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Information-rich Path Planning with General Constraints using Rapidly-exploring Random Trees

Author(s)
Levine, Daniel S.; Luders, Brandon Douglas; How, Jonathan P.
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Attribution-Noncommercial-Share Alike 3.0 Unported http://creativecommons.org/licenses/by-nc-sa/3.0/
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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.
Date issued
2010-04
URI
http://hdl.handle.net/1721.1/60027
Department
Massachusetts Institute of Technology. Aerospace Controls Laboratory; Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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
Other identifiers
AIAA 2010-3360

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