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- Genre: Masters Thesis
- Member of: ASU Electronic Theses and Dissertations
Description
The notion of the safety of a system when placed in an environment with humans and other machines has been one of the primary concerns of practitioners while deploying any cyber-physical system (CPS). Such systems, also called safety-critical systems, need to be exhaustively tested for erroneous behavior. This generates the need for coming up with algorithms that can help ascertain the behavior and safety of the system by generating tests for the system where they are likely to falsify. In this work, three algorithms have been presented that aim at finding falsifying behaviors in cyber-physical Systems. PART-X intelligently partitions while sampling the input space to provide probabilistic point and region estimates of falsification. PYSOAR-C and LS-EMIBO aims at finding falsifying behaviors in gray-box systems when some information about the system is available. Specifically, PYSOAR-C aims to find falsification while maximizing coverage using a two-phase optimization process, while LS-EMIBO aims at exploiting the structure of a requirement to find falsifications with lower computational cost compared to the state-of-the-art. This work also shows the efficacy of the algorithms on a wide range of complex cyber-physical systems. The algorithms presented in this thesis are available as python toolboxes.
ContributorsKhandait, Tanmay Bhaskar (Author) / Pedrielli, Giulia (Thesis advisor) / Fainekos, Georgios (Thesis advisor) / Gopalan, Nakul (Committee member) / Arizona State University (Publisher)
Created2022
Description
This work describes an approach for distance computation between agents in a
multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof-
Arrival (AoA) data and local trajectory data. Each agent in the swarm is able
to discretely determine distance and bearing to every other neighbor agent in the
swarm. From this information, I propose a lightweight method for sensor coverage
of an unknown area based on the work of Sameera Poduri. I also show that this
technique performs well with limited calibration distances.
multi-agent swarm. Unlike other approaches, this work relies solely on signal Angleof-
Arrival (AoA) data and local trajectory data. Each agent in the swarm is able
to discretely determine distance and bearing to every other neighbor agent in the
swarm. From this information, I propose a lightweight method for sensor coverage
of an unknown area based on the work of Sameera Poduri. I also show that this
technique performs well with limited calibration distances.
ContributorsMulford, Philip (Author) / Das, Jnaneshwar (Thesis advisor) / Takahashi, Timothy (Committee member) / Phelan, Patrick (Committee member) / Arizona State University (Publisher)
Created2020