High fidelity data acquisition of dynamic spatiotemporal phenomena for aquatic environmental research suggests the use of actuated sensors. Furthermore, characterization of the floor in aquatic environments is beneficial for environmental science, as well as can be applied to robot localization. The NIMS AQ cable robot platform is designed to meet these requirements and satisfy the constraints of large scale, in-field deployments. In addition to a set of water quality sensors it also carries an ultra-miniature side-scan sonar. In this paper we show the development of methods for autonomous range detection, spatial and semantic mapping in underwater environments. These methods are demonstrated to be important for future developments including localization, navigation, and path planning, particularly for 3D mobility. Experiments have been performed in both controlled environments and a lake environment and results are discussed.

Distributed, high-density spatiotemporal observations are proposed for answering many river related questions, including those pertaining to hydraulics and multi-dimensional river modeling, geomorphology, sediment transport and riparian habitat restoration. In spite of the recent advancements in technology, currently available systems have many constraints that preclude long term, remote, autonomous, high resolution monitoring in the real environment. We present here a case study of an autonomous, high resolution robotic spatial mapping of cross-sectional velocity and salt concentration in a river basin. The scientific objective of this investigation was to characterize the transport and mixing phenomena at the confluence of two distinctly different river streams - San Joaquin River and its tributary Merced River. Several experiments for analyzing the spatial and temporal trends at multiple cross-sections of the San Joaquin River were performed during the campaign from August 21-25, 2006. These include deterministic dense raster scans and in-field adapted experimental design. Preliminary analysis from these experiments illustrating the range of investigations is presented with the focus on adaptive experiments that enable sparse sampling to provide larger spatial coverage without discounting the dynamics in the phenomena. Lessons learned during the campaign are discussed to provide useful insights for similar robotic investigations in aquatic environments.