Peter Stone's Selected Publications

Classified by TopicClassified by Publication TypeSorted by DateSorted by First Author Last NameClassified by Funding Source


From Pixels to Multi-Robot Decision-Making: A Study in Uncertainty

Peter Stone, Mohan Sridharan, Daniel Stronger, Gregory Kuhlmann, Nate Kohl, Peggy Fidelman, and Nicholas K. Jong. From Pixels to Multi-Robot Decision-Making: A Study in Uncertainty. Robotics and Autonomous Systems , 54(11):933–43, November 2006. Special issue on Planning Under Uncertainty in Robotics.
Official versionfrom the RAS publisher's webpage.

Download

[PDF]260.5kB  [postscript]3.6MB  

Abstract

Mobile robots must cope with uncertainty from many sources along the path from interpreting raw sensor inputs to behavior selection to execution of the resulting primitive actions. This article identifies several such sources and introduces methods for i) reducing uncertainty and ii) making decisions in the face of uncertainty. We present a complete vision-based robotic system that includes several algorithms for learning models that are useful and necessary for planning, and then place particular emphasis on the planning and decision-making capabilities of the robot. Specifically, we present models for autonomous color calibration, autonomous sensor and actuator modeling, and an adaptation of particle filtering for improved localization on legged robots. These contributions enable effective planning under uncertainty for robots engaged in goal-oriented behavior within a dynamic, collaborative and adversarial environment. Each of our algorithms is fully implemented and tested on a commercial off-the-shelf vision-based quadruped robot.

BibTeX Entry

@Article{RAS06,
	Author="Peter Stone and Mohan Sridharan and Daniel Stronger and Gregory Kuhlmann and Nate Kohl and Peggy Fidelman and Nicholas K.\ Jong",
	title="From Pixels to Multi-Robot Decision-Making:  A Study in Uncertainty",
	journal="Robotics and Autonomous Systems ",
	year="2006",
	volume="54",number="11",month="November",
	pages="933--43",
	note="Special issue on Planning Under Uncertainty in Robotics.",
	abstract={
	          Mobile robots must cope with uncertainty from many
	          sources along the path from interpreting raw sensor
	          inputs to behavior selection to execution of the
	          resulting primitive actions.  This article
	          identifies several such sources and introduces
	          methods for i) reducing uncertainty and ii) making
	          decisions in the face of uncertainty.  We present a
	          complete vision-based robotic system that includes
	          several algorithms for learning models that are
	          useful and necessary for planning, and then place
	          particular emphasis on the planning and
	          decision-making capabilities of the robot.
	          Specifically, we present models for autonomous color
	          calibration, autonomous sensor and actuator
	          modeling, and an adaptation of particle filtering
	          for improved localization on legged robots.  These
	          contributions enable effective planning under
	          uncertainty for robots engaged in goal-oriented
	          behavior within a dynamic, collaborative and
	          adversarial environment.  Each of our algorithms is
	          fully implemented and tested on a commercial
	          off-the-shelf vision-based quadruped robot.
	         },
	wwwnote={<a href="http://dx.doi.org/10.1016/j.robot.2006.05.010">Official version
from the <a href="http://www.elsevier.com/locate/robot">RAS</a> publisher's webpage.},
}	

Generated by bib2html.pl (written by Patrick Riley ) on Wed Jul 09, 2014 11:54:41