Use of Geofencing to Minimize Accidental Death/Injury to Bystanders or Property

Use of Geofencing to Minimize Accidental Death/Injury to Bystanders or Property

Before delving into the specific examples of unmanned aerial vehicle mishaps and applications of lessons learned, there are a few points worth mentioning to gain a greater understanding of the technical innovations being made in the world of UAS. First, there are many lessons learned from manned flight mishaps and present a unique risk management opportunities, specifically with the specific application of those risk areas in terms of automated function monitoring and situational awareness. 

Engine out and glide considerations, specifically determination of safe landing areas away from populated ones based on known glide distance, is a predominant training area for junior pilots and ties into a sound decision making matrix and building situational awareness. The topic of individual UAV operator preparedness and training is an existing area of concern, but worth mentioning repeatedly. Commercial and private UAV operators may not be receiving the training required in order to maintain a solid grasp of the operating flight environment, and how to react accordingly with their surroundings (Murdock, 2015). Private UAV users may not be educated on flight restrictions for drones in their area or areas of high risk for collisions, in 2015 a private drone was flying above a news helicopter violating the 400-foot altitude restriction and the restrictions of flying unmanned aircraft in the heavily congested flight zone nearly causing a midair collision (Duecy, 2015). Collision with a drone and the rotor system of a helicopter can have disastrous results, and according to the FAA there are nearly 25 reported incidents every month involving drones that are flying too close to commercial aircraft (Duecy, 2015). 

Certain features inherent in the design of the UAV may increase safety such as geofencing, which is software that uses GPS or radio frequency identification (RFID) to draw a virtual boundary where the vehicle may not operate (Allianz, 2016). This integration of RFID geofencing into the NAS may be effective in minimizing the possibility of privately used drones entering congested and controlled airspace and high collision risk areas (Murdock, 2015). Geofencing legislation is currently underway in order to eliminate human error from the equation of UAV mishaps through built-in software, firmware and GPS tracking in the drone itself (Murdock, 2015). For emergency procedures, geofencing could prove to be a viable asset in terms of crash avoidance into populated areas for UAV flight malfunctions by placing them in large public areas such as parks, museums and sensitive areas (i.e. the white house lawn). 

In 2013, a drone crashed into the grandstand at Virginia Motorsports Park during the Great Bull Run, injuring several civilians (Weil, 2013). Additionally, in 2015 a drone crash landed on the center lawn of the white house in Washington, D.C. which led to a counterintelligence scare due to the small size of the vehicle flying in restricted airspace and not being seen on RADAR (Forrest, 2015). It is doubtful that civilian drone operators practice emergency procedures with their vehicles, and should not be able to fly in the aforementioned zones due to lack of general aviation training, emergency training and situational awareness. While this may be an oversimplification of the problem, this may lead towards a more safe and controlled environment for both manned and unmanned systems. 

Emergency procedure mode operations present an override condition and supersede the existing tasking that may be considered secondary or tertiary based on the changing conditions. In the application of manned flight as an example, a warning annunciator or light may change the priority of the flight from flying A-to-B, to landing immediately in a safe area free from hazards or people. Through establishing no fly zones for UAV’s ahead of time and ensuring they cannot enter via RFID or other features allows for reduced risk of midair collisions and potential flight malfunctions that would cause uncontrolled flight into the terrain (Allianz, 2016).  

References:

Allianz. (2016). Rise of the Drones: Managing the Unique Risks Associated with Unmanned Aircraft Systems. Retrieved from https://www.agcs.allianz.com/assets/PDFs/Reports/AGCS_Rise_of_the_drones_report.pdf

Duecy, L. (2015). Helicopter Crew Spots Drone Flying Feet Above KOMO Chopper. Retrieved from http://komonews.com/news/local/helicopter-crew-spots-drone-flying-feet-above-komo-chopper 

Forrest, C. (2015, March 20). 12 Drone Disasters That Show Why the FAA Hates Drones. Retrieved from http://www.techrepublic.com/article/12-drone-disasters-that-show-why-the-faa-hates-drones/

Murdock, S. (2015, August 21). 5 Points Which Senator Schumer Might Consider in Drafting his UAS Geofencing Legislation (No Drone Zones). Retrieved from http://jdasolutions.aero/blog/schumer-geofencing-provision/

Weil, M. (2013, August 26). Drone Crashes into Virginia Bull Run Crowd. Retrieved from The Washington Post, https://www.washingtonpost.com/local/drone-crashes-into-virginia-bull-run-crowd/2013/08/26/424e0b9e-0e00-11e3-85b6-d27422650fd5_story.html?utm_term=.2bbedf7c05c9

Wireless Energy Transfer to Overcome Electrical Energy Limitations to UAV Mission Effectiveness

Wireless Energy Transfer to Overcome Electrical Energy Limitations to UAV Mission Effectiveness

Given all the technological advances in unmanned systems over the last several years, electrical demands and battery life have been a limiting factor when discussing UAV mission life and overall system capabilities. Effectively managing and designing a system that is conscientious of power demands depending on sensor/payload duty cycle, as well as required times of higher than normal power regimes (i.e. takeoff and landing) where electric motors may be running outside of predetermined ranges (Hepperle, 2009). Increasing battery life using different chemical composite structures, such as lithium polymer, lithium-ion, nickel-cadmium, and nickel-hydrogen may be expensive and too heavy for a vehicle to carry and effectively execute the prescribed mission (Hepperle, 2009). 

In order to maximize vehicle efficiency in terms of airborne time and minimizing weight to increase payload/sensor capability, it may become necessary to externally power a vehicle while it is in operation. Operation of a tethered drone using a hardwire connection could prove problematic as an electrical umbilical cable could yield issues in vehicle maneuverability, and severely limit the intended flight path (Glaser, 2016). However, line of sight wireless transmissions using millimeter and microwave technology, there is a potential for a vehicle to receive constant power during regular operation so long as it remains within a predetermined area. For some of these technological innovations to become realized, a military application may enter the technical sphere first prior to incorporation into a commercial or private market. 

Every year the U.S. Combating Terrorism Technical Support Office puts out a Broad Agency Announcement (BAA), which highlights technical issues and requirements for future research areas the United States Government wishes to purchase or invest in (Nordrum, 2016). In November of 2016, the BAA requested technology research areas for the intent to wirelessly recharge drones while in flight, with a clear emphasis on meeting new demands in both its counterterrorism and counterinsurgency efforts (Nordrum, 2016). Based on this announcement, it is a strong indication that the need for increased range and flight time for unmanned vehicles is not only a technical innovation that is requested for research, but also a critical application area for the US Department of Defense, Homeland Security and possibly feed into US Military service branch requirements (Nordrum, 2016).

Despite the research request at the discretion of the DoD, commercial companies such as Facebook, and Amazon are already heavily researching methods of increasing flight time for their unmanned vehicles (Morris, 2016). Research efforts to increase the sophistication of electrical subsystems internal or external to the UAV include (but are not limited to): photovoltaics for fixed wing type UAVs, recharge stations integrated into existing structures such as telephone poles or buildings, and focused electromagnetic fields (such as microwaves) and lasers to transmit energy (Morris, 2016).

References:

Glaser, A. (2016, October 12). Wireless Charging Could Keep Drones in the Air for Much Longer. Retrieved from https://www.recode.net/2016/10/12/13257790/wireless-charging-drones-air-longer-solar-power-batteries 

Hepperle, M. (2009). Electric Flight – Potential and Limitations. German Aerospace Center: Institute of Aerodynamics and Flow Technology, STO-MP-AVT-209. Retrieved from http://www.mh-aerotools.de/company/paper_14/MP-AVT-209-09.pdf.

Morris, D. (2016, September 24). Demo Shows Drone Flying Under Wireless Power. Retrieved from Fortune Magazine, http://fortune.com/2016/09/24/drone-flies-wireless-power/

Nordrum, A. (2016, December 12). Wanted: In-flight Drone Charging, Itty-Bitty Spy Cams, and More. Retrieved from http://spectrum.ieee.org/tech-talk/aerospace/military/wanted-wearable-chemical-sensors-wireless-recharging-stations-for-drones-and-ittybitty-spy-cams

Artificial Intelligence-based Functionality for Unmanned Aerial Systems

Artificial Intelligence-based Functionality for Unmanned Aerial Systems

In an article released in late 2016, the topic of Artificial Intelligence (AI), and the level at which AI will be or should be incorporated into UAV use, is discussed heavily with reference to drive and functionality for the autonomous system to exist (Karpowicz, 2016). The drive for a technologically superior product in the world of unmanned aerial systems could be to minimize costs and human-machine interface (HMI) requirements across a multitude of users and operators. Developmental and maintenance costs are relatively unknown as the finite position or task in which a system may use an autonomous decision making framework changes based on other variables such as operating environment, design mission, and end-user requirements (Karpowicz, 2016). 

Parallel circumstances can be drawn at a basic level to the requirements set in place to maneuver a driverless car, where a basic operating requirement may be solely moving from A to B given all the external influences/factors. Variables and factors that would hinder performance could be other drivers on the road, communication links and constraints to communications channels between unmanned systems, and the specific road conditions in which the vehicle is designated for travel (Karpowicz, 2016).. Similarly, this stance can be taken into the frame for an aerial vehicle, where other unmanned systems and manned systems alike are required to share the designated airspace for continuous and safe operation. Exploring concepts such as Sense and Avoid (SAA) which can be meant to serve as a secondary or tertiary measure for collision avoidance, is also an important part of the AI concerns for advancing UAV technology (Knight, 2017). Ensuring variables are adequately accounted for and researched to not only find risk areas, but preprogram risk mitigation measures into the AI logic is extremely important for AI integration into UAS.

The level at which a vehicle is designed to perform AI-based functions is certainly at the discretion of the user/operator, and should be clearly annotated in the end-user requirements and expressed in the preliminary design review. Whether a UAV is required to execute an entire electronic intelligence (ELINT) mission from takeoff, to data collection/relay, to landing and shutdown or simply have automated landing and terminal area procedures is up to the user. Design and engineering level efforts will be directly affected by these requirements, but as the skies become more congested with multitudes of unmanned vehicles, the need to effectively monitor, as opposed to control, these vehicles may rely heavily on AI-based advancements.

References:

Karpowicz, J. (2016, September 8). How Will Advances in AI Impact the Approach to UAV Technology?. Retrieved from http://www.expouav.com/news/latest/will-advances-ai-impact-approach-uav-technology/

Knight, W. (2017, January 4). 5 Big Predictions for Artificial Intelligence in 2017. Retrieved from https://www.technologyreview.com/s/603216/5-big-predictions-for-artificial-intelligence-in-2017/