Performance of Several Optimization Methods on Robot Trajectory Planning Problems

Abstract

Listen

Translate article

Previous article Next article Performance of Several Optimization Methods on Robot Trajectory Planning ProblemsJoseph G. Ecker, Michael Kupferschmid, and Samuel P. MarinJoseph G. Ecker, Michael Kupferschmid, and Samuel P. Marinhttps://doi.org/10.1137/0915084PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstractThis paper provides a comparison and analysis of the performance of a special purpose algorithm and several specific available nonlinear programming codes applied to a robot trajectory problem.[1] Mokhtar S. Bazaraa and , C. M. Shetty, Nonlinear programming, John Wiley & Sons, New York-Chichester-Brisbane, 1979xiv+560 80f:90110 0476.90035 Google Scholar[2] C. deBoor, A practical guide to splines, Applied Mathematical Sciences, Vol. 27, Springer-Verlag, New York, 1978xxiv+392 80a:65027 0406.41003 CrossrefGoogle Scholar[3] A. Ech-Cherif, , J. G. Ecker, , M. Kupferschmid and , S. P. Marin, Robot Trajectory Planning by Nonlinear Programming, General Motors Research Publication, GMR-6460, General Motors Research Laboratories, Warren, MI, 1988, December Google Scholar[4] J. G. Ecker and , M. Kupferschmid, A computational comparison of the ellipsoid algorithm with several nonlinear programming algorithms, SIAM J. Control Optim., 23 (1985), 657–674 10.1137/0323042 86j:90127 0581.90078 LinkISIGoogle Scholar[5] J. G. Ecker and , M. Kupferschmid, Introduction to Operations Research, Wiley, New York, 1988 0647.90053 Google Scholar[6] D. Goldfarb and , A. Idnani, A numerically stable dual method for solving strictly convex quadratic programs, Math. Programming, 27 (1983), 1–33 84k:90058 0537.90081 CrossrefISIGoogle Scholar[7] Users Manual Math/Library, Sugarland, TX, 1987, April Google Scholar[8] M. Kupferschmid and , J. G. Ecker, EA3: A Practical Implementation of an Ellipsoid Algorithm for Nonlinear Programming, Dept. Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, NY, 1984 Google Scholar[9] L. S. Lasdon, , A. Waren, , A. Jain and , M. W. Ratner, Design and testing of a generalized reduced gradient code for nonlinear programming, Trans. Math. Software, 4 (1978), 34–50 10.1145/355769.355773 0378.90080 CrossrefGoogle Scholar[10] S. P. Marin, Optimal Parametrization of Curves in RN, General Motors Research Publication, GMR-4878, General Motors Research Laboratories, Warren, MI, 1985, August Google Scholar[11] S. P. Marin, Optimal parametrization of curves for robot trajectory design, IEEE Trans. Automat. Control, 33 (1988), 14–31 10.1109/9.393 CrossrefISIGoogle Scholar[12] M. J. D. Powell, VMCWD: A Fortran Subroutine for Constrained Optimization, Paper DAMTP, 1982/NA4, Dept. of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, England, 1982 Google Scholar[13] K. Schittkowski, NLPQL: a FORTRAN subroutine solving constrained nonlinear programming problems, Ann. Oper. Res., 5 (1986), 485–500 89d:90200 CrossrefGoogle ScholarKeywordsnonlinear programmingalgorithm evaluationrobot modeling Previous article Next article FiguresRelatedReferencesCited ByDetails On the Minimum-Time Control Problem for Differential Drive Robots with Bearing ConstraintsJournal of Optimization Theory and Applications, Vol. 173, No. 3 | 11 April 2017 Cross Ref Calculating a near time-optimal jerk-constrained trajectory along a specified smooth pathThe International Journal of Advanced Manufacturing Technology, Vol. 45, No. 9-10 | 19 April 2009 Cross Ref Volume 15, Issue 6| 1994SIAM Journal on Scientific Computing1251-1505 History Submitted:05 December 1988Accepted:21 September 1993Published online:13 July 2006 InformationCopyright © 1994 Society for Industrial and Applied MathematicsKeywordsnonlinear programmingalgorithm evaluationrobot modelingMSC codes90PDF Download Article & Publication DataArticle DOI:10.1137/0915084Article page range:pp. 1401-1412ISSN (print):1064-8275ISSN (online):1095-7197Publisher:Society for Industrial and Applied Mathematics