CN109344477B - 6-degree-of-freedom mechanical arm inverse kinematics solving method - Google Patents
6-degree-of-freedom mechanical arm inverse kinematics solving method Download PDFInfo
- Publication number
- CN109344477B CN109344477B CN201811098865.0A CN201811098865A CN109344477B CN 109344477 B CN109344477 B CN 109344477B CN 201811098865 A CN201811098865 A CN 201811098865A CN 109344477 B CN109344477 B CN 109344477B
- Authority
- CN
- China
- Prior art keywords
- mechanical arm
- population
- individuals
- random
- inverse kinematics
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
Landscapes
- Physics & Mathematics (AREA)
- Geometry (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Mathematical Analysis (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Computational Mathematics (AREA)
- Manipulator (AREA)
Abstract
The invention discloses a 6-degree-of-freedom mechanical arm inverse kinematics solving method, which relates to the field of industrial robots, and comprises the steps of firstly, endowing a certain number of random samples for a mechanical arm joint space by utilizing a Monte Carlo method, calculating the tail end position of a mechanical arm corresponding to each sample, and selecting the sample meeting a target position condition as a part of an initial population of a genetic algorithm; the other part of the initial population of the genetic algorithm consists of random individuals, and the inverse kinematics solution of the mechanical arm is solved through the genetic algorithm. On one hand, the initial population of the genetic algorithm is optimized by the Monte Carlo method, and the searching speed and the searching success rate of the genetic algorithm are effectively improved; on the other hand, the diversity of the genetic population is ensured by adding random individuals, and the global search capability of the genetic algorithm is improved.
Description
Technical Field
The invention relates to the field of industrial robots, in particular to a hybrid solving multi-degree-of-freedom mechanical arm inverse kinematics solving method based on the combination of a Monte Carlo method and a genetic algorithm.
Background
The inverse kinematics solution of the mechanical arm is the basis of industrial robot application, is a prerequisite condition of mechanical arm control, and is directly related to tasks such as robot off-line programming, trajectory planning, real-time control and the like. The inverse kinematics plays an important role in the robotics, and the programming control of the robot end effector according to the space pose can be realized only by converting the space pose into a joint variable.
Inverse kinematics of a serial mechanical arm is difficult to solve, and various scholars conduct a great deal of research and study. The solution method of the inverse kinematics solution is mainly divided into an analytical method, a numerical method, a geometric method and the like. If the analytic method is simple and intuitive, the operation speed is high, but only the mechanical arm structure meeting specific geometric conditions can be solved by using the analytic method: i.e. the last 3 joint axes intersect at one point or there are 3 joint axes parallel to each other. Through analysis of a student Doty, only 5 types of 24-type 6-degree-of-freedom orthogonal mechanical arm structures have analytical solutions, and whether analytical solutions exist in other structures cannot be determined. An iterative algorithm for solving joint variables by using a jacobian matrix is a basic idea of a numerical method, and various nonlinear optimization methods are applied to numerical methods of inverse solution, but the characteristics of large computation amount and low convergence rate generally exist. In recent years, some researchers have improved the calculation speed to some extent by combining analysis with numerical calculation. The method relates to the inverse operation of a matrix and the operation of an inverse trigonometric function, and has defects in the aspects of calculation amount, limitation conditions and the like.
Another type of inverse kinematics solution is solved by using an intelligent algorithm, such as a genetic algorithm, a particle swarm algorithm, a neural network, and the like. The intelligent algorithm solves the inverse kinematics, has few limiting conditions and wide application range, but has the defects of slow convergence, local minimum, incapability of ensuring the precision of the result and the like. According to the method, a group of random initial joint angles is used as an initial population, and the result of each iteration is gradually converged to a real solution through continuous iteration updating. However, the selection dependence of the correlation algorithm on the initial population is high, and the result of the calculation is directly influenced by the quality of the initial population.
The D-H matrix (D-H method) is a universal pose calculation method proposed by two scholars of Denavit and Hartenberg in 1955. This method fixes a coordinate system on each link of the robot and then uses a 4 x 4 homogeneous transformationThe matrix describes the spatial relationship between two adjacent connecting rods. The pose of the end effector relative to the base coordinate system can be finally deduced through sequential transformation, so that a kinematic equation of the robot is established. For a 6 degree-of-freedom robotic arm, the D-H matrix may be identified as follows, where A isiIs the transformation matrix of the ith link, R is the rotation matrix of 3 x 3, P is the position matrix of 1 x 3, and I is usually the transformation matrix T of a mechanical arm with 1, 6 degrees of freedom6Comprises the following steps:
disclosure of Invention
The invention aims to provide a 6-degree-of-freedom mechanical arm inverse kinematics solving method, so that the defects of low solving speed and low precision of the existing kinematics inverse solution are overcome.
In order to achieve the aim, the invention provides a 6-degree-of-freedom mechanical arm inverse kinematics solving method, which comprises the following steps of:
s1, establishing a D-H matrix of the mechanical arm according to the parameters of the mechanical arm;
s2, determining the threshold values of S3, namely the tail end position and the target position P of the mechanical arm according to the structure of the mechanical arm and the parameters of the mechanical armEThe maximum distance of (c);
s3, giving random angles to all joints of the mechanical arm by adopting a Monte Carlo method to generate random samples, calculating the tail end position of the mechanical arm corresponding to each random sample through the D-H matrix, and reserving the tail end position of the mechanical arm meeting the conditions as a sample;
and S4, taking the sample reserved in S3 as a seed group, adding new random populations to form an initial population of the genetic algorithm, and solving the inverse kinematics by using the genetic algorithm.
Further, the parameters of the mechanical arm include: the length, the torsion angle, the offset and the joint angle of each mechanical arm.
Further, the S3 specifically includes the following steps:
s31, giving random angles to all joints of the mechanical arm to generate random samples;
s32, substituting the random samples into the D-H matrix of S1, and calculating the position of the tail end of the corresponding mechanical arm
S33, judging reserved PMWhile satisfying the following conditions as shown in formula (1);
in the formula (1), thx，thy，thzThe identification of the threshold value in the x, y and z directions;
s34, if the random sample corresponds to the end position PMIf the condition of S33 is met, the corresponding sample is stored to a population array D1;
s35, circulating the operations of S32-S34 until the calculation of all random samples is completed;
and S36, finishing the circulation to obtain a population array D1.
Further, the S4 specifically includes the following steps:
s40, adding a new random sample group D2 of joint angles on the basis of the population group D1 to form an initial population D of the genetic algorithm;
s41, encoding the initial population D;
s42, setting parameters of a genetic algorithm;
s43, calculating a fitness function value of the mechanical arm terminal pose matrix corresponding to each individual in the population;
s44, carrying out genetic selection on individuals in the S41 population according to the fitness function value of S43;
s45, judging whether the genetically selected individuals obtained in S44 meet the crossing probability, and if the genetically selected individuals meet the crossing probability, performing genetic crossing and storing the genetically selected individuals into a new population; if the genetically selected individual does not meet the crossover probability, proceed to S46;
s46, judging whether the genetically selected individuals which do not meet the crossover probability meet the variation probability, if so, performing variation crossover and storing the individuals into a new population, otherwise, removing the individuals;
s47, adding new random individuals on the basis of the new population obtained through the operations of S45 and S46, and enabling the new population to keep the scale of the original population;
s48, judging whether the population obtained in S47 reaches a completion condition, wherein the completion condition comprises a fitness function value Fit of the best individual in the population<The number of epsilon or S43-S47 cycles is within 100-300 times, the achievement of any condition is regarded as the achievement of the completion condition, and the value range of epsilon is 1 x 10-7-2*10-7(ii) a If the completion condition is not met, replacing the population of the previous iteration with the new population obtained in the step S47, and repeating the operations of S43-S47; if the completion condition is reached, exiting;
and S49, outputting the result, namely the inverse kinematics solution.
Further, the encoding method of S41 is real number encoding.
Further, the selection in S44 adopts a competition selection method, and the individuals with the excessive fitness function value are discarded.
Further, the S45 intersection is a multi-point intersection.
Further, in the step S40, the number of individuals in the population D is set to be T, and the number of individuals in the random population D2 is set to be T2The calculation formula of (c) is:
in the formula (2), T1The number of individuals in population array D1 obtained at S36.
Further, the fitness function in S44 is constructed as shown in formula (3):
Fit＝|ΔP|+|ΔO| (3)
in the formula (3), | Δ P | is a position offset amount, and | Δ O | is an attitude offset amount;
in the formula (4), the reaction mixture is,
the end position of the mechanical arm corresponding to the genetic individual,
x, y, z coordinates, P of the end position of the robotic arm corresponding to the genetic individualEA target position for the end of the robotic arm;
in the formula (5), the reaction mixture is,
a jth column and an ith row of a manipulator end attitude matrix corresponding to the genetic individual,
is the jth column and ith row of the robot arm end target body attitude matrix.
Compared with the prior art, the invention has the following beneficial effects:
1. the 6-degree-of-freedom mechanical arm inverse kinematics solving method avoids the inverse of a calculation matrix and an inverse trigonometric function, has small operand and has no limitation condition.
2. According to the 6-degree-of-freedom mechanical arm inverse kinematics solving method provided by the invention, the initial sample of the genetic algorithm is optimized by the Monte Carlo method, the searching speed and the searching success rate of the genetic algorithm can be effectively improved, meanwhile, part of random samples are reserved to keep the diversity of the population, and the global searching capability of the algorithm is ensured.
3. The 6-degree-of-freedom mechanical arm inverse kinematics solving method provided by the invention is simple, the error is less than 1%, and the operability is strong.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of UR5 robot;
FIG. 2 is an encoding format of a genetic algorithm;
fig. 3 is a flowchart of S3;
fig. 4 is a flowchart of S4;
FIG. 5 is a diagram illustrating the result of solving the inverse kinematics solution of the present invention;
fig. 6 is a flowchart of a 6-degree-of-freedom robot arm inverse kinematics solution method according to the present invention.
Detailed Description
The technical solutions in the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the present invention describes in detail the 6-degree-of-freedom robot arm inverse kinematics solution method of the present invention, taking UR5 robot (6-degree-of-freedom robot arm) as an example.
The 6 degree-of-freedom mechanical arm requires to be in an attitude OETo position PE. Wherein the content of the first and second substances,
in order to reach the position of the mobile phone,
x, y, z coordinates of the target position at the tail end of the mechanical arm; attitude OEThe rotation amount of the mechanical arm is calculated,
the rotation angle of the end of the mechanical arm to the coordinate axis of the base.
The main parameters of each joint of the UR5 mechanical arm are as follows: a isiFor the length of the ith link, αiIs the torsion angle of the ith connecting rod, diIs the offset of the connecting rod, thetaiThe joint angle corresponding to the ith connecting rod.
Table 1 UR5 main arm parameters
| i | ai(m) | αi(°) | di(m) | θi |
| 1 | 0 | 90 | 0.08916 | (-360,360) |
| 2 | -0.425 | 0 | 0 | (-360,360) |
| 3 | -0.39225 | 0 | 0 | (-360,360) |
| 4 | 0 | -90 | 0.10915 | (-360,360) |
| 5 | 0 | -90 | 0.09456 | (-360,360) |
| 6 | 0 | 0 | 0.0823 | (-360,360) |
As shown in fig. 1, the 6-degree-of-freedom mechanical arm inverse kinematics solution method provided by the invention comprises the following steps:
and S1, establishing a D-H matrix of the mechanical arm according to the parameters of the mechanical arm.
S2, determining the threshold value of S3 according to the structure of the mechanical arm and the parameters of the mechanical arm, wherein the threshold value is the tail end and the target position P of the mechanical armEThe parameters of the robotic arm include: length, twist angle, offset and joint angle of each mechanical arm. In this embodiment, the threshold th in three directions x, y and zx，thy，thzAll take 0.1 cm.
S3, giving random angles to all joints of the mechanical arm by adopting a Monte Carlo method to generate random samples, and calculating the corresponding relation of each random sample through a D-H matrixOnly the arm end positions satisfying the condition are kept as samples, i.e., the population group D1. In this example, the number of samples is 20 ten thousand. According to the characteristics of the mechanical arm of the UR5 robot, the rotation angle range of all 6 joints is [0,360 ]. The sample data form is as follows: [ theta ] of1,θ2,θ3,θ4,θ5,θ6]. As shown in fig. 3, S3 specifically includes the following steps:
s31, giving random angles to all joints of the mechanical arm, and generating a random sample of the joint angles;
s32, substituting the random sample of the joint angle into the D-H matrix of S1, and calculating the tail end position corresponding to the random sample of the joint angle
S33, judging reserved PMWhile satisfying the following conditions as shown in formula (1);
in the formula (1), thx，thy，thzThe identification of the threshold value in the x, y and z directions;
s34, determining the end position P corresponding to the random sample of the joint angleMIf the condition of S33 is met, the corresponding sample is stored in a population array D1;
s35, circulating the operations of S32-S34 until the calculation of all random samples is completed;
and S36, finishing the circulation, and obtaining a population array D1 meeting the condition of S33.
S4, based on the population array D1 retained in S36, adding a new joint angle population D2, combining them into an initial population D of the genetic algorithm, and using the genetic algorithm to obtain an inverse solution of kinematics, as shown in fig. 4, S4 specifically includes the following steps:
s40, adding a new random sample D2 of the joint angle on the basis of the population array D1 to form an initial population D of the genetic algorithm; setting individual of population DNumber T, number of individuals in population D22Determined by equation (2):
in formula (2), T1The number of individuals in population array D1 obtained at S36;
s41, encoding the initial population D in a real number encoding mode, wherein J is shown in figure 21-J66 joints corresponding to the robot arm, J11、J12And J13The values of the hundred, ten and ones positions of the angle of the joint 1 respectively;
s42, setting parameters of a genetic algorithm, wherein in the embodiment, the cross probability is 0.8, and the mutation probability is 0.4;
and S43, calculating a fitness function value of the mechanical arm terminal pose matrix corresponding to each individual in the population. The pose matrix of the tail end of the mechanical arm is calculated by D-H, and the structure of the fitness function is shown as a formula (3):
Fit＝|ΔP|+|ΔO| (3)
in the formula (3), | Δ P | is a position offset amount, and | Δ O | is an attitude offset amount;
in the formula (4), the reaction mixture is,
the end position of the mechanical arm corresponding to the genetic individual,
x, y, z coordinates, P, of the end position of the robotic arm corresponding to the genetic individualEIs the target position of the end of the mechanical arm;
in the formula (5), the reaction mixture is,
a jth column and an ith row of a manipulator end attitude matrix corresponding to the genetic individual,
the ith row is the jth column of the attitude matrix of the target body at the tail end of the mechanical arm;
s44, carrying out genetic selection on individuals in the S41 population according to the fitness function value of S43, wherein a competition selection method is selected in the embodiment, and individuals with overlarge fitness function values are discarded;
s45, judging whether the genetically selected individuals obtained in S44 meet the crossing probability, if so, performing genetic crossing and storing the genetically selected individuals in a new population; if the genetically selected individual does not meet the crossover probability, proceed to S46; the embodiment adopts multipoint intersection;
s46, judging whether the genetically selected individuals which do not meet the crossover probability meet the variation probability, if so, performing variation crossover and storing the individuals into a new population, otherwise, removing the individuals;
s47, adding new random individuals on the basis of the new population obtained through the operations of S45 and S46, and enabling the new population to keep the scale of the original population;
s48, judging whether the population individuals obtained in the S47 reach the completion conditions, wherein the completion conditions comprise fitness function values Fit<The number of epsilon or S43-S47 cycles is within 100-300 times, the achievement of any condition is regarded as the achievement of the completion condition, and the value range of epsilon is 1 x 10-7-2*10-7(ii) a If the completion condition is not met, replacing the population of the previous iteration with a new population, and repeating the operations of S43-S47; if the completion condition is reached, exiting; the completion condition of this embodiment is the fitness function value Fit<E, e is 0.0000002, and the iteration number is 300;
s49, outputting a result, that is, an inverse kinematics solution, as shown in fig. 5, where an abscissa is the number of iterations, and a ordinate is a fitness function value, it can be found from fig. 5 that the inverse kinematics solution of the mechanical arm can be obtained through about 40 iterations in this embodiment, and an error of the inverse solution is approximately equal to zero, that is, an error of a value after iteration is less than 1%, so that the operability of the present invention is strong.
In summary, the 6-degree-of-freedom mechanical arm inverse kinematics solving method provided by the invention has the advantages that the initial sample of the genetic algorithm is generated by adopting the Monte Carlo method, the convergence rate of the genetic algorithm is improved by optimizing the initial sample of the genetic algorithm, the diversity of the sample is kept by the random sample, and the global searching capability of the genetic algorithm is ensured, so that the inverse kinematics solving method provided by the invention has the advantages of high speed and high precision, and is suitable for the situations of off-line and on-line track planning.
The above disclosure is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or modifications within the technical scope of the present invention, and shall be covered by the scope of the present invention.
Claims (6)
1. A6-degree-of-freedom mechanical arm inverse kinematics solving method is characterized by comprising the following steps: the method comprises the following steps:
s1, establishing a D-H matrix of the mechanical arm according to the parameters of the mechanical arm;
s2, determining the threshold value of S3, namely the tail end position and the target position P of the mechanical arm according to the structure of the mechanical arm and the parameters of the mechanical armEThe maximum distance of (d);
s3, giving random angles to all joints of the mechanical arm by adopting a Monte Carlo method to generate random samples, calculating the tail end position of the mechanical arm corresponding to each random sample through the D-H matrix, and reserving the tail end position of the mechanical arm meeting the conditions as a sample;
the S3 specifically includes the following steps:
s31, giving random angles to all joints of the mechanical arm to generate random samples;
s32, substituting the random sampleD-H matrix of S1, calculating the position of the corresponding mechanical arm end
S33, judging reserved PMWhile satisfying the following conditions as shown in formula (1);
in the formula (1), thx，thy，thzThe identification of the threshold value in the x, y and z directions;
s34, if the random sample corresponds to the end position PMIf the condition of S33 is met, the corresponding sample is stored to a population array D1;
s35, circulating the operations of S32-S34 until the calculation of all random samples is completed;
s36, finishing the circulation to obtain a population array D1;
s4, taking the sample reserved in S3 as a seed group, adding new random populations to form an initial population of the genetic algorithm, and obtaining a kinematic inverse solution by using the genetic algorithm;
the S4 specifically includes the following steps:
s40, adding a new random sample group D2 of joint angles on the basis of the population group D1 to form an initial population D of the genetic algorithm;
s41, encoding the initial population D;
s42, setting parameters of a genetic algorithm;
s43, calculating a fitness function value of the mechanical arm terminal pose matrix corresponding to each individual in the population;
s44, carrying out genetic selection on individuals in the S41 population according to the fitness function value of S43;
the fitness function in S44 is constructed as shown in equation (3):
Fit＝|ΔP|+|ΔO| (3)
in the formula (3), | Δ P | is a position offset amount, and | Δ O | is an attitude offset amount;
in the formula (4), the reaction mixture is,
the end position of the mechanical arm corresponding to the genetic individual,
x, y, z coordinates, P, of the end position of the robotic arm corresponding to the genetic individualEIs the target position of the end of the mechanical arm;
in the formula (5), the reaction mixture is,
a jth column and an ith row of a manipulator end attitude matrix corresponding to the genetic individual,
the ith row is the jth column of the attitude matrix of the target body at the tail end of the mechanical arm;
s45, judging whether the genetically selected individuals obtained in S44 meet the crossing probability, and if the genetically selected individuals meet the crossing probability, performing genetic crossing and storing the genetically selected individuals into a new population; if the genetically selected individual does not meet the crossover probability, proceed to S46;
s46, judging whether the genetically selected individuals which do not meet the crossover probability meet the variation probability, if so, performing variation crossover and storing the individuals into a new population, otherwise, removing the individuals;
s47, adding new random individuals on the basis of the new population obtained through the operations of S45 and S46, and enabling the new population to keep the scale of the original population;
s48, judging whether the population obtained in S47 reaches a completion condition, wherein the completion condition comprises a fitness function value Fit of the best individual in the population<The number of epsilon or S43-S47 cycles is within 100-300 times, the achievement of any condition is regarded as the achievement of the completion condition, and the value range of epsilon is 1 x 10-7-2*10-7(ii) a If the completion condition is not met, replacing the population of the previous iteration with the new population obtained in the step S47, and repeating the operations of S43-S47; if the completion condition is reached, exiting;
and S49, outputting the result, namely the inverse kinematics solution.
2. The 6-degree-of-freedom mechanical arm inverse kinematics solution method according to claim 1, wherein: the parameters of the mechanical arm comprise: the length, the torsion angle, the offset and the joint angle of each mechanical arm.
3. The 6-degree-of-freedom mechanical arm inverse kinematics solution method according to claim 1, wherein: the encoding mode of S41 is real number encoding.
4. The 6-degree-of-freedom mechanical arm inverse kinematics solution method according to claim 1, wherein: and selecting in the S44 by adopting a competition selection method, and discarding individuals with overlarge fitness function values.
5. The 6-degree-of-freedom mechanical arm inverse kinematics solution method according to claim 1, wherein: the S45 intersection is a multipoint intersection.
6. The 6-degree-of-freedom robot arm inverse kinematics solution method according to claim 1, wherein: in S40, the number of individuals in the population D is set to be T, and the number of individuals in the random population D2 is set to be T2The calculation formula of (2) is as follows:
in the formula (2), T1The number of individuals in population array D1 obtained at S36.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811098865.0A CN109344477B (en) | 2018-09-20 | 2018-09-20 | 6-degree-of-freedom mechanical arm inverse kinematics solving method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811098865.0A CN109344477B (en) | 2018-09-20 | 2018-09-20 | 6-degree-of-freedom mechanical arm inverse kinematics solving method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109344477A CN109344477A (en) | 2019-02-15 |
| CN109344477B true CN109344477B (en) | 2022-06-24 |
Family
ID=65306178
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811098865.0A Active CN109344477B (en) | 2018-09-20 | 2018-09-20 | 6-degree-of-freedom mechanical arm inverse kinematics solving method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109344477B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111241747B (en) * | 2020-01-13 | 2023-07-25 | 佛山科学技术学院 | Mechanical arm inverse kinematics solving method, system, computer equipment and storage medium |
| CN111283681B (en) * | 2020-02-28 | 2022-03-15 | 东南大学 | Six-degree-of-freedom mechanical arm inverse solution method based on SCAPSO switching |
| CN111914980B (en) * | 2020-05-30 | 2023-12-01 | 中南大学 | Redundant mechanical arm kinematics resolving method based on analytic solution and improved genetic algorithm |
| CN112347409A (en) * | 2020-09-30 | 2021-02-09 | 北京工业大学 | Redundancy mechanical arm inverse solution method for optimizing BP neural network based on genetic algorithm |
| CN116160444B (en) * | 2022-12-31 | 2024-01-30 | 中国科学院长春光学精密机械与物理研究所 | Mechanical arm kinematics inverse solution optimization method and device based on clustering algorithm |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104678766A (en) * | 2015-01-29 | 2015-06-03 | 苏州大学 | Method for solving optimal batting configuration for flight ball body operation of humanoid manipulator |
| CN206230507U (en) * | 2016-10-27 | 2017-06-09 | 桂林电子科技大学 | A kind of outdoor autonomous mobile robot with the laser radar with built-in stabilizers |
-
2018
- 2018-09-20 CN CN201811098865.0A patent/CN109344477B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104678766A (en) * | 2015-01-29 | 2015-06-03 | 苏州大学 | Method for solving optimal batting configuration for flight ball body operation of humanoid manipulator |
| CN206230507U (en) * | 2016-10-27 | 2017-06-09 | 桂林电子科技大学 | A kind of outdoor autonomous mobile robot with the laser radar with built-in stabilizers |
Non-Patent Citations (4)
| Title |
|---|
| 东辉 等."基于工作空间密度函数的平面冗余机器人的逆运动学求解算法".《机械工程学报》.2015,第51卷(第17期), * |
| 彭翊."脊柱微创手术机器人结构设计与手术仿真研究".《中国优秀硕士学位论文全文数据库 (信息科技辑)》.2017, * |
| 林阳 等."基于多种群遗传算法的一般机器人逆运动学求解".《机械工程学报》.2017,第53卷(第3期), * |
| 王俊龙 等."一种新的六自由度机械臂运动学反解方法研究".《计算机工程与应用》.2013,第49卷(第22期), * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109344477A (en) | 2019-02-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109344477B (en) | 6-degree-of-freedom mechanical arm inverse kinematics solving method | |
| CN110682286B (en) | Real-time obstacle avoidance method for cooperative robot | |
| CN111347429A (en) | Collision detection mechanical arm path planning method based on improved ant colony algorithm | |
| CN111761582B (en) | Mobile mechanical arm obstacle avoidance planning method based on random sampling | |
| CN111409077A (en) | Robot terminal multi-target pose approximation method based on joint angle compensation | |
| CN110489707B (en) | GAN network-based robot inverse kinematics solving method | |
| CN111914980B (en) | Redundant mechanical arm kinematics resolving method based on analytic solution and improved genetic algorithm | |
| CN113325799B (en) | Spot welding robot operation space smooth path planning method for curved surface workpiece | |
| Yoon et al. | Optimum design of 6-DOF parallel manipulator with translational/rotational workspaces for haptic device application | |
| CN112109084A (en) | Terminal position compensation method based on robot joint angle compensation and application thereof | |
| CN111645079B (en) | Device and method for planning and controlling mechanical arm path of live working robot | |
| CN116038702B (en) | Seven-axis robot inverse solution method and seven-axis robot | |
| CN113119109A (en) | Industrial robot path planning method and system based on pseudo-distance function | |
| CN111660296A (en) | Method for optimizing flexibility of robot based on global space condition number | |
| Zhao et al. | A learning-based multiscale modelling approach to real-time serial manipulator kinematics simulation | |
| CN113043278B (en) | Mechanical arm track planning method based on improved whale searching method | |
| CN117444958A (en) | Mechanical arm joint angle rapid extraction method based on GRU neural network and firework algorithm | |
| CN116237950A (en) | Robot tail end accurate control method and equipment based on sectional motion planning strategy | |
| CN113434982B (en) | Inverse kinematics solution method of electric intelligent bionic climbing robot | |
| Nagata et al. | Neural network-based inverse kinematics for an industrial robot and its learning method | |
| CN113021345A (en) | Method, device and equipment for controlling inverse kinematics of mechanical arm and readable storage medium | |
| Li et al. | Path planning of a robotic manipulator for pruning apple trees based on RRT-connect algorithm | |
| Dong et al. | A novel algorithm by combining nonlinear workspace partition with neural networks for solving the inverse kinematics problem of redundant manipulators | |
| CN112828891B (en) | Detection method and detection device for robot path planning effectiveness and computer readable storage medium | |
| CN117733872B (en) | Series robot inverse kinematics control method based on directional performance |
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 |