Sampling-based motion planning with deterministic mu-calculus specifications
Name
Karaman-2009-Sampling-based motion planning with deterministic -calculus specifications.pdf
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1.85 MB
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Adobe PDF
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Author(s)
Karaman, Sertac
Frazzoli, Emilio
Alternative Title
Sampling-based motion planning with deterministic μ-calculus specifications
Date Issued
January 2010
Journal
Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009
Publisher
Institute of Electrical and Electronics Engineers
Citation
Karaman, S., and E. Frazzoli. “Sampling-based motion planning with deterministic μ-calculus specifications.” Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on. 2009. 2222-2229. ©2010 Institute of Electrical and Electronics Engineers.
Version
Final published version
Abstract
In this paper, we propose algorithms for the on-line computation of control programs for dynamical systems that provably satisfy a class of temporal logic specifications. Such specifications have recently been proposed in the literature as a powerful tool to synthesize provably correct control programs, for example for embedded systems and robotic applications. The proposed algorithms, generalizing state-of-the-art algorithms for point-to-point motion planning, incrementally build finite transition systems representing a discrete subset of dynamically feasible trajectories. At each iteration, local mu-calculus model-checking methods are used to establish whether the current transition system satisfies the specifications. Efficient sampling strategies are presented, ensuring the probabilistic completeness of the algorithms. We demonstrate the effectiveness of the proposed approach on simulation examples.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CDC.2009.5400278
