Planetary Environment Prediction Using Generative Modeling

Shrijit Singh*

Shreyansh Daftry*

Roberto Capobianco

* External authors

AIAA SciTech Forum 2022

2022

Abstract

Planetary rovers have a limited sensory horizon and operate in environments where limited information about the surrounding terrain is available. The rough and unknown nature of the terrain in planetary environments potentially leads to scenarios where the rover gets stuck
and has to replan its path frequently to escape such situations. For avoiding such scenarios,
we need to exploit spatial knowledge of the environment beyond the rover’s sensor horizon.
The solutions presented by existing approaches are limited to indoor environments which are
structured. Predicting spatial knowledge for outdoor environments, particularly planetary
environments, has not be done before. We attempt to solve planetary environment prediction
by exploiting generative learning to (1) learn the distribution of spatial landmarks like rocks
and craters which the rover encounter on the planetary surface during exploration and (2)
predict spatial landmarks beyond the sensor horizon. We aim to utilize the proposed approach
of environment prediction to improve path planning and decision-making processes needed for
safe planetary navigation.