Safe Control under Uncertainty with Probabilistic Signal Temporal Logic

• DOI: 10.15607/rss.2016.xii.017
• Publication Date: 2016-06-27T09:11:27Z
• Authors: Dorsa Sadigh; Ashish Kapoor
• Keywords: SIGNAL (programming language); Probabilistic logic network

Safe control of dynamical systems that satisfy temporal invariants expressing various safety properties is a challenging problem has drawn the attention many researchers.However, making assumption such are deterministic far from reality.For example, robotic system might employ camera sensor and machine learned to identify obstacles.Consequently, controller satisfy, will be function data associated classifier.We propose framework for achieving safe control.At heart our approach new Probabilistic Signal Temporal Logic (PrSTL), an expressive language define stochastic properties, enforce probabilistic guarantees on them.We also present efficient algorithm reason about controllers given constraints derived PrSTL specification.One key distinguishing features encoded logic adaptive changes as encounters additional updates its beliefs latent random variables properties.We demonstrate by deriving quadrotors autonomous vehicles in dynamic environments.