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Stochastic Programming Methods in Adaptive Optimal Trajectory Planning for Robots
Author(s) -
Marti K.
Publication year - 2002
Publication title -
zamm ‐ journal of applied mathematics and mechanics / zeitschrift für angewandte mathematik und mechanik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.449
H-Index - 51
eISSN - 1521-4001
pISSN - 0044-2267
DOI - 10.1002/1521-4001(200211)82:11/12<795::aid-zamm795>3.0.co;2-i
Subject(s) - feed forward , trajectory , computer science , trajectory optimization , control theory (sociology) , stochastic control , stochastic programming , optimal control , robot , adaptive control , stochastic optimization , a priori and a posteriori , process (computing) , field (mathematics) , stochastic process , stochastic modelling , dynamic programming , control engineering , mathematical optimization , control (management) , engineering , mathematics , artificial intelligence , algorithm , philosophy , statistics , physics , epistemology , astronomy , pure mathematics , operating system
In the optimal control of industrial, field, or service robots, the standard procedure is to determine first off‐line a feedforward control and a reference trajectory, based on some selected nominal values of the unknown stochastic model parameters, and to correct then the inevitable and increasing deviation of the state or performance of the robot from the prescribed state or performance of the system by on‐line measurement and control actions. Due to the stochastic variations of the model parameters, increasing measurement and correction actions are needed during the process. By optimal stochastic trajectory planning (OSTP), based on stochastic optimization methods, the available a priori and sample information about the robot and its working environment is incorporated into the control process. Consequently, more robust reference trajectories and feedforward controls are obtained which cause much less on‐line control actions. In order to maintain a high quality of the guiding functions, the reference trajectory and the feedforward control can be updated at some later time points such that additional information about the control process is available. After the presentation of the Adaptive Optimal Stochastic Trajectory Planning (AOSTP) procedure based on stochastic optimization methods, several numerical techniques for the computation of robust reference trajectories and feedforward controls under real‐time conditions are presented.