Intelligent Shipwreck Search Using Autonomous Underwater Vehicles

This paper presents an autonomous robot system that is designed to autonomously search for and geo-localize potential underwater archaeological sites. The system, based on Autonomous Underwater Vehicles, invokes a multi-step pipeline. First, the AUV constructs a high altitude scan over a large area to collect low-resolution side scan sonar data. Second, image processing software is employed to automatically detect and identify potential sites of interest. Third, a ranking algorithm assigns importance scores to each site. Fourth, an AUV path planner is used to plan a time-limited path that visits sites with a high importance at a low altitude to acquire high-resolution sonar data. Last, the AUV is deployed to follow this path. This system was implemented and evaluated during an archaeological survey located along the coast of Malta. These experiments demonstrated that the system is able to identify valuable archaeological sites accurately and efficiently in a large previously unsurveyed area. Also, the planned missions led to the discovery of a historical plane wreck whose location was previously unknown.

[1]  Hanumant Singh,et al.  Robotic tools for deep water archaeology: Surveying an ancient shipwreck with an autonomous underwater vehicle , 2010, J. Field Robotics.

[2]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[3]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[4]  Rory Quinn,et al.  Comparison of the maritime Sites and Monuments Record with side‐scan sonar and diver surveys: A case study from Rathlin Island, Ireland , 2002 .

[5]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[6]  Keld Helsgaun,et al.  An effective implementation of the Lin-Kernighan traveling salesman heuristic , 2000, Eur. J. Oper. Res..

[7]  Tie-Yan Liu,et al.  Learning to Rank for Information Retrieval , 2011 .

[8]  Martin Klein Side Scan Sonar , 2002 .

[9]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Venkatesh Saligrama,et al.  A multi-resolution approach for discovery and 3-D modeling of archaeological sites using satellite imagery and a UAV-borne camera , 2016, 2016 American Control Conference (ACC).

[11]  David P. Williams On adaptive underwater object detection , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Yann LeCun,et al.  Large-scale Learning with SVM and Convolutional for Generic Object Categorization , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[13]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  David P. Williams,et al.  A fast physics-based, environmentally adaptive underwater object detection algorithm , 2011, OCEANS 2011 IEEE - Spain.

[15]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[16]  Jamil Sawas,et al.  Cascade of boosted classifiers for automatic target recognition in synthetic aperture sonar imagery , 2013 .

[17]  Jaana Kekäläinen,et al.  Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

[18]  Timmy Gambin Side scan sonar and the management of underwater cultural heritage , 2014 .

[19]  Brian Bingham,et al.  New archaeological uses of autonomous underwater vehicles , 2001, MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295).

[20]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[21]  Hanumant Singh,et al.  Imaging Underwater for Archaeology , 2000 .