Autonomy and Perception for Space Mining

TL;DR

This paper presents a solution for autonomous lunar mining robots using machine learning-enabled vision and a multi-robot coordination system, addressing challenges like hazardous terrain and lack of satellite positioning.

Contribution

It introduces a novel autonomous robotic system with machine learning vision and coordination strategies for lunar mining, advancing current space robotics capabilities.

Findings

Achieved 3rd place and won the innovation award in NASA Space Robotics Challenge.

Developed machine learning vision to handle lunar terrain challenges.

Created a robust multi-robot coordination system for long-term lunar operations.

Abstract

Future Moon bases will likely be constructed using resources mined from the surface of the Moon. The difficulty of maintaining a human workforce on the Moon and communications lag with Earth means that mining will need to be conducted using collaborative robots with a high degree of autonomy. In this paper, we describe our solution for Phase 2 of the NASA Space Robotics Challenge, which provided a simulated lunar environment in which teams were tasked to develop software systems to achieve autonomous collaborative robots for mining on the Moon. Our 3rd place and innovation award winning solution shows how machine learning-enabled vision could alleviate major challenges posed by the lunar environment towards autonomous space mining, chiefly the lack of satellite positioning systems, hazardous terrain, and delicate robot interactions. A robust multi-robot coordinator was also developed to achieve long-term operation and effective collaboration between robots.