CN111515949B - Double-arm transmission and reception position selection method for double-arm cooperative robot - Google Patents

Double-arm transmission and reception position selection method for double-arm cooperative robot Download PDF

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CN111515949B
CN111515949B CN202010327087.9A CN202010327087A CN111515949B CN 111515949 B CN111515949 B CN 111515949B CN 202010327087 A CN202010327087 A CN 202010327087A CN 111515949 B CN111515949 B CN 111515949B
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master
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CN111515949A (en
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丛明
刘冬
赵申申
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Dalian University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1679—Programme controls characterised by the tasks executed
    • B25J9/1682—Dual arm manipulator; Coordination of several manipulators
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/0084—Programme-controlled manipulators comprising a plurality of manipulators
    • B25J9/0087—Dual arms
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/1605—Simulation of manipulator lay-out, design, modelling of manipulator
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—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

Abstract

A double-arm transfer position selection method for a double-arm cooperative robot belongs to the field of intelligent robots. Firstly, acquiring initial poses of a main arm and a slave arm; secondly, establishing an optimization model for solving the optimal transfer position of the master-slave arm through the initial pose of the master-slave arm and the relative pose constraint required to be met by the master-slave arm at the target position; and finally, solving the established optimal transfer and connection position optimization model by adopting an improved genetic algorithm to obtain the optimal transfer and connection positions of the master arm and the slave arm. The invention realizes that the end effector of the main arm and the slave arm can reach the transfer and connection position by changing a smaller pose to finish the transfer and connection operation of the object, thereby improving the efficiency of transferring and connecting the object by the double-arm cooperative robot, enabling the double-arm operation to be applied to more occasions and finishing the task safely, accurately and efficiently.

Description

Double-arm transmission and reception position selection method for double-arm cooperative robot
Technical Field
The invention belongs to the field of intelligent robots, and particularly relates to a double-arm transfer position selection method of a double-arm cooperative robot.
Background
The robot is widely applied to industries such as automobile assembly, 3C manufacturing, service industry, medical pharmacy and the like. In recent years, with the expansion of the application range of robots, the working environment becomes more and more complex, and various new operation tasks have made higher demands on the operation capability of the robots. For example, the robot and the human cooperate to complete the industrial production task, so that a light and flexible 'cooperative robot' is created according to the market demand. Cooperative robots can be divided into single-arm and two-arm robots. Compared with a single-arm robot, the double-arm robot has more degrees of freedom and complex structures, and the flexibility and the universality of the robot are improved. Through the cooperation between the both arms, can accomplish the task that single armed was difficult to accomplish such as both arms carry in coordination, shaft hole assembly, both arms letter sorting. The single-arm cooperative robot is not a simple combination of two single-arm robots, but is used as an independent robot system, and a high degree of coordination exists between the two arms. The transmission of objects between two arms is one of the two-arm cooperation techniques. Through the transmission and connection matching between the two arms, the working range of the single arm can be enlarged, and the repeated utilization rate of the tool is improved, so that the object transmission and connection technology between the two arms of the robot has wide application prospect. The transfer position of the double arms of the robot is not fixed due to the difference of the initial positions when the double arms of the robot perform the transfer operation.
Disclosure of Invention
In view of the above problems, the present invention provides a method for selecting a dual-arm transfer position of a dual-arm robot. A proper transfer position is selected for transfer of an object between two arms of the robot, and the distance between the transfer position and the initial position of the two arms is minimum, so that the transfer position can be reached by the posture of the two arms which changes slightly, and kinematic constraint that the two arms grab the object at the same time is satisfied at the position.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a method for selecting double-arm transmission and reception positions of a double-arm cooperative robot is characterized in that relative pose constraint information required to be met by an end effector of double arms of the double-arm cooperative robot at the transmission and reception positions is obtained according to the shape of an object to be transmitted, wherein a mechanical arm for holding an object in the double arms of the double-arm cooperative robot is set as a main arm, and the other mechanical arm is a slave arm; acquiring initial poses of the master arm and the slave arm; establishing an optimization model for solving the optimal transfer position of the main arm and the slave arm through the initial pose of the main arm and the slave arm and the relative pose constraint which needs to be met by the main arm and the slave arm at the target position, wherein the optimization model is established by optimizing the pose change amplitude of the end effector of the mechanical arm from the initial pose to the transfer position; and establishing a fitness function to be optimized according to the optimization model, performing iterative training by using an improved genetic algorithm according to the fitness function, and obtaining the optimal transfer and connection positions of the master arm and the slave arm, so that transfer and connection are performed at the optimal transfer and connection positions, the double-arm end effector of the robot moves from an initial pose to the transfer and connection positions, and the pose change amplitude is minimum. The whole implementation process of the method can be divided into two stages, namely a first stage, establishing an optimization model for solving the optimal transfer and connection position of the main arm and the slave arm; and in the second stage, solving the established optimal transfer and connection position optimization model by using an improved genetic algorithm to obtain the optimal transfer and connection positions of the master arm and the slave arm. The method comprises the following specific steps:
first, establish the end effector coordinate systems of the master arm and the slave arm, respectively using { E }1}、{E2Denotes that when the end effectors of both arms are determined to grasp the object simultaneously according to the shape of the object, { E1And { E } and2the relative pose constraint that needs to be satisfied
Figure GDA0003089552120000021
Representing the relative pose constraints that are met by the dual-arm end effector grasping an object simultaneously at the transfer position.
Secondly, acquiring initial poses of the master arm and the slave arm, obtaining joint angle data through angle sensor feedback at the joints of the mechanical arm, and then obtaining poses T of the two arms in a Cartesian space through a positive kinematic equation of the mechanical arminit. For a single arm, the pose T of the single arm end effector can be represented by a 4 x 4 matrix, T being a homogeneous transformation matrix representing the spatial pose:
Figure GDA0003089552120000022
wherein, R is a 3 × 3 rotation matrix representing the posture; p is a 3 × 1 vector representing a position; 0TIs a 0 matrix of 1 × 3.
Thirdly, through the initial pose T of the master arm and the slave arminitAnd the relative pose constraint which needs to be met by simultaneously grabbing the object at the transfer position by the master arm and the slave arm
Figure GDA0003089552120000023
Establishing an optimization model for solving the optimal transfer and connection positions of the master arm and the slave arm;
f1＝Q(θ1)+Q(θ2) (2)
Q(θ)＝αΔR+βΔP (3)
Figure GDA0003089552120000024
Figure GDA0003089552120000025
Figure GDA0003089552120000026
Figure GDA0003089552120000027
wherein, theta1And theta2A vector composed of joint angles of the master arm and the slave arm is shown, Q (theta) shows the change of the terminal pose of the single arm, and Q (theta)1)、Q(θ2) Respectively showing the pose changes of the end effectors of the master arm and the slave arm when the two arms move from the initial pose to the transfer position. Change of end position of both arms and use of f1And (4) showing. And delta R represents the attitude difference between the initial position of the tail end of the mechanical arm and the transmission position, delta P represents the distance difference between the initial position of the tail end of the mechanical arm and the transmission position, alpha and beta are weights, and the proportion of the position and the attitude can be controlled by adjusting the weights. P ═ Px,py,pz]TThe position vector of the end effector of the robot arm is expressed in meters, which is obtained from equation (1). PinitRepresenting a position vector, P, of the end effector of the robot arm at an initial poseexchRepresenting a position vector of the end effector of the robotic arm at the transfer position. Delta is [ delta ]x,δy,δz]TIs a 3 × 1 attitude vector expressed by the attitude R in formula (1) via the tet-blaine angle, with the unit rad. DeltainitRepresenting a 3 x 1 pose vector, δ, of the end effector of the robotic arm at an initial poseexchRepresenting a 3 x 1 pose vector of the end effector of the robotic arm at the transfer position. To ensure that the arms do not collide with the robot body at the optimal transfer position, l is set to indicate the front of the robotThe threshold value of the square security area is,
Figure GDA0003089552120000031
and
Figure GDA0003089552120000032
the positions of the end effectors of the master arm and the slave arm in the y-axis direction in the cartesian space are shown.
Establishing an optimization model for solving the target transfer and connection positions of the master arm and the slave arm:
Figure GDA0003089552120000033
when an optimal solution theta conforming to formula (8) is selected1And theta2And the optimal transfer position of the master arm and the slave arm required in the joint space is obtained.
Fourthly, solving the established optimal transfer and connection position optimization model by adopting an improved genetic algorithm to obtain the optimal transfer and connection positions of the master arm and the slave arm, wherein the method comprises the following specific steps of:
4.1) establishing a fitness function f in the genetic algorithm according to the optimization model in the formula (8)G：
fG＝f1+η2f2+η3f3 (9)
Figure GDA0003089552120000034
Figure GDA0003089552120000035
Wherein f is1Representing the sum of the poses of the end effectors of the master arm and the slave arm as the arms move from the initial pose to the relay position, f2An auxiliary function constructed to limit the pose of the two-arm end to meet the constraint at the transfer position, f3To limit the armsAn auxiliary function constructed when the tail end transmission pose is located in a safe area is determined by a formula (8); eta2，η3Is a penalty factor;
Figure GDA0003089552120000036
Ddist、Dexchrespectively representing the relative pose in the double-arm optimization process and the relative pose constraint to be met; ddistIs formed by
Figure GDA0003089552120000037
Obtained of DdistThe method is obtained by calculating the relative pose constraint T of the tail end of the double arm in the optimization process.
4.2) using the improved genetic algorithm to carry out iteration on the fitness function in the formula (9) to obtain an optimal solution, wherein the detailed process of using the improved genetic algorithm to carry out solution is as follows:
(1) determining a DH parameter value according to the structural form of the double-arm cooperative robot;
(2) setting initialization parameters of a genetic algorithm, generating an initial population, and representing genes of individuals by using a real value of a variable theta, wherein theta is a joint angle theta representing a main arm and a slave arm1And theta2The vector of components, the individual who improves the genetic algorithm is:
Figure GDA0003089552120000038
(3) judging whether the iteration times reach a set threshold value, if so, finishing the iteration process, otherwise, calculating a fitness function fG；
(4) Obtaining new iteration individuals through operations such as recombination, crossing, mutation and the like, wherein the crossing and mutation almost adopt a self-adaptive adjustment technology;
(5) calculating the population gene fitness again, replacing the worst gene with the elite gene, and circulating the steps;
(6) after the iteration is finished, the optimization calculation is carried out
Figure GDA0003089552120000041
To obtain theta1And theta2The optimal transfer position of the master arm and the slave arm is required in the joint space.
The invention has the beneficial effects that: the invention realizes that the end effector of the main arm and the slave arm can reach the transfer and connection position by changing a smaller pose to finish the transfer and connection operation of the object, thereby improving the efficiency of transferring and connecting the object by the double-arm cooperative robot, enabling the double-arm operation to be applied to more occasions and finishing the task safely, accurately and efficiently.
Drawings
FIG. 1 is a schematic diagram of a dual-arm joint of an object using a dual-arm cooperative robot in an example of the present invention;
FIG. 2 is a schematic diagram of the kinematic relationship between the two arms of the two-arm cooperative robot used in the example of the present invention and the object to be transferred;
FIG. 3 is a flow chart of an improved genetic algorithm for finding the location of a two-arm relay target of a two-arm cooperative robot used in an example of the present invention;
FIG. 4 is a simulation diagram of calculating target relay positions for dual arms of a dual-arm cooperative robot in an ROS simulation environment using a modified genetic algorithm in an example of the present invention. The two-arm initial pose graph of the two-arm cooperative robot in the ROS simulation environment is shown in the step (a), and the two-arm initial pose graph of the two-arm cooperative robot in the ROS simulation environment is shown in the step (b) in the optimal transfer position graph.
FIG. 5 is a comparison of an iterative process for calculating target transit positions using an improved genetic algorithm versus a generic genetic algorithm in an example of the present invention.
In the figure, 1 denotes a master arm, 2 denotes a slave arm, and l denotes a threshold value of a safety area in front of the robot.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present invention clearer, the technical solutions in the examples of the present invention will be clearly and completely described below with reference to the drawings in the examples of the present invention, and it is obvious that the described examples are a part of the embodiments of the present invention, but 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.
Fig. 1 is a schematic diagram of an example of the present invention for carrying out double-arm transfer and reception of an object by using a double-arm cooperative robot, as shown in fig. 1, and it can be seen from the schematic diagram that: for the initial position of the two arms of the two-arm cooperative robot, a plurality of transfer positions exist in the working space of the two arms, and the transfer of the object can be carried out at the transfer positions. When transferring and receiving an object, the main arm and the slave arm move from an initial position to a double-arm transferring and receiving position, and the object is transferred from the main arm to the slave arm at the double-arm transferring and receiving position, so that the transferring and receiving of the object between the double arms are completed. The double-arm cooperative robot adopted in the embodiment of the invention has 1 head with two degrees of freedom, 1 trunk, 1 chassis and 2 mechanical arms with seven degrees of freedom, the mechanical arms are light mechanical arms formed by modular joints, and a control system can also work independently to integrate a motor, a speed reducer, an encoder and a control circuit together. The cooperative robot communicates through EtherCAT.
Figure 2 is a schematic diagram of the relationship of movement between the arms and the object to be transferred during the two-arm transfer process used in the example of the invention,
in FIG. 2, the end effector coordinate systems of the master arm and the slave arm are { E, respectively1}、{E2And the common base coordinate of the master arm and the slave arm is { B }, and the process of completing the transmission and connection of the object between the two arms corresponds to four waypoints, wherein
Figure GDA0003089552120000051
Respectively showing the initial poses of the master arm and the slave arm,
Figure GDA0003089552120000052
setting relative pose constraint required to be satisfied by double-arm cooperative robot at transfer position for the required target transfer pose of the master arm and the slave arm
Figure GDA0003089552120000053
The relative pose matrix between the two-arm end effectors is represented as:
Figure GDA0003089552120000054
in the formula (14), the compound represented by the formula (I),
Figure GDA0003089552120000055
representing the relative pose constraints of the two-arm end effector,
Figure GDA0003089552120000056
and
Figure GDA0003089552120000057
the pose matrix of the double-arm end effector in the Cartesian space can be solved through positive kinematic equations of the main arm and the slave arm, and theta1And theta2The joint angle vector of the master arm and the slave arm is shown.
As can be seen from FIG. 2, the initial pose of the master arm and the slave arm is known
Figure GDA0003089552120000058
At the transfer position, the two arms can simultaneously grasp the object to be transferred, and at the same time, the relative pose of the end effectors of the two arms is restricted by
Figure GDA0003089552120000059
And (4) showing.
By initial pose of master arm and slave arm
Figure GDA00030895521200000510
And the relative pose constraint which is required to be met by the master arm and the slave arm at the transfer position
Figure GDA00030895521200000511
Establishing an optimization model for solving the target transfer and connection positions of the master arm and the slave arm;
f1＝Q(θ1)+Q(θ2) (15)
Q(θ)＝αΔR+βΔP (16)
Figure GDA00030895521200000512
Figure GDA00030895521200000513
Figure GDA00030895521200000514
Figure GDA00030895521200000515
wherein, theta1And theta2A vector composed of joint angles of the master arm and the slave arm is shown, Q (theta) shows the change of the terminal pose of the single arm, and Q (theta)1)、Q(θ2) Respectively, the poses of the end effectors of the master arm and the slave arm change when the two arms move from the initial poses to the transfer positions. Summation of pose changes of end-effectors of master and slave arms1And (4) showing. Wherein, Δ R represents the attitude difference between the initial position of the tail end of the mechanical arm and the transfer position, Δ P represents the distance difference between the initial position of the tail end of the mechanical arm and the transfer position, α and β are weights, and the proportion of the position and the attitude can be controlled by adjusting the weights. P ═ Px,py,pz]TThe position vector, P, representing the end effector of the robot arm, is obtained from the equation (1)initRepresenting a position vector, P, of the end effector of the robot arm at an initial poseexchRepresenting a position vector of the end effector of the robotic arm at the transfer position. Delta is [ delta ]x,δy,δz]TIs a 3 × 1 attitude vector of the attitude R in formula (1) via the Tett-Blaine angle, δinitIndicating the end-effector of a robot arm in an initial pose3 x 1 attitude vector, δexchRepresenting a 3 x 1 pose vector of the end effector of the robotic arm at the transfer position. To ensure that the arms do not collide with the robot body at the optimal transfer position, a threshold value is set, i represents a safety zone in front of the robot, p1 yAnd py 2The positions of the end effectors of the master arm and the slave arm in the y-axis direction in the cartesian space are shown.
Through the above, an optimization model for obtaining the optimal transfer and connection position of the master arm and the slave arm is established:
Figure GDA0003089552120000061
the first stage is completed, and an optimization model for solving the optimal transfer and connection position of the main arm and the slave arm is established; and then, entering a second stage, and solving the established optimal transfer and connection position optimization model by using an improved genetic algorithm to obtain the optimal transfer and connection positions of the master arm and the slave arm.
According to the optimization model in the formula (21), a fitness function f to be optimized is establishedG：
fG＝f1+η2f2+η3f3 (22)
Figure GDA0003089552120000062
Figure GDA0003089552120000063
Wherein f is1Representing the sum of the poses of the end effectors of the master arm and the slave arm as the arms move from the initial pose to the relay position, f2An auxiliary function constructed to limit the pose of the two-arm end to meet the constraint at the transfer position, f3An auxiliary function constructed to restrict the transfer pose of the end of the double arm from being located in a safe area, both determined by equation (21),η2、η3in order to be a penalty factor,
Figure GDA0003089552120000064
Ddist、Dexchand respectively representing the relative pose in the double-arm optimization process and the relative pose constraint to be met. DdistIs formed by
Figure GDA0003089552120000065
Obtained of DdistBy calculating relative pose constraints of the two-arm ends during the optimization process
Figure GDA0003089552120000066
And (4) obtaining the product.
Fig. 3 is a detailed process of the improved genetic algorithm used in the present invention for iteratively solving the optimal solution of the fitness function in equation (22), which comprises the following detailed steps:
(1) determining a DH parameter value according to the structural form of the double-arm cooperative robot;
(2) setting initialization parameters of a genetic algorithm, generating an initial population, and representing genes of individuals by using a real value of a variable theta, wherein theta is a joint angle theta representing a main arm and a slave arm1And theta2The vector of composition, the individual of the genetic algorithm is:
Figure GDA0003089552120000071
(3) judging whether the iteration times reach a set threshold value, if so, finishing the iteration process, otherwise, calculating a fitness function fG；
(4) Obtaining new iteration individuals through operations such as recombination, crossing, mutation and the like, wherein the crossing and mutation almost adopt a self-adaptive adjustment technology;
(5) calculating the population gene fitness again, replacing the worst gene with the elite gene, and circulating the steps;
(6) after the iteration is finished, the optimization calculation is carried out
Figure GDA0003089552120000072
To obtain theta1And theta2I.e. the target transfer and connection position of the master arm and the slave arm required in the joint space.
Fig. 4 shows a simulation diagram of calculating target relay positions for both arms of a two-arm cooperative robot in an ROS environment using an improved genetic algorithm in an example of the present invention. Setting the left arm of the double-arm robot as the main arm, wherein the joint angle theta of the main arm at the initial pose1Is (1.428-1.0620.623-0.600-2.1911.3350.391)]TIn units of rad. Setting the right arm of the double-arm robot as a slave arm, and setting the joint angle theta of the slave arm at the initial pose2Is [ -2.5210.7690.468-0.7013.051-1.067-2.081]TConstraining the relative pose of the two-arm end effector at the transfer position by DexchSet to [ 000.26 pi/20-pi/2]TAs shown in fig. 4 (a). Establishing an optimization model for solving the optimal transfer and connection positions of the master arm and the slave arm according to the known conditions, and then solving the established optimal transfer and connection position optimization model by using an improved genetic algorithm to obtain the optimal transfer and connection positions of the master arm and the slave arm, as shown in (b) of fig. 4, obtaining a joint angle theta of the master arm after iteration1Is (1.428-1.0620.623-0.600-2.1911.3350.391)]TAngle of articulation theta of slave arm2Is (1.428-1.0620.623-0.600-2.1911.3350.391)]T。
Fig. 5 shows the improved genetic algorithm and the unmodified genetic algorithm proposed herein, in which the optimal fitness value varies with the number of iterations in the iterative calculation process, and the main parameters of the genetic algorithm and the improved genetic algorithm are the same, except that the elite population is added in the calculation process and the adaptive variation and cross probability are different.
It can be seen from fig. 5 that the improved genetic algorithm performance is significantly better than the simple genetic algorithm. The improved genetic algorithm has high convergence speed and is not easy to vibrate, and the efficiency of the algorithm is obviously improved. The simulation in fig. 4 shows that the method provided by the present invention can select a suitable transmission position for the object transmission between the two arms of the robot, so that the relative pose constraint of the two-arm end effector can be satisfied by changing the smaller pose of the two-arm end effector, and the object transmission is completed. Finally, it should be noted that: the above examples are intended only to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the present embodiment example, it will be understood by those skilled in the art; the technical solutions described in the foregoing examples can be modified, or some technical features can be equally replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (1)

1. A method for selecting a double-arm transfer position of a double-arm cooperative robot is characterized by comprising the following steps:
first, establish the end effector coordinate systems of the master arm and the slave arm, respectively using { E }1}、{E2Denotes that when the end effectors of both arms are determined to grasp the object simultaneously according to the shape of the object, { E1And { E } and2the relative pose constraint that needs to be satisfied
Figure FDA0003089552110000017
Figure FDA0003089552110000018
Representing the relative pose constraints satisfied by the dual-arm end effector simultaneously grasping the object at the transfer position; the two arms of the two-arm cooperative robot are provided with an object holding mechanical arm as a main arm and the other mechanical arm as a slave arm;
secondly, acquiring initial poses of the master arm and the slave arm, obtaining joint angle data through angle sensor feedback at the joints of the mechanical arm, and then obtaining poses T of the two arms in a Cartesian space through a positive kinematic equation of the mechanical arminit(ii) a For a single arm, the pose T of the single arm end effector can be represented by a 4 x 4 matrix, T being a homogeneous transformation matrix representing the spatial pose:
Figure FDA0003089552110000011
wherein, R is a 3 × 3 rotation matrix representing the posture; p is a position vector representing the end effector of the mechanical arm; 0TA 0 matrix of 1 × 3;
thirdly, through the initial pose T of the master arm and the slave arminitAnd the relative pose constraint which needs to be met by simultaneously grabbing the object at the transfer position by the master arm and the slave arm
Figure FDA0003089552110000012
Establishing an optimization model for solving the optimal transfer and connection positions of the master arm and the slave arm;
f1＝Q(θ1)+Q(θ2) (2)
Q(θ)＝αΔR+βΔP (3)
Figure FDA0003089552110000013
Figure FDA0003089552110000014
Figure FDA0003089552110000015
Figure FDA0003089552110000016
wherein, theta1And theta2A vector composed of joint angles of the master arm and the slave arm is shown, Q (theta) shows the change of the terminal pose of the single arm, and Q (theta)1)、Q(θ2) Respectively showing the pose changes of the end effectors of the master arm and the slave arm when the two arms move from the initial pose to the transfer position; f. of1Indicating double armsThe sum of the pose changes of the end effectors of the master arm and the slave arm when moving from the initial pose to the transfer position; delta R represents the attitude difference between the initial position of the tail end of the mechanical arm and the transfer position, delta P represents the distance difference between the initial position of the tail end of the mechanical arm and the transfer position, alpha and beta are weights, and the proportion of the position and the attitude can be controlled by adjusting the weights; p ═ Px,py,pz]TThe position vector of the mechanical arm end effector is obtained by the formula (1) and is expressed in meters; pinitRepresenting a position vector, P, of the end effector of the robot arm at an initial poseexchRepresenting a position vector of the end effector of the robotic arm at the transfer position; delta is [ delta ]x,δy,δz]TIs an attitude vector represented by the attitude R in the formula (1) through a Tett-Blaine angle, and the unit is rad; deltainitRepresenting a pose vector, δ, of the end effector of the robotic arm at an initial poseexchRepresenting a pose vector of the end effector of the robotic arm at the transfer position; in order to ensure that the double arms do not collide with the main body of the robot at the optimal transfer position, a threshold value which represents a safety area in front of the robot is set,
Figure FDA0003089552110000021
and
Figure FDA0003089552110000022
representing the positions of the end effectors of the master arm and the slave arm in the y-axis direction in a cartesian space;
establishing an optimization model for solving the target transfer and connection positions of the master arm and the slave arm:
minf1＝Q(θ1)+Q(θ2)
Figure FDA0003089552110000023
when an optimal solution theta conforming to formula (8) is selected1And theta2The optimal transfer position of the master arm and the slave arm required in the joint space is obtained;
fourthly, solving the established optimal transfer and connection position optimization model by adopting an improved genetic algorithm to obtain the optimal transfer and connection positions of the master arm and the slave arm, wherein the method comprises the following specific steps of:
4.1) establishing a fitness function f in the genetic algorithm according to the optimization model in the formula (8)G：
fG＝f1+η2f2+η3f3 (9)
Figure FDA0003089552110000024
Figure FDA0003089552110000025
Wherein f is1Representing the sum of the poses of the end effectors of the master arm and the slave arm as the arms move from the initial pose to the relay position, f2An auxiliary function constructed to limit the pose of the two-arm end to meet the constraint at the transfer position, f3An auxiliary function constructed for limiting the transmission pose of the tail end of the double arm to be positioned in a safe area is determined by an equation (8); eta2，η3Is a penalty factor;
Figure FDA0003089552110000026
Ddist、Dexchrespectively representing the relative pose in the double-arm optimization process and the relative pose constraint to be met; ddistIs formed by
Figure FDA0003089552110000027
Obtained of DexchThe method is obtained by calculating the relative pose constraint T of the tail ends of the double arms in the optimization process;
4.2) using an improved genetic algorithm to carry out iteration on the fitness function in the formula (9) to obtain an optimal solution:
(1) determining a DH parameter value according to the structural form of the double-arm cooperative robot;
(2) setting initialization parameters of a genetic algorithm, generating an initial population, and representing genes of individuals by using a real value of a variable theta, wherein theta is a joint angle theta representing a main arm and a slave arm1And theta2The vector of components, the individual who improves the genetic algorithm is:
Figure FDA0003089552110000028
(3) judging whether the iteration times reach a set threshold value, if so, finishing the iteration process, otherwise, calculating a fitness function fG；
(4) Obtaining new iteration individuals through recombination, crossing and mutation operations, wherein the crossing and mutation processes adopt a self-adaptive adjustment technology;
(5) calculating the population gene fitness again, replacing the worst gene with the elite gene, and circulating the steps;
(6) after the iteration is finished, the optimization calculation is carried out
Figure FDA0003089552110000031
To obtain theta1And theta2The optimal transfer position of the master arm and the slave arm is required in the joint space.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023076724A1 (en) * 2021-11-01 2023-05-04 Dexterity, Inc. Robotic system to control multiple robots to perform a task cooperatively

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112123341B (en) * 2020-11-24 2021-03-02 季华实验室 Robot double-arm coordinated motion control method and device and electronic equipment
CN112809655B (en) * 2021-02-02 2022-10-04 无锡江锦自动化科技有限公司 Asymmetric double-arm cooperative robot system and modeling and working method thereof
CN115008478B (en) * 2022-08-09 2022-10-25 北京航空航天大学 Method and system for selecting grabbing pose of double-arm robot and storage medium
CN115890653A (en) * 2022-09-28 2023-04-04 华中科技大学 Multi-channel based cooperative control method and device for double-arm robot and readable medium

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007276086A (en) * 2006-04-11 2007-10-25 Yaskawa Electric Corp Robot and method of controlling robot
CN204578033U (en) * 2015-05-06 2015-08-19 长春理工大学 Climbing deicing robot
CN108255058A (en) * 2018-01-18 2018-07-06 山东大学深圳研究院 Service robot inverse kinematics method and apparatus under intelligent space
CN109202942A (en) * 2017-06-30 2019-01-15 发那科株式会社 The simulator of hand control device, hand control method and hand
CN109623815A (en) * 2018-12-19 2019-04-16 江苏科技大学 A kind of compensation of undulation double SCM and method for unmanned pick-up boat

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2007276086A (en) * 2006-04-11 2007-10-25 Yaskawa Electric Corp Robot and method of controlling robot
CN204578033U (en) * 2015-05-06 2015-08-19 长春理工大学 Climbing deicing robot
CN109202942A (en) * 2017-06-30 2019-01-15 发那科株式会社 The simulator of hand control device, hand control method and hand
CN108255058A (en) * 2018-01-18 2018-07-06 山东大学深圳研究院 Service robot inverse kinematics method and apparatus under intelligent space
CN109623815A (en) * 2018-12-19 2019-04-16 江苏科技大学 A kind of compensation of undulation double SCM and method for unmanned pick-up boat

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023076724A1 (en) * 2021-11-01 2023-05-04 Dexterity, Inc. Robotic system to control multiple robots to perform a task cooperatively

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