期刊
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
卷 33, 期 2, 页码 268-304出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364913501564
关键词
belief space; information; uncertainty; Planning; control
类别
资金
- NSF [RI-1217991, CNS-0551685, CCF-0833199, CCF-0830753, IIS-0917266, IIS-0916053, EFRI-1240483]
- AFOSR [FA9550-08-1-0038]
- NSF/DNDO [2008-DN-077-ARI018-02]
- NIH [NCI R25 CA090301-11]
- DOE [DE-FC52-08NA28616, DE-AC02-06CH11357, B575363, B575366]
- THECB (NHARP award) [000512-0097-2009]
- Samsung
- Chevron
- IBM
- Intel
- Oracle/Sun
- King Abdullah University of Science and Technology [KUS-C1-016-04]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0916053] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [0830753] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [0833199] Funding Source: National Science Foundation
- Emerging Frontiers & Multidisciplinary Activities
- Directorate For Engineering [1240483] Funding Source: National Science Foundation
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.
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