Tech
Accounting for uncertainty to help engineers design complex systems
Designing a complex electronic device like a delivery drone involves juggling many choices, such as selecting motors and batteries that minimize cost while maximizing the payload the drone can carry or the distance it can travel.
Unraveling that conundrum is no easy task, but what happens if the designers don’t know the exact specifications of each battery and motor? On top of that, the real-world performance of these components will likely be affected by unpredictable factors, like changing weather along the drone’s route.
MIT researchers developed a new framework that helps engineers design complex systems in a way that explicitly accounts for such uncertainty. The framework allows them to model the performance tradeoffs of a device with many interconnected parts, each of which could behave in unpredictable ways.
Their technique captures the likelihood of many outcomes and tradeoffs, giving designers more information than many existing approaches which, at most, can usually only model best-case and worst-case scenarios.
Ultimately, this framework could help engineers develop complex systems like autonomous vehicles, commercial aircraft, or even regional transportation networks that are more robust and reliable in the face of real-world unpredictability.
“In practice, the components in a device never behave exactly like you think they will. If someone has a sensor whose performance is uncertain, and an algorithm that is uncertain, and the design of a robot that is also uncertain, now they have a way to mix all these uncertainties together so they can come up with a better design,” says Gioele Zardini, the Rudge and Nancy Allen Assistant Professor of Civil and Environmental Engineering at MIT, a principal investigator in the Laboratory for Information and Decision Systems (LIDS), an affiliate faculty with the Institute for Data, Systems, and Society (IDSS), and senior author of a paper on this framework.
Zardini is joined on the paper by lead author Yujun Huang, an MIT graduate student; and Marius Furter, a graduate student at the University of Zurich. The research will be presented at the IEEE Conference on Decision and Control.
Considering uncertainty
The Zardini Group studies co-design, a method for designing systems made of many interconnected components, from robots to regional transportation networks.
The co-design language breaks a complex problem into a series of boxes, each representing one component, that can be combined in different ways to maximize outcomes or minimize costs. This allows engineers to solve complex problems in a feasible amount of time.
In prior work, the researchers modeled each co-design component without considering uncertainty. For instance, the performance of each sensor the designers could choose for a drone was fixed.
But engineers often don’t know the exact performance specifications of each sensor, and even if they do, it is unlikely the senor will perfectly follow its spec sheet. At the same time, they don’t know how each sensor will behave once integrated into a complex device, or how performance will be affected by unpredictable factors like weather.
“With our method, even if you are unsure what the specifications of your sensor will be, you can still design the robot to maximize the outcome you care about,” says Furter.
To accomplish this, the researchers incorporated this notion of uncertainty into an existing framework based on category theory.
Using some mathematical tricks, they simplified the problem into a more general structure. This allows them to use the tools of category theory to solve co-design problems in a way that considers a range of uncertain outcomes.
By reformulating the problem, the researchers can capture how multiple design choices affect one another even when their individual performance is uncertain.
This approach is also simpler than many existing tools that typically require extensive domain expertise. With their plug-and-play system, one can rearrange the components in the system without violating any mathematical constraints.
And because no specific domain expertise is required, the framework could be used by a multidisciplinary team where each member designs one component of a larger system.
“Designing an entire UAV isn’t feasible for just one person, but designing a component of a UAV is. By providing the framework for how these components work together in a way that considers uncertainty, we’ve made it easier for people to evaluate the performance of the entire UAV system,” Huang says.
More detailed information
The researchers used this new approach to choose perception systems and batteries for a drone that would maximize its payload while minimizing its lifetime cost and weight.
While each perception system may offer a different detection accuracy under varying weather conditions, the designer doesn’t know exactly how its performance will fluctuate. This new system allows the designer to take these uncertainties into consideration when thinking about the drone’s overall performance.
And unlike other approaches, their framework reveals distinct advantages of each battery technology.
For instance, their results show that at lower payloads, nickel-metal hydride batteries provide the lowest expected lifetime cost. This insight would be impossible to fully capture without accounting for uncertainty, Zardini says.
While another method might only be able to show the best-case and worst-case performance scenarios of lithium polymer batteries, their framework gives the user more detailed information.
For example, it shows that if the drone’s payload is 1,750 grams, there is a 12.8 percent chance the battery design would be infeasible.
“Our system provides the tradeoffs, and then the user can reason about the design,” he adds.
In the future, the researchers want to improve the computational efficiency of their problem-solving algorithms. They also want to extend this approach to situations where a system is designed by multiple parties that are collaborative and competitive, like a transportation network in which rail companies operate using the same infrastructure.
“As the complexity of systems grow, and involves more disparate components, we need a formal framework in which to design these systems. This paper presents a way to compose large systems from modular components, understand design trade-offs, and importantly do so with a notion of uncertainty. This creates an opportunity to formalize the design of large-scale systems with learning-enabled components,” says Aaron Ames, the Bren Professor of Mechanical and Civil Engineering, Control and Dynamical Systems, and Aerospace at Caltech, who was not involved with this research.
Tech
Welcome to the Future of Noise Canceling
This blurring of the lines between audio and health devices looks set to be a trend across the industry. “We really want to make sure that we take care of our customers’ hearing,” says Miikka Tikander, the Helsinki-based head of audio at Bang & Olufsen. Tikander points to recent data about the decline in hearing health in young adults and reports that there was a lot of emphasis from manufacturers on ANC and hearing health at the AES’ Headphone Technology conference in Espoo, Finland this August.
“Apple has a big lead in that area,” he says. “We want to make sure that our headphones can adapt, make this choice [on when to block out sound] on your behalf, if you let it, of course. Some people don’t like that idea, but if there’s a noisy event in your surroundings, the headset can take care of it, just tune it out a bit and get you back to normal listening once you are away from that noise.”
Enter the “Sound Bubble”
Hearvana AI is one startup looking to go much further than the AirPods’ current suite of noise canceling and ambient noise features. Cofounded by Shyam Gollakota, a computer science & engineering professor at the University of Washington, and two of his students, Malek Itani and Tuochao Chen, Hearvana recently raised $6 million in a pre-seed round which included none other than Amazon’s Alexa Fund.
One of the startup’s first big innovations was “semantic hearing,” which was the first project they approached, around three years ago. The team built a hardware prototype—a pair of on-ear headphones with six microphones across the headband, connected to an Orange Pi microcontroller—to test out a model that had been trained to recognize 20 different types of ambient sounds. This included things like sirens, car horns, birdsong, crying babies, alarm clocks, pets, and people talking, and then allowed the user to isolate say, one person’s voice as a “spotlight,” and block out all the other frequencies.
“So I’m going to the beach and I want to listen to just ocean sounds and not the people talking next to me, or I’m in the house vacuum cleaning but I still want to listen to people knocking on the door or important sounds, like a baby crying,” explains Gollakota, who is based in Seattle. “And that’s what we solved first. This was the difference between a vacuum cleaner and a door knock. They sound pretty different, right?”
Tech
Looking for the Best Smart Scale? Step on Up
Other Smart Scales
Renpho MorphoScan for $150: The Renpho MorphoScan full-body scanner looks surprisingly similar to the Runstar FG2015, including a near-identical display attached to the handlebars. Well, spoiler alert, they are basically the same scale. They even use the same app to collect data (and you can even use both scales simultaneously with it). The only reason this scale isn’t our top pick for the category is that it’s $15 more expensive. You can rest assured that a price war is looming.
Arboleaf Body Fat Scale CS20W for $40: This affordable Bluetooth scale isn’t the most eye-catching I’ve tested, owing to its big, silver electrodes and an oversized display that comes across as a bit garish. While weight is easy to make out, the six additional statistics showcased are difficult to read, all displayed simultaneously. I like the Arboleaf app better than the scale, where five more metrics can be found in addition to the seven above, each featuring a helpful explanation when tapping on it. It’s a solid deal at this price, but the upsell to get an “intelligent interpretation report” for an extra $40 per year is probably safe to skip.
Hume Health Body Pod for $183: Hume Health’s Body Pod, another full-body scanner with handles, is heavily advertised—at least to the apps on my phone—and touted (by Hume) as the Next Big Thing in the world of body management. While the app is indeed glossy and inviting, I was shocked to discover how flimsy the hardware felt, that it lacked Wi-Fi, and that some features are locked behind a $100-a-year Hume Plus subscription plan. It works fine enough, but you can get results that are just as good with a cheaper device.
Garmin Index S2 for $191: Five years after its release, the Index S2 is still Garmin’s current model, a surprise for a company otherwise obsessed with fitness. It’s still noteworthy for its lovely color display, which walks you through its six body metrics (for up to 16 users) with each weigh-in. The display also provides your weight trend over time in graphical form and can even display the weather. The scale connects directly to Wi-Fi and Garmin’s cloud-based storage system, so you don’t need a phone nearby to track your progress, as with Bluetooth-only scales. A phone running the Garmin Connect app (Android, iOS) is handy, so you can keep track of everything over time. Unfortunately, as health apps go, Connect is a bit of a bear, so expect a learning curve—especially if you want to make changes to the way the scale works. You can turn its various LCD-screen widgets on or off in the app, but finding everything can be difficult due to the daunting scope of the Garmin ecosystem. The color screen is nice at first, but ultimately adds little to the package.
Omron BCM-500 for $92: With its large LCD panel, quartet of onboard buttons, and oversize silver electrodes, the Omron BCM-500 is an eye-catching masterwork of brutalist design. If your bathroom is decked out in concrete and wrought iron, this scale will fit right in. The Bluetooth unit syncs with Omron’s HeartAdvisor app (Android, iOS), but it provides all six of its body metrics directly on the scale, cycling through them with each weigh-in (for up to four users). It can be difficult to read the label for each of the data points, in part because the LCD isn’t backlit, but the app is somewhat easier to follow, offering front-page graphs of weight, skeletal muscle, and body fat. On the other hand, the presentation is rather clinical, and the app is surprisingly slow to sync. For a scale without a Wi-Fi connection, it’s rather expensive too.
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Tech
To Start Doing What You Want to Do, First Do Less
This applies not just to things you have to do, but also things you think you want to do. Maybe you think you should learn Spanish, but you haven’t done anything to actually learn Spanish. Admitting that you aren’t actually committed to the idea enough to do the work of learning Spanish can help close that loop. Letting go of that feeling that you should learn Spanish just might be the thing that frees up your mind enough that you decide to take up paddleboarding on a whim. The point is that the new year isn’t just a time for starting something new. It’s a time to let go of the things from that past that are no longer serving you.
In many ways this is the antidote to that ever-so-popular slogan “Just do it.” Just do it implies that you shouldn’t think about it, instead of deciding what you really want to do or should do. Maybe spend some time remembering why you wanted to do it in the first place, and if those reasons no longer resonate with you, just don’t do it.
If you like this idea, I highly recommend getting Allen’s book. It goes into much more detail on this idea and has some practical advice on letting go. You can still keep track of those things, in case you do decide, years from now, when you’re paddleboarding through the Sea of Cortez, that now you really do want to learn Spanish and are willing to do the work.
Remember to Live
I will confess, my enthusiasm for Getting Things Done has waned over the years. Not because the system doesn’t work, but because I have found my life more dramatically improved by doing less, not more. It’s not that I’ve stopped getting things done. It’s that I’ve found many of the things I felt like I should do were not really my idea; they were ideas I’d internalized from other places. I didn’t really want to do them, so I didn’t, then I felt guilty about it.
While everything I’ve written above remains good advice for starting a healthy habit and keeping it going, it’s worth spending some time and making sure you know why you want to do what you’re doing. I have been rereading Bertrand Russell’s In Praise of Idleness, and this line jumped out at me: “The modern man thinks that everything ought to be done for the sake of something else, and never for its own sake.”
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