Probabilistic Model Checking of Robots Deployed in Extreme Environments



Zhao, Xingyu ORCID: 0000-0002-3474-349X, Robu, Valentin, Flynn, David, Dinmohammadi, Fateme, Fisher, Michael and Webster, Matt ORCID: 0000-0002-8817-6881
(2019) Probabilistic Model Checking of Robots Deployed in Extreme Environments. In: 33rd AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, 2019.

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Abstract

Robots are increasingly used to carry out critical missions in extreme environments that are hazardous for humans. This requires a high degree of operational autonomy under uncertain conditions, and poses new challenges for assuring the robot's safety and reliability. In this paper, we develop a framework for probabilistic model checking on a layered Markov model to verify the safety and reliability requirements of such robots, both at pre-mission stage and during runtime. Two novel estimators based on conservative Bayesian inference and imprecise probability model with sets of priors are introduced to learn the unknown transition parameters from operational data. We demonstrate our approach using data from a real-world deployment of unmanned underwater vehicles in extreme environments.

Item Type: Conference or Workshop Item (Unspecified)
Additional Information: Version accepted at the 33rd AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, 2019
Uncontrolled Keywords: cs.AI, cs.AI
Depositing User: Symplectic Admin
Date Deposited: 04 Feb 2019 12:50
Last Modified: 05 Jun 2024 04:49
DOI: 10.1609/aaai.v33i01.33018066
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3031668