UAM, UTM, NextGen and NAS Integration

NASA and other aviation and transportation companies like Uber and Blade are developing a system of air transport in Urban areas to solve the issues of traffic congestions. While air transportation in urban areas such as the use of helicopters has been around for a while, the focus of research and technological development has been mainly in the development and integration of Unmanned Aerial Systems (UAS). NASA has been involved in the development of safe and efficient air transport and package delivery above populated areas (NASA, 2019).

In order to safely integrate these UAM vehicles and other  unmanned aerial vehicles into the National Airspace System(NAS), NASA has partnered with other UAV developers and the Federal Aviation Authority to develop the UAS Traffic Management System(UTM), with a focus on developing technologies and procedures (NASA, 2019).The bulk of UTM flight tests involve pilots maintaining constant visual contact with the UAS. However, UTM flight research is beginning to fly extended distances. The challenge now is identifying airspace operations requirements to enable safe visual and beyond visual line-of-sight drone flights in low-altitude airspace (FAA, 2019). UTM will also work with FAA’s Next Generation Air Transportation System (NextGen) to establish procedures for effective control of airspace from the surface to 400 ft AGL. The UTM will enable the management of low-altitude uncontrolled UAS operations  (FAA, 2019).

To effectively fly Beyond Line of Vision (BLOV), UAS operating within the NAS will have to be installed detect, sense, and avoid (DSA) systems as per FAA regulations. The DSA systems which includes systems like the Automated Dependent Surveillance-Broadcast (ADS-B) and Terrain alert and Collision Avoidance System (TCAS) will help UAS meet FAA regulatory requirements for integration into the NAS.

ERAU AVRL Crash Lab

 The ERAU AVRL simulation tool helps explores the design, assembly, and simulation of different Unmanned Aerial Systems (UAS) for different missions such as a missing hiker at the Yosemite, Agricultural survey and Crash lab inspection. For this activity, I selected the Virtual Crash Lab which involves inspecting a crashed Boeing 737. I selected to assemble and simulate three different UAS to assess their performance in this crash site environment.

The first UAS I tried using for this mission was the Tern fixed-wing UAS. I added a brushless electric motor, auto control, a GPS module, dipole antenna, EO camera with gimbal, temperature sensor, ERA Powerhouse 10000 battery. For the GCS, I used the GCS Trailer and large Dipole antenna. I, however, found the Tern fixed-wing UAS unsuitable due to the fact that it flew past the crash site very fast and did not enable for a good view or assessment of the crash site.  Because the crash site was also located over a small area. The UAS was also limited to a certain turn radius. When selecting waypoints for automated flights, a warning that the turn or angle would exceed the maximum turn radius of the aircraft.

Next, I tried the Gadfly quadcopter UAV with an empty weight of 1.6 lbs and a max weight of 3.562lbs. I added an X5 Red Electric motor, auto control, NDVI, and PSI 0015 IR Cameras, ERA Enterprise 2300 battery, Dipole antenna, and a GPS Module. The total takeoff weight was 2.5lbs, 900 m radio range, and about 18 mins of flight time. The big problem with the Gadfly quadcopter UAV was that both the NDVI and PSI 0015 IR cameras were attached and fixed to the UAS. This hindered viewing the crash site from different angles especially when the UAS was flying out of direct view of the crash site. The result is an incomplete picture or view of the crash site.

      The third UAS  I tried was the Condor Octorotor (professional) UAS. The Octorotor allowed for the assembly of an X5 Red Electric motor, Infrared sensor with gimbal and a LiDAR camera, GPS Module, Dipole antenna, ERA Powerhouse 10000 battery. Unlike the Gadfly Quadcopter, the angles of the Infrared sensor with gimbal and LiDAR cameras were adjustable allowing for a better view and assessment of the crash site.

The man-portable and handheld GCS enabled the operator to have a direct view of the UAS around the crash, unlike the Trailer type GCS. This can enable the operator to make adjustments when necessary especially when flying in manual control mode.

UAS Human Factors

Human factors in Unmanned Aerial Systems are the effects of interactions with UAS systems which can either be positive impacts on the system or negative impacts on the system due to limitations in system design or human errors. 

When it comes to the design and manufacture of unmanned system ground control systems, it has been noted that many designers do not involve unmanned system operators during designs of these systems.  The lack of aviator involve has led to cockpits that are wither misapplied (McCarley & Wickens, n.d.). Designers also tend to use video game and smartphone designs to develop unmanned system GCS which are in most cases divergent from aviation standards. The rush to put out a working model has also created underdeveloped or incomplete mission requirements. The result of these inconsistent or non-standardized GCS are pilot errors and confusions or inappropriate responses during emergencies which could lead to the loss of the UAS.

Another human factor in UAS operation is over automation and inadequate ineffective command interfaces. For many UAS especially the highly autonomous systems such as the RQ4, most of the operations are automated with the operator only having access to a throttle, keyboard and a mouse. In most of these highly autonomous systems, the pilot is more of a program manager than an operator. The Pilot also uses more text entries which can cause distractions and errors as the pilot/operator has to shift eyes from keyboard to textbox(Howe, 2017).

Solutions to overcome relying heavily on autonomy is to make the UAS systems more intuitive. Command interface should be more of a display, buttons, and layouts, and button guards for critical knobs to avoid mistakes when under duress. Challenges with video visuals and depth can be overcome with the development of technologies that aid in-dept perception such as heads up displays and stereoscopic vision technology. In the long run, standardization of GCS such as the ones seen in manned aircraft will lead to less confusion and errors which are usually more experienced in emergency situations.

 References

Howe, S. (2017). The leading human factors deficiencies in unmanned aircraft systems. Retrieved from https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20170005590.pdf

McCarley, J., & Wickens, C. (n.d.). Human factors concerns in uav flight. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.551.6883

 

Adaptive Cruise Control

The past decade has seen a rapid development of advanced driver assistance systems (ADAS). Through the development of intelligent systems based on detection onboard detection and perception, engineers aim to significantly improve road safety. When these systems are fully developed, advanced driver assistance systems (ADAS) can detect potential unsafe conditions early and avoid the possibility of a crash. One of the big advanced driver assistance systems (ADAS) applications currently in use is the Adaptive Cruise Control system (ACC) (Padhi, 2019).

The Adaptive Cruise Control system is usually detected through the Radar and Lidar sensor suite that is installed in front of the vehicle. The radar sensors help the Adaptive Cruise Control maintain the speed that the driver sets as long as the road in front are free of any vehicle or obstacles and then gently slows down by engaging the brake system when the vehicle detects slowing vehicles at a predetermined range (Bosch, 2018).

One challenge facing the Adaptive cruise control is that it works well when a car directly follows another car but often fails to detect stationary objects. Adaptive cruise control is programmed to focus on maintaining a safe distance from other moving vehicles and to ignore stationary objects as the human operator should be able to steer clear of stationary objects. The result is not always the case. This situation is most likely going to result in a crash. The software to complement and utilize the full potential of autonomous-vehicle hardware still has a way to go. Development timelines have stalled given the complexity and research-oriented nature of the problems (Padhi, 2019).

The ability to detect both moving and stationary vehicles and objects will be the major update to the Adaptive Cruise Control. The field of advanced driver assistance systems still has a long way to go due to the complexity of developing the necessary software technology.

References

Bosch. (2018). Adaptive cruise control. Retrieved from https://www.bosch-mobility-solutions.com/en/products-and-services/passenger-cars-and-light-commercial-vehicles/driver-assistance-systems/adaptive-cruise-control/

Padhi, A. (2019). Autonomous-driving disruption: Technology, use cases, and opportunities | McKinsey. Retrieved from https://www.mckinsey.com/industries/automotive-and-assembly/our-insights/autonomous-driving-disruption-technology-use-cases-and-opportunities

FIRRE UGV Joint Battlespace Command and Control System

The Joint Battlespace Command and Control System (JBC2S) is the command-and-control element for the Family of Integrated Rapid Response Equipment (FIRRE) such as the FIRRE UGV. JBC2S is a network-centric, geospatial command and control system that allows the field commander and above to plan and execute missions utilizing multiple and disparate manned and unmanned assets. It utilizes standard map formats (GeoTIFF, DNC, CADRG) for displaying map data and for tracking asset placement and movement.

JBC2S is a fusion of the framework of the Multiple-robot Operator Control Unit (MOCU), the functionality of the Multiple Resource Host Architecture (MRHA). The look of JBC2S is much more improved over the MRHA through the display of raster graphics data in addition to vector graphics data. The use of raster images reveals much more detail about the environment and presents a modern, state-of-the-art user interface (Kramer et al., 2006). JBC2S control station operates either in Monitor mode, in which the operator observes and monitors the status of the unmanned vehicles and sensors or the operator is in direct control of a single resource.

The FIRRE UGV provides telemetry data such as engine speed, engine temperature, hydraulic pressure, hydraulic temperature, track speed, fuel level, battery voltage, and obstacle detection data to the JBC2S through the MRHA IDD protocol. It also provides the pan/tilt positions of a SeaFLIR imager and the AN/PPS-5D radar located on the FIRRE UGV. JBC2S uses pan/tilt information to display coverage areas for these sensors on the map. The Platform Get Status response includes several flags such as GPS failure, emergency halt, low-battery warning, tamper alarm, and diagnostic failure. (Kramer et al., 2006).

The look and feel of the JBC2S is much better with the incorporation of the ArcGIS Engine architecture which is a library of embeddable GIS components such as the toolbar, ArcMap (2-D), ArcGlobe (3-D), and ArcScene (3-D) components.

Reference

Kramer, T. A., Laird, R. T., Dinh, M., Barngrover, C. M., Cruickshanks, J. R., & Gilbreath, G. A. (2006).  FIRRE joint battlespace command and control system for manned and unmanned assets (JBC2S). Paper presented at the , 6230 623020. doi:10.1117/12.666191 Retrieved from https://www.spiedigitallibrary.org/conference-proceedings-of-spie/6230/623020/FIRRE-joint-battlespace-command-and-control-system-for-manned-and/10.1117/12.666191.short

UAV Data Storage

In most cases, UAV flight time such as the DJI Phantom 4 Pro will fly between 5 minutes to a maximum of about 30 minutes maximum. For UAVs with the capability to take pictures and record videos, pilots can quickly realize that in about to minutes, their UAV camera’s SD card storage is quickly filling up. UAVs such as the DJI Phantom 4 Pro can allow a maximum of 128 GB of SD. Taking pictures and recording high-quality 4K videos can see one’s storage fill up very fast. 128GB model will allow owners to record up to about 6 hours of 4K video. many technologies are available to enable UAV operators to continue to fly and shoot video and take pictures on their UAV camera without completely running out of onboard camera storage.

One way to decrease the size of data produced from sensors is the use of codecs. A codec is a software that compresses videos to a manageable data size than the original file. One of the most popular type of codecs is H.264 Codec. An H.264 codec software enables the recording of HD digital video at very low data rates. The H.264 compresses video to about half the space required to save a standard digital video (MPEG-2) (DivX, 2019). Phantom 4 Pro allows for record UHD 4K (4096X2160) at 60fps, at a maximum bitrate of 100Mbps using the H.264 codec (DJI, 2019).

Another method of freeing up storage space is to directly upload images and videos on cloud storage. A good example is the Memery’s Dragonfly app that allows the operator to edit recent images and videos and then transfer the edited footage to cloud storage. The drawback to the Dragonfly app is the price. There are a lot of reasonably priced cloud storage alternatives such as Google One and Dropbox cloud storage services

References

DIVX. (2019, ). H.264 definition. Retrieved from https://www.divx.com/en/software/technologies/h264/

DJI. (2019). DJI Phantom 4 pro – specs, tutorials & guides – DJI. Retrieved from https://www.dji.com/phantom-4-pro/info

Sensors used in UAVs

Sensors used in UAVs are mostly categorized into Navigation Sensors and Sensors used for missions. Most of the sensors used for navigations on UAVs comprise of sensors such as Global Navigation Satellite Systems (GNSS) which includes the Global Positioning Systems and the Inertial Navigation System. GPS and INS complement each to the point that they are the preferred sensors for the majority of autopilot systems (Mejias, Lai, & Bruggemann 2015)

Other sensors used for navigations or surveillance include the Electro-Optical (EO) Sensors and Radio- Wave Sensors

Electro Optical (EO) Sensors;

i.                    Visible Spectrum: These are either digital still cameras or machine vision webcams that are used to take pictures or provide a continuous stream of images respectively. This type of sensor cameras is mostly used in aerial photography.

ii.                  Infrared sensors:  Infrared cameras that are sensitive to light at a long wavelength and form images using infrared radiation in the spectrum at wavelengths of 14,000nm.

iii.                Hyperspectral Imaging:  these are sensors that acquire image data simultaneously in multiple adjacent spectral bands. This type of sensor is mostly used for identifying different compositions of materials

Radio- Wave Sensors

Airborne Radio Detection and Ranging (Radar) and Light Detection and Ranging (Lidar) systems are used to determine the range, altitude, direction and speed of objects by measuring signal return time of transmitted controlled radio pulses. Radar sensors such as the Ground Proximity Warning Systems (GPWS) have been widely used in the aviation world. Radars are also recently being used in the automotive industry for collision warning systems. ( Mejias, Lai, & Bruggemann 2015)

Exteroceptors (External) and Proprioceptors(Internal)

Exteroceptors are sensors that allow the robot of unmanned systems to perceive or interact with its environment whole Proprioceptors sensors measure the internal kinematic and dynamic parameters of the unmanned system. Such parameters include the amount of torque exerted by the actuator. Exteroceptors are grouped into contact and non-contact sensors. The contact sensors perceive their environment by touching the objects and shaped in its environment while non-contact sensors obtain information about its environments without physical contact. Such non-contact sensors include pneumatic sensors, ultrasonic sensors, and optical sensors (Gupta, Arora, & Wescott, 2016)

Sensor Review
 https://ieeexplore-ieee-org.ezproxy.libproxy.db.erau.edu/xpls/icp.jsp?arnumber=4772754

For the sensor review, I have selected the journal article that uses the Miniature Strapdown Inertial Navigation System (mini INS) with inertial microelectromechanical systems (MEMS) for control of different UAVs in the autopilot mode. Inertial Navigation System sensors as noted earlier are used for navigation. The Miniature Strapdown Inertial Navigation System (mini INS suit this mission as it is low cost with small overall dimensions and consumes very low power. The sensor can provide the required accuracy of determining the attitude, position, and velocity of the UAV. The use of the inertial system as the main component of the autopilot provides the required flying accuracy with the capability of UAV destination to the desired waypoint at a given time and tracking the predefined path. (Kortunov, Dybska, Proskura, & Kravchuk, 2009)

The disadvantage of using this system in an autonomous mode is hampered since the instability of MEMS sensor characteristics causes fast accumulation of errors in the determination of navigation data. The effective approach to solving this problem is the integration of mini INS with different external measuring devices like GPS navigation, which is considered as the most precise facilities of determination of moving object position, magnetic compass, and air data sensor (Kortunov, Dybska, Proskura, & Kravchuk, 2009)

References

Gupta, A. K., Arora, S. K., & Wescott, J. R. (2016). Industrial automation and robotics: An introduction., 390-401. Retrieved from https://ebookcentral-proquest-com.ezproxy.libproxy.db.erau.edu/lib/erau/reader.action?docID=4895078&query=industrial+automation+and+robotics%C2%A0(Links%20to%20an%20external%20site.)#

Kortunov, V. I., Dybska, I. Y., Proskura, G. A., & Kravchuk, A. S. (2009). Integrated mini INS based on MEMS sensors for UAV control. Retrieved from https://ieeexplore-ieee-org.ezproxy.libproxy.db.erau.edu/xpls/icp.jsp?arnumber=4772754

Mejias, L., Lai, J., & Bruggemann, T. (2015). Sensors for missions Springer, Dordrecht. Retrieved from https://search.credoreference.com/content/entry/sprunmanned/sensors_for_missions/0