(Georgia Institute of Technology, 2015-11)
Lyons, Damian M.; Arkin, Ronald C.; Jiang, Shu; Harrington, Dagan; Tang, Feng; Tang, Peng
The effective use of autonomous robot teams in
highly-critical missions depends on being able to establish
performance guarantees. However, establishing a guarantee
for the behavior of an autonomous robot operating in an
uncertain environment with obstacles is a challenging problem.
This paper addresses the challenges involved in building a
software tool for verifying the behavior of a multi-robot
waypoint mission that includes uncertain environment
geometry as well as uncertainty in robot motion. One
contribution of this paper is an approach to the problem of apriori
specification of uncertain environments for robot
program verification. A second contribution is a novel method
to extend the Bayesian Network formulation to reason about
random variables with different subpopulations, introduced to
address the challenge of representing the effects of multiple
sensory histories when verifying a robot mission. The third
contribution is experimental validation results presented to
show the effectiveness of this approach on a two-robot,
bounding overwatch mission.