Information-rich Path Planning with General Constraints using Rapidly-exploring Random Trees
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 (pos...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
American Institute of Aeronautics and Astronautics
2010
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| Online Access: | http://hdl.handle.net/1721.1/60027 https://orcid.org/0000-0001-8576-1930 |
| Summary: | 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. |
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