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, 54:933–943, 2006. Special issue on Planning Under Uncertainty
in Robotics.
Contains material that was previously published in an ICRA-2006
paper. Online Version
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{stone: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",
volume = "54",
issue = "11",
year = "2006",
pages = "933--943",
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 = {Contains material that was previously published in an <a href="http://www.cs.utexas.edu/~pstone/Papers/2005icra/actsense.pdf">ICRA-2006 paper</a>. <a href="http://dx.doi.org/10.1016/j.robot.2006.05.010">Online Version</a>},
bib2html_pubtype = {Journal},
bib2html_rescat = {Robot Soccer, Robotics, Machine Learning}
}
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