# An Approach to Autonomous Science by Modeling Geological Knowledge in a Bayesian Framework

## TL;DR

This paper introduces a Bayesian network-based framework enabling autonomous robots to model, reason about scientific knowledge, and plan sensing actions for exploration missions, demonstrated through Mars-like environment experiments.

## Contribution

It presents a novel Bayesian reasoning approach combined with Monte Carlo Tree Search for high-level scientific autonomy in exploration robots.

## Key findings

- Significant performance improvements over alternative methods.
- Effective handling of large state spaces and uncertainty.
- Successful autonomous science mission execution in a Martian analog environment.

## Abstract

Autonomous Science is a field of study which aims to extend the autonomy of exploration robots from low level functionality, such as on-board perception and obstacle avoidance, to science autonomy, which allows scientists to specify missions at task level. This will enable more remote and extreme environments such as deep ocean and other planets to be studied, leading to significant science discoveries. This paper presents an approach to extend the high level autonomy of robots by enabling them to model and reason about scientific knowledge on-board. We achieve this by using Bayesian networks to encode scientific knowledge and adapting Monte Carlo Tree Search techniques to reason about the network and plan informative sensing actions. The resulting knowledge representation and reasoning framework is anytime, handles large state spaces and robust to uncertainty making it highly applicable to field robotics. We apply the approach to a Mars exploration mission in which the robot is required to plan paths and decide when to use its sensing modalities to study a scientific latent variable of interest. Extensive simulation results show that our approach has significant performance benefits over alternative methods. We also demonstrate the practicality of our approach in an analog Martian environment where our experimental rover, Continuum, plans and executes a science mission autonomously.