activity
20122016
most citedOptimal Control of MDPs with Temporal Logic Constraints

6 citations · 18 across the 5 of their papers we have counts for

collaborators

6 papers

cs.RO2016★ 2 cited

Resource-Performance Trade-off Analysis for Mobile Robot Design

Morteza Lahijanian, Maria Svorenova, Akshay A. Morye +6

The design of mobile autonomous robots is challenging due to the limited on-board resources such as processing power and energy. A promising approach is to generate intelligent sch…

cs.LO2016

Optimal Observation Mode Scheduling for Systems under Temporal Constraints

Eva Tesarova, Maria Svorenova, Jiri Barnat +1

Autonomous control systems use various sensors to decrease the amount of uncertainty under which they operate. While providing partial observation of the current state of the syste…

eess.SY2014★ 3 cited

Temporal Logic Control for Stochastic Linear Systems using Abstraction Refinement of Probabilistic Games

Maria Svorenova, Jan Kretinsky, Martin Chmelik +3

We consider the problem of computing the set of initial states of a dynamical system such that there exists a control strategy to ensure that the trajectories satisfy a temporal lo…

cs.RO2013★ 5 cited

Optimal Receding Horizon Control for Finite Deterministic Systems with Temporal Logic Constraints

Mária Svoreňová, Ivana Černá, Calin Belta

In this paper, we develop a provably correct optimal control strategy for a finite deterministic transition system. By assuming that penalties with known probabilities of occurrenc…

cs.LO2013★ 6 cited

Optimal Control of MDPs with Temporal Logic Constraints

Maria Svorenova, Ivana Cerna, Calin Belta

In this paper, we focus on formal synthesis of control policies for finite Markov decision processes with non-negative real-valued costs. We develop an algorithm to automatically g…

cs.RO2012★ 2 cited

Attraction-Based Receding Horizon Path Planning with Temporal Logic Constraints

Maria Svorenova, Jana Tumova, Jiri Barnat +1

Our goal in this paper is to plan the motion of a robot in a partitioned environment with dynamically changing, locally sensed rewards. We assume that arbitrary assumptions on the…