Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Formalizing Performance Evaluation of Mobile Manipulator Robots using CTML

Published

Author(s)

Omar Aboul-Enein, Yaping Jing, Roger V. Bostelman

Abstract

Computation Tree Measurement Language (CTML) is a newly developed formal language that offers simultaneous model verification and performance evaluation measures. While the theory behind CTML has been established, the language has yet to be tested on a real-world example. In this work, we wish to demonstrate the utility of CTML when it is applied to a new field. To accomplish this, an artifact-based performance measurement methodology developed at the National Institute of Standards and Technology (NIST) was selected for modeling. The performance measurement method assesses mobile manipulator robots, which hold the potential to enable more flexible, dynamic workflows within manufacturing environments. Contributions of this work include the modeling of robot tasks implemented for the performance measurement test using Petri nets, as well as the formulation and execution of sample queries using CTML. To demonstrate the advantages of CTML when compared to other temporal logics, the queries were re-formulated and evaluated using the PRISM Model Checker.
Proceedings Title
International Mechanical Engineering Congress and Exposition (IMECE) 2020
Conference Dates
November 15-19, 2020
Conference Location
Portland, OR, US

Keywords

Model verification, Performance evaluation, Petri net, Mobile manipulators, Survivability

Citation

Aboul-Enein, O. , Jing, Y. and Bostelman, R. (2020), Formalizing Performance Evaluation of Mobile Manipulator Robots using CTML, International Mechanical Engineering Congress and Exposition (IMECE) 2020, Portland, OR, US, [online], https://doi.org/10.1115/IMECE2020-23234, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=928432 (Accessed March 29, 2024)
Created November 20, 2020, Updated March 31, 2022