2026-09-19T11:33:35Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/853962026-06-06T00:49:11Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Jonathan P. How.
Klerman, Shoshana
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-03-06T15:39:02Z
2014-03-06T15:39:02Z
2012
2012
http://hdl.handle.net/1721.1/85396
870304756
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 57-58).
Particle chance constrained rapidly-exploring random trees (PCC-RRT) is a sampling-based path-planning algorithm which uses particles to approximate an uncertainty distribution. In this thesis, we study the use of PCC-RRT on an autonomous parafoil. Specifically, we explore the behavior of PCC-RRT in a computationally constrained environment by studying the tradeoff between the number of samples and number of particles per sample and its effect on miss distance in single-threaded coded with a time constraint. We compare the results generated with the PCC-RRT planner to the equivalent data from a nominal planner using rapidly-exploring random trees (RRT) to determine the effect of robustness.
by Shoshana Klerman.
M.Eng.
58 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
Electrical Engineering and Computer Science.
Path planning for autonomous parafoils using particle chance constrained rapidly-exploring random trees in a computationally constrained environment
Thesis
application/pdf
Publication
unknown
unknown
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