Unmanned Systems Maritime Search and Rescue: HUGIN 4500 Autonomous Underwater Vehicle

Unmanned Systems Maritime Search and Rescue: HUGIN 4500 Autonomous Underwater Vehicle

Mark C. Hardy

Embry Riddle University

A multi-purpose survey vessel carrying a HUGIN 4500 autonomous underwater vehicle (AUV) was recently dispatched to the Indian Ocean in hopes of locating the missing wreckage of Malaysian Airlines flight 370 (AUVAC, 2015). The harsh conditions and extreme depths in this region of the Indian Ocean have created an environment that is not conducive to human divers. Additionally, the thick layer of sediment present on the ocean floor throughout the search area has limited the performance of shipborne sensors. As a result, AUVs capable of operating at punishing depths and equipped with specialized sensor arrays are now assisting in the search effort (Saltarin, 2014; AUVAC, 2015).

The HUGIN class of AUVs is manufactured by Norwegian based Kongsberg Maritime. Three models of the HUGIN AUV are available, each classified by its depth rating (i.e. HUGIN 1000, 3000, 4500). The most capable of the HUGIN models is the HUGIN 4500 which has the ability to descend to a maximum depth of 4500 meters, or approximately 15,000 feet (Button et al., 2009).

The HUGIN 4500 has a torpedo style body type consisting of a titanium hull. The 4500 is powered by a pressure tolerant Aluminum Oxygen Semi Fuel Cell which is capable of powering propulsion and sensor systems for 60 hours at 4 knots (Kongsberg Maritime, 2009).

Communications for vehicle control and data transfer can be handled via acoustic link, radio link, WLAN, or by iridium satellite. The HUGIN 4500 is capable of operating in one of three modes; supervised, autonomous, and semi-autonomous. Supervised mode allows the operator to manage the device and/or reprogram mission parameters. Autonomous mode allows the AUV to operate independently utilizing a pre-programmed mission profile. Semi-autonomous mode is a combination of supervised and autonomous modes (Kongsberg Maritime, 2009).

An advanced aided inertial navigation system (AINS) provides position and guidance information for the HUGIN 4500. Additional proprioceptive sensors such as GPS, doppler velocity log (DVL), compass, pressure sensors, and the high precision acoustic positioning (HiPAP) system; all assist by providing relevant information to the HUGIN’s Kalman filter which then processes data from the various systems and adjusts for error utilizing sophisticated navigation algorithms. Figure 1 provides illustrates the connectivity between AINS and other interrelated systems (Kongsberg Maritime, 2009).

Figure 1. Structure of the HUGIN aided internal navigation system. From “Autonomous Underwater Vehicle, HUGIN Family” Kongsberg Maritime, 2009.

One key advantage of the HUGIN 4500 is its large and versatile payload bay. The HUGIN’s sensor array is built around a centralized Payload Processor which allows the various sensor components to function holistically (Kongsberg Maritime, 2009). The HUGIN’s ability to support the simultaneous operation of sensors, which employ differing techniques and varying capabilities, provides end users with information that is fused from multiple sources culminating in more accurate and detailed products as compared to results generated from data retrieved from individual systems themselves (Thurman, Riordan, & Toal; 2013).

The HUGIN 4500 can be equipped with a vast array of exteroceptive sensors. The following is an example of a HUGIN 4500 payload:

• High Resolution Interferometric Synthetic Aperture Sonar (HISAS) rated to 3000 m 
• Multibeam Echosounder 
• Sidescan sonar 
• Sub-bottom profiler 
• Still image camera 

Sonar (Sound Navigation and Ranging) refers to the basic principle of using sound waves to detect and/or locate objects, typically in the underwater domain (Hansen, R. 2011). Side scan sonar is a high frequency sonar sensor which transmits waves and measures return echoes to create an image of the ocean terrain. Side scan systems can cover a wide swath of area and is commonly used for mapping and/or object detection (NOAA, 2008; Zehner & Loggins, 2004).

Synthetic aperture sonar (SAS) is based on the principle of emitting successive pulses of sound along a known track to create a large synthetic array. SAS sensors are capable of producing precise imagery with centimeter level resolution from hundreds of meters away (Hansen, 2011). Interferometric SAS further enhances SAS technology by measuring the phase difference between sonar images taken by separate arrays to produce 3D imagery (Banks, Griffiths & Sutton, 2001).

The HISAS 1030 by Kongsberg Maritime is ideally suited for search and recovery operations as this Interferometric SAS system is able to provide detailed imagery of the ocean floor and is designed to detect and classify small objects in cluttered environments, such as an underwater debris field. Moreover, the HISAS 1030 is capable of producing high resolution bathymetric information which can subsequently be used to generate 3D terrain models (Kongsberg Maritime, 2009).

The HISAS 1030 can also be used as a conventional side scan sonar, which will not provide the high resolution of SAS but will allow for an increase in the average coverage rate. Fusing bathymetric information collected by the SAS with data collected by another acoustic sensor, known as the multi-beam echo sounder, is an example of optimizing the overall data collection effort by leveraging the complementary capabilities of multiple sensors (Kongsberg Maritime, 2010; Thurman et al., 2013).

The sub-bottom profiler, which utilizes lower frequency waves to penetrate the seabed, is another acoustic sensor that the HUGIN 4500 may employ to remotely sense what lies beneath the thick layer of sediment covering the ocean floor. Lastly, an electro optical camera system capable of capturing still imagery rounds out the suite of exteroceptive sensors which one might expect to see on the HUGIN 4500 tasked with locating the wreckage of Malaysian Airlines flight 370 (Kongsberg Maritime, 2009).

While it is clear that the HUGIN 4500 is a superb platform for a myriad of underwater exploration applications, it is only one AUV operating in a vast search area. As such, it may be prudent to consider the manner in which AUVs are deployed. Research conducted at the State University of Buffalo should serve as a framework for the implementation of “cooperative search and survey” methods utilizing a fleet of interconnected AUVs operating in a coordinated search effort (Yoon & Qiao, 2008). In order to accommodate such a system, the HUGIN 4500 would require an enhanced capability to send and receive large amounts of data acoustically. It would also need to be capable of sensing and/or communicating with other AUVs operating in the vicinity so as to avoid collisions and eliminate duplicative search efforts.

Sea based search and rescue efforts could be further augmented by immediately deploying unmanned aviation systems (UAS) to search areas to begin surveying for wreckage or locating survivors in a timely fashion. UAS systems could also provide additional cueing for seaborne unmanned assets operating on or beneath the surface by directing them to areas of interest as seen from above the ocean surface.

AUVs have the unique ability to operate in environments which are hazardous to human beings. Removing the requirement for human operators allows engineers to build smaller, more cost effective vessels with a focus on developing the payload suites necessary to collect desired information. While manned vessels are capable of carrying the same equipment and conducting the same missions as their AUV counterparts, the physiological limits of the human body would drastically increase cost and decrease overall efficiency.

References:

Autonomous Underwater Vehicle Application Center (AUVAC). (2015, January 14). MH370 Searchers to Look at Hard to Reach Sea Floor. Retrieved from http://auvac.org/community-information/community-news/view/2720

Button, Curtin, Dryden & Kamp. (2009). A Survey of Missions for Unmanned Undersea Vehicles. Retrieved from http://www.rand.org/content/dam/rand/pubs/monographs/2009/RAND_MG808.pdf

Hansen, R. (2011, September 12). Introduction to Synthetic Aperture Sonar. InTech. Retrieved from www.intechopen.com/download/pdf/18868

Kongsberg Maritime. (2009). Autonomous Underwater Vehicle, HUGIN Family. Retrieved from http://auvac.org/uploads/configuration_spec_sheets/HUGIN_Family_brochure_r2_lr.pdf

Kongsberg Maritime. (2010). HISAS 1030. Retrieved from http://www.km.kongsberg.com/ks/web/nokbg0397.nsf/AllWeb/86E9FFB43569CDEEC12576B9006D75C7/$file/HISAS_1030_brochure_v1_lowres_v2.pdf?OpenElement

National Oceanic and Atmospheric Administration (NOAA). (2008). Side Scan Sonar. Retrieved from http://oceanservice.noaa.gov/education/seafloor-mapping/how_sidescansonar.html

Saltarin, A. (2014, April 3). MH370: How Can Unmanned Submarines Help in the Search of the Missing Plane. Retrieved from http://www.techtimes.com/articles/5150/20140403/mh370-how-can-unmanned-submarines-help-in-the-search-of-the-missing-plane.htm

Thurman, E., Riordan, J., & Toal, D. (2013, July). Real-Time Adaptive Control of Multiple Co-located Acoustic Sensors for an Unmanned Underwater Vehicle. IEEE Journal of Oceanic Engineering, Vol. 38, NO. 3.

Yoon, S. & Qiao, C. (2008). Cooperative Search and Survey using Autonomous Underwater Vehicles (AUVs). Retrieved from ftp://ftp10.us.freebsd.org/pub/tech-reports/2008-04.pdf

Zehner, W. & Loggins, C. (2004). Selection Criteria for UUV Sonar System. Retrieved from http://ieeexplore.ieee.org.ezproxy.libproxy.db.erau.edu/stamp/stamp.jsp?tp=&arnumber=754729&tag=1

Bathymetric Lidar for UAS

The following is a summary of an article written by Ben Coxworth of Gizmag.com:

Bathymetric Lidar for UAS

A team of researchers at Georgia Tech University, led by Dr. Grady Tuell, are in the process of developing a bathymetric lidar system which may be small enough to be carried by an unmanned aerial system (UAS). Lidar, which stands for Light Detection and Ranging, utilizes lasers to emit pulsating beams of light which reflect off of the surface below. The lidar receiving unit then monitors the beam’s rate-of-return to accurately determine the distance from the sensor to the surface below.  By combining millions of these data points, lidar systems are able to produce precise 3D terrain models.

Bathymetric lidar systems transmit two laser beams. One beam in the infrared and another in the higher frequency green spectrum which is capable penetrating columns of water to produce 3D imagery of the ocean floor. Typical bathymetric systems are bulky and weigh in the range of 600 pounds, therefore limiting their use to manned aircraft which have the ability to handle a payload of that size. The Georgia Tech team has developed a CAD model of their system which would be about half the size of current systems. The group hopes to develop an even smaller device which could perhaps be deployed on small UAS platforms. 

The Georgia Tech system also offers significant advancements in bathymetric lidar processing speeds. Their system utilizes a computing technique called “total propagated uncertainty” (TPU) and is capable of processing up to 37 million data points per second. Comparatively, some current systems are only capable of processing approximately one thousand points per second.

As of December 2014, the system was deployed on a gantry apparatus and was being tested over a pool for further evaluation. Possible applications for the system include seaborne mine and submarine detection, under water mapping, and land management applications- as the sensor is also capable of penetrating foliage to detect objects beneath the forest canopy.

  

The continued miniaturization of sensor technologies, both exteroceptive and proprioceptive, lends well to future implementation of UAS and will ultimately lead to increased efficiencies and cost reductions across a wide array of industries.  

Reference:

Coxworth, B. (2014, December 8).  UAVs Could Map The Bottom of the Sea, Using New Lidar Tech. Retrieved from http://www.gizmag.com/uav-bathymetric-lidar/35113/

NOAA (2014). Lidar. Retrieved from http://www.nauticalcharts.noaa.gov/hsd/lidar.html