Trajectory learning for human-robot scientific data collection

We propose an integrated learning and planning framework that leverages knowledge from a human user along with prior information about the environment to generate trajectories for scientific data collection. The proposed framework combines principles from probabilistic planning with uncertainty modeling through nonparametric Bayesian methods to refine trajectories for execution by autonomous vehicles. The resulting techniques allow for trajectories specified by a user to be modified for reduced risk of collision and increased reliability. We test our approach in the underwater ocean monitoring domain, and we show that the proposed framework reduces the risk of collision with ship traffic by as much as 51% for an autonomous underwater vehicle operating in ocean currents. This work provides insight into the tools necessary for combining human-robot interaction with autonomous navigation.