Fast, Approximate State Estimation of Concurrent Probabilistic Hybrid Automata
It is an undeniable fact that autonomous systems are simultaneously becoming more common place, more complex, and deployed in more inhospitable environments. Examples include smart homes, smart cars, Mars rovers, unmanned aerial vehicles, and autonomous underwater vehicles. A common theme that all of these autonomous systems share is that in order to appropriately control them and prevent mission failure, they must be able to quickly estimate their internal state and the state of the world. A natural representation of many real world systems is to describe them in terms of a mixture of continuous and discrete variables. Unfortunately, hybrid estimation is typically intractable due to the large space of possible assignments to the discrete variables. In this thesis, we investigate how to incorporate conflict directed techniques from the consistency-based, model-based diagnosis community into a hybrid framework that is no longer purely consistency based. We introduce a novel search algorithm, A∗ with Bounding Conflicts, that uses conflicts to not only record infeasiblilities, but also learn where in the search space the heuristic function provided to the A∗ search is weak (possibly due to heavy to moderate sensor or process noise). Additionally, we describe a hybrid state estimation algorithm that uses this new search to perform estimation on hybrid discrete/continuous systems.