CN106945043B - Multi-arm cooperative control system of master-slave teleoperation surgical robot - Google Patents

Multi-arm cooperative control system of master-slave teleoperation surgical robot Download PDF

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CN106945043B
CN106945043B CN201710254171.0A CN201710254171A CN106945043B CN 106945043 B CN106945043 B CN 106945043B CN 201710254171 A CN201710254171 A CN 201710254171A CN 106945043 B CN106945043 B CN 106945043B
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joint
matrix
arm
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CN106945043A (en
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向洋
傅舰艇
熊亮
谢毅
王黎
张敏锐
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Chongqing Institute of Green and Intelligent Technology of CAS
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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
    • B25J9/16—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

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Abstract

The invention discloses a multi-arm cooperative control system of a master-slave teleoperation surgical robot, which comprises an industrial PC system and a bottom layer control system, wherein the industrial PC system comprises an industrial PC, an Ethernet and a CAN bus adapter card; the bottom layer control system comprises a digital quantity and analog quantity acquisition card and a servo driver; the industrial PC receives signals from other systems by using the Ethernet, is connected with a servo driver by using the CAN bus adapter card to control the motion of the mechanical arm, and collects and processes the signals from the multi-dimensional force sensor by using a digital quantity/analog quantity acquisition card. The invention completes the establishment of hardware architecture and the development of mechanical arm kinematics algorithm and multi-arm cooperative control software, and realizes the multi-arm cooperative control function of the surgical robot.

Description

Multi-arm cooperative control system of master-slave teleoperation surgical robot
Technical Field
The invention belongs to the field of surgical robots, particularly relates to a multi-arm cooperative control system of a master-slave teleoperation surgical robot, and belongs to the field of human-computer interaction.
Background
A master-slave teleoperated surgical robotic control system is generally composed of a master manipulator and several slave manipulator arms. The hand-operated arm is arranged beside an operating table, and an endoscope and various surgical instruments can be arranged at the tail end of the hand-operated arm to reach a focus in a patient body through a tiny wound. The doctor operates the master hand operation, can control from the terminal apparatus of hand and accomplish various operation operations, provides the operating environment of traditional operation for the surgeon, can assist the doctor to accomplish more meticulous operation action, reduces the damage that produces because tired maloperation or hand tremble caused during the operation. The multi-arm robot is a complex nonlinear system with high-order and strong coupling, and simultaneously, due to the variability of the working environment, high requirements are provided for the robustness of a robot system control method and the real-time performance of a control system. Based on the kinematics optimization solution of the single-arm robot, the application requirements that the tail end of the robot is constrained by the environment and needs to interact with the environment cannot be met. Compared with the traditional single-arm robot, the multi-arm cooperative robot designed aiming at the unstructured environment has the characteristic of being more flexible, and can realize the interaction function between the robots and between the robots. At present, master-slave teleoperation surgical robots have been developed abroad, but the master-slave teleoperation surgical robots do not have the force feedback function and the multi-slave arm cooperative control function.
Disclosure of Invention
In view of this, the invention provides a multi-arm cooperative control system for a master-slave teleoperation surgical robot, which utilizes a sliding mode control system and a multi-motion controller to perform parallel calculation, so as to realize a cooperative operation function among mechanical arms.
The invention aims to realize the technical scheme that a master-slave teleoperation surgical robot multi-arm cooperative control system is used for controlling a multi-arm surgical robot, the multi-arm surgical robot comprises a master hand operation end and a slave hand execution end, the master hand operation end is a 7DOF serial robot, the slave hand execution end consists of two 7DOF serial mechanical arms and two 6DOF serial mechanical arms, and a multi-dimensional force sensor and a motion controller are arranged at the tail end of each mechanical arm, and the master-slave teleoperation surgical robot multi-arm cooperative control system is characterized in that: the control system comprises an industrial PC system and a bottom layer control system, wherein the industrial PC system comprises an industrial PC, an Ethernet and a CAN bus adapter card; the bottom layer control system comprises a digital quantity and analog quantity acquisition card and a servo driver; the industrial PC receives signals from other systems by using the Ethernet, controls the motion of the mechanical arm by using the CAN bus adapter card and the servo driver, and collects and processes the signals from the multi-dimensional force sensor by using a digital quantity/analog quantity acquisition card; each mechanical arm receives a position control instruction transmitted by the master hand operation end, and the expected position of each joint is obtained through inverse kinematics calculation; sensing a contact force between the robot and the environment and a contact force between the mechanical arms through a multi-dimensional force sensor, solving through inverse dynamics to obtain information of each joint force and moment, and feeding the contact force back to a master hand of the surgical robot; acquiring the actual position of each joint through an absolute value position sensor; and the industrial PC coordinates the data transmission and the signal transmission of each motion controller to complete the cooperative control function of the multiple mechanical arms.
Further, the multi-arm cooperative control system of the surgical robot comprises a sliding mode controller, an adaptive fuzzy logic controller and a nonlinear observer; the sliding mode controller is used for accurately obtaining dynamic parameters of the system; the self-adaptive fuzzy logic controller is used for improving the control precision; the nonlinear observer is used for compensating the influence of the external environment on the stability and accuracy of the system.
Further, the control law of the sliding mode controller is tau ═ taueq+τsmIn which τ issmIs a control item of a sliding mode,
Figure BDA0001272912810000021
is a dynamic equivalent model of the robot, M (q) is an inertia matrix, J-1(q) is an inverse jacobian matrix,
Figure BDA0001272912810000022
is the cartesian spatial acceleration of the robot end,
Figure BDA0001272912810000023
for the derivative of the jacobian matrix,
Figure BDA00012729128100000220
the velocity of the joint space is taken as the velocity,
Figure BDA0001272912810000025
is an inertial term and a coriolis term, and g (q) is a gravity term; selecting sliding form surface
Figure BDA0001272912810000026
Where s is a 6 × 1-dimensional vector, c is a positive definite diagonal matrix, and e ═ qid-qi，
Figure BDA0001272912810000027
Denotes the derivative of e, qidFor desired position of joint, qiIs the actual position of the joint; the item of the sliding form is
Figure BDA0001272912810000028
k1A positive definite diagonal matrix of 6 x 6; m (q) is an inertia matrix; j. the design is a squareT(q) is a rank matrix of the jacobian matrix, s ═ s1,s2,…,si]Is a dieFuzzy logic input quantity, gamma ═ gamma1,γ2,…,γi]Is a fuzzy logic output quantity.
Further, the model of the non-linear observer is:
Figure BDA0001272912810000029
Figure BDA00012729128100000210
wherein K1And K2Is a positive definite matrix and the matrix is a negative definite matrix,
Figure BDA00012729128100000211
is indicative of an external torque disturbance,
Figure BDA00012729128100000212
an estimate of the external torque disturbance is represented,
Figure BDA00012729128100000213
Figure BDA00012729128100000214
is a vector of the velocity error of the joint,
Figure BDA00012729128100000215
to the error of joint velocity
Figure BDA00012729128100000216
The estimated amount of (a) is,
Figure BDA00012729128100000217
representing a joint acceleration error; m-1Representing an inverse inertia matrix, C represents an inertia term and a coriolis term,
Figure BDA00012729128100000218
representing joint space velocity, G representing the gravity term,
Figure BDA00012729128100000219
for joint input acceleration, tau epsilon R6Is to turn offThe torque is input.
Due to the adoption of the technical scheme, the invention has the following advantages:
the invention completes the establishment of hardware architecture and the development of mechanical arm kinematics algorithm and multi-arm cooperative control software, and realizes the multi-arm cooperative control function of the surgical robot.
Drawings
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:
FIG. 1 is a schematic diagram of a master-slave surgical robot control system according to the present invention;
FIG. 2 is a schematic diagram of the kinetic control strategy of the present invention;
FIG. 3 is a schematic diagram of a multi-arm collaborative work control system according to the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings; it should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.
FIG. 1 is a hardware architecture of a control system of the invention, which comprises a master hand operation end and a slave hand execution end, wherein the master hand operation end is a 7DOF tandem robot, the slave hand execution end consists of two 7DOF tandem mechanical arms and two 6DOF tandem mechanical arms, and the tail end of each mechanical arm is provided with a multi-dimensional force sensor. The control system hardware of the mechanical arm comprises an industrial PC system and a bottom layer control system. The industrial PC system comprises a visual human-computer interface, an industrial PC, an Ethernet card and a CAN bus adapter card; the bottom layer control system comprises a plurality of motion controllers, a digital quantity and analog quantity acquisition card, a servo driver, a motor, a multi-dimensional force sensor and an absolute encoder. The industrial PC receives signals from other systems by using the Ethernet, is connected with the servo driver by using the CAN bus adapter card to control the motion of the mechanical arm, and collects and processes signals from the multi-dimensional force sensor and other sensors by using a digital quantity/analog quantity acquisition card.
FIG. 3 is a schematic diagram of a multi-arm cooperative work control system of the present invention, wherein each mechanical arm receives a position control command transmitted from a master hand operation end, and the expected position of each joint is obtained through inverse kinematics calculation; sensing a contact force between the robot and the environment and a contact force between the mechanical arms through a multi-dimensional force sensor, solving through inverse dynamics to obtain information of each joint force and moment, and feeding the contact force back to a master hand of the surgical robot; acquiring the actual position of each joint through an absolute value position sensor; and a sliding mode controller is adopted to control each joint, so that multi-arm cooperative operation control is realized.
The master-slave teleoperation surgical robot multi-arm cooperative control system comprises a sliding mode controller, a self-adaptive fuzzy logic controller and a nonlinear observer; the sliding mode controller is used for accurately obtaining dynamic parameters of the system; the self-adaptive fuzzy logic controller is used for improving the control precision; the nonlinear observer is used for compensating the influence of the external environment on the stability and accuracy of the system.
The control law of the sliding mode controller is tau ═ taueq+τsmIn which τ issmIs a control item of a sliding mode,
Figure BDA0001272912810000031
is a dynamic equivalent model of the robot, M (q) is an inertia matrix, J-1(q) is an inverse jacobian matrix,
Figure BDA0001272912810000032
is the cartesian spatial acceleration of the robot end,
Figure BDA0001272912810000033
for the derivative of the jacobian matrix,
Figure BDA0001272912810000038
the velocity of the joint space is taken as the velocity,
Figure BDA0001272912810000035
is an inertial term and a coriolis term, and g (q) is a gravity term; selecting sliding form surface
Figure BDA0001272912810000036
Figure BDA0001272912810000037
Denotes the derivative of e, where s is a 6 × 1 dimensional vector, c is a positive definite diagonal matrix, and e ═ qid-qi，qidFor desired position of joint, qiIs the actual position of the joint; item of sliding form tausmCan be designed as
Figure BDA0001272912810000041
k1,k2Is a positive definite diagonal matrix of 6 x 6.
Introducing fuzzy logic control strategy, where the fuzzy logic output quantity gamma is only related to s, where gamma is FLC(s), FLC(s) is function of fuzzy language decision set, and s is1,s2,…,si]For fuzzy logic inputs, γ ═ γ1,γ2,…,γi]In order to provide a fuzzy logic output,
Figure BDA0001272912810000042
definition of
Figure BDA0001272912810000043
Figure BDA0001272912810000044
Which is indicative of the current parameters of the device,
Figure BDA0001272912810000045
which is indicative of the initial parameters of the device,
Figure BDA0001272912810000046
Figure BDA0001272912810000047
to represent
Figure BDA0001272912810000048
Estimate of, ψki(si) The fuzzy basis functions are represented by a function of the fuzzy basis,
Figure BDA0001272912810000049
is notA deterministic compensation term; the sliding mode term τ can then be substitutedsmIs modified into
Figure BDA00012729128100000410
k1,k2A positive definite diagonal matrix of 6 x 6; m (q) epsilon R6×6An inertia matrix; j. the design is a squareT(q) is a rank matrix of the jacobian matrix, s ═ s1,s2,…,si]Is a fuzzy logic input quantity.
The model of the nonlinear observer is:
Figure BDA00012729128100000411
wherein K1And K2Is a positive definite matrix and the matrix is a negative definite matrix,
Figure BDA00012729128100000412
an estimate of the external torque disturbance is represented,
Figure BDA00012729128100000413
an error in the acceleration of the joint is indicated,
Figure BDA00012729128100000414
Figure BDA00012729128100000415
is a vector of the velocity error of the joint,
Figure BDA00012729128100000416
to the error of joint velocity
Figure BDA00012729128100000417
The estimated amount of (a);
Figure BDA00012729128100000418
as an acceleration, M-1Representing an inverse inertia matrix, C represents an inertia term and a coriolis term,
Figure BDA00012729128100000419
representing joint velocity, G representing the gravity term,
Figure BDA00012729128100000420
denotes an external torque disturbance, τ ∈ R6Torque is input for the joint.
The design method of the nonlinear observer is to define the position error e as q-qdQ denotes the current position of the joint, qdRepresenting the target position of the joint by taking the first and second derivatives as
Figure BDA00012729128100000421
qd,
Figure BDA00012729128100000422
Joint angle position, velocity and acceleration; definition of
Figure BDA00012729128100000423
Figure BDA00012729128100000424
Is a vector of the velocity error of the joint,
Figure BDA00012729128100000425
to the error of joint velocity
Figure BDA00012729128100000426
An estimate of (a); definition of
Figure BDA00012729128100000427
Figure BDA00012729128100000428
In order to estimate the external disturbance torque,
Figure BDA00012729128100000429
is an estimate of the interference error; the step of mixing the said e with the said E,
Figure BDA00012729128100000430
substituting the expression into a kinetic equation to obtain
Figure BDA00012729128100000431
The nonlinear observer based on the iterative algorithm is designed as
Figure BDA00012729128100000432
Wherein K1And K2Is a positive definite matrix.
The kinetic equation is as follows:
Figure BDA00012729128100000433
wherein
Figure BDA00012729128100000434
For joint angular position, velocity acceleration, M (q) e R6×6Is a matrix of the inertia, and the inertia matrix,
Figure BDA00012729128100000435
is a matrix of Coriolis forces and centripetal forces, G (q) e R6For the gravity matrix, τ ∈ R6For inputting torque into joints, τd∈R6For the external moment applied to the mechanical arm, will
Figure BDA00012729128100000436
Substituting into the joint dynamics equation of the mechanical arm to obtain
Figure BDA00012729128100000437
Bringing the end of the robot arm to a desired pose
Figure BDA00012729128100000438
Instead of the former
Figure BDA00012729128100000439
Obtain a kinetic equivalent equation of
Figure BDA00012729128100000440
Establishing a homogeneous transformation matrix of the tandem robot by using DH parameters of the mechanical arm, and expressing a positive kinematic equation of the mechanical arm as x (t) phi (q), x (t) epsilon R6，q(t)∈R6Wherein x (t) is the end of the arm in Cartesian spacePose, q (t), is each joint position in joint space; deriving the normal kinematics equation to obtain a velocity equation of
Figure BDA0001272912810000051
Wherein,
Figure BDA0001272912810000052
is a Jacobian matrix; deriving the velocity equation to obtain an acceleration equation
Figure BDA0001272912810000053
Thus, the
Figure BDA0001272912810000054
The invention provides a multi-arm cooperative control system of a master-slave teleoperation surgical robot, which realizes a multi-arm cooperative operation control function by building control system hardware and compiling a control program according to the flow.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and it is apparent that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (1)

1. A master-slave teleoperation surgical robot multi-arm cooperative control system is used for controlling a multi-arm surgical robot, the multi-arm surgical robot comprises a master hand operation end and a slave hand execution end, the master hand operation end is a 7DOF serial robot, the slave hand execution end consists of two 7DOF serial mechanical arms and two 6DOF serial mechanical arms, and a multi-dimensional force sensor and a motion controller are arranged at the tail end of each mechanical arm, and the system is characterized in that: the control system comprises an industrial PC system and a bottom layer control system, wherein the industrial PC system comprises an industrial PC, an Ethernet and a CAN bus adapter card; the bottom layer control system comprises a digital quantity and analog quantity acquisition card and a servo driver; the industrial PC receives signals from other systems by using the Ethernet, controls the motion of the mechanical arm by using the CAN bus adapter card and the servo driver, and collects and processes the signals from the multi-dimensional force sensor by using a digital quantity/analog quantity acquisition card; each mechanical arm receives a position control instruction transmitted by the master hand operation end, and the expected position of each joint is obtained through inverse kinematics calculation; sensing a contact force between the robot and the environment and a contact force between the mechanical arms through a multi-dimensional force sensor, solving through inverse dynamics to obtain information of each joint force and moment, and feeding the contact force back to a master hand of the surgical robot; acquiring the actual position of each joint through an absolute value position sensor; the industrial PC coordinates the data transmission and the signal transmission of each motion controller to complete the cooperative control function of the multiple mechanical arms; the multi-arm cooperative control system of the surgical robot comprises a sliding mode controller, a self-adaptive fuzzy logic controller and a nonlinear observer; the sliding mode controller is used for accurately obtaining dynamic parameters of the system; the self-adaptive fuzzy logic controller is used for improving the control precision; the nonlinear observer is used for compensating the influence of an external environment on the stability, novelty and precision of the system;
the control law of the sliding mode controller is tau ═ taueq+τsmIn which τ issmIs a control item of a sliding mode,
Figure FDA0002282812070000011
is a dynamic equivalent model of the robot, M (q) is an inertia matrix, J-1(q) is an inverse jacobian matrix,
Figure FDA0002282812070000012
is the cartesian spatial acceleration of the robot end,
Figure FDA0002282812070000013
for the derivative of the jacobian matrix,
Figure FDA0002282812070000014
the velocity of the joint space is taken as the velocity,
Figure FDA0002282812070000015
is an inertial term and a coriolis term, and g (q) is a gravity term; selecting sliding form surface
Figure FDA0002282812070000016
Where s is a 6 × 1-dimensional vector, c is a positive definite diagonal matrix, and e ═ qid-qi，
Figure FDA0002282812070000017
Denotes the derivative of e, qidFor desired position of joint, qiIs the actual position of the joint; the sliding mode control item is
Figure FDA0002282812070000018
k1A positive definite diagonal matrix of 6 x 6; m (q) is an inertia matrix; j. the design is a squareT(q) is a rank matrix of the jacobian matrix, s ═ s1,s2,…,si]For fuzzy logic inputs, γ ═ γ1,γ2,…,γi]Is the fuzzy logic output quantity;
the model of the nonlinear observer is:
Figure FDA0002282812070000019
wherein K1And K2Is a positive definite matrix and the matrix is a negative definite matrix,
Figure FDA00022828120700000110
is indicative of an external torque disturbance,
Figure FDA00022828120700000111
an estimate of the external torque disturbance is represented,
Figure FDA00022828120700000112
Figure FDA00022828120700000113
is a vector of the velocity error of the joint,
Figure FDA00022828120700000114
to the error of joint velocity
Figure FDA00022828120700000115
The estimated amount of (a) is,
Figure FDA00022828120700000116
representing a joint acceleration error; m-1Representing an inverse inertia matrix, C represents an inertia term and a coriolis term,
Figure FDA00022828120700000117
representing joint space velocity, G representing the gravity term,
Figure FDA00022828120700000118
for joint input acceleration, tau epsilon R6Torque is input for the joint.
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