An interview with Markus Uster, head of product center positioning at u-blox about recent GNSS receiver innovations.
Uster
What was the most significant technical innovation in your GNSS receivers in the past five years?
The u-blox F9, launched in 2018, is our robust and versatile high-precision positioning technology platform. It was the first receiver to enable multi-band high-precision positioning solutions for mass-market industrial and automotive applications — and remains the benchmark for the industry today.
The platform combines multi-constellation (continuous reception of four satellite constellations) GNSS technology with dead reckoning and high-precision algorithms. It is also compatible with a variety of GNSS correction data services to achieve positioning accuracy down to the centimeter level.
The u-blox F9 platform is leading the next generation of high-precision navigation with its augmented reality, unmanned vehicles and various machine automation applications. It has since been integrated into a selection of modules catering to a wide range of applications.
What has it enabled users to do that they could not do before?
The u-blox F9 is a widely adopted multi-band GNSS platform for automotive and industrial applications. (Photo: u-blox)
In a nutshell, the u-blox F9 brought high-precision positioning to the mass market. The demand for scalable high-precision technology is growing rapidly, as evident in the automotive world with next-generation advanced driver-assistance systems (ADAS) and in robotics with applications such as UAVs and robotic lawnmowers. However, due to the complexity, size, power and cost restrictions of existing high-precision solutions, until now it has been difficult to meet the demands of these markets.
u-blox developed the u-blox F9 platform by building on the success of our NEO-M8P high-precision GNSS module series and drawing on our extensive experience in GNSS positioning technologies, including dead reckoning, multi-band, real-time kinematic (RTK) and GNSS correction services. The platform delivers the next level of scalable GNSS high-precision technology and shows how u-blox is consistently addressing challenges and driving the GNSS technology evolution.
What is a good example of this?
Integration of the u-blox F9 platform into various applications has proven quite successful in a diverse range of use cases. In the industrial realm, u-blox F9 technology enables mass adoption of commercial unmanned vehicle applications. One example is precision agriculture, where high-precision positioning cost-effectively enables vehicle guidance solutions to improve pass-to-pass accuracy resulting in improved crop yield and reduced consumption of pesticides, fertilizer and seeds. The u-blox F9 modules also paved the way for autonomous driving, including lane-level navigation for heads-up displays and vehicular infotainment systems, a prerequisite for highly automated and fully autonomous vehicles.
Spirent Communications plc has chosen Navmatix s.r.o., a Czech-based company that provides cloud infrastructure for real-time data delivery, to provide cloud infrastructure for its GNSS Foresight service.
Spirent GNSS Foresight is a cloud-based service delivering real-time data on the availability and quality of GNSS signals. The solution accurately forecasts when and where GNSS positioning and navigation will be most reliable through a combination of high-definition maps and precise orbital modelling. This makes it possible to obtain a clear picture of the operating environment at a moment’s notice.
GNSS Foresight will ultimately allow unmanned vehicles, air taxis and drones to operate beyond-visual-line-of-sight (BVLOS) safely.
The GNSS Foresight service enables flight in challenging environments by calculating GNSS availability for every meter, every second, from 1-100 meters altitude, for up to three days into the future. (Image: Spirent Communications)
Navmatix will provide the cloud infrastructure required to deliver GNSS forecast data as real-time data via an API. Navmatix will be deploying full operational and developmental support, including hosting for collection and processing the GNSS forecast data through its content delivery network (CDN). The CDN allows the end user to efficiently query, comprehend and interact with the data. Navmatix will handle the foundational infrastructure of the project, a significant phase in expansion of the company as a whole.
“Spirent Communications are pioneers in GNSS test and assurance solutions, and the Spirent GNSS Foresight service expands our solutions to help autonomous systems reliably use GNSS,” said Jeremy Bennington, vic president of PNT Assurance. “Navmatix has built a framework that can deliver mission-critical services, which is also reliable and scalable. We’re excited to be partnering with Navmatix and look forward to growing Navmatix’s CDN to support the growth of Spirent GNSS Foresight solution throughout its complete lifecycle.”
Because of the amount of data generated, the architecture delivers a robust and sophisticated solution, according to Navmatix. Being entirely cloud based, it allows for continual updates and remote access. The cloud infrastructure will provide the tools necessary to deliver Spirent GNSS Foresight services to Spirent customers worldwide.
Navmatix offers managed infrastructure solutions for the operation, development and ongoing maintenance of GNSS services worldwide.
When I saw that there was a plan for a whole bunch of unmanned, semi-autonomous racecars to compete at the Indianapolis Motor Speedway (Indy, or IMS) racetrack, I initially thought we might be headed to one significant mess of broken-up machines and potentially a lot of damage. I tracked the various announcements of the competition as things progressed, especially when a prize of $1 million dollars was put up by the Lilly Endowment in Indianapolis, and the majority of the field appeared to be potentially staffed by undergrad university teams.
Photo: Indy Autonomous Challenge
However, this isn’t the first time we’ve had unmanned, autonomous road vehicles in competition — we’ve seen highly instrumented SUVs in desert settings in Nevada and California, initially with pretty poor results, which began to improve significantly for the second time round, then vehicles in some simulated street settings with some mixed and also some pretty good results.
So, as the competition date grew closer for the Indy Autonomous Challenge (IAC), the number of published progress reports began to increase, and we began to better understand how the initial 40 teams might take on this seemingly impossible task — how on Earth will they replicate a regular Indy (also a class of racecar) race? Surely many unmanned racecars on the same track at the same time doing more than 150 mph would be catastrophic!
When you take a look, however, at the advances we’ve seen, which have enabled unmanned cars, trucks, taxis and such – surely this tech could stretch to meet these major objectives? But Dallara AV-21 Indy Light racecars avoiding hurtling walls passing by, cornering, getting in and out of the pits, coping with vehicles behind, ahead and overtaking — even a superior-equipped unmanned racecar at >150 mph — well that’s something we would really need to see.
Then you have to take a look at the outfits involved, providing support to the IAC teams – companies including Cisco, and motor sport units such as ADLINK, Ansys, Aptiv, Bridgestone, Luminar, Microsoft and Valvoline and the non-profit Energy Systems Network. The University teams from around the world themselves appeared to also have significant heritage and skill-levels.
As the 40 University teams started the long trek to get over the hurdles that this challenge presented, members from 21 of those institutions were actually able to make it to Indy, grouped into nine “national” teams. By October 23 the nine teams, with only one car each, were ready to test their autonomous vehicles on the actual track.
Clemson University established the baseline Dallara AV-21 vehicle and technology to be used by each team for the race, with sensors monitoring chassis motion, suspension, tires and powertrain. Each team would install its own guidance and avoidance system, with each vehicle equipped with six cameras, four lidars, RTK GNSS, associated radios and bags of computing running each team’s customized control system software. The object being for cars to exit pit-lane, accelerate, brake, establish an optimum line for each corner and flat, avoid obstacles, evaluate the track conditions and establish tolerable limits.
The teams were required to complete several stages of selection, from submission of initial proposals through demonstration of existing vehicle automation capability, simulated race performance, qualification testing at the Indy track — all leading to an anticipated head-to head race against the other qualifiers.
Then 20 days of planned testing stretched to 50, and three months of preparation passed with students working intensely throughout, curing the glitches, experimenting with how to increase lap speed, and pushing the limits while still keeping the cars intact.
Energy Systems Network managed the rules of the final competition in a way that reflected Indy qualification days prior the main race — they judged that the technology was not yet at a stage where multiple cars on the track at the same time would have been such a good idea. So, each car was to individually run a number of practice/qualification laps and the quickest car would be the winner.
During the first stage of live competition, cars were required to exit the pits and run a warmup lap, followed by two laps that were timed and a slow-down lap that required navigating around inflatable barriers on the front-stretch, and then return successfully back around the track into their pit-stop locations. There were several spins in the corners and several crashes, but the four surviving cars/teams were able to optimistically post speeds of more than 130 mph.
Photo: Indy Autonomous Challenge
The final phase involved the four teams taking their cars around a number of warm-up/practice laps, followed by four timed laps. Only the car from Germany’s Technical University of Munich was able to complete all laps with an average speed of ~136 mph, so that team ultimately won the $1 million prize. Even so, all teams were able to successfully mature their systems’ performance through the many months leading up to the IAC and their progress through the various qualification stages. Even the other three final qualifiers had much to celebrate as a result of the competition.
The sponsors supporting the various teams as they progressed through the Challenge may have spent more than $120 million, so that high-pressure development work will be invested back into many vehicle automation opportunities. After all, that was the main objective for the whole undertaking. We should hopefully begin to see safer, more capable self-driving vehicles emerge in the months to come as the technology is applied to more production vehicle automation.
Findings show accuracy of new sensors is improved by greater than an order of magnitude over current offerings.
Honeywell, with funding from the U.S. Defense Advanced Research Projects Agency (DARPA), is creating the next generation of inertial sensor technology that will one day be used in both commercial and defense navigation applications.
The HG1930 IMU. (Photo: Honeywell)
Findings gathered in Honeywell labs have shown the new sensors to be greater than an order of magnitude more accurate than Honeywell’s HG1930 inertial measurement unit (IMU) product, a tactical-grade product with more than 150,000 units currently in use.
An IMU uses gyroscopes, accelerometers and electronics to give precise rotation and acceleration data to enable a vehicle system to calculate where it is, what direction it is going and at what speed, even when GPS signals aren’t available.
There are various types of IMUs on the market, and some — like the next-generation version currently under development — use sensors based on micro-electromechanical systems (MEMS) technology to precisely measure motion.
“Typically, MEMS inertial sensors have been on the lower end of the performance scale, but this latest milestone shows we are changing that paradigm,” said Jenni Strabley, director of offering management for Inertial Sensors, Honeywell Aerospace. “With this next-generation MEMS technology, we’re increasing performance without having to significantly change the size or weight of the IMU. This is a game-changer for the navigation industry, where customers need highly accurate solutions but cannot afford to compromise on weight or size.”
Over the past few years, Honeywell has been working with DARPA to develop the next generation of high-precision navigation-grade IMU technology, under the Precise Robust Inertial Guidance for Munitions: Thermally Stabilized Inertial Guidance for Munitions program.
The new MEMS sensors will use different sensor designs and electronics to enable higher performance. They will serve a broad range of applications in autonomous land and air vehicles for both military and commercial customers, including future urban air mobility aircraft.
“Now that we have demonstrated that MEMS is capable of reaching these incredibly precise performance levels, it is the perfect time to start talking with potential users about how this technology could help their applications,” Strabley said. “We believe this new technology will have a variety of applications, such as onboard future vehicles that will fly in urban environments where lightweight, extremely precise navigation is critical to safer operations. Additionally, there are other applications that haven’t been invented yet but may be enabled by these types of technology innovations.”
Commercial sales of an IMU containing these next-generation sensors are still several years away, but one of the first products using this new technology is expected to be more than 50 times more accurate while roughly the same size as Honeywell’s IMU.
Honeywell has long been a pioneer in MEMS-based IMUs, including the HG1930. Honeywell’s lineage in navigation dates to the 1920s and since then Honeywell has developed and manufactured high-performance navigation solutions found on many aircraft and other vehicles worldwide.
Robotic Research LLC, a leading provider of autonomy and robotic technologies, will begin testing fully autonomous low-speed shuttles that are totally unmanned in the second quarter of this year.
Current commercial applications of low-speed shuttles use onboard safety attendants to monitor the safety inside and outside the vehicle. Robotic Research plans to start testing without onboard attendants.
The first step is to have the attendants in fixed on-site locations, with the future goal to move attendants to an offsite safety monitoring facility.
“Through our work with the U.S. government over the past four years, we have already demonstrated that fully autonomous trucks are a reality. We are committed to making our shuttle and bus manufacturing partners successful by accelerating state-of-the-art technologies for unmanned vehicles ahead of regulatory agencies’ progress,” said Alberto Lacaze, president of Robotic Research.
“The level of safety certification and redundancy necessary to drive fully autonomous vehicles is a significant undertaking that needs to be designed from the top down. Just adding more ADAS is not a reasonable or cost-effective pathway to full autonomy,” Lacaze said. “The advancements driven by the Robotic Research team will provide a product that significantly reduces the cost of operation and therefore improves market size.”
Current local, state and federal regulations for most commercial shuttle operations require the safety attendant to be inside the cab of the vehicle. However, many transit operators are seeking to change these regulations to allow remote attendants to oversee system safety operations. The change is integral to the viability of low-speed shuttles, which are an innovative solution to the first/last mile problem, which is the distance between a traveler’s origin or destination, and a transit station or stop.
Robotic Research has been developing and testing unmanned, autonomous operations for a wide range of vehicles for nearly a decade. The company currently provides autonomy kits that fully automate logistics convoy trucks for the U.S. government and several of its allied nation partners. Nearly 100 trucks have already been delivered. The tests for these vehicles have included operations with no safety attendants on board, with a single operator monitoring three unmanned vehicles.
Robotic Research’s AutoDrive autonomy kit is platform agnostic and can be retrofitted to vehicles of all sizes, from small, portable robots to large trucks and buses. The system provides autonomous functionality on surfaces ranging from urban-improved roads to off-road terrain, all while the vehicle is collecting and analyzing data to better enhance the future of autonomous vehicles and transportation.
Robotic Research’s technology provides automation to one of the largest international shuttle providers as well as to the largest U.S. manufacturer of commercial buses. The company’s AutoDrive kit also supports various autonomy programs in commercial and government sectors and is currently operating in communities and cities around the globe, including 30 states and four continents.
