TY - GEN
T1 - An adaptive sampling solution using autonomous underwater vehicles
AU - Chen, Baozhi
AU - Pandey, Parul
AU - Pompili, Dario
N1 - Funding Information: Work supported by the NSF CAREER Award No. OCI-1054234.
PY - 2012
Y1 - 2012
N2 - To achieve efficient and cost-effective sensing coverage of the vast under-sampled 3D aquatic volume, intelligent adaptive sampling strategies involving Autonomous Underwater Vehicles (AUVs) endowed with underwater wireless (acoustic) communication capabilities are essential. These AUVs should coordinate and steer through the region of interest, and cooperatively sense, preprocess and transmit measured data to onshore stations for processing and analysis. Given a scalar field to sample, i.e, a phenomenon like temperature or salinity distribution, the AUVs should coordinate to take measurements using minimal resources (time or energy) in order to reconstruct the field with admissible error. A novel adaptive sampling solution to minimize the sampling cost is proposed, which requires the AUVs to take a small number of samples from the field. We observe via simulations that our solution outperforms existing solutions that are based on Compressive Sensing (CS) techniques.
AB - To achieve efficient and cost-effective sensing coverage of the vast under-sampled 3D aquatic volume, intelligent adaptive sampling strategies involving Autonomous Underwater Vehicles (AUVs) endowed with underwater wireless (acoustic) communication capabilities are essential. These AUVs should coordinate and steer through the region of interest, and cooperatively sense, preprocess and transmit measured data to onshore stations for processing and analysis. Given a scalar field to sample, i.e, a phenomenon like temperature or salinity distribution, the AUVs should coordinate to take measurements using minimal resources (time or energy) in order to reconstruct the field with admissible error. A novel adaptive sampling solution to minimize the sampling cost is proposed, which requires the AUVs to take a small number of samples from the field. We observe via simulations that our solution outperforms existing solutions that are based on Compressive Sensing (CS) techniques.
KW - Adaptive Sampling
KW - Autonomous Underwater Vehicles
KW - Compressive Sensing
UR - http://www.scopus.com/inward/record.url?scp=84900485015&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84900485015&partnerID=8YFLogxK
U2 - 10.3182/20120919-3-IT-2046.00060
DO - 10.3182/20120919-3-IT-2046.00060
M3 - Conference contribution
SN - 9783902823601
T3 - IFAC Proceedings Volumes (IFAC-PapersOnline)
SP - 352
EP - 356
BT - 9th IFAC Conference on Manoeuvring and Control of Marine Craft, MCMC 2012 - Proceedings
PB - IFAC Secretariat
T2 - 9th IFAC Conference on Manoeuvring and Control of Marine Craft, MCMC 2012
Y2 - 19 September 2012 through 21 September 2012
ER -