Science on the fly: Enabling science autonomy during robotic traverse

Robotic explorers must be capable of autonomous navigation into unknown terrain as well as autonomous science to interpret their observations to guide exploration. In this research we have created a robot able to select science features, direct instruments, collect observations, build maps, and interpret this information to plan actions. We report on field experiments in California´s Amboy Crater lava field and demonstrate fundamental capabilities for adaptive exploration in geologic mapping tasks. We show feature detection and instrument visual servoing that enables automated science observation of dozens of targets. Gaussian process models are used to discover spatial and cross-sensor structure including correlations between different locations and sensing scales. The rover develops these relationships on the fly with only on-board computation, reinterpreting remote sensing data in light of the surface materials it observes. The rover learns spatial models of physical phenomena and guides its exploration into informative areas using Maximum Entropy Sampling to improve exploration efficiency. The Amboy Crater experiments show that science autonomy can play a useful role in facilitating geologic survey on kilometer scales.