Global drone manufacturers 3DR, DJI, GoPro and Parrot today are forming the Drone Manufacturers Alliance, a coalition intended to serve as the voice for drone manufacturers and their customers across civilian, governmental, recreational, commercial, nonprofit and public safety applications.
“We will advocate for policies that promote innovation and safety, and create a practical and responsible regulatory framework,” said Kara Calvert, director of the Drone Manufacturers Alliance. “There are significant economic and social benefits to drone operations in the U.S., and industry must work with policymakers to ensure a safe environment for flying.
“The Drone Manufacturers Alliance believes a carefully balanced regulatory framework requires input from all stakeholders and must recognize the value and necessity of continued technological innovation. By highlighting innovation and emphasizing education, we intend to work with policymakers to ensure drones continue to be safely integrated into the national airspace.”
Intergeo 2014 in Berlin hosted a flight zone event for unmanned aircraft systems (UAS) business applications, which led to the 2015 debut in Stuttgart, Germany, of Interaerial Solutions as an integrated topic platform. For the first time, Interaerial Solutions will run as a free-standing UAS platform Oct. 11–13 during Intergeo 2016 in Hamburg.
“Interaerial Solutions Expo. Forum. Flight Zone for UAS.” hosted by Hinte GmbH, now has a dedicated website, www.interaerial-solutions.com, and will serve as a showcase for manufacturers, UAS users and operators, accessories, software and end-to-end solutions.
The repositioning of Interaerial Solutions is the result of its organizers recognizing the rapid development of the UAS market and the high rate of innovation in this new technology. UAS manufacturing and the development of related solutions currently form the most dynamic growth generator in geo-based data capture, processing and the development of applications, Intergeo said in a news release.
“A new chapter in aviation history is unfolding, as UAS takes over the civilian market and unlocks huge potential for developing innovative applications in countless directions,” Christoph Hinte, CEO of Hinte, said in the news release. “We will be scripting the storyline at Interaerial Solutions. We are already the biggest platform in this field in the German-speaking world.”
Uwe Nortmann, managing director of UAV Dach e.V., Interaerial Solutions’ partner organization, already considers the Interaerial Solutions marketplace to be the leading trade fair for unmanned aircraft systems. “For me, the event’s main appeal lies in the way it reveals how a range of sectors can benefit from the fledgling technological developments surrounding UAS,” he said. “By replacing manned flights, UAS heralds vast potential savings in costs. The future lies in unmanned aircraft systems, and Interaerial Solutions is the platform to show this.”
About 80 exhibitors and approximately 3,200 visitors attended the event as part of Intergeo 2015. A third of those visitors placed an order at the trade fair or immediately afterwards, Intergeo said, and two-thirds of visitors at Interaerial Solutions rated Intergeo 2015 as either “important” or “very important” for investment decisions.
The event will maintain the same format this year as last year with an exhibition area, expert forum and outdoor flight zone. Exhibitors of the 2016 event include:
Suppliers of hardware for UAS.
UAS manufacturers.
Hardware manufacturers for remote sensing.
Manufacturers of UAS accessories.
Suppliers of evaluation software/photogrammetry.
Suppliers of UAS services.
Technology and services for data utilisation.
Education and training.
Service providers and dealers.
Insurersand consultants.
Publishers and associations.
Authorities.
“Interaerial Solutions already gave professional UAS manufacturers like us the chance last year to present our products to a large, enthusiastic trade audience,” said Daniel Schmitt, manager of RotorKonzept Multikoptermanufaktur. “At the same time, visitors to the trade fair were able to gain a comprehensive overview of the market. Interaerial Solutions is the most important exhibition platform of the year for RotorKonzept. We will definitely be on board again in Hamburg.”
Back in September at the Institute of Navigation GNSS+ convention in Tampa, Florida, one of the papers went a long way to explaining why and how more GNSS satellites in more constellations is better. The natural assumption is that because there are more satellites, a multi-constellation receiver can choose which ones have the best signal and which provide the best solution — and it’s not always the same satellites.
Best geometry together with best signal strength obviously provide the best solution, but this might change in, for instance, a downtown urban setting for a car using a satellite navigation system. While most Western car-nav systems use only GPS, the study by Martin Escher, Mirko Stanisak, and Ulf Bestmann at the Institute of Flight Guidance, Technical University in Braunschweig, Germany, clearly shows that there is an advantage to embedding multi-constellation receivers in these systems.
Skyplot of GPS, GLONASS, Galileo and BeiDou satellites at Braunschweig.
The above skyplot shows a perfect reception of all GNSS satellites during a period of 14 hours — 30 usable satellites — obtained with a high-quality antenna without any obstacles. Car driving downtown will almost never encounter such good GNSS reception.
The Technical University put two different receivers in a car under static, representative, urban conditions, and went about evaluating reception against that predicted by an in-house simulation. The high-precision survey-grade receiver receiver tracked signals from all four constellations, while a lower cost receiver used in some car-nav systems was configured to only track GPS and Beidou. In this scenario, valid signals were obscured by surrounding buildings and the total number of visible satellites was reduced from 23-30 to 11-18.
The measurements validated the university simulation model and demonstrated how the high-precision receiver was able to remove multipath and other diffracted or reflected signals, while the lower cost receiver collected all available signals and therefore suffered some accuracy degradation.
Braunschweig urban scenario.Predicted satellites reception with an elevation of up to 65 degrees often obstructed by buildings.
The area chosen for this demonstration is dominated by narrow roads with multi-story buildings on both sides of the road. To begin, only GPS positioning was used on the test route — representing the current state-of-the-art for most production car-nav systems. For large portions of the test drive, no GPS-only position solution was achieved because of insufficient GPS measurements.
While there was some improvement in tracking using a multi-constellation receiver, when GNSS differential corrections over a mobile telecom link were incorporated, tracking performance was significantly improved. But when inertial and wheel sensors were also added into the solution, almost perfect positioning was achieved over the whole route.
Multi-constellation with differential corrections and sensor aiding.
Given that commercial GPS/GLONASS corrections are now available almost everywhere over a large portion of the globe and some assisted GNSS services are beginning to add both Galileo and Beidou corrections, it’s possible that downtown loss of signal for car drivers may soon be a thing of the past. And, of course, many car-nav systems currently incorporate wheel sensor inputs for dead-reckoning when GNSS is lost.
Drone use in difficult locations
Another interesting ION GNSS 2015 paper from Adam Schultz, Russell Gilabert, and Maarten Uijt de Haag of The Ohio University details the way a couple of students and their professor set out to fly a drone down corridors and within the halls of the Engineering Department. They are hoping to soon get access to the extensive maintenance tunnel system at Ohio University for more autonomous flights using newer, smaller drones.
The objective is to investigate the requirements and use of drones for missions in remote or difficult locations for applications such as large building maintenance, search and rescue, and indoor mapping.
But watch out, people in the Engineering building, if you see an unmanned hex-copter heading toward you on your way to class! Sounds like great fun as the UAV research students see the shots of the scattering inhabitants via the onboard Point Grey FireFly MV color camera!
The UAV/drone is equipped with a navigation and mapping system for both outdoor and indoor environments, using multiple laser scanners, an inertial measurement unit (IMU), barometric height and GNSS, whenever its available.
The UAV is a 3DRobotics hex-copter with a payload that includes an onboard processor, two short-range and one long-range laser range scanners, autopilot, Xsense MTI IMU, GPS receiver and a standard Wi-Fi link to relay real-time maps, trajectories and video to the remote operator.
Ohio U Hex-copter with similar payload as flown through indoor environment (speed ~2m/s).
Guidance, navigation and control (GNC) of the unmanned hex-copter is accomplished by tactical and strategic modules. In known environments, the strategic GNC keeps track of the planned and actual flight trajectories and provides the next waypoints for the mission.
In unknown environments, the strategic GNC maintains a rough estimate of trajectory and the current map of the UAV’s location. The UAV can be flown either manually by the student managing the flight controller or, when in autonomous mode, by the internal UAV flight control computer. Laser scanners provide horizontal position estimation and altitude estimation, while also collecting mapping data.
The mission manager is programmed with a simple rule-based system that uses the system’s 2D and 3D maps to control the route. The drone flies autonomously through the corridors and rooms, while the UAS operator monitors progress on a laptop. The operator can manually take control of the UAV guidance at any time.
The autopilot provides magnetometer and inertial measurements that are used to loosely maintain heading when moving from outdoors to indoors. When indoors, the lidar, inertial and optical (LION) mission controller continuously outputs position and orientation and generates short 10-30 second trajectories for the flight controller — providing a series of waypoints and required velocities for the UAV to follow.
Map generated by the UAV mission controller (red) versus truth reference map (blue).
Should this research ultimately lead to a commercial UAV implementation, it sure would help solve the huge problem we have now for generating indoor maps. The current simultaneous localization and mapping (SLAM) method for generating these indoor maps usually means somebody walks throughout a mall or office building carrying one of several indoor location systems or even taking physical measurements. If a very small UAV were to be flown safely throughout such an indoor location, data would be collected quickly, hopefully with a lot less effort than current methods allow. There’s still a lot of research and development required, but this sure does look promising.
Tony Murfin
GNSS Aerospace
References
“Future Automotive GNSS Positioning in Urban Scenarios,” Martin Escher, Mirko Stanisak, Ulf Bestmann, ION GNSS+ 2015.
“Indoor Flight Demonstration Results of an Autonomous Multi-copter using Multiple Laser Inertial Navigation,” Adam Schultz, Russell Gilabert, and Maarten Uijt de Haag, ION GNSS+ 2015.
General Atomics Aeronautical Systems Inc. (GA-ASI) has successfully flown the Predator B/MQ-9 Reaper Extended Range (ER) Long Wing craft.
The long-wing Predator is retrofitted with improved long-endurance wings, greater internal fuel capacity and additional hard points for carrying external stores. The flight took place Feb. 18 at GA-ASI’s Gray Butte Flight Test Facility in Palmdale, Calif., on a test aircraft.
GA-ASI is a a manufacturer of remotely piloted aircraft (RPA) systems, radars, and electro-optic and related mission systems solutions.
“Predator B ER’s new 79-foot wing span not only boosts the RPA’s endurance and range, but also serves as proof-of-concept for the next-generation Predator B aircraft that will be designed for Type-Certification and airspace integration,” said Linden Blue, CEO. “The wing was designed to conform to STANAG 4671 [NATO Airworthiness Standard for RPA systems], and includes lightning and bird strike protection, non-destructive testing and advanced composite and adhesive materials for extreme environments.”
During the flight, Predator B ER Long Wing demonstrated its ability to launch, climb to 7,500 feet (initial flight test altitude), complete basic airworthiness maneuvers, and land without incident. A subsequent test program will be conducted to verify full operational capability.
Developed on Internal Research and Development (IRAD) funds, the new wing span is 13 feet longer, increasing the aircraft’s endurance from 27 hours to more than 40 hours.
Additional improvements include short-field takeoff and landing performance and spoilers on the wings that enable precision automatic landings. The wings also have provisions for leading-edge de-ice and integrated low- and high-band RF antennas.
An earlier version of Predator B ER featuring two wing-mounted fuel tanks is currently operational with the U.S. Air Force as MQ-9 Reaper ER.
The long wings are the first components to be produced as part of GA-ASI’s Certifiable Predator B (CPB) development project, which will lead to a certifiable production aircraft in early 2018.
Further hardware and software upgrades planned for CPB will include improved structural fatigue and damage tolerance, more robust flight control software and enhancements allowing operations in adverse weather.
Exploiting terrestrial signals of opportunity (SOPs) can significantly reduce the vertical dilution of precision (VDOP) of a GNSS navigation solution. Simulation and experimental results show that adding cellular SOP observables is more effective in reducing VDOP than adding GNSS space vehicle (SV) observables.
By Joshua J. Morales, Joe J. Khalife and Zaher M. Kassas
GNSS position solutions can in many cases suffer from a high vertical dilution of precision (VDOP) due to lack of space vehicle (SV) angle diversity. Signals of opportunity (SOPs) have been recently considered to enable navigation whenever GNSS signals become inaccessible or untrustworthy. Terrestrial SOPs are abundant and are available at varying geometric configurations, making them an attractive supplement to GNSS for reducing VDOP.
Common metrics used to assess the quality of the spatial geometry of GNSS SVs are the parameters of the geometric dilution of precision (GDOP); namely, horizontal dilution of precision (HDOP), time dilution of precision (TDOP), and VDOP. Several methods have been investigated for selecting the best GNSS SV configuration to improve the navigation solution by minimizing the GDOP. While the navigation solution is always improved by additional observables from GNSS SVs, the solution’s VDOP generally remains of lesser quality than the HDOP. GPS augmentation with terrestrial transmitters that transmit GPS-like signals have been shown to reduce VDOP. However, this requires installation of additional proprietary infrastructure.
This article studies VDOP reduction by exploiting terrestrial SOPs, particularly cellular code division multiple access (CDMA) signals, which have inherently low elevation angles and are free to use.
In GNSS-based navigation, the states of the SVs are readily available. For SOPs, however, even though the position states may be known a priori, the clock-error states are dynamic; hence, they must be continuously estimated. The states of SOPs can be made available through one or more receivers in the navigating receiver’s vicinity. Here, the estimates of such SOPs are exploited and the VDOP reduction is evaluated.
PROBLEM FORMULATION
Consider an environment comprising a receiver, M GNSS SVs, and N terrestrial SOPs. Each SOP will be assumed to emanate from a spatially stationary transmitter, and its state vector, xsop(n), will consist of its three-dimensional (3-D) position rsop(n) and clock bias cδtsop(n), where n=1,…,N and c is the speed of light. The receiver draws pseudorange observations from the GNSS SVs and from the SOPs. The observations are fused through an estimator whose role is to estimate the state vector of the receiver xr=[rrT, cδtr] T, where rr and cδtr are the 3D position and clock bias of the receiver, respectively. To simplify the discussion, assume that the pseudorange observation noise is independent and identically distributed across all channels with variance σ2. The estimator produces an estimate of the receiver’s state vector and associated estimation error covariance P =σ2(HTH)-1.
Without loss of generality, assume an East-North-Up (ENU) coordinate frame to be centered at . In this frame, the dilution of precision matrix G≡(HTH)-1 is completely determined by the azimuth and elevation angles from the receiver to each SV, denoted azsv(m) and elsv(m), respectively, and the receiver to each SOP, denoted azsop(n) and elsop(n), respectively, where m=1,…,M. Hence, the quality of the estimate depends on these angles and the pseudorange observation noise variance σ2. The diagonal elements of G, denoted gii, are the parameters of the dilution of precision (DOP) factors:
Therefore, the DOP values are directly related to the estimation error covariance; hence, the more favorable the azimuth and elevation angles, the lower the DOP values. If the observation noise was not independent and identically distributed, the weighted DOP factors must be used.
VDOP REDUCTION VIA SOPs
With the exception of GNSS receivers mounted on high-flying and space vehicles, all GNSS SVs are typically above the receiver, that is, the receiver-to-SV elevation angles are theoretically limited between 0°≤elsv(m)≤90°. GNSS receivers typically restrict the lowest elevation angle to some elevation mask, elsv,min, so to ignore GNSS SV signals that are heavily degraded due to the ionosphere, troposphere and multipath.
As a consequence, GNSS SV observables lack elevation angle diversity, and the VDOP of a GNSS-based navigation solution is degraded. For ground vehicles, elsv,min is typically between 5° and 20°. These elevation angle masks also apply to low-flying aircraft, such as small unmanned aerial vehicles (UAVs), whose flight altitudes are limited to 500 feet (approximately 152 meters) by the Federal Aviation Administration (FAA).
In GNSS + SOP-based navigation, the elevation angle span may effectively double, specifically –90°≤elsop(n)≤90°. For ground vehicles, useful observations can be made on terrestrial SOPs that reside at elevation angles of elsop(n)=0°. For aerial vehicles, terrestrial SOPs can reside at elevation angles as low as elsop(n)=–90°, for example, if the vehicle is flying directly above the SOP transmitter.
To illustrate the VDOP reduction by incorporating additional GNSS SV observations versus additional SOP observations, an additional observation at elnew is introduced, and the resulting VDOP(elnew) is evaluated. To this end, M SV azimuth and elevation angles were computed using GPS ephemeris files accessed from the Yucaipa, California, station from Garner GPS Archive, which are tabulated in Table 1.
Table 1. SV azimuth and elevation angle (degrees).
For each set of GPS SVs, the azimuth angle of an additional observation was chosen as a random sample from a uniform distribution between 0° and 360°, that is, aznew~U(0°,360°). The corresponding VDOP for introducing an additional measurement at a sweeping elevation angle –90°≤elnew≤90° are plotted in Figure 1 (a)–(d) for M=4,…,7, respectively.
Figure 1. A receiver has access to M GPS SVs from Table 1. Plots (a)- (d) show the VDOP for each GPS SV configuration before adding an additional measurement (red dotted line) and the resulting VDOP(elnew) for adding an additional measurement (blue curve) at an elevation angle –90°≤elnew≤90° for M=4,…,7, respectively.
The following can be concluded from these plots. First, while the VDOP is always improved by introducing an additional measurement, the improvement of adding an SOP measurement is much more significant than adding an additional GPS SV measurement. Second, for elevation angles inherent only to terrestrial SOPs, that is, –90°≤elsop(n)≤0°, the VDOP is monotonically decreasing for decreasing elevation angles.
SIMULATION RESULTS
To compare the VDOP of a GNSS-only navigation solution with a GNSS + SOP navigation solution, simulations were conducted using receivers mounted on ground and aerial vehicles.
Ground Receiver. The position of a receiver mounted on a ground vehicle was set to rr ≡(106 )•[– 2.431171,– 4.696750, 3.553778]T expressed in an Earth-Centered-Earth-Fixed (ECEF) coordinate frame. The elevation and azimuth angles of the GPS SV constellation above the receiver over a 24-hour period was computed using GPS SV ephemeris files from the Garner GPS Archive. The elevation mask was set to elsv,min≡20°. The azimuth and elevation angles of three SOPs, which were calculated from surveyed terrestrial cellular CDMA tower positions in the navigating receiver’s vicinity, were set to azsop≡[42.4°,113.4°,230.3° ]T and elsop ≡[3.53°,1.98°,0.95°]T, respectively. The resulting VDOP, HDOP, GDOP and associated number of available GPS SVs for a 24-hour period starting from midnight, Sept. 1, 2015, are plotted in Figure 2.
Figure 2. Fig. (a) represents the number of SVs with an elevation angle >20° as a function of time. Fig. (b)-(d) correspond to the resulting VDOP, HDOP, and GDOP, respectively, of the navigation solution using GPS only, GPS + 1 SOP, GPS + 2 SOPs, and GPS + 3 SOPs.
The following can be concluded from these plots. First, the resulting VDOP using GPS + N SOPs for N≥1 is always less than the resulting VDOP using GPS alone. Second, using GPS + N SOPs for N≥1 prevents large spikes in VDOP when the number of GPS SVs drops. Third, using GPS + N SOPs for N≥1 also reduces both HDOP and GDOP.
Unmanned Aerial Vehicle. The initial position of a receiver mounted on a UAV was set to rr ≡(106 )•[–2.504728, –4.65991, 3.551203]T. The receiver’s true trajectory evolved according to velocity random walk dynamics. Pseudorange observations on all available GPS SVs above an elevation mask set to elsv,min≡20° and three terrestrial SOPs were generated using a MATLAB-based simulator. The simulator used SV trajectories which were computed using GPS SV ephemeris files from Sept. 1, 2015, 10:00 to 10:03 a.m.
The positions of the SOPs were set to rsop(1)≡(106)•[– 2.504953,– 4.659550, 3.551292]T, rsop(2)≡(106)•[– 2.503655, –4.659645, 3.552050]T, and rsop(3)≡(106)•[– 2.504124,– 4.660430, 3.550646]T, which are the locations of surveyed cellular towers in the UAV’s vicinity. The UAV’s true trajectory, navigation solution from using only GPS SV pseudoranges, and navigation solution from using GPS and SOP pseudoranges are illustrated in Figure 3 (top). The corresponding 95th-percentile uncertainty ellipsoids for a sample set of navigation solutions are illustrated in Figure 3 (bottom).
Figure 3 . Simulation results for a UAV flying over downtown Los Angeles. Top: Illustration of the true trajectory (red curve), navigation solution from using pseudoranges from six GPS SVs (yellow curve), and navigation solution from using pseudoranges from six GPS SVs and three cellular CDMA SOPs (blue curve). Bottom: Illustration of uncertainty ellipsoid (yellow) of GPS only navigation solution and uncertainty ellipsoid (blue) of GPS + SOP navigation solution.
The following can be noted from these plots. First, the accuracy of the vertical component of the GPS-only navigation solution is worse than that of the GPS + SOP navigation solution. Second, the uncertainty in the vertical component of the GPS-only navigation solution is larger than that of the GPS + SOP navigation solution, which is captured by the yellow and blue uncertainty ellipsoids, respectively. Third, the accuracy of the horizontal component of the navigation solution is also improved by incorporating cellular SOP pseudorange observations alongside GPS SV pseudorange observations.
EXPERIMENTAL RESULTS
A field experiment was conducted using software-defined receivers (SDRs) to demonstrate the reduction of VDOP obtained from including SOP pseudoranges alongside GPS pseudoranges for estimating the states of a receiver. To this end, two antennas were mounted on a vehicle to acquire and track multiple GPS signals and three cellular base transceiver stations (BTSs) whose signals were modulated through CDMA. The GPS and cellular signals were simultaneously downmixed and synchronously sampled via two universal software radio peripherals (USRPs). These front-ends fed their data to the Multichannel Adaptive TRansceiver Information eXtractor (MATRIX) SDR, developed at the Autonomous Systems Perception, Intelligence and Navigation (ASPIN) Laboratory at the University of California, Riverside. The LabVIEW-based MATRIX SDR produced pseudorange observables from five GPS L1 C/A signals in view and the three cellular BTSs.
Figure 4 depicts the experimental hardware setup.
Figure 4. Experiment hardware setup.
The pseudoranges were drawn from a receiver located at rr≡(106)•[– 2.430701,– 4.697498, 3.553099]T, expressed in an ECEF frame, which was surveyed using a carrier-phase differential GPS receiver. The corresponding SOP state estimates were collaboratively estimated by receivers in the navigating receiver’s vicinity. The pseudoranges and SOP estimates were fed to a least-squares estimator, producing x^r and associated P from which the VDOP, HDOP, and GDOP were calculated and tabulated in Table 2 for M GPS SVs and N cellular CDMA SOPs. A sky plot of the GPS SVs used is shown in Figure 5.
Figure 5. Left: Sky plot of GPS SVs: 14, 21, 22, and 27 used for the four SV scenarios. Right: Sky plot of GPS SVs: 14, 18, 21, 22, and 27 used for the five SV scenarios. The elevation mask, elsv,min, was set to 20° (dashed circle).
The tower locations, receiver location and a comparison of the resulting 95th-percentile estimation uncertainty ellipsoids of for {M,N}={5,0} and {5,3} are illustrated in Figure 6.
Figure 6. Top: Cellular CDMA SOP tower locations and receiver location. Bottom: Uncertainty ellipsoid (yellow) of navigation solution from using pseudoranges from five GPS SVs and uncertainty ellipsoid (blue) of navigation solution from using pseudoranges from five GPS SVs and three cellular CDMA SOPs.
The corresponding vertical error was 1.82 meters and 0.65 meters respectively. Hence, adding three SOPs to the navigation solution that used five GPS SVs reduced the vertical error by 64.3 percent. Although this is a significant improvement over using GPS observables alone, improvements for aerial vehicles are expected to be even more significant, since they can exploit a full span of observable elevation angles as demonstrated in the simulation section.
Table 2. DOP values for M SVs + N SOPs.
CONCLUSION
This article studied the VDOP reduction of a GNSS-based navigation solution by exploiting terrestrial SOPs. It was demonstrated that the VDOP of a GNSS solution can be reduced by exploiting the inherently small elevation angles of terrestrial SOPs. Experimental results using ground vehicles equipped with SDRs demonstrated VDOP reduction of a GNSS navigation solution by exploiting a varying number of cellular CDMA SOPs. Incorporating terrestrial SOP observables alongside GNSS SV observables for VDOP reduction is particularly attractive for aerial systems, since a full span of observable elevation angles becomes available.
MANUFACTURERS
Two National Instruments universal software radio peripherals were used in the experiment. A Trimble 5700 receiver surveyed the experimental receiver location.
JOSHUA J. MORALES is pursuing a Ph.D. in electrical and computer engineering at the University of California, Riverside.
JOE J. KHALIFEH is a Ph.D. student at the University of California, Riverside.
ZAHER (ZAK) M. KASSAS is an assistant professor at the University of California, Riverside. He received a Ph.D. in electrical and computer engineering from the University of Texas at Austin. Previously, he was a research and development engineer with the LabVIEW Control Design and Dynamical Systems Simulation Group at National Instruments Corp.
This article is based on a technical paper presented at the 2016 ION ITM conference in Monterey, California.
A methane leak at a Southern California Gas (SoCalGas) storage facility has shone a spotlight on how unmanned aerial vehicles can be used to inspect utilities. The massive three-month leak — temporarily plugged on Feb. 12 — chased thousands of Los Angeles residents from their homes.
At least 2 percent of natural gas is wasted through methane leaks at production sites, according to the U.S. Department of Energy (DOE).
UAVs are already being used for some electrical grid and pipeline inspections, mostly in pilot programs, but their potential for hands-off long-distance monitoring is just starting to be realized.
Along with criminal charges, SoCalGas is facing regulatory mandates to improve air-quality monitoring at its facilities. Nationally, the DOE’s Advanced Research Project Agency-Energy is funding a program to accurately locate and measure methane emissions associated with natural gas production.
Bridger’s proposed leak detector uses lidar in combination with range and gas absorption measurements. (Illustration: Bridger Photonics)
The program has given one company, Bridger Photonics, a $2 million grant to develop a leak detector. Bridger plans to build a mobile methane sensing system capable of surveying a 10 x 10 meter well platform in just over five minutes with precision that exceeds existing technologies used for large-scale monitoring.
Bridger’s detector useslaser beams to generate 3D images that show the distance and concentration of a gas leak, even showing the types and concentration of the hydrocarbons.
Mounted on a UAV, the sensor would give inspectors access to complicated or obscured infrastructures at processing plants, drilling rigs and pipelines. The sensor could also be mounted on a vehicle.
Bridger’s goal is for its devices to be able to service up to 85 sites, and cost $1,400 to $2,220 a year to operate per wellsite. Bridger plans to field test its technology this year and make it available commercially in 2017.
Bridger’s imager
Bridger’s gas imager is a point-scanning lidar sensor that performs simultaneous range and gas absorption measurements, according to Mike Thorpe, chief technology officer of Bridger Photonics. The measurements are combined to derive high-accuracy estimates of the gas concentration.
The measurement beam is scanned around the scene to create 3D topographic images of hard targets overlaid with 2D maps of the gas concentration.
The datasets will be geo-registered using GPS and inertial measurements.
The prototype sensor will have the following performance specs:
1 kpps measurement rate
3-100 m range
1 cm down-range resolution
2 cm cross-range resolution
<3 ppm-m methane detection sensitivity for distances < 30 m
<15 ppm-m methane detection sensitivity for distances < 100 m
Bridger also is developing measurement approaches and algorithms to enable automatic leak detection and leak rate estimation.
Unmanned aerial vehicle maker DJI has launched the Phantom 4, a quadcopter drone that uses highly advanced computer vision and sensing technology to make professional aerial imaging easier.
The Phantom 4 expands on previous generations of DJI’s Phantom line by adding on-board intelligence that make piloting and shooting great shots easier through features such as its Obstacle Sensing System, ActiveTrack and TapFly functionality.
“With the Phantom 4, we are entering an era where even beginners can fly with confidence,” said DJI CEO Frank Wang. “People have dreamed about one day having a drone collaborate creatively with them. That day has arrived.”
The Phantom 4’s Obstacle Sensing System features two forward-facing optical sensors that scan for obstacles and automatically direct the aircraft around impediments when possible, reducing risk of collision, while ensuring flight direction remains constant.
If the system determines the craft cannot go around the obstacle, it will slow to a stop and hover until the user redirects it. Obstacle avoidance also engages if the user triggers the drone’s “Return to Home” function to reduce the risk of collision when automatically flying back to its take off point.
With ActiveTrack, the Phantom 4 allows users running the DJI Go app on iOS and Android devices to follow and keep the camera centered on the subject as it moves by tapping the subject on their smartphone or tablet. Perfectly framed shots of moving joggers or cyclists, for example, only require activating the ActiveTrack mode in the app.
The Phantom 4 understands three-dimensional images and uses machine learning to keep the object in the shot, even when the subject changes its shape or turns while moving. Users have full control over camera movement while in ActiveTrack mode — and can move the camera around the object while it is in motion as the Phantom 4 keeps the subject framed in the center of the shot autonomously. A “pause” button on the Phantom 4’s remote controller allows the user to halt an autonomous flight at any time, leaving the drone to hover.
By using the TapFly function in the DJI Go app, users can double-tap a destination for their Phantom 4 on the screen, and the Phantom 4 calculates an optimal flight route to reach the destination, while avoiding any obstructions in its path. Tap another spot and the Phantom 4 will smoothly transition towards that destination making even the beginner pilot look like a seasoned professional.
The Phantom 4’s camera, an aerial-optimized 4K imaging device, has undergone an upgrade that includes improved optics for better corner sharpness and reduced chromatic aberration. The Phantom 4 also has DJI’s signature Lightbridge video transmission system onboard, allowing users to see what their camera sees in HD and in real-time on their smart devices at a distance up to five kilometers (3.1 miles).
The Phantom 4’s form factor, the classic quadcopter, has been redesigned and redefined to emphasize elegance and smoother, more aerodynamic lines. Its frame incorporates a lightweight composite core to provide enhanced stability and more agile flight. The core features a redesigned gimbal that provides more stability and vibration dampening, and has been repositioned for a better center of gravity and to reduce the risk of propellers getting in the shot.
Refinements to motor efficiency, power management and a new intelligent battery have extended the Phantom 4’s flight time to 28 minutes, which means more time in the air to capture professional photos and video.
DJI crafted the Phantom 4 with reliability in mind, including redundant inertial measurement units (IMUs) and dual compasses onboard. It uses new push-and-lock propellers that are faster to install and more secure in flight.
In addition to intelligence and ease-of-use, the Phantom 4 is built for fun, DJI said. Its new “Sport Mode” for advanced flyers gives a taste of what drone racing feels like. In “Sport Mode,” the Phantom 4 can fly 20 meters per second (45 miles per hour) and ascends and descends more rapidly than in other modes. The craft’s acceleration and top speed in “Sport Mode” also mean it can reach locations for shots faster and capture shots users couldn’t get before.
“Though the Phantom 4 is easy to use, let’s not forget it is a high-performance aircraft powered by unparalleled DJI technology,” said Senior Product Manager Paul Pan.
Powervision Robot Inc., a robotics and industrial drone maker, has launched drone shaped like an egg that can be folded up and stored in a backpack.
Although PowerEgg was developed for the mainstream consumer market, it includes advanced technologies such as a 360-degree panoramic 4K HD camera on a 3-axis gimbal, real-time long-range video transmission and advanced “optical flow” sensors for indoor navigation.
Powervision CEO Wally Zheng called the design a “work of art.” “We think the oval shape is not only clean and pure but also has the structural and functional benefits.”
The Powervision team spent more than 18 months to create the PowerEgg. The structural design includes larger propellers that required advancements to transform from the compact egg shape to the larger flight mode. On the software side, Powervision concentrated on making the drone easier to fly, because the average consumer drone has a 10-hour learning curve.
Since its inception in 2010, Powervision has focused on commercial UAV-related products and services including smart drones, data visualization and forecasting.
PowerEgg will be available early in the second quarter of this year.
Industrial Networks (INet) applied for exemption to Section 333 of the FAA Modernization and Reform Act in late 2015 for the railcar inspection and inventory market space and began testing a new drone Automated Equipment Identification (AEI) reader, the Industrial Networks Rail Automation Drone (IRAD1), for railyard automation.
The plan requires safety testing and FAA approvals, but will give rail shippers a greater amount of flexibility in railyards, INet said in a Feb. 24 news release. The IRAD1 will be capable of fully autonomous scanning of the railyard for inventory and inspection of a railcar.
An elaborate collision detection and avoidance system is built into the drone to help avoid objects in the flight path and reinforce safety, the company says. That system gives the IRAD1 the ability to be a completely autonomous AEI scanner, which will lead to faster data collection and help the business reduce workforce requirements, INet said.
INet’s current collection of AEI-scanning tools includes stationary and handheld readers, and automates data collection in the field.
“Advancement in drone technology has allowed Industrial Networks to explore what we feel is the future of rail automation,” said Jimmy Finster, president of Industrial Networks. “We are continuously researching new and innovative ways to help our customers improve their operations and streamline their daily processes.”
Insurance companies in the U.S. and Canada have jumped on the UAV bandwagon, with many now offering coverage for commercial drone users. The insurance usually covers liability for any damage caused by the drone, with comprehensive options covering damage to the drone itself.
Unmanned Risk Management, which also insures helicopters and other aircraft, has insured drones in all 50 U.S. states and in other countries, and has insured the seven film operators that received Section 333 exemptions.
ProSight Specialty Insurance, which operates in the U.S. and UK, was given a Best in Biz Award partly for creating insurance for drone operators. ”It’s so prescient and forward-thinking given the burgeoning use of drones in today’s business world,” said a Best in Biz judge.
AIG has developed commercial UAV coverage designed for the exposure faced by remotely piloted, semi-autonomous and fully autonomous aircraft.
In Canada, Intact Insurance’s UAV coverage caters to small and medium-sized businesses that use or plan to use drones in their business operations.
Meanwhile, a Chicago law firm is now specializing in federal commercial drone law. Antonelli Law became the first law firm in the U.S. to be invited by drone maker DJI to participate in the company’s referral program for commercial drone users to help them receive Section 333 exemptions from the Federal Aviation Administration (FAA). In 2015, Antonelli Law filed more than 50 petitions with the FAA.
The firm also launched a specialized drone law service for police and fire departments, community colleges, universities and municipalities obtain FAA exemptions.
The Association for Unmanned Vehicle Systems International (AUVSI) has renamed its major annual conference — XPONENTIAL — and the 2016 edition will be held in New Orleans at the Morial Convention Center on the west bank of the Mississippi, May 2–5. The huge convention center is hosting the event across two large halls, with more than 350,000 square feet of space for up to 600 exhibits.
With 370 exhibitors already signed up, you might want to decide who to put on your visit list if you’ve never been to one of these AUVSI exhibitions. Because just roaming the show floor without a plan can lead to frustration and exhaustion — the show is huge, not only in square feet, but also in the number and size of the exhibits. Full-size helicopters, Humvee-type vehicles and drones — lots and lots of different types of unmanned air vehicles (UAVs) or drones for any and all applications.
There is everything a drone manufacturer might need to develop and integrate into the latest small (sUAV), medium or large quadcopter, hexcopter, octocopter, fixed wing or STOL (short take-off and landing) air vehicle. Plus, you’ll find ground vehicles and surface and underwater vehicles of all shapes and sizes.
Propellers, engines, payloads of all sorts including cameras, radars, IR and lasers, plus connectors and electrical, mechanical and electro-mechanical components and systems, manufacturing systems, 3D printing, modeling, designing, developing — all in all, too much stuff to even mention everything that goes into, onto and processes/tools for manufacturing a UAV.
But, of course, our interest might be more readily captured by the booths exhibiting flight-control systems, sensors, antennas, autopilots, inertial, satellite and terrestrial radios and services, computing, GNSS and other guidance systems — and even avionics for drones. UAV ground control systems (UAV + ground control system = unmanned air system or UAS) are also present in force, along with all their constituent pieces. A ground control system can be more complex than a larger UAV, or sometimes as simple as an app on a tablet.
Applications are also featured in exhibit groupings for survey and mapping, air and start-up. Also, a large number of U.S. states and related academic, research, test and development organizations are represented this year, along with dedicated Chinese, French, Canadian and UK exhibit areas.
There also seems to be some presence for insurance, legal, certification and training organizations aiming to support the emerging commercial opportunities that Federal Aviation Administration (FAA) Section 333 approvals have enabled. The FAA continues to grant Section 333 exemptions, which have allowed commercial, research and agency drones to fly in the U.S. National Airspace System (NAS) on a trial and operational basis.
The FAA issued a fact sheet in mid-December that outlined safety reasons for federal oversight of aviation and airspace, and explained federal responsibility in this area. The object appears to be to let states know that the FAA has federal jurisdiction, and is therefore in charge of regulating access to and operations in the U.S. NAS. The fact sheet perhaps also aims to slow down recent state and city efforts — such as those in Miami, Albany County and New Jersey — to publish their own ordinances and laws related to UAV activity.
Meanwhile, the FAA’s recent UAV registration requirements for anything unmanned that takes to the air in the U.S. have met with mixed reactions. U.S. drone operators have indeed already complied and registered more than 181,000 UAVs, but one individual has filed a suit against the FAA alleging Section 333 does not allow the FAA to make any new rules or regulations regarding model aircraft if they’re flown for hobby or recreational purposes. We’ll have to see how this all turns out — AUVSI, which represents a good portion of the UAS industry, has already come out supporting the FAA’s UAV registration program.
AUVSI continues to call for the FAA to publish regulations that would allow small UAVs to operate in the U.S. NAS. These small UAV regulations have been in the works for several years and have yet to be formally released or implemented by the FAA. AUVSI argues that if these regulations were to be released, the commercial UAV industry would really take off and produce billions in revenue and create thousands of jobs.
In order to help move UAV integration forward, NASA has been working on traffic management concepts for UAS. The first section of this system was tested in August, looking mostly at topics such as geofencing so drones automatically avoid certain restricted areas, and also trajectory planning.
Google and Amazon have also been looking into UAS Traffic Management (UTM) systems. Amazon has proposed a high-speed UAS transit corridor between 200 and 400 feet, with slower vehicles flying below, and larger ones above it. Verizon has also been exploring how cellular networks could be used to enhance drone safety in the future. The FAA’s Pathfinder Programs also aim to investigate areas, such as beyond-visual-line-of-sight flights, that may assist in the development of UTM.
So, XPONENTIAL 2016 is a great UAV show to put in your calendar (May 2-5 in New Orleans) if you have interest in learning more about UAV/UAS, or in moving further into the growing business of UAVs, plus lots of related activity promising growth for actual UAV commercial operations in the U.S. There is always a lot going on nowadays in the world of unmanned vehicles.
Qualcomm Technologies, Tencent and Zerotech announced and demonstrated at CES 2016 a commercial drone based on the Qualcomm Snapdragon Flight platform. The Consumer Electronics Show is being held this week in Las Vegas.
Tencent, China’s largest Internet service portal, and drone maker Zerotech have co-designed Ying, a small, lightweight drone that can be easily controlled right from a smartphone, leveraging the companies’ advanced software, and the computational power of the Qualcomm Snapdragon 801 processor, making it easy to capture video that can be streamed directly to your friends using QQ and Wexin.
The Ying drone uses the Snapdragon 4K capture to “supersample” the video image, providing a stabilized, corrected video and picture recording at 1080P as well as first person view at 720p that can be directly streamed or uploaded to Tencent’s drone social community platforms Weixin and QQ. Weixin (“we chat”) is a mobile text and voice messaging communication service developed by Tencent in China, first released in January 2011.
“We continue to bring a range of new research and development products to meet the needs and demands of our customers across various industries,” said Jianjun Yang, founder of Zerotech. “We’re excited to work with two companies who are technology leaders in their space — Qualcomm Technologies who has brought their mobile expertise to the consumer drone industry, and Tencent with its popular social networks, to bring a lightweight, highly integrated consumer drone that enables users to share their photos and videos instantly with their friends.”
“The consumer drone market is expected to soar in the next few years, and Ying is a good example that shows how Tencent is working closely with the fast growing drone market by enriching use cases of our core and leading social communication services,” said Roland Cai, vice president, IEG, Tencent. “Zerotech’s expertise in UAV manufacturing and, Qualcomm Technologies’ highly integrated drone development board coupled with our social networking platforms allows us to provide our hundreds of millions of active users with a competitive price on a high quality drone such as Ying that can share their experiences in real time.”
Snapdragon Flight is a highly optimized 58 x 40 millimeter board targeted specifically for consumer drones and robotics applications. Snapdragon Flight is based on the Snapdragon 801 processor, with GPS, 4K video capture and robust connectivity, along with advanced drone software and development tools, bringing cutting-edge mobile technologies to create a new class of consumer drones.
“Consumer drones are becoming the ultimate selfie camera but with advanced capabilities such as 4K capture and high performance computing and connectivity,” said Raj Talluri, senior vice president, Qualcomm Technologies. “We are tapping into our proven mobile technologies for the exciting drone opportunity and teaming up with Zerotech and Tencent enables us to support smaller, smarter drones that deliver real-time content to China’s largest social media network.”
CES attendees can check out the Ying and other drones and robotics at the Qualcomm Technologies Booth #25824 in South Hall. Attendees can also attend the official launch event for the Ying at Zerotech’s Booth #26035 in South Hall on Jan. 7 at 11 a.m. PT.
Intergeo 2014 in Berlin hosted a flight zone event for unmanned aircraft systems (UAS) business applications, which led to the 2015 debut in Stuttgart, Germany, of Interaerial Solutions as an integrated topic platform. For the first time, Interaerial Solutions will run as a free-standing UAS platform Oct. 11–13 during 
