A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed. To enable persistent sensing and estimation in such a setting, it is beneficial to have a time-varying underlying environmental model. Here we present a planning and learning method that enables an autonomous marine vehicle to perform persistent ocean monitoring tasks by learning and refining an environmental model. To alleviate the computational bottleneck caused by large-scale data accumulated, we propose a framework that iterates between a planning component aimed at collecting the most information-rich data, and a sparse Gaussian Process learning component where the environmental model and hyperparameters are learned online by taking advantage of only a subset of data that provides the greatest contribution. Our simulations with ground-truth ocean data shows that the proposed method is both accurate and efficient.
Scientists are able to gain a greater understanding of the environmental processes (e.g., physical, chemical or biological parameters) through environmental sensing and monitoring @cite_18 . However, many environmental monitoring scenarios involve large environmental space and require considerable amount of work for collecting the data. Increasingly, a variety of autonomous robotic systems including marine vehicles @cite_17 , aerial vehicles @cite_16 , and ground vehicles @cite_21 , are designed and deployed for environmental monitoring in order to replace the conventional method that deploys static sensors to areas of interest @cite_22 . Particularly, the autonomous underwater vehicles (AUVs) such as marine gliders are becoming popular due to their long-range (hundreds of kilometers) and long-term (weeks even months) monitoring capabilities @cite_14 @cite_15 @cite_25 .
@cite_18 @cite_14 @cite_22 @cite_21 @cite_15 @cite_16 @cite_25 @cite_17
2072931689 1453435668 2039802096 2119816061 2143872920 2120225005 2106353989 85781994
Robotic systems are increasingly being utilized as fundamental data-gathering tools by scientists, allowing new perspectives and a greater understanding of the planet and its environmental processes. Today's robots are already exploring our deep oceans, tracking harmful algal blooms and pollution spread, monitoring climate variables, and even studying remote volcanoes. This article collates and discusses the significant advancements and applications of marine, terrestrial, and airborne robotic systems developed for environmental monitoring during the last two decades. Emerging research trends for achieving large-scale environmental monitoring are also reviewed, including cooperative robotic teams, robot and wireless sensor network (WSN) interaction, adaptive sampling and model-aided path planning. These trends offer efficient and precise measurement of environmental processes at unprecedented scales that will push the frontiers of robotic and natural sciences. Abstract The Amundsen Sea is one of the most productive polynyas in the Antarctic per unit area and is undergoing rapid changes including a reduction in sea ice duration, thinning ice sheets, retreat of glaciers and the potential collapse of the Thwaites Glacier in Pine Island Bay. A growing body of research has indicated that these changes are altering the water mass properties and associated biogeochemistry within the polynya. Unfortunately difficulties in accessing the remote location have greatly limited the amount of in situ data that has been collected. In this study data from a Teledyne-Webb Slocum glider was used to supplement ship-based sampling along the Dotson Ice Shelf (DIS). This autonomous underwater vehicle revealed a detailed view of a meltwater laden outflow from below the western flank of the DIS. Circumpolar Deep Water intruding onto the shelf drives glacial melt and the supply of macronutrients that, along with ample light, supports the large phytoplankton blooms in the Amundsen Sea Polynya. Less well understood is the source of micronutrients, such as iron, necessary to support this bloom to the central polynya where chlorophyll concentrations are highest. This outflow region showed decreasing optical backscatter with proximity to the bed indicating that particulate matter was sourced from the overlying glacier rather than resuspended sediment. This result suggests that particulate iron, and potentially phytoplankton primary productivity, is intrinsically linked to the magnitude and duration of sub-glacial melt from Circumpolar Deep Water intrusions onto the shelf. Traditionally, environmental monitoring is achieved by a small number of expensive and high precision sensing unities. Collected data are retrieved directly from the equipment at the end of the experiment and after the unit is recovered. The implementation of a wireless sensor network provides an alternative solution by deploying a larger number of disposable sensor nodes. Nodes are equipped with sensors with less precision, however, the network as a whole provides better spatial resolution of the area and the users can have access to the data immediately. This paper surveys a comprehensive review of the available solutions to support wireless sensor network environmental monitoring applications. In this paper we present initial experiments towards environmental monitoring with a mobile platform. A prototype of a pollution monitoring robot was set up which measures the gas distribution using an ldquoelectronic noserdquo and provides three dimensional wind measurements using an ultrasonic anemometer. We describe the design of the robot and the experimental setup used to run trials under varying environmental conditions. We then present the results of the gas distribution mapping. The trials which were carried out in three uncontrolled environments with very different properties: an enclosed indoor area, a part of a long corridor with open ends and a high ceiling, and an outdoor scenario are presented and discussed. The glider coordinated control system (GCCS) uses a detailed glider model for prediction and a simple particle model for planning to steer a fleet of underwater gliders to a set of coordinated trajectories. The GCCS also serves as a simulation testbed for the design and evaluation of multivehicle control laws. In this brief, we describe the GCCS and present experimental results for a virtual deployment in Monterey Bay, CA and a real deployment in Buzzards Bay, MA. Unmanned Aircraft Systems (UAS) have evolved rapidly over the past decade driven primarily by military uses, and have begun finding application among civilian users for earth sensing reconnaissance and scientific data collection purposes. Among UAS, promising characteristics are long flight duration, improved mission safety, flight repeatability due to improving autopilots, and reduced operational costs when compared to manned aircraft. The potential advantages of an unmanned platform, however, depend on many factors, such as aircraft, sensor types, mission objectives, and the current UAS regulatory requirements for operations of the particular platform. The regulations concerning UAS operation are still in the early development stages and currently present significant barriers to entry for scientific users. In this article we describe a variety of platforms, as well as sensor capabilities, and identify advantages of each as relevant to the demands of users in the scientific research sector. We also briefly discuss the current state of regulations affecting UAS operations, with the purpose of informing the scientific community about this developing technology whose potential for revolutionizing natural science observations is similar to those transformations that GIS and GPS brought to the community two decades ago. A full-scale adaptive ocean sampling network was deployed throughout the month-long 2006 Adaptive Sam- pling and Prediction (ASAP) field experiment in Monterey Bay, California. One of the central goals of the field experiment was to test and demonstrate newly developed techniques for coordinated motion control of au- tonomous vehicles carrying environmental sensors to efficiently sample the ocean. We describe the field results for the heterogeneous fleet of autonomous underwater gliders that collected data continuously throughout the month-long experiment. Six of these gliders were coordinated autonomously for 24 days straight using feed- back laws that scale with the number of vehicles. These feedback laws were systematically computed using recently developed methodology to produce desired collective motion patterns, tuned to the spatial and tem- poral scales in the sampled fields for the purpose of reducing statistical uncertainty in field estimates. The implementation was designed to allow for adaptation of coordinated sampling patterns using human-in-the- loop decision making, guided by optimization and prediction tools. The results demonstrate an innovative tool for ocean sampling and provide a proof of concept for an important field robotics endeavor that integrates coordinated motion control with adaptive sampling. C In this paper we present strategies for adaptive sampling using Autonomous Underwater Vehicle (AUV) ∞eets. The central theme of our strategies is the use of feedback that integrates distributed in-situ measurements into a coordinated mission planner. The measurements consist of GPS updates and estimated gradients of the environmental flelds (e.g., temperature) that are used to navigate the AUV ∞eets enabling effective front tracking and or feature detection. To this efiect these ∞eets are required to translate to collect and seek good data, expand contract to efiect changes in sensor resolution, and rotate and reconflgure to maximize sensing coverage, all while retaining a prescribed formation. These strategies play a key role in directing a cooperative ∞eet of autonomous underwater gliders in the flrst experiment of the O‐ce of Naval Research sponsored Autonomous Ocean Sampling Network II (AOSN-II) project in Monterey Bay, during August-September 2003. We present the coordination framework and investigate the efiectiveness of our sampling strategies in the context of AOSN-II via detailed simulations.