Rise of the Falcon Heavy
On February 6th, 2018, Elon Musk’s aerospace company, SpaceX, launched the most powerful rocket in the modern fleet. You can watch it here. You can also see an animated version here. The news media had a lot of fun (as did, I am sure, the folks at SpaceX) with the payload; which was a Tesla Roadster sporting an empty space suit posed as the driver, with a “Don’t Panic” plaque on the dashboard and ‘Space Oddity’ on the radio (that no one can hear, because space). The payload is on its way to a Mars orbit, so it won’t be a hazard to navigation around Earth.
As stated before, the Falcon Heavy is the most powerful rocket operating today. It’s not the most powerful rocket ever deployed, the Saturn V still holds that title (see here for the phallic waving contest of rockets), but it can lift more than any other rocket today or in recent history.
Of course, we’ve been building and launching rockets into space for 60+ years now, so lobbing payloads into orbit atop highly explosive candles is not really a big deal, no matter how powerful the rocket. What is a big deal is re-usability. Well, not entirely, since the shuttle was ‘re-usable’, at least, after a complete tear down and refit. The two solid rocket boosters were also re-usable, once you fished them out of the ocean, and did a tear down and repack of the propellant (an incredibly dangerous job, since the solid fuel was quite toxic).
The big deal for the Falcon Heavy is this (you can skip ahead to the 1:00 minute mark):
That… that was sweet. Those two boosters came down perfect, which is impressive, considering that they were falling straight down at supersonic speeds (you can hear the sonic booms), lit their engines, and set down on target with nary a wobble. The main stage booster didn’t fair so well, as it had an issue with the ignition system and fell short of the ocean going platform 1.
The engineering, both of hardware and software, to get this right on a consistent basis (and they are getting to be pretty consistent) is considerable. The folks at SpaceX have every reason to be very proud of their work. They may be standing on the shoulders of giants, but they reached quite a bit higher themselves. And, not only does this reduce waste, it also reduces the cost of getting things into space.
One would think, if we can get a rocket body to fall from space, stabilize itself at supersonic speeds, and light off a rocket engine for a perfect touchdown, we can totally do autonomous cars! Well, yes and no. Yes, we have the tech, but while the two problems are related, they are not the same. Getting a rocket to self-land involves some very tricky physics. Computing fuel loads and reserves, making sure the rocket knows where it is in three dimensions, as well as its current velocity and acceleration, involves a suite of sensors and software all triple-checking every input. Giving it the ability to course-correct should wind or turbulence move it off target. Those boosters probably set down on fumes. If there is any fuel left in the tanks, it can probably be measured in ounces, and if it is more than expected, you can bet they will be making note of how much in case it makes sense to reduce the reserve.
The physics of a self driving car is a lot easier 2. Much lower speeds, only two dimensions to worry about, little worry about running out of fuel resulting in a crash. Even path-finding is no big deal. The big hurdle for autonomous cars is that they won’t be driving alone, or only among other autonomous cars, but among people, and people are always good for tossing a wrench in the works. So it’s not a hardware issue, it’s a software issue, and as we’ve talked about before, it’s a thorny one.
Ask any engineer, and they’ll tell you, hardware issues are always easier to handle than software. Getting a rocket to land itself was (mostly) a hardware issue. The software is nothing to sneeze at, but we mostly just needed the cost and weight of the sensors, actuators, and computing hardware to reach a certain point, and then suddenly it was cost effective to do it. This is a watershed moment. Now that SpaceX has shown the world it can be done, expect other companies and national space programs to follow suit. In 20 years, the idea of throwing away your main booster stage(s) will probably be the mark of a very small rocket, or a very backward organization.
Image by jurvetson 
- a drone ship named Of Course I Still Love You – an obvious nod to Banks’ Culture series – the other two are Just Read The Instructions and A Shortfall of Gravitas
- even in bad weather – it’s easy to recover from a loss of traction if each wheel has it’s own electric motor and brake which can be operated independently
I saw this video and it was pretty cool.
I’m actually hoping that the cost will come down enough that I’ll be able to have a “gravity moment”, like in the movie where Sandra Bullock is looking at the earth from orbit in a space suit. I’d pay good money to do that.Report
Spaceship one is probably a more affordable option.Report
If it’s not putting me in open space and in a EVA for at least 15 minutes, it ain’t what I want.Report
Ah, well, for that you will need a stable orbit, and so far, SS1 is not doing orbits, just ballistics.Report
100 thousand quatloos on the newcomers!Report
It has been my impression that autonomous landing control was the result of deep learning, which is the case in vehicles, at least in part. Is that not so?Report
The deep learning is not necessary to landing a rocket, but it permits a considerable increase in the reliability of clean landings. Having computer hardware that is powerful and light enough to do that was important.
To expand on that a bit, the math and control systems for computing course corrections and throttle positions and burn times are all well established. Having software that can bring all the various inputs together and quickly use them to adjust the outputs such that a soft landing is possible requires a bit of predictive, rather than simply reactive, capability. That is what the deep learning provides.
If we apply that to cars, deep learning is how cars learn to judge, “Is that pedestrian going to step out in front of me?” or “Is that car next to me going to change lanes on top of me?”, etc.Report
None of the published descriptions of the real-time landing algorithm that I’ve seen mention learning. They appear to use a standard optimization approach, solved repeatedly. My understanding is that much of what makes that feasible depends on some recent work at Stanford that takes a description of the problem and (in minutes/hours) builds a problem-specific piece of code that can solve the problem very quickly. That code, running on board the rocket, is fast enough to use the optimization results to control the engine, aerodynamic surfaces, etc.
Stanford’s blurb on the tool says that it works best for problems with less than 2,000 total coefficients (objective plus constraints). Choosing the right set of variables for that 2,000 might be a candidate for a neural network deep-learning sort of scheme.Report
@michael-cain
You got a link?
I ask because I had read that the learning algorithms were in the control software. Perhaps it’s a different set of learning algorithms, or it’s algorithms that are the result of learning?Report
Well, “deep learning” is optimization after all, so I can see how those terms would get conflated in the press.
That said, I’d love to see a link. Optimization problems make me a happy veronica.Report
This is a superficial description of the problem, with follow-on references to more technical stuff. Blackmore’s been publishing this kind of stuff for years. The hard part is solving the problem for the optimum control settings fast enough to be able to respond. Here’s an example of the full-on math from when he was still at JPL. This is a paper on the Stanford tool that generates nearly-deterministic-time code for solving the convex optimization problems that SpaceX uses. I’m probably over the link limit, aren’t I?
SpaceX has a summary page about their landing failures somewhere on their web site, or at least they used to. They seem to have had the software dialed in from the beginning.Report
Thank you, @michael-cain
PS I think the link limit is 4 before it gets buggered.
ETA: Gotta save this for another day, need to do a bug fix before release next week.
Veronica, I think you’ll enjoy those links.Report
I’m gonna need to take a cold bath.Report
It occurred to me that if I take the conventional rocket equations and negate the ISP and drag vector (drag becomes thrust), then my rocket launches with high acceleration and near zero fuel, gaining fuel on the way up. If I can solve a launch trajectory problem such that my hypothetical rocket intersects the real rocket’s position with the velocity vector reversed, matching its angular positions, then my launch profile is the time inverse of the desired landing profile.Report
So, here’s my problem statement.
Use the algorithms to build an object (probably propelled by compressed gas) that can be hurled into the air, possibly tumbling, and then make a controlled propulsive precision landing every time.
I will add this to my list of strange tasks. Two weeks ago I was working on a propane fueled afterburner for a 5″ diameter ducted fan. I quickly prototyped a variable convergent nozzle section out of steel, but need to make a convergent/divergent section for maximum impact as a special effect for a fake spaceship for an engineering summer camp.Report
Need a drawing if you want an opinion.Report
Ask any engineer, and they’ll tell you, hardware issues are always easier to handle than software.
Because hardware problems often get fixed in software.Report
I love the ambition this launch demonstrates. The U.S. space program has been frozen in the amber of the Apollo program for nearly 50 years.Report
It’s not hardware vs. software, it’s a deterministic problem. vs. a non-deterministic problem. Calculating the projected trajectory for a given set of rocket thrusts is just a matter of grinding the math. Determining whether and when a pedestrian will step off a curve requires information about said pedestrian’s mind that aren’t available to anybody with current technology (even the pedestrian’s self-awareness is inadequate.) So you need experience, guesswork, and theory of mind, which are all far more complex that trajectory equations.Report
My apologies if I gave the impression it’s a hardware vs. software question. It’s not, hasn’t been for a very long time. It’s a, as you say, deterministic problem. vs. a non-deterministic problem. Although I’d argue that landing a rocket softly on target is less deterministic than you think*, but certainly considerably more so that trying to figure out what a human is going to do.
*It isn’t getting on target that is an issue, we’ve been able to do that since the early days of guided weapons. It’s getting on target in one piece and in a state that re-use is cheaper than rebuild that get’s tricky. Managing rocket thrust when there is atmosphere and weather is much harder to do than managing it on, say, the moon. Our ability to throttle those rockets sufficiently has also advanced quite a bit since the days of Apollo.Report
On a somewhat related note, SpaceX is going to put up two microsats this weekend to test out Satellite based broadband internet. Final orbit of the microsats will be about 1100 km, which tells me they are planning to put up a constellation of microsats for broadband service. At 1100 km, the signal lag won’t be terrible (unlike HughesNet, which is GeoSynch, or 35K+ km). I mean, LA to NYC is over 4000 km, and most folks won’t notice the signal lag (and the wires that carry that internet are longer than 4000 km).Report
They’re longer than 4000km and the signals going through them move slower than radio waves.
On the flip side, the direct-ish trip from LA to NYC vs the trip from LA to NYC via an 1100km satellite the more important comparison.Report
On the gripping hand, with a few notable exceptions, propagation (speed of light) delay is seldom dominant in the total average delay calculation. LEO data service is much more likely to be dominated by transmission delay (how long to bang out the packet, one bit at a time, at the supported bits-per-second rate), queueing delay (how long will the packet sit in buffers in routers/switches), the quality of the terrestrial network the LEO satellite dumps your packets into, and for TCP, packet loss rate.
When I run
tracerouteto http://www.google.com this morning, there are eleven hops. The largest part of the total delay occurs during the first eight, which all involve Comcast boxes here in the Denver area.ReportThe distance to the satellite’s gonna add about 10% to the overall distance, tops.Report