The future of GPS
Astronautics professor Grace Gao is an authority on the Global Positioning System.
GPS has long been key to navigation on Earth, she says, but science is now shifting its focus outward to the frontiers of space. Gao is working on a GPS-like system for the moon. To keep costs low, this lunar positioning system will leverage Earth-based satellites complemented by a network of smaller satellites in lunar orbit. It could lead to autonomous vehicles on the moon and a new era of lunar exploration, Gao tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.
Transcript
[00:00:00] Grace Gao: This is the first mission, um, tech demo, uh, about, uh, having collaborative robots on the Moon surface. In the past, all the robots that, you know, you probably heard about different rovers on Mars, on Moon, they're all driving on their own, right?
[00:00:14] Russ Altman: Yeah.
[00:00:14] Grace Gao: So this is the first mission about having three of them to collaborate.
[00:00:24] Russ Altman: This is Stanford Engineering's The Future of Everything, and I'm your host, Russ Altman. If you're enjoying the podcast, please tell your family, friends, colleagues. Personal recommendations are one of the best ways to spread the news about The Future of Everything.
[00:00:38] Today, Stanford University's Grace Gao will tell us about the Global Positioning System, GPS navigation. You might think it's a done deal, but there are issues continuing in coverage, safety, and security. It's the future of GPS navigation.
[00:00:54] Before we get started, if you're enjoying the show, please remember to share it with family, friends, and colleagues. It's a great way to spread the news about the show, and to make sure that everybody is clued in on The Future of Everything.
[00:01:13] GPS, or the Global Positioning System, has become critical to our lives. We have it in our smartphones, cars, little devices that we use to exercise, and many other things. It has become a critical part of modern technology for knowing where you are. And also, we don't talk about that as much, but also, what time it is. These GPS devices have incredibly accurate atomic clocks.
[00:01:39] But you know what? GPS is not a static technology. There are still issues of coverage. If you've ever gone into a tunnel, all of a sudden your car and your smartphone have no idea where they are. There's issues of security. There are people both by mistake and on purpose who can mess with the GPS signal. And there are also issues of safety.
[00:01:59] Well, Grace Gao from Stanford University is a professor of aeronautics and astronautics, as well as electrical engineering. She's an expert in global positioning systems, how they work and what the challenges are. She'll tell us about all this and we're even going to go to the Moon.
[00:02:15] So Grace, you're an expert at navigation and autonomous vehicles. Uh, and in many of us have heard of GPS and we use it on our phones and we use it all over. But increasingly I'm seeing in both in your writing and in the world, they mentioned G N S S. So I first wanted to start out, what is GNSS and how does it relate to GPS?
[00:02:36] Grace Gao: Oh, good question. GNSS stands for Global Navigation Satellite Systems. So that's the general term for GPS. So GPS is the US system and there's a European system called Galileo, there's a Chinese system called Compass. And there's also a Russian system called GLONASS. Many other systems similar to GPS are having similar, um, uh, functions, uh, by different nations. And then the general term for all these satellite based navigation system is called the GNSS, that is.
[00:03:09] Russ Altman: Perfect. So that's, uh, and why don't we take a minute for people who don't think about GPS very much, uh, how does GPS basically work? We kind of know there's satellites. But, uh, tell me a little bit about what the, what every person should know about GPS.
[00:03:24] Grace Gao: Sure. Yeah. As you said, the GPS, um, it's global positioning system, and then it has, uh, consists of satellites, consists of, uh, ground monitoring stations, and also the user receivers. For the GPS satellites, and that's the US system, there are about thirty satellites, uh, orbiting in the medium Earth's orbit. So that's about twenty thousand kilometers above the Earth.
[00:03:47] And then you have ground monitoring stations to make sure that all the satellites, they're healthy. And then, of course, you know, we're users, as users, for example, in our smartphones, and then we have the GPS receiver that listens to the satellite signals transmitted by all these satellites and then decide and know where we are.
[00:04:07] Russ Altman: So just, I have, I've always had this question because I use a GPS thing when I go on bicycle rides. Um, is it talking to one satellite or is it talking to multiple satellites when it's looking for that signal?
[00:04:19] Grace Gao: Yeah, it talks to multiple satellites and then we have this term called trial operation. So if you talk to multiple satellites from different directions and then you can calculate where you are.
[00:04:30] Russ Altman: Okay, and one final preliminary question, and it arises because you mentioned that GNSS is this kind of confederation of systems from. Uh, do all those systems work together? You mentioned Russia, China, Europe, United States, maybe other places. Do they coordinate or are they separate systems?
[00:04:51] Grace Gao: I would say both. Uh, they, uh, both, uh, independent systems as well they, uh, cooperate in the way that if you have just one receiver, like one antenna or set of antennae for one receiver, you can receive signals from all these satellites from all these nations.
[00:05:09] So that requires the different nations to coordinate so that the signals are transmitted in the same or similar frequency spectrum. And then in this way, you don't need multiple antennas to listen to all these different frequencies. You just need one.
[00:05:23] Russ Altman: So there's a little bit of collaboration to make life easier. That's nice. Okay. So now I want to get into the frontier things that your lab and you are working on. And I know that a lot of it has to do with the reliability of the signal and safety. So maybe tell us what are one or two of the big outstanding issues. Because for many of us, GPS just works and it might be surprising to find out that people like you are doing a lot of work to both improve it and make it stronger. So what are the issues?
[00:05:53] Grace Gao: Yeah, um, good question. You know, the GPS is already in our smartphones, right? So they already work pretty well, but there are still, uh, issues. Um, the main thing is that the GPS, because it relies on the satellite signals, if the satellite signals are blocked by buildings or reflected by surfaces such as windows, it can cause errors.
[00:06:14] And then, so that makes a GPS positioning in, say, urban environment quite challenging. Another aspect is, you know, um, because the GPS is a, it's a, you know, major sensor used in many applications, um, and including like say autonomous driving. And there's also the timing aspect of GPS that is being used in many timing applications.
[00:06:37] And then, so the reliability and safety of these GPS signals are very important. So we call it the integrity. So that's basically the confidence level of your accuracy. So, you know, because I teach the GPS class here on campus, and then often people say, oh, if you're a GPS professor, tell me what is the cutting-edge technology that for GPS? Like how accurately we can achieve.
[00:07:01] Russ Altman: Yes.
[00:07:01] Grace Gao: And then I say, actually, I cannot answer this question because, uh, you know, the accuracy, it should be associated with the confidence. Uh, for example, for in our smartphone, we can claim probably about a five or ten meter accuracy or meter level accuracy, but ninety-five percent of the time. Uh, if you go in the tunnel, then you lose your GPS and then GPS is also used to guide airplanes, right?
[00:07:26] And for that, uh, the confidence of accuracy is much, much higher. It's ninety-nine point nine nine nine nine percent so like six nines. But then the accuracy requirement for using GPS to guide airplanes, it's much less than, you know, the accuracy requirement for smartphones because airplanes, they can tolerate to be say ten meter apart or a hundred meter apart, right? But we need that high guarantee or high confidence level.
[00:07:53] Russ Altman: Okay. So I love these issues that you've raised because they all seem that we should care about them. So let's start with the, uh, signal strength issue or you're in an urban environment. On my bike ride, there's, it's very annoying because the place where the mountain is the steepest, there's a lot of trees and rocks and it blocks my GPS.
[00:08:12] So the one part that I'm most interested because it's the steepest part, and I want to find out how fast I'm going, that's when I lose my GPS. So what are the options and what are the approaches that you and others are taking to get better coverage, higher confidence for more of the more places.
[00:08:29] Grace Gao: Yeah. Yeah. There are many approaches. First of all, you can use sensor fusion. You know, GPS is important sensor, but even our smartphone, for example, already has a inertial measurement units. We call it inertial measurements, right? And then you can also fuse a GPS and a sensor with, say, the vision sensor. Um, like say autonomous driving cars, they use LiDAR, they use vision, they use inertial measurements.
[00:08:55] And then if you put all these sensors together and they all work in different ways and then they complement with each other. So, um, that's the one thing to, uh, make a GPS or like a positioning in general better. Another way is to leverage the map. And sometimes when people think about sensors, they only think about sensors equipped, uh, in our device, like phones, cars, right?
[00:09:18] But think about our whole environment, the map, I consider that as another type of sensor. If you know very well of your map, say the three dimensional, even the three-dimensional map or the model of the environment, you can do better. And in our group, we actually have been recently working on using neural network. And there's something called a neural radiance field, that's a type of neural network to represent the 3D environment, uh, of the cities so that if you know exactly where the buildings are and where the reflection surfaces are, then you can utilize that to, um, mitigate or characterize all the GPS, say reflection, signal blockage. And in that, in this way, you actually turn these traditional considered noise, right? The actions into some useful positioning, additional positioning information.
[00:10:11] Russ Altman: Okay. So that's very exciting. And I'm especially like the idea that you, what you called it, I think, multimodal sensor integration. So you have some LiDAR, you have the bouncing waves off the buildings. But let me ask if you're doing that, then you're not in complete control of the system, like you build your GPS system. But now you're saying I need to collaborate with like local sensors that I might pick up. I'm driving down the street and now there's other sensors. So are we going to have a very, kind of, a very good collaborative computational, um, operating system where these different sensors can communicate with one another.
[00:10:49] Grace Gao: Yeah, yeah, um, definitely. I think you’re talking, uh, talking about two topics. One is about the sensor fusion within one device.
[00:10:57] Russ Altman: Okay.
[00:10:57] Grace Gao: So, within one, um, say autonomous driving car, uh, within one device, you can have multiple sensors.
[00:11:03] Russ Altman: Yes.
[00:11:03] Grace Gao: And that would be, I think, in your words, the controlled environment.
[00:11:07] Russ Altman: Yes.
[00:11:07] Grace Gao: The sensors and then, like, the designers of these devices can design how the sensors work together, uh, per device right?
[00:11:15] Russ Altman: Yes.
[00:11:15] Grace Gao: And, but you also, um, talks about another interesting aspect is the collaboration among devices.
[00:11:21] Russ Altman: Right, right. That's what I was thinking about in terms of like signing up to say, I'm available to help other people if they need my data.
[00:11:28] Grace Gao: Yes, yes, exactly. So if you share your information among devices, say, if you have a autonomous driving car on the road, and then you have other cars, you have some cars in front of you. Maybe they could perceive the traffic in front of you, they could perceive around the corner. Um, so there's a school.
[00:11:45] Russ Altman: Right.
[00:11:46] Grace Gao: And there's a crossing, uh, uh, like a road and then, uh, this will help you. And then, um, that's why I still see a lot of interesting, uh, work can be done to improve the safety and for all the localization navigation. Because currently I don't think, uh, the autonomous vehicles, uh, they collaborate among each other on the road. Each individual autonomous vehicle, they make these decisions on their own. They perceive the environment. They sense all the data on their own. But in the future, if they can all collaborate, um, it will just make a position and navigation a lot better in terms of better accuracy, better safety.
[00:12:23] Russ Altman: It's a very exciting idea. But right away, and I'm sure you have thought about this a lot, all of these issues about the security and privacy of your data. Do these car companies, let's say it's Tesla and Lucid and other companies, do they want to build systems where they benefit from each other? What is the status of the industry's willingness to kind of have open standards for communication across vendors?
[00:12:48] Grace Gao: Yeah, yeah. Very good question. I think the industry, they start to share more data. But I can see it can come in different stages.
[00:12:56] The first stage, it could be just within each company and then you can share data locally. And there's something called a platooning. You know, if you have multiple cars, so platooning, that means multiple cars or trucks, they drive together, right?
[00:13:11] And then, uh, you can think about analogy of a birds flocking. So if you have geese, uh, like a flying in the light. And then they are a lot more aerodynamic for the, those geese, right? And then, uh, for, uh, autonomous driving cars, if there's platooning and then the cars, they drive all together, then it's a better, um, aerodynamic.
[00:13:30] So that means a better fuel efficiency, and then they can just, with locally among these, uh, platooning cars or trucks, they can help each other in terms of share their sense. And they're also in close proximity of each other. And then, um, that will also make the navigation more safe and more efficient.
[00:13:48] Russ Altman: Yeah, that sounds that's an exciting. And yeah, I really like that because you can imagine a company initially building platoon support for its vehicles in its product line. And then they could coordinate with other companies, maybe later or separately. And you can build like stacks of interaction.
[00:14:05] Grace Gao: Yeah, exactly. Yeah.
[00:14:07] Russ Altman: So, okay. I know that you focus on safety as well. And I don't know if there's other things that you want to say. Obviously, the accuracy and getting good GPS at all times has a general effect on safety. But I think you're doing a lot of work on specific safety issues, uh, both on the ground and in the air. So what are some of the challenges that you're seeing there?
[00:14:28] Grace Gao: Yeah. Um, for safety, right? There's, uh, um, so safety against uncertainties. You can have a sensing uncertainty. You can have dynamic uncertain, a lot of uncertainties and there's safety against outliers. There are safety against failures, safety against edge cases, and also, you know, there are a lot of AI involved in the position navigation. So that's safety against unseen data sets.
[00:14:55] Russ Altman: Okay.
[00:14:55] Grace Gao: So, um, so this is definitely a very interesting field about how do we improve safety and reduce all these uncertainties and try to, uh, avoid the failure cases.
[00:15:06] Russ Altman: You've written a little bit, I believe, of course, I always review everybody's papers before we get on the, uh, and it was very interesting. You've written a little bit about, um, attacks, like where it's a, uh, a nefarious actor who's trying to, uh, mess up the system. And that I, wasn't aware of that. Is that a, I've heard a little bit about it, like in the case of conflicts. Um, how big of a problem is like people trying to mess up GPS signals?
[00:15:34] Grace Gao: Yeah, good question. Um, actually there are people, um, who purposely try to mess up the GPS system. Actually, a lot of the attacks is, uh, from people also in, unintentionally try to mess up the GPS system. Um, so there's a, for example, the famous case about, um, so the Newark airport, um, they use GPS to guide airplane landing. And then, um, one day they realized the GPS all got messed up at the Newark airport. And then later they realized, guess what, and then also they found this really interesting, like, the behavior of this potential, uh, like, attacker is that, um, in the morning, um, the, um, GPS of the airport got messed up.
[00:16:21] And then they will send people to check and then all this. And then somehow it was just very brief and then it was okay again, and in the afternoon it got again messed up. So just like that repeatedly every, every, every day.
[00:16:34] Russ Altman: And this was a clue to who the person might be.
[00:16:37] Grace Gao: Yeah. Do you want to give a guess what happened?
[00:16:40] Russ Altman: I have no idea. Newark airport. I've been there many times. I have no idea.
[00:16:45] Grace Gao: Yeah. So apparently it was a truck driver and then, you know, uh, because he worked for a trucking company, they have, uh, the trucking company installed all these, uh, GPS tracking devices in all the trucks to know where they are.
[00:16:58] Russ Altman: Yes.
[00:16:59] Grace Gao: This, uh, truck driver wanted to take a detour to visit his girlfriend.
[00:17:04] Russ Altman: Oh no.
[00:17:04] Grace Gao: And then, so he just, uh, searched this, uh, like how to mask, how to disable GPS. And then he basically bought something online that's
[00:17:15] Russ Altman: Oh no.
[00:17:15] Grace Gao: Essentially a GPS jammer. So he turned that jammer on and then, you know, he could see that, oh, you know, the tracking device couldn't know where he was anymore. And then because he's a truck route and then he would turn that on in the morning. And then also turn that on in the afternoon when he returned.
[00:17:33] Russ Altman: Okay, okay.
[00:17:33] Grace Gao: And that is how the GPS got jammed repetitively.
[00:17:37] Russ Altman: So talk about you, you mentioned edge cases. There's a great edge case.
[00:17:40] Grace Gao: Yeah, yeah, yeah. And then interestingly, when Pokémon Go was very popular. And then there was a big, like, surge of all these spoofing events because the people wanted to kind of uh, just,
[00:17:54] Russ Altman: Yeah.
[00:17:54] Grace Gao: Uh, monographate their positions to pretend that they were already somewhere. But they just wanted to sit in their own, like, uh, sofa, and then they
[00:18:03] Russ Altman: To get more points in the game.
[00:18:04] Grace Gao: To get more points, and then apparently that exactly, um, uh, you know, there were a lot of spoofing events.
[00:18:10] Russ Altman: This is The Future of Everything with Russ Altman. More with Grace Gao, next.
[00:18:30] Welcome back to The Future of Everything. I'm Russ Altman, and I'm speaking with Grace Gao from Stanford University.
[00:18:34] In the last segment, Grace helped us understand the basics of GPS, why there are outstanding issues, and how she and her colleagues are working on them.
[00:18:43] In the next segment, she's going to tell us something unexpected. She's trying to figure out how to bring GPS to the Moon. We're having more and more missions fly to the Moon, and when they get there, it's nice to know where they are.
[00:18:56] In this segment, Grace, I want to ask you about some exciting stuff you're doing with NASA about some of these issues, but on the Moon. Now, I don't even know, do we have GPS satellites circling the Moon?
[00:19:09] Grace Gao: Good question. We don't yet, not yet, but hopefully we'll have that in the future. Um, actually I've been working with NASA to set up a GPS like system for the Moon with a satellites orbiting around the Moon.
[00:19:24] Russ Altman: So meanwhile, I guess the question is, um, are these, uh, spaceships, are they using GPS and then how does it work when they leave the orbit of the Moon, like does GPS have anything to offer, or GNSS, does it have anything to offer space travel?
[00:19:42] Grace Gao: Yeah, good question. Um, GPS is mainly designed for the Earth, right? But there are side lobes of this Earth based GPS signals. And then that can reach a certain area of the space, such as low Earth orbit satellites and things like that. And then, you know, we can already do a positioning and navigation on the Moon. Um, even say half a century ago we sent,
[00:20:07] Russ Altman: Yeah, we flew there when I was seven.
[00:20:11] Grace Gao: Yeah. You know, during Apollo times, right? And then we sent astronauts to the Moon and then we were able to navigate them to the Moon surface, right? But then, uh, now this is an exciting era for an, the Moon exploration. Because, um, the US uh, has a planned to send another set of two astronauts to the Moon again after the, like, fifty years of Apollo.
[00:20:35] Russ Altman: Wow.
[00:20:35] Grace Gao: And then, um, in addition to the, uh, astronauts on the Moon, um, there are actually about a hundred missions planned, um, to send different rovers, different things to the Moon's, uh, surface on near the Moon orbit, uh, for the next decade. So there are really a lot of activities.
[00:20:53] And then in the Apollo time, when you just need to send uh, uh, astronaut, just one kind of, uh, thing to land on the Moon, you can do the point to point position. However, if there are say a hundred missions and each mission, they have different, uh, devices, different equipment, different sensors and people.
[00:21:13] And then you need to be, want to know where they are and then navigate all of them. Then just the, you know, the, uh, traditional point to point positioning, that's not a good enough. And then we know on Earth we have a GPS, so that's a global position. So it can position anyone, anywhere on Earth. And then as analogy, um, so then there's this idea of, uh, called a lunar net, right? If we have a network of satellites, and then we can create something like a GPS, but for the Moon.
[00:21:43] Russ Altman: And will this be from existing satellites where, uh, where we repurpose them? Or is this a whole new set of satellites that you want to send up? Or maybe both?
[00:21:53] Grace Gao: Yes, yes, um, it will be mostly a new set of satellites because you need to, not only the satellite, the satellites have to transmit navigational signals.
[00:22:01] Russ Altman: Yeah.
[00:22:01] Grace Gao: You have to design the signals and then, um, implement like the satellites transmit these signals and you have to design the whole architecture, right? And then, um, but the, it's not as easy as we already knew how to do GPS on Earth.
[00:22:16] Russ Altman: Right. It doesn't sound easy. Let me assure you, it doesn't sound easy.
[00:22:20] Grace Gao: Yeah. Yeah. Uh, because also, uh, main driver is the cost, right? And then, um, the terrestrial or the Earth based GPS is a pretty high cost because there's so many things rely on it. So many people using it. And then, um, I'll just give you an example. Each, uh, GPS satellite is equipped with, uh, three really high quality, expensive atomic cops to make sure the satellites transmit signals at the right time. And then we just, and then also this, because of, uh, it's a lot of, uh, sensors, a lot of, uh, um, uh, elements in those expensive GPS satellites.
[00:22:58] So it's also pretty bulky and heavy. But for the Moon if we want to, um, a lower cost system, then we need a cheaper sensors we don't, we hope we don't need to have atomic clocks. And then we also want the satellites to be lighter and smaller so that the launch cost is lower, right? And then, uh, we also want to maybe have, don't need to have so many ground monitoring stations because that's also additional cost to set up things on the Moon.
[00:23:27] So, uh, we are working on, actually me and my students, we're working on how to keep the cost low. Actually, one idea is that we can leverage, uh, the Earth based GPS system. I mentioned before, you know, there, the satellites transmit signals and there's side lobe signals. And then if you have these Moon orbiting navigational satellites and then once in a while and if they are in view of these Earth based GPS satellites we can leverage uh the these really accurate GPS satellite signals to do like the, uh, the Moon orbiting satellite, um, uh, the timing correction, the clock correction or the fact fractions.
[00:24:07] Russ Altman: Okay. So that's very interesting. So we have these atomic clock, we have a lot of redundant atomic clocks, high quality systems around Earth. Then if I'm remembering correctly, it's something like two hundred and fifty thousand miles away is, right, is the Moon and so there's going to. Do this sig, do the satellites, the existing satellites on Earth, do they create a strong enough signal to be received if there's a satellite going around the Moon, like, is that signal strong enough?
[00:24:35] Grace Gao: Very good question. So we have done some studies in simulation to show that it's, although it's weak. But we also have, um, very advanced signal processing algorithm to be able to utilize that. And then, um, as something really even quite exciting is NASA, they are sending this mission called the LuGRE mission. They are sending a GPS receiver to orbit around the Moon next year. And then so that we can have this in situ like real world signal reception to validate that this is doable.
[00:25:05] Russ Altman: That's very exciting. Now, one other question. So, okay. So the idea is you and your team are working very hard to say what's a lightweight, functional system we can get up circling around the Moon. Uh, how many do you need to get a credible GPS or lunar PS? Uh, how many satellites would you need up there?
[00:25:24] Grace Gao: Yeah. Yeah. Very good question. So this is another trade off, right? Um, it's, uh, you know, uh, basically there are many parameters related to that. Uh, it's a different orbital plane, different number of satellites. Uh, and then different coverage, and then I would think that this will come also in stages because of the first stage, you'll probably only need the coverage in the very interesting areas.
[00:25:48] You'll prioritize on that, right? You know, if, uh, for example, the South Pole area, the Moon, that's where like a scientist think that there might be water there. That's high interest area. And then we want to try to put our coverage in that area.
[00:26:02] Russ Altman: Yes. So, um, so you might start off with a few and then add as the scientific value is proven. Um, the other thing I wanted to ask is, what about the trip from Earth to because we're talking you just said that they're thinking about a person, a set of people going to the Moon. Um, do we have or need uh, navigational assistance for the plane, for the spaceships between the Earth and between the Moon when they're not in they're not in low Earth anymore, they're not yet to the Moon. Are they going to just navigate the same way Apollo did or are there GPS navigational assistance that they can take advantage of these days?
[00:26:45] Grace Gao: Yeah. Um, uh, for that, you'll need more than GPS. Um, there are also different techniques. Um, there are actually, uh, sensors, gigantic antennas on Earth. So you can think about the driving using your rearview mirrors. You have this big antennas, you can think about, you can see, quote unquote, see these signals from your rearview mirrors and these big antennas are basically guide you to drive forward.
[00:27:13] Russ Altman: Great. Okay. This is very exciting. And I'm sure that at these kinds of distances, the atomic clocks are absolutely critical because you're going to have timing and synchronization issues, that might, of course we have them on Earth, but they get, you know, literally times ten thousand or a hundred thousand when we're looking at these distances.
[00:27:31] Well, in the last few minutes, I think you're also working on people driving on the Moon. So we started talking about, you know, GPS in our cars and in our cell phones. But you know, we know about rovers. And I believe you have projects looking at how are we going to navigate, so part of it might be what you said about, well, where we're going to be driving initially might be the South Pole. So let's make sure we have good maps, good coverage. But what are some of the challenges about Moon driving?
[00:27:59] Grace Gao: Yeah, yeah. Very good question. So, um, I will mention about the two projects, um, I collaborated with NASA and then, uh, the first project is a pretty near term. And then, um, so me, um, and my collaborators are from NASA JPL. Uh, we are actually trying to send a trial of, uh, three rovers to explore the Moon surface, uh, scheduled to be launched next year.
[00:28:25] Russ Altman: So that's great.
[00:28:26] Grace Gao: This is the very first mission, you know, early on in our show, you know, you were taught, we talk about a collaboration among autonomous vehicles.
[00:28:34] Russ Altman: Yeah. It sounds like this might be a platoon.
[00:28:36] Grace Gao: Yeah, yeah, exactly. So this is the first mission, uh, um, tech demo, uh, about, uh, having collaborative robots on the Moon surface. In the past, all the robots that, you know, you probably heard about different rovers on Mars, on Moon they're all, um, driving on their own, right?
[00:28:54] So this is the first mission about having three of them to collaborate. And then another project I want to mention is also in collaboration with a different group from NASA. And then this is a little bit further in the future. And then that is about autonomous driving on the Moon. And then that is in the far side of the Moon. And some of them are permanently shadowed area of the Moon.
[00:29:18] Russ Altman: Yeah. The dark side. It's the mysterious dark side of the Moon.
[00:29:21] Grace Gao: Yes. And then, you know, vision will be quite challenging.
[00:29:25] Russ Altman: And in a funny way, the GPS becomes even more important.
[00:29:29] Grace Gao: Yeah. And then, but, uh, you know, and then this is, uh, this is about autonomous driving over also long distance, as long as about two thousand kilometers.
[00:29:39] Russ Altman: Wow.
[00:29:40] Grace Gao: Yeah.
[00:29:41] Russ Altman: So this is exciting. And that's not short term. That's a longer-term challenge.
[00:29:44] Grace Gao: That's a longer-term project. Yeah.
[00:29:46] Russ Altman: And hopefully by then you have more of your satellites up there going around the Moon.
[00:29:51] Grace Gao: Yeah, probably this will happen before a full constellation around the Moon. And me and my students have been working on how to leverage limited resources. Um, for example, all the missions, all NASA missions, they always have at least one communication satellite because you need to get the data from the Moon back to the Earth, right? And then, uh, we actually, me and my students, uh, we just had this paper about leveraging this one communication satellite. And then see if just that signal could already help the rovers to navigate in the dark side of the Moon.
[00:30:25] Russ Altman: And that really is important because with all of the competing budgetary pressure, not only on NASA, but the whole federal government. The more, the better you can do with less resources, the more likely you get approval so that you can do all these experiments.
[00:30:39] Grace Gao: Yeah, yeah, exactly.
[00:30:41] Russ Altman: Fantastic.
[00:30:42] Thanks to Grace Gao. That was the future of GPS navigation. Thanks for tuning into this episode. You know, we have more than 250 episodes in the archives, so you can find pretty good conversations about the future of almost everything. If you're enjoying the show or if it's helped you in any way, and I love that, any way, please consider rating and reviewing it.
[00:31:03] We like getting fives, but only if we deserve it. And of course, we'd love to hear your thoughts as well. You can connect with me on X or Twitter @RBAltman and with Stanford Engineering @StanfordENG.