(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.
(Georgia Institute of Technology, 2015-10)
Jiang, Shu; Arkin, Ronald C.
The objectives of this article are: 1) to present a taxonomy for mixed-initiative human-robot interaction and 2) to survey its state of practice through the examination of past research along each taxonomical dimension. The paper starts with some definitions of mixed-initiative interaction (MII) from the perspective of human-computer interaction (HCI) to introduce the basic concepts of MII. We then synthesize these definitions to the robotic context for mixed-initiative human-robot teams. A taxonomy for mixed-initiative in human-robot interaction is then presented. The goal of the taxonomy is to inform the design of mixed-initiative human-robot systems by identifying key elements of these systems. The state of practice of mixed-initiative human-robot interaction is then surveyed and examined along each taxonomical dimension.
(Georgia Institute of Technology, 2015-06)
Lyons, Damian M.; Arkin, Ronald C.; Jiang, Shu; Liu, Tsung-Ming; Nirmal, Paramesh
Certain robot missions need to perform predictably in a physical environment that may have significant uncertainty. One approach is to leverage automatic software verification techniques to establish a performance guarantee. The addition of an environment model and uncertainty in both program and environment, however, means the state-space of a model-checking solution to the problem can be prohibitively large. An approach based on behavior-based controllers in a process-algebra framework that avoids state-space combinatorics is presented here. In this approach, verification of the robot program in the uncertain environment is reduced to a filtering problem for a Bayesian Network. Validation results are presented for the verification of a multiple-waypoint and an autonomous exploration robot mission.