How to Formally Model Human in Collaborative Robotics

Askarpour, Mehrnoosh

doi:10.4204/eptcs.329.1

Cited by 4 publications

(1 citation statement)

References 74 publications

(77 reference statements)

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“…Experiment results for each measure with (min,avg,max) data regions due to respiration, deformed tissue layers (which affect the needle tip speed), and inhomogeneous properties of multi-layered tissue. Work on human-robot collaboration[4] may then provide a starting point for formal modeling and integration of more advanced human behavior.…”

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confidence: 99%

Online Strategy Synthesis for Safe and Optimized Control of Steerable Needles

Lehmann¹,

Rogalla²,

Neidhardt³

et al. 2021

Electron. Proc. Theor. Comput. Sci.

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Autonomous systems are often applied in uncertain environments, which require prospective action planning and retrospective data evaluation for future planning to ensure safe operation. Formal approaches may support these systems with safety guarantees, but are usually expensive and do not scale well with growing system complexity. In this paper, we introduce online strategy synthesis based on classical strategy synthesis to derive formal safety guarantees while reacting and adapting to environment changes. To guarantee safety online, we split the environment into region types which determine the acceptance of action plans and trigger local correcting actions. Using model checking on a frequently updated model, we can then derive locally safe action plans (prospectively), and match the current model against new observations via reachability checks (retrospectively). As use case, we successfully apply online strategy synthesis to medical needle steering, i.e., navigating a (flexible and beveled) needle through tissue towards a target without damaging its surroundings.

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mentioning

confidence: 99%

Online Strategy Synthesis for Safe and Optimized Control of Steerable Needles

Lehmann¹,

Rogalla²,

Neidhardt³

et al. 2021

Electron. Proc. Theor. Comput. Sci.

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

Lestingi,

Manglaviti,

Marinaro

et al. 2023

Softw Syst Model

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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.

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