual optimum, and selecting an individual optimum value as a global optimum;
3) substituting the globally optimal time into an equation (9) to obtain a coefficient matrix K, then substituting the obtained coefficient matrix into an equation (8) to obtain an expression of a 4-3-4 degree polynomial interpolation function, namely a particle position, and obtaining a particle speed value (time) by derivation;
4) the position and the speed (time) are substituted into the formula (7) for determination, if the value of the speed exceeds or is less than the limit value, the particles less than the limit value are reassigned to the minimum limit value Vmin(tmin) Reassigning values greater than the limit value to the maximum limit value Vmax(tmax) (ii) a If the value of the position exceeds or is less than the limit value, the particles less than the limit value will be reassigned to the minimum limit value θminIs greater than the limitThe particles with fixed value will be reassigned to the maximum limit value thetamax；
5) And respectively calculating the fitness value of each generation of particles, and comparing the calculated optimal value with the historical individual optimal value and the historical global optimal value at any moment. If the obtained optimal value is superior to the previously obtained optimal value, replacing the historical optimal value with a new optimal value, otherwise, keeping the historical optimal value until the iteration times are met;
6) substituting the finally obtained optimal value into the formula (7) and meeting the requirement of the formula (6) to obtain a position and speed (time) expression of the optimal value, and finishing algorithm optimization;
7) and making a motion curve of the mechanical arm of the circuit breaker assembling robot, and storing the motion curve of the mechanical arm of the circuit breaker assembling robot as the optimized working track of the circuit breaker assembling robot.
And finally, the mechanical arm moves according to the tracks of the specified speed and the acceleration to finish the task.
As shown in fig. 3, a movement path of the four-axis robot is mainly divided into three sections, a path between a and B is a first section, a path between B and C is a second section, and a path between C and D is a third section. A. B, C, D four points were taken from the path of the robot arm, the specific parameters of which are shown in Table 2.
TABLE 2
After the PSO optimization, fig. 4 is a comparison of the operation time of each joint of the four-axis robot before and after the optimization, from which it can be seen that the operation time of the mechanical arm is significantly shortened compared to before. Wherein the running time of the joint 1 before optimization is 1.7521s, and the running time after optimization is shortened by 12.4%; the running time of the joint 2 before optimization is 2.0561s, and the running time after optimization is shortened by 46.9%; the running time of the joint 3 before optimization is 3.0366s, and the running time after optimization is shortened by 57.3%; the running time of the joint 4 before optimization is 1.9312s, and the running time after optimization is shortened by 27.8%.
In order to make the mechanical arm smoothly move, the running time of each section of each joint is required to be the same. Therefore, the maximum value of the operation time of each section is used as the operation time constraint of each section of each joint, so that the phenomenon of uncoordinated motion with movement and static does not occur, and the smoothness of the motion of the mechanical arm is ensured.
As shown in table 3 below, the optimized running time of each joint is listed, the maximum running time of each joint is selected as the final running time, which is 0.8948s, 0.8449s, 0.2935s respectively, the total time is 2.0332s,
TABLE 3
As shown in table 4 below, the running time of each joint before optimization is listed, and the maximum running time of each joint is selected as the final running time, which is 1.6274s, 0.9726s, 0.5084s respectively, and the total time is 3.0366 s.
TABLE 4
Comparing table 3 and table 4, it can be seen that the total time consumption after optimization is reduced by 1.0034s and the total time of operation is reduced by 33.04%.
Fig. 5a to 5d are diagrams of the trajectories of the joints before and after optimization respectively, and it can be seen from fig. 5a to 5d that the running speed of the trajectory of each joint after optimization is significantly higher than that before optimization, and the speed change can be kept smooth, and the phenomenon of "vibration and seizure" of the mechanical arm is not caused. The acceleration is also quickly and accurately adjusted under the performance requirement of the mechanical arm so as to meet the requirement that the running speed of the mechanical arm keeps smooth and stable. When the specified path point is guaranteed to be completed, the operation time of the optimized mechanical arm is saved by about one third compared with the operation before optimization, and the working efficiency is improved.
As shown in fig. 6, in an embodiment of the present invention, a circuit breaker assembling robot is provided, including:
an initial trajectory acquisition unit 110, configured to determine a starting condition of the circuit breaker assembly robot and a working trajectory of the mechanical arm movement after starting, and decompose the determined working trajectory into a plurality of segmented working trajectories;
the mechanical arm pose simulation unit 120 is used for constructing a mechanical arm three-dimensional model of the circuit breaker assembling robot, acquiring poses of joints on each connecting rod when a mechanical arm in the mechanical arm three-dimensional model corresponds to the starting and ending positions of each segmented working track according to the starting and ending positions of each segmented working track, and further simulating real-time pose change conditions of each connecting rod on the mechanical arm and each segmented working track corresponding to each joint in the mechanical arm three-dimensional model according to the poses of the joints on each connecting rod when the mechanical arm in the mechanical arm three-dimensional model corresponds to the starting and ending positions of each segmented working track;
an objective function constructing unit 130, configured to construct an objective function based on the motion time of the mechanical arm, and convert all pose change conditions of the end of the mechanical arm into angle position change functions based on time changes of each joint on the mechanical arm under the constructed objective function conditions according to the simulated real-time pose change conditions of each segmented working track corresponding to each link and joint on the mechanical arm;
a working track optimizing unit 140, configured to optimize an obtained angular position change function of each joint on the mechanical arm with respect to time based on a particle swarm algorithm, obtain an optimal solution of an angular position of each joint of the mechanical arm, create a motion curve of the mechanical arm of the circuit breaker assembly robot according to the obtained optimal solution of the angular position of each joint of the mechanical arm, and further store the motion curve of the mechanical arm of the circuit breaker assembly robot as a working track optimized by the circuit breaker assembly robot;
and the operation unit 150 is used for operating the circuit breaker assembling robot according to the stored optimized working track after the starting condition of the circuit breaker assembling robot is reached.
The breaker assembling robot is a four-axis robot, and a mechanical arm three-dimensional model of the breaker assembling robot is a model established according to D-H parameters.
The embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, the working track of the mechanical arm motion is subdivided into a plurality of segmental working tracks, and the real-time pose change condition of each joint is simulated by the constructed mechanical arm three-dimensional model aiming at the starting and ending positions of the mechanical arm in each segmental working track, so that after a target function based on the motion time of the mechanical arm is converted into an angle position change function based on time change of each joint on the mechanical arm, the optimal solution is solved by adopting a particle swarm algorithm, the optimal angle position based on time change of each joint is obtained to manufacture the optimal running track of the mechanical arm, and the aim of improving the working efficiency of the circuit breaker assembling robot is fulfilled.
It should be noted that, in the foregoing system embodiment, each included system unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (5)
1. A method for realizing work track optimization of a circuit breaker assembling robot is characterized by comprising the following steps:
step S1, determining the starting condition of the breaker assembling robot and the working track of the mechanical arm after starting, and decomposing the determined working track into a plurality of sectional working tracks;
step S2, constructing a mechanical arm three-dimensional model of the circuit breaker assembly robot, acquiring the positions of joints on each connecting rod when a mechanical arm in the mechanical arm three-dimensional model corresponds to the positions of the starting points and the ending points of each segmented working track according to the positions of the starting points and the ending points of each segmented working track, and further simulating the real-time position change conditions of each connecting rod on the mechanical arm and each segmented working track corresponding to each joint in the mechanical arm three-dimensional model according to the positions of joints on each connecting rod when the mechanical arm in the mechanical arm three-dimensional model corresponds to the positions of the starting points and the ending points of each segmented working track;
s3, constructing an objective function based on the motion time of the mechanical arm, and converting the pose change condition of the tail end of the mechanical arm into an angle position change function based on time change of each joint on the mechanical arm under the condition of the constructed objective function according to the simulated real-time pose change condition of each segmented working track corresponding to each connecting rod and each joint on the mechanical arm;
step S4, optimizing the angle position change function of each joint on the mechanical arm with respect to time based on a particle swarm optimization to obtain an optimal solution of the angle position of each joint of the mechanical arm, making a motion curve of the mechanical arm of the breaker assembly robot according to the obtained optimal solution of the angle position of each joint of the mechanical arm, and further storing the motion curve of the mechanical arm of the breaker assembly robot as the optimized working track of the breaker assembly robot;
and S5, after the starting condition of the circuit breaker assembling robot is met, the mechanical arm of the circuit breaker assembling robot runs according to the stored optimized working track.
2. The method for realizing the work track optimization by the circuit breaker assembling robot according to claim 1, wherein the circuit breaker assembling robot is a four-axis robot, and a mechanical arm three-dimensional model of the circuit breaker assembling robot is a model established according to D-H parameters.
3. The method for optimizing the working trajectory of the circuit breaker assembling robot according to claim 1, wherein the step of optimizing the obtained angular position change function of each joint on the mechanical arm with respect to time based on the particle swarm optimization comprises the following steps:
s41, determining parameters of the particle swarm; the parameters comprise the initial particle swarm number, particle swarm convergence radius, particle swarm exclusion radius, individual learning factors, swarm learning factors and inertia weight;
s42, initializing a particle swarm, wherein the maximum iteration number and the initial iteration number are set to be 0, the speed and the corresponding speed direction of each particle are randomly set, and the spatial position of each particle in the particle swarm is randomly set;
s43, acquiring the current iteration frequency, and judging whether the acquired current iteration frequency is less than the maximum iteration frequency;
s44, if yes, adding one to the obtained current iteration number, performing traversal update on each particle in the particle swarm, and obtaining the speed of each particle after traversal update according to a preset individual learning factor, a preset swarm learning factor and an inertia weight, the optimal value of a single particle in the particle swarm in the current traversal evolution track and the optimal values of all particles in the particle swarm in the current traversal evolution track; updating the positions of the particles according to the obtained traversal updated particle speeds, performing exclusion judgment processing and convergence judgment processing on the particle swarm after the positions are updated in sequence, and returning to the step S43;
and S45, if not, terminating traversal updating of each particle in the particle swarm, screening out the optimal value of each particle in the particle swarm in the previous evolutionary trajectory after traversal is terminated, and further converting the screened optimal value of the particle swarm into the optimal solution of the angle position of each joint of the mechanical arm to be output.
4. A circuit breaker assembly robot, comprising:
the circuit breaker assembling robot comprises an initial track acquisition unit, a control unit and a control unit, wherein the initial track acquisition unit is used for determining starting conditions of the circuit breaker assembling robot and a working track of mechanical arm movement after starting, and decomposing the determined working track into a plurality of sectional working tracks;
the circuit breaker assembling robot comprises a mechanical arm pose simulation unit, a circuit breaker assembling robot control unit and a circuit breaker assembling robot control unit, wherein the mechanical arm pose simulation unit is used for constructing a mechanical arm three-dimensional model of the circuit breaker assembling robot, acquiring the poses of joints on each connecting rod when a mechanical arm in the mechanical arm three-dimensional model corresponds to the starting and ending positions of each segmented working track according to the starting and ending positions of each segmented working track, and further simulating the real-time pose change conditions of each connecting rod and each joint on the mechanical arm corresponding to each segmented working track in the mechanical arm three-dimensional model according to the poses of the joints on each connecting rod when the mechanical arm in the mechanical arm three-dimensional model corresponds;
the target function construction unit is used for constructing a target function based on the motion time of the mechanical arm, converting the pose change condition of the tail end of the mechanical arm into an angle position change function of each joint on the mechanical arm based on time change under the condition of the target function according to the simulated real-time pose change condition of each connecting rod and each joint on the mechanical arm corresponding to each segmented working track;
the working track optimizing unit is used for optimizing the angle position change function of each joint on the mechanical arm with respect to time based on a particle swarm algorithm to obtain an optimal solution of the angle position of each joint of the mechanical arm, and making a motion curve of the mechanical arm of the circuit breaker assembling robot according to the obtained optimal solution of the angle position of each joint of the mechanical arm, and further storing the motion curve of the mechanical arm of the circuit breaker assembling robot as the optimized working track of the circuit breaker assembling robot;
and the operation unit is used for operating the circuit breaker assembly robot according to the stored optimized working track after the starting condition of the circuit breaker assembly robot is reached.
5. The circuit breaker assembling robot of claim 4, wherein the circuit breaker assembling robot is a four-axis robot, and a robot arm three-dimensional model of the circuit breaker assembling robot is a model established according to D-H parameters.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910072697.6A CN109676610B (en) | 2019-01-25 | 2019-01-25 | Circuit breaker assembly robot and method for realizing work track optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910072697.6A CN109676610B (en) | 2019-01-25 | 2019-01-25 | Circuit breaker assembly robot and method for realizing work track optimization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109676610A CN109676610A (en) | 2019-04-26 |
| CN109676610B true CN109676610B (en) | 2021-07-06 |
Family
ID=66194850
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910072697.6A Active CN109676610B (en) | 2019-01-25 | 2019-01-25 | Circuit breaker assembly robot and method for realizing work track optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109676610B (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113093716B (en) * | 2019-12-19 | 2024-04-30 | 广州极飞科技股份有限公司 | Motion trail planning method, device, equipment and storage medium |
| CN111152224B (en) * | 2020-01-10 | 2022-05-10 | 温州大学 | Dual-optimization robot motion trajectory optimization method |
| CN112549043B (en) * | 2020-11-26 | 2022-02-11 | 广东博智林机器人有限公司 | Collision prediction method and device for construction operation equipment and construction operation equipment |
| CN112721174B (en) * | 2020-12-16 | 2022-10-14 | 同济大学 | External shaft optimization method under three-dimensional printing |
| CN113442144B (en) * | 2021-09-01 | 2021-11-19 | 北京柏惠维康科技有限公司 | Optimal pose determining method and device under constraint, storage medium and mechanical arm |
| CN114030222B (en) * | 2021-11-04 | 2023-12-01 | 温州大学 | Visual stamping line multi-machine coordination track optimization method and system |
| CN117773911B (en) * | 2023-11-03 | 2024-05-17 | 广东工业大学 | Obstacle avoidance method for industrial robot in complex environment |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101239346B (en) * | 2008-03-10 | 2010-09-15 | 江苏大学 | Optimizing method of spray coating robot spray gun track on complex curved surface |
| TWI426366B (en) * | 2011-01-17 | 2014-02-11 | Hon Hai Prec Ind Co Ltd | System and method for automatically searching and optimum arranging test points |
| US9427868B1 (en) * | 2015-02-24 | 2016-08-30 | Disney Enterprises, Inc. | Method for developing and controlling a robot to have movements matching an animation character |
| CN105676642B (en) * | 2016-02-26 | 2018-10-26 | 同济大学 | A kind of six-DOF robot station layout and run duration cooperative optimization method |
| CN106503373B (en) * | 2016-11-04 | 2020-07-28 | 湘潭大学 | B-spline curve-based double-robot coordinated assembly track planning method |
| CN107490965B (en) * | 2017-08-21 | 2020-02-07 | 西北工业大学 | Multi-constraint trajectory planning method for space free floating mechanical arm |
| CN108297105B (en) * | 2018-01-17 | 2021-07-06 | 广东工业大学 | Optimal trajectory planning method for task-level time of mechanical arm |
| CN108656117B (en) * | 2018-05-21 | 2021-04-16 | 武汉理工大学 | Mechanical arm space trajectory optimization method for optimal time under multi-constraint condition |
-
2019
- 2019-01-25 CN CN201910072697.6A patent/CN109676610B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN109676610A (en) | 2019-04-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109676610B (en) | Circuit breaker assembly robot and method for realizing work track optimization | |
| CN108932216B (en) | Robot inverse kinematics solving method based on particle swarm optimization algorithm | |
| Hashimoto et al. | Visual servoing with hand-eye manipulator-optimal control approach | |
| CN110238839B (en) | Multi-shaft-hole assembly control method for optimizing non-model robot by utilizing environment prediction | |
| CN108748160B (en) | Mechanical arm motion planning method based on multi-population particle swarm algorithm | |
| CN110134062B (en) | Multi-axis numerical control machine tool machining path optimization method based on reinforcement learning | |
| CN105511266A (en) | Delta robot locus programming method based on gravitation searching particle swarm algorithm | |
| CN108068113A (en) | 7-DOF humanoid arm flying object operation minimum acceleration trajectory optimization | |
| He et al. | Immune optimization based multi-objective six-DOF trajectory planning for industrial robot manipulators | |
| CN114789454B (en) | Robot digital twin track completion method based on LSTM and inverse kinematics | |
| Ruchanurucks et al. | Humanoid robot motion generation with sequential physical constraints | |
| CN116673963A (en) | Double mechanical arm cooperation flexible assembly system and method for unordered breaker parts | |
| CN111152220A (en) | Mechanical arm control method based on man-machine fusion | |
| CN113043278B (en) | Mechanical arm track planning method based on improved whale searching method | |
| CN111515954B (en) | Method for generating high-quality motion path of mechanical arm | |
| CN107511830B (en) | Adaptive adjustment realization method for parameters of five-degree-of-freedom hybrid robot controller | |
| CN111966040B (en) | Trajectory generation method for balancing impact dynamic load in robot pick-and-place task | |
| CN109807893B (en) | Method for smoothing motion model of welding robot | |
| CN109711527B (en) | Robot control method based on particle swarm optimization algorithm | |
| Fan et al. | Trajectory planning of vibration suppression for hybrid structure flexible manipulator based on differential evolution particle swarm optimization algorithm | |
| Kawagoshi et al. | Visual servoing using virtual space for both learning and task execution | |
| Jing et al. | Research on neural network PID adaptive control with industrial welding robot in multi-degree of freedom | |
| Wang et al. | Adaptive control for industrial welding robot with muti-degree of freedom using the improved neural network PID | |
| Li et al. | Research on Intelligent Control Decision of Manipulator Based on Deep Reinforcement Learning | |
| CN113885511B (en) | Track optimization method of flexible assembly robot of circuit breaker |
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 |