Path planning for autonomous parafoils using particle chance constrained rapidly-exploring random trees in a computationally constrained environment
Name
870304756-MIT.pdf
Description
Full printable version
Size
870.69 KB
Format
Adobe PDF
Checksum (MD5)
662acee928ea34432595686a22e88d02
Author(s)
Klerman, Shoshana
Advisor(s)
Jonathan P. How.
Date Issued
2012
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
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).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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