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Assessing terrain ahead of a robot when repeating previously driven safe paths can be accomplished by looking for geometric changes (e.g., due to the appearance of humans or other obstacles). Previous work has shown that the incorporation of data-driven learning and place-dependence are useful aspects of making terrain classification viable in challenging terrain. This paper presents a learning, place-dependent...
This paper presents a learned, place-dependent terrain-assessment classifier that improves over time. Whereas typical methods aim to assess all of the terrain in a given environment, we exploit the fact that many robotic navigation tasks are well-suited to visual-teach-and-repeat navigation where robot motion is restricted to previously driven paths. In such scenarios, we argue that general terrain...
This paper presents an approach to learning robot terrain assessment from human demonstration. An operator drives a robot for a short period of time, supervising the gathering of traversable and untraversable terrain data. After this initial training period, the robot can then predict the traversability of new terrain based on its experiences. We improve on current methods in two ways: first, we maintain...
This paper presents a proof-of-concept, rover-based system to locate the source(s) of methane gas on Mars. A distributed open-path spectrometer is mounted on a rover and pointed at several retro reflective signs to measure the line-of-sight methane concentration between the rover and sign. By moving the rover around and accumulating such measurements, the location of a methane source can be determined...
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