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Information-Theoretic Motion Planning for Constrained Sensor Networks

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Show simple item record Levine, Daniel Luders, Brandon How, Jonathan P. 2012-07-20T18:41:47Z 2012-07-20T18:41:47Z 2012-07-20
dc.description.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. en_US
dc.description.sponsorship AFOSR and USAF under grant (FA9550-08-1-0086) en_US
dc.language.iso en_US en_US
dc.rights An error occurred on the license name. en
dc.rights.uri An error occurred getting the license - uri. en
dc.subject informative planning en_US
dc.subject motion planning en_US
dc.subject rapidly-exploring random trees en_US
dc.subject mobile sensor networks en_US
dc.title Information-Theoretic Motion Planning for Constrained Sensor Networks en_US
dc.type Article en_US

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