Robotic Decision Making for Sensing in the Natural World

Date: 
Friday, October 28, 2011 - 10:36am

UCSB COMPUTER SCIENCE DEPARTMENT PRESENTS:

Monday, November 21, 2011
3:30 – 4:30 PM
Computer Science Conference Room, Harold Frank Hall Rm. 1132

HOST: Subhash Suri

SPEAKER: Geoffrey Hollinger
Postdoctoral Research Associate, USC
Robotic Embedded Systems Laboratory and Viterbi School of Engineering

Title: Robotic Decision Making for Sensing in the Natural World

Abstract:

There is growing interest in the use of robots to gather information
from natural environments. Examples include biological monitoring, mine
sweeping, oil spill cleanup, and seismic event detection. The increasing
capabilities of the robots themselves enable more sophisticated decision
making techniques that optimize information gathered and adapt as new
information is received. The question becomes: how do we develop path
planning algorithms for information gathering tasks that are capable of
dealing with the communication limitations, noisy sensing, and mobility
restrictions present in natural environments?

This talk considers two problems related to path planning for Autonomous
Underwater Vehicles (AUVs): (1) data gathering from an underwater sensor
network equipped with acoustic communication and (2) autonomous
inspection of the submerged portion of a ship hull. For the first
problem, I present path planning methods that extend algorithms for
variants of the Traveling Salesperson Problem (TSP) and show how these
algorithms can be integrated with realistic acoustic communication
models. For the second problem, I discuss techniques for constructing
watertight 3D meshes from sonar-derived point clouds and introduce
uncertainty modeling through non-parametric Bayesian regression.
Uncertainty modeling provides novel cost functions for planning the path
of the robot that allow for formal analysis through connections to
submodular optimization and active learning. Such theoretical analysis
provides insight into the underlying structure of active sensing
problems. Finally, I present experiments that demonstrate the high
performance of the proposed solutions versus the state of the art in
robot path planning.

Bio:

Geoffrey A. Hollinger is a Postdoctoral Research Associate in the
Robotic Embedded Systems Laboratory and Viterbi School of Engineering at
the University of Southern California. He is currently interested in
adaptive sensing and distributed coordination for robots operating with
limited communication. He has also worked on multi-robot search at
Carnegie Mellon University, personal robotics at Intel Research
Pittsburgh, active estimation at the University of Pennsylvania’s GRASP
Laboratory, and miniature inspection robots for the Space Shuttle at
NASA’s Marshall Space Flight Center. He received his Ph.D. (2010) and
M.S. (2007) in Robotics from Carnegie Mellon University and his B.S. in
General Engineering along with his B.A. in Philosophy from Swarthmore
College (2005).