Statistical Model Checking of Human-Robot Interaction Scenarios

Livia Lestingi
(Politecnico di Milano)
Mehrnoosh Askarpour
(McMaster University)
Marcello M. Bersani
(Politecnico di Milano)
Matteo Rossi
(Politecnico di Milano)

Robots are soon going to be deployed in non-industrial environments. Before society can take such a step, it is necessary to endow complex robotic systems with mechanisms that make them reliable enough to operate in situations where the human factor is predominant. This calls for the development of robotic frameworks that can soundly guarantee that a collection of properties are verified at all times during operation. While developing a mission plan, robots should take into account factors such as human physiology. In this paper, we present an example of how a robotic application that involves human interaction can be modeled through hybrid automata, and analyzed by using statistical model-checking. We exploit statistical techniques to determine the probability with which some properties are verified, thus easing the state-space explosion problem. The analysis is performed using the Uppaal tool. In addition, we used Uppaal to run simulations that allowed us to show non-trivial time dynamics that describe the behavior of the real system, including human-related variables. Overall, this process allows developers to gain useful insights into their application and to make decisions about how to improve it to balance efficiency and user satisfaction.

In Rafael C. Cardoso, Angelo Ferrando, Daniela Briola, Claudio Menghi and Tobias Ahlbrecht: Proceedings of the First Workshop on Agents and Robots for reliable Engineered Autonomy (AREA 2020), Virtual event, 4th September 2020, Electronic Proceedings in Theoretical Computer Science 319, pp. 9–17.
Published: 23rd July 2020.

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