Information-rich Path Planning with General Constraints using Rapidly-exploring Random Trees
Name
Levine.pdf
Description
Main article
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1.79 MB
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Author(s)
Levine, Daniel S.
Luders, Brandon Douglas
How, Jonathan P.
Date Issued
April 2010
Journal
AIAA Infotech@Aerospace 2010 Proceeding
Publisher
American Institute of Aeronautics and Astronautics
Citation
Levine, Daniel, Brandon Luders and Jonathan P. How. "Information-rich path planning with general constraints using rapidly-exploring random trees." AIAA Infotech@Aerospace 2010, 20-22 April 2010, Atlanta, Georgia.
Version
Original manuscript
Abstract
This paper 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 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 eld-of-view. An extension of IRRT for multi-agent missions is also presented.
IRRT is distinguished from previous solutions 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-o between information gathering and goal arrival.
MIT Department
Massachusetts Institute of Technology. Aerospace Controls Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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