SystemX Alliance Newsletter - June 2023

Lab Spotlight: Navigation and Autonomous Vehicles Lab (NAV Lab)

Professor Grace Gao leads the Navigation and Autonomous Vehicles (NAV) Lab at Stanford University. The NAV Lab focuses on robust GPS-based positioning, navigation, and timing, with applications to manned and unmanned aerial vehicles, autonomous driving cars, and space robotics. 

Bringing localization safety in aviation to urban mobility, such as autonomous driving and aerial taxis 

For safe vehicle localization and navigation, it is critical to address not only positioning accuracy, and availability, but also the confidence level of the accuracy, broadly defined as integrity. In aviation, safety of navigation using GPS has been achieved and continuously improved. However, such safety has not been achieved for urban mobility, such as autonomous driving and aerial taxis. These vehicles operate in complex environments instead of open sky, and use a variety of sensors beyond just GPS, such as LiDAR and camera.

The NAV lab is developing new methods to bring integrity from aviation into urban navigation, including state estimation algorithms that can handle multi-modal measurement errors and algorithms that can bound integrity risks.

No signal is also signal: when a GPS signal is blocked by buildings, this fact still provides useful information for positioning  

Urban environments pose a great challenge for GPS positioning. In “urban canyons,” GPS signals can be blocked or reflected by buildings, leading to positioning errors or failure to position. However, a lack of GPS signal can actually provide insight that the receiver is in the satellite “shadow” of a nearby building or other structure. 

The NAV lab has developed a novel technique which leverages geometric set representations to quickly and accurately compute the overlap of these shadows for reliable urban positioning. However, such shadow calculation relies on detailed 3D building models, which can be difficult to create and expensive to store. Leveraging neural radiance fields, a cutting-edge technique from the field of computer vision, the NAV Lab is working to develop novel representations of cities which can be used for applications such as GPS shadow matching. For environments in which GPS-based localization alone is insufficient to provide the precision and accuracy required for safe autonomous driving, these neural city maps can also be utilized for visual localization.

Safety under extreme dynamics 

The NAV lab also considers safe autonomous navigation under extreme dynamics, such as drifting, as seen in car racing. At these extremes, we would like to know how certain we are of the car’s position and velocity. In collaboration with the Toyota Research Institute, we are looking at providing uncertainty guarantees on state estimates while accounting for uncertainties introduced from imprecise sensor measurements, such as noisy GPS positioning outputs.

Shooting for the Moon: Moon-GPS system 

In addition to applications on Earth, the NAV lab is also working on navigation for space, and in particular, the Moon. We are entering a new era of Moon exploration. More than fifty years since the Apollo program, NASA's Artemis mission will land humans on the Moon again. Exploring the Moon also serves as a crucial stepping-stone for the success of future deep space missions. With the increase in human and robotic exploration, we must provide Positioning, Navigation, and Timing (PNT) services anywhere on the Moon. Current vision-based methods work poorly in the Moon's polar regions and not at all in the darkness of its night and permanently shadowed craters.

The NAV lab is creating technologies for a “Moon-GPS,” a lunar satellite navigation system with smaller satellites, each perhaps as small as a shoe box, compared to existing Earth-GPS satellites which are each as large as a truck. The lunar navigation satellites can function with clocks that are a thousand times cheaper than the atomic clocks on today's GPS satellites. The key idea is that the lunar satellite navigation system will listen to signals already broadcast by the Earth-GPS and process those signals to perform timing and ephemeris corrections.

Navigating a swarm of robots on the Moon 

The NAV lab is also collaborating with NASA Jet Propulsion Laboratory for the Cooperative Autonomous Distributed Robotic Explorers (CADRE) mission to send a robot swarm to the Moon surface in 2024. By utilizing multiple types of sensors distributed across the multiple rovers including cameras, wheel encoders, and ranging radios, the NAV lab designs navigation algorithms to optimize the geometry of the rover team for better positioning accuracy and robustness to system failures. 

Article, photos, and video credits to NAV Lab

Reminder for Members: Student Thesis Defenses Webpage

We have a webpage that is a one-stop-shop for Student Thesis Defenses! To view upcoming defenses as well as recordings of past presentations, click here: Student Thesis Defenses. Please note: you must be logged in to the SystemX website to view this page.

Students & Faculty Awards/Recognitions

• Prof. H.-S. Philip Wong received the 2023 Symposium on VLSI Technology and Circuits Test of Time Award for the paper "Monte Carlo modeling of threshold variation due to dopant fluctuations" by Frank, D.J.; Taur, Y.; Ieong, M.; Wong, H.-S.P. presented in the 1999 Symposium of VLSI Technology and Circuits.
• Jung Il Choi, Mayank Jain, Philip Levis, Kannan Srinivasan, and Sachin Katti received a Test of Time Award from ACM SIGMOBILE for the paper "Achieving Single Channel, Full Duplex Wireless Communication," from MobiCom 2010.
• Jack Melchert and Kartik Prabhu (Advisor: Prof. Priyanka Raina) won the Apple PhD Fellowship
• Prof. Stephen Boyd received the 2023 Richard E. Bellman Control Heritage Award "for pioneering contributions and sustained leadership in the development and application of advanced optimization algorithms" from the American Automatic Control Council
• Prof. Danielle Mai was selected for the 2023 Spring Arthur K. Doolittle Award from the American Chemical Society Division of Polymeric Materials: Science and Engineering (PMSE). This award recognizes an outstanding presentation during a PMSE symposium at each of the Fall and Spring ACS National Meetings
• Brendan Wirtz and Winnie Huang (Advisor: Prof. Danielle Mai) won NSF Graduate Research Fellowships
• Mike Burroughs (Advisor: Prof. Danielle Mai) was selected as an ACS PMSE Future Faculty Scholar
• Ashwin Kanhere and Tara Mina (Advisor: Prof. Grace Gao) won the Community Impact Award
• Prof. Felipe Jornada received the NSF CAREER Award in February 2023
• Obumneme Godson Osele (Advisor: Prof. Allison Okamura) received the Stanford Community Impact Award
• Alaa Eldin Abdelaal (Advisor: Prof. Allison Okamura) received recognition as an RSS pioneer, among "a cohort of the world's top early-career researchers"