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CORR
2011
Springer

Active Markov Information-Theoretic Path Planning for Robotic Environmental Sensing

8 years 9 months ago
Active Markov Information-Theoretic Path Planning for Robotic Environmental Sensing
Recent research in multi-robot exploration and mapping has focused on sampling environmental fields, which are typically modeled using the Gaussian process (GP). Existing information-theoretic exploration strategies for learning GPbased environmental field maps adopt the non-Markovian problem structure and consequently scale poorly with the length of history of observations. Hence, it becomes computationally impractical to use these strategies for in situ, realtime active sampling. To ease this computational burden, this paper presents a Markov-based approach to efficient information-theoretic path planning for active sampling of GP-based fields. We analyze the time complexity of solving the Markov-based path planning problem, and demonstrate analytically that it scales better than that of deriving the non-Markovian strategies with increasing length of planning horizon. For a class of exploration tasks called the transect sampling task, we provide theoretical guarantees on the acti...
Kian Hsiang Low, John M. Dolan, Pradeep K. Khosla
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2011
Where CORR
Authors Kian Hsiang Low, John M. Dolan, Pradeep K. Khosla
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