Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-23T12:17:14.755Z Has data issue: false hasContentIssue false
  • English
  • Français

Numerical robot kinematics based on stochastic and molecular simulation methods

Published online by Cambridge University Press:  09 March 2009

Thomas Kastenmeier  and
Franz J. Vesely
Thomas Kastenmeier
Affiliation:
Institute of Experimental Physics, University of Vienna, Boltzmanngasse 5, A-1090 Vienna (Austria) e-mail: [email protected].
Franz J. Vesely
Affiliation:
Institute of Experimental Physics, University of Vienna, Boltzmanngasse 5, A-1090 Vienna (Austria) e-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Summary

Multilink robot arms are geometrically similar to chain molecules. We investigate the performance of molecular simulation methods, combined with stochastic methods for optimization, when applied to problems of robotics. An efficient and flexible algorithm for solving the inverse kinematic problem for redundant robots in the presence of obstacle's (and other constraints) is suggested. This “Constrained Kinematics/Stochastic Optimization” (CKSO) method is tested on various standard problems.

Keywords

Numerical kinematicsMolecular simulationMultilink robot armsRedundant robotsSimulated annealing.
Type
Article
Information
Robotica , Volume 14 , Issue 3 , May 1996 , pp. 329 - 337
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Desoyer, K., Kopacek, P. and Troch, I., Industrieroboter and Handhabungssysteme (R. Oldenburg Verlag, MUnchen-Wien, 1985).Google Scholar
2.Allgeuer, H., “Kinematische Steuerung von Robotern mit redundanten Freiheitsgraden”. Dissertation (Technical University, Wien, 1992).Google Scholar
3.Pfeiffer, F. and Reithmeier, E., Roboterdynamik (Teubner, Stuttgart, 1987).Google Scholar
4. G.Kneller, R. and Hinsen, K., Phys. Rev. 50(2), 1559–1569 (1994).Google Scholar
5.Abdel-Rahman, T.M.Int. J. Rob. Res. 10(4), 382–395 (1991).CrossRefGoogle Scholar
6.Barojas, J. and Levesque, D., Phys. Rev. A7(3), 1092–1105 (1973).CrossRefGoogle Scholar
7.Ryckaert, J.-P., Ciccotti, G. and Berendsen, H.J.C., J. Computat. Phys. 23, 327–341 (1977).CrossRefGoogle Scholar
8.Kirkpatrick, S., Gelatt, C.D. Jr and Vecchi, M.P., Science 220, No. 4598, 671–680 (1983).CrossRefGoogle Scholar
9.Metropolis, N. and Ulam, S., J. Amer. Statist. Assoc. 44, 335–341 (1949).CrossRefGoogle Scholar
10.Yoshikawa, T., 1st International Symposium of Robotics Research,N.H., U.S.A.,August 25-Sept. 2, 1983 (MIT Press, Cambridge Mass., 1984) pp. 735–747.Google Scholar
11.Kastenmeier, Th., “Numerische Roboterkinematik mit stochastischen und molekulardynamischen Simulationsmethoden, insbesondere für redundante Roboter” Diploma Thesis (University of Vienna, 1995).Google Scholar