PDF

Description

Robotic systems are becoming more pervasive, and have the potential to significantly improve human lives. However, for these benefits to be realized it is critical that the safe operation of these systems be guaranteed. Reachability analysis has proven to be an effective tool for providing safety certificates for dynamical systems, given a model of the system. A major challenge in assuring safety, is that systems often have uncertainty due to the hard-to-model complex physical interactions, or lack of knowledge of the behavior of external agents, on which safety may depend.

This thesis uses Hamilton-Jacobi (HJ) reachability analysis to robustly guarantee safety for systems with uncertainty. In the presence of uncertainty there must be a balance between conservativeness as it pertains to safety and performance as it pertains to other system objectives, and we also account for this through reachability analysis. In addition, this thesis also explores methods for modifying the analysis as more data is collected from the robotics system, which ultimately allows for improved performance. This is referred to here as learning-based reachability analysis. The thesis concludes with a new HJ reachability formulation that enhances the learning-based analysis. The myriad of ideas presented throughout the thesis are demonstrated on various examples.

Details

Title
A Learning-Based Approach to Safety for Uncertain Robotic Systems
Creator
Akametalu, Anayo, Author
Published
2018-05-10
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2018-41
Type
Text
Format
technical reports
Extent
97 p
Archive
The Engineering Library
Usage Statement
Researchers may make free and open use of the UC Berkeley Library’s digitized public domain materials. However, some materials in our online collections may be protected by U.S. copyright law (Title 17, U.S.C.). Use or reproduction of materials protected by copyright beyond that allowed by fair use (Title 17, U.S.C. § 107) requires permission from the copyright owners. The use or reproduction of some materials may also be restricted by terms of University of California gift or purchase agreements, privacy and publicity rights, or trademark law. Responsibility for determining rights status and permissibility of any use or reproduction rests exclusively with the researcher. To learn more or make inquiries, please see our permissions policies (https://www.lib.berkeley.edu/about/permissions-policies).
Collection

Files

Statistics

from
to
Export
Download Full History