TY - JOUR
T1 - USC CINAPS builds bridges
T2 - Observing and monitoring the Southern California bight
AU - Smith, Ryan N.
AU - Das, Jnaneshwar
AU - Heidarsson, Hördur
AU - Pereira, Arvind M.
AU - Arrichiello, Filippo
AU - Cetnić, Ivona
AU - Darjany, Lindsay
AU - Garneau, Marie Éve
AU - Howard, Meredith D.
AU - Oberg, Carl
AU - Ragan, Matthew
AU - Seubert, Erica
AU - Smith, Ellen C.
AU - Stauffer, Beth A.
AU - Schnetzer, Astrid
AU - Toro-Farmer, Gerardo
AU - Caron, David A.
AU - Jones, Burton H.
AU - Sukhatme, Gaurav S.
PY - 2010/3
Y1 - 2010/3
N2 - 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 (e.g., [11], [17], and [29]). 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 amultisensor, 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.
AB - 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 (e.g., [11], [17], and [29]). 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 amultisensor, 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.
KW - Adaptive control
KW - Control architectures and programming
KW - Marine robotics, networked robots
KW - Networked teleoperation
UR - http://www.scopus.com/inward/record.url?scp=77949591909&partnerID=8YFLogxK
U2 - 10.1109/MRA.2010.935795
DO - 10.1109/MRA.2010.935795
M3 - Article
AN - SCOPUS:77949591909
SN - 1070-9932
VL - 17
SP - 20
EP - 30
JO - IEEE Robotics and Automation Magazine
JF - IEEE Robotics and Automation Magazine
IS - 1
M1 - 5430373
ER -