Performance Verification for Behavior-Based Robot Missions

Lyons, Damian M.; Arkin, Ronald C.; Jiang, Shengyuan; Liu, Tsung-Ming; Nirmal, Paramesh

doi:10.1109/tro.2015.2418592

Cited by 21 publications

(43 citation statements)

References 27 publications

(55 reference statements)

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“…This is as compared to dynamic analysis, which annotates code and evaluates execution traces for single runs or statistics for sets of runs. However, in prior work [24] [25], static analysis techniques have been extended with Dynamic Bayesian Networks [26] to produce probabilities associated with variable values. Integrating this with the RDA (or a points-to) filter in MLSA would mean that the results generated, while still sound, would be less conservative and more reflective of real run-time operation.…”

Section: Resultsmentioning

confidence: 99%

Lightweight Multilingual Software Analysis

Lyons

Bogar

Baird

2017

Challenges and Opportunities in ICT Research Projects

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Developer preferences, language capabilities and the persistence of older languages contribute to the trend that large software codebases are often multilingual -that is, written in more than one computer language. While developers can leverage monolingual software development tools to build software components, companies are faced with the problem of managing the resultant large, multilingual codebases to address issues with security, efficiency, and quality metrics. The key challenge is to address the opaque nature of the language interoperability interface: one language calling procedures in a second (which may call a third, or even back to the first), resulting in a potentially tangled, inefficient and insecure codebase. An architecture is proposed for lightweight static analysis of large multilingual codebases -the MLSA architecture. Its modular and table-oriented structure addresses the open-ended nature of multiple languages and language interoperability APIs. We focus here as an application on the construction of call-graphs that capture both inter-language and intra-language calls. The algorithms for extracting multilingual call-graphs from codebases are presented, and several examples of multilingual software engineering analysis are discussed. The state of the implementation and testing of MLSA is presented, and the implications for future work are discussed.1. The procedure-based interoperability API call is tested for in line 3 (e.g., PyObject_CallObject) 2. The name of the procedure, if it is statically available, is added to the modified call-graph CGM at lines 8 and 11, and the arguments to the procedure are modified to omit the first argument. 3. The node is labelled "ProcBased-Dynamic" at line 6 This algorithm does not include several steps typically associated with procedure-based interoperability calls. Continuing our python.h example:

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Section: Resultsmentioning

confidence: 99%

Lightweight Multilingual Software Analysis

Lyons

Bogar

Baird

2017

Challenges and Opportunities in ICT Research Projects

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“…The VIPARS (Verification in PARS) [13] module provides a performance verification functionality for MissionLab. The inputs for VIPARS are the mission program as designed in MissionLab's CfgEdit graphical interface, a set of designer selected library models of the robot, the sensor systems, the mission operating environment, and the mission performance criteria.…”

Section: A Mission Designmentioning

confidence: 99%

“…The PARS process algebra, extraction of flow functions and Bayesian Network filtering has been described in prior papers, and the reader is referred there for details [13] [9]. The bounding overwatch multirobot mission, without obstacles, is described in more detail in [8].…”

Section: Verification With Uncertain Geometrymentioning

confidence: 99%

“…(4). Lyons et al [13] develops an interleaving theorem and associated algorithm Sysgen with linear computational complexity, by which the parallel, connected network of process on the top line of eq. (4) can be converted to the TR process on the second line of eq.…”

Section: Verification With Uncertain Geometrymentioning

confidence: 99%

“…In that approach, the mission designer builds the robot program using the graphical MissionLab [10] [11] mission design editor. The mission is automatically translated [12] into an internal process algebra (PARS -Process Algebra for Robot Schemas) from which static analysis algorithms [13] extract a set of probabilistic relations for the program and environment variables. A Bayesian Network approach is constructed from these relations and used to verify the performance of the program.…”

Section: Introductionmentioning

confidence: 99%

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Probabilistic Verification of Multi-robot Missions in Uncertain Environments

Lyons

Arkin

Jiang

et al. 2015

2015 IEEE 27th International Conference on Tools With Artificial Intelligence (ICTAI)

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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 a-priori 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.

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Evolution of Formal Model-Based Assurance Cases for Autonomous Robots

Gleirscher

Foster

Nemouchi

2019

Software Engineering and Formal Methods

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An assurance case should carry sufficient evidence for a compelling argument that a system fulfils its guarantees under specific environmental assumptions. Assurance cases are often subject of maintenance, evolution, and reuse. In this paper, we demonstrate how evidence of an assurance case can be formalised, and how an assurance case can be refined using this formalisation to increase argument confidence and to react to changing operational needs. Moreover, we propose two argument patterns for construction and extension and we implement these patterns using the generic proof assistant Isabelle. We illustrate our approach for an autonomous mobile ground robot. Finally, we relate our approach to international standards (e.g. DO-178C, ISO 26262) recommending the delivery and maintenance of assurance cases.

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Performance Verification for Behavior-Based Robot Missions

Cited by 21 publications

References 27 publications

Lightweight Multilingual Software Analysis

Lightweight Multilingual Software Analysis

Probabilistic Verification of Multi-robot Missions in Uncertain Environments

Evolution of Formal Model-Based Assurance Cases for Autonomous Robots

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