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- W3090200014 abstract "Maintaining high levels of productivity for planetary rover missions is very difficult due to limited communication and heavy reliance on ground control. There is a need for autonomy that enables more adaptive and efficient actions based on real-time information. This paper presents an autonomous mapping and exploration approach for planetary rovers. We first describe a machine learning model that actively combines remote and rover measurements for mapping. We focus on spectroscopic data because they are commonly used to investigate surface composition. We then incorporate notions from information theory and non-myopic path planning to improve exploration productivity. Finally, we demonstrate the feasibility and successful performance of our approach via spectroscopic investigations of Cuprite, Nevada; a well-studied region of mineralogical and geological interest. We first perform a detailed analysis in simulations, and then validate those results with an actual rover in the field in Nevada." @default.
- W3090200014 created "2020-10-08" @default.
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- W3090200014 date "2020-05-01" @default.
- W3090200014 modified "2023-10-09" @default.
- W3090200014 title "Planetary Rover Exploration Combining Remote and In Situ Measurements for Active Spectroscopic Mapping" @default.
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- W3090200014 doi "https://doi.org/10.1109/icra40945.2020.9196973" @default.
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