4T焊接滾輪架機(jī)械設(shè)計,焊接,輪架,機(jī)械設(shè)計
Predictive sensor guided robotic manipulators in automated welding cells
Adolfo Bauchspiess, a, Sadek C. Absi Alfaro, b and Leszek A. Dobrzanski, c
a GRACO/UnB — Electrical Engineering Departments, Automation and Control Group, University of Brasília, 70910-900 Brasília, DF, Brazil
b GRACO/UnB — Mechanical Engineering Departments, Automation and Control Group, University of Brasília, 70910-900 Brasília, DF, Brazil
c Silesian University of Technology, Ul.Konarskiego 18a, 44-100 Gliwice, Poland
Available online 10 January 2001.
Abstract
This paper presents an on-line tracking optimization scheme for sensor guided robotic manipulators by associating sensor information, manipulator dynamics and a path generator model. Feedback linearization-decoupling permits the use of linear SISO prediction models for the dynamics of each robot joint. Scene interpretation of CCD-camera images generates spline fitted segments of future trajectory. In the sensor vision field the proposed optimization criteria minimizes the error between state variables of the prediction model and the state variables of the spline trajectory generator. These techniques, allied with separation of disturbance rejection and path-tracking performance by the proposed feed-forward following model predictive (FMP) servo-controller design, permits very high path tracking dynamics (and consequently small errors). Experimental results on implementation of a CCD-camera guided hydraulic robot and a welding robot demonstrates the practical relevance of the proposed approach.
Author Keywords: Machine vision; Welding; Robotic
Article Outline
1. Introduction
2. Dynamic system tracking
3. Following model predictive path tracking
4. Problem formulation
5. Calculation of the optimal control sequence
6. Superposition of the minimization horizon
6.1. The receding horizon approach
7. Velocity and acceleration reference
8. Simulation for a typical trajectory
9. Experimental results
10. Circle form test
11. CCD-camera guided weld robot
12. Conclusion
Acknowledgements
References
1. Introduction
Robotic manipulators have been used in welding cells for a long time in order to improve welding quality. Substitution of mancraft in welding cells where a robot welds only few different parts every time in the same manner such as in spot welding commonly used in the automobile industry is not a very difficult task. But in a flexible, just-in-time, and CAD customized production approach, very different parts are to be welded demanding an “intelligent” robot-welding concept.
Providing robots with abilities of an experienced welder is the visionary target of many research groups. The realization of such ambitious goals leads to the use of sensors which provide the robot with the necessary information, so that it can interact within their environment. Preferentially, the robot should autonomously find and precisely weld metal joining paths in order to fulfill some given manufacturing tasks [1].
A shortcut of the use of sensor-guided robots is that due to their mechanical inertia they can react only relatively slowly to changes in the trajectory information captured by the sensor system. In this paper, it will be shown how a sensor that can look ahead such as a CCD-camera can be used to improve substantially the seam tracking precision. The proposed algorithm virtually eliminates the tracking error by considering the dynamic model of the robot and the captured future trajectory information. Incorporating an internal trajectory generator model leads, thus, to the following model predictive servo-controller algorithm (FMP — for short [3]).
Non-linear control techniques [6, 7 and 12] can decouple and linearize robotic manipulator joints. So that each robot joint can be considered as a linear SISO system. Using such a model, the tracking problem of sensor guided manipulators can be treated in the linear domain. In particular, the discrete optimization of the predictive path-tracking problem with the proposed cost function leads to an analytical solution with guaranteed stability [3 and 11]. This new approach avoids the typical recursive solution usually employed for the Riccati equation [2].
Robotic manipulators equipped with sensors can automate industrial processes in an “intelligent manner”. Those are objects of intense research efforts in the field of artificial intelligence (AI): to build machines that consider the information captured from the surrounding environment in a proper (intelligent) manner. With the support of sensors the working trajectory of the robot can be obtained within a certain vision field which will be used here as the minimization horizon of the tracking error.
The proposed algorithm was implemented to control a hydraulic manipulator guided by a CCD-camera where it was shown that the FMP methodology significantly reduces the dynamic tracking error. Currently, this technique is being implemented to control a 6-DOF CCD-camera guided welding robot at GRACO.
2. Dynamic system tracking
Depending on the characteristics of tracking the following classification of problems are usual [2]:
? The tracking problem: the reference trajectory is a determined (arbitrary) function of time for 0
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