(Georgia Institute of Technology, 2016-11)
Lyons, Damian M.; Arkin, Ronald C.
Localization and mapping algorithms can allow a robot to navigate well in an unknown environment. However, whether such algorithms enhance any specific robot mission is currently a matter for empirical validation. In this paper we apply our MissionLab/VIPARS mission design and verification approach to an autonomous robot mission that uses probabilistic localization software. Two approaches to modeling probabilistic localization for verification are presented: a high-level approach, and a sample-based approach which allows run-time code to be embedded in verification. Verification and experimental validation results are presented for two waypoint missions using each method, demonstrating the accuracy of verification, and both are compared with verification of an odometry-only mission, to show the mission-specific benefit of localization.
(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, 2015-06)
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.