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Adaptive Optimal Stochastic Trajectory Planning

In: Online Optimization of Large Scale Systems

Author

Listed:
  • Andreas Aurnhammer

    (Universität der Bundeswehr München, Institut für Mathematik und Informatik)

  • Kurt Marti

    (Universität der Bundeswehr München, Institut für Mathematik und Informatik)

Abstract

In Optimal Stochastic Trajectory Planning of industrial or service robots the problem can be modelled by a variational problem under stochastic disturbances that compared to ordinary deterministic engineering techniques also accounts for stochastic model parameters. Using stochastic optimisation theory, this variational problem is transformed into a nonlinear mathematical program, that can be solved by means of standard optimisation routines like SQP. However, these methods are not applicable in the on-line control process of robots, since they are not capable of solving mathematical programs in real-time. Hence, Neural Networks are trained based on solutions obtained from a standard optimisation algorithm.

Suggested Citation

  • Andreas Aurnhammer & Kurt Marti, 2001. "Adaptive Optimal Stochastic Trajectory Planning," Springer Books, in: Martin Grötschel & Sven O. Krumke & Jörg Rambau (ed.), Online Optimization of Large Scale Systems, pages 521-543, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-04331-8_27
    DOI: 10.1007/978-3-662-04331-8_27
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