CN113033065B - Inverse kinematics solving method for two-joint rope-driven continuous mechanical arm - Google Patents
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Abstract
The invention discloses a method for solving inverse kinematics of a two-joint rope-driven continuous mechanical arm, which comprises the following steps: firstly, establishing a parameterized model of a two-joint rope-driven continuous mechanical arm by utilizing a piecewise constant curvature hypothesis; secondly, solving errors from the position point of the tail end of the mechanical arm to the target position point in position and posture, and taking the errors as a target function; then, quickly finding out a joint variable which enables an objective function to be minimum by using a particle swarm algorithm; and finally, analyzing to obtain the motion coupling amount between the two joints, and decoupling in the mapping from the joint space to the driving space to obtain the rope length variation. The invention can rapidly obtain the inverse kinematics solution of the rope-driven continuous mechanical arm, and takes the coupling between two joints into consideration, so that the inverse kinematics model is more accurate.
Description
Technical Field
The invention relates to a two-joint rope-driven continuous mechanical arm inverse kinematics solving method, in particular to a rope-driven continuous mechanical arm inverse kinematics solving method based on a particle swarm algorithm.
Background
Along with the diversification of space on-orbit tasks, the environment where the aircraft is located in space is more complex, and although the traditional mechanical arm design and control technology is mature, the traditional mechanical arm is generally formed by connecting a plurality of rigid connecting rods, the working space is relatively fixed, and in some narrow task spaces, the movement of the traditional rigid mechanical arm can be limited, so that the requirements of the tasks cannot be met. In bionic research on flexible living beings or animal flexible organs in the nature, researchers design flexible continuum mechanical arms. The mechanical arm has a continuous flexible connecting rod structure, has no rigid node, has excellent bending performance and can flexibly change the shape of the mechanical arm. The continuous mechanical arm replaces the traditional mechanical arm, is applied to an aerospace vehicle, and can be better adapted to a complex task space, so that the aircraft can be guaranteed to better complete an on-orbit task.
The continuous mechanical arm has the characteristics of strong flexibility and strong coupling, and the positive kinematic model established by utilizing the sectional constant curvature has very complex structure, so that the inverse kinematic solution is difficult to obtain like the traditional rigid mechanical arm. The numerical solution is obtained by utilizing the jacobian and Newton iteration method, and the problem that the jacobian is difficult to obtain and the calculation period is long is faced. Therefore, a novel inverse kinematics solving method of the rope-driven continuous mechanical arm is found, and the method has very important significance for quick control of the mechanical arm.
Disclosure of Invention
The invention aims to provide a method for solving inverse kinematics of a two-joint rope-driven continuous mechanical arm. Firstly, establishing a parameterized model of the two-joint continuous mechanical arm, then calculating errors of initial end points and target positions of the mechanical arm in positions and postures, taking the errors as an objective function, then rapidly solving related parameters by using a particle swarm algorithm, and determining the variable quantity of the driving rope length of the two-joint continuous mechanical arm by using the parameters.
The technical scheme for realizing the invention is as follows: a method for solving inverse kinematics of a two-joint rope-driven continuous mechanical arm comprises the following steps:
step 1, carrying out parameterization modeling on a two-joint rope-driven continuous mechanical arm by using a piecewise constant curvature hypothesis;
and 4, determining the coupling quantity between two joints of the continuous mechanical arm, and decoupling in the mapping from the driving space to the joint space to obtain the variable quantity of the driving rope length, so as to complete the inverse kinematics solution of the two-joint rope-driven continuous mechanical arm.
Compared with the prior art, the invention has the remarkable advantages that:
(1) According to the two-joint rope-driven continuous mechanical arm inverse kinematics solving method, the error between the tail end position and the target position of the mechanical arm is used as an objective function, and the particle swarm algorithm is utilized for optimization, so that a complex analysis formula can be avoided being solved, and inverse kinematics solution can be effectively obtained.
(2) According to the method for solving the inverse kinematics of the two-joint rope-driven continuous mechanical arm, which is disclosed by the invention, the kinematic coupling between the two joints of the continuous mechanical arm is considered, the coupling quantity is obtained through analysis, the decoupling is realized, and the accuracy of an inverse kinematics model is improved.
The invention is further described below with reference to the drawings and detailed description.
Drawings
FIG. 1 is a flow chart of the inverse kinematics solving method of the two-joint rope-driven continuous mechanical arm.
Fig. 2 is a schematic structural diagram of a two-joint rope-driven continuous mechanical arm in the invention.
FIG. 3 is a schematic diagram of parameterized modeling of a robotic arm in the present invention.
Fig. 4 is a diagram illustrating an embodiment of the present invention for solving the objective function by using a particle swarm algorithm.
Detailed Description
A method for solving inverse kinematics of a two-joint rope-driven continuous mechanical arm comprises the following steps:
step 1, carrying out parameterization modeling on a two-joint rope-driven continuous mechanical arm by utilizing a segmentation constant curvature assumption, wherein the parameterization modeling specifically comprises the following steps:
abstracting the movement of the mechanical arm as a change of a parameter including the known parameter of arc length L 1 、L 2 Parameter bending angle θ to be determined 1 、θ 2 Angle of curved plane
Taking the first joint as an example, when the mechanical arm moves, the first joint can be regarded as a continuous curve with equal curvature, and the basic parameters include arc length L 1 Angle of bending theta 1 Angle of curved plane
The parameterization of the second joint is similar to that of the first joint and comprises three parameters L 2 、θ 2 、
Wherein L is 1 、L 2 Is a known parameter, θ 1 、
θ 2 、
The joint angle parameter is the parameter to be solved.
step 2-1: the target pose at the tail end of the mechanical arm is
Wherein R is d ＝[r d1 r d2 r d3 ] T Is a direction matrix, P d ＝[p d1 p d2 p d3 ] T Is a position vector;
according to the segmentation constant curvature assumption, the current tail end pose of the mechanical arm
Wherein R is c (q)＝[r c1 (q) r c2 (q) r c2 (q)]，P c (q)＝[p c1 (q) p c2 (q) p c3 (q)] T Q is the joint angle parameter,
the T is c (q)＝T 1 T 2 ：
Wherein T is 1 And T 2 The current tail end pose of two joints of the mechanical arm respectively, L 1 、L 2 Arc length, theta, of two joints respectively 1 、θ 2 The bending angles of the two joints are respectively,
the bending plane angles of the two joints are respectively c=cos=s=sin.
Step 2.2, position error target P (q) = ||p d -P c (q) attitude error target
The objective function is E (q) =p (q) +o (q).
setting each particle position as
Initializing example population x= (X) 1 ,x 2 ,...,x n ) And the flying speed v= (V) of each particle 1 ,v 2 ,...,v n ) The particle swarm evolution formula is:
wherein k is the iteration number; omega (k) is inertia weight, and linearly decreasing inertia weight is adopted in iteration, so that the particle swarm algorithm has good global searching capability at the beginning and good local searching capability at the later stage; c 1 、c 2 Is a learning factor; r is (r) 1 、r 2 Is in [0,1 ]]Random numbers in between;
the best position of particle i after iterating k times;
The best position of the population of particles for the current cycle;
firstly initializing population, setting parameters, then entering algorithm circulation, calling objective function in each circulation, updating the position and speed of particles, updating the optimal position and global optimal position of individual particles until the value of the objective function E (q) is smaller than a set threshold value, and exiting the circulation to obtain an optimal solution
Step 4, when the two joints of the rope-driven continuous mechanical arm move, the driving ropes of the two joints are coupled, and when the two joints move independently, the driving ropes of the other joint are offset, so that a more accurate inverse kinematics model is obtained, the corresponding coupling amount is needed to be calculated, and compensation is performed;
determining the coupling quantity between two joints of the continuous mechanical arm, and decoupling in the mapping from a driving space to a joint space to obtain the driving rope length variation quantity, so as to complete the inverse kinematics solution of the two-joint rope-driven continuous mechanical arm, and specifically comprising the following steps:
step 4-1: when the driving rope of the first joint is pulled to enable the first joint to independently move, the second joint is kept unchanged, but a part of the driving rope of the second joint passing through the first joint can generate certain displacement;
determining the amount of coupling of the first joint motion to the second joint:
wherein r is 2 Is the distance between the center rod and the second joint driving rope, n 1 Is the first number of articular spacer discs;
step 4-2: determining the coupling amount of the second joint to the first joint:
when the driving rope of the second joint is pulled, the first joint also moves due to the continuity of the mechanical arm, so that the displacement of the driving rope is driven;
the bending angle of the first joint is
The angle of the bending plane is +.>
The amount of coupling the second joint motion causes to the first joint is:
wherein r is 1 Distance from the center rod to the first joint driving rope;
step 4-3: according to the determined coupling quantity, solving the mapping from the joint space to the driving space, and determining the change of the driving rope length:
wherein n is 2 The number of the second joint spacing discs is considered, and the driving rope is approximately a continuous straight line segment in the actual movement process of the rope-driven continuous mechanical arm.
A two-joint rope-driven continuous mechanical arm inverse kinematics solving system comprises the following modules:
the objective function construction module: the method comprises the steps of abstracting the motion of a mechanical arm into the change of parameters, and constructing an objective function aiming at the parameters to be determined;
and a parameter determining module: the method comprises the steps of utilizing a particle swarm algorithm to solve an objective function to obtain an optimal solution of parameters to be determined;
the coupling amount determining module: and determining the coupling quantity between the joints of the mechanical arm by utilizing the optimal solution of the obtained parameters, and obtaining the variable quantity of the driving rope length.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of:
step 1, carrying out parameterization modeling on a two-joint rope-driven continuous mechanical arm by using a piecewise constant curvature hypothesis;
and 4, determining the coupling quantity between two joints of the continuous mechanical arm, and decoupling in the mapping from the driving space to the joint space to obtain the variable quantity of the driving rope length, so as to complete the inverse kinematics solution of the two-joint rope-driven continuous mechanical arm.
A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, performs the steps of:
step 1, carrying out parameterization modeling on a two-joint rope-driven continuous mechanical arm by using a piecewise constant curvature hypothesis;
and 4, determining the coupling quantity between two joints of the continuous mechanical arm, and decoupling in the mapping from the driving space to the joint space to obtain the variable quantity of the driving rope length, so as to complete the inverse kinematics solution of the two-joint rope-driven continuous mechanical arm.
The invention will be further illustrated with reference to examples
Examples:
in the embodiment, each joint of the joint rope-driven continuous mechanical arm is 300mm long, each joint is provided with 5 spacing discs and driven by three driving ropes, the first joint driving ropes are uniformly distributed on a circle 18mm away from a central rod, the second joint driving ropes are uniformly distributed on a circle 16mm away from the central rod, the pose vector of a target position is [229.54 357.49 272.79-0.8319.5136-2.0689 ], wherein the first three parameters are position parameters x, y and z, the unit is mm, the last three parameters are Euler angles alpha, beta and gamma, and the unit is rad.
Referring to fig. 1, a method for solving inverse kinematics of a two-joint rope-driven continuous mechanical arm includes the following steps:
step 1, carrying out parameterization modeling on a two-joint rope-driven continuous mechanical arm by utilizing a segmentation constant curvature assumption, wherein the parameterization modeling specifically comprises the following steps:
as shown in fig. 2, the motion of the mechanical arm is abstracted into the change of parameters, including the known parameter arc length L 1 ＝300、L 2 =300, the parameter to be determined is the bending angle θ 1 、θ 2 Angle of curved plane
As shown in fig. 3;
taking the first joint as an example, when the mechanical arm moves, the first joint can be regarded as a continuous curve with equal curvature, and the basic parameters include arc length L 1 Angle of bending theta 1 Angle of curved plane
The parameterization of the second joint is similar to that of the first joint and comprises three parameters L 2 、θ 2 、
Wherein L is 1 、L 2 Is a known parameter, θ 1 、
θ 2 、
The joint angle parameter is the parameter to be solved.
step 2-1: the target pose at the tail end of the mechanical arm is as follows:
wherein R is d ＝[r d1 r d2 r d3 ] T Is a direction matrix:
wherein c=cos, s=sin;
P d ＝[p d1 p d2 p d3 ] T is a position vector;
according to the segmentation constant curvature assumption, the current tail end pose of the mechanical arm
Wherein R is c (q)＝[r c1 (q) r c2 (q) r c2 (q)]，P c (q)＝[p c1 (q) p c2 (q) p c3 (q)] T Q is the joint angle parameter,
the T is c (q)＝T 1 T 2 ：
Wherein T is 1 And T 2 The current tail end pose of two joints of the mechanical arm respectively, L 1 、L 2 Arc length, theta, of two joints respectively 1 、θ 2 Respectively two jointsIs provided with a bending angle of (a),
the bending plane angles of the two joints are respectively c=cos=s=sin.
Step 2.2, position error target P (q) = ||p d -P c (q) attitude error target
The objective function is E (q) =p (q) +o (q).
setting each particle position as
Initializing example population x= (X) 1 ,x 2 ,...,x n ) And the flying speed v= (V) of each particle 1 ,v 2 ,...,v n ) The particle swarm evolution formula is:
wherein k is the iteration number; omega (k) is inertia weight, and linearly decreasing inertia weight is adopted in iteration, so that the particle swarm algorithm has good global searching capability at the beginning and good local searching capability at the later stage; c 1 、c 2 Is a learning factor; r is (r) 1 、r 2 Is in [0,1 ]]Random numbers in between;
the best position of particle i after iterating k times;
The best position of the population of particles for the current cycle;
firstly initializing population, setting parameters, then entering algorithm circulation, calling objective function in each circulation, updating the position and speed of particles, updating the optimal position and global optimal position of individual particles until the value of the objective function E (q) is smaller than a set threshold value, and exiting the circulation to obtain an optimal solution
In this embodiment, the population number of the particle swarm is set to be 100, the maximum movement speed is 1, and the learning factor c 1 ＝1.3、c 2 =1.7, the maximum iteration number is 100, the maximum inertia weight is 0.9, the minimum inertia weight is 0.1, a program is written in MATLAB, the objective function in the step 2 is optimized, and the optimal solution to be solved is x best = (1.000,1.017,1.012,0.969), unit is rad, and algorithm running trace is shown in fig. 4.
Step 4, when the two joints of the rope-driven continuous mechanical arm move, the driving ropes of the two joints are coupled, and when the two joints move independently, the driving ropes of the other joint are offset, so that a more accurate inverse kinematics model is obtained, the corresponding coupling amount is needed to be calculated, and compensation is performed;
determining the coupling quantity between two joints of the continuous mechanical arm, and decoupling in the mapping from a driving space to a joint space to obtain the driving rope length variation quantity, so as to complete the inverse kinematics solution of the two-joint rope-driven continuous mechanical arm, and specifically comprising the following steps:
step 4-1: when the driving rope of the first joint is pulled to enable the first joint to independently move, the second joint is kept unchanged, but a part of the driving rope of the second joint passing through the first joint can generate certain displacement;
determining the amount of coupling of the first joint motion to the second joint:
wherein r is 2 Is the distance between the center rod and the second joint driving rope, n 1 Is the first number of articular spacer discs;
step 4-2: determining the coupling amount of the second joint to the first joint:
when the driving rope of the second joint is pulled, the first joint also moves due to the continuity of the mechanical arm, so that the displacement of the driving rope is driven;
the bending angle of the first joint is
The angle of the bending plane is +.>
The amount of coupling the second joint motion causes to the first joint is: />
Wherein r is 1 Distance from the center rod to the first joint driving rope;
step 4-3: according to the determined coupling quantity, the mapping from the joint space to the driving space is solved, and the change of the driving rope length is determined, wherein the unit is mm:
wherein n is 2 The number of the second joint spacing discs is considered, and the driving rope is approximately a continuous straight line segment in the actual movement process of the rope-driven continuous mechanical arm.
According to the two-joint rope-driven continuous mechanical arm inverse kinematics solving method, the error between the tail end position and the target position of the mechanical arm is used as an objective function, the particle swarm optimization is utilized, the kinematic coupling between two joints of the continuous mechanical arm is considered, the coupling amount is obtained through analysis, decoupling is realized, and the accuracy of an inverse kinematics model is improved.
Claims (7)
1. The inverse kinematics solving method of the two-joint rope-driven continuous mechanical arm is characterized by comprising the following steps of:
step 1, carrying out parameterization modeling on a two-joint rope-driven continuous mechanical arm by using a piecewise constant curvature hypothesis;
step 2, constructing an objective function;
step 3, solving an optimal solution of the objective function according to a particle swarm algorithm;
step 4, determining the coupling quantity between two joints of the continuous mechanical arm, and decoupling in the mapping from the driving space to the joint space to obtain the variable quantity of the driving rope length, so as to complete the inverse kinematics solution of the two-joint rope-driven continuous mechanical arm, wherein the method specifically comprises the following steps:
step 4-1: determining the amount of coupling of the first joint motion to the second joint:
wherein r is 2 Is the distance between the center rod and the second joint driving rope, n 1 Is the first number of articular spacer discs;
L 1 、L 2 indicating arc length, theta 1 、θ 2 Representing the bending angle of the parameter to be determined,
representing a curved plane angle;
step 4-2: determining the coupling amount of the second joint to the first joint:
the bending angle of the first joint is
The angle of the bending plane is +.>
The amount of coupling the second joint motion causes to the first joint is:
wherein r is 1 Distance from the center rod to the first joint driving rope;
step 4-3: determining a change in drive rope length according to the determined coupling amount:
wherein n is 2 Is the second number of articular spacer discs.
2. The method for solving the inverse kinematics of the two-joint rope-driven continuous mechanical arm according to claim 1, wherein the construction objective function in the step 2 specifically comprises the following steps:
step 2-1: the target pose at the tail end of the mechanical arm is
Wherein R is d ＝[r d1 r d2 r d3 ] T Is a direction matrix, P d ＝[p d1 p d2 p d3 ] T Is a position vector;
according to the segmentation constant curvature assumption, the current tail end pose of the mechanical arm
Wherein R is c (q)＝[r c1 (q) r c2 (q) r c3 (q)]，P c (q)＝[p c1 (q) p c2 (q) p c3 (q)] T Q is the joint angle parameter,
step 2.2, position error target P (q) = ||p d -P c (q) attitude error target
The objective function is E (q) =p (q) +o (q).
3. The method for solving inverse kinematics of a two-joint rope-driven continuous mechanical arm according to claim 2, wherein the T is c (q)＝T 1 T 2 ：
4. The method for solving the inverse kinematics of the two-joint rope-driven continuous mechanical arm according to claim 1, wherein the solving the optimal solution of the objective function according to the particle swarm algorithm in the step 3 is specifically:
setting each particle position as
Initializing example population x= (X) 1 ,x 2 ,...,x n ) And the flying speed v= (V) of each particle 1 ,v 2 ,...,v n ) The particle swarm evolution formula is:
wherein k is the iteration number; omega (k) is inertial weight, c 1 、c 2 Is a learning factor; z 1 、z 2 Is in [0,1 ]]Random numbers in between;
the optimal position of particle i after iterating k times;
The optimal position of the particle swarm is the current circulation;
firstly initializing population, setting parameters, then entering algorithm circulation, calling objective function in each circulation, updating the position and speed of particles, updating the optimal position and global optimal position of individual particles until the value of the objective function E (q) is smaller than a set threshold value, and exiting the circulation to obtain an optimal solution
5. The two-joint rope-driven continuous mechanical arm inverse kinematics solving system is characterized by comprising the following modules:
the objective function construction module: the method comprises the steps of abstracting the motion of a mechanical arm into the change of parameters, and constructing an objective function aiming at the parameters to be determined;
and a parameter determining module: the method comprises the steps of utilizing a particle swarm algorithm to solve an objective function to obtain an optimal solution of parameters to be determined;
the coupling amount determining module: determining the coupling quantity between joints of the mechanical arm by utilizing the optimal solution of the solved parameters to obtain the variable quantity of the driving rope length, wherein the variable quantity specifically comprises the following steps:
determining the amount of coupling of the first joint motion to the second joint:
wherein r is 2 Is the distance between the center rod and the second joint driving rope, n 1 Is the first number of articular spacer discs;
determining the coupling amount of the second joint to the first joint:
the bending angle of the first joint is
The angle of the bending plane is +.>
The amount of coupling the second joint motion causes to the first joint is:
wherein r is 1 Distance from the center rod to the first joint driving rope;
determining a change in drive rope length according to the determined coupling amount:
wherein n is 2 Is the second number of articular spacer discs.
6. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-4 when the computer program is executed.
7. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1-4.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105159096A (en) * | 2015-10-10 | 2015-12-16 | 北京邮电大学 | Redundancy space manipulator joint torque optimization method based on particle swarm algorithm |
| CN109901397A (en) * | 2019-03-25 | 2019-06-18 | 西北工业大学 | A kind of mechanical arm inverse kinematics method using particle swarm optimization algorithm |
| CN112336297A (en) * | 2020-10-31 | 2021-02-09 | 同济大学 | Method and system for controlling in-vivo introduction device, and computer-readable storage medium |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105159096A (en) * | 2015-10-10 | 2015-12-16 | 北京邮电大学 | Redundancy space manipulator joint torque optimization method based on particle swarm algorithm |
| CN109901397A (en) * | 2019-03-25 | 2019-06-18 | 西北工业大学 | A kind of mechanical arm inverse kinematics method using particle swarm optimization algorithm |
| CN112336297A (en) * | 2020-10-31 | 2021-02-09 | 同济大学 | Method and system for controlling in-vivo introduction device, and computer-readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 一种仿生轻量型机械臂的设计与分析;陈彦霖;《中国优秀硕士学位论文全文数据库》;20210215;第1-63页 * |
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