Skip to Main content Skip to Navigation
Conference papers

Stress Testing of Single-Arm Robots Through Constraint-Based Generation of Continuous Trajectories

Mathieu Collet 1 Arnaud Gotlieb 1 Nadjib Lazaar 2 Morten Mossige 3
2 COCONUT - Agents, Apprentissage, Contraintes
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : System Testing of Single-Arm Robots (SAR) is challenging as typical SAR involve multiple coordinated software-controlled subsystems, such as motion and action control, perception and anti-collision systems. Developing convincing test scenarios which place the SAR into highly CPU-demanding cases is complicated due to the huge number of possible robots' workspace configurations. In this paper, we introduces RobTest, a tool-supported method for stress testing of SAR, which generates automatically optimal collision-free trajectories. Initially specified by a cloud of points and a set of obstacles, these trajectories are piecewise linear paths in a cost-labelled oriented graph. By using advanced Constraint Programming (CP) techniques, such as constraint refutation over continuous domains and constraint optimization over graphs, RobTest can generate continuous trajectories which 1) avoid physical obstacles and 2) maximize the load of the various CPUs of the SAR. These trajectories result into automatically robot computer programs which place the SAR into high-demanding test scenarios. Our initial experimental evaluation of RobTest shows promising results.
Complete list of metadatas

Cited literature [17 references]  Display  Hide  Download

https://hal-lirmm.ccsd.cnrs.fr/lirmm-02089742
Contributor : Nadjib Lazaar <>
Submitted on : Thursday, April 4, 2019 - 10:12:36 AM
Last modification on : Monday, July 27, 2020 - 10:32:02 AM
Document(s) archivé(s) le : Friday, July 5, 2019 - 12:47:51 PM

File

AITest19-robtest.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Mathieu Collet, Arnaud Gotlieb, Nadjib Lazaar, Morten Mossige. Stress Testing of Single-Arm Robots Through Constraint-Based Generation of Continuous Trajectories. AiTest: Artificial Intelligence Testing, Apr 2019, San francisco, United States. pp.121-128, ⟨10.1109/AITest.2019.00014⟩. ⟨lirmm-02089742⟩

Share

Metrics

Record views

94

Files downloads

180