Assured decison-making for autonomous systems

As autonomous systems become more widely used in society, they will necessarily have to make more decisions in order to meet increasingly complex objectives. However, to facilitate greater deployment of autonomous systems, especially in safety-critical contexts, it is crucial to provide guarantees that the decisions made by these systems will be safe and achieve the desired objective. This dissertation studies techniques for assuring decision-making in complex and large-scale autonomous systems. The dissertation uses synthesis techniques from the fields of formal methods to provide guarantees of correctness with respect to specifications provided in temporal logic. Synthesis methods often suffer from scalability issues limiting their applicability in realistic systems. To address this issue, the dissertation provides abstraction methods and decentralized synthesis architectures to provide guarantees in systems with partial-information as well as large numbers of interacting agents. The dissertation provides a systematic approach to assured decision-making in this dissertation that is agnostic to the specifics of the implementation details of the autonomous systems. Such an approach avoids having to assure systems case-by-case and will facilitate certification and deployment of autonomous systems in more application areas. Finally, the dissertation illustrates this concept in traffic management for urban air mobility operations and provide a synthesis architecture that can adapt to changing specifications or vehicle capabilities.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Aerospace Engineering

Grantor

The University of Texas at Austin

Year dc:date.issued

2021

Author dc:creator

• Bharadwaj, Sudarshanan

Advisor dc:contributor.advisor

• Topcu, Ufuk

Committee members dc:contributor.committeemember

• Neogi, Natasha
• Stone, Peter H
• Tanaka, Takashi
• Clarke, John-Paul

• Formal methods
• Urban air mobility
• Robotics
• AI

Language dc:language.iso

en

Identifier URI

http://dx.doi.org/10.26153/tsw/35045

OAI identifier oai:identifier

oai:repositories.lib.utexas.edu:2152/108137