Archive for May, 2017

Advanced Underwater Drones May Help Find MH370

A fanciful depiction of a team of AUVs searching the sea floor as proposed by researchers at Virginia Tech

Despite an underwater search of the seabed in the Southern Indian Ocean (SIO) that covered 120,000 sq km, employed five search vessels, lasted 16 months, and cost Malaysia, Australia, and China a total of US$ 226 million, the MH370 wreckage remains elusive. The only debris from MH370 that has been found are parts that have drifted across the Indian Ocean and recovered from the shores of Eastern Africa.

Because of the need for specialized equipment capable of searching as deep as 6,000 m, operated by highly-trained crews in the exceptionally harsh conditions of the SIO, the underwater search is slow, expensive, and dangerous.

First, a bathymetric survey is undertaken to map the topography of the seabed. The bathymetric survey uses “multibeam sonar” transducers mounted on the hull of the survey vessel. By transmitting an acoustic pulse and measuring the time duration to receive an echo from the sea floor, the depth of the sea floor can be mapped with a resolution of about 100 m at a rate of 1200 sq km per day.

After the sea floor is mapped, the seabed is scanned for aircraft debris by pulling a “towfish” behind a search vessel. The towfish is lowered so that it “glides” just 100 – 150 m from the sea floor. The towfish is equipped with “side scan sonar” to search on either side of the towfish, and “multibeam sonar” to search below the towfish. This allows scanning the seabed out to a distance of 1 km to either side of the towfish at a resolution of about 70 cm and a rate of 133 sq km per day. If there is an object of interest, or the seabed is difficult to scan due to challenging topography, an autonomous underwater vehicle (referred to as an AUV or drone) can be deployed to get close to the sea floor and obtain high resolution images. For instance, the drones of the type used in the MH370 search have a resolution of about 10 cm and can scan the seabed at a rate of about 17 sq km per day.

Our estimates of the location of the crash site come mainly from two bodies of evidence: satellite data that was recorded for the brief intervals that MH370 transmitted signals to the Inmarsat communications network, and from drift models that estimate the crash site based on the timing and location of debris that has been recovered from the shores of Eastern Africa. Unfortunately, neither of these data sets is sufficiently precise to provide high confidence in the location of the wreckage.

Due to the expense of seabed searching, combined with the imprecision of using the existing data sets to estimate the location of the wreckage, some are suggesting that it is not economical to do further searching with our current technology. The argument is that further searching should be suspended until we gain additional information or insight that allows us to more precisely estimate the location, or until we develop new technology that allows us to more economically search large areas of the sea floor.

It is the promise of new technology that can more economically search large areas of the sea floor that led me to the work of Dr Dan Stilwell, a professor of electrical engineering at Virginia Tech. Dan’s team conducts research in the area of marine autonomy and robotics, and they have developed small, fast, high-performance, inexpensive AUVs for the US Navy. His research team is using their extensive inventory of technology to compete in the Ocean Discovery XPrize, which aims to accelerate innovations to improve the speed, scale, and image resolution of technologies used to explore the ocean floor.

Dan Stilwell (right) and his team with one of their AUVs

The XPrize contest will require mapping 500 sq km of ocean floor with a resolution of 5 m and at a depth of 4,000 m, and also to produce high resolution photographs of various objects on the seabed, all within 24 hours. That’s quite a challenge with existing technology. Nonetheless, the prospect of winning the US$ 7 million prize has attracted interest from 21 teams from around the world.

Dan’s approach is to use a “team” of small, low-cost AUVs to cooperatively survey and scan the ocean floor. Each AUV can travel at 4 knots for 24 hours on a single battery charge. Rather than using expensive inertial guidance systems to navigate, Dan and his team are using technology developed at the Woods Hole Oceanographic Institute, whereby all the AUVs acoustically communicate and navigate using a low-bandwidth, time-division multiple access (TDMA) network. In this approach, each AUV in the team is assigned a time slice and each AUV has a synchronized clock. Each AUV measures the time delay between transmission and receipt of pulses from each of the other AUVs, and from this and other information, the relative position of all the AUVs may be determined. One node remains at the surface, which provides an absolute GPS position reference. This approach to acoustic navigation has not previously achieved the accuracy that is required for the XPrize contest, but Dan’s team will implement a number of new tricks that they expect will provide a sufficient boost in navigational performance.

Can next generation AUV technology provide an economical way to search for MH370? Consider this: Dan estimates that it would take about four of his drones to match the scan rate of a single towfish. But there are compelling economic benefits to using a team of drones. For one, each drone is relatively inexpensive–Dan and his team can build one for about US$ 125,000. Secondly, a large team of AUVs can be deployed from a single surface vessel and crew, while a towfish requires a dedicated vessel. For instance, if a cooperative team of twelve AUVs is deployed from a single vessel, that vessel would be able to scan three times as much sea floor as a vessel deploying a towfish. As sonar sensors increase in performance and miniaturization, sea floor scanning with AUVs will become even faster and cheaper.

As we struggle to squeeze every last bit of information from the existing MH370 evidence, it may be that some of our resources are better directed to improving our ability to quickly and economically search large expanses of the sea floor. Research on autonomous vehicles like that performed at Virginia Tech by Dan and his team can help us.

Update on May 31, 2017.

I was recently in a discussion that included a well-known ocean explorer who happens to be a judge in the Ocean Discovery XPrize competition.  We were having a general discussion about searching for MH370 and ways to scan the ocean floor at high resolution, and he told us about the capabilities of Ocean Infinity. Like the team at Virginia Tech, their approach is to employ a team of AUVs. From their website:

Six HUGIN autonomous underwater vehicles (AUVs) are capable of operating in 6,000 m water depth collecting high resolution data at record breaking speeds. Our AUV fleet is accompanied by six unmanned surface vehicles (USVs) to ensure precise position and constant communication.

With multiple autonomous vehicles working simultaneously utilizing innovative technology, we are able to survey huge swaths of the seabed, quickly and with outstanding accuracy. We can operate in shallow waters but excel in extreme depths, working in dynamic environments ranging from the tropics to the Arctic ice.

Because of the size and complexity of each AUV/USV pair, the capital cost of the technology from Ocean Infinity would greatly exceed the capital cost of Virginia Tech’s technology, which uses small AUVs with innovative navigation systems. On the other hand, both approaches benefit from having a single host vessel supporting multiple underwater vehicles, which offers significant operating cost and scan rate improvements compared to the conventional towfish technology.

Ocean Infinity’s seabed exploration system is commercially available today, including underwater and surface vehicles, on-board support equipment, and the host vessel. This is an exciting possibility for conducting the search for MH370 in the near future.

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