Paulo Tabuada

TL;DR: An efficient algorithm for multi-robot motion planning from linear temporal logic (LTL) specifications is presented that is more than one order of magnitude faster than state-of-the-art sampling-based techniques for high-dimensional state spaces while supporting complex missions.

TL;DR: This work shows that testing the existence of a supervisor and building the supervisor can be done using tools developed for the classical DES supervisory control problem, by considering a family of automata with modified output maps, but without expanding the size of the state space and without incurring on exponential complexity on the number of attacks considered.

TL;DR: A new satisfiability modulo convex programming (SMC) framework that integrates SAT solving and convex optimization to efficiently reason about Boolean and conveX constraints at the same time, and can handle more complex problem instances than state-of-the-art alternative techniques based on SMT solving and mixed integer convex Programming.

TL;DR: This paper addresses the problem of determining the satisfiability of a Boolean combination of convex constraints over the real numbers, which is common in the context of hybrid system verification and control, and proposes a suite of algorithms that can trade complexity with the minimality of the generated infeasibility certificates.

TL;DR: In this paper, two algorithms for state reconstruction from sensor measurements that are corrupted with sparse, but otherwise arbitrary, "noise" are described. But their results are motivated by the need to secure cyber-physical systems against a malicious adversary that can arbitrarily corrupt sensor measurements.