Modeling Operator Behavior in the Safety Analysis of Collaborative Robotic Applications

Askarpour, Mehrnoosh; Mandrioli, Dino; Rossi, Matteo; Vicentini, Federico

doi:10.1007/978-3-319-66266-4_6

Cited by 16 publications

(10 citation statements)

References 31 publications

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“…Vicentini et al [VARM20] proposed a methodology to assess the physical safety of Human-Robot Collaboration (HRC) via a rich formal model of collaborative systems. The methodology features a model of both normative and erroneous human behavior [AMRV17,AMRV19] in order to detect the hazardous situations caused by human error, which are typically overlooked during the assessment. It allows engineers to incrementally refine the model of their system until all the safety violations are removed.…”

Section: Supporting the Design Of Robotic Applicationsmentioning

confidence: 99%

PuRSUE -from specification of robotic environments to synthesis of controllers

et al. 2020

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Developing robotic applications is a complex task, which requires skills that are usually only possessed by highly-qualified robotic developers. While formal methods that help developers in the creation and design of robotic applications exist, they must be explicitly customized to be impactful in the robotics domain and to support effectively the growth of the robotic market. Specifically, the robotic market is asking for techniques that: (i) enable a systematic and rigorous design of robotic applications though high-level languages; and (ii) enable the automatic synthesis of low-level controllers, which allow robots to achieve their missions. To address these problems we present the PuRSUE (Planner for RobotS in Uncontrollable Environments) approach, which aims to support developers in the rigorous and systematic design of high-level run-time control strategies for robotic applications. The approach includes PuRSUE-ML a high-level language that allows for modeling the environment, the agents deployed therein, and their missions. PuRSUE is able to check automatically whether a controller that allows robots to achieve their missions might exist and, then, it synthesizes a controller. We evaluated how PuRSUE helps designers in modeling robotic applications, the effectiveness of its automatic computation of controllers, and how the approach supports the deployment of controllers on actual robots. The evaluation is based on 13 scenarios derived from 3 different robotic applications presented in the literature. The results show that: (i) PuRSUE-ML is effective in supporting designers in the formal modeling of robotic applications compared to a direct encoding of robotic applications in low-level modeling formalisms; (ii) PuRSUE enables the automatic generation of controllers that are difficult to create manually; and (iii) the plans generated with PuRSUE are indeed effective when deployed on actual robots.

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Section: Supporting the Design Of Robotic Applicationsmentioning

confidence: 99%

PuRSUE -from specification of robotic environments to synthesis of controllers

et al. 2020

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“…Indeed, probabilities could be associated with hazards after they are detected by the formal verification runs, to better estimate the actual need to introduce RRMs. In particular, we considered in [62] a series of common human errors derived from previous analyses based on psychological operator models [75]. We consider deterministic facts resulting from errors (e.g., being late, being in the wrong location, doing the wrong sequence, and not respecting a postcondition).…”

Section: A Discussionmentioning

confidence: 99%

“…The inclusion of state wt helps us to generate some of these error types (omission, insertion). This issue has been tackled more thoroughly in [62].…”

Section: ) Model Of Robot Actionsmentioning

confidence: 99%

Safety Assessment of Collaborative Robotics Through Automated Formal Verification

et al. 2020

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A crucial aspect of physical human-robot collaboration (HRC) is to maintain a safe common workspace for human operator. However, close proximity between human-robot and unpredictability of human behavior raises serious challenges in terms of safety. This article proposes a risk analysis methodology for collaborative robotic applications, which is compatible with well-known standards in the area and relies on formal verification techniques to automate the traditional risk analysis methods. In particular, the methodology relies on temporal logic-based models to describe the different possible ways in which tasks can be carried out, and on fully automated formal verification techniques to explore the corresponding state space to detect and modify the hazardous situations at early stages of system design.Index Terms-Formal methods, human-robot collaboration (HRC), model-based risk assessment, robot safety, temporal logic. I. INTRODUCTIONH UMAN-ROBOT collaboration (HRC) in industrial settings enhances the flexibility and adaptability of robotic systems for production. Close proximity and hybrid task assignment between humans and robots have substantial impacts on the safety of human operators. Most importantly, shared dynamic environments cannot be effectively supported by static safety analyses. In particular, the hybrid human-robot tasks generate different possible execution workflows. Therefore, various task assignments among humans and robots can also change during operations. In realistic scenarios, the environment can also change, due to mobile resources, tool changing, modification of the layout, and process locations, so that the specifications of the system need to be re-evaluated. The safety of such evolving systems should not be verified as a static property, but rather according to the behavior of the system in actual situations.

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“…In industrial HRC, this approach is mainly pursued by the SAFER-HRCmethod, which is based on the formal language TRIO and the satisfiability checker ZOT [4]. The method has also been extended to cover erroneous worker behavior [18] and can be used in conjunction with a 3D simulator [19] for visualization. Besides industrial HRC, formal safety verification has also been applied to robot systems for personal assistance, medicine, and transport [20]- [22].…”

Section: B Novel Hazard Analysis Methodsmentioning

confidence: 99%

Testing Robot System Safety by creating Hazardous Human Worker Behavior in Simulation

Huck¹,

Ledermann²,

Kröger³

2021

Preprint

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We introduce a novel simulation-based approach to identify hazards that result from unexpected worker behavior in human-robot collaboration. Simulation-based safety testing must take into account the fact that human behavior is variable and that human error can occur. When only the expected worker behavior is simulated, critical hazards can remain undiscovered. On the other hand, simulating all possible worker behaviors is computationally infeasible. This raises the problem of how to find interesting (i.e., potentially hazardous) worker behaviors given a limited number of simulation runs. We frame this as a search problem in the space of possible worker behaviors. Because this search space can get quite complex, we introduce the following measures: (1) Search space restriction based on workflow-constraints, (2) prioritization of behaviors based on how far they deviate from the nominal behavior, and (3) the use of a risk metric to guide the search towards high-risk behaviors which are more likely to expose hazards. We demonstrate the approach in a collaborative workflow scenario that involves a human worker, a robot arm, and a mobile robot.

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Modeling Operator Behavior in the Safety Analysis of Collaborative Robotic Applications

Cited by 16 publications

References 31 publications

PuRSUE -from specification of robotic environments to synthesis of controllers

PuRSUE -from specification of robotic environments to synthesis of controllers

Safety Assessment of Collaborative Robotics Through Automated Formal Verification

Testing Robot System Safety by creating Hazardous Human Worker Behavior in Simulation

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