Title:
Barrier Functions and Model Free Safety With Applications to Fixed Wing Collision Avoidance

dc.contributor.advisor Egerstedt, Magnus B.
dc.contributor.author Squires, Eric G.
dc.contributor.committeeMember Coogan, Samuel
dc.contributor.committeeMember Wardi, Yorai
dc.contributor.committeeMember Pippin, Charles
dc.contributor.committeeMember Kira, Zsolt
dc.contributor.department Electrical and Computer Engineering
dc.date.accessioned 2021-09-15T15:45:06Z
dc.date.available 2021-09-15T15:45:06Z
dc.date.created 2021-08
dc.date.issued 2021-07-29
dc.date.submitted August 2021
dc.date.updated 2021-09-15T15:45:06Z
dc.description.abstract Robotics is now being applied to a diversity of real-world applications and in many areas such as industrial, medical, and mobile robotics, safety is a critical consideration for continued adoption. In this thesis we therefore investigate how to develop algorithms that improve the safety of autonomous systems using both a model-based and model-free framework. To begin, we make a variety of assumptions (e.g., that a model is known, there is a single safety constraint, there are no communication limits, and that the state can be sensed everywhere), and show how to guarantee the safety of the system. The contribution of the initial approach is a generalization of an existing method for creating a barrier function, which is a function similar to a Lyapunov function that can be used to make safety guarantees. We then investigate relaxing these initial assumptions. In some cases, new additional assumptions are required, performance may be reduced, or safety guarantees may no longer be available. We motivate the thesis with collision avoidance for fixed wing aircraft which can be viewed as a pairwise constraint on each pair of aircraft. This introduces the need for considering multiple safety factors simultaneously, and we show that an additional assumption is needed in this case. We then relax the assumption that the vehicles have unlimited communication and find that safety can still be guaranteed. However, it is possible in this case that the overriding safety controller may be more invasive than if more communication is allowed. When we then further relax the assumption that the state can be sensed at all times, safety can still be guaranteed in some specified situations but the system may be more permissive in approaching safety boundaries. We finally remove the assumption of a known model for dynamics. Although removing this assumption means the system is no longer guaranteed to be safe, the benefit is that it allows a safety designer to build a far less invasive override to get more performance out of the system.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/65101
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject swarm
dc.subject machine-learning
dc.subject model-free
dc.subject safety
dc.subject barrier
dc.title Barrier Functions and Model Free Safety With Applications to Fixed Wing Collision Avoidance
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Egerstedt, Magnus B.
local.contributor.author Egerstedt, Magnus B.
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
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relation.isAuthorOfPublication dd4872d3-2e0d-435d-861d-a61559d2bcb6
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relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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