TY - GEN
T1 - Filter-based stochastic abstractions for constrained planning with limited sensing
AU - Poonawala, Hasan A.
AU - Topcu, Ufuk
PY - 2016/12/27
Y1 - 2016/12/27
N2 - As the complexity of the specifications that must be met by a system increases, hierarchical control protocols that merge control and planning decisions at multiple levels of abstraction become necessary. For such hierarchical reasoning, a suitable finite-state abstraction for dynamical systems evolving over continuous state spaces may be needed. The implementation of existing controllers derived using a finite-state abstraction often require that the current continuous state be known exactly, in order to guarantee that the required transitions in the finite-state abstraction occur. When the measurements are partial or noisy, the true state is unknown, and these controllers cannot be implemented. We propose an abstraction that can be used to overcome the uncertainty in the state resulting from imperfect measurement, at the cost of providing only probabilistic guarantees. The abstraction is based on the filter used to maintain an estimate of the true state. We show how the abstraction can be used to create a time-varying policy which maximizes the minimum probability that a target discrete state is reached in finite time from any initial state.
AB - As the complexity of the specifications that must be met by a system increases, hierarchical control protocols that merge control and planning decisions at multiple levels of abstraction become necessary. For such hierarchical reasoning, a suitable finite-state abstraction for dynamical systems evolving over continuous state spaces may be needed. The implementation of existing controllers derived using a finite-state abstraction often require that the current continuous state be known exactly, in order to guarantee that the required transitions in the finite-state abstraction occur. When the measurements are partial or noisy, the true state is unknown, and these controllers cannot be implemented. We propose an abstraction that can be used to overcome the uncertainty in the state resulting from imperfect measurement, at the cost of providing only probabilistic guarantees. The abstraction is based on the filter used to maintain an estimate of the true state. We show how the abstraction can be used to create a time-varying policy which maximizes the minimum probability that a target discrete state is reached in finite time from any initial state.
UR - http://www.scopus.com/inward/record.url?scp=85010739541&partnerID=8YFLogxK
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U2 - 10.1109/CDC.2016.7798768
DO - 10.1109/CDC.2016.7798768
M3 - Conference contribution
AN - SCOPUS:85010739541
T3 - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
SP - 3319
EP - 3324
BT - 2016 IEEE 55th Conference on Decision and Control, CDC 2016
Y2 - 12 December 2016 through 14 December 2016
ER -