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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...
The recently reported v planning algorithm is modified to handle on-the-fly dynamic updates to the obstacle map. The modified algorithm called All-Pair-Dynamic-Planning(APDP), models the problem of robot path planning in the framework of finite state probabilistic automata and solves the all-pair planning problem in one setting. We use the concept of renormalized measure of regular languages to plan...
This paper presents a novel approach to autonomous intelligent navigation of mobile robotic platforms, which is based on the concept of language-theoretic discrete-event supervisory control. The proposed algorithm combines real-time sensor data and model-based information on motion dynamics into a probabilistic finite state automaton model to dynamically compute a time-varying supervisory algorithm...
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