Analyzing the impact of human errors on interactive service robotic scenarios via formal verification

• Published in: Software and systems modeling Vol. 23; no. 2; pp. 473 - 502
• Main Authors: Lestingi, Livia, Manglaviti, Andrea, Marinaro, Davide, Marinello, Luca, Askarpour, Mehrnoosh, Bersani, Marcello M., Rossi, Matteo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024; Springer Nature B.V
• Subjects: Compilers; Computer Science; Designers; Human behavior; Human error; Impact analysis; Information Systems Applications (incl.Internet); Interpreters; IT in Business; Mission planning; Programming Languages; Programming Techniques; Robotics; Robots; Software Engineering; Software Engineering/Programming and Operating Systems; Special Section Paper; Statistical models; Verification; Human errors; Statistical model checking; Stochastic Hybrid Automata; Human–robot interaction; Service robotics; Formal modeling; Formal verification
• ISSN: 1619-1366; 1619-1374
• DOI: 10.1007/s10270-023-01125-1

Developing robotic applications with human–robot interaction for the service sector raises a plethora of challenges. In these settings, human behavior is essentially unconstrained as they can stray from the plan in numerous ways, constituting a critical source of uncertainty for the outcome of the robotic mission. Application designers require accessible and reliable frameworks to address this issue at an early development stage. We present a model-driven framework for developing interactive service robotic scenarios, allowing designers to model the interactive scenario, estimate its outcome, deploy the application, and smoothly reconfigure it. This article extends the framework compared to previous works by introducing an analysis of the impact of human errors on the mission’s outcome. The core of the framework is a formal model of the agents at play—the humans and the robots—and the robotic mission under analysis, which is subject to statistical model checking to estimate the mission’s outcome. The formal model incorporates a formalization of different human erroneous behaviors’ phenotypes, whose likelihood can be tuned while configuring the scenario. Through scenarios inspired by the healthcare setting, the evaluation highlights how different configurations of erroneous behavior impact the verification results and guide the designer toward the mission design that best suits their needs.