Information-Theoretic Motion Planning for Constrained Sensor Networks
Name
Levine12_JACIC_submitted.pdf
Size
2.99 MB
Format
Adobe PDF
Checksum (MD5)
7866771a276f27578b0b2a3ddab0078f
Author(s)
Levine, Daniel
Luders, Brandon
How, Jonathan P.
Date Issued
July 20, 2012
Abstract
This paper considers the problem of online informative motion planning for a network of heterogeneous sensing agents, each subject to dynamic constraints, environmental constraints, and sensor limitations. Prior work has not yielded algorithms that are amenable to such general constraint characterizations. In this paper, we propose the Information-rich Rapidly-exploring Random Tree (IRRT) algorithm as a solution to the constrained informative motion planning problem that embeds metrics on uncertainty reduction at both the tree growth and path selection levels. IRRT possesses a number of beneficial properties, chief among them being the ability to find dynamically feasible, informative paths on short timescales, even subject to the aforementioned constraints. The utility of IRRT in efficiently localizing stationary targets is demonstrated in a progression of simulation results with both single-agent and multiagent networks. These results show that IRRT can be used in real-time to generate and execute information-rich paths in tightly constrained environments.
Subjects
informative planning
motion planning
rapidly-exploring random trees
mobile sensor networks
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Aerospace Controls Laboratory
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