Cite

Abbasi-Yadkori, Y., Modayil, J. and Szepesvari, C. (2010). Extending rapidly-exploring random trees for asymptotically optimal anytime motion planning, IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, pp. 127-132.Search in Google Scholar

Asfour, T., Gyarfas, F., Azad, P. and Dillmann, R. (2006). Imitation learning of dual-arm manipulation tasks in humanoid robots, IEEE/RAS International Conference on Humanoid Robots (Humanoids 2006), Genoa, Italy, pp. 40-47.Search in Google Scholar

Bell, B. M. and Burke, J. V. (2008). Algorithmic differentiation of implicit functions and optimal values, in C. H. Bischof, H. M. Bücker, P. D. Hovland, U. Naumann and J. Utke (Eds.), Advances in Automatic Differentiation, Springer, Berlin/Heidelberg, pp. 67-77.10.1007/978-3-540-68942-3_7Search in Google Scholar

Benson, H. Y., Shanno, D. F. and Vanderbei, R. J. (2001). Interior-point methods for nonconvex nonlinear programming: Filter methods and merit functions, Technical Report ORFE-00-06, Operations Research and Financial Engineering, Princeton University, Princeton, NJ.Search in Google Scholar

Benson, H. Y., Shanno, D. F. and Vanderbei, R. J. (2002). A comparative study of large-scale nonlinear optimization algorithms, Technical Report ORFE-01-04, Operations Research and Financial Engineering, Princeton University, Princeton, NJ.Search in Google Scholar

Błaszczyk, J., Karbowski, A. and Malinowski, K. (2007). Object library of algorithms for dynamic optimization problems: Benchmarking SQP and nonlinear interior point methods, International Journal of Applied Mathematics and Computer Science 17(4): 515-537, DOI: 10.2478/v10006-007- 0043-y.Search in Google Scholar

Błaszczyk, J. P. (2007). Object Library of Algorithms for Dynamic Optimization: A Study on Effectiveness of Sequential Quadratic Programming and Nonlinear Interior Point Methods, Ph.D. thesis, Warsaw University of Technology, Warsaw, (in Polish).Search in Google Scholar

Byrd, R. H., Gilbert, J. C. and Nocedal, J. (2000). A trust region method based on interior point techniques for nonlinear programming, Mathematical Programming 89(1): 149-185.10.1007/PL00011391Search in Google Scholar

Byrd, R. H., Hribar, M. E. and Nocedal, J. (1999). An interior point algorithm for large scale nonlinear programming, SIAM Journal on Optimization 9(4): 877-900.10.1137/S1052623497325107Search in Google Scholar

Canny, J. (1988). The Complexity of Robot Motion Planning, MIT Press, Cambridge, MA.Search in Google Scholar

Cortés, J., Siméon, T. and Laumond, J.-P. (2002). A random loop generator for planning the motions of closed kinematic chains using PRM methods, IEEE International Conference on Robotics and Automation ICRA, Washington, DC, USA, pp. 2141-2146.Search in Google Scholar

Daniel, J. (1971). Approximate Minimisation of Functionals, Prentice Hall, Englewood Cliffs, NJ.Search in Google Scholar

de Boor, C. (1978). Practical Guide to Splines, Springer, New York, NY/Heidelberg.10.1007/978-1-4612-6333-3Search in Google Scholar

Dolan, E. D. and Moré, J. J. (2002). Benchmarking optimization software with performance profiles, Mathematical Programming 91(2): 201-213.10.1007/s101070100263Search in Google Scholar

Fiacco, A. V. and McCormick, G. P. (1968). Nonlinear Programming: Sequential Unconstrained Minimization Techniques, John Wiley and Sons, New York, NY/London.Search in Google Scholar

Fiser, A., Do, R. and Sali, A. (2000). Modeling of loops in protein structure, Protein Science 9(9): 1753-1773.10.1110/ps.9.9.1753214471411045621Search in Google Scholar

Fletcher, R. and Leyffer, S. (2002). Nonlinear programming without a penalty function, Mathematical Programming 91(2): 239-269.10.1007/s101070100244Search in Google Scholar

Haegele, M., Nilsson, K. and Pires, J. N. (2008). Springer Handbook of Robotics, Springer, Berlin/Heidelberg.Search in Google Scholar

Han, L. and Amato, N. (2000). A kinematics-based probabilistic roadmap method for closed chain systems, Workshop on Algoritmic Foundations of Robotics, Dartmouth, Hanover, NH, USA, pp. 233-245.Search in Google Scholar

Han, L., Rudolph, L., Blumenthal, J. and Valodzin, I. (2006). Stratified deformation space and path planning for a planar closed chain with revolute joints, in S. Akella, N. Amato, W. Huang and B. Mishra (Eds.), International Workshop on Algorithmic Foundations of Robotics WAFR, Springer Tracts in Advanced Robotics, Vol. 47, Springer, New York, NY, pp. 235-250.Search in Google Scholar

Kallmann, M., Aubel, A., Abaci, T. and Thalmann, D. (2003). Planning collision-free reaching motions for interactive object manipulation and grasping, Eurographics 22(3): 313-322.10.1111/1467-8659.00678Search in Google Scholar

Kanehiro, F., Lamiraux, F., Kanoun, O., Yoshida, E. and Laumond, J.-P. (2008). A local collision avoidance method for non-strictly convex polyhedra, Proceedings of Robotics: Science and Systems (RSS) IV, Zurich, Switzerland, pp. 151-158.Search in Google Scholar

Kavraki, L. E., Svestka, P., Latombe, J.-C. and Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces, IEEE Transactions on Robotics and Automation 12(4): 566-580.10.1109/70.508439Search in Google Scholar

Kuffner, J. J. and LaValle, S. M. (2000). RRT-connect: An efficient approach to single-query path planning, IEEE International Conference on Robotics and Automation, San Francisco, CA, USA, pp. 995-1001.Search in Google Scholar

Latombe, J.-C. (1991). Robot Motion Planning, Kluwer, Boston, MA.10.1007/978-1-4615-4022-9Search in Google Scholar

LaValle, S. (2006). Planning Algorithms, Cambridge University Press, Cambridge.10.1017/CBO9780511546877Search in Google Scholar

Liu, G. and Trinkle, J. (2005). Complete path planning for planar closed chains among point obstacles, Proceedings of Robotics: Science and Systems (RSS) I, Cambridge, MA, USA, pp. 33-40.Search in Google Scholar

Merlet, J. (2000). Parallel Robots, Kluwer, Dordrecht.10.1007/978-94-010-9587-7Search in Google Scholar

Morales, J. L., Nocedal, J., Waltz, R. A., Liu, G. and Goux, J.-P. (2001). Assessing the potential of interiormethods for nonlinear optimization, Technical Report OTC 2001/4, Optimization Technology Center, Northwestern University, Evanston, IL.Search in Google Scholar

Ratliff, N., Zucker, M., Bagnell, J. A. and Srinivasa, S. (2009). CHOMP: Gradient optimization techniques for efficient motion planning, IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan, pp. 489-494.Search in Google Scholar

Szynkiewicz, W. (2003). Motion planning for multi-robot systems with closed kinematic chains, 9th IEEE International Conference on Methods and Models in Automation and Robotics, Międzyzdroje, Poland, pp. 779-786.Search in Google Scholar

Szynkiewicz, W. and Gosiewski, A. (1995). Motion space analysis and trajectory planning for dual-arm system, 2nd International Symposium on Methods and Models in Automation and Robotics, Międzyzdroje, Poland, pp. 503-510.Search in Google Scholar

Tang, X., Thomas, S. and Amato, N. (2007). Planning with reachable distances: Fast enforcement of closure constraints, IEEE International Conference on Robotics and Automation ICRA, Rome, Italy, pp. 2694-2699.Search in Google Scholar

Tits, A. L.,Wächter, A., Bakhtiari, S., Urban, T. J. and Lawrence, C. (2002). A primal-dual interior-point method for nonlinear programming with strong global and local convergence properties, Technical Report TR 2002-29, Institute for Systems Research, University of Maryland, College Park, MD.Search in Google Scholar

Trinkle, J. and Milgram, R. (2002). Complete path planning for closed kinematic chains with spherical joints, International Journal of Robotics Research 21(9): 773-789.10.1177/0278364902021009119Search in Google Scholar

Ulbrich, M., Ulbrich, S. and Vicente, L. N. (2004). A globally convergent primal-dual interior-point filter method for nonlinear programming, Mathematical Programming 100(2): 379-410.10.1007/s10107-003-0477-4Search in Google Scholar

Vanderbei, R. J. and Shanno, D. F. (1997). An interior-point algorithm for non-convex nonlinear programming, Technical Report SOR-97-21, Statistics and Operations Research, Princeton University, Princeton, NJ.Search in Google Scholar

Wächter, A. (2002). An Interior Point Algorithm for Large-Scale Nonlinear Optimization with Applications in Process Engineering, Ph.D. dissertation, Carnegie Mellon University, Pittsburgh, PA.Search in Google Scholar

Wächter, A. and Biegler, L. T. (2000). Failure of global convergence for a class of interior point methods for nonlinear programming, Mathematical Programming 88(3): 565-574.10.1007/PL00011386Search in Google Scholar

Wächter, A. and Biegler, L. T. (2005). Line search filter methods for nonlinear programming: Motivation and global convergence, SIAM Journal on Optimization 16(1): 1-31.10.1137/S1052623403426556Search in Google Scholar

Wächter, A. and Biegler, L. T. (2006). On the implementation of a primal-dual interior-point filter line-search algorithm for large-scale nonlinear programming, Mathematical Programming 106(1): 25-57.10.1007/s10107-004-0559-ySearch in Google Scholar

Waltz, R. A. and Plantenga, T. (2006). KNITRO 5.0 User's Manual, Ziena Optimization, Inc., http://www.ziena.com/docs/knitroman.pdf. http://www.ziena.com/docs/knitroman.pdfSearch in Google Scholar

Yakey, J., LaValle, S. and Kavraki, L. (2001). Randomized path planning for linkages with closed kineamtic chains, IEEE Transactions on Robotics and Automation 17(6): 951-958.10.1109/70.976030Search in Google Scholar

Yershova, A. and LaValle, S. (2009). Motion planning for highly constrained spaces, in K. R. Kozłowski (Ed.) Robot Motion and Control 2009, Springer, Berlin/Heidelberg, pp. 297-306.10.1007/978-1-84882-985-5_27Search in Google Scholar

Zefran, M. and Kumar, V. (1997). A variational calculus framework for motion planning, IEEE International Conference on Robotics and Automation ICRA, Albuquerque, NM, USA, pp. 415-420.Search in Google Scholar

Zhang, J. and Knoll, A. (1995). An enhanced optimization approach for generating smooth robot trajectories in the presence of obstacles, Proceedings of the European Chinese Automation Conference, London, UK, pp. 263-268.Search in Google Scholar

Zieliński, C. and Winiarski, T. (2010). Motion generation in the MRROC++ robot programming framework, International Journal of Robotics Research 29(4): 386-413.10.1177/0278364909348761Search in Google Scholar

ISSN:
1641-876X
Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Mathematics, Applied Mathematics