Abstract:

The problem of multi-robot patrol has become a canonical problem in
multi-robot, in which a team of mobile robots is required to jointly visit
some target area in order to monitor change in state. The goal of the robots
can vary from optimizing point-visit frequency, to maximizing the chances of
detecting an adversary that tries to pass through the patrol path
undetected.
In this talk I will describe theoretical results that are used as a baseline
for my work, in which strategies for the patrolling robots can be found
efficiently based on, among others, a Markovian modeling of the world. I
will then describe various adaptations of the theoretical results to handle
real world constraints, including description of new patrolling strategies,
reevaluation of coordination restrictions, and development of new
adversarial models.


Short bio:

Noa Agmon is a Postdoctoral Fellow in the Department of Computer Science,
the University of Texas at Austin. She received her PhD in Computer Science
from Bar-Ilan University, and her MSc in Computer Science from the Weizmann
Institute. Her research focuses on various aspects of multi-robot systems,
including multi-robot patrolling, robot navigation and multi-agent planning
in adversarial environments.