Yim, Mark

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  • Publication
    STAM: A System of Tracking and Mapping in Real Environments
    (2004-12-01) Zhang, Ying; Ackerson, Lee; Duff, David; Eldershaw, Craig; Yim, Mark
    We have implemented a system of tracking mobile robots and mapping an unstructured environment, using up to 25 wireless sensor nodes in an indoor setting. These sensor nodes form an ad hoc network of beacons, self-localize with respect to three anchor nodes, and then track the locations of mobile robots in the field. The system described here was motivated by search and rescue applications, and has been demonstrated in real physical environments.
  • Publication
    Automatic Configuration Recognition Methods in Modular Robots
    (2008-03-01) Park, Michael; Chitta, Sachin; Teichman, Alex; Yim, Mark
    Recognizing useful modular robot configurations composed of hundreds of modules is a significant challenge. Matching a new modular robot configuration to a library of known configurations is essential in identifying and applying control schemes. We present three different algorithms to address the problem of (a) matching and (b) mapping new robot configurations onto a library of known configurations. The first method solves the problem using graph isomorphisms and can identify configurations that share the same underlying graph structure, but have different port connections amongst the modules. The second approach compares graph spectra of configuration matrices to find a permutation matrix that maps a given configuration to a known one. The third algorithm exploits the unique structure of the problem for the particular robots used in our research to achieve impressive gains in performance and speed over existing techniques, especially for larger configurations. With these three algorithms, this paper presents novel solutions to the problem of configuration recognition and sheds light on theoretical and practical issues for long-term advances in this important area of modular robotics. Results and examples are provided to compare the performance of the three algorithms and discuss their relative advantages.
  • Publication
    Towards Robotic Self-reassembly After Explosion
    (2007-10-29) Yim, Mark; Shirmohammadi, Babak; Sastra, Jimmy; Park, Michael; Taylor, Camillo J; Dugan, Michael
    This paper introduces a new challenge problem: designing robotic systems to recover after disassembly from high-energy events and a first implemented solution of a simplified problem. It uses vision-based localization for self-reassembly. The control architecture for the various states of the robot, from fully-assembled to the modes for sequential docking, are explained and inter-module communication details for the robotic system are described.
  • Publication
    Special Issue on the Grand Challenges of Robotics
    (2007-03-01) Ostrowski, James; Yim, Mark; Tapus, Adriana
  • Publication
    Using Smart Cameras to Localize Self-Assembling Modular Robots
    (2007-09-25) Shirmohammadi, Babak; Taylor, Camillo J; Yim, Mark; Sastra, Jimmy; Park, Mike
    In order to realize the goal of self assembling or self reconfiguring modular robots the constituent modules in the system need to be able to gauge their position and orientation with respect to each other. This paper describes an approach to solving this localization problem by equipping each of the modules in the ensemble with a smart camera system. The paper describes one implementation of this scheme on a modular robotic system and discusses the results of a self assembly experiment.