Nova Ray Unmanned Underwater Vehicle Control Station Analysis

           

               The Nova Ray unmanned underwater vehicle (UUV) is one of the most unique 
unmanned submersibles on the market. Instead of taking on the torpedo shape favored 
by a majority of unmanned underwater vehicles, the Nova Ray takes its styling cues 
(and its name) from the stingray, utilizing a big pair of wings to stabilize itself under
water (Rees, 2017). Here is a picture of the Nova Ray, ready to go into the water:

            The picture makes it clear that the craft is controlled by a tether, but what kind
of control station is it tethered to? According to Coral Partners, the company behind 
the submersible, each Nova Ray ships with an integrated Control Console (CC) 
packaged in a Pelican case (Nova Ray, n.d.). Below is an image of the CC:

            According to Nova Ray (n.d.), the CC is comprised of the following 
components: laptop computer, ten-inch LCD monitor, and four-axis joystick. Doing 
a little research revealed that a typical four-axis joystick moves up, down, left and 
right (the first two axes), has a rotating knob (the third axis) and one or more buttons
(the fourth axis), giving a tremendous amount of control options from a single stick 
(Engineering 360, n.d.). This stick is used to control both the movement of the Nova
Ray itself and its camera. The laptop and the LCD monitor combine to display both 
data that the Nova Ray is gathering and the data generated by its internal sensors, as

shown in the picture above: the laptop screen shows images of the Nova Ray’s pitch,
attitude,and other variables not readily visible, while the LCD screen shows the images 
of fish the Nova Ray is recording.

            Unfortunately, details about the software don’t get any more specific. according 
to Nova Ray (n.d.), the command and control software the CC uses is Windows-based 
and proprietary, but does not go into any more detail. Still, by looking at the screens, 
we can see that the images on the laptop are quite similar to what we may find in an 
airplane cockpit, particularly the attitude gauge on the left side of the laptop screen that 
displays the Nova Ray’s position relative to the horizon. I think it is safe to assume that 
most of the functions within the software are designed to mimic an airplane instrument 
panel as much as possible, especially given that UAV Propulsion Tech states over and 
over in their sales material that the Nova Ray “flies” in the water.

            Overall, I think the Nova Ray’s control console seems well-designed, but there 
is one modification I would make: a twin-stick controller. As a gamer, I’m used to using 
the left stick of a controller to control movement while using the right stick to control a 
camera, and this control setup is so widely used in gaming that I think there would be 
a lot of potential crossover between the controls of a video game and that of the Nova 
Ray. This is not a novel idea; as far back as 2008, companies like Lockheed Martin were 
developing video game-style twin-stick controllers for their drones (Hambling, 2008). 
Potential users are already trained in the use of two sticks, and I think that if this was 
made an option (like a USB accessory that can be plugged into the included laptop), I 
think the Nova Ray could find a wider audience.

            Still, the Nova Ray is an interesting craft, and its command and control console 
seems well put-together. Throw in the option to use a gaming controller, and I just 
might think about making a purchase.

           

References

Engineering 360 (n.d.). 4-axis control industrial joysticks datasheets. Retrieved from

     

     https://www.globalspec.com/ds/1399/areaspec/axis_four

Hambling, D. (2018). Game controllers driving drones, nukes. Retrieved from

     

     https://www.wired.com/2008/07/wargames/

Nova Ray (n.d.). NOVA RAY inspection class remotely operated vehicle (ROV). Retrieved from

     

     http://www.novaray.com/novaray_outline.htm

Rees, M. (2017). UAV Propulsion Tech to distribute Nova Ray ROV. Retrieved from

     

     https://www.unmannedsystemstechnology.com/2017/10/uav-propulsion-tech-distribute-nova-

    

     ray-rov/

Simtoo: Data and Sensors

The Simtoo Dragonfly bills itself as the “world’s first foldable drone” (Simtoo, n.d.). Its

dimensions are tiny, as shown in the picture below, taken directly from Simtoo’s website:

A UAV this small creates interesting challenges for designers, both in terms of sensors and memory storage.

The UAV uses micro SD cards to store both photos and videos (Custer, 2016). This is a small version of the swappable memory system used by larger drones, and it is a perfect fit for the Dragonfly, as micro SD cards have the footprint of a thumbnail but can have storage capacities in excess of two hundred gigabytes (Estrada, 2017). Eliminating onboard storage was no doubt one of the keys in keeping the Dragonfly small.

According to Simtoo (n.d.), the Dragonfly has six sensors: a GPS, a gyroscope, a barometer, a magnetometer, an accelerometer, and a camera capable of taking 4K photos and video. The spec sheet doesn’t indicate how much power each device draws, but it does state that its working voltage is 3.7 volts, meaning that the Dragonfly utilizes 3.7 volts, on average, during one hour of flight (Simtoo, n.d.). From this number, we can infer that the sensors working together do not draw much power at all; in fact, the biggest consumer of battery power on the Dragonfly is the motor, which is rated at 100 watts.

The sensor with the biggest impact on the Dragonfly’s data treatment strategy is obviously the camera; as mentioned earlier, the camera is capable of 4K images, which can quickly consume the available empty space on the inserted micro SD Card. It becomes clear why Simtoo chose to make micro-SD cards the sole source of onboard memory, as users can swap cards quickly when ultra-high definition images consume too much of the installed card’s storage space.

The Dragonfly seems to work well, but it is not perfect. The swappable SD cards have the potential to give the operator virtual acres of space, but nothing is in place to compensate for the drone running out of storage in the middle of a crucial shot. For this reason, I am proposing that Simtoo add cloud-based storage to its system. Cloud storage has the ability to give the Dragonfly nearly limitless space, enabling uninhibited access for the user as they no longer need to watch and see how much memory is left and how much shooting they can do before cards need to be swapped.

Simtoo can take this concept further, by adding basic photo editing options to their storage cloud. This way, a pilot can take a photo with the drone, access it in the cloud, edit it, and then submit for publishing to Facebook, Twitter, or any number of other social media sites.

The Simtoo Dragonfly is an intriguing UAV. It is a drone whose tiny size is its biggest selling point, a point around which the rest of the system’s design revolves; this design is the reason the designers looked for the smallest possible device to provide onboard storage. It is also the reason the UAV’s systems draw as little power as possible. I think the Dragonfly has potential, but that it needs cloud storage to fully reach that potential.

References

Simtoo (n.d.). Dragonfly. Retrieved from http://www.simtoo.com/page.php?id=132#page1 on 4

     October, 2018.

Custer, C. (2016). Dragonfly drone wants to be a slightly cheaper alternative to DJI (review).

     

     Retrieved from https://www.techinasia.com/simtoo-dragonfly-drone-review-skies

Estrada, M. (2017). The 5 highest capacity micro SD cards you can get right now on Amazon.

     

     Retrieved from https://bgr.com/2017/02/28/microsd-256gb-amazon-large-capacity-

     

     microsdxc/

Sensor Placement in Photography and Racing Drones

Sensor Placement in Photography and Racing Drones

Sensor placement is one of the most crucial elements of unmanned system design, and

the purpose of the unmanned system dictates exactly where and how critical sensors are 
placed.Quadcopter drones (the four-rotor helicopter drones that have become 
ubiquitous in electronics stores) all look similar from a distance, but vary wildly 
in their sensor placement depending on their purpose. Two such unmanned systems 
are photography drones and FPV racing drones, which superficially look similar 
but are actually worlds apart in both purpose and sensor placement.

           

DJI is one of the industry leaders in hobby and commercial photography drones, 
and their Phantom 4 drone stands at the top of their professional-level UAV 
catalog (Mellors, n.d.). According to Mellors (n.d.), the chief feature of the 
Phantom 4 is the 4K camera, which enables the user to record ultra-high 
resolution video. Below is an image of the drone:

This is a great example of the drone’s camera placement. It is mounted on a 
gimbal on the bottom of the craft, which gives it the ability to both rotate and 
pan up and down.  This allows the camera to adjust to a wide variety of 
positions while the drone is hovering in place, giving the pilot the ability 
to capture a wide variety of images and angles without having to reposition 
the drone. The gimbal also compensates for the UAV’s movement in the air, 
enabling the camera to record stable footage even if the craft is moving 
(Mellors, n.d.). Sensor placement is key in the DJI’s functionality as a
photography tool.

            

Camera drones aren’t the only quadcopters in the skies; first-person view (FPV)
racing drones are also becoming popular. FPV drone racing is a growing sport 
where competitors fly their drones through pre-built tracks (Drone Enthusiast, 2016). 
The “FPV” is the key term here; operators utilize displays which show a first-person 
view from the drone, almost as if the pilot is sitting in the drone’s cockpit. The 
FPV requirement dictates very specific mounting for the racing UAV’s camera, 
namely its nose:

This is the Walkera Rodeo 150 FPV racing drone. Just like the DJI Phantom 4, 
it is a quadcopter, but that is where the similarities end. The Rodeo is a lightweight 
craft; it only weighs a hundred and fifty grams, and is designed for indoor and 
outdoor racing (Brown, n.d.). According to Brown (n.d.), it is also designed for 
aerobatic flight.

            

Since this is an FPV racing drone, the camera is mounted to the UAV’s nose,
centered in such a way as to simulate a cockpit view. According to Brown (n.d.), 
the camera’s position is fixed in place, with the optics allowing for a wide-angle 
view of one hundred and ten degrees. As Brown (n.d.) points out, this is a suboptimal 
position for photography, which removes the drone from consideration for serious 
photographic work. However, in its intended use as an FPV viewfinder, the drone
works very well, with the UAV’s electronics transmitting the camera’s

data to a viewing device with almost no lag.

            

Sensor placement is a critical element of UAV design, and the drone’s purpose often

decides where key sensors are placed. The most important sensor on both a racing 
drone or a photography UAV is the camera, although camera placement varies wildly 
from one platform to another. The DJI Phantom 4 carries its camera on its belly in 
order to allow for maximum photographic flexibility; the Walkera Rodeo 150 carries 
its camera on its nose, in order to give its pilot the best possible view when careening 
through a racetrack at high speeds. Both drones work well in their intended roles, and 
sensor placement is key to their successful function.

                                                               References

Brown, J. Walkera Rodeo 150: a compact FPV quadcopter for racing. Retrieved from

    

     http://mydronelab.com/reviews/walkera-rodeo-150.html on 27 September 2018.

Drone Enthusiast (2016). FPV drone racing – the UAV sport about to hit the big time.
     
     Retrieved from https://www.dronethusiast.com/fpv-drone-racing/ on 

     27 September 2018.

Mellors, J. (n.d.). DJI Phantom 4 review for photographers. Retrieved from

     

     https://shotkit.com/dji-phantom-4-review-for-photographers/ on 27 September 2018.

           

           

                                                       US Navy Bluefin-12(D)

In November of 2017, an Argentinian Navy submarine named the ARA San Juan
disappeared while traveling from an Argentinian naval base in Tierra del Fuego to its home port
in Mar del Plata (McKirdy, 2017). According to McKirdy (2017), the submarine, if underwater,
was in danger of running out of oxygen in as little as seven days. The vessel’s disappearance
sparked a frantic search from both the Argentine Navy and its international partners.  One of
those partners, the US Navy, deployed four unmanned underwater vehicles (UUVs) in support of
the search (Werner, 2017). According to Werner (2017), one of those vehicles was the Bluefin-
12(D).

The Bluefin-12(D), manufactured by General Dynamics, is a torpedo-shaped, highly
modular UUV capable of diving to a maximum depth of fifteen hundred meters (General
Dynamics, n.d.). According to General Dynamics (n.d.), its primary prioproceptive sensor is an
inertial measurement unit (IMU), which internally monitors the UUV’s speed and pitch in the
water. General Dynamics (n.d.) also states that one of the craft’s chief exteroreceptors is a
doppler velocity log (DVL), which measures the doppler effect created by the UUV as it
travels through water and then uses that information to confirm the IMU’s data. Rounding out
the suite of navigation sensors are a GPS and a compass, all of which aid the Bluefin in
navigating deep water.

The US Navy made an official statement that the Bluefin-12(D) was being utilized in the
search, but did not specify what search and rescue-specific modifications were made to the craft
(US Navy, 2017). However, a quick look at the Navy’s official photo of the Bluefin shows that it
is missing one modification that I think would greatly improve the craft’s ability to carry out its
mission: a manipulator arm.

                                      Bluefin-12(D). Source: US Navy Public Affairs Office.

I believe that a manipulator arm would greatly increase the Bluefin’s effectiveness, as it
would give the craft the ability to move debris and remove obstacles, thereby clearing a path for
manned rescue efforts. While not specified in the craft’s spec sheet, the Bluefin’s modularity
indicates that it can be mounted with a manipulator arm without too much difficulty.

Unmanned aerial vehicles (UAVs) can be combined with unmanned surface and
underwater vehicles to maximize search effectiveness in maritime environments. In 2017, a
research team from the Air Force Engineering University in China conducted a study that
determined that UAVs can be far more effective than manned aircraft in maritime search-and-
rescue operations (Lei, Jianbo, & Shukui, 2017). According to Lei et al. (2017), the ability of
UAVs to maintain sustained operations while providing both automatic and manual scanning
greatly increases the odds of rescuers finding lost personnel. By utilizing both a UAV and a
UUV, rescuers can systematically search large areas of water for longer periods of time (and at a
much lower cost) than by utilizing manned craft for the same purpose.

UUVs have many advantages over their manned counterparts when it comes to maritime
operations. They are generally far less expensive to operate than manned craft and present no
danger to pilots or crew while operating (McPhail, 2002). They are also small and compact in a
way that manned craft cannot be. The Bluefin-12(D) is small enough to be maneuverable
in tight spaces, a potentially crucial ability when attempting to navigate wreckage. A craft the
size of the Bluefin is far too small to accommodate a human crew.

The story of the Argentine submarine does not have a happy ending; the US Navy called
off its search for vehicle six weeks after it disappeared (Chaplain, 2017). As of May of 2018,
the submarine is still missing, with little hope of survival for its forty-four person crew
(Goldman, 2018). However, while this operation may have failed thus far, the increasing use of
UUVs in maritime search and rescue operations may increase the number of lives saved in the
future.


                                                                 References

Chaplain, C. (2017). US Navy ends search for missing Argentine submarine ARA San Juan.
     https://www.standard.co.uk/news/world/us-navy-ends-search-for-missing-argentine-
     submarine-ara-san-juan-a3727766.html

General Dynamics (n.d.). Bluefin-12D autonomous underwater vehicle (AUV). Retrieved from
     https://gdmissionsystems.com/en/products/underwater-vehicles/bluefin-12-d-autonomous-
     underwater-vehicle

Goldman, J. (2018). 6 months after Argentine submarine went missing, families feel ‘invisible’.
     https://abcnews.go.com/International/months-argentine-submarine-missing-families-feel-
     invisible/story?id=55146472

Lei, Z., Jianbo, H., & Shukui, X. (2017). Marine search and rescue of UAV in long-distance
     security modeling simulation. Polish Maritime Research, 95(24), 192-199.

McKirdy, E. (2017). Argentina’s missing submarine: what we know. Retrieved from
     https://edition.cnn.com/2017/11/20/americas/argentina-submarine-what-we-know/index.html
     on 18 November, 2018.

McPhail, S. (2002). Autonomous underwater vehicles: are they the ideal sensor platforms for
     ocean margin science? Ocean Margin Systems, 79-97.

US Navy. (2017). US Navy deploys unmanned submersibles in Argentine submarine search.
     Retrieved from https://www.navy.mil/submit/display.asp?story_id=103420

Werner, B. (2017). US Navy undersea teams now underway as part of Argentine submarine
     search. Retrieved from https://news.usni.org/2017/11/22/u-s-navy-unmanned-underseateams-
     now-underway-part-argentine-submarine-search

Original article: A self-driving car in every driveway? Solid-state LIDAR is the key.

Author: Nick Mokey

URL: https://www.digitaltrends.com/cars/solid-state-lidar-for-self-driving-cars/

                                                                       Summary:

LIDAR is an exteroceptive sensor that is crucial in the operation of self-driving vehicles. It works on the same principle as radar, but utilizes light instead of radio waves. Here is an image of a conventional LIDAR system on an autonomous vehicle:

The module you see on top of these cars is the LIDAR system. It spins rapidly while emitting beams of light. As this light bounces back towards the system, the LIDAR calculates the positions and angles of the reflected light to create a three-dimensional image of the car's surroundings. The car then uses this data to plot a path through its environment. LIDAR is a key exteroceptive sensor on many self-driving cars.

It is also unreliable and expensive. A conventional LIDAR system requires many moving parts, all built around a console that must spin very rapidly in order to work properly. Any system with multiple parts and a high rate of spin speed is potentially prone to many different types of failure. And the cost of one system (upwards of $75,000) means that it costs more than many of the cars to which it is mated. LIDAR is critical technology, but is unrealistic as a mass-market solution in its current form.

Enter solid-state LIDAR. This is what a solid-state module looks like:

This solid-state system, developed by Veoldyne, is one of many modules currently in development. It is a fraction of the size and a fraction of the cost of a conventional spinning LIDAR.

So how does it work? Simply put, "solid state" means no moving parts, and a solid-state LIDAR works by utilizing an array of light emitters to scan a focused slice of the surrounding environment. According to Quanergy, a manufacturer quoted in the article, this drives the price point of a single solid-state LIDAR system to under $1,000, with technology improvements lowering the price further still. A solid-state LIDAR system can only see a limited slice of its environment (something like 90-120 degrees, depending on the unit), necessitating the use of multiple systems for full 360-degree coverage. However, given the unit's size and price point, it is still far more practical and cost effective to install three or more solid-state units on a car than to use just one conventional LIDAR system.

The vast majority of automobile manufacturers agree that LIDAR is key to fully autonomous, self-driving vehicles. LIDAR is a key extereceptive sensor in these vehicles, and solid-state LIDAR promises to make self-driving cars affordable to the masses.

Unmanned Aerial Systems: The Wave of the Future, for Better or Worse

Development of unmanned systems is progressing forward at a rapid pace. The market is exploding with new models in the air, ground, and maritime domains. Flying drones, both commercial and hobby, are becoming cheaper and more available; ground drones are being used as delivery systems; and maritime drones are entering military service in both combat and search-and-rescue roles. However, although drone technology is exploding in all domains, it is the unmanned aerial systems, or UASs, that will be the dominant drones of the future. And they will be dominant because of two key words: versatility and range. 

UASs are the most versatile of all the drone types on the market. For example, one look at the H Robotics website (http://www.hrobotics.co.uk/index.html) shows that they offer their drone in nine different configurations, including broadcasting, surveying, and gas leak monitoring. Professional photographers use drones to take images that would be either prohibitively expensive or outright impossible without UAVs (Bernstein, 2015). Search and rescue units are using drones to locate missing persons (Hodapp, 2015). The list goes on. Unmanned ground systems (UGSs) and unmanned maritime systems (UMSs) can also be used in a variety of roles, but none are as encompassing as the UAVs. 

UAVs also have the biggest range, by far. UGVs are limited to operating on the ground and UMVs are limited to operating in water. However, UAVs can affect all three domains. UAVs can be used for search and rescue operations over water as much as over land; they can be used for deliveries to ground locations; and they can go farther and faster than either of the other two types of unmanned systems. 

I think UAVs will have the greatest impact on society over the next two decades. UAVs are the most popular, most common, and most well-known drones on the market. They can be seen in every toy store, and more and more companies are using them for commercial purposes. I think they will be most commonly used in a photography/videography/surveillance role, since we as a society love taking and sharing images.

What impact will this have? I believe it will have a similar impact to that of smartphones. The smartphone was (and is) a world-changing device, because it gives users the ability to take high-quality images and videos and share them instantly with the rest of the world. This has eroded privacy, but it has also uncovered injustice and led to the exposure of serious issues that would otherwise never come to light. I believe UAVs will do the same thing. The ability of these drones to take high-resolution pictures from virtually any angle will lead to more and more people living their lives in a sort of fishbowl, constantly mindful of the fact that someone is watching. This is bad for privacy but good for justice and law enforcement. It’s a double-edged sword.

Ultimately, I think UAVs will be the most impactful because they most closely align with human nature. For whatever reason, people love to take pictures and observe each other. UAVs make that possible more than any other unmanned system, which is why they will be the most impactful drone in the future.

References

Bernstein, B. (2015). Top 3 best drones for drone photography and 4K video. Retrieved from

    

     http://heavy.com/tech/2015/07/top-best-drones-to-buy-for-drone-photography-4k-video-

     

     surveillance/

Hodapp, P. (2015). Search and rescue teams aim to save lives with off-the-shelf drones.

     Retrieved from https://makezine.com/2015/12/15/search-and-rescue-teams-aim-to-save-lives-

     off-the-shelf-drones/

Studying Orcas with the Wave Glider SV3

            Orcas, more commonly known as killer whales, are some of the most intelligent and mysterious creatures in the ocean. Orcas are a very culture-oriented species, with different pods developing different preferences for food, different migratory patterns, and even different dialects (Stiffler, 2011). In fact, when it comes to language, orcas may be as sophisticated as humans (Crawford, 2013). Some scientists have devoted their entire lives to cracking the code of the orca language and learning how to speak to these animals, but to no avail. Where humans have failed, however, drones may succeed, especially one drone in particular: the Wave Glider SV3 by liquid robotics.

The Wave Glider is a fully autonomous surface unmanned maritime vehicle (UMV) that has achieved record-breaking feats of autonomous sailing (Coxworth, 2012). Highly modular, it can be fitted with a variety of devices for observation and research. It can also be fitted with recording devices capable of recording the sounds made by killer whales when they communicate. This data, gathered over long periods of time, may give researchers the key they need to finally understand what orcas are saying to each other.

But how would such a research plan be implemented? And how would it deal with four key issues of drone use, namely privacy, ethics, safety, and loss of link/loss of system control?

Step one, in my opinion, is to introduce the Wave Glider into waters near the orca pod it is directed to follow. The drone needs to be close enough to the pod to record both video and audio and to allow the whales to become accustomed to its presence, but far enough away to not be considered a threat. This keeping of distance would preserve the whales’ privacy, which is key to keeping the drone operational. If the orcas perceive the Wave Glider as a threat, the drone will literally be dead in the water.

Step two is to follow the designated pod. This would present the most difficult challenge, as killer whales travel much faster than the Wave Glider. The best solution here may be to leave the UMV in the whales’ territory, knowing that they will come back.

Step three is to record and transmit as much audio and video data as possible. Linking visuals to sound is key to interpreting what the whales are saying, and the more data is gathered, the better.

It’s a simple plan, but I think it can work, once the other aspects of drone use are taken into consideration.

We’ve already covered privacy; the Wave Glider needs to respect the privacy of the whales, or risk being upended and torn apart. What about ethics, however? Is it ethical to spy on these animals (which some believe have the intelligence of humans) to try and decipher their language?

I believe the answer is yes, as long as that knowledge is put to ethical use. If we ever gain the ability to communicate with killer whales, we can use that ability to guide them out of potentially dangerous or overfished waters and to a location more suitable for the pod.

Safety plays a big part in this project as well, with the safety of the whales and other marine life being paramount. Again, if the whales regard the Wave Glider as a threat, they may smash into it to damage it and hurt themselves in the process. This is where gradual introduction of the Wave Glider in the orcas’ environment is crucial. I know from experience that orcas will tolerate small boats within a few hundred yards of their location, because I did a kayak whale-watching tour in 2014 and watched them do exactly that. The key: the whales were used to seeing the kayaks, and knew that they weren’t threats.

Loss of link/loss of system control is the final consideration, and an important one. Losing connection to the drone would negate the value of the experiment. Loss of system control may mean the drone wandering out of the whales’ territory, or worse – getting too close to the whales and getting demolished in the process. This can only be mitigated by periodic human monitoring and blind luck. Having a human repair and retrieval team on standby would negate the efficacy and cost savings of the experiment; if a human crew can be in the water watching the drone, it can also be in the water watching the whales themselves. Periodic monitoring, however, would identify problems within a reasonable amount of time and give a response team the chance to go to the drone and correct the issue. Ultimately, though, being problem-free will come down to preparation and luck. Prepare the drone for the mission as best as possible, then trust luck to take it the rest of the way.

Killer whales talk, but no one knows what they’re saying. The Wave Glider SV3 can help change that.

References

Coxworth, B. (2012). Wave Glider aquatic robots set world record. Retrieved from

     

     https://newatlas.com/wave-gliders-set-record/21840/

Crawford, L. (2013). Killer whales are non-human persons. Retrieved from

         

     http://greymattersjournal.com/killer-whales-are-non-human-persons/

Stiffler, L. (2011). Understanding orca culture. Retrieved from

     

     https://www.smithsonianmag.com/science-nature/understanding-orca-culture-12494696/