A SolarCleano F1A robot tackles a tough cleaning challenge on a solar farm in Saudi Arabia. (Photo: SolarCleano)
SolarCleano, based in Garnich, Luxembourg, makes robots that clean large solar panel installations using GNSS receivers and corrections from Swift Navigation. We asked Christophe Timmermans, SolarCleano’s managing director, a few questions about its technology.
How often do solar panels need to be cleaned?
For decades, it was believed that solar panels did not need to be cleaned due to their angle to the ground and rain. Nowadays, however, the cleaning of solar panels is widely accepted as necessary to optimize a plant’s return on investment (ROI).
How much time per sq. meter do your machines take to clean solar panels?
To provide the fastest possible ROI to our customers, we developed a range of robots to best address the needs of various solar plant layouts. A large utility-scale project with high level of soiling losses in a desert environment will need a very fast and reactive cleaning solution such as our SolarBridge B1, which can clean 24/7/365 fully autonomously. The most suitable solution for a farm rooftop in Germany that needs to be cleaned three to four times a year might be our F1 model, which can clean the equivalent of up to two soccer fields a day. It is designed for rooftops, floating panels and mid-size plants up to 50 MW. While the speed of cleaning is a very important variable, the quality of cleaning is often considered as the driver to performance, which is why we propose different types of brushes depending on the soiling types. Plus, the robot speed can be modified according to the soiling level.
Why do robots need GNSS receivers to clean solar panels?
Moving on inclined, wet glass surfaces makes odometry unreliable because robots might occasionally slip. Therefore, GNSS is the most reliable way to continuously monitor their exact position. Our robots also need path planning because they cannot operate randomly like lawn mowers. Safety is obviously a major concern; we need a very high localization accuracy to ensure that robots don’t fall off the panels. Finally, the largest solar plants are developed in dry, remote locations with high irradiation such as the Sahara, Atacama and Australian deserts. GNSS allows us to have very accurate localization even in those remote areas. In addition, this solution can easily be installed on already-existing solar plants with little capital expenditure.
What spatial accuracy requirements do the robots have for this task?
Safety is our absolute priority. Therefore, our robots need an accuracy of less than 3 cm. They also need to be aware, in real time, of changes in their surroundings, such as maintenance teams, animals and uneven ground.
On large solar farms, GNSS receivers always have a clear line of sight to the satellites and do not suffer from multipath. So, what are the key technical challenges?
Our robots have the additional advantage that they do not need to drive very fast. However, we need to manage fleets of robots on the other side of the world in regions difficult to access and with harsh weather conditions, such as very high or low temperatures and the accumulation of dust behind panels due to air vortices. We need to be able to perform remote maintenance and solve any issue from our control center in Luxembourg. These challenges make our robots increasingly robust. With a current fleet of more than 300 robots around the world, we collect lessons every day to ensure a greater reliability for our upcoming generations of robots.
Why did you choose to partner with Swift Navigation?
We share a vision with Swift: “Accessible automated solutions serving sustainable goals.” We also share other important values, such as “iterate quickly” and “focus on what matters.”
Surveyors used ComNav equipment to construct a hospital in Burkina Faso. (Photo: ComNav)
Line of sight to GNSS satellites is sometimes obscured by buildings and trees, which also cause multipath, as does nearby water. These conditions require an RTK receiver with multipath mitigation. Often, surveying must occur on property corners or on uneven ground, where it is hard to place surveying equipment. For these reasons, reliability and accuracy are essential, especially in harsh environments. Ground control points require 1-2mm accuracy and topo surveys 1-2cm accuracy. Surveying for AEC also requires software that processes digital files.
ComNav has focused on GNSS core technology innovation and applications for 10 years. The Quantum III technology includes algorithms to suppress multipath and supports all GNSS constellations, allowing the users to acquire and keep RTK centimeter accuracy even in harsh environments. The built-in tilt IMU will help where the exact location to be surveyed is hard to reach. For example, the T300 Plus and N Series GNSS receivers support a maximum pole tilt of 60° and keep the compensation accuracy within 2.5cm, making the field work more efficient, convenient and reliable.
With the Survey Master software’s stake-out points, users can import DXF or DWG files directly and the software can stake out the point, line and surface in CAD.
In April 2021, the government of Burkina Faso used ComNav GNSS T300Plus to provide ground control points survey for the construction of a hospital.
The land security and topographic surveying were completed within only six days, less than half the time that had been scheduled for those tasks. This greatly expedited the construction of the hospital and helped with the fight against infectious diseases, including COVID-19.
Celestia Technologies Group (CTG) is taking part in the ADACORSA project, a European initiative designed to unlock the potential of long-range and beyond-visual-line-of-sight (BVLOS) drones and give Europe a world-class drone industry.
ADACORSA — Airborne Data Collection on Resilient System Architecture — is a major collaborative project launched in May 2020 that aims to demonstrate the safety and efficiency of drones or unmanned aerial vehicles (UAVs) in extended out-of-line-of-sight operation ranges.
Specifically, it draws on European expertise in developing sensor and communication technologies for UAVs to underpin their role and reliable capability in long-range applications, including observation, analysis and transport, taking them one step further toward being integrated into conventional airspace.
ADASCORA also seeks to increase public and regulatory acceptance of modern UAV or drone technology. More than 49 specialist companies from 12 European countries are expected to contribute know-how and practical support. The project also aims to research and develop innovative components and systems for airborne observation and detection, telecommunication and data processing along the electronics value-chain.
Task Forces Established
To meet ADACORSA’s ambitious targets, task forces have been set up, one of which will be led by CTG. The company will lead the development of electronic components for reliable and fail-operational environment perception and run one project demonstrator designed to integrate unmanned aircraft systems safely into the common European airspace and ensure that they operate correctly in a multi-unmanned aircraft system environment.
CTG is a Dutch supplier and part of a pan-European company group providing innovative technology products, systems and services to space, aerospace, defense, telecommunications and scientific markets.
Galileo + EGNOS Transponder
CTG will use its expertise in on-board UAV electronics to develop a lightweight, high-performance transponder capable of sending and receiving accurate identification and location data for unmanned aerial vehicles.
Positioning will be based on Galileo, supplemented by its European Geostationary Navigation Overlay Service (EGNOS), allowing all airspace users to know the location of the vehicle and contribute to safety while supporting other on-board systems such as detect-and-avoid equipment.
The transponder will be based on conventional aviation technologies such as Mode S Interrogator and Automatic Dependent Surveillance-Broadcast (ADS-B) and will integrate new concepts including network identification, meaning the vehicle can fly safely in various scenarios. These include in locations close to airports, in drone fleet operations and within the U-Space environment. U-space is a set of European services and procedures designed to support safe, efficient and secure access to airspace for drones.
ADACORSA has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No. 876019. The JU receives support from the European Union’s Horizon 2020 research and innovation program and Germany, Netherlands, Austria, Romania, France, Sweden, Cyprus, Greece, Lithuania, Portugal, Italy, Finland and Turkey.
A technique developed by researchers at the University of Illinois at Urbana-Champaign distinguishes a reflected non-line-of-sight (NLOS) signal of a particular satellite from the LOS signal and characterizes the NLOS signal as coming from a virtual mirror-image satellite in the direction of the signal reflection point. By using information on the position and orientation of the reflector, the NLOS signal can be treated as an additional LOS signal.
By Yuting Ng and Grace Xingxin Gao
INNOVATION INSIGHTS with Richard Langley
THIS ARTICLE IS ABOUT VIRTUAL SATELLITES. No, we don’t mean physical objects that are almost satellites. That’s the common everyday meaning of the word virtual. We mean it in the sense used in computing to describe something that is not physically present but made to appear so by software (and perhaps aided by hardware). The word was first used in this sense by computer scientists in the 1950s in the term virtual memory to describe a memory management technique. It is now widely used in computing, most commonly as virtual reality. But what is a virtual satellite then?
As we all know, GPS satellite signals are quite weak. The antenna of a standard GPS receiver needs to have a clear line-of-sight (LOS) view to the satellites for successful signal tracking and position determination. Buildings and other structures will block signals coming from certain directions. In built-up areas, this can result in fewer LOS signals than the minimum of four needed for unaided positioning. Even with four or more LOS signals, the receiver-satellite geometry may be poor resulting in a large dilution of precision and poor positioning accuracy as a result. It is true that augmentations such as wheel sensors and inertial measurement units coupled with dead reckoning may permit an acceptable level of positioning accuracy for some kinematic applications, but the accuracy will degrade over time if satellite blockage continues unabated. And yes, multi-GNSS can help in these situations with receivers availing themselves of additional LOS signals from the GLONASS, Galileo, and BeiDou systems and in Japan, QZSS. But Galileo, BeiDou and QZSS are still in development with a variable number of satellites available at a given location during the day. Is there anything else that can be done to improve the availability of GPS signals?
In fact, there are often more GPS signals arriving at a receiver’s antenna than just the LOS signals. These are non-line-of-sight (NLOS) signals that bounce off nearby structures before arriving at the antenna. We call the phenomenon multipath and, as we have discussed before in this column, multipath typically reduces positioning performance when the NLOS signals from a particular satellite combine with the LOS signal to distort a receiver’s standard correlator outputs thereby biasing pseudorange and carrier-phase measurements. Various techniques have been developed to reject multipath signals at the antenna or in the receiver while others have been developed to lessen the effect of these signals and so minimize their impact on position solutions. On the other hand, non-positioning GPS applications have been developed to use reflections from the Earth’s surface to measure snow depth, ground moisture content, and ocean-surface roughness. But could we somehow use multipath signals to improve positioning applications rather than degrade them?
In this month’s column, we look at a technique developed by researchers at the University of Illinois at Urbana-Champaign that distinguishes a reflected NLOS signal of a particular satellite from the LOS signal and characterizes the NLOS signal as coming from a virtual mirror-image satellite in the direction of the signal reflection point. By using information on the position and orientation of the reflector, the NLOS signal can be treated as an additional LOS signal, albeit from a ghost satellite. The authors have demonstrated that the technique works well in practice and in one difficult positioning environment, obtained an improvement in horizontal position accuracy of 40 meters — a reflected blessing indeed.
Building obstructions and reflections present serious challenges to GPS receivers operating in urban environments. In such environments, buildings may obstruct GPS signals, leading to reduced GPS signal availability. In addition, buildings may reflect GPS signals, resulting in reception of non-line-of-sight (NLOS) signals. NLOS GPS signals are delayed versions of the line-of-sight (LOS) signals. As such, they lead to pseudorange errors, resulting in positioning errors. Conventional approaches treat NLOS GPS signals as unwanted interference to be rejected or mitigated.
Conventional approaches reject NLOS GPS signals at multiple stages of GPS signal processing. Antenna-based approaches include the use of right-hand-circularly-polarized (RHCP) antennas and controlled reception pattern antennas (CRPA). Correlator-based approaches include the use of the narrow correlator, the double-delta correlator, the multipath estimating delay lock loop (MEDLL) and the vision correlator by various receiver manufacturers. In addition, receiver autonomous integrity monitoring (RAIM) approaches reject pseudoranges with inconsistent positioning residuals.
Besides rejecting NLOS GPS signals, conventional approaches also make use of robust filtering and joint signal tracking techniques to mitigate the effects of these signals. Robust filtering techniques include the use of Bayesian filters such as Kalman filters and particle filters. Joint signal tracking techniques include vector tracking and direct position estimation (DPE). A list of existing approaches addressing NLOS GPS signals is provided in TABLE 1.
TABLE 1. Approaches for rejecting and mitigating NLOS GPS signals.
In contrast to conventional approaches that reject or mitigate the effects of NLOS GPS signals, we propose transforming NLOS GPS signals from being unwanted interference to becoming additional useful navigation signals. In addition, we provide a navigation solution under reduced GPS signal availability.
RELATED WORK
In our approach to using NLOS GPS signals, we make use of DPE and 3D map-aided positioning. The following sections provide an overview of these techniques.
Direct Position Estimation. DPE is an unconventional joint signal tracking and navigation technique that directly estimates the GPS receiver’s navigation parameters from the GPS raw signal. It does so by directly comparing the expected signal reception of multiple potential navigation candidates against the actual received signal. The navigation solution is then estimated as the navigation candidate with the highest overall correlation between the expected and the actual received signal. This overall correlation is an accumulation of signal correlations across all available satellites, with replica signal parameters aligned to the candidate navigation parameters. In this manner, DPE jointly uses signal correlations from all available satellites to produce a robust navigation solution.
3D Map-Aided Positioning Techniques. State-of-the-art approaches use available 3D maps to predict NLOS signal reception. Apart from rejecting and/or mitigating the effects of NLOS pseudoranges, state-of-the-art approaches leverage the benefits of NLOS pseudoranges, constructively using the affected pseudorange measurements through special treatment of NLOS paths during trilateration. Using 3D building models, they model NLOS paths as LOS paths from satellites to virtual receivers located at receiver mirror-image positions. However, these approaches are limited by the issue of reduced signal availability due to multipath fading in addition to building obstruction. Under reduced signal availability, the navigation solution obtained via trilateration is degraded. With further reduction in signal availability — the number of available pseudorange measurements reduced to fewer than four — conventional calculation of the GPS navigation solution via trilateration with four unknowns is not possible.
In contrast to state-of-the-art approaches addressing NLOS signal reception at the GPS pseudorange measurement level, we directly address and constructively use NLOS signals at the GPS signal level via DPE using NLOS signals.
OUR APPROACH: DPE USING NLOS SIGNALS
We first model NLOS signals as LOS signals to virtual satellites at satellite mirror-image positions, as shown in FIGURE 1. This approach is similar to using virtual transmitters for multipath-assisted wireless indoor positioning. We calculate these satellite mirror-image positions and velocities using knowledge of building reflection surfaces estimated from available 3D maps.
FIGURE 1. NLOS signal transformed from being (top) an unwanted interference to becoming (bottom) an additional LOS signal to a virtual satellite at the satellite mirror-image position.
We then integrate these NLOS signals into GPS positioning via DPE. We modify the expected signal reception used in DPE to include NLOS signal information, as shown in FIGURE 2. Our approach deeply integrates this information and accurately describes the actual received signal.
FIGURE 2. Overall correlation in DPE, with the NLOS signal treated as an additional LOS signal to a virtual satellite at the satellite mirror-image position.
In addition, our approach provides a navigation solution under reduced signal availability. FIGURE 3 shows a block diagram of our approach.
FIGURE 3. Block diagram of DPE using NLOS signals and involving calculation of satellite position, velocity and time (PVT) and batch correlation using a fast Fourier transform (FFT).
IMPLEMENTATION AND EXPERIMENT RESULTS
We implemented DPE using NLOS signals with commercial front-end components and our software platform, PyGNSS. We conducted an experiment in front of the 53 meters by 40 meters wind tunnel located at NASA’s Ames Research Center, Mountain View, California (see FIGURE 4).
FIGURE 4. Experiment setup in front of the 53 meters by 40 meters wind tunnel located at NASA’s Ames Research Center, Mountain View, California. (a) data collection equipment; (b) wide-angle photograph of the wind tunnel’s air-intake port.
The material of the vertical surface of the wind tunnel’s air-intake port is a metal wire mesh with a grid spacing of 1.8 centimeters by 1.8 centimeters, as shown in FIGURE 5. This grid spacing is approximately one tenth of the carrier wavelength of the GPS L1 signal; the mesh wire radius is much less than the grid spacing. Thus, the vertical surface of the air-intake port acts as a reflector of GPS L1 signals.
FIGURE 5. Metal wire mesh on the vertical surface of the wind tunnel’s air-intake port. (Left) close-up photograph showing the grid spacing of 1.8 centimeters by 1.8 centimeters; (right) photograph from another perspective showing wire mesh covering the entire vertical surface of the air-intake port.
We estimated the normal vector and a point on the wind tunnel’s reflection surface using a geo-referenced 3D point cloud available on line through the National Oceanic and Atmospheric Administration’s (NOAA’s) Data Access Viewer tool. We refined the estimate using iterative closest point map-matching with a lidar scan (FIGURE 6).
FIGURE 6. Building reflection surface estimated from NOAA Data Access Viewer (DAV) point cloud, refined using map-matching with a lidar scan.
We then determined possible LOS and NLOS paths from satellite elevation-azimuth plots. Plotted in FIGURE 7 are the satellite positions, the satellite mirror-image positions and the building reflection surface. An NLOS path to a satellite exists if the corresponding LOS path to the satellite mirror-image intersects the building reflection surface. In our experiment, LOS paths exist to satellite PRNs 5, 7, 27 and 28 and an NLOS path exists to satellite PRN 5. Thus, both LOS and NLOS signals from satellite PRN 5 are present. This is verified by examining the amplitude of the in-phase prompt correlations over time. Only the in-phase prompt correlations of satellite PRN 5 exhibit a sinusoidal behavior characteristic of having both LOS and NLOS signals, as shown in FIGURE 8.
FIGURE 7. Elevation-azimuth plot with satellites highlighted using green boxes and satellite mirror-images highlighted using red boxes. The 3D point cloud of the wind tunnel’s air-intake port is plotted using grey dots. The path to the mirror-image of satellite PRN 5 passes through the surface of the wind tunnel. Thus, an NLOS path to satellite PRN 5 exists. In addition, LOS paths exist to satellite PRNs 5, 7, 27 and 28.FIGURE 8. Only the in-phase prompt correlation of satellite PRN 5 exhibits a sinusoidal behavior characteristic of having both LOS and NLOS signal components.
We then performed DPE, including the signal correlation contribution from the NLOS path to satellite PRN 5, where the NLOS path is represented as a LOS path to the satellite mirror-image. The overall correlation result, including the signal correlation from the NLOS path to satellite PRN 5, is shown in FIGURE 9. The color of the position markers, plotted using Google Maps, represents the overall correlation amplitude. Red indicates a high overall correlation amplitude and blue indicates a low overall correlation amplitude. The navigation solution is directly estimated as a correlation-weighted mean of the navigation candidates.
FIGURE 9. Normalized overall correlation with contributions from all satellites, including the satellite mirror-image of PRN 5.
The result, as compared to that estimated using pseudoranges from scalar tracking followed by trilateration, is shown in FIGURE 10. DPE using NLOS GPS signals demonstrated improved horizontal positioning accuracy by 40 meters.
FIGURE 10. DPE using NLOS GPS signals demonstrates improved horizontal positioning accuracy by 40 meters. This is in comparison to the navigation result obtained using pseudoranges estimated from conventional scalar tracking followed by trilateration.
CONCLUSION
In summary, we proposed DPE using NLOS signals to mitigate the issues of NLOS GPS signal reception and reduced GPS signal availability in urban navigation. We modeled NLOS signals as LOS signals to virtual satellites at satellite mirror-image positions. In this manner, NLOS signals are transformed from being unwanted interference to becoming additional useful navigation signals. We then created expected signal receptions to include NLOS GPS signal information at multiple potential navigation candidates and use DPE for positioning. Finally, we experimentally demonstrated a reduction in horizontal positioning error by 40 meters. This is in comparison to the navigation result obtained using pseudoranges estimated from conventional scalar tracking followed by trilateration.
ACKNOWLEDGMENTS
The authors thank the Safe Autonomous Flight Environment (SAFE50) and the Unmanned Aircraft System Traffic Management teams at NASA’s Ames Research Center, where the lead author was hosted for the summer of 2016, for their equipment support. The authors also thank Akshay Shetty for collecting and map-matching the lidar scan to the geo-referenced 3D point cloud.
This article is based on the paper “Direct Position Estimation Utilizing Non-Line-of-Sight (NLOS) GPS Signals” presented at ION GNSS+ 2016, the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation, held Sept. 12–16, 2016, in Portland, Oregon.
YUTING NG received her B.S. degree in electrical engineering and her M.S. degree in aerospace engineering from the University of Illinois at Urbana-Champaign (UIUC) in 2014 and 2016, respectively. Her research interests are advanced signal processing, satellite navigation systems and radar.
GRACE XINGXIN GAO is an assistant professor in the Aerospace Engineering Department at UIUC. She obtained her Ph.D. degree in electrical engineering from the GPS Laboratory at Stanford University in 2008. Before joining UIUC in 2012, she was a research associate at Stanford University.
FURTHER READING
• Authors’ Conference Paper
“Direct Position Estimation Utilizing Non-Line-of-Sight (NLOS) GPS Signals” by Y. Ng and G.X. Gao in Proceedings of ION GNSS+ 2016, the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 12–16, 2016, pp. 1279–1284.
“Mitigating Jamming and Meaconing Attacks Using Direct GPS Positioning” by Y. Ng and G.X. Gao in Proceedings of IEEE/ION PLANS 2016, the Position, Location, and Navigation Symposium, Savannah, Georgia, April 11–14, 2016, pp. 1021–1026, doi: 10.1109/PLANS.2016.7479804.
“Evaluation of GNSS Direct Position Estimation in Realistic Multipath Channels” by P. Closas, C. Fernández-Prades, J. Fernández-Rubio, M. Wis, G. Vecchione, F. Zanier, J.A. Garcia-Molina and M. Crisci in Proceedings of ION GNSS+ 2015, the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation, Tampa, Florida, Sept. 14–18, 2015, pp. 3693–3701.
“On the Maximum Likelihood Estimation of Position” by P. Closas, C. Fernández-Prades and J. Fernández-Rubio in Proceedings of ION GNSS 2006, the 19th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, Sept. 26–29, 2006, pp. 1800–1810.
“NLOS Correction/Exclusion for GNSS Measurement Using RAIM and City Building Models” by L.-T. Hsu, Y. Gu and S. Kamijo in Sensors, Vol. 15, No. 7, 2015, pp. 17329–17349, doi: 10.3390/s150717329.
“GPS Multipath Detection and Rectification Using 3D Maps” by S. Miura, S. Hisaka and S. Kamijo in Proceedings of ITSC 2013, the 16th International IEEE Conference on Intelligent Transportation Systems, The Hague, The Netherlands, Oct. 6–9, 2013, pp. 1528–1534, doi: 10.1109/ITSC.2013.6728447.
“Urban Multipath Detection and Mitigation with Dynamic 3D Maps for Reliable Land Vehicle Localization” by M. Obst, S. Bauer and G. Wanielik in Proceedings of IEEE/ION PLANS 2012, the Position, Location, and Navigation Symposium, Myrtle Beach, South Carolina, April 23–26, 2012, pp. 685–691, doi: 10.1109/PLANS.2012.6236944.
• Virtual Transmitters
“Simultaneous Localization and Mapping in Multipath Environments” by C. Gentner, B. Ma, M. Ulmschneider, T. Jost and A. Dammann in Proceedings of IEEE/ION PLANS 2016, the Position, Location, and Navigation Symposium, Savannah, Georgia, April 11–14, 2016, pp. 807–815, doi: 10.1109/PLANS.2016.7479776.
Ubiqomm has unveils its ubiquitous high-speed data connectivity solution, especially designed for enterprise drone fleets engaged in present line of sight (LOS) and, in the future, beyond visual line of sight (BVLOS) flights.
“The application for drones is increasing exponentially as companies apply drone technology for surveying and performing emergency operations in remote locations, and other use cases including traffic monitoring in cities, and above stadium live-streaming of sporting events. Consistent high-speed data connectivity between drones and ground control centers is becoming mission critical,” said Saum Vahdat, VP of Marketing and Business Development at Ubiqomm.
Ubiqomm’s wireless solution encompasses a network of base stations on the ground and communications devices mounted on drones. Each base station is capable of supporting all drones within its 25km+ coverage radius. The seamless handoff with the adjacent base station ensures ubiquitous coverage in a large area while connecting drones to a cloud backhaul.
Ubiqomm’s unique patented solution uses multiple techniques, such as innovative antenna design for both base stations and drones, mobility management, and interference mitigation, together achieving very high bandwidth efficiency. Vahdat added “Dubbed as “Wi-Fi in the Sky,” Ubiqomm’s solution enables 10x lower CapEx and OpEx as compared to terrestrial LTE networks while enabling very high-data rates of 200 Mbps between drones and base stations.”
Ubiqomm is partnering with Skyriver, an affiliated company in the Bridgewest Group portfolio of businesses with expertise in wireless broadband network design and deployment, in the millimeter wave and lower spectrum bands. Together, the two companies are offering demonstrations to companies that are interested in leveraging Ubiqomm’s technology for their own products and services.
The demonstration includes multiple drones flying within a region approximately 25km away from their San Diego base station. Each drone will be transmitting multiple 1080p video streams to the base station, utilizing secure high-speed links (200+ Mbps).
Ubiqomm and Skyriver are seeking industry partners for development, testing and trials of UAV traffic management (UTM) protocols in addition to the “Wi-Fi in the Sky” network solution, paving the way for BVLOS flight operations.
Last month I wrote about the drone industry experiencing giddy enthusiasm. One of the points I mentioned was the upcoming Commercial UAV Expo, in which there were predicted to be 100+ exhibitors and 500-700 attendees — an exhibitor-to-attendee ratio of 1:5-7, an unusually low ratio for a conference. At INTERGEO in September, from where I wrote last month’s column, the ratio was 1:31.
Well, I attended the Commercial UAV Expo in Las Vegas last week. The organizers reported ~1,500 attendees instead of the predicted 500-700. Apparently, attendance even surprised the organizers because they ran out of attendee bags by the time I picked up my badge the day before the conference began.
It was a very good conference because there were legitimate users and potential users of drone technology. During sessions, the audience was focused, more so than at most conferences I’ve attended. I think the reason is clear. The audience, consisting of drone users, potential users and manufacturers, wants to know where in the rapidly developing drone market is there a chance to make money?
One of the more interesting presenters was Commonwealth Edison, an electric utility based in Chicago with more than 5 million customers. ComEd discussed its experience and applications for drones from substation tower inspections to transmission line surveys. A representative from CNN, the news organization, spoke about how they are using drones to capture images and videos of breaking news events such as the recent refugee crisis in Europe. Chad Colby, a farmer who claims more than 3,500 drone flights and is active on the drone conference speaking circuit, showed the audience the value of drones in agriculture, which is one of the no-brainer markets for drones. Presentations such as these and a handful of others struck home with the audience because they present meaningful, that is to say monetizable content.
Commonwealth Edison’s use cases for drones.CNN use case for drones.Chad Colby/Nolan Berg describe the impact of drones in the ag market.
Moving from current uses to future uses, British Petroleum (BP) displayed its drone wish list — likely one that most drone dreamers would like to see:
Platforms (hardware/software):
Interoperability
Continuous operation
Autonomous air, land, water
Robots that can maneuver around a facility
Non-military pricing
Regulations:
Tech standards — iSafe, ANSI, HSAC
Beyond line of sight
Data exchange formats
Certification programs
Night operations
Payloads:
Miniaturized
Varied – full EM spectrum, acoustic, gas sensing
Several of the items on BP’s wish list were recurring themes at the conference, with the big elephant in the room being beyond-visual-line-of-sight (BVLOS). The Federal Aviation Administration (FAA) has largely not allowed BLOS operations even for 333 Exemption holders like me. Following is an excerpt from the CoA (Certificate of Waiver or Authorization) issued by the FAA:
d. The PIC is responsible to ensure visual observer(s) are: – Able to see the UA and the surrounding airspace throughout the entire flight
The Visual Line of Sight (VLOS) requirement seriously inhibits the value of drones for commercial use. When you consider that a rotorcraft (helicopter) might be less than two feet in diameter, it doesn’t have to travel very far before it’s difficult to see (without the aid of binoculars or similar devices, which are prohibited). However, rotorcraft are very flexible in that they can be controlled in a small area. They can hover and they can land in very small or constrained areas relatively safely. Fixed-wing (airplane) drones are a different story. At 30-50 miles per hour, it doesn’t take long for a fixed-wing drone to be out of VLOS. So, practically speaking, a fixed-wing drone for production-oriented flying is very limited, unless the operator disregards the FAA VLOS rule.
The other challenge with fixed-wing drones is the take-off, and more importantly, the landing space required to bring a fixed-wing drone back to earth in one piece. One fixed-wing manufacturer said you’ll need several hundred feet to land their aircraft, and that’s assuming a full payload (maximum weight). One has to wonder how fixed-wing drones will be deployed. One can quickly see how impractical it may be to launch a fixed-wing drone in something less than a city park, high school sports field or a crop field.
Ignoring the FAA VLOS (and other) rules is clearly what is happening. There is seemingly no constraint for manufacturers to tell prospective buyers “go ahead and operate on your own property, no one will care.” Farms, mining operations and some construction sites might be so rural that there’s not a human being in sight. In those scenarios, it seems the “no harm, no foul” rule is in effect, or more likely “don’t ask, don’t tell.” It’s definitely happening, to the point that critics are arguing that the FAA rules are so restrictive that it promotes illegal operations. Even a former NTSB (National Transportation Safety Board) member wrote an article entitled “Unreasonable UAS Rules Promote Culture of Non-Compliance”.
However, just when you think it’s a drone free-for-all to fly where you want, the FAA pulls one out of its hat like it did last week and proposed a $1.9M fine to a Chicago-based company, SkyPan International, for conducting 65 drone flights without authorization. Mind you, these weren’t flights in rural Iowa taking pictures of corn fields. According to the FAA, the company flew 43 missions in New York City’s restricted airspace without prior authorization. Well, now we know where the FAA’s tolerance lies.
Back to the Commercial UAV Expo. While the enthusiasm during the technical sessions showed some restraint, it knew no bounds in some areas of the exhibition area. Vendors, especially the venture capital-funded ones, were looking to book orders now. Prices ranged from sub-$1,000 for a “prosumer” drone for snapping high-resolution images to a $100,000+ for the drone equipped with lidar or other specialized payload.
The exhibit hall at the Commercial UAV Expo.
Please don’t take my message the wrong way. There’s a lot of opportunity for drones in the commercial market segments, from agriculture to utility inspection to photography — but the game is very early. While the technical hurdles can be conquered, the regulatory hurdles are substantial. The FAA is working on rules for BVLOS, but as the FAA chairman said, a solution for that is a few years from now.
