2 research outputs found
Stochastic Robustness Interval for Motion Planning with Signal Temporal Logic
In this work, we present a novel robustness measure for continuous-time
stochastic trajectories with respect to Signal Temporal Logic (STL)
specifications. We show the soundness of the measure and develop a monitor for
reasoning about partial trajectories. Using this monitor, we introduce an STL
sampling-based motion planning algorithm for robots under uncertainty. Given a
minimum robustness requirement, this algorithm finds satisfying motion plans;
alternatively, the algorithm also optimizes for the measure. We prove
probabilistic completeness and asymptotic optimality, and demonstrate the
effectiveness of our approach on several case studies
Automaton-Guided Control Synthesis for Signal Temporal Logic Specifications
This paper presents an algorithmic framework for control synthesis of
continuous dynamical systems subject to signal temporal logic (STL)
specifications. We propose a novel algorithm to obtain a time-partitioned
finite automaton from an STL specification, and introduce a multi-layered
framework that utilizes this automaton to guide a sampling-based search tree
both spatially and temporally. Our approach is able to synthesize a controller
for nonlinear dynamics and polynomial predicate functions. We prove the
correctness and probabilistic completeness of our algorithm, and illustrate the
efficiency and efficacy of our framework on several case studies. Our results
show an order of magnitude speedup over the state of the art.Comment: 8 pages, 3 figures, to appear in CDC 202