CN116330288A - Man-machine sharing control method and system for mobile mechanical arm - Google Patents
Man-machine sharing control method and system for mobile mechanical arm Download PDFInfo
- Publication number
- CN116330288A CN116330288A CN202310326739.0A CN202310326739A CN116330288A CN 116330288 A CN116330288 A CN 116330288A CN 202310326739 A CN202310326739 A CN 202310326739A CN 116330288 A CN116330288 A CN 116330288A
- Authority
- CN
- China
- Prior art keywords
- mechanical arm
- slave
- control
- fuzzy
- joint
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- 230000008859 change Effects 0.000 claims abstract description 30
- 239000012636 effector Substances 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 8
- 239000011159 matrix material Substances 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000006073 displacement reaction Methods 0.000 claims description 5
- 230000003993 interaction Effects 0.000 claims description 5
- 238000013507 mapping Methods 0.000 claims description 5
- 238000013139 quantization Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 4
- 239000013598 vector Substances 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 6
- 230000007613 environmental effect Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000011217 control strategy Methods 0.000 description 2
- 230000001276 controlling effect Effects 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005352 clarification Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000009897 systematic effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a man-machine sharing control method and a man-machine sharing control system for a mobile mechanical arm, wherein the man-machine sharing control method and the man-machine sharing control system comprise an upper computer teleoperation system and a lower computer control system; the upper computer teleoperation system comprises a main industrial personal computer, a mechanical arm main hand electrically connected with the industrial personal computer and a sharing controller; the lower computer control system comprises an embedded industrial personal computer and a mechanical arm slave; the invention realizes the dynamic change of the shared control weight, adds the fuzzy control into the consideration of the problem of the limitation of the operation environment, and reduces the complete dependence on the algorithm through the shared control, thereby enabling the mechanical arm to still complete the corresponding task under the condition of limited operation environment.
Description
Technical Field
The invention relates to a robot control method, in particular to a man-machine sharing control method and system for a mobile mechanical arm.
Background
Along with the continuous development of robot control technology, the intelligent level of robots is also continuously improved. When facing more complex working environments or tasks involving fine operations, the traditional control mode comprising remote control and autonomous control cannot meet the corresponding task demands, so that man-machine sharing control is introduced into a robot system, namely, a method of combining an operator direct control instruction and a robot local autonomous control is combined, and the adaptability of the robot under different environments is enhanced. The current robot sharing control system generally comprises direct control of an operator on a robot, autonomous control of a robot part and sharing control of the two. In the case of direct control of the robot by an operator, manual control instructions are mostly adopted, namely, the operator directly controls the robot through equipment such as a keyboard, remote sensing and the like, but sometimes is influenced by environment and system time delay; the autonomous control of the robot adopts the technologies of path planning, navigation, positioning and the like, and highly depends on a corresponding algorithm; the sharing control part adopts a robot control system with multiple fuzzy rules, and the sharing control method is typified by fuzzy control, neural networks and the like.
In the existing sharing control method, the robot (mechanical arm) is controlled by adopting a fuzzy control scheme under the condition of environmental limitation due to the limitations of automation level and sensor technology, so that the task completion degree is low. For example, when the working environment of the robot changes, the adaptive performance of the shared control system is generally limited by the limitations of the intelligent fuzzy control algorithm.
Disclosure of Invention
The invention aims to: aiming at the problems, the invention provides the man-machine sharing control method and system for the mobile mechanical arm, which realize the dynamic change of the sharing control weight, make the fuzzy control add the consideration of the limiting problem of the operation environment, and reduce the complete dependence on the algorithm through the sharing control, so that the mechanical arm can still complete the corresponding task under the condition of limited operation environment.
The technical scheme is as follows: the technical scheme adopted by the invention is that the man-machine sharing control system of the mobile mechanical arm comprises an upper computer teleoperation system and a lower computer control system;
the upper computer teleoperation system comprises a main industrial personal computer, a mechanical arm main hand electrically connected with the industrial personal computer and a sharing controller; the lower computer control system comprises an embedded industrial personal computer and a mechanical arm slave;
the manipulator slave hand is provided with a sensor and a 3D camera, the sensor acquires first state information and feeds back the first state information to a joint change output module in the embedded industrial personal computer for processing, the joint change output module outputs joint change quantity of the manipulator slave hand, and the first state information comprises degree of freedom state quantity of an end effector of the manipulator slave hand; the 3D camera is used for collecting image information;
the embedded industrial personal computer transmits the joint variation and the image information to the main industrial personal computer, and the main industrial personal computer processes the image information to obtain the distance between the slave hand of the mechanical arm and the target object; the fuzzy controller in the host industrial personal computer outputs a sharing control coefficient according to the second state information and the fuzzy control rule and sends the sharing control coefficient to the sharing controller; the second state information comprises the joint variation of the mechanical arm from the hand and the distance between the mechanical arm from the hand and the target object;
the industrial personal computer sends a master-slave control instruction to the shared controller; the embedded industrial personal computer sends an autonomous control instruction to the shared controller; the sharing controller outputs a sharing control instruction and sends the sharing control instruction to the slave arm slave hand to control the slave arm to move;
the industrial personal computer is provided with a man-machine interaction interface.
The system also comprises a mobile platform, wherein the mobile platform is used for carrying the slave manipulator of the mechanical arm and driving the slave manipulator of the mechanical arm to the corresponding position.
The sharing controller outputs a sharing control instruction S c The method comprises the following steps:
S c (η)＝(I o -I A )η+I A
wherein I is o I is master-slave control instruction A Is an autonomous control instruction of the robot, and eta is a shared control coefficient.
The master arm and the slave arm of the mechanical arm adopt master-slave isomorphic configuration, the master-slave isomorphic mechanical arm adopts a mode based on joint space mapping control, all joint angles of a master end controller are mapped into a slave joint space synchronously in a certain proportion, and the slave arm operation of the mechanical arm is controlled.
The mechanical arm adopts a point-to-point track planning mode from the autonomous control of the hand, and performs track tracking in cooperation with a PID controller; and planning the track of the point-to-point by adopting a fifth-order polynomial.
The joint change output module calculates the joint change amount from the degree of freedom of an end effector of a hand through the mechanical arm, and the calculation formula is as follows:
wherein DeltaX is a variable matrix of the end effector, deltaQ is a joint variable matrix, and Q is a channel quantity comprising the rotation angle and displacement distance of the joint; n represents the number of joints, m is the number of degrees of freedom of the end effector; f represents the mapping between each element in the degree of freedom vector set of the end effector and the channel quantity q, denoted as x=f (q);
the fuzzy rule of the fuzzy controller comprises: the larger the joint variation is, the larger the sharing control coefficient is, and the smaller the joint variation is, the smaller the sharing control coefficient is;
the controller adopts a Mamdani fuzzy reasoning method to realize fuzzy reasoning.
Its ambiguous implication relationship
Fuzzy aggregation of the arm joint variable Q and the distance L between the arm end and the target object>
And->
The calculation formula for obtaining the fuzzy implication relation by the Cartesian combination set is as follows:
wherein the method comprises the steps of
And->
Respectively representing the change Q of the joint of the mechanical arm and the membership degree of each fuzzy set on each domain after the distance L between the tail end of the mechanical arm and the target object is quantized, < + >>
Representing membership degree of implication relation of fuzzy reasoning on input object,/>
Membership degree of fuzzy set on its domain after quantization for shared control coefficient, R i Implication relation corresponding to each fuzzy control rule;
the total output of fuzzy logic reasoning is:
in the formula, wherein
And quantizing the input object to obtain intersections of membership degrees of each fuzzy set, wherein i is the fuzzy rule number.
For the membership function of the fuzzy set of the joint variable Q of the mechanical arm and the distance L between the mechanical arm and the target object, a mode of combining a trapezoidal membership function and a triangular membership function is adopted, when the fuzzy set is positioned at the middle degree, the triangular membership function is adopted, and the trapezoidal membership function is adopted at the extreme degrees at the two ends.
For the membership function of the fuzzy set sharing the control coefficient eta, a mode of combining a triangle membership function and an S-type membership function is adopted, when the fuzzy set is positioned at the middle degree, the triangle membership function is adopted, and the S-type membership is adopted at the extreme degrees at the two ends.
The beneficial effects are that: compared with the prior art, the invention has the following advantages: the invention adopts a control system of an upper computer structure and a lower computer structure, and establishes a set of man-machine sharing control method, namely man-machine sharing control based on master-slave control of an operator and track tracking of a mechanical arm. The joint change output device and the fuzzy controller are combined aiming at the man-machine sharing control strategy, so that the mechanical arm can dynamically adjust the control weight according to the surrounding environment in the process of advancing to the target object. Meanwhile, in the sharing control method, the special characteristics of the mechanical arm compared with other robots are fully considered, namely, the joints generate joint changes including angle changes and displacement changes in the motion process, and the mechanical arm control system can optimize the running posture of the mechanical arm by means of master-slave control of an operator under the environment facing the limited operation space by adjusting the joint change amount, so that the task is completed, and meanwhile, the complexity of a system algorithm is not increased.
Drawings
FIG. 1 is a general framework of a mobile robotic human-machine shared control system;
FIG. 2 is a graph of shared control coefficients versus operating modes;
FIG. 3 is a mobile robotic arm human-machine sharing control block diagram;
FIG. 4 is a block diagram of a master-slave control system;
FIG. 5 is a PID principle control block diagram;
FIG. 6 is a block diagram of a fuzzy controller;
fig. 7 is a mobile robot man-machine sharing control flow chart.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
The invention relates to a man-machine sharing control system of a mobile mechanical arm, which adopts a control system of an upper computer and a lower computer, wherein the teleoperation system of the upper computer consists of a computer and a main mechanical arm (a main hand), the industrial personal computer is responsible for a man-machine interaction interface, an operator sends a master-slave control instruction to a controller by operating the main hand, and an intelligent control system is formed by an embedded industrial personal computer at the control system part of the lower computer and is responsible for controlling fingers according to sharingAnd controlling the mechanical arm (slave hand) positioned on the mobile platform, feeding back the environmental information acquired by the sensor to the upper computer, displaying the environmental information to an operator through a man-machine interaction interface, and sending an autonomous control instruction, wherein the overall frame of the man-machine sharing control system of the specific mobile mechanical arm is shown in figure 1. The working mechanism of the sharing control system is that after the upper computer and the lower computer are in communication connection in a wired mode, an operator checks the gesture of the mechanical arm through a man-machine interaction interface and issues a master-slave control instruction, meanwhile, the intelligent control system outputs an autonomous control instruction according to environment information and the state of the mechanical arm, and finally, the sharing controller outputs the sharing control instruction after receiving the master-slave control instruction, the autonomous control instruction and the environment information. A specific sharing control flow is shown in fig. 7. Wherein the designed shared control instruction S is output c The method comprises the following steps:
S c (η)＝(I o -I A )η+I A (1)
wherein I is o I is master-slave control instruction A Is an autonomous control instruction of the robot, and eta is a shared control coefficient. The magnitude of the shared control coefficient η, which is obtained by the equation (1), determines the magnitude of the weight that the operator and the robot arm control autonomously in the shared control. The control mode of determining the size of the η coefficient is classified into autonomous control, operator master-slave control and man-machine sharing control, as shown in fig. 2.
(1) When η=0, the operation mode is an automatic control mode of the mechanical arm, the operation instruction output by the shared controller is the automatic control of the mechanical arm, at this time, the operator does not participate in the control of the mechanical arm, and the mechanical arm performs the automatic control according to the visual feedback environment information.
(2) When eta is more than 0 and less than 1, the operation mode is a man-machine sharing control mode, namely the operation instruction output by the sharing controller is a weighted fusion instruction of a master-slave control instruction of an operator and an autonomous control instruction of the mechanical arm. In this mode, when η=0.5, the operator and the robot each occupy half of the control authority. With the increasing value of η, the control weight occupied by the operator increases.
(3) When η=1, the operation mode is an operator master-slave control mode, and the operation command output by the shared controller is an operator master-slave control command, and the mechanical arm is completely controlled by the operator, that is, the operator controls the mechanical arm (slave hand) by the master hand.
Based on the man-machine sharing control system of the mobile mechanical arm, the man-machine sharing control method combining the master-slave control-based operator control and the automatic mechanical arm control based on track tracking is mainly used for designing an effective man-machine sharing control strategy, integrating the master-slave control instruction of the operator with the automatic mechanical arm control instruction and realizing dynamic distribution of weights in the control process.
The principle of the method is that firstly, the operator state, the mechanical arm state and the environment information are integrated, a fuzzy controller model is established for receiving, and a sharing control coefficient eta is output according to a designed sharing control rule. Then master-slave control instruction I of operator according to shared control coefficient eta 0 And an autonomous control command I for a robotic arm A Fusing and outputting a sharing control instruction S c Thereby achieving the man-machine sharing control of the mechanical arm. A block diagram of the man-machine sharing control of the mobile robot is shown in fig. 3.
According to the mobile mechanical arm man-machine sharing control block diagram, the mechanical arm man-machine sharing control method comprises three parts: (1) operator master-slave control; (2) autonomous control of robotic arm trajectory tracking; and (3) man-machine sharing control of the combination of the two.
(1) As a preferable technical scheme of the invention, a master-slave control mode is adopted to realize the control of an operator on the mechanical arm, and the master-slave control system comprises the following parts: the system comprises a master control end, a control system, a slave control end and a feedback system. A specific master-slave control system is shown in fig. 4. The operator transmits control signals via the control system to the slave hand via the master hand.
The master hand and the slave hand adopt a master-slave isomorphic configuration, namely the structures of the master hand and the slave hand are identical, and only the size is different. The method is characterized in that a joint space mapping control mode is adopted for the master-slave isomorphic mechanical arm, and all joint angles of a master-end controller are synchronously mapped into a slave joint space in a certain proportion. Meanwhile, the distance between the tail end of the mechanical arm and the target object is measured by means of the 3D camera, and is fed back to an operator through the control system, so that the operator can control the slave hand in real time in the operation process, and the control instruction is optimized according to the environmental change.
(2) As a preferable technical scheme of the invention, the mechanical arm grabbing unit is carried on the robot moving unit and can reach corresponding positions along with the robot moving platform. The mechanical arm adopts a point-to-point track planning mode and carries out track tracking by matching with a PID controller, and the mechanical arm is specifically expressed as follows:
(21) The mechanical arm track planning adopts a point-to-point planning mode, the point-to-point planning mode is used as a mode of joint space planning, and when constraint conditions of a starting point and a termination point are given and two-point tracks are required to be connected, the problem is that the point-to-point track planning mode is adopted. The technical scheme adopts a fifth order polynomial to carry out planning, and the general form is as follows:
θ(t)＝a 0 +a 1 (t-t s )+a 2 (t-t s ) 2 +a 3 (t-t s ) 3 +a 4 (t-t s ) 4 +a 5 (t-t s ) 5 (2)
six unknowns are given with six constraint conditions, namely angular displacement, angular velocity and angular acceleration of a starting point and an end point respectively, and constraint condition equations are that
Solving the equation set to obtain six unknowns, wherein t= (T) e -t s )。
Substituting the parameters into the fifth-order polynomial to obtain the required track.
(22) Aiming at the problem that after the track planning of the mechanical arm, the invention proposes to track the planned track by adopting a PID controller. The PID controller consists of proportional control, integral control and differential control, and three parameters of PID are automatically regulated according to errors of the system and the change rate of the errors, so that the PID controller has better control performance. The specific structure is shown in fig. 5.
Ratio control (P): the output of the proportional controller is proportional to the difference value of the current state from the target state, if the proportion is large, the target value is approached more quickly, but the proportion is large and is easy to overshoot; at small ratios, overshoot is reduced, but the response time becomes very long. And, when there is only proportional control, there is a steady state error in the system output.
Integral control (I): the output of the integrating controller is proportional to the integral of the input error information number. Integral control is typically used to eliminate steady state errors of the system.
Differential control (D): the output of the differential control is proportional to the rate of change of the input error signal. Differential control may be used to reduce overshoot of a purely proportional control.
Further, regarding the PID controller, there is the following formula:
wherein e (i), i=0, 1,2,3, k, k is the corresponding systematic error, k p ，k i ，k d Respectively PID controller parameters. The three parameters are debugged to a group of more suitable values according to the task, and the debugging process is generally as follows: first, let k i ，k d The P controller is independently adjusted until the system response reaches an excellent result, namely the response speed can be accepted, and overshoot is small. Then fix k p Integral controller k is transferred d 。
(3) As a preferable technical scheme of the invention, in order to realize man-machine sharing control and reasonably distribute control weights between an operator and a mechanical arm, the invention provides a mode of combining a joint change output device and a fuzzy controller to finally obtain a sharing control coefficient eta.
1) Joint change output device
The purpose of the joint change output device is to calculate the change quantity of the joint through the inverse operation of the motion equation of the mechanical arm and the change inverse transformation of the mechanical arm end effector, and output the change quantity to the fuzzy controller. The specific method is as follows:
the rotation angle and displacement distance of the joint are collectively referred to as the channel amount, denoted as q, while the target state is denoted as a vector group x= (X) 1 ，x 2 ，x 3 ，....，x m ) T X is the set of angular and linear velocity vectors of the end effector, each element X of which can be found from Q, noted as x=f (Q), by introducing a jacobian matrix to relate Δq (change in joint) to Δx (end effector), the joint degrees of freedom are found by deriving the following formula:
expanding the above equation, fully differentiating each term in X, biasing all q to the right of the equation. The following formula is developed:
where the number of terms m in X is the number of degrees of freedom of the end effector and the columns of the matrix equal the number of joints in Q, abbreviated as follows:
ΔX m×1 ＝J m×n (q)·ΔQ n×1 (8)
from the above equation, in the inverse operation, the degree of freedom of each joint is inversely calculated by using the degree of freedom of the end effector, that is, Δx is multiplied by the inverse (pseudo-inverse) of the jacobian matrix to obtain the change in Q.
2) Fuzzy controller
And a fuzzy logic method is adopted, the joint variable quantity Q and the distance L between the tail end of the mechanical arm and the target object are taken as inputs, a fuzzy rule is established according to expert experience in actual work, the two information are fused, and finally a sharing control coefficient is output. The design of the fuzzy logic controller is as follows:
(31) Determining fuzzy controller structure
The invention adopts a fuzzy controller structure as shown in fig. 6, and the working principle is that firstly, the input accurate quantity is subjected to fuzzification processing and converted into corresponding fuzzification quantity. And then finishing fuzzy reasoning according to the generalized principle, and finally, carrying out clear processing on the reasoning result to obtain an output accurate value, namely the value of eta.
(32) Determining fuzzy sets of inputs and outputs
The basic argument of the mechanical arm joint variation Q is [0,1], the fuzzy argument is {0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1}, and the mechanical arm joint variation Q is divided into 5 fuzzy sets { VS, s, M, B, VB }, which correspond to { very small, medium, large, very large }, respectively.
The basic argument of the distance L between the tail end of the mechanical arm and the target object is [0,1], the fuzzy argument is {0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1}, the quantization factor is 0.1, and the distance between the tail end of the mechanical arm and the target object is divided into 5 fuzzy sets { VC, C, M, F, VF }, which correspond to { very near, medium, far, very far }, respectively.
The basic argument of the shared control coefficient eta is [0,1], the fuzzy argument is {0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1}, and the shared control coefficient eta is divided into 5 fuzzy sets { VS, S, M, B, VB }, which correspond to { very small, medium, large, very large }, respectively.
(33) Membership function of fuzzy set
The method comprises the steps that a trapezoid membership function is adopted at two ends of a mechanical arm joint change Q and a distance L between the tail end of the mechanical arm and a target object, and a triangular membership function is adopted in the middle of the mechanical arm joint change Q. For the shared control coefficient eta, a mode of combining a triangle membership function and an S-type membership function is adopted. The method comprises the steps of carrying out a first treatment on the surface of the Taking the mechanical arm joint variable Q as an example, when the fuzzy set is { S, M and B }, the membership function adopts triangle membership, and when the fuzzy set is { VS, VB }, trapezoid membership is adopted, so that the membership function of the mechanical arm joint variable Q is formed, and the membership function of the distance L between the tail end of the mechanical arm and the target object is the same.
For the membership function of the fuzzy set sharing the control coefficient eta, a mode of combining a triangle membership function and an S-type membership function is adopted, when the fuzzy set is { S, M, B }, the triangle membership is adopted, and the S-type membership is adopted at { VS, VB }, so that the membership function sharing the control coefficient eta is formed.
(34) Establishing fuzzy rules
The fuzzy rule is designed based on the following principle:
1. when the distance between the tail end of the mechanical arm and the target object is far, the mechanical arm is carried on the mobile platform and still in the process of approaching the target object, and at the moment, an operator controls the mobile platform.
2. When the mechanical arm gradually approaches the target object, the mechanical arm is possibly dithered due to the inherent limitations of the mechanical arm structure and the autonomous path planning algorithm, and the problem that the target position is not reachable is solved, at the moment, the sharing control coefficient eta is increased, the proportion of an operator in sharing control is increased, and the path of the mechanical arm is adjusted through the upper layer decision-making capability of the operator.
3. When the distance between the tail end of the mechanical arm and the target object is very close, in order to avoid the tension influence on the control accuracy of an operator, the sharing control coefficient eta should be reduced, the proportion of the mechanical arm autonomous control in the sharing control is increased, and the autonomous control is completed by means of mechanical arm track tracking.
4. When the joint variation is large, the working space of the mechanical arm is limited, the sharing control coefficient eta is increased, the specific gravity of an operator in sharing control is improved, namely, the joint variation is limited by virtue of master-slave control, and collision is avoided.
5. And when the variation of the joint is smaller, the sharing control coefficient eta is reduced, and the advantage of autonomous control of the mechanical arm is fully exerted.
(35) Fuzzy reasoning and defuzzification
The invention adopts MamdaThe ni method carries out fuzzy reasoning, and the fuzzy implication relation of the ni method
(Q, L) can be determined by blurring the set +.>
And->
Specifically, the change Q of the joint of the mechanical arm and the distance L between the tail end of the mechanical arm and the target object are used as fuzzy set +.>
And->
The calculation is performed as shown in the formula (6).
Wherein the method comprises the steps of
And->
Representing the membership of each fuzzy set on its domain after quantization of two input objects, +.>
Membership of fuzzy sets on their domain after quantization for the output object.
The total output of fuzzy logic reasoning is shown in equation 7:
bringing the result of equation 6 into 7 to obtain U * The method comprises the steps of carrying out a first treatment on the surface of the Finally, U is subjected to the average maximum membership method * The shared control coefficient eta is obtained by performing the clarification.
Specifically, by adopting the control system and the fuzzy rule, the execution steps of the sharing control are as follows:
a. firstly, randomly determining the position of a target object and the initial position of a mechanical arm in a given environment;
b. according to a track planning algorithm, designing a path of the mechanical arm reaching the position of the target object;
c. according to the track tracking algorithm, the mechanical arm autonomously tracks the planned track
d. Judging the distance between the mechanical arm and the target object, dividing the distance, and dividing a working area into an operator manual control area, a mechanical arm track tracking area and an unclampable area;
e. in the manual control area of the operator, the operator adopts a master-slave control mode; in the track tracking area of the mechanical arm, the mechanical arm is used for autonomous control; in the non-grabbing area, the mechanical arm does not receive a control instruction;
f. the sharing controller receives the master-slave control instruction and the autonomous control instruction, performs weighted fusion on the master-slave control instruction and the autonomous control instruction to obtain a sharing control instruction, and outputs the sharing control instruction to the mechanical arm;
g. and the mechanical arm receives the sharing control instruction and finally reaches the position of the target object.
Claims (9)
1. A mobile mechanical arm man-machine sharing control system is characterized in that: comprises an upper computer teleoperation system and a lower computer control system;
the upper computer teleoperation system comprises a main industrial personal computer, a mechanical arm main hand electrically connected with the industrial personal computer and a sharing controller; the lower computer control system comprises an embedded industrial personal computer and a mechanical arm slave;
the manipulator slave hand is provided with a sensor and a 3D camera, the sensor acquires first state information and feeds back the first state information to a joint change output module in the embedded industrial personal computer for processing, the joint change output module outputs joint change quantity of the manipulator slave hand, and the first state information comprises degree of freedom state quantity of an end effector of the manipulator slave hand; the 3D camera is used for collecting image information;
the embedded industrial personal computer transmits the joint variation and the image information to the main industrial personal computer, and the main industrial personal computer processes the image information to obtain the distance between the slave hand of the mechanical arm and the target object; the fuzzy controller in the host industrial personal computer outputs a sharing control coefficient according to the second state information and the fuzzy control rule and sends the sharing control coefficient to the sharing controller; the second state information comprises the joint variation of the mechanical arm from the hand and the distance between the mechanical arm from the hand and the target object;
the industrial personal computer sends a master-slave control instruction to the shared controller; the embedded industrial personal computer sends an autonomous control instruction to the shared controller; the sharing controller outputs a sharing control instruction and sends the sharing control instruction to the slave arm slave hand to control the slave arm to move;
the industrial personal computer is provided with a man-machine interaction interface.
2. The mobile robotic arm human-machine sharing control system of claim 1, wherein: the system also comprises a mobile platform, wherein the mobile platform is used for carrying the slave manipulator of the mechanical arm and driving the slave manipulator of the mechanical arm to the corresponding position.
3. The mobile robotic arm human-machine sharing control system of claim 1, wherein: the sharing controller outputs a sharing control instruction S c The method comprises the following steps:
S c (η)＝(I o -I A )η+I A
wherein I is o I is master-slave control instruction A Is an autonomous control instruction of the robot, and eta is a shared control coefficient.
4. The mobile robotic arm human-machine sharing control system of claim 1, wherein: the master arm and the slave arm of the mechanical arm adopt master-slave isomorphic configuration, the master-slave isomorphic mechanical arm adopts a mode based on joint space mapping control, all joint angles of a master end controller are mapped into a slave joint space synchronously in a certain proportion, and the slave arm operation of the mechanical arm is controlled.
5. The mobile robotic arm human-machine sharing control system of claim 1, wherein: the mechanical arm adopts a point-to-point track planning mode from the autonomous control of the hand, and performs track tracking in cooperation with a PID controller; and planning the track of the point-to-point by adopting a fifth-order polynomial.
6. The sharing control method applied to the man-machine sharing control system of the mobile mechanical arm according to claim 1, wherein the joint change output module calculates the joint change amount from the degree of freedom of the end effector of the hand through the mechanical arm, and the calculation formula is as follows:
wherein DeltaX is a variable matrix of the end effector, deltaQ is a joint variable matrix, and Q is a channel quantity comprising the rotation angle and displacement distance of the joint; n represents the number of joints, m is the number of degrees of freedom of the end effector; f represents the mapping between each element in the degree of freedom vector set of the end effector and the channel quantity q, denoted as x=f (q);
the fuzzy rule of the fuzzy controller comprises: the larger the joint variation is, the larger the sharing control coefficient is, and the smaller the joint variation is, the smaller the sharing control coefficient is;
the controller adopts a Mamdani fuzzy reasoning method to realize fuzzy reasoning.
7. The sharing control method according to claim 6, wherein the fuzzy implication relation calculation formula is:
wherein the method comprises the steps of
And->
Respectively representing the change Q of the joint of the mechanical arm and the membership degree of each fuzzy set on each domain after the distance L between the tail end of the mechanical arm and the target object is quantized, < + >>
Representing the membership of implication relationships for fuzzy reasoning on input objects, +.>
Membership degree of fuzzy set on its domain after quantization for shared control coefficient, R i Implication relation corresponding to each fuzzy control rule;
the total output of fuzzy logic reasoning is:
in U * For the total output of the fuzzy logic reasoning,
and quantizing the input object to obtain intersections of membership degrees of each fuzzy set, wherein i is the fuzzy rule number.
8. The sharing control method according to claim 6, wherein: for the membership function of the fuzzy set of the joint variable Q of the mechanical arm and the distance L between the mechanical arm and the target object, a mode of combining a trapezoidal membership function and a triangular membership function is adopted, when the fuzzy set is positioned at the middle degree, the triangular membership function is adopted, and the trapezoidal membership function is adopted at the extreme degrees at the two ends.
9. The sharing control method according to claim 6, wherein: for the membership function of the fuzzy set sharing the control coefficient eta, a mode of combining a triangle membership function and an S-type membership function is adopted, when the fuzzy set is positioned at the middle degree, the triangle membership function is adopted, and the S-type membership is adopted at the extreme degrees at the two ends.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310326739.0A CN116330288A (en) | 2023-03-30 | 2023-03-30 | Man-machine sharing control method and system for mobile mechanical arm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310326739.0A CN116330288A (en) | 2023-03-30 | 2023-03-30 | Man-machine sharing control method and system for mobile mechanical arm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116330288A true CN116330288A (en) | 2023-06-27 |
Family
ID=86883756
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310326739.0A Pending CN116330288A (en) | 2023-03-30 | 2023-03-30 | Man-machine sharing control method and system for mobile mechanical arm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116330288A (en) |
-
2023
- 2023-03-30 CN CN202310326739.0A patent/CN116330288A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP2676397B2 (en) | Dynamic trajectory generation method for dynamic system | |
| Boukens et al. | Design of an intelligent optimal neural network-based tracking controller for nonholonomic mobile robot systems | |
| CN105772917B (en) | A kind of three joint spot welding robot's Trajectory Tracking Control methods | |
| CN111687827B (en) | Control method and control system for coordinating and operating weak rigid member by two robots | |
| Li et al. | Development of articulated robot trajectory planning | |
| CN113848905B (en) | Mobile robot track tracking method based on neural network and self-adaptive control | |
| CN115157238A (en) | Multi-degree-of-freedom robot dynamics modeling and trajectory tracking method | |
| Hu et al. | Prescribed time tracking control without velocity measurement for dual-arm robots | |
| Xiu et al. | Path-following based on fuzzy line-of-sight guidance for a bionic snake robot with unknowns | |
| Souères et al. | Dynamical sequence of multi-sensor based tasks for mobile robots navigation | |
| Korayem et al. | Regulation of cost function weighting matrices in control of WMR using MLP neural networks | |
| CN117047774A (en) | Multi-joint hydraulic mechanical arm servo control method and system based on sliding film self-adaption | |
| CN107894709A (en) | Controlled based on Adaptive critic network redundancy Robot Visual Servoing | |
| Okuma et al. | A neural network compensator for uncertainties of robotic manipulators | |
| CN113867157B (en) | Optimal trajectory planning method and device for control compensation and storage device | |
| CN116330288A (en) | Man-machine sharing control method and system for mobile mechanical arm | |
| Wu et al. | Design of ugv trajectory tracking controller in ugv-uav cooperation | |
| CN115533920A (en) | Collaborative planning method and system for solving inverse kinematics of rope-driven mechanical arm and computer storage medium | |
| Zhang et al. | Modular design of fuzzy controller integrating deliberative and reactive strategies | |
| SHOUYU et al. | Fuzzy trajectory planning and its application to robot manipulator path control | |
| Li et al. | Formation Tracking for a Multi-Auv System Based on an Adaptive Sliding Mode Method in the Water Flow Environment | |
| Yang et al. | An obstacle avoidance and trajectory tracking algorithm for redundant manipulator end | |
| Koubaa et al. | Intelligent control for nonholonomic mobile robot including actuator dynamics | |
| Gao et al. | Motion planning and coordinated control for mobile manipulators | |
| Swain-Oropeza et al. | Sensor-based navigation in cluttered environments |
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 |