No abstract

Since 2004, NASA has been working to return to the Moon. In contrast to the Apollo missions, two key objectives of the current exploration program is to establish significant infrastructure and an outpost. Achieving these objectives will enable long-duration stays and long-distance exploration of the Moon. To do this, robotic systems will be needed to perform tasks which cannot, or should not, be performed by crew alone. In this paper, we summarize our work to develop "utility robots" for lunar surface operations, present results and lessons learned from field testing, and discuss directions for future research.

Robotic reconnaissance ("recon") has the potential to significantly improve scientific and technical return from lunar surface exploration. In particular, robotic recon can be used to improve traverse planning, reduce operational risk, and increase crew productivity. To study how robotic recon can benefit human exploration, we recently conducted a field experiment at Black Point Lava Flow (BPLF), Arizona. In our experiment, a simulated ground control team at NASA Ames teleoperated a planetary rover to scout geology traverses at BPLF. The recon data was then used to plan revised traverses. Two-man crews subsequently performed both types of traverses using the NASA "Lunar Electric Rover" (LER) and simulated extra-vehicular activity (EVA) suits. This paper describes the design of our experiment, presents our preliminary results and discusses directions for future research.

No abstract

We are studying how "robotic follow-up" can improve future planetary exploration. Robotic follow-up, which we define as augmenting human field work with subsequent robot activity, is a field exploration technique designed to increase human productivity and science return. To better understand the benefits, requirements, limitations and risks associated with this technique, we are conducting analog field tests with human and robot teams at the Haughton Crater impact structure on Devon Island, Canada. In this paper, we discuss the motivation for robotic follow-up, describe the scientific context and system design for our work, and present results and lessons learned from field testing.