gregkuhlmann

Peter Stone, Mohan Sridharan, Daniel Stronger, Gregory Kuhlmann, Nate Kohl, Peggy Fidelman, and Nicholas K. Jong
Robotics and Autonomous Systems, 2006.

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.

@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",
  note="Special issue on Planning Under Uncertainty in Robotics."
)