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An optimization-based path-following control scheme for the lateral control of a nonholonomous vehicle is proposed. It is formulated as a nonlinear model predictive controller. The penalty function is chosen in a way that the result of the optimization is conceptually similar to the output of a traditional “Follow-the-Carrot” or “Pure Pursuit” approach as long as no constraints are active. This allows...
A significant generalization to the language-measure-theoretic path planning algorithm v* is presented that accounts for average dynamic uncertainties in plan execution. The planning problem thus can be solved with parametric input from the dynamics of the robotic platform under consideration. Applicability of the algorithm is demonstrated in a simulated maze solution and by experimental validation...
The v*-planning algorithm is generalized to handle finite memory obstacle dynamics. A sufficiently long observation sequence of obstacle dynamics is algorithmically compressed via symbolic dynamic filtering to obtain a probabilistic finite state model which is subsequently integrated with the navigation automaton to generate an overall model reflecting both navigation constraints and obstacle dynamics...
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