This project is attempting to elucidate the basic principles governing environmental field model synthesis based on the integration of adaptive robot sampling with human decision-making. In so doing, we are addressing two fundamental technical challenges: 1) Model-Based Asset Allocation and 2) Sampling-Based Model Refinement. The overall goal is to couple human decision-making with probabilistic modeling and learning in a decision support system enabling environmental field model discovery and refinement. The first challenge, Model-Based Asset Allocation, involves synthesis of large-scale, low-resolution data with human scientific expertise to make timely, model-informed asset allocation decisions. The second challenge, Sampling-Based Model Refinement, involves small-scale, high-resolution autonomous cooperative selection and execution of robot sampling trajectories. Both challenges involve the handling of multivariate, multi-resolution, temporally evolving fields. The second challenge’s high-resolution local field characterization cyclically feeds and refines the first challenge’s high-level model. We will demonstrate these general principles in the specific domain of coastal ocean exploration, but expect them to be broadly applicable across robotics and computational intelligence.
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