Abstract
This paper presents a method that allows mobile systems with uncertainty in motion and sensing to react to unknown environments while high-level specifications are satisfied. Although previous works have addressed the problem of synthesising controllers under uncertainty constraints and temporal logic specifications, reaction to dynamic environments has not been considered under this scenario. The method uses feedback-based information roadmaps (FIRMs) to break the curse of history associated with partially observable systems. A transition system is incrementally constructed based on the idea of FIRMs by adding nodes on the belief space. Then, a policy is found in the product Markov decision process created between the transition system and a Rabin automaton representing a linear temporal logic formula. The proposed solution allows the system to react to previously unknown elements in the environment. To achieve fast reaction time, a FIRM considering the probability of violating the specification in each transition is used to drive the system towards local targets or to avoid obstacles. The method is demonstrated with an illustrative example.
Felipe J. Montana is supported by the Mexican National Council of Science and Technology (CONACyT). Jun Liu is supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada and the Canada Research Chairs (CRC) program.
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Montana, F.J., Liu, J., Dodd, T.J. (2017). Sampling-Based Reactive Motion Planning with Temporal Logic Constraints and Imperfect State Information. In: Petrucci, L., Seceleanu, C., Cavalcanti, A. (eds) Critical Systems: Formal Methods and Automated Verification. AVoCS FMICS 2017 2017. Lecture Notes in Computer Science(), vol 10471. Springer, Cham. https://doi.org/10.1007/978-3-319-67113-0_9
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