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Mobile Robot Laboratory
Mobile Robot Laboratory
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ItemPerformance Verification for Behavior-Based Robot Missions(Georgia Institute of Technology, 2015-06) Lyons, Damian M. ; Arkin, Ronald C. ; Jiang, Shu ; Liu, Tsung-Ming ; Nirmal, ParameshCertain 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.
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ItemVerifying and Validating Multirobot Missions(Georgia Institute of Technology, 2014) Lyons, Damian M. ; Arkin, Ronald C. ; Jiang, Shu ; Harrington, Dagan ; Liu, Tsung-MingWe have developed an approach that can be used by mission designers to determine whether or not a performance guarantee for their mission software, when carried out under the uncertain conditions of a real-world environment, will hold within a threshold probability. In this paper we demonstrate its utility for verifying multirobot missions, in particular a bounding overwatch mission.
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ItemA Software Tool for the Design of Critical Robot Missions with Performance Guarantees(Georgia Institute of Technology, 2013-03) Lyons, Damian M. ; Arkin, Ronald C. ; Nirmal, Paramesh ; Jiang, Shu. ; Liu, Tsung-MingDeploying a robot as part of a counter-weapons of mass destruction mission demands that the robotic software operates with high assurance. A unique feature of robotic software development is the need to perform predictably in a physical environment that may only be poorly characterized in advance. In this paper, we present an approach to building high assurance software for robot missions carried out in uncertain environments. The software development framework and the verification algorithm, VIPARS, are described in detail. Results are presented for missions including motion and sensing uncertainty, interaction with obstacles, and the use of sensors to guide behavior.
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ItemPerformance Guarantees for C - WMD Robot Missions(Georgia Institute of Technology, 2013) Jiang, Shu ; Arkin, Ronald C. ; Lyons, Damian M. ; Liu, Tsung-Ming ; Harrington, DaganRobotics has been considered as one of the five key technology areas for defense against attacks with weapons of mass destruction (WMD). However, due to the mass impact nature of WMD, failures of counter-WMD (C-WMD) missions can have catastrophic consequences. To ensure robots’ success in carrying out C-WMD missions, we have developed a novel verification framework in providing performance guarantees for behavior-based and probabilistic robot algorithms in complex real-world environments. This paper describes the system architecture and discusses how the verification framework can be used to provide pre-mission performance guarantees for robots in executing C-WMD missions.
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ItemPerformance Verification for Behavior-based Robot Missions(Georgia Institute of Technology, 2013) Lyons, Damian M. ; Arkin, Ronald C. ; Jiang, Shu ; Liu, Tsung-Ming ; Nirmal, Paramesh ; Deeb, J.Certain robot missions need to perform predictably in a physical environment that may only be poorly characterized in advance. This requirement raises many issues for existing approaches to software verification. An approach based on behavior-based controllers in a process-algebra framework is proposed by Lyons et al [15] to side-step state combinatorics. In this paper we show that this approach can be used to generate a Dynamic Bayesian Net work for the problem, and that verification is reduced to a filtering problem for this network. We present validation results for the verification of a multiple waypoint robot mission using this approach.
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ItemGetting it Right the First time: Robot Mission Guarantees in the Presence of Uncertainty(Georgia Institute of Technology, 2013) Lyons, Damian M. ; Arkin, Ronald C. ; Nirmal, P. ; Jiang, Shu ; Liu, Tsung-Ming ; Deeb, J.Certain robot missions need to perform predictably in a physical environment that may only be poorly characterized in advance. We have previously developed an approach to establishing performance guarantees for behavior-based controllers in a process-algebra framework. We extend that work here to include random variables, and we show how our prior results can be used to generate a Dynamic Bayesian Network for the coupled system of program and environment model. Verification is reduced to a filtering problem for this network. Finally, we present validation results that demonstrate the effectiveness of the verification of a multiple waypoint robot mission using this approach.
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ItemVerifying Performance for Autonomous Robot Missions with Uncertainty(Georgia Institute of Technology, 2012) Lyons, Damian M. ; Arkin, Ronald C. ; Liu, Tsung-Ming ; Jiang, Shu ; Nirmal, ParameshEstablishing performance guarantees for robot missions is especially important for C-WMD applications. Software verification techniques, such as model checking (Clark 1999, Jhala & Majumdar 2009), can be applied to robotic applications but characteristics of this application area, including addition of a robot environment model and handling continuous spatial location well, exacerbate state explosion, a key weakness of these methods. We have proposed an approach to verifying robot missions that shifts the focus from state-based analysis onto the solution of a set of flow equations (Lyons et al. 2012). The key novelty introduced in this paper is a probabilistic spatial representation for flow equations. We show how this representation models the spatial situation for robot motion with environments or controllers that include discrete choice (constraints). A model such as we propose here is useful only if it can accurately predict robot motion. We conclude by presenting three validation results that show this approach has strong predictive power ; that is, that the verifications it produces can be trusted.