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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.
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.
[PDF]260.5kB [postscript]3.6MB
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.
@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.},
}
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