AUV-enabled adaptive underwater surveying for optimal data collection

Williams, David P.

doi:10.1007/s11370-011-0102-y

Cited by 29 publications

(9 citation statements)

References 38 publications

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“…Image quality was measured by the coherence, or ping-to-ping correlation, of the synthetic aperture sonar (SAS) data. A threshold, defining the admissible data was based on "extensive experience visually assessing the quality of sonar images" [13] and verified in a sensitivity analysis that assessed detection performance as a function of admissible coherence [13], [14]. A more recent approach of an AUV adapting its tracks using the coherence in SAS as a metric for quality is described in [15].…”

Section: B Related Workmentioning

confidence: 99%

Coverage Path Planning With Track Spacing Adaptation for Autonomous Underwater Vehicles

Yordanova

Gips

2020

IEEE Robot. Autom. Lett.

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In this paper we address the mine countermeasures (MCM) search problem for an autonomous underwater vehicle (AUV) surveying the seabed using a side-looking sonar. We propose a coverage path planning method that adapts the AUV track spacing with the objective of collecting better data. We achieve this by shifting the coverage overlap at the tail of the sensor range where the lowest data quality is expected. To assess the algorithm, we collected data from three at-sea experiments. The adaptive survey allowed the AUV to recover from a situation where the sensor range was overestimated and resulted in reducing area coverage gaps. In another experiment, the adaptive survey showed a 4.2% improvement in data quality for nearly 30% of the 'worst' data.

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Section: B Related Workmentioning

confidence: 99%

Coverage Path Planning With Track Spacing Adaptation for Autonomous Underwater Vehicles

Yordanova

Gips

2020

IEEE Robot. Autom. Lett.

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“…The underwater robotics community has recently begun examining problems of AUV dive planning for such applications as mine sweeping [33] and seabed classification [32]. These works focus on the dive planning problem, but they do not integrate methods for measuring 3D reconstruction accuracy.…”

Section: Related Workmentioning

confidence: 99%

Active and Adaptive Dive Planning for Dense Bathymetric Mapping

Hollinger

Mitra

Sukhatme

2013

Springer Tracts in Advanced Robotics

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We examine the problem of planning dives for an Autonomous Underwater Vehicle (AUV) to generate a dense bathymetric map using sidescan sonar. The three key challenges in this scenario are (1) proper modeling of the local uncertainty of the 3D reconstruction, (2) efficient dive planning to reduce this uncertainty, and (3) determination of when to re-plan adaptively based on new information. To address these challenges, we propose using non-parametric Bayesian regression to model the expected accuracy of the map, which provides principled cost functions for planning subsequent dives. In addition, we propose an efficient greedy method to reduce this uncertainty, and we show that it achieves theoretically bounded performance given assumptions on the sensor model and the form of the uncertainty function. We present experiments on the propeller-driven YSI EcoMapper AUV equipped with a sidescan sonar in an inland lake. The experiments demonstrate the benefit of efficient dive planning, with our results providing performance gains of up to 83% versus standard lawnmower patterns.

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“…By estimating the correlation coefficients as a function of range and setting a threshold under which data is considered to be of insufficient quality, one can estimate the maximum imaging range achievable. As in [3], the imaging quality threshold for this study is set to a minimum correlation coefficient value of 2/3 (corresponding to a SNR threshold of 3 dB).…”

Section: Ping-to-ping Correlationmentioning

confidence: 99%