Ping 2020 ADS-B in Small UAS for Sense and Avoid Operations

Ping 2020 ADS-B in Small UAS for Sense and Avoid Operations

Research Analysis

Miguel A. Linares

Embry-Riddle Aeronautical University

  

Introduction

         Obstacle and traffic avoidance has been a growing concern to the Federal Aviation Administration (FAA) since the introduction of unmanned systems operations in the National Airspace System. (NAS). Larger UAVs like the MQ-9 Predator B are equipped with transponders that enable Air Traffic Controllers (ATC) to detect and identify the aircraft up to a certain range. Using radar technology, a Radio Frequency (RF) signal is sent out interrogating the transponders for aircraft information (Rogers, 2016). Although this technology works well for large UAV operations in the NAS it is unfeasible to include such hardware in small scale drones that also pose a potential hazard to other aerial platforms manned and unmanned in the NAS. Sense and avoid is, as the name describes, the ability of a system to detect and maneuver to prevent collisions. It has also become a term that mirrors the FAA Visual Flight Rules (VFR) term “See and Avoid” stating the pilot’s responsibility to fly actively avoiding traffic as a requirement to operate in the NAS while 1,000’ above, 500’ below, and 2,000’ horizontal from clouds and the visibility is at least 3 statute miles (FAR, 2011). Nonetheless, it is nearly impossible for a manned aircraft pilot to avoid collisions with small UAS (SUAS) if he/she cannot see or detect them.

uAvionix Ping 2020 Description

         One solution to this problem that the FAA is looking to implement by the year 2020 is Automatic Dependent Surveillance – Broadcast (ADS-B). This technology involves equipping aircraft, to include SUAS, with a sensor much like a transponder that can communicate with ATC and other aircraft via Line of Sight (LOS) and ground stations, to share information on satellite based navigation, position, altitude, and identification (TRIG, 2016).

        uAvionix has developed a full range ADS-B transceiver that is small enough to install in SUAS. This sensor is capable of detecting aircraft in real time, who are transmitting on the 1090MHz and 978MHz bands within 100 statute miles. It then generates the reports of potential threats based on a programmable spherical radius. Although the international standard for high altitude flight (at or above 18,000 feet) is the 1090MHz band for transmissions, the Ping2020 transmits on the 978MHz band which is within minimums for flight below the 18,000’ mark. This is particularly ideal for SUAS as they generally operate at lower altitudes. The fact that it transmits on a smaller band also equates to lower power consumption, which enhances the compatibility with SUAS. Measuring a minute 25x39x12mm and weighing only 20 grams, the required input power of the sensor is 6-29V and 30W peak. This low power consumption makes it a great match for SUAS. The sensor uses a MavLink for its input and output data interface. The final factor that makes the Ping2020 an ideal sense-and-avoid option for SUAS is its ability to integrate directly with the Pixhawk Autopilot and with DJI SUAS via a DJI software development kit (uAvionix, 2016).

Conclusion

              While the Ping2020 ADS-B sensor provides the “sense” portion of sense-and-avoid, it still relies on operator input or autonomous system reaction to maneuver and avoid collisions with the detected traffic. Also this system does not enable autonomous navigation indoors to avoid colliding against objects in the SUAS’s surroundings. This requires the use of technologies like LIDAR and vision based obstacle detection, along with the algorithms for autonomous avoidance maneuvers. Nonetheless, in the realm of “Sense and Avoid” in terms of the FAA’s “See and Avoid” requirements for NAS operations, the Ping2020 ADS-B sensor is the ideal choice for SUAS due to its size, power requirements, transmission capabilities, and interface assimilation features.

References

FAR. (2011). Sec. 91.155 — Basic VFR weather minimums. Retrieved from Rising Up Aviation: http://www.risingup.com/fars/info/part91-155-FAR.shtml

Rogers, T. (2016). Transponders, How They Work . Retrieved from Avionics List; new and used avionics: http://www.avionicslist.com/articles/how_transponders_work.php

TRIG. (2016). Introduction to ADS-B. Retrieved from Trig Avionics: http://www.trig-avionics.com/knowledge-bank/ads-b/introduction-to-ads-b/

uAvionix. (2016). ping2020 Overview. Retrieved from uAvionix: http://www.uavionix.com/products/ping2020/

Control Station Analysis of Unmanned Maritime Vehicle

Bluefin-21 AUV Control Station Analysis

Research Study

Miguel A. Linares

Embry-Riddle Aeronautical University

Introduction

         Command and control of unmanned systems requires the provision for the operator to monitor and manipulate the system whether the system is fully automated or manually remote operated. There has also been an evolution of the data presentation architectures and methods used in conjunction with the equally numerous types of unmanned systems. This study will focus on the hardware, software, and user interface of the command and control station of the Bluefin-21 Autonomous Underwater Vehicle (AUV) and the negative aspects and challenges that it faces, and possible recommendations in overcoming such challenges.

Bluefin-21 AUV Control Station Description

         The Bluefin-21 AUV, owned by General Dynamics (GD) and Bluefin Robotics, is a highly capable unmanned underwater system used in offshore surveying, search and salvage, archaeology and exploration, oceanography, mine countermeasure and unexploded ordnance location applications. It is capable of carrying multiple sensor payloads with high energy capacity at depths of nearly 15,000 feet below the surface. (Keller, 2014) The system performs its pre-programmed mission autonomously and is monitored via its Operator Tool Suite. It is a user interface that provides data display of mission and vehicle status during all phases of the operation. The vehicle’s acoustic tracking transponder and acoustic modem send INS navigation and system status data to the control station for the operator to monitor. The system’s iridium antenna can also send larger data signals between the operator and the vehicle such as redirection commands but only when the vehicle antenna is above the surface of the water. (Bluefin, 2016) The operator software used is based on a Windows operating system and can be run from any desktop or laptop computer. This makes the hardware requirements of the control station very low and even portable. The user interface displays data in three different ways consisting of the Mission Planner, Dashboard, and Lantern, depending on the mission phase. (Bluefin, 2016)

        The mission planning and verification phase is used to create the path and depth that the vehicle is to follow based on a chart seen on the graphical tool. The operator also sets specific safety settings, constraints, and recapture points and courses of action. The dashboard provide tracking of the vehicle’s position based on the chart on the graphical tool where the plan was created. It also includes vehicle status, attitude, and position as well as current behavior. In the dashboard, the operator can also use specific diagnostic tools for post-mission maintenance of the sensors and subsystems. Finally, the lantern display is used in the post-mission processing of the collected data. This includes the combination of survey lines and other sensor-based collected imagery with the vehicle position and contact data for accurate product delivery. Lantern also allows zooming, contact measurement, and height above ground functions for better data analysis, and the geographical based information enhances the accuracy of targets identified. (Bluefin, 2016)

Challenges and Recommendations

            One of the main negative aspects of this interface on the Bluefin-21 is that it is isolated to that specific vehicle and does not enable multiple vehicle operations if desired. Another disadvantage is the inability to handle operations of other systems outside of the Bluefin Robotics family; even if those systems are part of the General Dynamics family. One solution presented by GD is the open architecture computing infrastructure and design of their Common Dispay System (CSD) family of control stations. These are actual command and control consoles that have interchangeable components and support the operation of the entire GD fleet of unmanned systems. (General Dynamics, 2016) While this widens the operational flexibility, naval military also employs aerial platforms. For this type of user, a control station more along the lines of Textron Systems’ Universal Ground Control Station (UGCS), would provide commonality and interoperability across platforms and domains. (Textron, 2016) Another benefit of having multiple vehicle commonality of control is that UAVs can enable communications relays beyond line of sight with a USV or even a UUV that has a link to a surface buoy or can come up to send the signal. The challenge of control of multiple unmanned systems from a common control station has been tackled by many such as QuinetiQ, who has also demonstrated this capability with some limitations but continues to improve on the station’s ability to run services even for platforms of different manufacturers. (Cheng, 2014)

Conclusion

            The Bluefin-21 has an operator tool suite that is likely good enough to meet a small scale enterprise utilizing this single AUV. It also translates into lower hardware costs noting it can be run from a Windows OS based laptop computer that is hooked up to the acoustic modem as part of the communications network. It provides a simple and easy to use three-tier interface for mission planning, monitoring, and data processing. However, for larger enterprises such as naval military operations encompassing salvage search, mine detection, and defense countermeasures that require an aerial component and systems, an interoperable and common multi-platform control station may be required for better mission accomplishment.

References

Bluefin. (2016). Bluefin-21. Retrieved from Bluefin Robotics: http://www.bluefinrobotics.com/vehicles-batteries-and-services/bluefin-21

Bluefin. (2016). Operator Software. Retrieved from Bluefin Robotics: http://www.bluefinrobotics.com/technology/operator-software/

Bluefin. (2016). Sensor Integration. Retrieved from Bluefin Robotics: http://www.bluefinrobotics.com/technology/sensor-integration/

Cheng, J. (2014, April 11). The quest for a universal remote for unmanned systems. Retrieved from Defense Systems: https://defensesystems.com/articles/2014/04/11/uav-common-control-qinetiq.aspx

General Dynamics. (2016). OPEN CI – Open Architecture Computing Infrastructure. Retrieved from General Dynamics Mission Systems: https://gdmissionsystems.com/maritime-strategic/open-ci/

Keller, J. (2014, April 14). Bluefin Robotics wins $7.1 million contract to develop Navy's next-generation underwater drones. Retrieved from Military & Aerospace: http://www.militaryaerospace.com/articles/2014/04/bluefin-black-pearl.html

Textron. (2016). UNIVERSAL GROUND CONTROL STATION (UGCS). Retrieved from Textron Systems: http://www.textronsystems.com/what-we-do/unmanned-systems/UGCS

Studying DJI's Phantom 4 in Data Protocols and Format

Phantom 4 UAS Data Protocol and Format

Research Analysis

Miguel A. Linares

Embry-Riddle Aeronautical University

 

Introduction

         This study focuses on DJI’s Phantom 4 Aerial Photography UAV in respect to its data capture, storage, and management as well as the relevance of its sensors’ power usage on its general data strategy. The world of small UAS has taken a number of directions from drone racing for the thrill enthusiast, to aerial imagery applications in agriculture, fire-fighting, and even film-making. For all these applications, an important aspect of operations is the connectivity and transfer of data from the point of collection to the end user or operator. The type of method used can vary depending on how the UAV is used and what purpose it is serving.

 

Phantom 4 UAS Description

         The Phantom 4 is a small quadcopter UAV that precedes and improves upon the Phantom 3 Pro. This improved Phantom is equipped with more sensors expanding functionality and capability. It has two sonar radar sensors facing forward and two facing downward. There are also two ultrasonic sensors on the belly of the UAV’s main body. (Popper, 2016)  These front facing sensors send data to its intelligent flight computer which creates a 3D volumetric map of the environment and calculates a modification to the flightpath enabling the system to avoid obstacles in their 94° field of view (FOV) with detection range of up to 50 feet. (Korey, 2016) The downward facing sensors help the system hover in place and navigate mainly indoors and where a GPS connection is unobtainable. (DroneWorld, 2016) Other sensors in the system include the link connectivity sensor, which would trigger a return-to-home (RTH) mode if it senses a loss of connection with the controller. It also has a power level sensor that informs the operator of the remaining battery left via a “status LED” light bar. (Patterson, 2016) The main camera on the Phantom can take stills with 12 Megapixel quality and video recording quality of UHD (4096 x 2160) with a maximum video bitrate of 60 Mbps. The power source is a 15.2 V, 5350 mAh, LiPo 4S battery that enables approximately 28 minutes of flight time. (DJI , 2016)     

Data Format, Protocols and Storage Methods

         The Phantom 4 UAV can be used in numerous applications. When used for professional aerial photography and videography, as is the main purpose of this UAS, the data collected is generally not time critical and can wait to be downloaded from the on-board storage device once the craft lands. The UAV uses a Micro SD card with up to 64GB capacity and must have a Class 10 or UHS-1 rating. The still image data captured by the camera can be in JPEG and DNG formats, while the video capture can be MP4, MOV, and MPEG-4. (DJI , 2016) The Phantom does allow for a form of instant data transfer from the UAV while it is still in flight. However, although the camera sensor can take video with UHD and 4K quality, its download capability is limited to 1080p video and JPEGs, not 4K or higher quality imagery. (Popper, 2016)

Data Treatment Strategy Recommendations

Due to the power usage from flight and by sensor and subsystems utilization, batteries are quickly drained thus diminishing the operational endurance of the UAV. For the Phantom, I’d recommend removing onboard storage and generate an instantaneous data transfer of whatever is recorded. This will ensure the safety of the data being captured despite the fate of the UAV should an accident occur.

The way to achieve this robust connection between the UAV and the operator controller is via a large-aperture multiple quantum well modulating retroreflector in optical forms of data transfer. The emergence of free-space optical communications and the technological advancements of lasers open up a more appealing venue for data transfer due to the shorter wavelengths, which also provide lower probability of signal interception and jamming. Also, establishing the use of this connection, which enables lower power consumption and higher data transfer rates with greater bandwidth, instead of using a 2.4 GHz ISM the system will allow for the quick transfer of larger UHD quality video; a function it is currently unable perform. This larger video can be downloaded to the user’s interface but only until after the craft lands. This form of connection would also extended flight time due to greater power allocation to flight functions and to lighter payloads being carried in optical communications equipment compared to its radio frequency communications counterparts. (Gilbreath, et al., 2001) This in turn maximizes the UAV’s utility and expands its envelope of operational applications.

 

Conclusion

              There numerous ways to handle data processing and management when it comes to an unmanned system’s functionality structure, which is mainly derived with the end user’s needs and convenience in mind. In the case of DJI’s Phantom 4 UAV, the data collection revolves around high quality still images and videos. The protocols and methods used for the processing and management of this data rely largely on storage devices on-board the system limiting the user to post-flight data retrieval of the high quality imagery, and lower quality imagery downloads in-flight. An alternative method was proposed discussing the use of a simple modulator coupled with an optical retroreflector instead of RF to transfer data in near-real time, enabling larger and more secure data transfer and acquisition while consuming less energy and extending operational endurance.

References

 DJI . (2016, September 25). Phantom 4 Info. Retrieved from DJI: https://www.dji.com/phantom-4/info

DroneWorld. (2016, April 30). DJI Phantom 4 Specs. Retrieved from Drone World: http://www.drone-world.com/dji-phantom-4-specs/

Gilbertson, S. (2016, April 22). Review: DJI Phantom 4. Retrieved from Wired: https://www.wired.com/2016/04/review-dji-phantom-4/

Gilbreath, G., Rabinovich, S., Meehan, J., Vilcheck, J., Mahon, R., Burris, R., . . . Montes, M. J. (2001, July). Large-aperture multiple quantum well modulating retroreflector for free-space optical data transfer on unmanned aerial vehicles. Optical Engineering, 1348-1356.

Korey. (2016, March 3). DJI Phantom 4: What makes it different? Retrieved from My First Drone: http://myfirstdrone.com/phantom-4/dji-phantom-4-what-has-changed/

Patterson, J. (2016, July 12). DJI Phantom 4 In Depth Part 2: The Remote Controller . Retrieved from HeliGuy: https://www.heliguy.com/blog/2016/07/12/dji-phantom-4-in-depth-the-remote-controller/

Popper, B. (2016, March 1). DJI's revolutionary Phantom 4 drone can dodge obstacles and track humans. Retrieved from The Verge: http://www.theverge.com/2016/3/1/11134130/dji-phantom-4-drone-autonomous-avoidance-tracking-price-video