CN104626168A - Robot force position compliant control method based on intelligent algorithm - Google Patents
Robot force position compliant control method based on intelligent algorithm Download PDFInfo
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Abstract
The invention discloses a robot force position compliant control method based on an intelligent algorithm, and belongs to the field of mechatronics. The control method includes the following steps: a control system comprehensively works out an interaction force value of the combination position of the tail end of a mechanism arm and a workpiece by measuring currents of joint servo motors and joint rotation positions of a robot based on a position impedance control mode; an interaction force value of the robot and the environment is predicted through the adoption of a prediction algorithm and is compared with the interaction force value obtained through calculating, output processed with an energy balance correction algorithm is actual force perception of the control system, the control system accordingly conducts assembly robot track tail end position setting, control signals of the joint servo motors are accordingly formed to be used for controlling the servo motors, and therefore force-position compliant control is achieved. By means of the robot force position compliant control method, during operation such as assembling, machining and polishing, the robot is required to be in contact with an operation object in the working process, the contact force is kept within a set section in the working process, and the good working effect is obtained.
Description
Technical field
The invention belongs to field of electromechanical integration, specifically, relate to a kind of Robot Force position Shared control method based on intelligent algorithm.
Background technology
Along with progress and the manufacturing development of science and technology, market constantly increases the demand that sanding and polishing is processed.Sanding and polishing machine people can realize high efficiency, high-quality automation polishing, provides a kind of effective solution for replacing manual polishing.
The core of milling robot is power control technology, by the card polishing quality of trying hard to keep of controlled working track and milling tools end, namely all will control the position of robot and power these two aspects.Develop more ripe position control humanoid robot both at home and abroad at present, a lot of research has also been carried out to force control robot, but most of force control robot is all position-based servo to be realized, its response time is long, directly can not control power, the precision that the power that have impact on controls and effect.
Most power controls research and all adopts wrist force sensor to measure and the contact force feeding back robot end and contact environment.But wrist force sensor price general charged is higher, ratio of rigidity Robot actions end is low, easily damages, and cannot be applied in the practical application in industry situation such as high temperature, high burn into strong jamming.Joint torque controls to form closed loop by torque feedback and replaces the closed-loop control of end power, in the system of robot end's power open loop, finally completes high-quality power control, can overcome all drawbacks of wrist force sensor.
Due to robot model inaccuracy own and the various interference that are subject to, often satisfied Control platform cannot be obtained.For this reason, Chinese scholars proposes multiple nonlinear control system, and in these control methods, computed moment control is the most simple and effective.In robot location's control procedure, adopt the computed moment control based on neural networks compensate unknown disturbances, neural network parameter study does not need system priori.When after robot end and environmental exposure, utilize torque to convert and obtain actual contact force.
Because above-mentioned defect, the design people, actively in addition research and innovation, to found a kind of mankind of adding experience, improve institute's dynamometry signal anti-interference and accuracy, there is fast response time, the Robot Force position Shared control method based on intelligent algorithm of safe and reliable feature.
Summary of the invention
The technical problem to be solved in the present invention overcomes above-mentioned defect, the experience of a kind of mankind of adding is provided, improves anti-interference and the accuracy of institute's dynamometry signal, there is fast response time, the Robot Force position Shared control method based on intelligent algorithm of safe and reliable feature.
For solving the problem, the technical solution adopted in the present invention is:
Based on the Robot Force position Shared control method of intelligent algorithm, it is characterized in that: control method is as follows:
The location-based impedance-controlled fashion of control system, by robot measurement each joint servo current of electric and each articulation position, data combination goes out the mutual force value of arm end and workpiece junction; Adopt the mutual force value of prediction algorithm prediction machine human and environment, and compare with the above-mentioned mutual force value obtained of resolving, output through the process of balancing energy correcting algorithm is the perception of control system actual forces, control system carries out the setting of assembly robot trailing end position accordingly, each joint servo motor control signal is formed with this, servomotor is controlled, realizable force-position Shared control with this.
As a kind of technical scheme of optimization, the concrete steps of control method are as follows:
1), motion controller is planned new location point, and is converted into analogue speed signal;
2), speed ring receive analogue speed signal, control electric current loop carry out servomotor Current Control;
3), by encoder obtain actual position signal, measure servomotor electric current and obtain dtc signal;
4), by the conversion of power position import motion controller into after actual position signal, calculate the deviation of exerting oneself with position, adjustment produces new location point.
As a kind of technical scheme of optimization, step 1) in motion controller plan that the step of new location point is as follows:
(1) the fresh target point of robot end's motion, is calculated according to given mission requirements;
(2) contact force/moment values resolving out, is read from data buffer zone;
(3), resolve value according to power/moment to calculate and the position of adjustment aim point with the deviation information expecting to contact value;
(4), carry out robot trajectory planning, send into ready queue;
(5), trajectory planning is performed;
(6), while execution step (5), current contact force/moment information is gathered;
(7), get back to step (l), repeat.
As a kind of technical scheme of optimization, step 4) in the specific implementation of power position conversion as follows:
(1) the joint torque signal of servomotor, is received;
(2), according to the joint torque signal obtained by electric current, calculate environmental interaction power by Jacobian matrix, and adopt RBF neural algorithm predicts robot and environmental interaction power to prevent from shaking;
(3) reciprocal force, using step (2) obtained is as the input of balancing energy device, and balancing energy device obtains the perception of control system actual forces through the output of balancing energy correcting algorithm process;
(4), actual forces perception input motion controller is carried out track position setting;
(5), get back to step (l), repeat.
As a kind of technical scheme of optimization,
In step 1) start before operation, need demarcate each joint torque, obtain the torque under Light Condition, as the basis calculating band and carry torque under state.
Owing to have employed technique scheme, compared with prior art, the present invention is based on human experience's information and the comprehensive power of acceleration information-position to control milling robot and be studied.First the corresponding power required for workpiece of manual polishing is measured, then carried out filtering, gravity compensation and sensor coordinate system to the force signal collection measured to demarcate, improve anti-interference and the accuracy of institute's dynamometry signal, finally expert system is entered to above-mentioned algorithm and carry out reductive analysis, draw and the polishing force value that corresponding workpiece is suitable by data signal, polishing power is set by controller.This method adds the experience of the mankind, has fast response time, safe and reliable feature.
The power of operating space any direction and position can be assigned on each joint by Jacobian matrix and selection matrix by the present invention in actual applications, and also the power in each joint can being done normal solution, to calculate robot end stressed.Utilization power-position Hybrid mode technology can in the operations such as assembling, processing, polishing, and the course of work is required, and robot contacts with manipulating object, and makes contact force remain on the interval of setting in the course of the work, to obtain good working effect.
Below in conjunction with the drawings and specific embodiments, the invention will be further described simultaneously.
Accompanying drawing explanation
Fig. 1 is control system distinguishing hierarchy figure in an embodiment of the present invention;
Fig. 2 be polish in an embodiment of the present invention tap operation Control System Software flow chart.
Detailed description of the invention
Embodiment:
As shown in Figure 1, based on the Robot Force position Shared control method of intelligent algorithm, control method is as follows:
The location-based impedance-controlled fashion of control system, by measuring servomotor electric current and articulation position, RBF neural algorithm predicts robot and environmental interaction force value is adopted to input balancing energy state, output through the process of balancing energy correcting algorithm is the perception of control system actual forces, control system carries out the setting of assembling track position accordingly, form Serve Motor Control signal, control servomotor with this, realizable force position Shared control.
Control system comprises motion controller, driver and motor and related software, algorithm,
Before starting operation, need demarcate each joint torque, obtain the torque under Light Condition, as the basis of torque under calculating band year state.
Concrete steps after it are as follows:
1), motion controller is planned new location point, and is converted into analogue speed signal.
Motion controller plans that the step of new location point is as follows:
(1) the fresh target point of robot end's motion, is calculated according to given mission requirements.
(2) contact force/moment values resolving out, is read from data buffer zone.
(3), resolve value according to power/moment to calculate and the position of adjustment aim point with the deviation information expecting to contact value.
(4), carry out robot trajectory planning, send into ready queue.
(5), trajectory planning is performed.
(6), while execution step (5), current contact force/moment information is gathered.
(7), get back to step (l), repeat.
2), speed ring receive analogue speed signal, control electric current loop carry out servomotor Current Control.
3), by encoder obtain actual position signal, measure servomotor electric current and obtain dtc signal.
4), by the conversion of power position import motion controller into after actual position signal, calculate the deviation of exerting oneself with position, adjustment produces new location point.
Wherein the specific implementation of power position conversion is as follows:
(1) the joint torque signal of servomotor, is received;
(2), according to the joint torque signal obtained by electric current, calculate environmental interaction power by Jacobian matrix, and adopt RBF neural algorithm predicts robot and environmental interaction power to prevent from shaking;
(3) reciprocal force, using step (2) obtained is as the input of balancing energy device, and balancing energy device obtains the perception of control system actual forces through the output of balancing energy correcting algorithm process;
(4), actual forces perception input motion controller is carried out the setting of assembling track position;
(5), get back to step (l), repeat.
The present embodiment can adopt high-performance industrial computer to design the power-positioner of a robotic asssembly, to realize the prediction of effective power and balancing energy, obtains the authenticity of power perception and the accuracy of TRAJECTORY CONTROL when system stability.The experiments such as assembling are carried out, the correctness of verification system by transformer and power supply.
In robot system, at robot end's sectional fixture (adsorption head) and workpiece, and comprehensively go out workpiece and assembling object contact power by resolving of each joint torque of robot, just solving an equation by each joint encoders measured angular displacement and by robot motion and calculating workpiece pose.Be difficult to directly obtain for the actual detection contact force in impedance control system, adopt and gather each servomotor torque value through the method obtaining the contact force of end effector and external environment condition that converts, substitute and directly adopt force snesor feedback end effector contact force signal; To expectation contact force signal, design a kind of experimental technique and obtain assembly force and the amount of being assembled under its effect, estimate out the contact force scope of expectation, then according to neural network prediction contact force, controller thus to reference locus carry out online local trace adjustment, to meet matching requirements.
In robot location's control procedure, adopt the computed moment control based on neural networks compensate unknown disturbances, neural network parameter study does not need system priori.When after robot end and environmental exposure, utilize torque to convert and obtain actual contact force.
When robot be in freely-movable pattern do not contact time, employing be position-position control mode; When robot is in restricted mode, namely when robot end and absorption surface, employing be position-force control mode in setting constant force regional extent.
The present embodiment also illustrates the problems referred to above by a detailed description of the invention:
Robot polishing system take tap as polishing object.
Robot polishing system comprises:
(1) belt grinder
Utilize the High Rotation Speed in polishing abrasive band can carry out grinding to surface of the work, make it smooth bright, increase brightness and the fineness of product.
A robot cell configures 2 groups of sanders, according to the abrasive band of technological requirement, workpiece shapes selection different thicknesses and width, and can arrange suitable polishing parameter.
(2) motor, be responsible for often organizing sander front and back position and move, the quantity of its motor matches with belt grinder with position relationship.
(3) frequency converter, Main Function is the variable frequency adjustment of abrasive band contact wheel rotating speed.
(4) contact wheel is executing agency, meets kirsite grip surface surface finish requirements for polishing.
(5) dust collection is reserved dust collecting.
(6) strainer, adopt cylinder tensioning abrasive band mode, strainer can manual adjustments position.
(7) force feedback governor motion, is calculated by joint torque, is resolved that to obtain end stressed by Jacobian matrix.
Robot polishing system in present embodiment is based on the power-position hybrid control system of FUZZY ALGORITHMS FOR CONTROL, by installing pressure sensor additional in belt sander guide rail platform air cylinder, be used for measuring force and moment size suffered by X-direction under sensor coordinate system, and by have devised wave filter, filtering is carried out to the data measured, and consider the quality of milling tools, be provided with gravity compensation link, the position of conversion robot can obtain organizing force value more, the data of least square method to gained are utilized to be optimized, draw optimal value, thus complete the coordinate system of force snesor is demarcated.
Robot polishing system have employed FUZZY ALGORITHMS FOR CONTROL, controls control system, uses MATLAB to emulate, effect operational excellence.Output signal passes to force controller, robot is regulated to the power relative constancy kept between milling tools and workpiece, thus ensures the effect of polishing.
As shown in Figure 2, the working-flow of robot polishing system is as follows:
The software function of control system is the polishing work of coordinating robot and polishing grinding belt sander, and main purpose is the constant of maintenance polishing power.
Polishing detailed process is correct after workpiece is got by robot, notice master controller starts sander, sander coordinates robot buffing work-piece, and constantly carry out force feedback and regulate constant with guarantee power, this best polishing power is intersected to form by human experience's information and acceleration information.
After this operation has been polished, robot notice master controller cuts out this operation sander and starts subsequent processing polishing work after carrying out workpiece correction, repeats above process until all process steps has been polished.
The present invention is not limited to above-mentioned preferred embodiment, and anyone should learn the structure change made under enlightenment of the present invention, and every have identical or akin technical scheme with the present invention, all belongs to protection scope of the present invention.
Claims (5)
1., based on the Robot Force position Shared control method of intelligent algorithm, it is characterized in that:
Control method is as follows:
The location-based impedance-controlled fashion of control system, by robot measurement each joint servo current of electric and each articulation position, data combination goes out the mutual force value of arm end and workpiece junction; Adopt the mutual force value of prediction algorithm prediction machine human and environment, and compare with the above-mentioned mutual force value obtained of resolving, output through the process of balancing energy correcting algorithm is the perception of control system actual forces, control system carries out the setting of assembly robot trailing end position accordingly, each joint servo motor control signal is formed with this, servomotor is controlled, realizable force-position Shared control with this.
2., according to the Robot Force position Shared control method based on intelligent algorithm described in claim 1, it is characterized in that:
The concrete steps of control method are as follows:
1), motion controller is planned new location point, and is converted into analogue speed signal;
2), speed ring receive analogue speed signal, control electric current loop carry out servomotor Current Control;
3), by encoder obtain actual position signal, measure servomotor electric current and obtain dtc signal;
4), by the conversion of power position import servo controlling card into after actual position signal, calculate the deviation of exerting oneself with position, adjustment produces new location point.
3., according to the Robot Force position Shared control method based on intelligent algorithm described in claim 2, it is characterized in that:
Step 1) in motion controller plan that the step of new location point is as follows:
(1) the fresh target point of robot end's motion, is calculated according to given mission requirements;
(2) contact force/moment values resolving out, is read from data buffer zone;
(3), resolve value according to power/moment to calculate and the position of adjustment aim point with the deviation information expecting to contact value;
(4), carry out robot trajectory planning, send into ready queue;
(5), trajectory planning is performed;
(6), while execution step (5), current contact force/moment information is gathered;
(7), get back to step (l), repeat.
4., according to the Robot Force position Shared control method based on intelligent algorithm described in claim 2, it is characterized in that:
Step 4) in power position conversion specific implementation as follows:
(1) the joint torque signal of servomotor, is received;
(2), according to the joint torque signal obtained by electric current, calculate environmental interaction power by Jacobian matrix, and adopt RBF neural algorithm predicts robot and environmental interaction power to prevent from shaking;
(3) reciprocal force, using step (2) obtained is as the input of balancing energy device, and balancing energy device obtains the perception of control system actual forces through the output of balancing energy correcting algorithm process;
(4), actual forces perception input motion controller is carried out the setting of assembling track position;
(5), get back to step (l), repeat.
5., according to the Robot Force position Shared control method based on intelligent algorithm described in claim 2, it is characterized in that:
In step 1) start before operation, need demarcate each joint torque, obtain the torque under Light Condition, as the basis calculating band and carry torque under state.
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| CN111730598B (en) * | 2020-07-06 | 2022-01-04 | 武汉联影智融医疗科技有限公司 | Robot force position interlocking control method, device and system |
| CN111730598A (en) * | 2020-07-06 | 2020-10-02 | 武汉联影智融医疗科技有限公司 | Robot force position interlocking control method, device and system |
| CN111752252A (en) * | 2020-07-18 | 2020-10-09 | 大连交通大学 | Automatic flexible butt joint assembly control system and method |
| CN112405531A (en) * | 2020-11-06 | 2021-02-26 | 广东电网有限责任公司电力科学研究院 | Position domain impedance control method and device, storage medium and operation robot |
| CN112809666A (en) * | 2020-12-17 | 2021-05-18 | 安徽工业大学 | 5-DOF mechanical arm force and position tracking algorithm based on neural network |
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