Water as a medium poses a number of challenges for robots, limiting the progress of research in underwater robotics vis-à-vis ground or aerial robotics. The primary challenges are satellite based positioning and radio communication being unusable due to high attenuation of electromagnetic waves in water. We have developed miniature, agile, easy to carry and deploy Autonomous Underwater Vehicles (AUVs) equipped with a suite of sensors for underwater environmental sensing. We previously demonstrated adaptive sampling and feature tracking, and gathered data from a lake for limnological research, with the AUV performing inertial navigation. In this paper, we demonstrate a new underwater acoustic positioning system, which allows on-board estimation of AUV position.Our system uses absolute time information from GNSS for initial clock synchronization and uses one-way-travel-time for range measurements, which makes it scalable in the number of robots. It is easily deployable and does not rely on any installed infrastructure in the environment. We describe various hardware and software components of our system, and present results from experiments in Lake Geneva.

The primary and somewhat interrelated challenges affecting deployment of Autonomous Underwater Vehicles (AUVs) are navigation and communication. We have developed miniature, agile, easy to carry and deploy AUVs equipped with a suite of sensors for underwater environmental sensing. In this paper, we propose a support system for multiple AUVs where a group of Autonomous Surface Vehicles (ASVs) coordinate to provide external positioning reference. They transmit an acoustic ranging pulse and then broadcast their position using acoustic communication. Communication errors are detected by using a novel approach where data decoding is coupled with navigation. Our system achieves scalability in the number of AUVs by using one-way travel time for ranging and making the AUVs passive receivers. Further, it allows the ASVs to be repositioned during a mission, so that they can provide positioning aid from a closer range. This is an advantage especially in shallow water environments, where range measurement errors increase significantly with increasing distance. We describe our system in detail and evaluate it with simulations based on real data as well as field experiments.

Applications of robots for gathering data in underwater environments has been limited due to the challenges posed by the medium. We have developed a miniature, agile, easy to carry and deploy Autonomous Underwater Vehicle (AUV) equipped with a suite of sensors for underwater environmental sensing. We have also developed a compact high resolution fast temperature sensing module for the AUV for microstructure and turbulence measurements in water bodies. In this paper, we describe a number of algorithms and subsystems of the AUV that enable autonomous real-world operation, and present the data gathered in an experimental campaign in collaboration with limnologists. We demonstrate adaptive sampling missions where the AUV could autonomously locate a zone of interest and adapt its trajectory to stay in it. Further, it could execute specific behaviors to accommodate special sensing requirements necessary to enhance the quality of the data collected. In these missions, the AUV could autonomously trace a feature and capture horizontal variation in various quantities, including turbidity and temperature fluctuations, allowing limnologists to study lake phenomena in an additional dimension.

One of the main challenges in underwater robot localization is the scarcity of external positioning references. Therefore, accurate inertial localization in between external position updates is crucial for applications such as underwater environmental sampling. In this paper, we present a framework for estimating kinematic and dynamic model parameters used for inertial navigation. Accurate values of these parameters result in better trajectory estimation. Our approach can run online as well as offline, with either choice providing different advantages. Further, our framework can correct errors in the past trajectory at each estimation step. By doing so, we are able to provide improved geo-references for past as well as future spatial measurements made by the robots. This has an impact on adaptive sampling methods, which use geotagged measurements for building local spatial distributions and choose future sampling points. We present results from field experiments and demonstrate improvement in trajectory estimation accuracy. We also experimentally show that with optimal parameter estimates, robots can tolerate longer intervals in external positioning updates for a specified acceptable level of estimation error.

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