Safety Assessment of Collaborative Robotics Through Automated Formal Verification

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

doi:10.1109/tro.2019.2937471

Cited by 61 publications

(46 citation statements)

References 57 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.…”

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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“…Few researchers and their teams [3,[16][17][18][19][20][21] discussed different industrial environments, the safety approaches that should be followed, and some real-life case studies. it was showed that lead designers must develop and evaluate safe, human-centered, ergonomics, and efficient collaborative assembly workstations, where the operator's feedback was provided in regards to occupational health and safety.…”

Section: Literature Reviewmentioning

confidence: 99%

Human-Robot Interaction and Collaboration (HRI-C) Utilizing Top-View RGB-D Camera System

Tashtoush¹,

Garcia²,

Landa³

et al. 2021

IJACSA

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In this study, a smart and affordable system that utilizes an RGB-D camera to measure the exact position of an operator with respect to an adjacent robotic manipulator was developed. This developed technology was implemented in a simulated human operation in an automated manufacturing robot to achieve two goals; enhancing the safety measures around the robot by adding an affordable smart system for human detection and robot control and developing a system that will allow the between the human-robot collaboration to finish a predefined task. The system utilized an Xbox Kinect V2 sensor/camera and Scorbot ER-V Plus to model and mimics the selected applications. To achieve these goals, a geometric model for the Scorbot and Xbox Kinect V2 was developed, a robotics joint calibration was applied, an algorithm of background segmentation was utilized to detect the operator and a dynamic binary mask for the robot was implemented, and the efficiency of both systems based on the response time and localization error was analyzed. The first application of the Add-on Safety Device aims to monitor the working-space and control the robot to avoid any collisions when an operator enters or gets closer. This application will reduced and remove physical barriers around the robots, expand the physical work area, reduce the proximity limitations, and enhance the human-robots interaction (HRI) in an industrial environment while sustaining a low cost. The system was able to respond to human intrusion to prevent any collision within 500 ms on average, and it was found that the system's bottleneck was PC and robot inter-communication speed. The second application was developing a successful collaborative scenario between a robot and a human operator, where a robot will deposit an object on the operator's hand, mimicking a real-life human-robot collaboration (HRC) tasks. The system was able to detect the operator's hand and it's location then command the robot to place an object on the hand, the system was able to place the object within a mean error of 2.4 cm, and the limitation of this system was the internal variables and data transmitting speed between the robot controller and main computer. These results are encouraging and ongoing work aims to experiment with different operations and implement gesture detection in real-time collaboration tasks while keeping the human operator safe and predicting their behavior.

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“…Other works implement formal languages to capture collaboration: for example, Webster et al [11] develop Brahms models of a robotic personal assistant and then perform modelchecking with the tool SPIN. Vicentini et al [12] use LTL formulae to model collaborative tasks and develop an innovative risk assessment technique. Some works exploit learning techniques to develop their model.…”

Section: Related Workmentioning

confidence: 99%