@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.},
}
Distributed Algorithms |
|
Distributed Watchpoints: Debugging Large Multi-Robot Systems | pdf bib |
|
Michael De Rosa, Seth Copen Goldstein, Peter Lee, Jason D. Campbell, Padmanabhan Pillai, and Todd C. Mowry.
In Proceedings of the IEEE International Conference on Robotics and Automation ICRA '07,
April, 2007.
|
| @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},
}
|
|
Internal Localization of Modular Robot Ensembles | pdf bib |
|
Stanislav Funiak, Padmanabhan Pillai, Jason D. Campbell, and Seth Copen Goldstein.
In Workshop on Self-Reconfiguring Modular Robotics at the IEEE International Conference on Intelligent Robots and Systems (IROS) '07,
October, 2007.
|
| @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},
}
|
|
Distributed Inference in Dynamical Systems | pdf bib |
|
Stanislav Funiak, Carlos Guestrin, Mark Paskin, and Rahul Sukthankar.
In Advances in Neural Information Processing Systems 19,
pages 433–440, December, 2006.
|
| @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.},
}
|
|
Distributed Localization of Networked Cameras | pdf bib |
|
Stanislav Funiak, Carlos Guestrin, Rahul Sukthankar, and Mark Paskin.
In Fifth International Conference on Information Processing in Sensor Networks (IPSN'06),
pages 34–42, April, 2006.
|
| @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.},
}
|
Graphical Models |
|
Distributed Inference in Dynamical Systems | pdf bib |
|
Stanislav Funiak, Carlos Guestrin, Mark Paskin, and Rahul Sukthankar.
In Advances in Neural Information Processing Systems 19,
pages 433–440, December, 2006.
|
| @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.},
}
|
|
Distributed Localization of Networked Cameras | pdf bib |
|
Stanislav Funiak, Carlos Guestrin, Rahul Sukthankar, and Mark Paskin.
In Fifth International Conference on Information Processing in Sensor Networks (IPSN'06),
pages 34–42, April, 2006.
|
| @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.},
}
|
Sensing |
|
Internal Localization of Modular Robot Ensembles | pdf bib |
|
Stanislav Funiak, Padmanabhan Pillai, Jason D. Campbell, and Seth Copen Goldstein.
In Workshop on Self-Reconfiguring Modular Robotics at the IEEE International Conference on Intelligent Robots and Systems (IROS) '07,
October, 2007.
|
| @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},
}
|
|
Distributed Inference in Dynamical Systems | pdf bib |
|
Stanislav Funiak, Carlos Guestrin, Mark Paskin, and Rahul Sukthankar.
In Advances in Neural Information Processing Systems 19,
pages 433–440, December, 2006.
|
| @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.},
}
|
|
Distributed Localization of Networked Cameras | pdf bib |
|
Stanislav Funiak, Carlos Guestrin, Rahul Sukthankar, and Mark Paskin.
In Fifth International Conference on Information Processing in Sensor Networks (IPSN'06),
pages 34–42, April, 2006.
|
| @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.},
}
|
Probabilistic Inference |
|
Internal Localization of Modular Robot Ensembles | pdf bib |
|
Stanislav Funiak, Padmanabhan Pillai, Jason D. Campbell, and Seth Copen Goldstein.
In Workshop on Self-Reconfiguring Modular Robotics at the IEEE International Conference on Intelligent Robots and Systems (IROS) '07,
October, 2007.
|
| @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},
}
|
|
Distributed Inference in Dynamical Systems | pdf bib |
|
Stanislav Funiak, Carlos Guestrin, Mark Paskin, and Rahul Sukthankar.
In Advances in Neural Information Processing Systems 19,
pages 433–440, December, 2006.
|
| @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.},
}
|
|
Distributed Localization of Networked Cameras | pdf bib |
|
Stanislav Funiak, Carlos Guestrin, Rahul Sukthankar, and Mark Paskin.
In Fifth International Conference on Information Processing in Sensor Networks (IPSN'06),
pages 34–42, April, 2006.
|
| @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.},
}
|