2026-09-19T05:23:21Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1431972022-06-16T03:24:06Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Leonard, John J.
Thumma, Nicole
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-06-15T13:02:53Z
2022-06-15T13:02:53Z
2022-02
2022-02-22T18:32:03.588Z
https://hdl.handle.net/1721.1/143197
The quality of localization for AUVs using inter-agent ranging measurements is heavily dependent on the configuration of the agents. This work introduces a potential field planner with an E Optimality force to minimize the impact of network configuration on localization when navigating between waypoints in pre-determined trajectories. For this work, the trajectories are generated by Localization-Constrained Graph Planning (LCGP) as the basis for path planning. This system takes waypoints and improves the localizability of the timesteps in between the waypoints, relative to directly going from wach waypoint to the next. A total of 39 tests were conducted with four weight configurations and two noise scenarios with three environments. These tests indicated that the inclusion of an E Optimality force lowered the average localization error as compared to the baseline by up to 56% in the simpler environments, but failed to make a significant difference in a more complex environment.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Distributed Potential Field Method for Cooperative Range-Only Localization in Multi-Robot Networks
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="a158d419-a365-4706-abec-346c0e162068">
<Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type>
<Title>Distributed Potential Field Method for Cooperative Range-Only Localization in Multi-Robot Networks</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2022-02</PublicationDate>
<Authors>
<Author>
<DisplayName>Thumma, Nicole</DisplayName>
<Affiliation>
<OrgUnit>
</OrgUnit>
</Affiliation>
</Author>
</Authors>
<Editors>
</Editors>
<Publishers>
<Publisher>
<DisplayName>Massachusetts Institute of Technology</DisplayName>
<OrgUnit />
</Publisher>
</Publishers>
<License>http://rightsstatements.org/page/InC-EDU/1.0/</License>
<Abstract>The quality of localization for AUVs using inter-agent ranging measurements is heavily dependent on the configuration of the agents. This work introduces a potential field planner with an E Optimality force to minimize the impact of network configuration on localization when navigating between waypoints in pre-determined trajectories. For this work, the trajectories are generated by Localization-Constrained Graph Planning (LCGP) as the basis for path planning. This system takes waypoints and improves the localizability of the timesteps in between the waypoints, relative to directly going from wach waypoint to the next. A total of 39 tests were conducted with four weight configurations and two noise scenarios with three environments. These tests indicated that the inclusion of an E Optimality force lowered the average localization error as compared to the baseline by up to 56% in the simpler environments, but failed to make a significant difference in a more complex environment.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>