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 explore the utility of robotic vision towards addressing several major challenges in autonomous mining in the lunar environment: lack of satellite positioning systems, navigation in hazardous terrain, and delicate robot interactions. Specifically, we describe and report the results of robotic vision algorithms that we developed for Phase 2 of the NASA Space Robotics Challenge, which was framed in the context of autonomous collaborative robots for mining on the Moon. The competition provided a simulated lunar environment that exhibits the complexities alluded to above. We show how machine learning-enabled vision could help alleviate the challenges posed by the lunar environment. A robust multi-robot coordinator was also developed to achieve long-term operation and effective collaboration between robots.
翻译:利用从月球表面开采的资源来建造未来的月球基地。 维持月球上人类劳动力的难度和与地球的通信落后意味着需要使用高度自主的协作机器人进行采矿。 在本文中,我们探讨了机器人愿景对于应对月球环境中自主采矿的若干重大挑战的效用:缺乏卫星定位系统、危险地形导航和微妙的机器人互动。具体地说,我们描述并报告了我们为美国航天局空间机器人挑战第二阶段开发的机器人愿景算法的结果,该算法是在自动协作机器人为在月球上采矿而设计的。竞赛提供了一个模拟月球环境,展示了上面提到的复杂情况。我们展示了机器学习的愿景如何帮助减轻月球环境带来的挑战。还开发了一个强大的多机器人协调员,以实现机器人的长期运作和有效协作。