Benjamin J. Choi
ABSTRACT
We present a formal verification method that provides a model-based approach to human-robot interaction (HRI) in medical settings by utilizing linear temporal logic (LTL). We define high-level HRI procedures with an LTL-based framework to create algorithmically sound robots that can function independently in dynamic HRI environments. Our approach's theoretical infallibility confers particular advantages for medical robots, where safety and informative communications are crucial. In order to establish the viability of our proposed method, and with the ongoing COVID-19 pandemic in mind, we developed an LTL knowledge base for a medical robot tasked with HRI-intensive roles of patient reception and triage. We designed robotic simulations based on our LTL architecture to test our approach, employing randomized inputs to generate unpredictable HRI environments. We then conducted formal verification via an automata-theoretic approach by evaluating our simulated robot against generalized Büchi automata. We hope our LTL-based approach can enable future achievements in HRI.
- Yousef Alimohamadi, Mojtaba Sepandi, Maryam Taghdir, and Hadiseh Hosamirudsari. 2020. Determine the most common clinical symptoms in COVID- 19 patients: a systematic review and meta-analysis. Journal of preventive medicine and hygiene 61, 3 (2020), E304.Google Scholar
- Mehrnoosh Askarpour, Dino Mandrioli, Matteo Rossi, and Federico Vicentini. 2016. SAFER-HRC: Safety analysis through formal verification in human-robot collaboration. In International Conference on Computer Safety, Reliability, and Security. Springer, 283--295.Google ScholarCross Ref
- Amit Bhatia, Lydia E Kavraki, and Moshe Y Vardi. 2010. Sampling-based motion planning with temporal goals. In 2010 IEEE International Conference on Robotics and Automation. IEEE, 2689--2696.Google ScholarCross Ref
- Rafael H Bordini, Michael Fisher, and Maarten Sierhuis. 2009. Formal verification of human-robot teamwork. In Proceedings of the 4th ACM/IEEE international conference on Human robot interaction. 267--268.Google ScholarDigital Library
- Georgios E Fainekos, Hadas Kress-Gazit, and George J Pappas. 2005. Temporal logic motion planning for mobile robots. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation. IEEE, 2020--2025.Google ScholarCross Ref
- Cameron Finucane, Gangyuan Jing, and Hadas Kress-Gazit. 2010. LTLMoP: Experimenting with language, temporal logic and robot control. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 1988--1993.Google ScholarCross Ref
- Sertac Karaman and Emilio Frazzoli. 2008. Complex mission optimization for multiple-UAVs using linear temporal logic. In 2008 american control conference. IEEE, 2003--2009.Google Scholar
- Zeashan Hameed Khan, Afifa Siddique, and Chang Won Lee. 2020. Robotics Utilization for Healthcare Digitization in Global COVID-19 Management. International Journal of Environmental Research and Public Health 17, 11 (2020), 3819.Google ScholarCross Ref
- Livia Lestingi, Mehrnoosh Askarpour, Marcello M Bersani, and Matteo Rossi. 2020. Formal Verification of Human-Robot Interaction in Healthcare Scenarios. In International Conference on Software Engineering and Formal Methods. Springer, 303--324.Google Scholar
- John J Palmieri and Theodore A Stern. 2009. Lies in the doctor-patient relationship. Primary care companion to the Journal of clinical psychiatry 11, 4 (2009), 163.Google Scholar
- Amir Pnueli. 1977. The temporal logic of programs. In 18th Annual Symposium on Foundations of Computer Science (sfcs 1977). IEEE, 46--57.Google ScholarDigital Library
- David Porfirio, Allison Sauppé, Aws Albarghouthi, and Bilge Mutlu. 2018. Authoring and verifying human-robot interactions. In Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology. 75--86.Google ScholarDigital Library
- David Porfirio, Allison Sauppé, Aws Albarghouthi, and Bilge Mutlu. 2019. Computational tools for human-robot interaction design. In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 733--735.Google ScholarCross Ref
- Kristin Y Rozier. 2011. Linear temporal logic symbolic model checking. Computer Science Review 5, 2 (2011), 163--203.Google ScholarDigital Library
- Lauren Vogel. 2019. Why do patients often lie to their doctors?Google Scholar
- MattWebster, Maha Salem, Clare Dixon, Michael Fisher, and Kerstin Dautenhahn. 2014. Formal verification of an autonomous personal robotic assistant. (2014).Google Scholar
- Ning Xu, Jie Li, Yifeng Niu, and Lincheng Shen. 2016. An LTL-based motion and action dynamic planning method for autonomous robot. IFAC-Papers OnLine 49, 5 (2016), 91--96.Google ScholarCross Ref
Index Terms
- Formal Verification for Human-Robot Interaction in Medical Environments
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