Run-Time Dependency Graph Models for Independently Developed Robotic Software Components

Nagrath, Vineet; Schlegel, Christian

doi:10.1007/978-3-031-10464-0_62

Cited by 1 publication

(1 citation statement)

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“…The functional and non‐functional properties of these (heterogeneous) components require careful analysis in order to determine their compatibilities toward higher‐level system goals. A change in the non‐functional property of one component may have a severe effect on other components 2 . An additional level of complexity results from the need for autonomous and robotic systems to adopt different operational configurations under different environmental contexts 3,4 .…”

Section: Introductionmentioning

confidence: 99%

SCARS: Suturing wounds due to conflicts between non‐functional requirements in autonomous and robotic systems

Roy,

Bag,

Deb

et al. 2023

Softw Pract Exp

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In autonomous and robotic systems, the functional requirements (FRs) and non‐functional requirements (NFRs) are gathered from multiple stakeholders. The different stakeholder requirements are associated with different components of the robotic system and with the contexts in which the system may operate. This aggregation of requirements from different sources (multiple stakeholders) often results in inconsistent or conflicting sets of requirements. Conflicts among NFRs for robotic systems heavily depend on features of actual execution contexts. It is essential to analyze the inconsistencies and conflicts among the requirements in the early planning phase to design the robotic systems in a systematic manner. In this work, we design and experimentally evaluate a framework, called SCARS, providing: (a) a domain‐specific language extending the ROS2 Domain Specific Language (DSL) concepts by considering the different environmental contexts in which the system has to operate, (b) support to analyze their impact on NFRs, and (c) the computation of the optimal degree of NFR satisfaction that can be achieved within different system configurations. The effectiveness of SCARS has been validated on the iRobot Create3 robot using Gazebo simulation.

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Section: Introductionmentioning

confidence: 99%