Kimberly Shillcutt

Technology and Field Demonstration of Robotic Search for Antarctic Meteorites

Meteorites are the only significant source of material from other planets and asteroids, and therefore are of immense scientific value. Antarctica’s frozen and pristine environment has proven to be the best place on earth to harvest meteorite specimens. The lack of melting and surface erosion keep meteorite falls visible on the ice surface in pristine condition for thousands of years. In this article, we describe the robotic technologies and field demonstration that enabled the first discovery of Antarctic meteorites by a robot. Using a novel autonomous control architecture, specialized science sensing, combined manipulation and visual servoing, and Bayesian classification, the Nomad robot found and classified five indigenous meteorites during an expedition to the remote site of Elephant Moraine in January 2000. This article first overviews Nomad’s mechatronic systems and details the control architecture that governs the robot’s autonomy and classifier that enables the autonomous interpretation of scientific data. It then focuses on the technical results achieved during field demonstrations at Elephant Moraine. Finally, the article discusses the benefits and limitations of robotic autonomy in science missions. Science autonomy is shown as a capable and expandable architecture for exploration and in situ classification. Inefficiencies in the existing implementation are explained with a focus on important lessons that outline future work.

The Science Autonomy System of the Nomad robot

The Science Autonomy System (SAS) is a hierarchical control architecture for exploration and in situ science that integrates sensing, navigation, classification and mission planning. The Nomad robot demonstrated the capabilities of the SAS during a January 2000 expedition to Elephant Moraine, Antarctica where it accomplished the first meteorite discoveries made by a robot. In the paper, the structure and functionality of the three-tiered SAS are detailed. Results and lessons learned are presented with a focus on important future research.

Robotic Antarctic meteorite search: outcomes

Automation of the search for and classification of Antarctic meteorites offers a unique case for early demonstration of robotics in a scenario analogous to geological exploratory missions to other planets and to the Earths extremes. Moreover, the discovery of new meteorite samples is of great value because meteorites are the only significant source of extraterrestrial material available to scientists. In this paper we focus on the primary outcomes and technical lessons learned from the first field demonstration of autonomous search and in situ classification of Antarctic meteorites by a robot. Using a novel autonomous control architecture, specialized science sensing, combined manipulation and visual servoing, and Bayesian classification, the Nomad robot classified five indigenous meteorites during an expedition to the remote site of Elephant Moraine in January 2000. Nomads expedition proved the rudiments of science autonomy and exemplified the merits of machine learning techniques for autonomous geological classification in real-world settings. On the other hand, the expedition showcased the difficulty in executing reliable robotic deployment of science sensors and a limited performance in the speed and coverage of autonomous search.

A Concept for Robotic Lunar South Pole Exploration

The lunar south pole region may contain frozen volatiles such as water and carbon dioxide in surface depressions which are permanently dark. The low Sun angles of the region create these permanently dark areas and also provide nearby regions of long term sunlight and moderate temperatures which benefit robotic exploration. In this paper a concept for a robotic explorer named Icebreaker is presented. It is designed to take advantage of the south pole environment and to find and analyze frozen volatiles. Icebreaker is an innovative new spacecraft concept which combines the functionality of traditional landing craft and mobile robots into one integrated vehicle. This type of vehicle will allow larger science packages to be delivered to the planets. Icebreaker will acquire samples with a drill and determine the presence and composition of volatiles inside cold traps using a Regolith Evolved Gas Analyzer (REGA).

Solar navigational planning for robotic explorers

This research considers the suns motion and terrain in navigational planning. Robotic exploration of remote areas depends heavily on efficient use of power resources. An orbital ephemeris and terrain data are incorporated into a robotic planner to predict shadowing and solar power generation. The simulation and evaluation of coverage patterns are described, as well as searches for continually sunlit paths or for nearby locations suitable for recharging or communicating with Earth. Aspects of the research were implemented as part of the Robotic Antarctic Meteorite Search project, for which simulations and field test results are given.

Path Planning for Orbital Motions

Path planning algorithms determine a path from start to goal locations, generally minimizing a cost such as time, distance, fuel, or other parameters. Often, this cost is estimated by the straight-line distance between two points or configurations. However, for a free flying space robot, a straight line is not necessarily the least costly path, because discrete thrusters generate arc-shaped trajectories defined by the current orbit of the robot. So, a way to consider non-straight paths must be found. This research uses the geometries in Voronoi diagrams to determine and define the path to take for a free flying space robot called AERCam. The current work deals with a two-dimensional world, though the extension to three dimensions is planned. Object-free corridors through which AERCam may safely maneuver are also defined. This work will lead to methods for determining the optimal path.