Information-rich path planning under general constraints using Rapidly-exploring Random Trees
Name
668232182-MIT.pdf
Description
Full printable version
Size
9.14 MB
Format
Adobe PDF
Checksum (MD5)
c16dc9905362620dd2775aea8e6ca074
Author(s)
Levine, Daniel S., Ph. D. Massachusetts Institute of Technology.
Advisor(s)
Jonathan P. How.
Date Issued
2010
Publisher
Massachusetts Institute of Technology
Abstract
This thesis introduces the Information-rich Rapidly-exploring Random Tree (IRRT), an extension of the RRT algorithm that embeds information collection as predicted using Fisher information matrices. The primary contribution of this trajectory generation algorithm is target-based information maximization in general (possibly heavily constrained) environments, with complex vehicle dynamic constraints and sensor limitations, including limited resolution and narrow field-of-view. Extensions of IRRT both for decentralized, multiagent missions and for information-rich planning with multimodal distributions are presented. IRRT is distinguished from previous solution strategies by its computational tractability and general constraint characterization. A progression of simulation results demonstrates that this implementation can generate complex target-tracking behaviors from a simple model of the trade-off between information gathering and goal arrival.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 99-104).
Subjects
Aeronautics and Astronautics.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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