Steven Schaffer

Sensor web enables rapid response to volcanic activity

Rapid response to the onset of volcanic activity allows for the early assessment of hazard and risk [Tilling, 1989]. Data from remote volcanoes and volcanoes in countries with poor communication infrastructure can only be obtained via remote sensing [Harris et al., 2000]. By linking notifications of activity from ground-based and spacebased systems, these volcanoes can be monitored when they erupt. Over the last 18 months, NASAs Jet Propulsion Laboratory (JPL) has implemented a Volcano Sensor Web (VSW) in which data from ground-based and space-based sensors that detect current volcanic activity are used to automatically trigger the NASA Earth Observing 1 (EO-1) spacecraft to make highspatial-resolution observations of these volcanoes.

Autonomous Spectral Discovery and Mapping Onboard the EO-1 Spacecraft

Imaging spectrometers are valuable instruments for space exploration, but their large data volumes limit the number of scenes that can be downlinked. Missions could improve science yield by acquiring surplus images and analyzing them onboard the spacecraft. This onboard analysis could generate surficial maps, summarizing scenes in a bandwidth-efficient manner to indicate data cubes that warrant a complete downlink. Additionally, onboard analysis could detect targets of opportunity and trigger immediate automated follow-up measurements by the spacecraft. Here, we report a first step toward these goals with demonstrations of fully automatic hyperspectral scene analysis, feature discovery, and mapping onboard the Earth Observing One (EO-1) spacecraft. We describe a series of overflights in which the spacecraft analyzes a scene and produces summary maps along with lists of salient features for prioritized downlink. The onboard system uses a superpixel endmember detection approach to identify compositionally distinctive features in each image. This procedure suits the limited computing resources of the EO-1 flight processor. It requires very little advance information about the anticipated spectral features, but the resulting surface composition maps agree well with canonical human interpretations. Identical spacecraft commands detect outlier spectral features in multiple scenarios having different constituents and imaging conditions.

Distributed network scheduling

We investigate missions where communications resources are limited, requiring autonomous planning and execution. Unlike typical networks, spacecraft networks are also suited to automated planning and scheduling because many communications can be planned in advance. Because the network of spacecraft can represent multiple missions, missions will be reluctant to give up control of the spacecraft. Because communication among spacecraft is often intermittent (due to orbital and resource constraints), a spacecraft that can make scheduling decisions autonomously will be more responsive to unexpected events. Thus, a centralized planning system will not be sufficient to enable reactive communications, so we propose a distributed network scheduling system.

AEGIS autonomous targeting for the Curiosity rover's ChemCam instrument

AEGIS (Autonomous Exploration for Gathering Increased Science) is a software suite that will imminently be operational aboard NASAs Curiosity Mars rover, allowing the rover to autonomously detect and prioritize targets in its surroundings, and acquire geochemical spectra using its ChemCam instrument. ChemCam, a Laser-Induced Breakdown Spectrometer (LIBS), is normally used to study targets selected by scientists using images taken by the rover on a previous sol and relayed by Mars orbiters to Earth. During certain mission phases, ground-based target selection entails significant delays and the use of limited communication bandwidth to send the images. AEGIS will allow the science team to define the properties of preferred targets, and obtain geochemical data more quickly, at lower data penalty, without the extra ground-inthe-loop step. The system uses advanced image analysis techniques to find targets in images taken by the rovers stereo navigation cameras (NavCam), and can rank, filter, and select targets based on properties selected by the science team. AEGIS can also be used to analyze images from ChemCams Remote Micro Imager (RMI) context camera, allowing it to autonomously target very fine-scale features - such as veins in a rock outcrop - which are too small to detect with the range and resolution of NavCam. AEGIS allows science activities to be conducted in a greater range of mission conditions, and saves precious time and command cycles during the rovers surface mission. The system is currently undergoing initial tests and checkouts aboard the rover, and is expected to be operational by late 2015. Other current activities are focused on science team training and the development of target profiles for the environments in which AEGIS is expected to be used on Mars.