NASA JPL are developing autonomous capabilities that could allow future Mars rovers to go farther, faster and do more science. Training machine learning models on the Maverick2, their team developed and optimized models for Drive-By Science and Energy-Optimal Autonomous Navigation.

Four generations of rovers have traversed the red planet gathering scientific data, sending back evocative photographs, and surviving incredibly harsh conditions — all using on-board computers less powerful than an iPhone 1. The latest rover, Perseverance, was launched on July 30, 2020, and engineers are already dreaming of a future generation of rovers.

While a major achievement, these missions have only scratched the surface (literally and figuratively) of the planet and its geology, geography, and atmosphere.

«The surface area of Mars is approximately the same as the total area of the land on Earth,» said Masahiro (Hiro) Ono, group lead of the Robotic Surface Mobility Group at the NASA Jet Propulsion Laboratory (JPL) — which has led all the Mars rover missions — and one of the researchers who developed the software that allows the current rover to operate.

«Imagine, you’re an alien and you know almost nothing about Earth, and you land on seven or eight points on Earth and drive a few hundred kilometers. Does that alien species know enough about Earth?» Ono asked. «No. If we want to represent the huge diversity of Mars we’ll need more measurements on the ground, and the key is substantially extended distance, hopefully covering thousands of miles.»

Travelling across Mars’ diverse, treacherous terrain with limited computing power and a restricted energy diet — only as much sun as the rover can capture and convert to power in a single Martian day, or sol — is a huge challenge.

Story Source: Materials provided by University of Texas at Austin, Texas Advanced Computing Center. Original written by Aaron Dubrow. Note: Content may be edited for style and length.