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