This paper addresses the problem of goal-directed robot path planning in the presence of uncertainties that are induced by bounded environmental disturbances and actuation errors. The offline infinite-horizon optimal plan is locally updated by online finite-horizon adaptive replanning upon observation of unexpected events (e.g., detection of unanticipated obstacles). The underlying theory is developed as an extension of a grid-based path planning algorithm, called , which was formulated in the framework of probabilistic finite state automata (PFSA) and language measure from a control-theoretic perspective. The proposed concept has been validated on a simulation test bed that is constructed upon a model of typical autonomous underwater vehicles (AUVs) in the presence of uncertainties.
Robot Path Planning in Uncertain Environments: A Language-Measure-Theoretic Approach
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received January 23, 2014; final manuscript received May 31, 2014; published online October 21, 2014. Assoc. Editor: Jongeun Choi. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. Approved for public release; distribution is unlimited.
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Jha, D. K., Li, Y., Wettergren, T. A., and Ray, A. (October 21, 2014). "Robot Path Planning in Uncertain Environments: A Language-Measure-Theoretic Approach." ASME. J. Dyn. Sys., Meas., Control. March 2015; 137(3): 034501. https://doi.org/10.1115/1.4027876
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