Distributed Inference in Dynamical Systems

@inproceedings{derosa-icra07,
  abstract = {Tightly-coupled multi-agent systems such as modular
     robots frequently exhibit properties of interest that span
     multiple modules. These properties cannot easily be detected from
     any single module, though they might readily be detected by
     combining the knowledge of multiple modules. Testing for
     distributed conditions is especially important in debugging or
     verifying the correctness of software for modular robots. We have
     developed a technique we call distributed watchpoint triggers
     which can efficiently recognize such distributed conditions. Our
     watchpoint description language can handle a variety of temporal,
     spatial, and logical properties spanning multiple robots. This
     paper presents that language, describes our fully-distributed,
     online mechanism for detecting distributed conditions in a
     running system, and evaluates the performance of our
     implementation. We found that the performance of the system is
     highly dependent on the program being debugged, scales linearly
     with ensemble size, and is small enough to make the system
     practical in all but the worst case scenarios.},
  author = {De~Rosa, Michael and Goldstein, Seth Copen and Lee, Peter
     and Campbell, Jason D. and Pillai, Padmanabhan and Mowry, Todd
     C.},
  booktitle = {Proceedings of the IEEE International Conference on
     Robotics and Automation {ICRA '07}},
  venue = {IEEE International Conference on Robotics and Automation
     (ICRA)},
  title = {Distributed Watchpoints: Debugging Large Multi-Robot
     Systems},
  year = {2007},
  month = {April},
  keywords = {Debugging, Distributed Algorithms},
  url = {http://www.cs.cmu.edu/~claytronics/papers/derosa-icra07.pdf},
}

@inproceedings{funiak-iros07,
  author = {Funiak, Stanislav and Pillai, Padmanabhan and Campbell,
     Jason D. and Goldstein, Seth Copen},
  title = {Internal Localization of Modular Robot Ensembles},
  booktitle = {Workshop on Self-Reconfiguring Modular Robotics at the
     IEEE International Conference on Intelligent Robots and Systems
     (IROS) '07},
  venue = {Workshop on Self-Reconfigurable Robots/Systems and
     Applications at IROS},
  year = {2007},
  month = {October},
  abstract = {The determination of the relative position and pose of
     every robot in a modular robotic ensemble is a necessary
     preliminary step for most modular robotic tasks. Localization is
     particularly important when the modules make local noisy
     observations and are not significantly constrained by inter-robot
     latches. In this paper, we propose a robust hierarchical approach
     to the {\em internal localization} problem that uses normalized
     cut to identify subproblems with small localization error. A key
     component of our solution is a simple method to reduce the cost
     of normalized cut computations. The result is a robust algorithm
     that scales to large, non-homogeneous ensembles. We evaluate our
     algorithm in simulation on ensembles of up to 10,000 modules,
     demonstrating substantial improvements over prior work.},
  keywords = {Probabilistic Inference, Sensing, Localization,
     Distributed Algorithms},
  url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-iros07.pdf},
}

@inproceedings{funiak-nips06,
  title = {Distributed Inference in Dynamical Systems},
  author = {Funiak, Stanislav and Guestrin, Carlos and Paskin, Mark
     and Sukthankar, Rahul},
  booktitle = {Advances in Neural Information Processing Systems 19},
  venue = {Advances in Neural Information Processing Systems},
  editor = {B. Scholkopf and J. Platt and T. Hoffman},
  publisher = {MIT Press},
  address = {Cambridge, MA},
  pages = {433--440},
  year = {2006},
  month = {December},
  url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-nips06.pdf},
  keywords = {Probabilistic Inference, Sensing, Distributed
     Algorithms, Graphical Models},
  abstract = {We present a robust distributed algorithm for
     approximate probabilistic inference in dynamical systems, such as
     sensor networks and teams of mobile robots. Using assumed density
     filtering, the network nodes maintain a tractable representation
     of the belief state in a distributed fashion. At each time step,
     the nodes coordinate to condition this distribution on the
     observations made throughout the network, and to advance this
     estimate to the next time step. In addition, we identify a
     significant challenge for probabilistic inference in dynamical
     systems: message losses or network partitions can cause nodes to
     have inconsistent beliefs about the current state of the system.
     We address this problem by developing distributed algorithms that
     guarantee that nodes will reach an informative consistent
     distribution when communication is re-established. We present a
     suite of experimental results on real-world sensor data for two
     real sensor network deployments: one with 25 cameras and another
     with 54 temperature sensors.},
}

@inproceedings{funiak-ipsn06,
  author = {Funiak, Stanislav and Guestrin, Carlos and Sukthankar,
     Rahul and Paskin, Mark},
  title = {Distributed Localization of Networked Cameras},
  booktitle = {Fifth International Conference on Information
     Processing in Sensor Networks (IPSN'06)},
  venue = {International Conference on Information Processing in
     Sensor Networks (IPSN'06)},
  month = {April},
  pages = {34--42},
  year = {2006},
  keywords = {Probabilistic Inference, Sensing, Distributed
     Algorithms, Graphical Models, Localization},
  url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-ipsn06.pdf},
  abstract = {Camera networks are perhaps the most common type of
     sensor network and are deployed in a variety of real-world
     applications including surveillance, intelligent environments and
     scientific remote monitoring. A key problem in deploying a
     network of cameras is calibration, i.e., determining the location
     and orientation of each sensor so that observations in an image
     can be mapped to locations in the real world. This paper proposes
     a fully distributed approach for camera network calibration. The
     cameras collaborate to track an object that moves through the
     environment and reason probabilistically about which camera poses
     are consistent with the observed images. This reasoning employs
     sophisticated techniques for handling the difficult
     nonlinearities imposed by projective transformations, as well as
     the dense correlations that arise between distant cameras. Our
     method requires minimal overlap of the cameras' fields of view
     and makes very few assumptions about the motion of the object. In
     contrast to existing approaches, which are centralized, our
     distributed algorithm scales easily to very large camera
     networks. We evaluate the system on a real camera network with 25
     nodes as well as simulated camera networks of up to 50 cameras
     and demonstrate that our approach performs well even when
     communication is lossy.},
}

@inproceedings{funiak-nips06,
  title = {Distributed Inference in Dynamical Systems},
  author = {Funiak, Stanislav and Guestrin, Carlos and Paskin, Mark
     and Sukthankar, Rahul},
  booktitle = {Advances in Neural Information Processing Systems 19},
  venue = {Advances in Neural Information Processing Systems},
  editor = {B. Scholkopf and J. Platt and T. Hoffman},
  publisher = {MIT Press},
  address = {Cambridge, MA},
  pages = {433--440},
  year = {2006},
  month = {December},
  url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-nips06.pdf},
  keywords = {Probabilistic Inference, Sensing, Distributed
     Algorithms, Graphical Models},
  abstract = {We present a robust distributed algorithm for
     approximate probabilistic inference in dynamical systems, such as
     sensor networks and teams of mobile robots. Using assumed density
     filtering, the network nodes maintain a tractable representation
     of the belief state in a distributed fashion. At each time step,
     the nodes coordinate to condition this distribution on the
     observations made throughout the network, and to advance this
     estimate to the next time step. In addition, we identify a
     significant challenge for probabilistic inference in dynamical
     systems: message losses or network partitions can cause nodes to
     have inconsistent beliefs about the current state of the system.
     We address this problem by developing distributed algorithms that
     guarantee that nodes will reach an informative consistent
     distribution when communication is re-established. We present a
     suite of experimental results on real-world sensor data for two
     real sensor network deployments: one with 25 cameras and another
     with 54 temperature sensors.},
}

@inproceedings{funiak-ipsn06,
  author = {Funiak, Stanislav and Guestrin, Carlos and Sukthankar,
     Rahul and Paskin, Mark},
  title = {Distributed Localization of Networked Cameras},
  booktitle = {Fifth International Conference on Information
     Processing in Sensor Networks (IPSN'06)},
  venue = {International Conference on Information Processing in
     Sensor Networks (IPSN'06)},
  month = {April},
  pages = {34--42},
  year = {2006},
  keywords = {Probabilistic Inference, Sensing, Distributed
     Algorithms, Graphical Models, Localization},
  url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-ipsn06.pdf},
  abstract = {Camera networks are perhaps the most common type of
     sensor network and are deployed in a variety of real-world
     applications including surveillance, intelligent environments and
     scientific remote monitoring. A key problem in deploying a
     network of cameras is calibration, i.e., determining the location
     and orientation of each sensor so that observations in an image
     can be mapped to locations in the real world. This paper proposes
     a fully distributed approach for camera network calibration. The
     cameras collaborate to track an object that moves through the
     environment and reason probabilistically about which camera poses
     are consistent with the observed images. This reasoning employs
     sophisticated techniques for handling the difficult
     nonlinearities imposed by projective transformations, as well as
     the dense correlations that arise between distant cameras. Our
     method requires minimal overlap of the cameras' fields of view
     and makes very few assumptions about the motion of the object. In
     contrast to existing approaches, which are centralized, our
     distributed algorithm scales easily to very large camera
     networks. We evaluate the system on a real camera network with 25
     nodes as well as simulated camera networks of up to 50 cameras
     and demonstrate that our approach performs well even when
     communication is lossy.},
}

@inproceedings{funiak-iros07,
  author = {Funiak, Stanislav and Pillai, Padmanabhan and Campbell,
     Jason D. and Goldstein, Seth Copen},
  title = {Internal Localization of Modular Robot Ensembles},
  booktitle = {Workshop on Self-Reconfiguring Modular Robotics at the
     IEEE International Conference on Intelligent Robots and Systems
     (IROS) '07},
  venue = {Workshop on Self-Reconfigurable Robots/Systems and
     Applications at IROS},
  year = {2007},
  month = {October},
  abstract = {The determination of the relative position and pose of
     every robot in a modular robotic ensemble is a necessary
     preliminary step for most modular robotic tasks. Localization is
     particularly important when the modules make local noisy
     observations and are not significantly constrained by inter-robot
     latches. In this paper, we propose a robust hierarchical approach
     to the {\em internal localization} problem that uses normalized
     cut to identify subproblems with small localization error. A key
     component of our solution is a simple method to reduce the cost
     of normalized cut computations. The result is a robust algorithm
     that scales to large, non-homogeneous ensembles. We evaluate our
     algorithm in simulation on ensembles of up to 10,000 modules,
     demonstrating substantial improvements over prior work.},
  keywords = {Probabilistic Inference, Sensing, Localization,
     Distributed Algorithms},
  url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-iros07.pdf},
}

@inproceedings{funiak-nips06,
  title = {Distributed Inference in Dynamical Systems},
  author = {Funiak, Stanislav and Guestrin, Carlos and Paskin, Mark
     and Sukthankar, Rahul},
  booktitle = {Advances in Neural Information Processing Systems 19},
  venue = {Advances in Neural Information Processing Systems},
  editor = {B. Scholkopf and J. Platt and T. Hoffman},
  publisher = {MIT Press},
  address = {Cambridge, MA},
  pages = {433--440},
  year = {2006},
  month = {December},
  url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-nips06.pdf},
  keywords = {Probabilistic Inference, Sensing, Distributed
     Algorithms, Graphical Models},
  abstract = {We present a robust distributed algorithm for
     approximate probabilistic inference in dynamical systems, such as
     sensor networks and teams of mobile robots. Using assumed density
     filtering, the network nodes maintain a tractable representation
     of the belief state in a distributed fashion. At each time step,
     the nodes coordinate to condition this distribution on the
     observations made throughout the network, and to advance this
     estimate to the next time step. In addition, we identify a
     significant challenge for probabilistic inference in dynamical
     systems: message losses or network partitions can cause nodes to
     have inconsistent beliefs about the current state of the system.
     We address this problem by developing distributed algorithms that
     guarantee that nodes will reach an informative consistent
     distribution when communication is re-established. We present a
     suite of experimental results on real-world sensor data for two
     real sensor network deployments: one with 25 cameras and another
     with 54 temperature sensors.},
}

@inproceedings{funiak-ipsn06,
  author = {Funiak, Stanislav and Guestrin, Carlos and Sukthankar,
     Rahul and Paskin, Mark},
  title = {Distributed Localization of Networked Cameras},
  booktitle = {Fifth International Conference on Information
     Processing in Sensor Networks (IPSN'06)},
  venue = {International Conference on Information Processing in
     Sensor Networks (IPSN'06)},
  month = {April},
  pages = {34--42},
  year = {2006},
  keywords = {Probabilistic Inference, Sensing, Distributed
     Algorithms, Graphical Models, Localization},
  url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-ipsn06.pdf},
  abstract = {Camera networks are perhaps the most common type of
     sensor network and are deployed in a variety of real-world
     applications including surveillance, intelligent environments and
     scientific remote monitoring. A key problem in deploying a
     network of cameras is calibration, i.e., determining the location
     and orientation of each sensor so that observations in an image
     can be mapped to locations in the real world. This paper proposes
     a fully distributed approach for camera network calibration. The
     cameras collaborate to track an object that moves through the
     environment and reason probabilistically about which camera poses
     are consistent with the observed images. This reasoning employs
     sophisticated techniques for handling the difficult
     nonlinearities imposed by projective transformations, as well as
     the dense correlations that arise between distant cameras. Our
     method requires minimal overlap of the cameras' fields of view
     and makes very few assumptions about the motion of the object. In
     contrast to existing approaches, which are centralized, our
     distributed algorithm scales easily to very large camera
     networks. We evaluate the system on a real camera network with 25
     nodes as well as simulated camera networks of up to 50 cameras
     and demonstrate that our approach performs well even when
     communication is lossy.},
}

@inproceedings{funiak-iros07,
  author = {Funiak, Stanislav and Pillai, Padmanabhan and Campbell,
     Jason D. and Goldstein, Seth Copen},
  title = {Internal Localization of Modular Robot Ensembles},
  booktitle = {Workshop on Self-Reconfiguring Modular Robotics at the
     IEEE International Conference on Intelligent Robots and Systems
     (IROS) '07},
  venue = {Workshop on Self-Reconfigurable Robots/Systems and
     Applications at IROS},
  year = {2007},
  month = {October},
  abstract = {The determination of the relative position and pose of
     every robot in a modular robotic ensemble is a necessary
     preliminary step for most modular robotic tasks. Localization is
     particularly important when the modules make local noisy
     observations and are not significantly constrained by inter-robot
     latches. In this paper, we propose a robust hierarchical approach
     to the {\em internal localization} problem that uses normalized
     cut to identify subproblems with small localization error. A key
     component of our solution is a simple method to reduce the cost
     of normalized cut computations. The result is a robust algorithm
     that scales to large, non-homogeneous ensembles. We evaluate our
     algorithm in simulation on ensembles of up to 10,000 modules,
     demonstrating substantial improvements over prior work.},
  keywords = {Probabilistic Inference, Sensing, Localization,
     Distributed Algorithms},
  url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-iros07.pdf},
}

@inproceedings{funiak-nips06,
  title = {Distributed Inference in Dynamical Systems},
  author = {Funiak, Stanislav and Guestrin, Carlos and Paskin, Mark
     and Sukthankar, Rahul},
  booktitle = {Advances in Neural Information Processing Systems 19},
  venue = {Advances in Neural Information Processing Systems},
  editor = {B. Scholkopf and J. Platt and T. Hoffman},
  publisher = {MIT Press},
  address = {Cambridge, MA},
  pages = {433--440},
  year = {2006},
  month = {December},
  url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-nips06.pdf},
  keywords = {Probabilistic Inference, Sensing, Distributed
     Algorithms, Graphical Models},
  abstract = {We present a robust distributed algorithm for
     approximate probabilistic inference in dynamical systems, such as
     sensor networks and teams of mobile robots. Using assumed density
     filtering, the network nodes maintain a tractable representation
     of the belief state in a distributed fashion. At each time step,
     the nodes coordinate to condition this distribution on the
     observations made throughout the network, and to advance this
     estimate to the next time step. In addition, we identify a
     significant challenge for probabilistic inference in dynamical
     systems: message losses or network partitions can cause nodes to
     have inconsistent beliefs about the current state of the system.
     We address this problem by developing distributed algorithms that
     guarantee that nodes will reach an informative consistent
     distribution when communication is re-established. We present a
     suite of experimental results on real-world sensor data for two
     real sensor network deployments: one with 25 cameras and another
     with 54 temperature sensors.},
}

@inproceedings{funiak-ipsn06,
  author = {Funiak, Stanislav and Guestrin, Carlos and Sukthankar,
     Rahul and Paskin, Mark},
  title = {Distributed Localization of Networked Cameras},
  booktitle = {Fifth International Conference on Information
     Processing in Sensor Networks (IPSN'06)},
  venue = {International Conference on Information Processing in
     Sensor Networks (IPSN'06)},
  month = {April},
  pages = {34--42},
  year = {2006},
  keywords = {Probabilistic Inference, Sensing, Distributed
     Algorithms, Graphical Models, Localization},
  url = {http://www.cs.cmu.edu/~claytronics/papers/funiak-ipsn06.pdf},
  abstract = {Camera networks are perhaps the most common type of
     sensor network and are deployed in a variety of real-world
     applications including surveillance, intelligent environments and
     scientific remote monitoring. A key problem in deploying a
     network of cameras is calibration, i.e., determining the location
     and orientation of each sensor so that observations in an image
     can be mapped to locations in the real world. This paper proposes
     a fully distributed approach for camera network calibration. The
     cameras collaborate to track an object that moves through the
     environment and reason probabilistically about which camera poses
     are consistent with the observed images. This reasoning employs
     sophisticated techniques for handling the difficult
     nonlinearities imposed by projective transformations, as well as
     the dense correlations that arise between distant cameras. Our
     method requires minimal overlap of the cameras' fields of view
     and makes very few assumptions about the motion of the object. In
     contrast to existing approaches, which are centralized, our
     distributed algorithm scales easily to very large camera
     networks. We evaluate the system on a real camera network with 25
     nodes as well as simulated camera networks of up to 50 cameras
     and demonstrate that our approach performs well even when
     communication is lossy.},
}