2026-09-19T10:10:02Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/836862022-01-13T07:54:05Zcom_1721.1_7582com_1721.1_7581col_1721.1_131024
Russell L. Tedrake.
Cantum, Cecilia G
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Massachusetts Institute of Technology. Department of Mechanical Engineering
2014-01-09T19:44:54Z
2014-01-09T19:44:54Z
2013
http://hdl.handle.net/1721.1/83686
863160821
Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 32-33).
The DARPA Robotic Challenge (DRC) required the development of user interface, perception, and planning and control modules for a robotic humanoid. This paper focuses on the planning and control component for the manipulation qualification task of the virtual section of the DRC. Nonlinear algorithms were employed for the planning systems, such as the grasp optimization system and the robot state trajectory computation system. However, for closed-loop control, a linear proportional-derivative (PD) joint position controller was used. The nonlinear algorithms used for the planning systems may be improved, but their current functionality allows the successful completion of the manipulation qualification task. Also, even though PD controllers seem appropriate for the closed-loop control, PID controllers might yield a higher level of accuracy if tuned properly. In conclusion, a linear controller appears sufficient for certain control of the highly nonlinear ATLAS humanoid robot and Sandia hand as long as accurate optimization and planning systems complement such control.
by Cecilia G. Cantu.
S.B.
33 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
copyright. They may be viewed from this source for any purpose, but
reproduction or distribution in any format is prohibited without written
permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
Planning and control for simulated robotic Sandia hand for the DARPA Robotic Challenge
Thesis
application/pdf
false
Publication
unknown
unknown
<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="ab1af927-496b-49bc-82bb-3ed7833e5729">
<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>Planning and control for simulated robotic Sandia hand for the DARPA Robotic Challenge</Title>
<PublishedIn>
<Publication>
</Publication>
</PublishedIn>
<PublicationDate>2013</PublicationDate>
<Authors>
<Author>
<DisplayName>Cantum, Cecilia G</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>Mechanical Engineering.</Keyword>
<Abstract>The DARPA Robotic Challenge (DRC) required the development of user interface, perception, and planning and control modules for a robotic humanoid. This paper focuses on the planning and control component for the manipulation qualification task of the virtual section of the DRC. Nonlinear algorithms were employed for the planning systems, such as the grasp optimization system and the robot state trajectory computation system. However, for closed-loop control, a linear proportional-derivative (PD) joint position controller was used. The nonlinear algorithms used for the planning systems may be improved, but their current functionality allows the successful completion of the manipulation qualification task. Also, even though PD controllers seem appropriate for the closed-loop control, PID controllers might yield a higher level of accuracy if tuned properly. In conclusion, a linear controller appears sufficient for certain control of the highly nonlinear ATLAS humanoid robot and Sandia hand as long as accurate optimization and planning systems complement such control.</Abstract>
<Access xmlns="http://purl.org/coar/access_right"
>
</Access>
</Publication>