TY - GEN
T1 - Network of nano-landers for in-situ characterization of asteroid impact studies
AU - Kalita, Himangshu
AU - Asphaug, Erik
AU - Schwartz, Stephen
AU - Thangavelautham, Jekan
N1 - Publisher Copyright:
Copyright © 2017 by the International Astronautical Federation (IAF). All rights reserved.
PY - 2017
Y1 - 2017
N2 - Exploration of asteroids and comets can give insight into the origins of the solar system and can be instrumental in planetary defence and in-situ resource utilization (ISRU). Asteroids, due to their low gravity are a challenging target for surface exploration. Current missions envision performing touch-and-go operations over an asteroid surface. In this work, we analyse the feasibility of sending scores of nano-landers, each 1 kg in mass and volume of 1U, or 1000 cm3. These landers would hop, roll and fly over the asteroid surface. The landers would include science instruments such as stereo cameras, hand-lens imagers and spectrometers to characterize rock composition. A network of nano-landers situated on the surface of an asteroid can provide unique and very detailed measurements of a spacecraft impacting onto an asteroid surface. A full-scale, artificial impact experiment onto an asteroid can help characterize its composition and geology and help in the development of asteroid deflection techniques intended for planetary defence. Scores of nano-landers could provide multiple complementary views of the impact, resultant seismic activity and trajectory of the ejecta. The nano-landers can analyse the pristine, unearthed regolith shielded from effects of UV and cosmic rays and that may be millions of years old. Our approach to formulating this mission concepts utilizes automated machine learning techniques in the planning and design of space systems. We use a form of Darwinian selection to select and identify suitable number of nano-landers, the on-board instruments and control system to explore and navigate the asteroid environment. Scenarios are generated in simulation and evaluated against quantifiable mission goals such as area explored on the asteroid and amount of data recorded from the impact event. Our earlier work in this field applied to excavation robotics has shown that a machine-learning approach can discover creative solutions that exceed the capability of human devised solutions. In this work, we once again intend to compare a human-devised system to these machine evolved-systems. The results from these mission formulation and preliminary design studies will be used to identify a pathway towards a future asteroid CubeSat mission.
AB - Exploration of asteroids and comets can give insight into the origins of the solar system and can be instrumental in planetary defence and in-situ resource utilization (ISRU). Asteroids, due to their low gravity are a challenging target for surface exploration. Current missions envision performing touch-and-go operations over an asteroid surface. In this work, we analyse the feasibility of sending scores of nano-landers, each 1 kg in mass and volume of 1U, or 1000 cm3. These landers would hop, roll and fly over the asteroid surface. The landers would include science instruments such as stereo cameras, hand-lens imagers and spectrometers to characterize rock composition. A network of nano-landers situated on the surface of an asteroid can provide unique and very detailed measurements of a spacecraft impacting onto an asteroid surface. A full-scale, artificial impact experiment onto an asteroid can help characterize its composition and geology and help in the development of asteroid deflection techniques intended for planetary defence. Scores of nano-landers could provide multiple complementary views of the impact, resultant seismic activity and trajectory of the ejecta. The nano-landers can analyse the pristine, unearthed regolith shielded from effects of UV and cosmic rays and that may be millions of years old. Our approach to formulating this mission concepts utilizes automated machine learning techniques in the planning and design of space systems. We use a form of Darwinian selection to select and identify suitable number of nano-landers, the on-board instruments and control system to explore and navigate the asteroid environment. Scenarios are generated in simulation and evaluated against quantifiable mission goals such as area explored on the asteroid and amount of data recorded from the impact event. Our earlier work in this field applied to excavation robotics has shown that a machine-learning approach can discover creative solutions that exceed the capability of human devised solutions. In this work, we once again intend to compare a human-devised system to these machine evolved-systems. The results from these mission formulation and preliminary design studies will be used to identify a pathway towards a future asteroid CubeSat mission.
KW - Asteroids
KW - Exploration
KW - Machine learning
KW - Mobility
KW - Robot swarms
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M3 - Conference contribution
AN - SCOPUS:85048853314
SN - 9781510855373
T3 - Proceedings of the International Astronautical Congress, IAC
SP - 10382
EP - 10392
BT - 68th International Astronautical Congress, IAC 2017
PB - International Astronautical Federation, IAF
T2 - 68th International Astronautical Congress: Unlocking Imagination, Fostering Innovation and Strengthening Security, IAC 2017
Y2 - 25 September 2017 through 29 September 2017
ER -