Distributed Localization of Modular Robot Ensembles

@inproceedings{funiak-rss08,
  author = {Funiak, Stanislav and Pillai, Padmanabhan and
     Ashley-Rollman, Michael P. and Campbell, Jason D. and Goldstein,
     Seth Copen},
  title = {Distributed Localization of Modular Robot Ensembles},
  booktitle = {Proceedings of Robotics: Science and Systems},
  year = {2008},
  month = {October},
  abstract = {Internal localization, the problem of estimating
     relative pose for each module (part) of a modular robot is a
     prerequisite for many shape control, locomotion, and actuation
     algorithms. In this paper, we propose a robust hierarchical
     approach that uses normalized cut to identify dense subregions
     with small mutual localization error, then progressively merges
     those subregions to localize the entire ensemble. Our method
     works well in both 2D and 3D, and requires neither exact
     measurements nor rigid inter-module connectors. One key component
     of our solution is a simple method to reduce the cost of
     normalized cut computations. Most of the other computations can
     be effectively distributed. The result is a robust algorithm that
     scales to large, non-homogeneous ensembles. We evaluate our
     algorithm in accurate 2D and 3D simulations of scenarios with up
     to 10,000 modules.},
  keywords = {Distributed Systems, Localization}
}

@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},
  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-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)},
  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.}
}

@mastersthesis{reshko04,
  author = {Reshko, Greg},
  title = {Localization Techniques for Synthetic Reality},
  school = {Carnegie Mellon University},
  keywords = {Localization},
  year = {2004}
}

@inproceedings{funiak-rss08,
  author = {Funiak, Stanislav and Pillai, Padmanabhan and
     Ashley-Rollman, Michael P. and Campbell, Jason D. and Goldstein,
     Seth Copen},
  title = {Distributed Localization of Modular Robot Ensembles},
  booktitle = {Proceedings of Robotics: Science and Systems},
  year = {2008},
  month = {October},
  abstract = {Internal localization, the problem of estimating
     relative pose for each module (part) of a modular robot is a
     prerequisite for many shape control, locomotion, and actuation
     algorithms. In this paper, we propose a robust hierarchical
     approach that uses normalized cut to identify dense subregions
     with small mutual localization error, then progressively merges
     those subregions to localize the entire ensemble. Our method
     works well in both 2D and 3D, and requires neither exact
     measurements nor rigid inter-module connectors. One key component
     of our solution is a simple method to reduce the cost of
     normalized cut computations. Most of the other computations can
     be effectively distributed. The result is a robust algorithm that
     scales to large, non-homogeneous ensembles. We evaluate our
     algorithm in accurate 2D and 3D simulations of scenarios with up
     to 10,000 modules.},
  keywords = {Distributed Systems, Localization}
}

@article{mderosa-ijrr-2008,
  author = {De~Rosa, Michael and Goldstein, Seth Copen and Lee, Peter
     and Campbell, Jason D. and Pillai, Padmanabhan},
  journal = {International Journal of Robotics Research},
  keywords = {Debugging, Distributed Systems},
  month = {Special Issue on Modular Robotics},
  url = {http://www.cs.cmu.edu/~claytronics/papers/mderosa-ijrr-2008.pdf},
  number = {3},
  title = {Distributed Watchpoints: Debugging Large Modular Robotic
     Systems},
  abstract = {Distributed systems frequently exhibit properties of
     interest which span multiple entities. These properties cannot
     easily be detected from any single entity, but can be readily be
     detected by combining the knowledge of multiple entities. Testing
     for distributed properties is especially important in debugging
     or verifying software for modular robots. We have developed a
     technique we call distributed watchpoint triggers which can
     efficiently recognize distributed conditions. Our watchpoint
     description language can handle a variety of temporal, spatial,
     and logical properties spanning multiple robots. This paper
     presents the specification language, describes the distributed
     online mechanism for detecting distributed conditions in a
     running system, and evaluates the performance of our
     implementation.},
  volume = {27},
  also = {Distributed Watchpoints: Debugging Large Multi-Robot
     Systems, (icra07)},
  year = {2008}
}

@inproceedings{rister-icra07,
  author = {Rister, Benjamin D. and Campbell, Jason D. and Pillai,
     Padmanabhan and Mowry, Todd C.},
  title = {Integrated Debugging of Large Modular Robot Ensembles},
  booktitle = {Proceedings of the IEEE International Conference on
     Robotics and Automation {ICRA '07}},
  keywords = {Debugging, Distributed Systems},
  month = {April},
  abstract = {Creatively misquoting Thomas Hobbes, the process of
     software debugging is nasty, brutish, and all too long. This
     holds all the more true in robotics, which frequently involves
     concurrency, extensive nondeterminisism, event-driven components,
     complex state machines, and difficult platform limitations.
     Inspired by the challenges we have encountered while attempting
     to debug software on simulated ensembles of tens of thousands of
     modular robots, we have developed a new debugging tool
     particularly suited to the characteristics of highly parallel,
     event- and state-driven robotics software. Our state capture and
     introspection system also provides data that may be used in
     higher-level debugging tools as well. We report on the design of
     this promising debugging system, and on our experiences with it
     so far.},
  year = {2007},
  url = {http://www.cs.cmu.edu/~claytronics/papers/rister-icra07.pdf}
}

@inproceedings{derosa-rss06,
  author = {De~Rosa, Michael and Goldstein, Seth Copen and Lee, Peter
     and Campbell, Jason D. and Pillai, Padmanabhan},
  booktitle = {Robotics: Science and Systems Workshop on
     Self-Reconfigurable Modular Robots},
  title = {Distributed Watchpoints: Debugging Very Large Ensembles of
     Robots},
  see = {derosa2007-icra07},
  month = {August},
  year = {2006},
  keywords = {Debugging, Distributed Systems},
  address = {Philadelphia, PA},
  talk = {http://www.cs.cmu.edu/~seth/papers/derosa-rss06.pdf},
  url = {http://www.cs.cmu.edu/~claytronics/papers/derosa-rss06.pdf},
  abstract = {We describe a debugging tool for modular robotics that
     introduces the concept of distributed watchpoint triggers. This
     technique can initiate debugging actions (system halt, global
     snapshot, logging, etc.) in an ensemble of robots based on
     temporal, physical, and logical conditions distributed over
     multiple robots. Our technique is specifically designed to be
     effective in debugging modular robotic ensembles, where many
     important types of failure conditions can be detected within
     small, physically connected subsets of the total ensemble.}
}