Team for Research in
Ubiquitous Secure Technology

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

• jeremyEt-Al-CASE2008-finalSubmission-FINAL.pdf · application/pdf · 1532 kbytes

• 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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