Wednesday, August 17, 2016

Sense and Avoid Technology for UASs... Risk Mitigation vs. Complete Risk Avoidance

The integration of unmanned aerial systems (UASs) into national airspace (NAS) has been an ongoing process that is slowly but surely making small steps to a future filled with UASs. There are multiple facets that make integrating UASs into NAS a complicated process. Questions that deal with aircraft registration, operator training, ethical responsibility, hacking and lost link procedures are all on the table when it comes to integration into NAS. One topic that has been at the front and center of the integration process is sense and avoid standards and responsibilities for UASs. Companies like Amazon, Google, and Intel are all working solutions to this challenge, but the Military and government entities like NASA are also looking for solutions to getting more robust sense and avoid technology into the skies fast.

Currently in Part 91.113 of the federal aviation regulation, it lays out the rules of the sky in terms of right of way for all aircraft. One key verbiage used is that aircraft must “see and avoid”. This does not include the ability to sense and avoid, which is the method by which a UAS would accomplish this same task. Due to this, these regulations are very limiting to UAS operation. For military UAS this means that we must have visual observers that are trained and qualified observing the aircraft at all times while operating in NAS. This creates a huge logistical addition to typical training missions and is a cumbersome task to accomplish when it comes to personnel and crew management. It is reported that the FAA is attempting to alter Part 91.113 this year in order to include sense and avoid technology as a legal substitute for see and avoid (Carey, 2013).

The Army specifically has been working with a ground based sense and avoid system (GBSAA) to augment their ability to fly in NAS. I have worked with this technology during my time as a UAS commander in the Army and have seen how beneficial proper implementation could be for units stationed in the US. GBSAA works very similarly to ground based ATC radar systems, the only difference is that it is completely dedicated to a particular UAS mission. The radar picture of both participating and non-participating aircraft is collected via LSTAR ground sensors and overlaid on the ground control station’s display and moving map that is utilized by the operator to navigate the UAS (SRC Inc., 2016). When fully functional and approved by the FAA, this system would allow the Army to fly large UASs through Military Operations Airspace (MOA). For locations like Fort Campbell, Kentucky, this would greatly expand the operational area for the multiple UAS units stationed there. 

Depiction of Ground Based Sense and Avoid System by SRC Inc. 

In the civilian sector, there is a greater focus on autonomous sense and avoid capabilities. This technology will allow for smaller UASs to go further and farther than ever before. For companies like Amazon, who want to be able to deliver merchandise via UAS, it will be imperative that they can utilize autonomous drones that will legally be authorized to travel beyond line of sight as long as they are equipped with autonomous sense and avoid technology (Popper, 2016).

Regulators are attempting to find a perfect answer to the sense and avoid issue, but the technology is currently very good, but not perfect. Some argue that the technology needs to be better, while others argue that aviation has always been about risk mitigation and not risk avoidance. Companies like Intel have produced sense and avoid systems that could reduce risk nearly to zero, but not promise a perfect solution to every scenario (Popper, 2016). Many feel that the same risk acceptance levels applied to manned aviation should be carried over to unmanned aviation rather than attempting to create a more stringent and difficult standard to achieve. What do you think is the best way forward? Please feel free to respond in the comments section below.    

References:

Carey, B. (2013, July 22). FAA Plans Unmanned 'Sense and Avoid' Rule in 2016. Retrieved August 17, 2016, from http://www.ainonline.com/aviation-news/air-transport/2013-07-22/faa-plans-unmanned-sense-and-avoid-rule-2016

FAA. (2004). Part 91 GENERAL OPERATING AND FLIGHT RULE. Retrieved August 17, 2016, from http://rgl.faa.gov/Regulatory_and_Guidance_Library/rgFAR.nsf/0/934f0a02e17e7de086256eeb005192fc!OpenDocument

Popper, B. (2016, January 16). What's really standing in the way of drone delivery? Retrieved August 17, 2016, from http://www.theverge.com/2016/1/16/10777144/delivery-drones-regulations-safety-faa-autonomous-flight

SRC Inc. (2016). Ground-Based Sense and Avoid Radar System. Retrieved August 17, 2016, from http://www.srcinc.com/what-we-do/radar-and-sensors/gbsaa-radar-system.html

Thursday, August 11, 2016

UAS Strengths and Weaknesses

In the world of Military Intelligence there are many methods of intelligence collection that span well beyond typical Electro Optic Infrared (EO/IR) imagery and real time HD video. The use of hyperspectral imagery has long been a great source of information that could either stand alone or even augment other forms of imagery. Hyperspectral imagery allows people to see beyond the surface of what they are looking at by examining the specific portion of the electromagnetic spectrum that an examined material is reflecting (Richter, n.d). This data, when compared to databases, can tell the observer exactly what material they are looking at, how much moisture is in soil, and even what types of minerals are present in top soil. From a defense perspective this can help determine the difference between true vegetation and camouflage or even if some kind of metal device has been planted in the ground. From a civil perspective this imagery can help farmers determine crop viability and soil conditions in support of precision agriculture.

The military currently utilizes satellites, manned aircraft and some large UASs controlled at the national level to gather most of their hyperspectral imagery (Military & Aerospace Electronics, 2013).  This means if a particular unit wants recently collected hyperspectral imagery of an area it will need to send requests up the chain and hope that the request can be processed in a timely mater. The lag between request and collection often causes users to end up with outdated products or no products at all.

As hyperspectral sensors become cheaper, smaller, and more accessible they are starting to make their way into the hands of the public. One great example of putting the power of hyperspectral sensors into the hands of the public is in the form of the Precision Hawk. The Precision Hawk is a small UAS that is hand launched and flown completely autonomously around a preassigned area. Upon landing the system uploads to a standard laptop and processes the data almost immediately. This UAS is well inside the price range of a small scale farmer and provides high definition hyperspectral imagery to a user for a very small cost and with little training or skill (Precision Hawk In., 2016).



In order to mitigate some of the challenges that exist in obtaining military hyperspectral imagery, the military is looking into making smaller and more accessible collection platforms that can be pushed down to a more tactical level. Taking notes from small platforms like the Precision Hawk, perhaps a hand launched small UAS like the MQ-11 Raven can be equipped with advanced hyperspectral sensors. Defense sensor developers are even working on ground based hyperspectral sensors that could be put on small tactical vehicles (Military & Aerospace Electronics, 2013).    


Resources
Military & Aerospace Electronics. (2013, January 1). Hyperspectral imaging sensors come into their own for aerospace and defense applications. Retrieved August 11, 2016, from http://www.militaryaerospace.com/articles/print/volume-24/issue-1/product-intelligence/hyperspectral-imaging-sensors-come-into-their-own-for-aerospace-.html

Precision Hawk Inc. (2016). EMPOWERING THE COMMERCIAL DRONE INDUSTRY. Retrieved August 11, 2016, from http://www.precisionhawk.com/  

Richter, R. (n.d.). Hyperspectral Sensors for Military Applications. Retrieved August 11, 2016, from http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA469649