Incremental Control Synthesis in Probabilistic Environments with Temporal Logic Constraints

In this paper, we present a method for optimal control synthesis of a plant that interacts with a set of agents in a graph-like environment. The control specification is given as a temporal logic statement about some properties that hold at the vertices of the environment. The plant is assumed to be deterministic, while the agents are probabilistic Markov models. The goal is to control the plant such that the probability of satisfying a syntactically co-safe Linear Temporal Logic formula is maximized. We propose a computationally efficient incremental approach based on the fact that temporal logic verification is computationally cheaper than synthesis. We present a case-study where we compare our approach to the classical non-incremental approach in terms of computation time and memory usage.Comment: Extended version of the CDC 2012 pape

Dynamics-Based Reactive Synthesis and Automated Revisions for High-Level Robot Control

The aim of this work is to address issues where formal specifications cannot be realized on a given dynamical system subjected to a changing environment. Such failures occur whenever the dynamics of the system restrict the robot in such a way that the environment may prevent the robot from progressing safely to its goals. We provide a framework that automatically synthesizes revisions to such specifications that restrict the assumed behaviors of the environment and the behaviors of the system. We provide a means for explaining such modifications to the user in a concise, easy-to-understand manner. Integral to the framework is a new algorithm for synthesizing controllers for reactive specifications that include a discrete representation of the robot's dynamics. The new approach is demonstrated with a complex task implemented using a unicycle model.Comment: 25 pages, 8 figure

Optimal temporal logic control of autonomous vehicles

Thesis (Ph.D.)--Boston UniversityTemporal logics, such as Linear Temporal Logic (LTL) and Computation Tree Logic (CTL), are extensions of propositional logic that can capture temporal relations. Even though temporal logics have been used in model checking of finite systems for quite some time, they have gained popularity as a means for specifying complex mission requirements in path planning and control synthesis problems only recently. This dissertation proposes and evaluates methods and algorithms for optimal path planning and control synthesis for autonomous vehicles where a high-level mission specification expressed in LTL (or a fragment of LTL) must be satisfied. In summary, after obtaining a discrete representation of the overall system, ideas and tools from formal verification and graph theory are leveraged to synthesize provably correct and optimal control strategies. The first part of this dissertation focuses on automatic planning of optimal paths for a group of robots that must satisfy a common high level mission specification. The effect of slight deviations in traveling times on the behavior of the team is analyzed and methods that are robust to bounded non-determinism in traveling times are proposed. The second part focuses on the case where a controllable agent is required to satisfy a high-level mission specification in the presence of other probabilistic agents that cannot be controlled. Efficient methods to synthesize control policies that maximize the probability of satisfaction of the mission specification are presented. The focus of the third part is the problem where an autonomous vehicle is required to satisfy a rich mission specification over service requests occurring at the regions of a partitioned environment. A receding horizon control strategy that makes use of the local information provided by the sensors on the vehicle in addition to the a priori information about the environment is presented. For all of the automatic planning and control synthesis problems that are considered, the proposed algorithms are implemented, evaluated, and validated through experiments and/or simulations