Autonomous science during large‐scale robotic survey

Abstract

Today's planetary exploration robots rarely travel beyond the yesterday imagery. However, advances in autonomous mobility will soon permit single-command site surveys of multiple kilometers. Here scientists cannot see the terrain in advance, and explorer robots must navigate and collect data autonomously. Onboard science data understanding can improve these surveys with image analysis, pattern recognition, learned classification, and information-theoretic planning. We report on field experiments near Amboy Crater, California, that demonstrate fundamental capabilities for autonomous surficial mapping of geologic phenomena with a visible near-infrared spectrometer. We develop an approach to "science on the fly" that adapts the robot's exploration using collected instrument data. We demonstrate feature detection and visual servoing to acquire spectra from dozens of targets without human intervention. The rover interprets spectra onboard, learning spatial models of science phenomena that guide it toward informative areas. It discovers spatial structure (correlations between neighboring regions) and cross-sensor structure (correlations between different scales). The rover uses surface observations to reinterpret satellite imagery and improve exploration efficiency. C 2011 Wiley Periodicals, Inc.

Adaptive sensing of time series with application to remote exploration

“…METHOD Here we propose a simple method for introducing nonstationarity to permit adaptive sampling in GP time series. It is related to previous work that warps the input space with a monotonic function, as in [11], or augments Gaussian processes with additional input dimensions, as in [17]- [19]. These modifications create covariance relationships that are nonstationary with respect to original time index.…”

“…Here we illustrate adaptive acquisition of Visible Near Infrared (VNIR) spectra by a rover, during a long traverse that took place during field experiments at Amboy Crater [17]. The rover carried a mast-mounted VNIR spectrometer capable of measuring the 400-2000 nm range.…”

Sensor-Driven Area Coverage for an Autonomous Fixed-Wing Unmanned Aerial Vehicle

“…Again, originally developed for ground robotics [35] and more recently used for planetary exploration [36], the problem can be posed through the use of information as an optimal control problem [37]. For example, in [38], an algorithm using information is presented to define optimal paths for a UAV to navigate through a region and obtain information about objects of interest.…”

Smart, texture‐sensitive instrument classification for in situ rock and layer analysis

“…Further, direct analysis of identified rocks enables the characterization of the local environment by its distribution of rock sizes, colors, and compositions. A large amount of research has already been invested in methods for automatically identifying rocks in such images [Gulick et al, 2001;Castano et al, 2007;Thompson et al, 2011;Gong and Liu, 2012].…”