2026-09-19T04:04:28Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1291292026-06-06T00:49:21Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Sertac Karaman.
Balabanska, Nadya L.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2021-01-06T17:41:43Z
2021-01-06T17:41:43Z
2020
2020
https://hdl.handle.net/1721.1/129129
1227274206
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 20-21).
This contribution is an optimization-based method for robotic path-planning that is able to recover vehicle controls in addition to discovering an optimized, feasible trajectory from start to goal for vehicles with arbitrary dynamics. The motion planner extends the application of factor-graph optimization commonly used in simultaneous localization and mapping tasks to the path-planning task, specifically the "timed elastic band" trajectory optimization approach [1] for control input extraction functionality. This is achieved by the introduction of control input-dependent vertices into the factor-graph along with a way to systematically design dynamics violation costs without relying on hand-picked geometric parameters. An implementation of the planner successfully recovers vehicle control inputs and produces feasible trajectories in simulation testing.
by Nadya L. Balabanska.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
27 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Motion planning with dynamic constraints through pose graph optimization
Thesis
application/pdf
2021-01-06T17:41:43Z
Publication
Master
EECS
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="40abefd1-4092-4e14-817d-531dbedbdfa3">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Language>eng</Language>
<Title>Motion planning with dynamic constraints through pose graph optimization</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2020</PublicationDate>
<Authors>
<Author>
<DisplayName>Balabanska, Nadya L.</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://dspace.mit.edu/handle/1721.1/7582</License>
<Keyword>Electrical Engineering and Computer Science.</Keyword>
<Abstract>This contribution is an optimization-based method for robotic path-planning that is able to recover vehicle controls in addition to discovering an optimized, feasible trajectory from start to goal for vehicles with arbitrary dynamics. The motion planner extends the application of factor-graph optimization commonly used in simultaneous localization and mapping tasks to the path-planning task, specifically the "timed elastic band" trajectory optimization approach [1] for control input extraction functionality. This is achieved by the introduction of control input-dependent vertices into the factor-graph along with a way to systematically design dynamics violation costs without relying on hand-picked geometric parameters. An implementation of the planner successfully recovers vehicle control inputs and produces feasible trajectories in simulation testing.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>