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USC CINAPS Builds Bridges

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TLDR
Connecting all of these components, which form an aquatic robotic system, in synchronous operation can greatly assist the scientists in improving the overall understanding of the complex ocean environment.
Abstract
More than 70% of our earth is covered by water, yet we have explored less than 5% of the aquatic environment. Aquatic robots, such as autonomous underwater vehicles (AUVs), and their supporting infrastructure play a major role in the collection of oceanographic data. To make new discoveries and improve our overall understanding of the ocean, scientists must make use of these platforms by implementing effective monitoring and sampling techniques to study ocean upwelling, tidal mixing, and other ocean processes. Effective observation and continual monitoring of a dynamic system as complex as the ocean cannot be done with one instrument in a fixed location. A more practical approach is to deploy a collection of static and mobile sensors, where the information gleaned from the acquired data is distributed across the network. Additionally, orchestrating a multisensor, long-term deployment with a high volume of distributed data involves a robust, rapid, and cost-effective communication network. Connecting all of these components, which form an aquatic robotic system, in synchronous operation can greatly assist the scientists in improving our overall understanding of the complex ocean environment.

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Journal ArticleDOI

Robots for Environmental Monitoring: Significant Advancements and Applications

TL;DR: 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.
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Marine protistan diversity.

TL;DR: Studies of protistan diversity provide insight regarding how species richness and community composition contribute to ecosystem function and support the development of predictive models that describe how microbial communities will respond to natural or anthropogenically mediated changes in environmental conditions.
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Persistent ocean monitoring with underwater gliders: Adapting sampling resolution

TL;DR: In this paper, a path planning algorithm and a speed control algorithm for underwater gliders are proposed, which together give informative trajectories for the glider to persistently monitor a patch of ocean.
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Planning and Implementing Trajectories for Autonomous Underwater Vehicles to Track Evolving Ocean Processes Based on Predictions from a Regional Ocean Model

TL;DR: In this paper, the authors present algorithms that determine paths for AUVs to track evolving features of interest in the ocean by considering the output of predictive ocean models, while traversing the computed path, the vehicle provides near-real-time, in situ measurements back to the model with the intent to increase the skill of future predictions in the local region.
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Risk-aware Path Planning for Autonomous Underwater Vehicles using Predictive Ocean Models

TL;DR: In this paper, two stochastic planners, a minimum expected risk planner and a risk-aware Markov decision process, were proposed to improve the safety and reliability of AUV operation in coastal regions.
References
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Journal ArticleDOI

An Incremental Self-Deployment Algorithm for Mobile Sensor Networks

TL;DR: The incremental deployment algorithm for mobile sensor networks is described and the results from an extensive series of simulation experiments are presented to both validate the algorithm and illuminate its empirical properties.
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Experiments with a Large Heterogeneous Mobile Robot Team: Exploration, Mapping, Deployment and Detection

TL;DR: The design and experimental validation of a large heterogeneous mobile robot team built for the DARPA Software for Distributed Robotics (SDR) program is described and selected results from supervised experiments conducted in a 600 m 2 indoor environment are presented.
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Coverage, Exploration and Deployment by a Mobile Robot and Communication Network

TL;DR: An efficient minimalist algorithm which assumes that global information is not available is presented which deploys a network of radio beacons which assists the robot in coverage and also used by the robot for navigation.
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Slocum Gliders: Robust and ready

TL;DR: Since the initial tests, a wide range of physical and optical sensors have been integrated into the Glider allowing measurements of temperature, salinity, depth averaged currents, surface currents, fluorescence, apparent and inherent optical properties.
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Cooperative Control for Ocean Sampling: The Glider Coordinated Control System

TL;DR: 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.
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