A Model-driven Approach for the Formal Analysis of Human-Robot Interaction Scenarios

Lestingi, Livia; Askarpour, Mehrnoosh; Bersani, Marcello M.; Rossi, Matteo

doi:10.1109/smc42975.2020.9283204

Cited by 3 publications

(7 citation statements)

References 20 publications

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“…A modeling pattern to incorporate a stochastic characterization of fatigue into the formal model of human behavior. In [13,14], we presented an early version of the model formalizing an interactive robotic scenario as a Network of Hybrid Automata, with probabilistic edges capturing human choices made out of free will. This paper details the refined version of the formal model as a Network of Stochastic Hybrid Automata (SHA).…”

Section: Reconfigurationmentioning

confidence: 99%

“…3. A model of the robot's battery charge and discharge dynamics refined (compared to [13,14]) to fit the behavior of the robotic agent used for the experimental validation. The model fitting procedure increases the accuracy of the SHA modeling the battery when compared against field observations.…”

Section: Reconfigurationmentioning

confidence: 99%

“…In the forthcoming sections, we present the automata constituting the SHA network, i.e., the humans, the robot and its battery, and the orchestrator, primarily focusing on the human behaviors modeled by the patterns Human Follower, Human Leader, and Human Recipient, which this work extends with respect to [13,14] with a stochastic characterization of physical fatigue, and a factorization of the common modeling pattern capturing the periodic sensor reading update. Comparable SHA for the Human Applicant, Human Rescuer, and Human Competitor patterns are introduced in [16].…”

Section: Formal Modeling Hri With Uncertain Human Behaviorsmentioning

confidence: 99%

“…Compared to [13,14], we extend SHA modeling humans by introducing D(h op ), ξ op , and ξ rand, op in all 629 op pub h instances. Enriching the SHA with these features strengthens the results obtained with SMC as 630 they account not only for the uncertainty due to human autonomy, but also for the natural variability of 631 the fatigue phenomenon.…”

Section: Human-robot Interaction Patterns Modelmentioning

confidence: 99%

See 3 more Smart Citations

Specification, stochastic modeling and analysis of interactive service robotic applications

Lestingi

Zerla

Bersani

et al. 2023

Robotics and Autonomous Systems

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Section: Reconfigurationmentioning

confidence: 99%

Section: Reconfigurationmentioning

confidence: 99%

Section: Formal Modeling Hri With Uncertain Human Behaviorsmentioning

confidence: 99%

Section: Human-robot Interaction Patterns Modelmentioning

confidence: 99%

See 2 more Smart Citations

Specification, stochastic modeling and analysis of interactive service robotic applications

Lestingi

Zerla

Bersani

et al. 2023

Robotics and Autonomous Systems

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“…Considering the complexity and tight interactions of CPSs, Statistical Model Checker (SMC) is proposed [13] [14] to tackle two obstacles [15] of modern CPSs. SMC is a simulation-based approach to sample the behaviours and check conformance to the temporal formula.…”

Section: Related Work a Cps Verificationmentioning

confidence: 99%

Adaptive Model Verification for Modularized Industry 4.0 Applications

et al. 2022

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Cyber-Physical Systems (CPSs) are the core of Industry 4.0 applications, integrating advanced technologies such as sensing, data analytics, and artificial intelligence. This kind of combination typically consists of networked sensors and decision-making processes in which sensor-generated data drive the control decisions. Hence, the trustworthiness of the sensors is essential to guarantee performance, safety and quality during operation. Formal model verification techniques are a valuable tool allowing strong reasoning about the high-level design of CPSs. However, the uncertainty exhibited by the underlying sensor networks is often ignored. Manufacturing processes typically involve composition of various modular CPSs that work as a whole, such as multiple Collaborative Robots (cobots) working together as a production line, which improves the flexibility and resilience of the production process. It is still challenging to verify this class of compositional process while also considering uncertainty. We propose a novel verification framework for modular CPSs that combines sensor-level data-driven fault detection and system-level model-driven probabilistic model checking. The resulting framework can rigorously quantify sensor readings' trustworthiness, enabling formal reasoning for system failure prediction and reliability analysis. We validated our approach on a cobots-based manufacturing process.

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Formal Verification of Safety Properties of Collaborative Robotic Applications including Variability

Rathmair

Luckeneder

Haspl

et al. 2021

2021 30th IEEE International Conference on Robot &Amp; Human Interactive Communication (RO-MAN)

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A Model-driven Approach for the Formal Analysis of Human-Robot Interaction Scenarios

Cited by 3 publications

References 20 publications

Specification, stochastic modeling and analysis of interactive service robotic applications

Specification, stochastic modeling and analysis of interactive service robotic applications

Adaptive Model Verification for Modularized Industry 4.0 Applications

Formal Verification of Safety Properties of Collaborative Robotic Applications including Variability

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