Surface Telerobotics: Development and Testing of a Crew Controlled Planetary Rover System

In planning for future exploration missions, architecture and study teams have made numerous assumptions about how crew can be telepresent on a planetary surface by remotely operating surface robots from space (i.e. from a flight vehicle or deep space habitat). These assumptions include estimates of technology maturity, existing technology gaps, and operational risks. These assumptions, however, have not been grounded by experimental data. Moreover, to date, no crew-controlled surface telerobot has been fully tested in a high-fidelity manner. To address these issues, we developed the "Surface Telerobotics" tests to do three things: 1) Demonstrate interactive crew control of a mobile surface telerobot in the presence of short communications delay. 2) Characterize a concept of operations for a single astronaut remotely operating a planetary rover with limited support from ground control. 3) Characterize system utilization and operator work-load for a single astronaut remotely operating a planetary rover with limited support from ground control

Failure is Not an Option: Policy Learning for Adaptive Recovery in Space Operations

This letter considers the problem of how robots in long-term space operations can learn to choose appropriate sources of assistance to recover from failures. Current assistant selection methods for failure handling are based on manually specified static lookup tables or policies, which are not responsive to dynamic environments or uncertainty in human performance. We describe a novel and highly flexible learning-based assistant selection framework that uses contextual multiarm bandit algorithms. The contextual bandits exploit information from observed environment and assistant performance variables to efficiently learn selection policies under a wide set of uncertain operating conditions and unknown/dynamically constrained assistant capabilities. Proof of concept simulations of long-term human-robot interactions for space exploration are used to compare the performance of the contextual bandit against other state-of-the-art assistant selection approaches. The contextual bandit outperforms conventional static policies and noncontextual learning approaches, and also demonstrates favorable robustness and scaling properties

The Mistastin Lake Impact Structure As A Terrestrial Analogue Site For Lunar Science And Exploration

The impact cratering record on the Moon is important for many reasons, from understanding early solar system chronology to probing the lunar interior. In order to maximize scientific return from future lunar missions, it is useful to: 1) study terrestrial impact craters to better understand impact processes and products, and 2) develop appropriate human and robotic exploration strategies aligned with geological goals. This research shows that the intermediate-size Mistastin Lake impact structure, in northern Labrador, Canada, is an unparalleled lunar analogue site, which includes both an anorthositic target and an almost complete suite of impact lithologies, including proximal ejecta deposits. New remote sensing, field mapping, and microscopy data are used to develop new structural and geological models of the Mistastin Lake impact structure. The results of this study show that a multi-stage ejecta emplacement model is required to explain the observations. It is also shown that impact melt-bearing breccias or “suevites” at Mistastin were emplaced as flows, were never airborne, and were formed from the mixing of impact melt flows with underlying lithic materials. In order to maximize scientific return from future lunar missions, this work also focused on developing appropriate human and robotic exploration strategies aligned with geological goals. We show that precursor reconnaissance missions provide surface geology visualization at resolutions and from viewpoints not achievable from orbit. Within such a mission concept, geological tasks are best divided between fixed-executional approaches, in which tasks are fairly repetitive and are carried out by an unskilled surface agent, and an adaptive-exploratory approach, where a skilled agent makes observations and interpretations and the field plan can adapt to these findings as the agent progresses. Operational considerations that help increase scientific return include: extensive pre-mission planning using remote sensing data; defining flexible plans and science priorities to respond to changing conditions; including mutually cross-trained scientists and engineers on the field team; and adapting traverses to accommodate field crew input and autonomy. A phased approach for human exploration proved successful in incorporating astronaut feedback and allowed more autonomy for astronauts to determine optimal sampling localities and sites for detailed observations