Team for Research in
Ubiquitous Secure Technology

Actuator Networks for Navigating an Unmonitored Mobile Robot.
Jeremy Schiff, Anand Kulkarni, Danny Bazo, Vincent Duindam, Ron Alterovitz, Dezhen Song, Ken Goldberg

Citation
Jeremy Schiff, Anand Kulkarni, Danny Bazo, Vincent Duindam, Ron Alterovitz, Dezhen Song, Ken Goldberg. "Actuator Networks for Navigating an Unmonitored Mobile Robot.". IEEE Conference on Automation Science and Engineering (CASE)., August 2008.

Abstract
Abstract—Building on recent work in sensor-actuator networks and distributed manipulation, we consider the use of pure actuator networks for localization-free robotic navigation. We show how an actuator network can be used to guide an unobserved robot to a desired location in space and introduce an algorithm to calculate optimal actuation patterns for such a network. Sets of actuators are sequentially activated to induce a series of static potential fields that robustly drive the robot from a start to an end location under movement uncertainty. Our algorithm constructs a roadmap with probability-weighted edges based on motion uncertainty models and identifies an actuation pattern that maximizes the probability of successfully guiding the robot to its goal. Simulations of the algorithm show that an actuator network can robustly guide robots with various uncertainty models through a two-dimensional space. We experiment with additive Gaussian Cartesian motion uncertainty models and additive Gaussian polar models. Motion randomly chosen destinations within the convex hull of a 10-actuator network succeeds with with up to 93.4% probability. For n actuators, and m samples per transition edge in our roadmap, our runtime is O(mn6).

Electronic downloads

Citation formats  
  • HTML
    Jeremy Schiff, Anand Kulkarni, Danny Bazo, Vincent Duindam,
    Ron Alterovitz, Dezhen Song, Ken Goldberg. <a
    href="http://www.truststc.org/pubs/706.html"
    >Actuator Networks for Navigating an Unmonitored Mobile
    Robot.</a>, <i>IEEE Conference on Automation
    Science and Engineering (CASE).</i>, August 2008.
  • Plain text
    Jeremy Schiff, Anand Kulkarni, Danny Bazo, Vincent Duindam,
    Ron Alterovitz, Dezhen Song, Ken Goldberg. "Actuator
    Networks for Navigating an Unmonitored Mobile Robot.".
    <i>IEEE Conference on Automation Science and
    Engineering (CASE).</i>, August 2008.
  • BibTeX
    @article{SchiffKulkarniBazoDuindamAlterovitzSongGoldberg08_ActuatorNetworksForNavigatingUnmonitoredMobileRobot,
        author = {Jeremy Schiff and Anand Kulkarni and Danny Bazo
                  and Vincent Duindam and Ron Alterovitz and Dezhen
                  Song and Ken Goldberg},
        title = {Actuator Networks for Navigating an Unmonitored
                  Mobile Robot.},
        journal = {IEEE Conference on Automation Science and
                  Engineering (CASE).},
        month = {August},
        year = {2008},
        abstract = {Abstract—Building on recent work in
                  sensor-actuator networks and distributed
                  manipulation, we consider the use of pure actuator
                  networks for localization-free robotic navigation.
                  We show how an actuator network can be used to
                  guide an unobserved robot to a desired location in
                  space and introduce an algorithm to calculate
                  optimal actuation patterns for such a network.
                  Sets of actuators are sequentially activated to
                  induce a series of static potential fields that
                  robustly drive the robot from a start to an end
                  location under movement uncertainty. Our algorithm
                  constructs a roadmap with probability-weighted
                  edges based on motion uncertainty models and
                  identifies an actuation pattern that maximizes the
                  probability of successfully guiding the robot to
                  its goal. Simulations of the algorithm show that
                  an actuator network can robustly guide robots with
                  various uncertainty models through a
                  two-dimensional space. We experiment with additive
                  Gaussian Cartesian motion uncertainty models and
                  additive Gaussian polar models. Motion randomly
                  chosen destinations within the convex hull of a
                  10-actuator network succeeds with with up to 93.4%
                  probability. For n actuators, and m samples per
                  transition edge in our roadmap, our runtime is
                  O(mn6).},
        URL = {http://www.truststc.org/pubs/706.html}
    }
    

Posted by Jessica Gamble on 5 Apr 2010.
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