Title:
Probabilistic Verification of Multi-robot Missions in Uncertain Environments

dc.contributor.author Lyons, Damian M.
dc.contributor.author Arkin, Ronald C.
dc.contributor.author Jiang, Shu
dc.contributor.author Harrington, Dagan
dc.contributor.author Tang, Feng
dc.contributor.author Tang, Peng
dc.contributor.corporatename Georgia Institute of Technology. College of Computing en_US
dc.contributor.corporatename Georgia Institute of Technology. School of Interactive Computing en_US
dc.contributor.corporatename Fordham University en_US
dc.contributor.corporatename Georgia Institute of Technology. Mobile Robot Laboratory
dc.contributor.corporatename Georgia Institute of Technology. Institute for Robotics and Intelligent Machines
dc.date.accessioned 2017-05-04T16:06:56Z
dc.date.available 2017-05-04T16:06:56Z
dc.date.issued 2015-11
dc.description © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. en_US
dc.description DOI: 10.1109/ICTAI.2015.22 en_US
dc.description.abstract 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. en_US
dc.identifier.citation Lyons, D. M., Arkin, R. C., Jiang, S., Harrington, D., Tang, F., & Tang, P. (2015). Probabilistic Verification of Multi-robot Missions in Uncertain Environments. 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI), Vietri sul Mare, 2015, pp. 56-63. en_US
dc.identifier.doi 10.1109/ICTAI.2015.22 en_US
dc.identifier.uri http://hdl.handle.net/1853/56671
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original Institute of Electrical and Electronics Engineers
dc.relation.ispartofseries Mobile Robot Laboratory en_US
dc.subject Behavior-based robots en_US
dc.subject Multi-robot missions en_US
dc.subject Probabilistic verification en_US
dc.subject Validation en_US
dc.title Probabilistic Verification of Multi-robot Missions in Uncertain Environments en_US
dc.type Text
dc.type.genre Proceedings
dspace.entity.type Publication
local.contributor.author Arkin, Ronald C.
local.contributor.corporatename College of Computing
local.contributor.corporatename Mobile Robot Laboratory
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
relation.isAuthorOfPublication e853e35f-f419-4348-9619-6f0c7abef2c7
relation.isOrgUnitOfPublication c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isOrgUnitOfPublication 488966cd-f689-41af-b678-bbd1ae9c01d4
relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
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