FIRM: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements

A.-a. Agha-mohammadi, S. Chakravorty, N. M. Amato

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184 Scopus citations


In this paper we present feedback-based information roadmap (FIRM), a multi-query approach for planning under uncertainty which is a belief-space variant of probabilistic roadmap methods. The crucial feature of FIRM is that the costs associated with the edges are independent of each other, and in this sense it is the first method that generates a graph in belief space that preserves the optimal substructure property. From a practical point of view, FIRM is a robust and reliable planning framework. It is robust since the solution is a feedback and there is no need for expensive replanning. It is reliable because accurate collision probabilities can be computed along the edges. In addition, FIRM is a scalable framework, where the complexity of planning with FIRM is a constant multiplier of the complexity of planning with PRM. In this paper, FIRM is introduced as an abstract framework. As a concrete instantiation of FIRM, we adopt stationary linear quadratic Gaussian (SLQG) controllers as belief stabilizers and introduce the so-called SLQG-FIRM. In SLQG-FIRM we focus on kinematic systems and then extend to dynamical systems by sampling in the equilibrium space. We investigate the performance of SLQG-FIRM in different scenarios. © The Author(s) 2013.
Original languageEnglish (US)
Pages (from-to)268-304
Number of pages37
JournalThe International Journal of Robotics Research
Issue number2
StatePublished - Nov 15 2013
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: The authors are grateful to the anonymous reviewers for their helpful suggestions as well as Aditya Mahadevan and Daniel Tomkins for many fruitful discussions and their help with experiments. This work is supported in part by NSF award RI-1217991. Additionally, the work of Agha-mohammadi and Chakravorty is supported in part by AFOSR Grant FA9550-08-1-0038 and the work of Agha-mohammadi and Amato is supported in part by NSF awards CNS-0551685, CCF-0833199, CCF-0830753, IIS-0917266, IIS-0916053, EFRI-1240483, by NSF/DNDO award 2008-DN-077-ARI018-02, by NIH NCI R25 CA090301-11, by DOE awards DE-FC52-08NA28616, DE-AC02-06CH11357, B575363, B575366, by THECB NHARP award 000512-0097-2009, by Samsung, Chevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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