Probabilistic Model Checking of Robots Deployed in Extreme Environments
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.
Cite
Text
Zhao et al. "Probabilistic Model Checking of Robots Deployed in Extreme Environments." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33018066Markdown
[Zhao et al. "Probabilistic Model Checking of Robots Deployed in Extreme Environments." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/zhao2019aaai-probabilistic/) doi:10.1609/AAAI.V33I01.33018066BibTeX
@inproceedings{zhao2019aaai-probabilistic,
title = {{Probabilistic Model Checking of Robots Deployed in Extreme Environments}},
author = {Zhao, Xingyu and Robu, Valentin and Flynn, David and Dinmohammadi, Fateme and Fisher, Michael and Webster, Matt},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {8066-8074},
doi = {10.1609/AAAI.V33I01.33018066},
url = {https://mlanthology.org/aaai/2019/zhao2019aaai-probabilistic/}
}