This work was designed to find a way to optimally (or near optimally) sample spatiotemporal phenomena based on limited sensing capability, and to create a model that can be run to estimate uncertainties, as well as to estimate covariances. The goal was to maximize (or minimize) some function of the overall uncertainty.
The uncertainties and covariances were modeled presuming a parametric distribution, and then the model was used to approximate the overall information gain, and consequently, the objective function from each potential sense. These candidate sensings were then cross-checked against operation costs and feasibility. Consequently, an operations plan was derived that combined both operational constraints/ costs and sensing gain.
Probabilistic modeling was used to perform an approximate inversion of the model, which enabled calculation of sensing gains, and subsequent combination with operational costs. This incorporation of operations models to assess cost and feasibility for specific classes of vehicles is unique.
This work was done by Steve A. Chien and David R. Thompson of Caltech, and Kian Hsiang Low of the National University of Singapore for NASA’s Jet Propulsion Laboratory. NPO-48664
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Adaptive Sampling of Spatiotemporal Phenomena With Optimization Criteria
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Overview
The document titled "Adaptive Sampling of Spatiotemporal Phenomena With Optimization Criteria" from NASA's Jet Propulsion Laboratory discusses advanced methodologies for optimizing sensor placement and data collection in dynamic environments, particularly in oceanic contexts. It emphasizes the importance of adaptive sampling, which aims to maximize information gain while minimizing uncertainty in measurements of spatiotemporal phenomena, such as salinity fields.
The core concept revolves around the idea of selecting a subset of measurement locations (V) from a larger set (A) to reduce residual uncertainty about unmeasured locations (U). This is framed as a submodular optimization problem, which allows for efficient solutions through decomposition techniques. The document highlights that once global model parameters are established from data, the sensor placement problem can be addressed using tractable convex solutions.
The challenges of ocean sampling are underscored, including the dynamic nature of ocean currents, which can affect the navigation and positioning of sensing platforms. The document notes that traditional point-to-point navigation strategies are often inadequate due to these complexities. Instead, it advocates for adaptive sampling strategies that account for the unique mobility and measurement capabilities of various sensor platforms, such as autonomous underwater vehicles (AUVs), gliders, and floats.
The document also discusses the need for sensor command policies tailored to the specific characteristics of the vehicles used in the study. It suggests that integrating science-sensitive and current-sensitive planning can enhance the efficiency of data collection. For instance, simulations have demonstrated that adjusting the depths of Argo floats to exploit predicted currents can significantly improve the information gain and inter-float distances.
In summary, the document presents a comprehensive overview of the methodologies and challenges associated with adaptive sampling in environmental monitoring. It emphasizes the integration of information theory, spatial statistics, and robotics to develop effective strategies for optimizing sensor deployment in complex and dynamic environments. The research aims to advance the field of robotic environmental monitoring, providing insights that could have broader applications in various scientific and technological domains.
