# Probabilistic Model Checking of Robots Deployed in Extreme Environments

## TL;DR

This paper presents a probabilistic model checking framework for verifying the safety and reliability of robots operating in extreme environments, using layered Markov models and novel estimators based on Bayesian inference.

## Contribution

It introduces a layered Markov model framework and two new estimators for learning transition parameters from operational data, enhancing verification during pre-mission and runtime.

## Key findings

- Successfully verified safety and reliability of underwater robots in extreme conditions.
- Demonstrated effectiveness of estimators with real-world deployment data.
- Enhanced assurance of autonomous robot operation in hazardous 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.